51
|
Chen M, Qi Y, Zhang S, Du Y, Cheng H, Gao S. Screening of genes related to programmed cell death in esophageal squamous cell carcinoma and construction of prognostic model based on transcriptome analysis. Expert Rev Anticancer Ther 2024; 24:905-915. [PMID: 38975629 DOI: 10.1080/14737140.2024.2377184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 06/19/2024] [Indexed: 07/09/2024]
Abstract
OBJECTIVES To screen programmed cell death (PCD)-related genes in esophageal squamous cell carcinoma (ESCC) based on transcriptomic data and to explore its clinical value. METHODS Differentially expressed PCD genes (DEPCDGs) were screened from ESCC transcriptome and clinical data in TCGA database. Univariate COX and LASSO COX were performed on prognostically DEPCDGs in ESCC to develop prognostic model. Differences in immune cell infiltration in different RiskScore groups were determined by ssGSEA and CIBERSORT. The role of RiskScore in immunotherapy response was explored using Tumor Immune Dysfunction and Exclusion (TIDE) and IMvigor210 cohorts. RESULTS Fourteen DEPCDGs associated with prognosis were tapped in ESCC. These DEPCDGs form a RiskScore with good predictive performance for prognosis. RiskScore demonstrated excellent prediction accuracy in three data sets. The abundance of M2 macrophages and Tregs was higher in the high RiskScore group, and the abundance of M1 macrophages was higher in the low RiskScore group. The RiskScore also showed good immunotherapy sensitivity. RT-qPCR analysis showed that AUP1, BCAP31, DYRK2, TAF9 and UBQLN2 were higher expression in KYSE-150 cells. Knockdown BCAP31 inhibited migration and invasion. CONCLUSION A prognostic risk model can predict prognosis of ESCC and may be a useful biomarker for risk stratification and immunotherapy assessment.
Collapse
Affiliation(s)
- Min Chen
- School of Information Engineering, Henan University of Science and Technology, Luoyang, China
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China
- Medical College, Henan University of Science and Technology, Luoyang, China
| | - Yijun Qi
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China
- Medical College, Henan University of Science and Technology, Luoyang, China
| | - Shenghua Zhang
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China
- Medical College, Henan University of Science and Technology, Luoyang, China
| | - Yubo Du
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China
- Medical College, Henan University of Science and Technology, Luoyang, China
| | - Haodong Cheng
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China
- Medical College, Henan University of Science and Technology, Luoyang, China
| | - Shegan Gao
- School of Information Engineering, Henan University of Science and Technology, Luoyang, China
- State Key Laboratory of Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Henan Key Laboratory of Cancer Epigenetics, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- Cancer Hospital, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China
- Medical College, Henan University of Science and Technology, Luoyang, China
| |
Collapse
|
52
|
Zheng C, Liu J, Li Y, Xu P, Zhang B, Wei R, Zhang W, Liu B, Huang J. A 2PLM-RANK multidimensional forced-choice model and its fast estimation algorithm. Behav Res Methods 2024; 56:6363-6388. [PMID: 38409459 DOI: 10.3758/s13428-023-02315-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/06/2023] [Indexed: 02/28/2024]
Abstract
High-stakes non-cognitive tests frequently employ forced-choice (FC) scales to deter faking. To mitigate the issue of score ipsativity derived, many scoring models have been devised. Among them, the multi-unidimensional pairwise preference (MUPP) framework is a highly flexible and commonly used framework. However, the original MUPP model was developed for unfolding response process and can only handle paired comparisons. The present study proposes the 2PLM-RANK as a generalization of the MUPP model to accommodate dominance RANK format response. In addition, an improved stochastic EM (iStEM) algorithm is devised for more stable and efficient parameter estimation. Simulation results generally supported the efficiency and utility of the new algorithm in estimating the 2PLM-RANK when applied to both triplets and tetrads across various conditions. An empirical illustration with responses to a 24-dimensional personality test further supported the practicality of the proposed model. To further aid in the application of the new model, a user-friendly R package is also provided.
Collapse
Affiliation(s)
- Chanjin Zheng
- Department of Educational Psychology, Faculty of Education, East China Normal University, Shanghai, China.
| | - Juan Liu
- Beijing Insight Online Management Consulting Co.,Ltd, Beijing, China
| | - Yaling Li
- Beijing Insight Online Management Consulting Co.,Ltd, Beijing, China
| | - Peiyi Xu
- Department of Educational Psychology, Faculty of Education, East China Normal University, Shanghai, China
- Beijing Insight Online Management Consulting Co.,Ltd, Beijing, China
| | - Bo Zhang
- School of Labor and Employment Relations and Department of Psychology, University of Illinois Urbana-Champaign, Champaign, USA
| | - Ran Wei
- Beijing Insight Online Management Consulting Co.,Ltd, Beijing, China
| | - Wenqing Zhang
- Department of Educational Psychology, Faculty of Education, East China Normal University, Shanghai, China
- Beijing Insight Online Management Consulting Co.,Ltd, Beijing, China
| | - Boyang Liu
- Beijing Insight Online Management Consulting Co.,Ltd, Beijing, China
| | - Jing Huang
- Educational Psychology and Research Methodology, Purdue University, West Lafayette, IN, USA
| |
Collapse
|
53
|
Uanhoro JO. Handling dependent samples in meta-analytic structural equation models: A Wishart-based approach. Behav Res Methods 2024; 56:6101-6118. [PMID: 38308147 DOI: 10.3758/s13428-024-02340-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/09/2024] [Indexed: 02/04/2024]
Abstract
We present an approach to meta-analytic structural equation models that relies on hierarchical modeling of sample covariance matrices under the assumption that the matrices are Wishart. The approach handles the commonplace fixed- and random-effects meta-analytic SEMs, and solves the problem of dependent covariance matrices where more than one covariance matrix is obtained from a single study or study author. The ability of the approach to adequately recover parameters is examined via a simulation study. The approach is implemented in the bayesianmasem R package and a demonstration shows applications of the model.
Collapse
Affiliation(s)
- James Ohisei Uanhoro
- Research, Measurement & Statistics, Department of Educational Psychology, University of North Texas, Denton, TX, 76205, USA.
| |
Collapse
|
54
|
Wang X, Jin L, Zhang X, Li M, Zhu A, Zhang M, Fan H. Transcriptomic profiling and risk assessment in bladder cancer: Insights from copper death-related genes. Cell Signal 2024; 121:111237. [PMID: 38810861 DOI: 10.1016/j.cellsig.2024.111237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 05/23/2024] [Accepted: 05/26/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND The study aimed to investigate the role of copper death-related genes (CRGs) in bladder cancer (BC) for improved prognosis assessment. METHODS Multi-omics techniques were utilized to analyze CRG expression in BC tissues from TCGA and GEO databases. Consensus clustering categorized patients into molecular subtypes based on clinical characteristics and immune cell infiltration. RESULTS An innovative risk assessment model identified eight critical genes associated with BC risk. In vitro and in vivo experiments validated LIPT1's significant impact on copper-induced cell death, proliferation, migration, and invasion in BC. CONCLUSION This multi-omics analysis elucidates the pivotal role of CRGs in BC progression, suggesting enhanced risk assessment through molecular subtype categorization and identification of key genes like LIPT1. Insights into these mechanisms offer the potential for improved diagnosis and treatment strategies for BC patients.
Collapse
Affiliation(s)
- Xu Wang
- Department of Urology, The Second Hospital of Jilin University, Changchun 130022, PR China
| | - Long Jin
- Department of Urology, The Second Hospital of Jilin University, Changchun 130022, PR China
| | - Xiaoyu Zhang
- Department of Urology, The Second Hospital of Jilin University, Changchun 130022, PR China
| | - Mingyu Li
- Department of Urology, The Second Hospital of Jilin University, Changchun 130022, PR China
| | - Ankang Zhu
- Department of Urology, The Second Hospital of Jilin University, Changchun 130022, PR China
| | - Ming Zhang
- Department of Urology, The Second Hospital of Jilin University, Changchun 130022, PR China
| | - Haitao Fan
- Department of Urology, The Second Hospital of Jilin University, Changchun 130022, PR China.
| |
Collapse
|
55
|
Leroux A, Crainiceanu C, Zeger S, Taub M, Ansari B, Wager TD, Bayman E, Coffey C, Langefeld C, McCarthy R, Tsodikov A, Brummet C, Clauw DJ, Edwards RR, Lindquist MA. Statistical modeling of acute and chronic pain patient-reported outcomes obtained from ecological momentary assessment. Pain 2024; 165:1955-1965. [PMID: 38718196 DOI: 10.1097/j.pain.0000000000003214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 01/11/2024] [Indexed: 05/18/2024]
Abstract
ABSTRACT Ecological momentary assessment (EMA) allows for the collection of participant-reported outcomes (PROs), including pain, in the normal environment at high resolution and with reduced recall bias. Ecological momentary assessment is an important component in studies of pain, providing detailed information about the frequency, intensity, and degree of interference of individuals' pain. However, there is no universally agreed on standard for summarizing pain measures from repeated PRO assessment using EMA into a single, clinically meaningful measure of pain. Here, we quantify the accuracy of summaries (eg, mean and median) of pain outcomes obtained from EMA and the effect of thresholding these summaries to obtain binary clinical end points of chronic pain status (yes/no). Data applications and simulations indicate that binarizing empirical estimators (eg, sample mean, random intercept linear mixed model) can perform well. However, linear mixed-effect modeling estimators that account for the nonlinear relationship between average and variability of pain scores perform better for quantifying the true average pain and reduce estimation error by up to 50%, with larger improvements for individuals with more variable pain scores. We also show that binarizing pain scores (eg, <3 and ≥3) can lead to a substantial loss of statistical power (40%-50%). Thus, when examining pain outcomes using EMA, the use of linear mixed models using the entire scale (0-10) is superior to splitting the outcomes into 2 groups (<3 and ≥3) providing greater statistical power and sensitivity.
Collapse
Affiliation(s)
- Andrew Leroux
- Department of Biostatistics and Informatics, Anschutz Medical Campus, University of Colorado, Aurora, CO, United States
| | - Ciprian Crainiceanu
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States
| | - Scott Zeger
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States
| | - Margaret Taub
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States
| | - Briha Ansari
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States
| | - Tor D Wager
- Department of Psychological and Brain Science, Dartmouth College, Hanover, NH, United States
| | - Emine Bayman
- Departments of Biostatistics and
- Anesthesia, University of Iowa, Iowa City, IA, United States
| | | | - Carl Langefeld
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston Salem, NC, United States
- The Comprehensive Cancer Center of Wake Forest University, Winston Salem, NC, United States
| | - Robert McCarthy
- Department of Anesthesiology, Rush University, Chicago, IL, United States
| | | | - Chad Brummet
- Anesthesiology, University of Michigan, Ann Arbor, MI, United States
| | - Daniel J Clauw
- Anesthesiology, University of Michigan, Ann Arbor, MI, United States
| | - Robert R Edwards
- Harvard Medical School, Department of Anesthesiology, Pain Management Center, Brigham and Women's Hospital, Chestnut Hill, MA, United States
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States
| |
Collapse
|
56
|
Horvat C, Chauvel C, Casalegno JS, Benchaib M, Ploin D, Nunes MC. RSV Severe Infection Risk Stratification in a French 5-Year Birth Cohort Using Machine-learning. Pediatr Infect Dis J 2024; 43:819-824. [PMID: 38713818 PMCID: PMC11319071 DOI: 10.1097/inf.0000000000004375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/05/2024] [Indexed: 05/09/2024]
Abstract
BACKGROUND Respiratory syncytial virus (RSV) poses a substantial threat to infants, often leading to challenges in hospital capacity. With recent pharmaceutical developments to be used during the prenatal and perinatal periods aimed at decreasing the RSV burden, there is a pressing need to identify infants at risk of severe disease. We aimed to stratify the risk of developing a clinically severe RSV infection in infants under 1 year of age. METHODS This retrospective observational study was conducted at the Hospices Civils de Lyon, France, involving infants born between 2014 and 2018. This study focused on infants hospitalized with severe and very severe acute lower respiratory tract infections associated with RSV (SARI-WI group). Data collection included perinatal information and clinical data, with machine-learning algorithms used to discriminate SARI-WI cases from nonhospitalized infants. RESULTS Of 42,069 infants, 555 developed SARI-WI. Infants born in November were very likely (>80%) predicted SARI-WI. Infants born in October were very likely predicted SARI-WI except for births at term by vaginal delivery and without siblings. Infants were very unlikely (<10%) predicted SARI-WI when all the following conditions were met: born in other months, at term, by vaginal delivery and without siblings. Other infants were possibly (10-30%) or probably (30-80%) predicted SARI-WI. CONCLUSIONS Although RSV preventive measures are vital for all infants, and specific recommendations exist for patients with high-risk comorbidities, in situations where prioritization becomes necessary, infants born just before or within the early weeks of the epidemic should be considered as a risk group.
Collapse
Affiliation(s)
- Côme Horvat
- From the Hospices Civils de Lyon, Hôpital Femme Mère Enfant, Service de Réanimation Pédiatrique et d’Accueil des Urgences, Bron, France
| | - Cécile Chauvel
- Center of Excellence in Respiratory Pathogens (CERP), Hospices Civils de Lyon and Centre International de Recherche en Infectiologie (CIRI), Équipe Santé publique, épidémiologie et écologie évolutive des maladies infectieuses (PHE3ID), Inserm U1111, CNRS UMR5308, ENS de Lyon, Université Claude Bernard - Lyon 1, Lyon, France
| | - Jean-Sebastien Casalegno
- Hospices Civils de Lyon, Hôpital de la Croix-Rousse, Centre de Biologie Nord, Institut des Agents Infectieux, Laboratoire de Virologie, Lyon, France
- Centre International de Recherche en Infectiologie (CIRI), Laboratoire Vir’Path, Inserm U1111, CNRS UMR5308, ENS de Lyon, Université Claude Bernard - Lyon 1, Lyon, France
| | - Mehdi Benchaib
- Hospices Civils de Lyon, Hôpital Femme Mère Enfant, Service de Médecine et de la Reproduction, Bron, France
| | - Dominique Ploin
- From the Hospices Civils de Lyon, Hôpital Femme Mère Enfant, Service de Réanimation Pédiatrique et d’Accueil des Urgences, Bron, France
- Hospices Civils de Lyon, Hôpital Femme Mère Enfant, Service de Médecine et de la Reproduction, Bron, France
| | - Marta C. Nunes
- Center of Excellence in Respiratory Pathogens (CERP), Hospices Civils de Lyon and Centre International de Recherche en Infectiologie (CIRI), Équipe Santé publique, épidémiologie et écologie évolutive des maladies infectieuses (PHE3ID), Inserm U1111, CNRS UMR5308, ENS de Lyon, Université Claude Bernard - Lyon 1, Lyon, France
- South African Medical Research Council, Vaccines & Infectious Diseases Analytics Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| |
Collapse
|
57
|
Chen X, Hu X, Li G, Grover CE, You J, Wang R, Liu Z, Qi Z, Luo X, Peng Y, Zhu M, Zhang Y, Lu S, Zhang YM, Lin Z, Wendel JF, Zhang X, Wang M. Genetic Regulatory Perturbation of Gene Expression Impacted by Genomic Introgression in Fiber Development of Allotetraploid Cotton. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2401549. [PMID: 39196795 DOI: 10.1002/advs.202401549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 07/23/2024] [Indexed: 08/30/2024]
Abstract
Interspecific genomic introgression is an important evolutionary process with respect to the generation of novel phenotypic diversity and adaptation. A key question is how gene flow perturbs gene expression networks and regulatory interactions. Here, an introgression population of two species of allopolyploid cotton (Gossypium) to delineate the regulatory perturbations of gene expression regarding fiber development accompanying fiber quality change is utilized. De novo assembly of the recipient parent (G. hirsutum Emian22) genome allowed the identification of genomic variation and introgression segments (ISs) in 323 introgression lines (ILs) from the donor parent (G. barbadense 3-79). It documented gene expression dynamics by sequencing 1,284 transcriptomes of developing fibers and characterized genetic regulatory perturbations mediated by genomic introgression using a multi-locus model. Introgression of individual homoeologous genes exhibiting extreme low or high expression bias can lead to a parallel expression bias in their non-introgressed duplicates, implying a shared yet divergent regulatory fate of duplicated genes following allopolyploidy. Additionally, the IL N182 with improved fiber quality is characterized, and the candidate gene GhFLAP1 related to fiber length is validated. This study outlines a framework for understanding introgression-mediated regulatory perturbations in polyploids, and provides insights for targeted breeding of superior upland cotton fiber.
Collapse
Affiliation(s)
- Xinyuan Chen
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xiubao Hu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Guo Li
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Corrinne E Grover
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Jiaqi You
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ruipeng Wang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zhenping Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zhengyang Qi
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xuanxuan Luo
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yabin Peng
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Mengmeng Zhu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuqi Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Sifan Lu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zhongxu Lin
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jonathan F Wendel
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Xianlong Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Maojun Wang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| |
Collapse
|
58
|
Ponthier L, Franck B, Autmizguine J, Labriffe M, Ovetchkine P, Marquet P, Åsberg A, Woillard JB. Application of machine-learning models to predict the ganciclovir and valganciclovir exposure in children using a limited sampling strategy. Antimicrob Agents Chemother 2024:e0086024. [PMID: 39194260 DOI: 10.1128/aac.00860-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Accepted: 07/31/2024] [Indexed: 08/29/2024] Open
Abstract
Intravenous ganciclovir and oral valganciclovir display significant variability in ganciclovir pharmacokinetics, particularly in children. Therapeutic drug monitoring currently relies on the area under the concentration-time (AUC). Machine-learning (ML) algorithms represent an interesting alternative to Maximum-a-Posteriori Bayesian-estimators for AUC estimation. The goal of our study was to develop and validate an ML-based limited sampling strategy (LSS) approach to determine ganciclovir AUC0-24 after administration of either intravenous ganciclovir or oral valganciclovir in children. Pharmacokinetic parameters from four published population pharmacokinetic models, in addition to the World Health Organization growth curve for children, were used in the mrgsolve R package to simulate 10,800 pharmacokinetic profiles of children. Different ML algorithms were trained to predict AUC0-24 based on different combinations of two or three samples. Performances were evaluated in a simulated test set and in an external data set of real patients. The best estimation performances in the test set were obtained with the Xgboost algorithm using a 2 and 6 hours post dose LSS for oral valganciclovir (relative mean prediction error [rMPE] = 0.4% and relative root mean square error [rRMSE] = 5.7%) and 0 and 2 hours post dose LSS for intravenous ganciclovir (rMPE = 0.9% and rRMSE = 12.4%). In the external data set, the performance based on these two sample LSS was acceptable: rMPE = 0.2% and rRMSE = 16.5% for valganciclovir and rMPE = -9.7% and rRMSE = 17.2% for intravenous ganciclovir. The Xgboost algorithm developed resulted in a clinically relevant individual estimation using only two blood samples. This will improve the implementation of AUC-targeted ganciclovir therapeutic drug monitoring in children.
Collapse
Affiliation(s)
- Laure Ponthier
- Pharmacology and Transplantation, INSERM U1248, Université de Limoges, Limoges, France
- Department of Pediatrics, University Hospital of Limoges, Limoges, France
| | - Bénédicte Franck
- Department of Clinical and Biological Pharmacology and Pharmacovigilance, Clinical Investigation Center CIC-P 1414, Rennes, France
| | - Julie Autmizguine
- Department of Pharmacology and Physiology, Université de Montréal, Montreal, Quebec, Canada
- Research Center, Center Hospitalier Universitaire Sainte-Justine, Montreal, Quebec, Canada
- Department of Pediatrics, Center Hospitalier Universitaire Sainte-Justine, Montreal, Quebec, Canada
| | - Marc Labriffe
- Pharmacology and Transplantation, INSERM U1248, Université de Limoges, Limoges, France
- Department of Pharmacology, Toxicology and Pharmacovigilance, University Hospital of Limoges, Limoges, France
| | - Philippe Ovetchkine
- Department of Pharmacology and Physiology, Université de Montréal, Montreal, Quebec, Canada
| | - Pierre Marquet
- Pharmacology and Transplantation, INSERM U1248, Université de Limoges, Limoges, France
- Department of Pharmacology, Toxicology and Pharmacovigilance, University Hospital of Limoges, Limoges, France
| | - Anders Åsberg
- Department of Transplantation Medicine, Oslo University Hospital-Rikshospitalet, Oslo, Norway
- Section of Pharmacology and Pharmaceutical Biosciences, Department of Pharmacy, University of Oslo, Oslo, Norway
| | - Jean-Baptiste Woillard
- Pharmacology and Transplantation, INSERM U1248, Université de Limoges, Limoges, France
- Department of Pharmacology, Toxicology and Pharmacovigilance, University Hospital of Limoges, Limoges, France
| |
Collapse
|
59
|
Yang CH, Li MH, Wen SF, Chang SM. Modeling multiple-criterion diagnoses by heterogeneous-instance logistic regression. Stat Med 2024. [PMID: 39189687 DOI: 10.1002/sim.10202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/10/2024] [Accepted: 08/07/2024] [Indexed: 08/28/2024]
Abstract
Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD) that causes a significant burden in caregiving and medical costs. Clinically, the diagnosis of MCI is determined by the impairment statuses of five cognitive domains. If one of these cognitive domains is impaired, the patient is diagnosed with MCI, and if two out of the five domains are impaired, the patient is diagnosed with AD. In medical records, most of the time, the diagnosis of MCI/AD is given, but not the statuses of the five domains. We may treat the domain statuses as missing variables. This diagnostic procedure relates MCI/AD status modeling to multiple-instance learning, where each domain resembles an instance. However, traditional multiple-instance learning assumes common predictors among instances, but in our case, each domain is associated with different predictors. In this article, we generalized the multiple-instance logistic regression to accommodate the heterogeneity in predictors among different instances. The proposed model is dubbed heterogeneous-instance logistic regression and is estimated via the expectation-maximization algorithm because of the presence of the missing variables. We also derived two variants of the proposed model for the MCI and AD diagnoses. The proposed model is validated in terms of its estimation accuracy, latent status prediction, and robustness via extensive simulation studies. Finally, we analyzed the National Alzheimer's Coordinating Center-Uniform Data Set using the proposed model and demonstrated its potential.
Collapse
Affiliation(s)
- Chun-Hao Yang
- Institute of Statistics and Data Science, National Taiwan University, Taipei City, Taiwan
| | - Ming-Han Li
- Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan
| | - Shu-Fang Wen
- Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan
| | - Sheng-Mao Chang
- Department of Statistics, National Taipei University, New Taipei City, Taiwan
| |
Collapse
|
60
|
Ji F, Qian H, Sun Z, Yang Y, Shi M, Gu H. A novel model based on lipid metabolism-related genes associated with immune microenvironment predicts metastasis of breast cancer. Discov Oncol 2024; 15:372. [PMID: 39190262 DOI: 10.1007/s12672-024-01253-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Accepted: 08/20/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND Breast cancer (BC) is the most prevalent malignant tumor among women worldwide and a significant cause of cancer-related deaths in females. Recent studies have shown that lipid metabolism-related genes (LMRGs) exhibit prognostic potential in various types of tumors, including BC. Our study aimed to establish a novel model to predict the metastasis of BC. METHODS Clinical information and corresponding RNA data of patients with BC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Consensus clustering was performed to identify novel molecular subgroups. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues using Expression, microenvironment cell populations counter, microenvironment cell populations counter, and single-sample gene set enrichment analyses were employed to determine the tumor immune microenvironment and immune status of the identified subgroups. Functional analyses, including Gene Ontology and gene set enrichment analyses, were conducted to elucidate the underlying mechanisms. A prognostic risk model was constructed using the Least Absolute Shrinkage and Selection Operator algorithm and multivariate Cox regression analysis. RESULTS This study identified differential gene expression between patients with BC exhibiting metastasis and those without metastasis using public databases. Using the obtained data, we established predictive models based on six LMRGs. Furthermore, consensus clustering and prognostic score grouping analysis revealed that differentially expressed LMRGs influence tumor prognosis by regulating tumor immunity. To facilitate clinical application, we developed a nomogram integrating the risk model and clinical characteristics to accurately predict the prognosis of patients with BC. CONCLUSION We developed and validated a novel signature associated with LMRGs for predicting disease-free survival in patients with BC. The expression of LMRGs correlates with the immune microenvironment of patients with BC, providing new insights and improved strategies for the diagnosis and treatment of BC.
Collapse
Affiliation(s)
- Fan Ji
- Department of Radiology, Medical School, Affiliated Hospital of Nantong University, Nantong University, Nantong, China
| | - Hongyan Qian
- Cancer Research Center Nantong, Nantong Tumor Hospital, The Affiliated Tumor Hospital of Nantong University, Nantong University, Nantong, China
| | - Zhouna Sun
- Cancer Research Center Nantong, Nantong Tumor Hospital, The Affiliated Tumor Hospital of Nantong University, Nantong University, Nantong, China
| | - Ying Yang
- Cancer Research Center Nantong, Nantong Tumor Hospital, The Affiliated Tumor Hospital of Nantong University, Nantong University, Nantong, China
| | - Minxin Shi
- Cancer Research Center Nantong, Nantong Tumor Hospital, The Affiliated Tumor Hospital of Nantong University, Nantong University, Nantong, China.
| | - Hongmei Gu
- Department of Radiology, Medical School, Affiliated Hospital of Nantong University, Nantong University, Nantong, China.
| |
Collapse
|
61
|
Tu H, Su J, Gong K, Li Z, Yu X, Xu X, Shi Y, Sheng J. A dynamic model to predict early occurrence of acute kidney injury in ICU hospitalized cirrhotic patients: a MIMIC database analysis. BMC Gastroenterol 2024; 24:290. [PMID: 39192202 DOI: 10.1186/s12876-024-03369-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 08/13/2024] [Indexed: 08/29/2024] Open
Abstract
BACKGROUND This study aimed to develop a tool for predicting the early occurrence of acute kidney injury (AKI) in ICU hospitalized cirrhotic patients. METHODS Eligible patients with cirrhosis were identified from the Medical Information Mart for Intensive Care database. Demographic data, laboratory examinations, and interventions were obtained. After splitting the population into training and validation cohorts, the least absolute shrinkage and selection operator regression model was used to select factors and construct the dynamic online nomogram. Calibration and discrimination were used to assess nomogram performance, and clinical utility was evaluated by decision curve analysis (DCA). RESULTS A total of 1254 patients were included in the analysis, and 745 developed AKI. The mean arterial pressure, white blood cell count, total bilirubin level, Glasgow Coma Score, creatinine, heart rate, platelet count and albumin level were identified as predictors of AKI. The developed model had a good ability to differentiate AKI from non-AKI, with AUCs of 0.797 and 0.750 in the training and validation cohorts, respectively. Moreover, the nomogram model showed good calibration. DCA showed that the nomogram had a superior overall net benefit within wide and practical ranges of threshold probabilities. CONCLUSIONS The dynamic online nomogram can be an easy-to-use tool for predicting the early occurrence of AKI in critically ill patients with cirrhosis.
Collapse
Affiliation(s)
- Huilan Tu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Junwei Su
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Kai Gong
- Department of Infectious Diseases, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Zhiwei Li
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xia Yu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Xianbin Xu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Yu Shi
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, 310003, China.
| | - Jifang Sheng
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, 310003, China.
| |
Collapse
|
62
|
Kleinsasser MJ, Mistry R, Hsieh HF, McCarthy WJ, Raghunathan T. Person explanatory multidimensional item response theory with the instrument package in R. Behav Res Methods 2024:10.3758/s13428-024-02490-5. [PMID: 39187738 DOI: 10.3758/s13428-024-02490-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/31/2024] [Indexed: 08/28/2024]
Abstract
We present the new R package instrument to perform Bayesian estimation of person explanatory multidimensional item response theory. The package implements an exploratory multidimensional item response theory model and a higher-order multidimensional item response theory model, a type of confirmatory multidimensional item response theory. Explanation of person parameters is accomplished by fixed and random effect linear regression models. Estimation is carried out using Hamiltonian Monte Carlo in Stan. In this article, we provide a detailed description of the models; we use the instrument package to demonstrate fitting explanatory item response models with fixed and random effects (i.e., mixed modeling) of person parameters in R; and, we perform a simulation study to evaluate the performance of our implementation of the models.
Collapse
Affiliation(s)
| | - Ritesh Mistry
- Department of Health Behavior and Health Education, University of Michigan, Ann Arbor, Michigan, USA
| | - Hsing-Fang Hsieh
- Department of Health Behavior and Health Education, University of Michigan, Ann Arbor, Michigan, USA
| | - William J McCarthy
- Department of Health Policy and Management, University of California Los Angeles, Los Angeles, California, USA
| | | |
Collapse
|
63
|
Većkalov B, Geiger SJ, Bartoš F, White MP, Rutjens BT, van Harreveld F, Stablum F, Akın B, Aldoh A, Bai J, Berglund F, Bratina Zimic A, Broyles M, Catania A, Chen A, Chorzępa M, Farahat E, Götz J, Hoter-Ishay B, Jordan G, Joustra S, Klingebiel J, Krajnc Ž, Krug A, Andersen TL, Löloff J, Natarajan D, Newman-Oktan S, Niehoff E, Paerels C, Papirmeister R, Peregrina S, Pohl F, Remsö A, Roh A, Rusyidi B, Schmidt J, Shavgulidze M, Vellinho Nardin V, Wang R, Warner K, Wattier M, Wong CY, Younssi M, Ruggeri K, van der Linden S. A 27-country test of communicating the scientific consensus on climate change. Nat Hum Behav 2024:10.1038/s41562-024-01928-2. [PMID: 39187712 DOI: 10.1038/s41562-024-01928-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 06/13/2024] [Indexed: 08/28/2024]
Abstract
Communicating the scientific consensus that human-caused climate change is real increases climate change beliefs, worry and support for public action in the United States. In this preregistered experiment, we tested two scientific consensus messages, a classic message on the reality of human-caused climate change and an updated message additionally emphasizing scientific agreement that climate change is a crisis. Across online convenience samples from 27 countries (n = 10,527), the classic message substantially reduces misperceptions (d = 0.47, 95% CI (0.41, 0.52)) and slightly increases climate change beliefs (from d = 0.06, 95% CI (0.01, 0.11) to d = 0.10, 95% CI (0.04, 0.15)) and worry (d = 0.05, 95% CI (-0.01, 0.10)) but not support for public action directly. The updated message is equally effective but provides no added value. Both messages are more effective for audiences with lower message familiarity and higher misperceptions, including those with lower trust in climate scientists and right-leaning ideologies. Overall, scientific consensus messaging is an effective, non-polarizing tool for changing misperceptions, beliefs and worry across different audiences.
Collapse
Affiliation(s)
- Bojana Većkalov
- Department of Psychology, Faculty of Social and Behavioural Sciences, University of Amsterdam, Amsterdam, the Netherlands
| | - Sandra J Geiger
- Environmental Psychology, Department of Cognition, Emotion and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria.
| | - František Bartoš
- Department of Psychology, Faculty of Social and Behavioural Sciences, University of Amsterdam, Amsterdam, the Netherlands
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
| | - Mathew P White
- Cognitive Science Hub, University of Vienna, Vienna, Austria
| | - Bastiaan T Rutjens
- Department of Psychology, Faculty of Social and Behavioural Sciences, University of Amsterdam, Amsterdam, the Netherlands
| | - Frenk van Harreveld
- Department of Psychology, Faculty of Social and Behavioural Sciences, University of Amsterdam, Amsterdam, the Netherlands
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Federica Stablum
- University of Trento, Department of Psychology and Cognitive Science, Trento, Italy
| | - Berkan Akın
- Department of Psychology, Faculty of Social and Behavioural Sciences, University of Amsterdam, Amsterdam, the Netherlands
- University of Mannheim, Department of Psychology, School of Social Sciences, Mannheim, Germany
| | - Alaa Aldoh
- Department of Psychology, Faculty of Social and Behavioural Sciences, University of Amsterdam, Amsterdam, the Netherlands
| | - Jinhao Bai
- Liberal Arts Program, Faculty of Humanities, Tel Aviv University, Tel Aviv, Israel
| | - Frida Berglund
- Department of Psychology, Uppsala University, Uppsala, Sweden
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Aleša Bratina Zimic
- Department of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
| | - Margaret Broyles
- Department of Industrial Engineering and Operations Research, Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY, USA
| | - Andrea Catania
- Department of Psychology, University of Malta, Msida, Malta
| | - Airu Chen
- Department of Psychology, Columbia University, New York, NY, USA
| | - Magdalena Chorzępa
- Department of Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Eman Farahat
- Department of Psychology, Behavioural and Economic Science, University of Warwick, Coventry, UK
| | - Jakob Götz
- Department of Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Motivation Psychology, Department of Occupational, Economic and Social Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Bat Hoter-Ishay
- Department of Psychology, Columbia University, New York, NY, USA
| | - Gesine Jordan
- Department of Behavioural and Cognitive Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Siri Joustra
- Department of Psychology, Faculty of Social Sciences, Radboud University, Nijmegen, the Netherlands
| | - Jonas Klingebiel
- School of General Studies, Columbia University, New York, NY, USA
| | - Živa Krajnc
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
- Department of Psychology, University of Maribor, Maribor, Slovenia
| | - Antonia Krug
- Institute of Psychology, University of Innsbruck, Innsbruck, Austria
| | - Thomas Lind Andersen
- Child and Adolescent Mental Health Center, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
| | - Johanna Löloff
- Department of Psychology, Heidelberg University, Heidelberg, Germany
| | - Divya Natarajan
- Department of Cognitive Science, Barnard College, Columbia University, New York, NY, USA
| | | | - Elena Niehoff
- Environmental Policy Group, Wageningen University & Research, Wageningen, the Netherlands
| | - Celeste Paerels
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA
| | | | - Steven Peregrina
- Department of Psychology, Columbia University, New York, NY, USA
| | - Felicia Pohl
- Faculty of Psychology, Warsaw International Studies in Psychology, University of Warsaw, Warsaw, Poland
| | - Amanda Remsö
- Department of Psychology, Faculty of Education, Kristianstad University, Kristianstad, Sweden
| | - Abigail Roh
- Department of Psychology, Columbia University, New York, NY, USA
| | - Binahayati Rusyidi
- Social Welfare Department & Center for CSR, Social Entrepreneurship & Community Empowerment, FISIP, Universitas Padjadjaran, Jatinangor-Sumedang, Indonesia
| | - Justus Schmidt
- University of Mannheim, Department of Psychology, School of Social Sciences, Mannheim, Germany
| | | | - Valentina Vellinho Nardin
- Department of Psychology, Faculty of Psychology and Education Sciences, University of Porto, Porto, Portugal
| | - Ruixiang Wang
- Columbia College, Columbia University, New York, NY, USA
| | - Kelly Warner
- Department of Psychology, Columbia University, New York, NY, USA
| | - Miranda Wattier
- Department of Psychology, Columbia University, New York, NY, USA
| | - Chloe Y Wong
- Barnard College, Columbia University, New York, NY, USA
| | | | - Kai Ruggeri
- Department of Health Policy and Management, Mailman School of Public Health, Columbia University, New York, NY, USA.
- Policy Research Group, Centre for Business Research, Judge Business School, University of Cambridge, Cambridge, UK.
| | - Sander van der Linden
- Department of Psychology, School of the Biological Sciences, University of Cambridge, Cambridge, UK
| |
Collapse
|
64
|
Kamp M, Pain O, Lewis CM, Ramsay M. Ancestry-aligned polygenic scores combined with conventional risk factors improve prediction of cardiometabolic outcomes in African populations. Genome Med 2024; 16:106. [PMID: 39187845 PMCID: PMC11346299 DOI: 10.1186/s13073-024-01377-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 08/12/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND Cardiovascular diseases (CVD) are a major health concern in Africa. Improved identification and treatment of high-risk individuals can reduce adverse health outcomes. Current CVD risk calculators are largely unvalidated in African populations and overlook genetic factors. Polygenic scores (PGS) can enhance risk prediction by measuring genetic susceptibility to CVD, but their effectiveness in genetically diverse populations is limited by a European-ancestry bias. To address this, we developed models integrating genetic data and conventional risk factors to assess the risk of developing cardiometabolic outcomes in African populations. METHODS We used summary statistics from a genome-wide association meta-analysis (n = 14,126) in African populations to derive novel genome-wide PGS for 14 cardiometabolic traits in an independent African target sample (Africa Wits-INDEPTH Partnership for Genomic Research (AWI-Gen), n = 10,603). Regression analyses assessed relationships between each PGS and corresponding cardiometabolic trait, and seven CVD outcomes (CVD, heart attack, stroke, diabetes mellitus, dyslipidaemia, hypertension, and obesity). The predictive utility of the genetic data was evaluated using elastic net models containing multiple PGS (MultiPGS) and reference-projected principal components of ancestry (PPCs). An integrated risk prediction model incorporating genetic and conventional risk factors was developed. Nested cross-validation was used when deriving elastic net models to enhance generalisability. RESULTS Our African-specific PGS displayed significant but variable within- and cross- trait prediction (max.R2 = 6.8%, p = 1.86 × 10-173). Significantly associated PGS with dyslipidaemia included the PGS for total cholesterol (logOR = 0.210, SE = 0.022, p = 2.18 × 10-21) and low-density lipoprotein (logOR = - 0.141, SE = 0.022, p = 1.30 × 10-20); with hypertension, the systolic blood pressure PGS (logOR = 0.150, SE = 0.045, p = 8.34 × 10-4); and multiple PGS associated with obesity: body mass index (max. logOR = 0.131, SE = 0.031, p = 2.22 × 10-5), hip circumference (logOR = 0.122, SE = 0.029, p = 2.28 × 10-5), waist circumference (logOR = 0.013, SE = 0.098, p = 8.13 × 10-4) and weight (logOR = 0.103, SE = 0.029, p = 4.89 × 10-5). Elastic net models incorporating MultiPGS and PPCs significantly improved prediction over MultiPGS alone. Models including genetic data and conventional risk factors were more predictive than conventional risk models alone (dyslipidaemia: R2 increase = 2.6%, p = 4.45 × 10-12; hypertension: R2 increase = 2.6%, p = 2.37 × 10-13; obesity: R2 increase = 5.5%, 1.33 × 10-34). CONCLUSIONS In African populations, CVD and associated cardiometabolic trait prediction models can be improved by incorporating ancestry-aligned PGS and accounting for ancestry. Combining PGS with conventional risk factors further enhances prediction over traditional models based on conventional factors. Incorporating data from target populations can improve the generalisability of international predictive models for CVD and associated traits in African populations.
Collapse
Affiliation(s)
- Michelle Kamp
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, The University of the Witwatersrand, Johannesburg, South Africa.
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, Psychology & Neuroscience, London, UK.
| | - Oliver Pain
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, Psychology & Neuroscience, London, UK
- Department of Medical & Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Michèle Ramsay
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, The University of the Witwatersrand, Johannesburg, South Africa.
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| |
Collapse
|
65
|
Fang X, Yu WY, Zhu CM, Zhao N, Zhao W, Xie TT, Wei LJ, Sun XR, Xie J, Zhao Y. Chromosome instability functions as a potential therapeutic reference by enhancing chemosensitivity to BCL-XL inhibitors in colorectal carcinoma. Acta Pharmacol Sin 2024:10.1038/s41401-024-01372-y. [PMID: 39187678 DOI: 10.1038/s41401-024-01372-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 07/30/2024] [Indexed: 08/28/2024] Open
Abstract
Chromosome instability (CIN) and subsequent aneuploidy are prevalent in various human malignancies, influencing tumor progression such as metastases and relapses. Extensive studies demonstrate the development of chemoresistance in high-CIN tumors, which poses significant therapeutic challenges. Given the association of CIN with poorer prognosis and suppressed immune microenvironment observed in colorectal carcinoma (CRC), here we aimed to discover chemotherapeutic drugs exhibiting increased inhibition against high-CIN CRC cells. By using machine learning methods, we screened out two BCL-XL inhibitors Navitoclax and WEHI-539 as CIN-sensitive reagents in CRC. Subsequent analyses using a CIN-aneuploidy cell model confirmed the vulnerability of high-CIN CRC cells to these drugs. We further revealed the critical role of BCL-XL in the viability of high-CIN CRC cells. In addition, to ease the evaluation of CIN levels in clinic, we developed a three-gene signature as a CIN surrogate to predict prognosis, chemotherapeutic and immune responses in CRC samples. Our results demonstrate the potential value of CIN as a therapeutic target in CRC treatment and the importance of BCL-XL in regulating survival of high-CIN CRC cells, therefore representing a valuable attempt to translate a common trait of heterogeneous tumor cells into an effective therapeutic target.
Collapse
Affiliation(s)
- Xiao Fang
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225001, China
- Clinical Medical College, Yangzhou University, Yangzhou, 225009, China
- Jiangsu Key Laboratory of Experimental & Translational Non-coding RNA Research, Yangzhou, 225001, China
| | - Wen-Ying Yu
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225001, China
- Jiangsu Key Laboratory of Experimental & Translational Non-coding RNA Research, Yangzhou, 225001, China
| | - Chun-Miao Zhu
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225001, China
- Jiangsu Key Laboratory of Experimental & Translational Non-coding RNA Research, Yangzhou, 225001, China
| | - Nan Zhao
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225001, China
- Jiangsu Key Laboratory of Experimental & Translational Non-coding RNA Research, Yangzhou, 225001, China
| | - Wei Zhao
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225001, China
- Jiangsu Key Laboratory of Experimental & Translational Non-coding RNA Research, Yangzhou, 225001, China
| | - Ting-Ting Xie
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225001, China
- Jiangsu Key Laboratory of Experimental & Translational Non-coding RNA Research, Yangzhou, 225001, China
| | - Li-Jie Wei
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225001, China
- Jiangsu Key Laboratory of Experimental & Translational Non-coding RNA Research, Yangzhou, 225001, China
| | - Xi-Ran Sun
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225001, China
- Jiangsu Key Laboratory of Experimental & Translational Non-coding RNA Research, Yangzhou, 225001, China
| | - Juan Xie
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225001, China
- Jiangsu Key Laboratory of Experimental & Translational Non-coding RNA Research, Yangzhou, 225001, China
| | - Ya Zhao
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225001, China.
- Jiangsu Key Laboratory of Experimental & Translational Non-coding RNA Research, Yangzhou, 225001, China.
| |
Collapse
|
66
|
Hinch R, Panovska-Griffiths J, Ward T, Charlett A, Watkins N, Fraser C. Quantification of the time-varying epidemic growth rate and of the delays between symptom onset and presenting to healthcare for the mpox epidemic in the UK in 2022. Sci Rep 2024; 14:19755. [PMID: 39187529 PMCID: PMC11347625 DOI: 10.1038/s41598-024-68154-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 07/19/2024] [Indexed: 08/28/2024] Open
Abstract
The mpox epidemic in the UK began in May 2022, with rates of new cases unexpectedly and rapidly declining during August 2022. Interpreting trends in infection requires disentangling the underlying growth rate of cases from the delay from symptom onset to presenting to healthcare. We developed a nowcasting Bayesian method which incorporates time-varying delays (EpiLine) to quantify the changes in the delay from symptom onset to healthcare presentation and the underlying mpox growth rate over the period May-August 2022 in the UK. We show that the mean delay between symptom onset and healthcare presentation for mpox in the UK decreased from 22 days in early May 2022 to 10 days by early June and 8 days in August 2022. When we account for these dynamic delays, the time-varying growth rate declined gradually and continuously in the UK during the May-August 2022 period. Not accounting for varying time delays would have incorrectly characterised the growth rate by a sharp increase followed by a rapid decline in mpox cases. Our results highlight the importance of correctly quantifying the delay between symptom onset to healthcare presentation when characterising the epidemic growth of mpox in the UK. The gradual reduction in the rate of epidemic spread, which pre-dated the vaccine roll-out, is consistent with gradual risk reduction or acquired immunity amongst the highest risk individuals. Our study highlights the need for public health agencies to record the delays from symptom onset to healthcare presentation early in an outbreak.
Collapse
Affiliation(s)
- Robert Hinch
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jasmina Panovska-Griffiths
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- The Queen's College, University of Oxford, Oxford, UK.
- UK Health Security Agency, London, UK.
| | | | | | | | - Christophe Fraser
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| |
Collapse
|
67
|
Masson-Grehaigne C, Lafon M, Palussière J, Leroy L, Bonhomme B, Jambon E, Italiano A, Cousin S, Crombé A. Single- and multi-site radiomics may improve overall survival prediction for patients with metastatic lung adenocarcinoma. Diagn Interv Imaging 2024:S2211-5684(24)00166-9. [PMID: 39191636 DOI: 10.1016/j.diii.2024.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Accepted: 07/09/2024] [Indexed: 08/29/2024]
Abstract
PURPOSE The purpose of this study was to assess whether single-site and multi-site radiomics could improve the prediction of overall survival (OS) of patients with metastatic lung adenocarcinoma compared to clinicopathological model. MATERIALS AND METHODS Adults with metastatic lung adenocarcinoma, pretreatment whole-body contrast-enhanced computed tomography examinations, and performance status (WHO-PS) ≤ 2 were included in this retrospective single-center study, and randomly assigned to training and testing cohorts. Radiomics features (RFs) were extracted from all measurable lesions with volume ≥ 1 cm3. Radiomics prognostic scores based on the largest tumor (RPSlargest) and the average RF values across all tumors per patient (RPSaverage) were developed in the training cohort using 5-fold cross-validated LASSO-penalized Cox regression. Intra-patient inter-tumor heterogeneity (IPITH) metrics were calculated to quantify the radiophenotypic dissimilarities among all tumors within each patient. A clinicopathological model was built in the training cohort using stepwise Cox regression and enriched with combinations of RPSaverage, RPSlargest and IPITH. Models were compared with the concordance index in the independent testing cohort. RESULTS A total of 300 patients (median age: 63.7 years; 40.7% women; median OS, 16.3 months) with 1359 lesions were included (200 and 100 patients in the training and testing cohorts, respectively). The clinicopathological model included WHO-PS = 2 (hazard ratio [HR] = 3.26; P < 0.0001), EGFR, ALK, ROS1 or RET mutations (HR = 0.57; P = 0.0347), IVB stage (HR = 1.65; P = 0.0211), and liver metastases (HR = 1.47; P = 0.0670). In the testing cohort, RPSaverage, RPSlargest and IPITH were associated with OS (HR = 85.50, P = 0.0038; HR = 18.83, P = 0.0082 and HR = 8.00, P = 0.0327, respectively). The highest concordance index was achieved with the combination of clinicopathological variables and RPSaverage, significantly better than that of the clinicopathological model (concordance index = 0.7150 vs. 0.695, respectively; P = 0.0049) CONCLUSION: Single-site and multi-site radiomics-based scores are associated with OS in patients with metastatic lung adenocarcinoma. RPSaverage improves the clinicopathological model.
Collapse
Affiliation(s)
- Cécile Masson-Grehaigne
- Department of Diagnostic and Interventional Oncologic Imaging, Institut Bergonié, 33076 Bordeaux, France; Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France
| | - Mathilde Lafon
- Department of Medical Oncology, Institut Bergonié, 33076 Bordeaux, France
| | - Jean Palussière
- Department of Diagnostic and Interventional Oncologic Imaging, Institut Bergonié, 33076 Bordeaux, France
| | - Laura Leroy
- Department of Medical Oncology, Institut Bergonié, 33076 Bordeaux, France
| | | | - Eva Jambon
- Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France
| | - Antoine Italiano
- Department of Medical Oncology, Institut Bergonié, 33076 Bordeaux, France; SARCOTARGET Team, Bordeaux Institute of Oncology (BRIC) INSERM U1312, Bordeaux 33076, France
| | - Sophie Cousin
- Department of Medical Oncology, Institut Bergonié, 33076 Bordeaux, France
| | - Amandine Crombé
- Department of Diagnostic and Interventional Oncologic Imaging, Institut Bergonié, 33076 Bordeaux, France; Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France; SARCOTARGET Team, Bordeaux Institute of Oncology (BRIC) INSERM U1312, Bordeaux 33076, France.
| |
Collapse
|
68
|
Long F, Chen Y, Zhang Q, Li Q, Wang Y, Wang Y, Li H, Zhao Y, McNamara RK, DelBello MP, Sweeney JA, Gong Q, Li F. Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis. Mol Psychiatry 2024:10.1038/s41380-024-02710-6. [PMID: 39187625 DOI: 10.1038/s41380-024-02710-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 08/18/2024] [Accepted: 08/19/2024] [Indexed: 08/28/2024]
Abstract
Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = -2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.
Collapse
Affiliation(s)
- Fenghua Long
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yufei Chen
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Qian Zhang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Qian Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yaxuan Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Yitian Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Haoran Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Youjin Zhao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Robert K McNamara
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA
| | - John A Sweeney
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China
| | - Fei Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, PR China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, Sichuan, PR China.
| |
Collapse
|
69
|
McGehee DL, Saben JL, Sims CR, Turner D, Thakali KM, Diaz EC, Sobik SR, Edwards T, Krukowski RA, Williams DK, Børsheim E, Andres A. Childhood cardiometabolic risk factors associated with the perinatal environment of the maternal-paternal-child triad. Pediatr Obes 2024:e13162. [PMID: 39183454 DOI: 10.1111/ijpo.13162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 07/05/2024] [Accepted: 07/29/2024] [Indexed: 08/27/2024]
Abstract
INTRODUCTION Clustering of cardiometabolic risk factors in childhood significantly increases the risk of atherosclerotic cardiovascular disease later in life. Identification of modifiable parental factors that contribute to offspring cardiometabolic health is critical for the prevention of disease. The objective was to identify factors associated with child cardiometabolic risk factors at age 5 years. METHODS Triads from a longitudinal cohort were recalled at 5 years (n = 68). Dietary intake, anthropometrics, physical activity and serum-based risk factors were collected. Best subset selection, linear and logistic regressions were used to identify triad variables associated with increased risk of cardiometabolic risk factor clustering at age 5 years. RESULTS In this cohort, best subset modelling revealed that increased paternal fat mass, serum low-density lipoproteins and triglycerides, maternal dietary added sugar and being female were associated with increased odds of offspring having two or more cardiometabolic risk factors at age 5 years. CONCLUSIONS Dietary and exercise interventions prior to conception targeting paternal adiposity and dyslipidaemia as well as maternal dietary habits could decrease children's cardiometabolic risk in later life.
Collapse
Affiliation(s)
- Diamond L McGehee
- Arkansas Children's Nutrition Center, Little Rock, Arkansas, USA
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | | | - Clark R Sims
- Arkansas Children's Nutrition Center, Little Rock, Arkansas, USA
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Donald Turner
- Arkansas Children's Nutrition Center, Little Rock, Arkansas, USA
| | - Keshari M Thakali
- Arkansas Children's Nutrition Center, Little Rock, Arkansas, USA
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Eva C Diaz
- Arkansas Children's Nutrition Center, Little Rock, Arkansas, USA
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
- Arkansas Children's Research Institute, Little Rock, Arkansas, USA
| | - Sarah R Sobik
- Arkansas Children's Nutrition Center, Little Rock, Arkansas, USA
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Timothy Edwards
- Arkansas Children's Nutrition Center, Little Rock, Arkansas, USA
- Arkansas Children's Research Institute, Little Rock, Arkansas, USA
| | | | - D Keith Williams
- Arkansas Children's Nutrition Center, Little Rock, Arkansas, USA
- Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Elisabet Børsheim
- Arkansas Children's Nutrition Center, Little Rock, Arkansas, USA
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
- Arkansas Children's Research Institute, Little Rock, Arkansas, USA
| | - Aline Andres
- Arkansas Children's Nutrition Center, Little Rock, Arkansas, USA
- Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
- Arkansas Children's Research Institute, Little Rock, Arkansas, USA
| |
Collapse
|
70
|
Tirrell Z, Norman A, Hoyle M, Lybrand S, Parkinson B. Bring Out Your Dead: A Review of the Cost Minimisation Approach in Health Technology Assessment Submissions to the Australian Pharmaceutical Benefits Advisory Committee. PHARMACOECONOMICS 2024:10.1007/s40273-024-01420-9. [PMID: 39182009 DOI: 10.1007/s40273-024-01420-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/21/2024] [Indexed: 08/27/2024]
Abstract
OBJECTIVES Published literature has levied criticism against the cost-minimisation analysis (CMA) approach to economic evaluation over the past two decades, with multiple papers declaring its 'death'. However, since introducing the requirements for economic evaluations as part of health technology (HTA) decision-making in 1992, the cost-minimisation analysis (CMA) approach has been widely used to inform recommendations about the public subsidy of medicines in Australia. This research aimed to highlight the breadth of use of CMA in Australia and assess the influence of preconditions for the approach on subsidy recommendations METHODS: Relevant information was extracted from Public Summary Documents of Pharmaceutical Benefits Advisory Committee (PBAC) meetings in Australia considering submissions for the subsidy of medicines that included a CMA and were assessed between July 2005 and December 2022. A generalised linear model was used to explore the relationship between whether medicines were recommended and variables that reflected the primary preconditions for using CMA set out in the published PBAC Methodology Guidelines. Other control variables were selected through the Bolasso Method. Subgroup analysis was undertaken which replicated this modelling process. RESULTS While the potential for inferior safety or efficacy reduced the likelihood of recommendation (p < 0.01), the effect sizes suggest that the requirements for CMA were not requisite for recommendation. CONCLUSION The Australian practice of CMA does not strictly align with the PBAC Methodology Guidelines and the theoretically appropriate application of CMA. However, within the confines of a deliberative HTA decision-making process that balances values and judgement with available evidence, this may be considered acceptable, particularly if stakeholders consider the current approach delivers sufficient clarity of process and enables patients to access medicines at an affordable cost.
Collapse
Affiliation(s)
- Zachary Tirrell
- Macquarie University, Macquarie University Centre for the Health Economy, Macquarie Park, NSW, Australia.
- Macquarie Business School, Macquarie University, Macquarie Park, Australia.
- Australian Institute for Health Innovation, Macquarie University, Macquarie Park, Australia.
| | - Alicia Norman
- Macquarie University, Macquarie University Centre for the Health Economy, Macquarie Park, NSW, Australia
- Macquarie Business School, Macquarie University, Macquarie Park, Australia
- Australian Institute for Health Innovation, Macquarie University, Macquarie Park, Australia
| | - Martin Hoyle
- Macquarie University, Macquarie University Centre for the Health Economy, Macquarie Park, NSW, Australia
- Macquarie Business School, Macquarie University, Macquarie Park, Australia
- Australian Institute for Health Innovation, Macquarie University, Macquarie Park, Australia
| | - Sean Lybrand
- Macquarie University, Macquarie University Centre for the Health Economy, Macquarie Park, NSW, Australia
| | - Bonny Parkinson
- Macquarie University, Macquarie University Centre for the Health Economy, Macquarie Park, NSW, Australia
- Macquarie Business School, Macquarie University, Macquarie Park, Australia
- Australian Institute for Health Innovation, Macquarie University, Macquarie Park, Australia
| |
Collapse
|
71
|
Zhou X, Wang S, Yu D, Niu T. Investigating CR1 as an indicated Gene for mild cognitive impairment in type 2 diabetes mellitus. Diabetol Metab Syndr 2024; 16:206. [PMID: 39182129 PMCID: PMC11344402 DOI: 10.1186/s13098-024-01449-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Accepted: 08/19/2024] [Indexed: 08/27/2024] Open
Abstract
OBJECTIVE Type 2 diabetes mellitus (T2DM) has beenis known as an important risk factor for cognitive impairment. Meanwhile, the liver plays a central role in the development of T2DM and insulin resistance. The present study attempted to identify and validate marker genes for mild cognitive impairment (MCI) in patients with T2DM. METHODS In this study, insulin resistance-related differentially expressed genes were identified from the liver tissues of individuals with T2DM and those with normal glucose tolerance using the Gene Expression Omnibus database and MCI-associated genes were identified using the GeneCards database. Next, enrichment analysis was performed with overlapping T2DM and MCI genes, followed by the identification of specific genes using the LASSO logistic regression and SVM-RFE algorithms. An important experiment involved the implementation of clinical and in vitro validation using real-time quantitative polymerase chain reaction (RT-qPCR). Finally, multiple linear regression, binary logistic regression, and receiver operating characteristic curve analyses were performed to investigate the relationship between the key gene and cognitive function in these patients. RESULT The present study identified 40 overlapping genes between MCI and T2DM, with subsequent enrichment analysis revealing their significant association with the roles of neuronal and glial projections. The marker gene complement receptor 1(CR1) was identified for both diseases using two regression algorithms. Based on RT-qPCR validation in 65 T2DM patients with MCI (MCI group) and 65 T2DM patients without MCI (NC group), a significant upregulation of CR1 mRNA in peripheral blood mononuclear cells was observed in the MCI group (P < 0.001). Furthermore, the CR1 gene level was significantly negatively associated with MoCA and MMSE scores, which reflect the overall cognitive function, and positively correlated with TMTB scores, which indicate the executive function. Finally, elevated CR1 mRNA levels were identified as an independent risk factor for MCI (OR = 1.481, P < 0.001). CONCLUSION These findings suggest that CR1 is an important predictor of MCI in patients with T2DM. Thus, CR1 has potential clinical significance, which may offer new ideas and directions for the management and treatment of T2DM. The identification and clinical validation of dysregulated marker genes in both T2DM and MCI can offer valuable insights into the intrinsic association between these two conditions. The current study insights may inspire the development of novel strategies for addressing the complicated issues related to cognitive impairment associated with diabetes.
Collapse
Affiliation(s)
- Xueling Zhou
- School of Medicine, Southeast University, Nanjing, China
- Department of Endocrinology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China
| | - Shaohua Wang
- School of Medicine, Southeast University, Nanjing, China.
- Department of Endocrinology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China.
| | - Dandan Yu
- School of Medicine, Southeast University, Nanjing, China
- Department of Endocrinology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China
| | - Tong Niu
- School of Medicine, Southeast University, Nanjing, China
- Department of Endocrinology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China
| |
Collapse
|
72
|
Wang S, Yang Z, Hu K, Guo C, Xiao J, Meng G, Liu X, Li Q. Developmental patterns and computational mechanisms of school-aged children's fairness-related decision making under social comparison. J Exp Child Psychol 2024; 247:106047. [PMID: 39182459 DOI: 10.1016/j.jecp.2024.106047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 07/31/2024] [Accepted: 07/31/2024] [Indexed: 08/27/2024]
Abstract
The developmental patterns and computational mechanisms underlying the impact of unfair offers and social comparisons on school-aged children's fairness-related decision making remain unclear. To address this, we recruited 190 children aged 8 to 12 years (52.1% female) in a multi-responder ultimatum game. Results revealed an age-related decline in children's tendency to reject unfair offers, partially mediated by emotions, alongside a slight increase in rejecting inferior social comparisons. Computational modeling identified two distinct motivations guiding children's rejection behavior: inequity aversion and inferior social comparison avoidance. Furthermore, there was significant variability in responses to superior social comparisons, with some children displaying aversion and others seeking. Our refined model enhances the explanatory power of inequity aversion theory in complex multi-player social scenarios, validating and refining existing theories. In addition, the exploration of superior social comparison tendencies reveals individual heterogeneity, enriching our understanding of children's social comparisons. These findings contribute to elucidating the developmental patterns and internal mechanisms of children's socialization processes, offering implications for promoting their social adaptation and mental health.
Collapse
Affiliation(s)
- Shujing Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhong Yang
- Institute of Brain Science and Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China
| | - Kesong Hu
- Department of Psychological Science, University of Arkansas, Little Rock, AR 72204, USA
| | - Chunyan Guo
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing 100048, China
| | - Jing Xiao
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing 100048, China
| | - Guangteng Meng
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xun Liu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Qi Li
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing 100048, China.
| |
Collapse
|
73
|
Stechemesser A, Koch N, Mark E, Dilger E, Klösel P, Menicacci L, Nachtigall D, Pretis F, Ritter N, Schwarz M, Vossen H, Wenzel A. Climate policies that achieved major emission reductions: Global evidence from two decades. Science 2024; 385:884-892. [PMID: 39172830 DOI: 10.1126/science.adl6547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 07/19/2024] [Indexed: 08/24/2024]
Abstract
Meeting the Paris Agreement's climate targets necessitates better knowledge about which climate policies work in reducing emissions at the necessary scale. We provide a global, systematic ex post evaluation to identify policy combinations that have led to large emission reductions out of 1500 climate policies implemented between 1998 and 2022 across 41 countries from six continents. Our approach integrates a comprehensive climate policy database with a machine learning-based extension of the common difference-in-differences approach. We identified 63 successful policy interventions with total emission reductions between 0.6 billion and 1.8 billion metric tonnes CO2. Our insights on effective but rarely studied policy combinations highlight the important role of price-based instruments in well-designed policy mixes and the policy efforts necessary for closing the emissions gap.
Collapse
Affiliation(s)
- Annika Stechemesser
- Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
- Mercator Research Institute on Global Commons and Climate Change (MCC), Berlin, Germany
- Institute of Physics, University of Potsdam, Potsdam, Germany
| | - Nicolas Koch
- Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
- Mercator Research Institute on Global Commons and Climate Change (MCC), Berlin, Germany
- IZA Institute of Labor Economics, Bonn, Germany
| | - Ebba Mark
- Climate Econometrics, Nuffield College, University of Oxford, Oxford, UK
- Smith School of Enterprise and the Environment, University of Oxford, Oxford, UK
- Institute for New Economic Thinking, University of Oxford, Oxford, UK
| | - Elina Dilger
- Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
| | - Patrick Klösel
- Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
- Mercator Research Institute on Global Commons and Climate Change (MCC), Berlin, Germany
| | - Laura Menicacci
- Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
| | - Daniel Nachtigall
- Organisation for Economic Co-operation and Development (OECD), Paris, France
| | - Felix Pretis
- Climate Econometrics, Nuffield College, University of Oxford, Oxford, UK
- Department of Economics, University of Victoria, Victoria, BC, Canada
| | - Nolan Ritter
- Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
- Mercator Research Institute on Global Commons and Climate Change (MCC), Berlin, Germany
| | - Moritz Schwarz
- Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
- Climate Econometrics, Nuffield College, University of Oxford, Oxford, UK
- Smith School of Enterprise and the Environment, University of Oxford, Oxford, UK
- Faculty of Economics and Management, Technische Universität Berlin, Berlin, Germany
| | - Helena Vossen
- Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
| | - Anna Wenzel
- Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
| |
Collapse
|
74
|
Chen J, Liu X, Zhang Z, Su R, Geng Y, Guo Y, Zhang Y, Su M. Early Diagnostic Markers for Esophageal Squamous Cell Carcinoma: CNA Gene Identification and cfDNA Detection. J Transl Med 2024:102127. [PMID: 39182610 DOI: 10.1016/j.labinv.2024.102127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 08/17/2024] [Accepted: 08/19/2024] [Indexed: 08/27/2024] Open
Abstract
The high mortality rate of esophageal squamous cell carcinoma (ESCC) is exacerbated by the absence of early diagnostic markers. The pronounced heterogeneity of mutations in ESCC renders copy number alterations (CNAs) more prevalent among patients. The identification of CNA genes within esophageal squamous dysplasia (ESD), a precancerous stage of ESCC, is crucial for advancing early detection efforts. Utilization of liquid biopsies via droplet-based digital PCR (ddPCR) offers a novel strategy for detecting incipient tumor traces. This study undertook a thorough investigation of CNA profiles across ESCC development stages, integrating data from existing databases and prior investigations to pinpoint and confirm CNA markers conducive to early detection of ESCC. Targeted sequencing was employed to select potential early detection genes, followed by the establishment of prediction models for ESCC early detection using ddPCR. Our analysis reveals widespread CNAs during the ESD stage, mirroring the CNA landscape observed in ESCC. A total of 40 CNA genes were identified as highly frequent in both ESCC and ESD lesions, through a comprehensive gene-level CNA analysis encompassing ESD and ESCC tissues, ESCC cell lines, and pan-cancer datasets. Subsequent validation of five candidate markers via ddPCR underscored the efficacy of combined predictive models-encompassing PIK3CA, SOX2, EGFR, MYC, and CCND1-in early ESCC screening, as evidenced by the area under the curve (AUC) values exceeding 0.92 (p<0.0001) across various detection contexts. The findings highlight the significant utility of CNA genes in the early screening of ESCC, presenting robust models that could facilitate early detection, broad-scale population screening, and adjunctive diagnosis.
Collapse
Affiliation(s)
- Jiamin Chen
- Institute of Clinical Pathology& Department of Pathology, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Xi Liu
- Institute of Clinical Pathology& Department of Pathology, Shantou University Medical College, Shantou 515041, Guangdong, China; Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Zhihua Zhang
- Institute of Clinical Pathology& Department of Pathology, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Ruibing Su
- Institute of Clinical Pathology& Department of Pathology, Shantou University Medical College, Shantou 515041, Guangdong, China; Department of Cardiology, The Second Affiliated Hospital of Shantou University Medical College, Shantou 515041, China
| | - Yiqun Geng
- Guangdong Provincial International Collaborative Center of Molecular Medicine, Laboratory of Molecular Pathology, Shantou University Medical College, Shantou 515041, China
| | - Yi Guo
- Cancer Hospital of Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Yimin Zhang
- Clinical research center, Shantou Central Hospital, Guangdong Province, Shantou 515041, China
| | - Min Su
- Institute of Clinical Pathology& Department of Pathology, Shantou University Medical College, Shantou 515041, Guangdong, China; Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Shantou University Medical College, Shantou 515041, Guangdong, China.
| |
Collapse
|
75
|
Deere R, Pallmann P, Shepherd V, Brookes-Howell L, Carson-Stevens A, Davies F, Dunphy E, Gupta P, Hickson M, Hill V, Ingarfield K, Ivins N, Jones F, Letchford R, Lowe R, Nash S, Otter P, Prout H, Randell E, Sewell B, Smith D, Trubey R, Wainwright T, Busse M, Button K. MulTI-domain self-management in older People wiTh OstEoarthritis and multi-morbidities: protocol for the TIPTOE randomised controlled trial. Trials 2024; 25:557. [PMID: 39180101 PMCID: PMC11344358 DOI: 10.1186/s13063-024-08380-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 08/06/2024] [Indexed: 08/26/2024] Open
Abstract
BACKGROUND Four out of five people living with osteoarthritis (OA) also suffer with at least one other long-term health condition. The complex interaction between OA and multiple long-term conditions (MLTCs) can result in difficulties with self-care, restricted mobility, pain, anxiety, depression and reduced quality of life. The aim of the MulTI-domain Self-management in Older People wiTh OstEoarthritis and Multi-Morbidities (TIPTOE) trial is to evaluate the clinical and cost-effectiveness of the Living Well self-management support intervention, co-designed with people living with OA, integrated into usual care, in comparison to usual care alone. METHODS TIPTOE is a multi-centre, two-arm, individually randomised controlled trial where 824 individuals over 65 years old with knee and/or hip joint pain from their OA affected joint and at least one other long-term health condition will be randomised to receive either the Living Well Self-Management support intervention or usual care. Eligible participants can self-refer onto the trial via a website or be referred via NHS services across Wales and England. Those randomised to receive the Living Well support intervention will be offered up to six one-to-one coaching sessions with a TIPTOE-trained healthcare practitioner and a co-designed book. Participants will be encouraged to nominate a support person to assist them throughout the study. All participants will complete a series of self-reported outcome measures at baseline and 6- and 12-month follow-up. The primary outcome is symptoms and quality of life as assessed by the Musculoskeletal Health Questionnaire (MSK-HQ). Routine data will be used to evaluate health resource use. A mixed methods process evaluation will be conducted alongside the trial to inform future implementation should the TIPTOE intervention be found both clinically and cost-effective. An embedded 'Study Within A Project' (SWAP) will explore and address barriers to the inclusion of under-served patient groups (e.g. oldest old, low socioeconomic groups, ethnic groups). DISCUSSION TIPTOE will evaluate the clinical and cost-effectiveness of a co-designed, living well personalised self-management support intervention for older individuals with knee and/or hip OA and MLTCs. The trial has been designed to maximise inclusivity and access. TRIAL REGISTRATION ISRCTN 16024745 . Registered on October 16, 2023.
Collapse
Affiliation(s)
- Rachel Deere
- Centre for Trials Research, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Philip Pallmann
- Centre for Trials Research, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Victoria Shepherd
- Centre for Trials Research, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Lucy Brookes-Howell
- Centre for Trials Research, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Andrew Carson-Stevens
- Division of Population Medicine, School of Medicine, Cardiff University, Cardiff, UK
| | - Ffion Davies
- Centre for Trials Research, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Emma Dunphy
- Healthcare NHS Foundation Trust is Homerton Hospital, Homerton Row, London, E9 6SR, UK
| | - Preeti Gupta
- Cardiff and Vale University Health Board, Heath Park, Cardiff, CF14 4XN, UK
| | - Mary Hickson
- School of Health Professions, University of Plymouth, Plymouth, UK
| | - Val Hill
- Public and Patient Involvement Member C/O Centre for Trials Research, Cardiff University, Cardiff, UK
| | - Kate Ingarfield
- Centre for Trials Research, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Nicola Ivins
- Centre for Trials Research, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Fiona Jones
- Population Health Research Institute, St George's University, London, UK
- Bridges Self-Management, St George's University, London, UK
| | - Robert Letchford
- Cardiff and Vale University Health Board, Heath Park, Cardiff, CF14 4XN, UK
| | - Rachel Lowe
- Centre for Trials Research, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Sarah Nash
- Centre for Trials Research, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Paula Otter
- Bridges Self-Management, St George's University, London, UK
| | - Hayley Prout
- Centre for Trials Research, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Elizabeth Randell
- Centre for Trials Research, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Bernadette Sewell
- Faculty of Medicine, Health and Life Science, Swansea Centre for Health Economics, Swansea University, Swansea, UK
| | - Debs Smith
- Public and Patient Involvement Member C/O Centre for Trials Research, Cardiff University, Cardiff, UK
| | - Robert Trubey
- Centre for Trials Research, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Tom Wainwright
- Orthopaedic Research Institute, Bournemouth University, Bournemouth, UK
| | - Monica Busse
- Centre for Trials Research, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Kate Button
- School of Healthcare Sciences, Cardiff University, Cardiff, UK.
| |
Collapse
|
76
|
Yang L, Wei X, Sun P, Wang J, Zhou X, Zhang X, Luo W, Zhou Y, Zhang W, Fang S, Chao J. Deciphering the spatial organization of fibrotic microenvironment in silica particles-induced pulmonary fibrosis. JOURNAL OF HAZARDOUS MATERIALS 2024; 478:135540. [PMID: 39178783 DOI: 10.1016/j.jhazmat.2024.135540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 07/24/2024] [Accepted: 08/14/2024] [Indexed: 08/26/2024]
Abstract
Silicosis represents a form of interstitial lung disease induced by the inhalation of silica particles in production environments. A key pathological characteristic of silica-induced pulmonary fibrosis is its localized tissue heterogeneity, which presents significant challenges in analyzing transcriptomic data due to the loss of important spatial context. To address this, we integrate spatial gene expression data with single-cell analyses and achieve a detailed mapping of cell types within and surrounding fibrotic regions, revealing significant shifts in cell populations in normal and diseased states. Additionally, we explore cell interactions within fibrotic zones using ligand-receptor mapping, deepening our understanding of cellular dynamics in these areas. We identify a subset of fibroblasts, termed Inmt fibroblasts, that play a suppressive role in the fibrotic microenvironment. Validating our findings through a comprehensive suite of bioinformatics, histological, and cell culture studies highlights the role of monocyte-derived macrophages in shifting Inmt fibroblast populations into profibrotic Grem1 fibroblast, potentially disrupting lung homeostasis in response to external challenges. Hence, the spatially detailed deconvolution offered by our research markedly advances the comprehension of cell dynamics and environmental interactions pivotal in the development of pulmonary fibrosis.
Collapse
Affiliation(s)
- Liliang Yang
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, Department of Physiology, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Xinyan Wei
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, Department of Physiology, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, China
| | - Piaopiao Sun
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, Department of Physiology, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Jing Wang
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, Department of Physiology, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Xinbei Zhou
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, Department of Physiology, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, China
| | - Xinxin Zhang
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, Department of Physiology, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Wei Luo
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, Department of Physiology, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, China; School of Medicine, Xizang Minzu University, Xianyang, Shanxi 712082, China
| | - Yun Zhou
- Department of Health Management, School of Health Science, West Yunnan University of Applied Sciences, Dali, Yunnan 671000 China
| | - Wei Zhang
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, Department of Physiology, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Shencun Fang
- Department of Respiratory Medicine, Nanjing Chest hospital, The Affiliated Brain Hospital of Nanjing Medical University, China.
| | - Jie Chao
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, Department of Physiology, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, China; School of Medicine, Xizang Minzu University, Xianyang, Shanxi 712082, China.
| |
Collapse
|
77
|
Barbati G, Gregorio C, Scagnetto A, Indennidate C, Cappelletto C, Di Lenarda A. Effectiveness of PCSK9 inhibitors: A Target Trial Emulation framework based on Real-World Electronic Health Records. PLoS One 2024; 19:e0309470. [PMID: 39173034 PMCID: PMC11341039 DOI: 10.1371/journal.pone.0309470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 07/29/2024] [Indexed: 08/24/2024] Open
Abstract
Low-Density Lipoprotein (LDL) cholesterol is one of the main target for cardiovascular (CV) prevention and therapy. In the last years, Proprotein Convertase Subtilisin-Kexin type 9 inhibitors (PCSK9-i) has emerged as a key therapeutic target to lower LDL and were introduced for prevention of CV events. Recently (June 2022) the Italian Medicines Agency (AIFA) modified the eligibility criteria for the use of PCSK9-i. We designed an observational study to estimate the prevalence of eligible subjects and evaluate the effectiveness of PCSK9-i applying a Target Trial Emulation (TTE) approach based on Electronic Health Records (EHR). Subjects meeting the eligibility criteria were identified from July 2017 (when PCSK9-i became available) to December 2020. Outcomes were all-cause death and the first hospitalization. Among eligible subjects, we identified those treated at date of the first prescription. Inverse Probability of Treatment Weights (IPTW) were estimated including demographic and clinical covariates, history of treatment with statins and the month/year eligibility date. Competing risk models on weighted cohorts were used to derive the Average Treatment Effect (ATE) and the Conditional Average Treatment Effect (CATE) in subgroups of interest. Out of 1976 eligible subjects, 161 (8%) received treatment with PCSK9-i. Treated individuals were slightly younger, predominantly male, had more severe CV conditions, and were more often treated with statin compared to the untreated subjects. The latter exhibited a higher prevalence of non-CV comorbidities. A significant absolute and relative risk reduction of death and a lower relative risk for the first hospitalization was observed. The risk reduction for death was confirmed in CATE analysis. PCSk9-i were prescribed to a minority of eligible subjects. Within the TTE framework, the analysis confirmed the association between PCSK9-i and lower risk of events, aligning with findings from randomized clinical trials (RCTs). In our study, PCSK9-i provided protection specifically against all-cause death, expanding upon the evidence from RCTs that had primarily focused on composite CV outcomes.
Collapse
Affiliation(s)
- Giulia Barbati
- Biostatistics Unit, Department of Medical Sciences, University of Trieste, Trieste, Italy
| | - Caterina Gregorio
- Biostatistics Unit, Department of Medical Sciences, University of Trieste, Trieste, Italy
- MOX—Modelling and Scientific Computing Laboratory, Department of Mathematics, Politecnico di Milano, Milano, Italy
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden
| | - Arjuna Scagnetto
- Cardiovascular Center, Territorial Specialistic Department, University Hospital and Health Services of Trieste, Trieste, Italy
| | - Carla Indennidate
- Cardiovascular Center, Territorial Specialistic Department, University Hospital and Health Services of Trieste, Trieste, Italy
| | - Chiara Cappelletto
- Cardiovascular Center, Territorial Specialistic Department, University Hospital and Health Services of Trieste, Trieste, Italy
| | - Andrea Di Lenarda
- Cardiovascular Center, Territorial Specialistic Department, University Hospital and Health Services of Trieste, Trieste, Italy
| |
Collapse
|
78
|
Panahi B. Global transcriptome analysis identifies critical functional modules associated with multiple abiotic stress responses in microalgae Chromochloris zofingiensis. PLoS One 2024; 19:e0307248. [PMID: 39172989 PMCID: PMC11341014 DOI: 10.1371/journal.pone.0307248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 06/27/2024] [Indexed: 08/24/2024] Open
Abstract
In the current study, systems biology approach was applied to get a deep insight regarding the regulatory mechanisms of Chromochloris zofingiensis under overall stress conditions. Meta-analysis was performed using p-values combination of differentially expressed genes. To identify the informative models related to stress conditions, two distinct weighted gene co-expression networks were constructed and preservation analyses were performed using medianRankand Zsummary algorithms. Moreover, functional enrichment analysis of non-preserved modules was performed to shed light on the biological performance of underlying genes in the non-preserved modules. In the next step, the gene regulatory networks between top hub genes of non-preserved modules and transcription factors were inferred using ensemble of trees algorithm. Results showed that the power of beta = 7 was the best soft-thresholding value to ensure a scale-free network, leading to the determination of 12 co-expression modules with an average size of 128 genes. Preservation analysis showed that the connectivity pattern of the six modules including the blue, black, yellow, pink, greenyellow, and turquoise changed during stress condition which defined as non-preserved modules. Examples of enriched pathways in non-preserved modules were Oxidative phosphorylation", "Vitamin B6 metabolism", and "Arachidonic acid metabolism". Constructed regulatory network between identified TFs and top hub genes of non-preserved module such as Cz06g10250, Cz03g12130 showed that some specific TFs such as C3H and SQUAMOSA promoter binding protein (SBP) specifically regulates the specific hubs. The current findings add substantially to our understanding of the stress responsive underlying mechanism of C. zofingiensis for future studies and metabolite production programs.
Collapse
Affiliation(s)
- Bahman Panahi
- Department of Genomics, Branch for Northwest & West Region, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Tabriz, Iran
| |
Collapse
|
79
|
Riou J, Studer E, Fesser A, Schuster TM, Low N, Egger M, Hauser A. Surveillance of SARS-CoV-2 prevalence from repeated pooled testing: application to Swiss routine data. Epidemiol Infect 2024; 152:e100. [PMID: 39168632 DOI: 10.1017/s0950268824000876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2024] Open
Abstract
Surveillance of SARS-CoV-2 through reported positive RT-PCR tests is biased due to non-random testing. Prevalence estimation in population-based samples corrects for this bias. Within this context, the pooled testing design offers many advantages, but several challenges remain with regards to the analysis of such data. We developed a Bayesian model aimed at estimating the prevalence of infection from repeated pooled testing data while (i) correcting for test sensitivity; (ii) propagating the uncertainty in test sensitivity; and (iii) including correlation over time and space. We validated the model in simulated scenarios, showing that the model is reliable when the sample size is at least 500, the pool size below 20, and the true prevalence below 5%. We applied the model to 1.49 million pooled tests collected in Switzerland in 2021-2022 in schools, care centres, and workplaces. We identified similar dynamics in all three settings, with prevalence peaking at 4-5% during winter 2022. We also identified differences across regions. Prevalence estimates in schools were correlated with reported cases, hospitalizations, and deaths (coefficient 0.84 to 0.90). We conclude that in many practical situations, the pooled test design is a reliable and affordable alternative for the surveillance of SARS-CoV-2 and other viruses.
Collapse
Affiliation(s)
- Julien Riou
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Department of Epidemiology and Health Systems, Unisanté, Center for Primary Care and Public Health & University of Lausanne, Lausanne, Switzerland
| | - Erik Studer
- Federal Office of Public Health, Liebefeld, Switzerland
| | - Anna Fesser
- Federal Office of Public Health, Liebefeld, Switzerland
| | | | - Nicola Low
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Matthias Egger
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Centre for Infectious Disease Epidemiology and Research, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Anthony Hauser
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- INSERM, Sorbonne Université, Pierre Louis Institute of Epidemiology and Public Health, Paris, France
| |
Collapse
|
80
|
Yang S, Guo J, Xiong Y, Han G, Luo T, Peng S, Liu J, Hu T, Zha Y, Lin X, Tan Y, Zhang J. Unraveling the genetic and molecular landscape of sepsis and acute kidney injury: A comprehensive GWAS and machine learning approach. Int Immunopharmacol 2024; 137:112420. [PMID: 38851159 DOI: 10.1016/j.intimp.2024.112420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 05/29/2024] [Accepted: 06/03/2024] [Indexed: 06/10/2024]
Abstract
OBJECTIVES This study aimed to explore the underlying mechanisms of sepsis and acute kidney injury (AKI), including sepsis-associated AKI (SA-AKI), a frequent complication in critically ill sepsis patients. METHODS GWAS data was analyzed for genetic association between AKI and sepsis. Then, we systematically applied three distinct machine learning algorithms (LASSO, SVM-RFE, RF) to rigorously identify and validate signature genes of SA-AKI, assessing their diagnostic and prognostic value through ROC curves and survival analysis. The study also examined the functional and immunological aspects of these genes, potential drug targets, and ceRNA networks. A mouse model of sepsis was created to test the reliability of these signature genes. RESULTS LDSC confirmed a positive genetic correlation between AKI and sepsis, although no significant shared loci were found. Bidirectional MR analysis indicated mutual increased risks of AKI and sepsis. Then, 311 key genes common to sepsis and AKI were identified, with 42 significantly linked to sepsis prognosis. Six genes, selected through LASSO, SVM-RFE, and RF algorithms, showed excellent predictive performance for sepsis, AKI, and SA-AKI. The models demonstrated near-perfect AUCs in both training and testing datasets, and a perfect AUC in a sepsis mouse model. Significant differences in immune cells, immune-related pathways, HLA, and checkpoint genes were found between high- and low-risk groups. The study identified 62 potential drug treatments for sepsis and AKI and constructed a ceRNA network. CONCLUSIONS The identified signature genes hold potential clinical applications, including prognostic evaluation and targeted therapeutic strategies for sepsis and AKI. However, further research is needed to confirm these findings.
Collapse
Affiliation(s)
- Sha Yang
- Guizhou University Medical College, Guiyang 550025, Guizhou Province, China
| | - Jing Guo
- Guizhou University Medical College, Guiyang 550025, Guizhou Province, China
| | - Yunbiao Xiong
- Department of Neurosurgery, Guizhou Provincial People's Hospital, Guiyang, China
| | - Guoqiang Han
- Department of Neurosurgery, Guizhou Provincial People's Hospital, Guiyang, China
| | - Tao Luo
- Department of Neurosurgery, Guizhou Provincial People's Hospital, Guiyang, China
| | - Shuo Peng
- Department of Neurosurgery, Guizhou Provincial People's Hospital, Guiyang, China
| | - Jian Liu
- Guizhou University Medical College, Guiyang 550025, Guizhou Province, China; Department of Neurosurgery, Guizhou Provincial People's Hospital, Guiyang, China
| | - Tieyi Hu
- Department of Neurology, the Affiliated Dazu Hospital of Chongqing Medical University , China
| | - Yan Zha
- Guizhou University Medical College, Guiyang 550025, Guizhou Province, China; Department of Nephrology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Xin Lin
- Department of Nephrology, Guizhou Provincial People's Hospital, Guiyang, China.
| | - Ying Tan
- Department of Neurosurgery, Guizhou Provincial People's Hospital, Guiyang, China.
| | - Jiqin Zhang
- Department of Anesthesiology, Guizhou Provincial People's Hospital, Guiyang, China.
| |
Collapse
|
81
|
Luo S, Liu Z, Zhang J, Chen Y, Lei Y, Gao X, Liu C, Chen Y, Liu C, Yan P, Chen Y, Li H, Zhao C, Wang H, Wang K, Wang C, Tian R, Yang P. Three-gene signature revealing the dynamics of lymphocyte infiltration in subchondral bone during osteoarthritis progression. Int Immunopharmacol 2024; 137:112431. [PMID: 38897125 DOI: 10.1016/j.intimp.2024.112431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 06/04/2024] [Accepted: 06/05/2024] [Indexed: 06/21/2024]
Abstract
Osteoarthritis (OA), a degenerative joint disorder, has an unclear immune infiltration mechanism in subchondral bone (SCB). Thus, this study aims to discern immune infiltration variations in SCB between early- and late-stages of OA and identify pertinent biomarkers. Utilizing the GSE515188 bulk-seq profile from the Gene Expression Omnibus database, we performed single-sample gene-set enrichment analysis alongside weighted gene co-expression network analysis to identify key cells and immune-related genes (IRGs) involved in SCB at both stages. At the meanwhile, differentially expressed genes (DEGs) were identified in the same dataset and intersected with IRGs to find IR-DEGs. Protein-protein interaction network and enrichment analyses and further gene filtering using LASSO regression led to the discovery of potential biomarkers, which were then validated by ROC curve analysis, single-cell RNA sequencing, qRT-PCR, western blot and immunofluorescence. ScRNA-seq analysis using GSE196678, qRT-PCR, western blot and immunofluorescence results confirmed the upregulation of their expression levels in early-stage OA SCB samples. Our comprehensive analysis revealed lymphocytes infiltration as a major feature in early OA SCB. A total of 13 IR-DEGs were identified, showing significant enrichment in T- or B-cell activation pathways. Three of them (CD247, POU2AF1, and TNFRSF13B) were selected via the LASSO regression analysis, and results from the ROC curve analyses indicated the diagnostic efficacy of these 3 genes as biomarkers. These findings may aid in investigating the mechanisms of SCB immune infiltration in OA, stratifying OA progression, and identifying relevant therapeutic targets.
Collapse
Affiliation(s)
- Sen Luo
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Zeyu Liu
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Jiewen Zhang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Yuanyuan Chen
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Yutian Lei
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Xu Gao
- Department of Orthopedics, Honghui Hospital, Xi'an, Shaanxi, China
| | - ChengYan Liu
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Yutao Chen
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Chenkun Liu
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Peng Yan
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Yang Chen
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Heng Li
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Chuanchuan Zhao
- Department of Operating Room, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Haifan Wang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Kunzheng Wang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Chunsheng Wang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China
| | - Run Tian
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China.
| | - Pei Yang
- Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China.
| |
Collapse
|
82
|
Tang X, Liu Y, Zhao J, Fu C, Yang W. Subtyping of gastric cancer based on basement membrane genes that stratifies the prognosis, immune infiltration and therapeutic response. Discov Oncol 2024; 15:362. [PMID: 39164593 PMCID: PMC11336019 DOI: 10.1007/s12672-024-01238-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 08/13/2024] [Indexed: 08/22/2024] Open
Abstract
Gastric cancer (GC) is highly heterogeneous and prone to metastasis, which are obstacles to the effectiveness of treatment. The basement membrane (BM) acts as a barrier to tumor cell invasion and metastasis. It is critical to investigate the relationship between BM status, metastasis, and patient prognosis. In several large cohorts, we investigated BM gene expression-based molecular classification and risk-prognosis models for GC, examined tumor microenvironment (TME) differences among different molecular subtypes, and developed risk models in predicting prognosis, immunotherapy effectiveness, and chemotherapy resistance. Three GC subtypes (BMclusterA/B/C) based on BM gene expression status were discovered. Each of the three GC subtypes has unique immune infiltration and activated oncogenic signals. Moreover, a 6-gene score (BMscore) predictive model was developed. The low BMscore group had a high tumor mutation burden, high immunogenicity, and low RHOJ expression levels, implying that individuals with GC in this category may be more susceptible to immunotherapy and treatment. The EMT subtype showed a considerably higher BMscore than the other subtypes in the Asian Organization for Research on Cancer (ACRG) molecular classification. Endothelial cells, smooth muscle cells, and fibroblasts may be engaged in regulating BM reorganization in GC progression, according to single-cell transcriptome analyses. In conclusion, we defined a novel molecular classification of GC based on BM genes, developed a prognostic risk model, and elucidated the cell subpopulations involved in BM remodeling at the single-cell level. This study has deepened the understanding of the relationship between GC metastasis and BM alterations, achieved prognostic stratification, and guided therapy.
Collapse
Affiliation(s)
- Xin Tang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushanhu Road, Hefei, 230031, Anhui, China
- Science Island Branch, Graduate School of University of Science and Technology of China, Hefei, 230026, China
| | - Yu Liu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushanhu Road, Hefei, 230031, Anhui, China
- Science Island Branch, Graduate School of University of Science and Technology of China, Hefei, 230026, China
| | - Jiarong Zhao
- Medical Pathology Center, Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, China
| | - Changfang Fu
- Department of Pharmacy, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, China.
- Anhui Provincial Key Laboratory of Precision Pharmaceutical Preparations and Clinical Pharmacy, Hefei, 230001, Anhui, China.
| | - Wulin Yang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushanhu Road, Hefei, 230031, Anhui, China.
- Science Island Branch, Graduate School of University of Science and Technology of China, Hefei, 230026, China.
| |
Collapse
|
83
|
Lekkas D, Jacobson NC. Breaking the silence: leveraging social interaction data to identify high-risk suicide users online using network analysis and machine learning. Sci Rep 2024; 14:19395. [PMID: 39169143 PMCID: PMC11339441 DOI: 10.1038/s41598-024-70282-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 08/14/2024] [Indexed: 08/23/2024] Open
Abstract
Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, STB risk detection may be improved through development of uniquely insightful digital markers. Focusing on Sanctioned Suicide, an online pro-choice suicide forum, this work derived 17 egocentric network features to capture dynamics of social interaction and engagement within this uniquely uncensored community. Using network data generated from over 3.2 million unique interactions of N = 192 individuals, n = 48 of which were determined to be highest risk users (HRUs), a machine learning classification model was trained, validated, and tested to predict HRU status. Model prediction dynamics were analyzed using introspection techniques to uncover patterns in feature influence and highlight social phenomena. The model achieved a test AUC = 0.73 ([0.61, 0.85], 95% CI), suggesting that network-based socio-behavioral patterns of online interaction can signal for heightened suicide risk. Transitivity, density, and in-degree centrality were among the most important features driving this performance. Moreover, predicted HRUs tended to be targets of social exchanges with lesser frequency and possessed egocentric networks with "small world" network properties. Through the implementation of an underutilized method on an unlikely data source, findings support future incorporation of network-based social interaction features in descriptive, predictive, and preventative STB research.
Collapse
Affiliation(s)
- Damien Lekkas
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway, Suite 300, Office #313S, Lebanon, NH, 03766, USA.
- Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, USA.
| | - Nicholas C Jacobson
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway, Suite 300, Office #313S, Lebanon, NH, 03766, USA
- Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, USA
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
- Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA
| |
Collapse
|
84
|
Tsai HHD, Ford LC, Burnett SD, Dickey AN, Wright FA, Chiu WA, Rusyn I. Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human-Induced Pluripotent Stem-Cell-Derived Cardiomyocytes. Chem Res Toxicol 2024; 37:1428-1444. [PMID: 39046974 DOI: 10.1021/acs.chemrestox.4c00193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
Environmental chemicals may contribute to the global burden of cardiovascular disease, but experimental data are lacking to determine which substances pose the greatest risk. Human-induced pluripotent stem cell (iPSC)-derived cardiomyocytes are a high-throughput cardiotoxicity model that is widely used to test drugs and chemicals; however, most studies focus on exploring electro-physiological readouts. Gene expression data may provide additional molecular insights to be used for both mechanistic interpretation and dose-response analyses. Therefore, we hypothesized that both transcriptomic and functional data in human iPSC-derived cardiomyocytes may be used as a comprehensive screening tool to identify potential cardiotoxicity hazards and risks of the chemicals. To test this hypothesis, we performed concentration-response analysis of 464 chemicals from 12 classes, including both pharmaceuticals and nonpharmaceutical substances. Functional effects (beat frequency, QT prolongation, and asystole), cytotoxicity, and whole transcriptome response were evaluated. Points of departure were derived from phenotypic and transcriptomic data, and risk characterization was performed. Overall, 244 (53%) substances were active in at least one phenotype; as expected, pharmaceuticals with known cardiac liabilities were the most active. Positive chronotropy was the functional phenotype activated by the largest number of tested chemicals. No chemical class was particularly prone to pose a potential hazard to cardiomyocytes; a varying proportion (10-44%) of substances in each class had effects on cardiomyocytes. Transcriptomic data showed that 69 (15%) substances elicited significant gene expression changes; most perturbed pathways were highly relevant to known key characteristics of human cardiotoxicants. The bioactivity-to-exposure ratios showed that phenotypic- and transcriptomic-based POD led to similar results for risk characterization. Overall, our findings demonstrate how the integrative use of in vitro transcriptomic and phenotypic data from iPSC-derived cardiomyocytes not only offers a complementary approach for hazard and risk prioritization, but also enables mechanistic interpretation of the in vitro test results to increase confidence in decision-making.
Collapse
Affiliation(s)
- Han-Hsuan D Tsai
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
| | - Lucie C Ford
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
| | - Sarah D Burnett
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
| | - Allison N Dickey
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27603, United States
| | - Fred A Wright
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27603, United States
- Department of Statistics and Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27603, United States
| | - Weihsueh A Chiu
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
| | - Ivan Rusyn
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
| |
Collapse
|
85
|
Kerioui M, Iasonos A, Gönen M, Arfé A. New clinical trial design borrowing information across patient subgroups based on fusion-penalized regression models. Stat Methods Med Res 2024:9622802241267355. [PMID: 39158499 DOI: 10.1177/09622802241267355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/20/2024]
Abstract
In cancer research, basket trials aim to assess the efficacy of a drug using baskets, wherein patients are organized into subgroups according to their tumor type. In this context, using information borrowing strategy may increase the probability of detecting drug efficacy in active baskets, by shrinking together the estimates of the parameters characterizing the drug efficacy in baskets with similar drug activity. Here, we propose to use fusion-penalized logistic regression models to borrow information in the setting of a phase 2 single-arm basket trial with binary outcome. We describe our proposed strategy and assess its performance via a simulation study. We assessed the impact of heterogeneity in drug efficacy, prevalence of each tumor types and implementation of interim analyses on the operating characteristics of our proposed design. We compared our approach with two existing designs, relying on the specification of prior information in a Bayesian framework to borrow information across similar baskets. Notably, our approach performed well when the effect of the drug varied greatly across the baskets. Our approach offers several advantages, including limited implementation efforts and fast computation, which is essential when planning a new trial as such planning requires intensive simulation studies.
Collapse
Affiliation(s)
- Marion Kerioui
- Department of Biostatistics, Memorial Sloan Kettering Cancer Centre, New York, NY, USA
| | - Alexia Iasonos
- Department of Biostatistics, Memorial Sloan Kettering Cancer Centre, New York, NY, USA
| | - Mithat Gönen
- Department of Biostatistics, Memorial Sloan Kettering Cancer Centre, New York, NY, USA
| | - Andrea Arfé
- Department of Biostatistics, Memorial Sloan Kettering Cancer Centre, New York, NY, USA
| |
Collapse
|
86
|
Hu J, Ning Y, Ma Y, Sun L, Chen G. Characterization of RNA Processing Genes in Colon Cancer for Predicting Clinical Outcomes. Biomark Insights 2024; 19:11772719241258642. [PMID: 39161926 PMCID: PMC11331464 DOI: 10.1177/11772719241258642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 05/05/2024] [Indexed: 08/21/2024] Open
Abstract
Objective Colon cancer is associated with multiple levels of molecular heterogeneity. RNA processing converts primary transcriptional RNA to mature RNA, which drives tumourigenesis and its maintenance. The characterisation of RNA processing genes in colon cancer urgently needs to be elucidated. Methods In this study, we obtained 1033 relevant samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to explore the heterogeneity of RNA processing phenotypes in colon cancer. Firstly, Unsupervised hierarchical cluster analysis detected 4 subtypes with specific clinical outcomes and biological features via analysis of 485 RNA processing genes. Next, we adopted the least absolute shrinkage and selection operator (LASSO) as well as Cox regression model with penalty to characterise RNA processing-related prognostic features. Results An RNA processing-related prognostic risk model based on 10 genes including FXR1, MFAP1, RBM17, SAGE1, SNRPA1, SRRM4, ADAD1, DDX52, ERI1, and EXOSC7 was identified finally. A composite prognostic nomogram was constructed by combining this feature with the remaining clinical variables including TNM, age, sex, and stage. Genetic variation, pathway activation, and immune heterogeneity with risk signatures were also analysed via bioinformatics methods. The outcomes indicated that the high-risk subgroup was associated with higher genomic instability, increased proliferative and cycle characteristics, decreased tumour killer CD8+ T cells and poorer clinical prognosis than the low-risk group. Conclusion This prognostic classifier based on RNA-edited genes facilitates stratification of colon cancer into specific subgroups according to TNM and clinical outcomes, genetic variation, pathway activation, and immune heterogeneity. It can be used for diagnosis, classification and targeted treatment strategies comparable to current standards in precision medicine. It provides a rationale for elucidation of the role of RNA editing genes and their clinical significance in colon cancer as prognostic markers.
Collapse
Affiliation(s)
- Jianwen Hu
- Gastrointestinal Surgery Department, Peking University First Hospital, Beijing, China
- Laboratory Department of Anzhen Hospital, Capital Medical University, Beijing, China
| | - Yingze Ning
- Department of Thoracic Surgery, Peking University Third Hospital, Beijing, China
| | - Yongchen Ma
- Endoscopy Center, Peking University First Hospital, Beijing, PR China
| | - Lie Sun
- Gastrointestinal Surgery Department, Peking University First Hospital, Beijing, China
| | - Guowei Chen
- Gastrointestinal Surgery Department, Peking University First Hospital, Beijing, China
| |
Collapse
|
87
|
Naujokaitis-Lewis I, Endicott S, Gaudreault E, Maisonneuve F, Robinson SA. Milkweed in agricultural field margins - A neonicotinoid exposure route for pollinators at multiple life stages. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 951:175622. [PMID: 39163943 DOI: 10.1016/j.scitotenv.2024.175622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 08/15/2024] [Accepted: 08/16/2024] [Indexed: 08/22/2024]
Abstract
Neonicotinoid insecticides move from targeted crops to wildflowers located in adjacent field margins, acting as a potential exposure source for wild pollinators and insect species of conservation concern, including monarch butterflies. Monarchs rely on milkweed over multiple life stages, including as a host plant for eggs and a food source for both larvae (leaves) and adults (flowers). Milkweeds, which are closely associated with field margins, can contain neonicotinoid residues, but previous assessments are constrained to a single plant tissue type. In 2017 and 2018, we sampled milkweeds from 95 field margins adjacent to crop fields (corn, soybean, hay, wheat, and barley) in agricultural landscapes of eastern Ontario, Canada. Milkweeds were sampled during the flower blooming period and leaves and flower tissues were analysed. The neonicotinoids acetamiprid, clothianidin, thiamethoxam, and thiacloprid were detected. Maximum concentrations in leaf samples included 10.30 ng/g of clothianidin in 2017, and 24.4 ng/g of thiamethoxam in 2018. Clothianidin and thiamethoxam percent detections in flowers (72 % and 61 %, respectively) were significantly higher than detections in leaves (24 % and 31 %, respectively). Thiamethoxam concentrations were significantly higher in paired flower samples than leaf samples (median 0.33 ng/g vs <0.07 ng/g) while clothianidin concentrations also trended higher in flowers (median 0.18-0.55 ng/g vs <0.18 ng/g). Only thiamethoxam showed significant differences between years, and we found no effect of crop type, with hay, soybean and corn fields all yielding 50-56 % detections in leaves. We found significantly higher concentrations in older milkweed flowers than young flowers or leaves (medians 0.87 ng/g vs <0.18 ng/g and 0.45 ng/g vs <0.07 ng/g for clothianidin and thiamethoxam, respectively). Our results highlight the importance of considering variation in milkweed tissue type and age of flowers in neonicotinoid exposure risk assessments. Efforts to increase milkweed availability in agricultural landscapes should consider how exposure to neonicotinoids can be mitigated.
Collapse
Affiliation(s)
- Ilona Naujokaitis-Lewis
- Landscape Science and Technology Division, National Wildlife Research Centre, Environment and Climate Change Canada, Ottawa, ON, Canada.
| | - Sarah Endicott
- Landscape Science and Technology Division, National Wildlife Research Centre, Environment and Climate Change Canada, Ottawa, ON, Canada
| | - Emma Gaudreault
- Production Insurance, Business Risk Management Branch, Ministry of Agriculture and Food, Kelowna, BC, Canada
| | - France Maisonneuve
- Ecotoxicology and Wildlife Health Division, National Wildlife Research Centre, Environment and Climate Change Canada, Ottawa, ON, Canada
| | - Stacey A Robinson
- Ecotoxicology and Wildlife Health Division, National Wildlife Research Centre, Environment and Climate Change Canada, Ottawa, ON, Canada.
| |
Collapse
|
88
|
Fukae M, Rogers J, Garcia R, Tachibana M, Shimizu T. Bayesian sparse regression for exposure-response analyses of efficacy and safety endpoints to justify the clinical dose of valemetostat for adult T-cell leukemia/lymphoma. CPT Pharmacometrics Syst Pharmacol 2024. [PMID: 39155584 DOI: 10.1002/psp4.13203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 06/28/2024] [Accepted: 07/01/2024] [Indexed: 08/20/2024] Open
Abstract
Valemetostat is an oral inhibitor of enhancer of zeste homolog (EZH) 2 and EZH1 approved in Japan for the treatment of adult T-cell leukemia/lymphoma (ATLL). To support the approved daily dose of 200 mg and inform dose adjustments in patients with ATLL, Bayesian exposure-response analyses were conducted using data from two clinical trials. The analyses included two efficacy endpoints, overall response by central and investigator assessments in patients with ATLL (n = 38, 150-200 mg), and six safety endpoints in patients with non-Hodgkin lymphoma (n = 102, 150-300 mg), which included grade ≥3 laboratory values for anemia, absolute neutrophil count decreased, and platelet count decreased; any grade ≥3 treatment-emergent adverse event (TEAE); and dose reductions and dose interruptions due to TEAEs. A slightly positive relationship was observed between unbound exposure and efficacy endpoints. A steeper relationship was observed in safety endpoints, compared with efficacy. Candidate covariate effects, except intercepts of the baseline laboratory values, were regularized via spike and slab priors in a Bayesian framework; only the laboratory values for corresponding hematologic TEAEs were shown to be of substantial impact. The target exposure range was established by defining a modified region of practical equivalence (184-887 ng·h/mL), which was expected to provide satisfactory efficacy and acceptable safety within the range of available exposure data. The simulated exposure range considering inter-individual variability showed that 200 mg could reach target exposure in the overall population and across subpopulations of interest, supporting the use of valemetostat 200 mg in patients with ATLL.
Collapse
Affiliation(s)
| | - James Rogers
- Metrum Research Group, Tariffville, Connecticut, USA
| | - Ramon Garcia
- Metrum Research Group, Tariffville, Connecticut, USA
| | | | | |
Collapse
|
89
|
Liu Y, Yao X, Zhao W, Xu J, Zhang H, Huang T, Wu C, Yang J, Tang C, Ye Q, Hu W, Wang Q. A comprehensive analysis of TRP-related gene signature, and immune infiltration in patients with colorectal cancer. Discov Oncol 2024; 15:357. [PMID: 39154317 PMCID: PMC11330954 DOI: 10.1007/s12672-024-01227-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Accepted: 08/07/2024] [Indexed: 08/19/2024] Open
Abstract
BACKGROUND Transient receptor potential (TRP) channels are involved in the development and progression of tumors. However, their role in colorectal cancer (CRC) remains unclear, and this study aims to investigate the role of TRP-related genes in CRC. METHODS Data was obtained from The Cancer Genome Atlas (TCGA) database, and analyses were conducted on the GSE14333 and GSE38832 datasets to assess the prognosis and mark TRP-related genes (TRGs). Subsequently, clustering analysis and immune infiltration analysis were performed to explore the relevant TRGs. In vitro validation of key TRGs' gene and protein expression was conducted using human colon cancer cells. RESULTS Compared to normal tissues, 8 TRGs were significantly upregulated in CRC, while 11 were downregulated. TRPA1 was identified as a protective prognostic factor, whereas TRPM5 (HR = 1.349), TRPV4 (HR = 1.289), and TRPV3 (HR = 1.442) were identified as prognostic risk factors. Receiver operating characteristic (ROC) curves and Kaplan-Meier (KM) analyses yielded similar results. Additionally, lower expression of TRPA1 and higher expression of TRPV4 and TRPM5 were negatively correlated with patient prognosis, and experimental validation confirmed the underexpression of TRPA1 and overexpression of TRPV4 and TRPM5 in CRC cell lines. CONCLUSION This study identifies a TRP channel-related prognosis in CRC, providing a novel approach to stratifying CRC prognosis.
Collapse
Affiliation(s)
- Yicheng Liu
- Department of Anorectal Surgery, Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Xiaobing Yao
- Gastrointestinal surgery, Yueyang hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wenjun Zhao
- Department of Anorectal Surgery, Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Jin Xu
- Department of Anorectal Surgery, Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Haiyan Zhang
- Department of Anorectal Surgery, Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Ting Huang
- Department of Anorectal Surgery, Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Chuang Wu
- Department of Anorectal Surgery, Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Jiajia Yang
- Department of Anorectal Surgery, Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Cheng Tang
- Department of Anorectal Surgery, Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China
| | - Qianqian Ye
- Department of Anorectal Surgery, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Weiye Hu
- Department of Liver, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Qingming Wang
- Department of Anorectal Surgery, Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China.
- Department of Anorectal Surgery, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| |
Collapse
|
90
|
Fritsche M, Majumdar A, Strickland L, Liebana Garcia S, Bogacz R, Lak A. Temporal regularities shape perceptual decisions and striatal dopamine signals. Nat Commun 2024; 15:7093. [PMID: 39154025 PMCID: PMC11330509 DOI: 10.1038/s41467-024-51393-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 08/05/2024] [Indexed: 08/19/2024] Open
Abstract
Perceptual decisions should depend on sensory evidence. However, such decisions are also influenced by past choices and outcomes. These choice history biases may reflect advantageous strategies to exploit temporal regularities of natural environments. However, it is unclear whether and how observers can adapt their choice history biases to different temporal regularities, to exploit the multitude of temporal correlations that exist in nature. Here, we show that male mice adapt their perceptual choice history biases to different temporal regularities of visual stimuli. This adaptation was slow, evolving over hundreds of trials across several days. It occurred alongside a fast non-adaptive choice history bias, limited to a few trials. Both fast and slow trial history effects are well captured by a normative reinforcement learning algorithm with multi-trial belief states, comprising both current trial sensory and previous trial memory states. We demonstrate that dorsal striatal dopamine tracks predictions of the model and behavior, suggesting that striatal dopamine reports reward predictions associated with adaptive choice history biases. Our results reveal the adaptive nature of perceptual choice history biases and shed light on their underlying computational principles and neural correlates.
Collapse
Affiliation(s)
- Matthias Fritsche
- Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, UK.
| | - Antara Majumdar
- Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, UK
| | - Lauren Strickland
- Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, UK
- Institute of Behavioral Neuroscience, University College London, London, UK
| | | | - Rafal Bogacz
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Armin Lak
- Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, UK.
| |
Collapse
|
91
|
Yuan Z, Wang Y, Xu S, Zhang M, Tang J. Construction of a prognostic model for colon cancer by combining endoplasmic reticulum stress responsive genes. J Proteomics 2024; 309:105284. [PMID: 39159861 DOI: 10.1016/j.jprot.2024.105284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 07/31/2024] [Accepted: 08/15/2024] [Indexed: 08/21/2024]
Abstract
Endoplasmic reticulum stress may affect the occurrence and development of cancer. However, its effect on the prognosis of colon cancer (CC) patients is not clear yet. Herein, based on TCGA database, we screened 15 endoplasmic reticulum stress responsive genes (ERSRGs) associated with the prognosis of CC patients by Cox regression. By LASSO and multivariate Cox regression analyses, a prognostic risk assessment model involving 12 genes (DNAJB2, EIF4A1, YPEL4, COQ10A, IRX3, ASPHD1, NTRK2, TRIM39, XBP1, GRIN2B, LRRC59, and RORC) was built. The survival curves indicated that patients in the low-risk group had good prognosis. ROC curves demonstrated a good performance of this 12-gene prognostic model, and the Riskscore could be considered as an independent prognostic factor. Patients in low-risk group benefit more from immune checkpoint inhibitor and immune checkpoint blockade (ICB) treatment. Besides, the enrichment analysis suggested a remarkable difference in Ca2+ signaling in both groups. Finally, based on the cMAP database, we identified several potential drugs that could target high-risk groups, such as Dasatinib, GNF-2, Saracatinib, and WZ-1-84. To sum up, our research constructed an ERSRGs-characteristic prognostic model. The model is a promising biomarker for prediction of clinical outcomes and immune therapy response of CC patients. SIGNIFICANCE: Based on the transcriptomic data of colon cancer in the TCGA database, this study screens 12 endoplasmic reticulum stress-related genes (ERSRGs), including DNAJB2, EIF4A1, YPEL4, COQ10A, IRX3, ASPHD1, NTRK2, TRIM39, XBP1, asphD1, NTRK2. GRIN2B, LRRC59, and RORC, and a prognostic model was constructed. This model can be used as a predictor of prognosis and immunotherapy response in colon cancer patients. At the same time, model-based prediction of drugs can also be a potential option for colon cancer treatment in the future.
Collapse
Affiliation(s)
- Zhibin Yuan
- Department of General Surgery, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - Yi Wang
- Department of General Surgery, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - Song Xu
- Department of General Surgery, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China
| | - Meng Zhang
- Department of General Surgery, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China.
| | - Jianjun Tang
- Department of General Surgery, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China.
| |
Collapse
|
92
|
Aubona G, Mezzomo P, Sedio BE, Staab M, Volf M. Neighbourhood effects on herbivory damage and chemical profiles in short-rotation coppice willows and their hybrids. PHYTOCHEMISTRY 2024; 228:114249. [PMID: 39155032 DOI: 10.1016/j.phytochem.2024.114249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 08/08/2024] [Accepted: 08/12/2024] [Indexed: 08/20/2024]
Abstract
Short rotation coppices (SRCs) represent an important source of biomass. Since they are grown in various mixtures, SRCs represent an excellent opportunity for assessing the effects of local plant neighbourhoods on their performance. We used a common garden experiment consisting of plots that varied in genotype diversity of SRC willows to test for the effects of chemical traits of individual plants and chemical variation in the plots where they grew on insect herbivory. We also explored whether the composition of willows planted in a plot affected their chemistry. To do this, we performed untargeted metabolomics and quantified various chemical traits related to the total set of metabolites we detected, flavonoids, and salicinoids in four willow genotypes. We measured the leaf herbivory that the plants suffered. The genotypes differed in most chemical traits, yet we found only limited effects of individual traits on herbivory damage. Instead, herbivory damage was positively correlated with structural variation in salicinoids in a plot. When analysing the effects of plot chemical variation on herbivory damage separately for each genotype, we found both positive and negative correlations between the two, suggesting both associational resistance and susceptibility. Finally, we also observed a significant effect of the interaction between genotype and plot composition on structural variation in plant chemistry. Overall, our results suggest that high chemical variation in mixed willow SRCs does not necessarily lower the herbivory damage, possibly due to spillover effects of insect herbivores among genotypes. Our results also show that different genotypes respond differently to plot composition in terms of herbivory damage and chemical composition, which may affect their suitability for growing in mixed stands.
Collapse
Affiliation(s)
- Gibson Aubona
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic; Faculty of Science, Department of Zoology, University of South Bohemia, Ceske Budejovice, Czech Republic
| | - Priscila Mezzomo
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic; Faculty of Science, Department of Zoology, University of South Bohemia, Ceske Budejovice, Czech Republic
| | - Brian E Sedio
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA; Smithsonian Tropical Research Institute, Ancón, Panama
| | - Michael Staab
- Ecological Networks, Technical University Darmstadt, Darmstadt, Germany
| | - Martin Volf
- Biology Centre of the Czech Academy of Sciences, Ceske Budejovice, Czech Republic; Faculty of Science, Department of Zoology, University of South Bohemia, Ceske Budejovice, Czech Republic.
| |
Collapse
|
93
|
McCain JSP, Britten GL, Hackett SR, Follows MJ, Li GW. Microbial reaction rate estimation using proteins and proteomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.13.607198. [PMID: 39185172 PMCID: PMC11343155 DOI: 10.1101/2024.08.13.607198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Microbes transform their environments using diverse enzymatic reactions. However, it remains challenging to measure microbial reaction rates in natural environments. Despite advances in global quantification of enzyme abundances, the individual relationships between enzyme abundances and their reaction rates have not been systematically examined. Using matched proteomic and reaction rate data from microbial cultures, we show that enzyme abundance is often insufficient to predict its corresponding reaction rate. However, we discovered that global proteomic measurements can be used to make accurate rate predictions of individual reaction rates (median R 2 = 0.78). Accurate rate predictions required only a small number of proteins and they did not need explicit prior mechanistic knowledge or environmental context. These results indicate that proteomes are encoders of cellular reaction rates, potentially enabling proteomic measurements in situ to estimate the rates of microbially mediated reactions in natural systems. Significance One of the most basic phenotypes of a microbe is its set of associated reaction rates, but quantifying these rates in situ remains extremely challenging, especially in natural systems. We used molecular data and statistical models to estimate microbial rates in steady state cultures. We found that many reaction rates are highly predictable using proteomic data, though single proteins are typically not informative for their associated reaction rates. This result suggests that gene expression data from complex microbial communities could be used to estimate in situ reaction rates, providing new clues into the lives and environmental function of microbes.
Collapse
|
94
|
Ondo I, Dhanjal-Adams KL, Pironon S, Silvestro D, Colli-Silva M, Deklerck V, Grace OM, Monro AK, Nicolson N, Walker B, Antonelli A. Plant diversity darkspots for global collection priorities. THE NEW PHYTOLOGIST 2024. [PMID: 39152543 DOI: 10.1111/nph.20024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 07/07/2024] [Indexed: 08/19/2024]
Abstract
More than 15% of all vascular plant species may remain scientifically undescribed, and many of the > 350 000 described species have no or few geographic records documenting their distribution. Identifying and understanding taxonomic and geographic knowledge shortfalls is key to prioritising future collection and conservation efforts. Using extensive data for 343 523 vascular plant species and time-to-event analyses, we conducted multiple tests related to plant taxonomic and geographic data shortfalls, and identified 33 global diversity darkspots (those 'botanical countries' predicted to contain most undescribed and not yet recorded species). We defined priority regions for future collection according to several socio-economic and environmental scenarios. Most plant diversity darkspots are found within global biodiversity hotspots, with the exception of New Guinea. We identify Colombia, Myanmar, New Guinea, Peru, Philippines and Turkey as global collection priorities under all environmental and socio-economic conditions considered. Our study provides a flexible framework to help accelerate the documentation of global plant diversity for the implementation of conservation actions. As digitisation of the world's herbaria progresses, collection and conservation priorities may soon be identifiable at finer scales.
Collapse
Affiliation(s)
- Ian Ondo
- Royal Botanic Gardens, Kew, Richmond, TW9 3AE, UK
- UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), Cambridge, CB3 0DL, UK
| | | | - Samuel Pironon
- Royal Botanic Gardens, Kew, Richmond, TW9 3AE, UK
- UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), Cambridge, CB3 0DL, UK
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, E1 4DQ, UK
| | - Daniele Silvestro
- Department of Biology, University of Fribourg, Fribourg, 1700, Switzerland
- Department of Biological and Environmental Sciences, Gothenburg Global Biodiversity Centre, University of Gothenburg, Gothenburg, 41319, Sweden
| | | | - Victor Deklerck
- Royal Botanic Gardens, Kew, Richmond, TW9 3AE, UK
- Meise Botanic Garden, Meise, 1860, Belgium
| | - Olwen M Grace
- Royal Botanic Gardens, Kew, Richmond, TW9 3AE, UK
- Royal Botanic Garden Edinburgh, Edinburgh, EH3 5LR, UK
| | | | | | | | - Alexandre Antonelli
- Royal Botanic Gardens, Kew, Richmond, TW9 3AE, UK
- Department of Biological and Environmental Sciences, Gothenburg Global Biodiversity Centre, University of Gothenburg, Gothenburg, 41319, Sweden
- Department of Biology, University of Oxford, Oxford, OX1 3RB, UK
| |
Collapse
|
95
|
Lu EH, Ford LC, Rusyn I, Chiu WA. Reducing uncertainty in dose-response assessments by incorporating Bayesian benchmark dose modeling and in vitro data on population variability. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2024. [PMID: 39148436 DOI: 10.1111/risa.17451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 07/17/2024] [Accepted: 07/25/2024] [Indexed: 08/17/2024]
Abstract
There are two primary sources of uncertainty in the interpretability of toxicity values, like the reference dose (RfD): estimates of the point of departure (POD) and the absence of chemical-specific human variability data. We hypothesize two solutions-employing Bayesian benchmark dose (BBMD) modeling to refine POD determination and combining high-throughput toxicokinetic modeling with population-based toxicodynamic in vitro data to characterize chemical-specific variability. These hypotheses were tested by deriving refined probabilistic estimates for human doses corresponding to a specific effect size (M) in the Ith population percentile (HDM I) across 19 Superfund priority chemicals. HDM I values were further converted to biomonitoring equivalents in blood and urine for benchmarking against human data. Compared to deterministic default-based RfDs, HDM I values were generally more protective, particularly influenced by chemical-specific data on interindividual variability. Incorporating chemical-specific in vitro data improved precision in probabilistic RfDs, with a median 1.4-fold reduction in uncertainty variance. Comparison with US Environmental Protection Agency's Exposure Forecasting exposure predictions and biomonitoring data from the National Health and Nutrition Examination Survey identified chemicals with margins of exposure nearing or below one. Overall, to mitigate uncertainty in regulatory toxicity values and guide chemical risk management, BBMD modeling and chemical-specific population-based human in vitro data are essential.
Collapse
Affiliation(s)
- En-Hsuan Lu
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas, USA
| | - Lucie C Ford
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas, USA
| | - Ivan Rusyn
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas, USA
| | - Weihsueh A Chiu
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas, USA
| |
Collapse
|
96
|
Kamil NN, Xiao S, Syed Salleh SN, Xu H, Zhuang CC. Nonlinear impacts of climate anomalies on oil palm productivity. Heliyon 2024; 10:e35798. [PMID: 39170218 PMCID: PMC11337023 DOI: 10.1016/j.heliyon.2024.e35798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/18/2024] [Accepted: 08/04/2024] [Indexed: 08/23/2024] Open
Abstract
Oil palm contributes to various global needs as one of the most productive oil crops, but there exist ongoing concerns regarding its yield reductions and associated environmental impacts resulting from land conversion. This is the first detailed report investigating the nonlinear threats to estate-level oil palm yields posed by El Niño Southern Oscillation (ENSO) in the equatorial Pacific Ocean, a major driver of climate variability. Using the Malaysian Palm Oil Board administrative records on monthly performances reported by oil palm estates through the e-submissions portal spanning from January 2015 to June 2023, we focused on elucidating the impacts of ENSO on fresh fruit bunch yield, oil extraction rate, and oil yield. We found that both El Niño and La Niña conditions, characterized by extreme levels of ENSO indices cumulated over lags of 0-23 months prior to harvest, were associated with statistically significant reductions in yields. Lag association patterns unveiled that production risks were linked to pre-harvest exposure to extreme ENSO indices in various time windows. Subgroup analyses further revealed that the effects were pronounced in labor-intensive estates and those lacking fertilizer investments. This study underscores the necessity for adaptation strategies in response to future climate anomalies.
Collapse
Affiliation(s)
- Nur Nadia Kamil
- Economics and Industry Development Division, Malaysian Palm Oil Board, Selangor, Malaysia
| | - Saizi Xiao
- School of Economics, University of Nottingham Malaysia, Selangor, Malaysia
| | - Sharifah Nabilah Syed Salleh
- Department of Economics, Kulliyyah of Economics and Management Sciences, International Islamic University Malaysia, Kuala Lumpur, Malaysia
| | - Hongbing Xu
- Department of Occupational and Environmental Health, Peking University School of Public Health, Beijing, China
| | | |
Collapse
|
97
|
Lin Q, Zheng Z, Ni H, Xu Y, Nie H. Cellular senescence-Related genes define the immune microenvironment and molecular characteristics in severe asthma patients. Gene 2024; 919:148502. [PMID: 38670389 DOI: 10.1016/j.gene.2024.148502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 04/16/2024] [Accepted: 04/23/2024] [Indexed: 04/28/2024]
Abstract
Recent studies have shown that cellular senescence is involved in the pathogenesis of severe asthma (SA). The objective of this study was to investigate the role of cellular senescence-related genes (CSGs) in the pathogenesis of SA. Here, 54 differentially expressed CSGs were identified in SA patients compared to healthy control individuals. Among the 54 differentially expressed CSGs, 3 CSGs (ETS2, ETS1 and AURKA) were screened using the LASSO regression analysis and logistic regression analysis to establish the CSG-based prediction model to predict severe asthma. Moreover, we found that the protein expression levels of ETS2, ETS1 and AURKA were increased in the severe asthma mouse model. Then, two distinct senescence subtypes of SA with distinct immune microenvironments and molecular biological characteristics were identified. Cluster 1 was characterized by increased infiltration of immature dendritic cells, regulatory T cells, and other cells. Cluster 2 was characterized by increased infiltration levels of eosinophils, neutrophils, and other cells. The molecular biological characteristics of Cluster 1 included aerobic respiration and oxidative phosphorylation, whereas the molecular biological characteristics of Cluster 2 included activation of the immune response and immune receptor activity. Then, we established an Random Forest model to predict the senescence subtypes of SA to guide treatment. Finally, potential drugs were searched for each senescence subgroup of SA patients via the Connectivity Map database. A peroxisome proliferator-activated receptor agonist may be a potential therapeutic drug for patients in Cluster 1, whereas a tachykinin antagonist may be a potential therapeutic drug for patients in Cluster 2. In summary, CSGs are likely involved in the pathogenesis of SA, which may lead to new therapeutic options for SA patients.
Collapse
Affiliation(s)
- Qibin Lin
- Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, China
| | - Zhishui Zheng
- Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, China
| | - Haiyang Ni
- Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, China
| | - Yaqing Xu
- Department of Geriatric Medicine, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, China.
| | - Hanxiang Nie
- Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, China.
| |
Collapse
|
98
|
Beall J, Li H, Martin-Harris B, Neelon B, Elm J, Graboyes E, Hill E. Bayesian hierarchical profile regression for binary covariates. Stat Med 2024; 43:3432-3446. [PMID: 38853284 DOI: 10.1002/sim.10119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/01/2023] [Accepted: 04/30/2024] [Indexed: 06/11/2024]
Abstract
Dysphagia, a common result of other medical conditions, is caused by malfunctions in swallowing physiology resulting in difficulty eating and drinking. The Modified Barium Swallow Study (MBSS), the most commonly used diagnostic tool for evaluating dysphagia, can be assessed using the Modified Barium Swallow Impairment Profile (MBSImP™). The MBSImP assessment tool consists of a hierarchical grouped data structure with multiple domains, a set of components within each domain which characterize specific swallowing physiologies, and a set of tasks scored on a discrete scale within each component. We lack sophisticated approaches to extract patterns of physiologic swallowing impairment from the MBSImP task scores within a component while still recognizing the nested structure of components within a domain. We propose a Bayesian hierarchical profile regression model, which uses a Bayesian profile regression model in conjunction with a hierarchical Dirichlet process mixture model to (1) cluster subjects into impairment profile patterns while respecting the hierarchical grouped data structure of the MBSImP, and (2) simultaneously determine associations between latent profile cluster membership for all components and the outcome of dysphagia severity. We apply our approach to a cohort of patients referred for an MBSS and assessed using the MBSImP. Our research results can be used to inform appropriate intervention strategies, and provide tools for clinicians to make better multidimensional management and treatment decisions for patients with dysphagia.
Collapse
Affiliation(s)
- Jonathan Beall
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Hong Li
- Division of Public Health Sciences, University of California Davis School of Medicine, Sacramento, California, USA
| | - Bonnie Martin-Harris
- Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA
| | - Brian Neelon
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Jordan Elm
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Evan Graboyes
- Department of Otolaryngology-Head & Neck Surgery, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Elizabeth Hill
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA
| |
Collapse
|
99
|
Wang X, Liu T, Sheng Y, Zhang Y, Qiu C, Li M, Cheng Y, Li S, Wang Y, Wu C. Identification and verification of four candidate biomarkers for early diagnosis of osteoarthritis by machine learning. Heliyon 2024; 10:e35121. [PMID: 39157341 PMCID: PMC11328075 DOI: 10.1016/j.heliyon.2024.e35121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 07/22/2024] [Accepted: 07/23/2024] [Indexed: 08/20/2024] Open
Abstract
Background Osteoarthritis (OA) is a common chronic joint disease. This study aimed to investigate possible OA diagnostic biomarkers and to verify their significance in clinical samples. Methods We exploited three datasets from the Gene Expression Omnibus (GEO) database, serving as the training set. We first determined differentially expressed genes and screened candidate diagnostic biomarkers by applying three machine learning algorithms (Random Forest, Least Absolute Shrinkage and Selection Operator logistic regression, Support Vector Machine-Recursive Feature Elimination). Another GEO dataset was used as the validation set. The test set consisted of RNA-sequenced peripheral blood samples collected from patients and healthy donors. Blood samples and chondrocytes were collected for quantitative real-time PCR to confirm expression levels. Receiver operating characteristic curves were generated for individual and combined biomarkers. Results In total, 251 DEGs were screened, where B3GALNT1, SCRG1 and ZNF423 were screened by all three algorithms. The area under the curve (AUC) of various biomarkers in our test set did not reach as high as that in public datasets. GRB10 exhibited highest AUC of 0.947 in the training set but 0.691 in our test set, while the favorable combined model comprising B3GALNT1, GRB10, KLF9 and SCRG1 demonstrated an AUC of 0.986 in the training set, 1.000 in the validation set and 0.836 in our test set. Conclusion We identified a combined model for early diagnosis of OA that includes B3GALNT1, GRB10, KLF9 and SCRG1. This finding offers new avenues for further exploration of mechanisms underlying OA.
Collapse
Affiliation(s)
- Xinyu Wang
- Department of Molecular Orthopaedics, National Center for Orthopaedics, Beijing Research Institute of Traumatology and Orthopaedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China
- Department of Anesthesiology, National Center for Orthopaedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China
| | - Tianyi Liu
- Department of Molecular Orthopaedics, National Center for Orthopaedics, Beijing Research Institute of Traumatology and Orthopaedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yueyang Sheng
- Department of Molecular Orthopaedics, National Center for Orthopaedics, Beijing Research Institute of Traumatology and Orthopaedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China
| | - Yanzhuo Zhang
- Department of Molecular Orthopaedics, National Center for Orthopaedics, Beijing Research Institute of Traumatology and Orthopaedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China
| | - Cheng Qiu
- Department of Orthopaedic Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Manyu Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China
| | - Yuxi Cheng
- Xiangya Stomatological Hospital & Xiangya School of Stomatology, Central South University, Changsha, Hunan, 410008, China
| | - Shan Li
- Department of Molecular Orthopaedics, National Center for Orthopaedics, Beijing Research Institute of Traumatology and Orthopaedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China
| | - Ying Wang
- Department of Molecular Orthopaedics, National Center for Orthopaedics, Beijing Research Institute of Traumatology and Orthopaedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China
| | - Chengai Wu
- Department of Molecular Orthopaedics, National Center for Orthopaedics, Beijing Research Institute of Traumatology and Orthopaedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China
| |
Collapse
|
100
|
Rohde MD, French B, Stewart TG, Harrell FE. Bayesian transition models for ordinal longitudinal outcomes. Stat Med 2024; 43:3539-3561. [PMID: 38853380 DOI: 10.1002/sim.10133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/22/2024] [Accepted: 05/23/2024] [Indexed: 06/11/2024]
Abstract
Ordinal longitudinal outcomes are becoming common in clinical research, particularly in the context of COVID-19 clinical trials. These outcomes are information-rich and can increase the statistical efficiency of a study when analyzed in a principled manner. We present Bayesian ordinal transition models as a flexible modeling framework to analyze ordinal longitudinal outcomes. We develop the theory from first principles and provide an application using data from the Adaptive COVID-19 Treatment Trial (ACTT-1) with code examples in R. We advocate that researchers use ordinal transition models to analyze ordinal longitudinal outcomes when appropriate alongside standard methods such as time-to-event modeling.
Collapse
Affiliation(s)
- Maximilian D Rohde
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Benjamin French
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Thomas G Stewart
- School of Data Science, University of Virginia, Charlottesville, Virginia, USA
| | - Frank E Harrell
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| |
Collapse
|