Screening of genes related to programmed cell death in esophageal squamous cell carcinoma and construction of prognostic model based on transcriptome analysis

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.

A 2PLM-RANK multidimensional forced-choice model and its fast estimation algorithm

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.

Handling dependent samples in meta-analytic structural equation models: A Wishart-based approach

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.

Transcriptomic profiling and risk assessment in bladder cancer: Insights from copper death-related genes

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.

Statistical modeling of acute and chronic pain patient-reported outcomes obtained from ecological momentary assessment

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.

RSV Severe Infection Risk Stratification in a French 5-Year Birth Cohort Using Machine-learning

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.

Genetic Regulatory Perturbation of Gene Expression Impacted by Genomic Introgression in Fiber Development of Allotetraploid Cotton

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.

Application of machine-learning models to predict the ganciclovir and valganciclovir exposure in children using a limited sampling strategy

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.

Modeling multiple-criterion diagnoses by heterogeneous-instance logistic regression

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.

A novel model based on lipid metabolism-related genes associated with immune microenvironment predicts metastasis of breast cancer

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.

A dynamic model to predict early occurrence of acute kidney injury in ICU hospitalized cirrhotic patients: a MIMIC database analysis

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.

Person explanatory multidimensional item response theory with the instrument package in R

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.

A 27-country test of communicating the scientific consensus on climate change

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.

Ancestry-aligned polygenic scores combined with conventional risk factors improve prediction of cardiometabolic outcomes in African populations

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.

Chromosome instability functions as a potential therapeutic reference by enhancing chemosensitivity to BCL-XL inhibitors in colorectal carcinoma

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.

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

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.

Single- and multi-site radiomics may improve overall survival prediction for patients with metastatic lung adenocarcinoma

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.

Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis

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.

Childhood cardiometabolic risk factors associated with the perinatal environment of the maternal-paternal-child triad

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.

Bring Out Your Dead: A Review of the Cost Minimisation Approach in Health Technology Assessment Submissions to the Australian Pharmaceutical Benefits Advisory Committee

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.

Investigating CR1 as an indicated Gene for mild cognitive impairment in type 2 diabetes mellitus

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.

Developmental patterns and computational mechanisms of school-aged children's fairness-related decision making under social comparison

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.

Climate policies that achieved major emission reductions: Global evidence from two decades

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.

Early Diagnostic Markers for Esophageal Squamous Cell Carcinoma: CNA Gene Identification and cfDNA Detection

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.

MulTI-domain self-management in older People wiTh OstEoarthritis and multi-morbidities: protocol for the TIPTOE randomised controlled trial

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.

Deciphering the spatial organization of fibrotic microenvironment in silica particles-induced pulmonary fibrosis

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.

Effectiveness of PCSK9 inhibitors: A Target Trial Emulation framework based on Real-World Electronic Health Records

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.

Global transcriptome analysis identifies critical functional modules associated with multiple abiotic stress responses in microalgae Chromochloris zofingiensis

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.

Surveillance of SARS-CoV-2 prevalence from repeated pooled testing: application to Swiss routine data

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.

Unraveling the genetic and molecular landscape of sepsis and acute kidney injury: A comprehensive GWAS and machine learning approach

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.

Three-gene signature revealing the dynamics of lymphocyte infiltration in subchondral bone during osteoarthritis progression

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.

Subtyping of gastric cancer based on basement membrane genes that stratifies the prognosis, immune infiltration and therapeutic response

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.

Breaking the silence: leveraging social interaction data to identify high-risk suicide users online using network analysis and machine learning

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.

Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human-Induced Pluripotent Stem-Cell-Derived Cardiomyocytes

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.

New clinical trial design borrowing information across patient subgroups based on fusion-penalized regression models

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.

Characterization of RNA Processing Genes in Colon Cancer for Predicting Clinical Outcomes

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.

Milkweed in agricultural field margins - A neonicotinoid exposure route for pollinators at multiple life stages

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.

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

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.

A comprehensive analysis of TRP-related gene signature, and immune infiltration in patients with colorectal cancer

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.

Temporal regularities shape perceptual decisions and striatal dopamine signals

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.

Construction of a prognostic model for colon cancer by combining endoplasmic reticulum stress responsive genes

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.

Neighbourhood effects on herbivory damage and chemical profiles in short-rotation coppice willows and their hybrids

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.

Microbial reaction rate estimation using proteins and proteomes

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.

Plant diversity darkspots for global collection priorities

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.

Reducing uncertainty in dose-response assessments by incorporating Bayesian benchmark dose modeling and in vitro data on population variability

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.

Nonlinear impacts of climate anomalies on oil palm productivity

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.

Cellular senescence-Related genes define the immune microenvironment and molecular characteristics in severe asthma patients

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.

Bayesian hierarchical profile regression for binary covariates

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.

Identification and verification of four candidate biomarkers for early diagnosis of osteoarthritis by machine learning

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.

Bayesian transition models for ordinal longitudinal outcomes

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.