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Chen Y, Zhou C, Zhang X, Chen M, Wang M, Zhang L, Chen Y, Huang L, Sun J, Wang D, Chen Y. Construction of a novel radioresistance-related signature for prediction of prognosis, immune microenvironment and anti-tumour drug sensitivity in non-small cell lung cancer. Ann Med 2025; 57:2447930. [PMID: 39797413 PMCID: PMC11727174 DOI: 10.1080/07853890.2024.2447930] [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] [Received: 06/30/2024] [Revised: 11/26/2024] [Accepted: 12/12/2024] [Indexed: 01/13/2025] Open
Abstract
BACKGROUND Non-small cell lung cancer (NSCLC) is a fatal disease, and radioresistance is an important factor leading to treatment failure and disease progression. The objective of this research was to detect radioresistance-related genes (RRRGs) with prognostic value in NSCLC. METHODS The weighted gene coexpression network analysis (WGCNA) and differentially expressed genes (DEGs) analysis were performed to identify RRRGs using expression profiles from TCGA and GEO databases. The least absolute shrinkage and selection operator (LASSO) regression and random survival forest (RSF) were used to screen for prognostically relevant RRRGs. Multivariate Cox regression was used to construct a risk score model. Then, Immune landscape and drug sensitivity were evaluated. The biological functions exerted by the key gene LBH were verified by in vitro experiments. RESULTS Ninety-nine RRRGs were screened by intersecting the results of DEGs and WGCNA, then 11 hub RRRGs associated with survival were identified using machine learning algorithms (LASSO and RSF). Subsequently, an eight-gene (APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2 and TSPAN2) risk score model was established and demonstrated to be an independent prognostic factor in NSCLC on the basis of Cox regression analysis. The immune landscape and sensitivity to anti-tumour drugs showed significant disparities between patients categorized into different risk score subgroups. In vitro experiments indicated that overexpression of LBH enhanced the radiosensitivity of A549 cells, and knockdown LBH reversed the cytotoxicity induced by X-rays. CONCLUSION Our study developed an eight-gene risk score model with potential clinical value that can be adopted for choice of drug treatment and prognostic prediction. Its clinical routine use may assist clinicians in selecting more rational practices for individuals, which is important for improving the prognosis of NSCLC patients. These findings also provide references for the development of potential therapeutic targets.
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Affiliation(s)
- Yanliang Chen
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, Gansu, China
| | - Chan Zhou
- Department of Geriatrics, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Xiaoqiao Zhang
- Department of Geriatrics, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Min Chen
- Department of Geriatrics, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Meifang Wang
- Department of Pulmonary and Critical Care Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Lisha Zhang
- Department of Obstetrics, Tangshan Caofeidian District Hospital, Tangshan, Hebei, China
| | - Yanhui Chen
- Department of Neuroscience and Endocrinology, Tangshan Caofeidian District Hospital, Tangshan, Hebei, China
| | - Litao Huang
- Department of Clinical Research Management, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Junjun Sun
- Department of Emergency Surgery, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, Hubei, , China
| | - Dandan Wang
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, Gansu, China
| | - Yong Chen
- Department of Radio-Chemotherapy, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China
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Zhang T, Chen Y, Xiang Z. Machine learning-based integration develops a disulfidptosis-related lncRNA signature for improving outcomes in gastric cancer. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2025; 53:1-13. [PMID: 39701937 DOI: 10.1080/21691401.2024.2440415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 11/05/2024] [Accepted: 11/25/2024] [Indexed: 12/21/2024]
Abstract
Gastric cancer remains one of the deadliest cancers globally due to delayed detection and limited treatment options, underscoring the critical need for innovative prognostic methods. Disulfidptosis, a recently discovered programmed cell death triggered by disulphide stress, presents a fresh avenue for therapeutic exploration. This research examines disulfidptosis-related long noncoding RNAs (DRLs) in gastric cancer, with the goal of leveraging these lncRNAs as potential markers to enhance patient outcomes and treatment approaches. Comprehensive genomic and clinical data from stomach adenocarcinoma (STAD) were obtained from The Cancer Genome Atlas (TCGA). Employing least absolute shrinkage and selection operator (LASSO) regression analysis, a prognostic model was devised incorporating five key DRLs to forecast survival rates. The effectiveness of this model was validated using Kaplan-Meier survival plots, receiver operating characteristic (ROC) curves, and extensive functional enrichment studies. The importance of select lncRNAs and the expression variability of genes tied to disulfidptosis were validated via quantitative real-time PCR (qRT-PCR) and Western blot tests, establishing a solid foundation for their prognostic utility. Analyses of functional enrichment and tumour mutation burden highlighted the biological importance of these DRLs, connecting them to critical cancer pathways and immune responses. These discoveries broaden our comprehension of the molecular framework of gastric cancer and bolster the development of tailored treatment plans, highlighting the substantial role of DRLs in clinical prognosis and therapeutic intervention.
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Affiliation(s)
- Tianze Zhang
- Department of Gastrointestinal Surgery, The Second Hospital of Shandong University, Jinan, China
| | - Yuqing Chen
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan, China
| | - Zhiping Xiang
- Head and Neck Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Zheng Z, Chen J, Xu J, Jiang B, Li L, Li Y, Dai Y, Wang B. Peripheral blood RNA biomarkers can predict lesion severity in degenerative cervical myelopathy. Neural Regen Res 2025; 20:1764-1775. [PMID: 39104114 PMCID: PMC11688566 DOI: 10.4103/nrr.nrr-d-23-01069] [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: 06/28/2023] [Revised: 10/10/2023] [Accepted: 11/23/2023] [Indexed: 08/07/2024] Open
Abstract
JOURNAL/nrgr/04.03/01300535-202506000-00027/figure1/v/2024-08-05T133530Z/r/image-tiff Degenerative cervical myelopathy is a common cause of spinal cord injury, with longer symptom duration and higher myelopathy severity indicating a worse prognosis. While numerous studies have investigated serological biomarkers for acute spinal cord injury, few studies have explored such biomarkers for diagnosing degenerative cervical myelopathy. This study involved 30 patients with degenerative cervical myelopathy (51.3 ± 7.3 years old, 12 women and 18 men), seven healthy controls (25.7 ± 1.7 years old, one woman and six men), and nine patients with cervical spondylotic radiculopathy (51.9 ± 8.6 years old, three women and six men). Analysis of blood samples from the three groups showed clear differences in transcriptomic characteristics. Enrichment analysis identified 128 differentially expressed genes that were enriched in patients with neurological disabilities. Using least absolute shrinkage and selection operator analysis, we constructed a five-gene model (TBCD, TPM2, PNKD, EIF4G2, and AP5Z1) to diagnose degenerative cervical myelopathy with an accuracy of 93.5%. One-gene models (TCAP and SDHA) identified mild and severe degenerative cervical myelopathy with accuracies of 83.3% and 76.7%, respectively. Signatures of two immune cell types (memory B cells and memory-activated CD4+ T cells) predicted levels of lesions in degenerative cervical myelopathy with 80% accuracy. Our results suggest that peripheral blood RNA biomarkers could be used to predict lesion severity in degenerative cervical myelopathy.
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Affiliation(s)
- Zhenzhong Zheng
- Department of Spine Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan Province, China
| | - Jialin Chen
- Department of Spine Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan Province, China
| | - Jinghong Xu
- Department of Spine Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan Province, China
| | - Bin Jiang
- Department of Spine Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan Province, China
| | - Lei Li
- Department of Spine Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan Province, China
| | - Yawei Li
- Department of Spine Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan Province, China
| | - Yuliang Dai
- Department of Spine Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan Province, China
| | - Bing Wang
- Department of Spine Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan Province, China
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Souza Bulhões RD, Pimentel JS, Rodrigues PC. Bayesian spatio-temporal modeling of severe acute respiratory syndrome in Brazil: A comparative analysis across pre-, during, and post-COVID-19 eras. Infect Dis Model 2025; 10:466-476. [PMID: 39834649 PMCID: PMC11743096 DOI: 10.1016/j.idm.2024.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 11/09/2024] [Accepted: 12/13/2024] [Indexed: 01/22/2025] Open
Abstract
This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome (SARS) across the diverse health regions of Brazil from 2016 to 2024. Leveraging extensive datasets that include SARS cases, climate data, hospitalization records, and COVID-19 vaccination information, our study employs a Bayesian spatio-temporal generalized linear model to capture the intricate dependencies inherent in the dataset. The analysis reveals significant variations in the incidence of SARS cases over time, particularly during and between the distinct eras of pre-COVID-19, during, and post-COVID-19. Our modeling approach accommodates explanatory variables such as humidity, temperature, and COVID-19 vaccine doses, providing a comprehensive understanding of the factors influencing SARS dynamics. Our modeling revealed unique temporal trends in SARS cases for each region, resembling neighborhood patterns. Low temperature and high humidity were linked to decreased cases, while in the COVID-19 era, temperature and vaccination coverage played significant roles. The findings contribute valuable insights into the spatial and temporal patterns of SARS in Brazil, offering a foundation for targeted public health interventions and preparedness strategies.
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Affiliation(s)
- Rodrigo de Souza Bulhões
- Department of Statistics, IME, Federal University of Bahia, Salvador, BA, Brazil
- Department of Statistical Methods, IM, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | | | - Paulo Canas Rodrigues
- Department of Statistics, IME, Federal University of Bahia, Salvador, BA, Brazil
- Econometrics and Business Statistics, Monash University, Australia
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Chen G, Liu W, Lin Y, Zhang J, Huang R, Ye D, Huang J, Chen J. Predicting bone metastasis risk of colorectal tumors using radiomics and deep learning ViT model. J Bone Oncol 2025; 51:100659. [PMID: 39902382 PMCID: PMC11787686 DOI: 10.1016/j.jbo.2024.100659] [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: 08/19/2024] [Revised: 12/13/2024] [Accepted: 12/23/2024] [Indexed: 02/05/2025] Open
Abstract
Background Colorectal cancer is a prevalent malignancy with a significant risk of metastasis, including to bones, which severely impacts patient outcomes. Accurate prediction of bone metastasis risk is crucial for optimizing treatment strategies and improving prognosis. Purpose This study aims to develop a predictive model combining radiomics and Vision Transformer (ViT) deep learning techniques to assess the risk of bone metastasis in colorectal cancer patients using both plain and contrast-enhanced CT images. Materials and methods We conducted a retrospective analysis of 155 colorectal cancer patients, including 81 with bone metastasis and 74 without. Radiomic features were extracted from segmented tumors on both plain and contrast-enhanced CT images. LASSO regression was applied to select key features, which were then used to build traditional machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, LightGBM, and XGBoost. Additionally, a dual-modality ViT model was trained on the same CT images, with a late fusion strategy employed to combine outputs from the different modalities. Model performance was evaluated using AUC-ROC, accuracy, sensitivity, and specificity, and differences were statistically assessed using DeLong's test. Results The ViT model demonstrated superior predictive performance, achieving an AUC of 0.918 on the test set, significantly outperforming all traditional radiomics-based models. The SVM model, while the best among traditional models, still underperformed compared to the ViT model. The ViT model's strength lies in its ability to capture complex spatial relationships and long-range dependencies within the imaging data, which are often missed by traditional models. DeLong's test confirmed the statistical significance of the ViT model's enhanced performance, highlighting its potential as a powerful tool for predicting bone metastasis risk in colorectal cancer patients. Conclusion The integration of radiomics with ViT-based deep learning offers a robust and accurate method for predicting bone metastasis risk in colorectal cancer patients. The ViT model's ability to analyze dual-modality CT imaging data provides greater precision in risk assessment, which can improve clinical decision-making and personalized treatment strategies. These findings underscore the promise of advanced deep learning models in enhancing the accuracy of metastasis prediction. Further validation in larger, multicenter studies is recommended to confirm the generalizability of these results.
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Affiliation(s)
- Guanfeng Chen
- Radiology Department of Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China
| | - Wenxi Liu
- Radiology Department of The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Yingmin Lin
- Thyroid and Breast Surgery Department of the Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Jie Zhang
- Radiology Department of Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China
| | - Risheng Huang
- Radiology Department of Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China
| | - Deqiu Ye
- Radiology Department of Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China
| | - Jing Huang
- Radiology Department of The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Jieyun Chen
- Radiology Department of Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China
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Tan Y, Zhou M, Wang J, Song Y, Li Q, Huang Z, Li Y, Wang Y, Zhang J, Quan W, Tian J, Yin L, Dong W, Liu B. Heart rate variability in subthreshold depression and major depressive disorder. J Affect Disord 2025; 373:306-313. [PMID: 39761755 DOI: 10.1016/j.jad.2025.01.003] [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] [Received: 07/25/2024] [Revised: 01/02/2025] [Accepted: 01/03/2025] [Indexed: 02/06/2025]
Abstract
BACKGROUND The relationship between heart rate variability (HRV) and major depressive disorder (MDD) has been well explored. However, current researches lack an observation of HRV in subthreshold depression (SubD), which increases the risk of MDD and presents significant societal challenges. METHODS This study compared resting state HRV among 128 MDD patients, 131 SubD individuals and 222 healthy controls (HC) recruited from the hospital, physical examination center, and colleges. Depression, anxiety and sleep quality were assessed by the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7) and Pittsburgh Sleep Quality Index (PSQI) scale. Statistical analyses including Kruskal-Wallis test, spearman correlation and binary logistic regression were conducted to investigate relationships between HRV and emotional issues. RESULTS The results revealed increasing trends in HRV across three groups, and the top three indices correlated with self-reported symptoms were standard deviation of the Poincaré plot perpendicular along the line of identity (SD2), standard deviation of all RR intervals (SDNN) and low frequency (LF). After adjusting for demographics, lower HRV was significantly associated with increased MDD risk compared to HC or SubD. Additionally, LF, SDNN and SD2 exhibited significant associations between SubD and HC. CONCLUSIONS This study is the first attempt to explore the HRV in SubD and compares it with MDD/HC. Our findings indicate that HRV changes are evident in minor depression, with indices in SubD lower than HC yet higher than MDD. SDNN, LF, and SD2 emerge as potential biomarkers for identifying depression early.
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Affiliation(s)
- Yinliang Tan
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing 100191, China
| | - Meihong Zhou
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing 100191, China
| | - Jiuju Wang
- Peking University Sixth Hospital, Beijing 100191, China; Peking University Institute of Mental Health, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China
| | - Yanping Song
- Peking University Sixth Hospital, Beijing 100191, China; Peking University Institute of Mental Health, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China
| | - Qiang Li
- Beijing Medical Science and Technology Promotion Center, Beijing 101101, China
| | - Zetao Huang
- Peking University Sixth Hospital, Beijing 100191, China; Peking University Institute of Mental Health, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China
| | - Ying Li
- National Clinical Research Center for Mental Disorders, Beijing 100088, China; National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, China
| | - Yuxin Wang
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing 100191, China
| | - Jingbo Zhang
- Beijing Medical Science and Technology Promotion Center, Beijing 101101, China
| | - Wenxiang Quan
- Peking University Sixth Hospital, Beijing 100191, China; Peking University Institute of Mental Health, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China
| | - Ju Tian
- Peking University Sixth Hospital, Beijing 100191, China; Peking University Institute of Mental Health, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China
| | - Lina Yin
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing 100191, China
| | - Wentian Dong
- Peking University Sixth Hospital, Beijing 100191, China; Peking University Institute of Mental Health, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing 100191, China.
| | - Baohua Liu
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing 100191, China.
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Lee J, Lim Y, Seo DG, Lee MK, Schalet BD, Fischer F, Rose M, Kang D, Cho J. A Multinational Comparison Study of the Patient-Reported Outcomes Measurement Information System Anxiety, Depression, and Anger Item Bank in the General Population. Int J Methods Psychiatr Res 2025; 34:e70012. [PMID: 39740187 DOI: 10.1002/mpr.70012] [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: 12/05/2024] [Revised: 12/05/2024] [Accepted: 12/19/2024] [Indexed: 01/02/2025] Open
Abstract
OBJECTIVES This study aimed to compared Patient-Reported Outcomes Measurement Information System (PROMIS) anxiety, depression, and anger item bank among Korean, US and Dutch general population. METHODS Between December 2021 and January 2022, we surveyed representative Korean participants (N = 2699). Then we compared the mean T-scores of PROMIS anxiety, depression, and anger full items bank among Korean, US (N = 1696) and the Dutch (N = 1002) populations. Differential item-functioning (DIF) analyses were also performed. We also compared each score by age group, sex, presence of comorbidities, and general health status. RESULTS In Korean, the mean T-scores for anxiety, depression, and anger were 45.3 (standard deviation [SD] = 11.6), 48.4 (SD = 11.2), and 44.9 (SD = 12.6), respectively. Among the general population in Korea, patients aged 35-44 years and those with comorbidities had higher anxiety, depression, and anger scores. In the DIF analyses between the US and Korean populations, 28%, 32%, and 45% were flagged for uniform or non-uniform DIF in anxiety, depression and anger, respectively. CONCLUSIONS Considering the cultural differences, we recommend using a harmonized approach that includes country-specific reference values while retaining a standardized core set of items to enable cross-country comparability.
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Affiliation(s)
- Jiseon Lee
- Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Samsung Medical Center, Center for Clinical Epidemiology, Seoul, South Korea
| | - Yeonjung Lim
- Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Samsung Medical Center, Center for Clinical Epidemiology, Seoul, South Korea
| | - Dong Gi Seo
- Department of Psychology, Hallym University, Chuncheon-si, South Korea
| | - Minji K Lee
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin D Schalet
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Felix Fischer
- Department of Psychosomatic Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Matthias Rose
- Department of Psychosomatic Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Danbee Kang
- Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Samsung Medical Center, Center for Clinical Epidemiology, Seoul, South Korea
| | - Juhee Cho
- Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Samsung Medical Center, Center for Clinical Epidemiology, Seoul, South Korea
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Chen Q, Li X, Yang Y, Ni J, Chen J. Combined Analysis of Human and Experimental Rat Samples Identified Biomarkers for Ischemic Stroke. Mol Neurobiol 2025; 62:3794-3812. [PMID: 39325100 PMCID: PMC11790756 DOI: 10.1007/s12035-024-04512-x] [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: 03/28/2024] [Accepted: 09/18/2024] [Indexed: 09/27/2024]
Abstract
The genetic transcription profile and underlying molecular mechanisms of ischemic stroke (IS) remain elusive. To address this issue, four mRNA and one miRNA expression profile of rats with middle cerebral artery occlusion (MCAO) were acquired from the Gene Expression Omnibus (GEO) database. A total of 780 differentially expressed genes (DEGs) and 56 miRNAs (DEMs) were screened. Gene set and functional enrichment analysis revealed that a substantial number of immune-inflammation-related pathways were abnormally activated in IS. Through weighted gene co-expression network analysis, the turquoise module was identified as meaningful. By taking the intersection of the turquoise module genes, DEM-target genes, and all DEGs, 354 genes were subsequently obtained as key IS-related genes. Among them, six characteristic genes were identified using the least absolute shrinkage and selection operator. After validation with three external datasets, transforming growth factor beta 1 (Tgfb1) was selected as the hub gene. This finding was further confirmed by gene expression pattern analysis in both the MCAO model rats and clinical IS patients. Moreover, the expression of the hub genes exhibited a negative correlation with the modified Rankin scale score (P < 0.05). Collectively, these results expand our knowledge of the genetic profile and molecular mechanisms involved in IS and suggest that the Tgfb1 gene is a potential biomarker of this disease.
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Affiliation(s)
- Qingfa Chen
- Department of Rehabilitation, Fujian Medical University Union Hospital, Fuzhou, 350001, Fujian, China
| | - Xiaolu Li
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530022, Guangxi, China
| | - Ye Yang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530022, Guangxi, China
| | - Jun Ni
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350004, Fujian, China.
| | - Jianmin Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350004, Fujian, China.
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Moller CI, Badcock PB, Hetrick SE, Rice S, Berk M, Dean OM, Chanen AM, Gao C, Davey CG, Cotton SM. Assessing Suicidal Ideation in Young People With Depression: Factor Structure of the Suicidal Ideation Questionnaire. OMEGA-JOURNAL OF DEATH AND DYING 2025; 90:1502-1530. [PMID: 36067753 DOI: 10.1177/00302228221124388] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Evaluating suicidal ideation in young people seeking mental health treatment is an important component of clinical assessment and treatment planning. To reduce the burden of youth suicide, we need to improve our understanding of suicidal ideation, its underlying constructs, and how ideation translates into suicidal behaviour. Using exploratory factor analysis, we investigated the dimensionality of the Suicidal Ideation Questionnaire (SIQ) among 273 participants aged 15-25 with Major Depressive Disorder. Area under the receiver operating characteristic curve (AUROC) analysis was used to explore associations between latent factors and actual suicidal behaviour. Findings suggested that the SIQ assesses multiple factors underlying suicidal ideation. AUROC analyses demonstrated that latent factors relating to both active and passive suicidal ideation predicted past-month suicidal behaviour and suicide attempt. These findings contribute to an improved understanding of the complexities of suicidal ideation and relationships with suicidal behaviour in young people with depression.
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Affiliation(s)
- Carl I Moller
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Paul B Badcock
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Sarah E Hetrick
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
- Department of Psychological Medicine, University of Auckland, Auckland, New Zealand
| | - Simon Rice
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Michael Berk
- School of Medicine, Barwon Health, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, Deakin University, Geelong, Australia
- Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
| | - Olivia M Dean
- School of Medicine, Barwon Health, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, Deakin University, Geelong, Australia
- Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
| | - Andrew M Chanen
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Caroline Gao
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Christopher G Davey
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
- Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia
| | - Sue M Cotton
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
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Wang X, Sarangi V, Wickland DP, Li S, Chen D, Aubrey Thompson E, Jenkinson G, Asmann YW. Identification of Gene Regulatory Networks Associated with Breast Cancer Patient Survival Using an Interpretable Deep Neural Network Model. EXPERT SYSTEMS WITH APPLICATIONS 2025; 262:125632. [PMID: 39676894 PMCID: PMC11643596 DOI: 10.1016/j.eswa.2024.125632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2024]
Abstract
Artificial neural networks have recently gained significant attention in biomedical research. However, their utility in survival analysis still faces many challenges. In addition to designing models for high accuracy, it is essential to optimize models that provide biologically meaningful insights. With these considerations in mind, we developed a deep neural network model, MaskedNet, to identify genes and pathways whose expression at the time of diagnosis is associated with overall survival. MaskedNet was trained using TCGA breast cancer transcriptome and clinical data, and the model's final output was the predicted logarithm of the hazard ratio for death. The trained model was interpreted using SHapley Additive exPlanations (SHAP), a technique grounded in robust mathematical principles that assigns importance scores to input features. Compared to traditional Cox proportional hazards regression, MaskedNet had higher accuracy, as measured by Harrell's C-index. We also found that aggregating outputs from several model runs identified multiple genes and pathways associated with overall survival, including IFNG and PIK3CA genes, along with their related pathways. To further elucidate the role of the IFNG gene, tumors were partitioned into two groups based on low and high IFNG SHAP values, respectively. Tumors with lower IFNG SHAP values exhibited higher IFNG expression and better overall survival, which were linked to more abundant presence of M1 macrophages and activated CD4+ and CD8+ T cells in the tumor microenvironment. The association of the IFNG pathway with overall survival was validated in the trastuzumab arm of the NCCTG-N9831 trial, an independent breast cancer study.
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Affiliation(s)
- Xue Wang
- Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Rd. S., Jacksonville, FL, USA, 32224
| | - Vivekananda Sarangi
- Department of Quantitative Health Sciences, Mayo Clinic, 200 1st St SW, Rochester, MN, USA, 55905
| | - Daniel P. Wickland
- Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Rd. S., Jacksonville, FL, USA, 32224
| | - Shaoyu Li
- Mathematics & Statistics Department, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, USA, 28223
| | - Duan Chen
- Mathematics & Statistics Department, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, USA, 28223
| | - E. Aubrey Thompson
- Department of Cancer Biology, Mayo Clinic, 4500 San Pablo Rd. S., Jacksonville, FL, USA, 32224
| | - Garrett Jenkinson
- Department of Quantitative Health Sciences, Mayo Clinic, 200 1st St SW, Rochester, MN, USA, 55905
- Department of Data Science, Johnson & Johnson Innovative Medicine R&D, 1400 McKean Rd, Springhouse, PA 19477
| | - Yan W. Asmann
- Department of Quantitative Health Sciences, Mayo Clinic, 4500 San Pablo Rd. S., Jacksonville, FL, USA, 32224
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11
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Caycho-Rodríguez T, Lee SA, Vilca LW, Carbajal-León C, Reyes-Bossio M, Delgado-Campusano M, Gallegos M, Carranza Esteban R, Noe-Grijalva M. Measurement of Risk Factors Associated With bereavement Severity and Deterioration by COVID-19: A Spanish Validation Study of the Pandemic Grief Risk Factors. OMEGA-JOURNAL OF DEATH AND DYING 2025; 90:1609-1632. [PMID: 36066339 DOI: 10.1177/00302228221124987] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The present study translated and evaluated the psychometric evidence of the Pandemic Grief Risk Factors (PGRF) in a sample of 363 people from the general population of Peru who suffered the death of a loved one by COVID-19 (63-4% women and 36.6% men, where 78.5% were between 18 and 29 years old). The findings indicated that the PGRF is a unidimensional and reliable measure. The PGRF items can differentiate between individuals with different levels of risk factors and thus cover a wide range of the latent construct. Also, a greater sense of distress for each of the risk factors for pandemic grief is necessary to answer the higher response categories. Risk factors significantly and positively predict COVID-19-associated dysfunctional grief. The results indicated that the PGRF in Spanish is a measure with adequate psychometric properties to measure risk factors for pandemic grief.
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Affiliation(s)
| | - Sherman A Lee
- Psychology, Christopher Newport University, Newport News, VA, USA
| | - Lindsey W Vilca
- South American Center for Education and Research in Public Health, Universidad Norbert Wiener, Lima, Perú
| | - Carlos Carbajal-León
- South American Center for Education and Research in Public Health, Universidad Norbert Wiener, Lima, Perú
| | - Mario Reyes-Bossio
- Facultad de Psicología, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
| | | | - Miguel Gallegos
- Departamento de Psicología, Facultad de Ciencias de la Salud, Universidad Católica del Maule, Talca, Chile
- Programa de Pós-Graduação em Psicologia, Pontifícia Universidade Católica de Minas Gerais, Belo Horizonte, MG, Brasil
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12
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Wang Y, Zhang C, Zhao S, Xu J, Han J. Predictive Effect of FT3 Within the Euthyroid Range on LDL-C in Patients With Type 2 Diabetes: A Cross-Sectional Analysis of Inpatients in China. Clin Exp Pharmacol Physiol 2025; 52:e70021. [PMID: 39787620 DOI: 10.1111/1440-1681.70021] [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: 10/24/2024] [Revised: 12/06/2024] [Accepted: 12/16/2024] [Indexed: 01/12/2025]
Abstract
Evidence regarding the relationship between free triiodothyronine (FT3) and low-density lipoprotein cholesterol (LDL-C) remains limited. This study aimed to evaluate the association between FT3 and LDL-C levels in patients with type 2 diabetes mellitus (T2DM) who exhibit normal thyroid function. Between June 2022 and October 2023, a total of 3011 inpatients with T2DM and euthyroid status were continuously and non-selectively recruited from a Chinese hospital. The average age of the included individuals was 56.92 ± 12.56 years, with 1430 (47.49%) males. The mean FT3 concentration was 4.35 ± 0.56 pmol/L. A logistic regression model was applied to analyse the relationship between the FT3 and LDL-C levels, while smooth curve fitting was employed to investigate potential nonlinear associations between these variables. This study demonstrated a positive correlation (0.05 [95% CI: 0.02-0.07; p = 0.0018]) and nonlinear relationship (p = 0.0014) between FT3 and LDL-C levels in Chinese patients with diabetes. Specifically, when FT3 was below 4.28 pmol/L, LDL-C levels increased alongside rising FT3 concentration. However, when FT3 reached or exceeded 4.28 pmol/L, LDL-C levels plateaued and tended to stabilise. These findings suggest that maintaining FT3 within the range of 2.76 to 4.28 pmol/L may be most beneficial for mitigating the progression of cardiovascular disease in patients with T2DM. Our research is important for identifying the optimal FT3 range to delay the progression of cardiovascular disease in patients with T2DM. These findings provide valuable insights to guide clinicians in preventing and managing cardiovascular disease in this population.
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Affiliation(s)
- Yujue Wang
- Department of Research, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Chi Zhang
- Department of Medical Insurance, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Shangshuang Zhao
- Department of Endocrinology and Metabolism, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Jinmei Xu
- Department of Endocrinology and Metabolism, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Jun Han
- Department of Endocrinology and Metabolism, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
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13
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Tulva K, Pirajev A, Zeb A, Aksoy AE, Bello A, Lee B, Guðjónsson BF, Helgadottir SB, Jagomäe T, García-Llorca A, Eysteinsson T, Jürgenson M, Plaas M, Vasar E, Kaasik A, Hickey MA. Early trigeminal and sensory impairment and lysosomal dysfunction in accurate models of Wolfram syndrome. Exp Neurol 2025; 385:115099. [PMID: 39662795 DOI: 10.1016/j.expneurol.2024.115099] [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: 08/30/2024] [Revised: 11/04/2024] [Accepted: 12/04/2024] [Indexed: 12/13/2024]
Abstract
Wolfram syndrome (WS) is a rare condition caused by homozygous or compound heterozygous mutations in the WFS1 gene primarily. It is diagnosed on the basis of early-onset diabetes mellitus and optic nerve atrophy. Patients complain of trigeminal-like migraines and show deficits in vibration sensation, but the underlying cause is unknown. Using accurate cell models and two separate, accurate rodent models of WS that show excellent face and construct validity, here we have examined trigeminus, sensation and sensory neuronal function in WS. Analysis of ex vivo and in vivo MRI sequences revealed profound trigeminal atrophy in each rodent model, a novel finding in WS. Optic nerve atrophy is a diagnostic sign in WS, and trigeminal atrophy occurred at the time of earliest loss of optic nerve volume. We also observed deficits in mechanical sensation in our mouse WS model, and pathological analysis revealed extensive inflammation in trigeminal sensory nucleus, both of which are novel findings in WS. Sensory neurons (dorsal root ganglia) showed impaired calcium handling upon depolarisation and reduced mitochondrial membrane potential. Finally, lysosomes were smaller, soma lysosome content was decreased and importantly, lysosome acidity was impaired in sensory neurons, all of which are novel findings in WS. We validated these findings using two separate publicly available datasets, both from WS patient fibroblast-derived neural stem cells. We observed a highly significant functional enrichment of GO cellular component lysosome-related terms among the differentially expressed proteins and genes, with the majority of lysosome-related proteins being downregulated. These data reveal extensive impairments in the trigeminal pathway and nociceptive neurons in WS that may contribute to trigeminal and sensory symptoms observed in patients. Moreover, we note that mutations in WFS1 are relatively common and, given the importance of WFS1 for sensory function, our data may also shed light on sensory impairments in general.
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