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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Britny Farahdel Bishal Thapaliya Pranav Suresh Bhaskar Ray Vince D. Calhoun Jingyu Liu Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Atlanta USA

ISBN: (纸本)9781665429825

The general psychopathology factor (p factor), derived from a wide range of psychological symptoms, is proposed to approximate an individual’s tendency to develop a broad range of psychiatric disorders. The aim of this study was to extract the general p factor in a bifactor model from confirmatory factor analysis (CFA), as well as internalizing and externalizing factors, and test the stability of the factors derived from different behavioral measures. Using data from the Adolescent Brain and Cognitive Development (ABCD) study, we compared the latent factors derived from Child Behavior Checklist measure (a standard approach) with those from broader measures. Multiple linear regression was used to assess the relationship of the p factor with other behavioral or cognition measures and clinical diagnoses. The results showed that the p factor had greater stability and a higher correlation between models (r=0.87) than internalizing and externalizing factors (r=0.32,0.02, respectively). The p factor explained significant variances in all dimensional behavioral variables as well as neurocognition and was significantly related to all mental disorders available. A mixed-effects model was constructed to measure the association of the p factor with screen time activity, sleep duration, physical activity, household income, and gender (altogether, these explained 8.02% of the variance in the p factor). The findings from this study also show that the general p factor defined in the bifactor model has sufficient determinacy.

关键词： Correlation Mental disorders Conferences Linear regression Mental health Biomedical measurement Stability analysis

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Any-Way Independent Component Analysis with Reference

Any-Way Independent Component Analysis with Reference

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IEEE International Symposium on Biomedical Imaging

作者： Kuaikuai Duan Rogers F. Silva Jingyu Liu Oktay Agcaoglu Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia USA Department of Computer Science Georgia State University Atlanta USA Department of Psychology Georgia State University Atlanta GA USA

Multimodal fusion with reference allows a prior-guided exploration of coherent multimodal patterns to facilitate the interpretation. Multiset canonical correlation analysis with reference + joint independent component analysis (mCCAR + jICA) is one such method, and it is limited to decompose the same number of components for each modality, and two-step processing (i.e., mCCAR for correlation optimization, jICA for independence optimization) may hinder the optimization arriving at an optimal global solution. To overcome these limitations, we propose aNy-way ICA with reference, which simultaneously maximizes the independence of sources, cross-modality correlations, and correlations between reference and modalities without orthogonality requirements on the loadings, and allows different number of sources for different modalities. Simulation results demonstrated that aNy-way ICA with reference recovered the designed linkages between reference and modalities, and the designed cross-modality correlations, as well as recovered loading and component matrices with improved accuracy, compared to mCCAR+jICA under noisy conditions.

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Potential alleviation of bone mineral density loss with Janus kinase inhibitors in rheumatoid arthritis

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Clinical Rheumatology 2024年第1期43卷 117-128页

作者： Chen, Yun-Wen Chen, Hsin-Hua Huang, Wen-Nan Chen, Jun-Peng Chen, Yi-Hsing Chen, Yi-Ming Division of Allergy Immunology and Rheumatology Taichung Veterans General Hospital Taichung Taiwan Division of General Medicine Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan Faculty of Medicine National Yang-Ming Chiao Tung University Taipei Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung Taiwan Institute of Biomedical Science and Rong Hsing Research Center for Translational Medicine & Big Data Center Chung Hsing University Taichung Taiwan Institute of Public Health and Community Medicine Research Center National Yang-Ming University Taipei Taiwan College of Business and Management Ling Tung University Taichung Taiwan Biostatistics Task Force of Taichung Veterans General Hospital Taichung Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Institute of Biomedical Science and Rong Hsing Research Center for Translational Medicine National Chung Hsing University Taichung Taiwan

Objective: Rheumatoid arthritis (RA) is characterized by localized bone loss, general osteoporosis and increased fracture risks. Tumour necrosis factor inhibitors (TNFi), non-tumour necrosis factor inhibitors (non-TNFi) biologic, Janus kinase inhibitors (JAKi) had shown the suppression effects to osteoclast activation and improvement of bone mineral density (BMD). Anti-cyclic citrullinated peptide antibody (ACPA) is associated with osteoclast activation and the resultant bone loss. However, few studies have compared BMD changes among patients with RA treated with targeted therapies that have different mechanisms of action. Methods: This retrospective study recruited patients with RA who had undergone BMD testing twice. Changes in the BMD were compared using the generalized estimating equation (GEE) in treatment groups that received conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs), TNFi, non-TNFi biologics, and JAKi. Results: In total, 362 patients with RA were enrolled (csDMARDs, n = 153, TNFi, n = 71, non-TNFi biologics, n = 108, JAKi, n = 30). We observed greater changes in femoral BMD (left, 0.06, 95% CI 0.01–0.12, p = 0.016;right, 0.09, 95% CI 0.04–0.15, p = 0.001 by GEE) following JAKi treatment as compared with other treatments. Compared to the ACPA-negative group, patients with ACPA positivity exhibited greater improvement in the femoral BMD (left, 0.09, 95% CI 0.02–0.15, p = 0.008;right, 0.11, 95% CI 0.05–0.18, p = 0.001). Conclusion: Compared to other targeted therapies, JAKi might exert a more potent effect to prevent BMD loss, specifically in ACPA-positive patients with RA, and could be a potential therapeutic option to mitigate generalized bone loss. Key Points •JAKi therapy inhibits systemic bone loss in patients with RA. •ACPA-positive RA patients exhibited a greater BMD improvement than ACPA-negative RA patients. •JAKi might more potently prevent BMD decline than conventional synthetic or biological DMARDs. © 2023, The Author(s

关键词： Bone loss Bone mineral density Janus kinase inhibitor Non-tumour necrosis factor inhibitor Osteoporosis Rheumatoid arthritis Tumour necrosis factor inhibitor

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Cryptotanshinone Suppresses the STAT3/BCL-2 Pathway to Provoke Human Bladder Urothelial Carcinoma Cell Death

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Environmental Toxicology 2025年第4期40卷 624-635页

作者： Tung, Min-Che Chang, Ge-Man Dai, Wen-Chyi Hsu, Chen-Hsuan Chang, Hsiang-Chun Yang, Wei-Ting Ho, Yann-Jen Lu, Chien-Hsing Chen, Yi-Hsin Chang, Chia-Che Division of Urology Department of Surgery Tungs' Taichung MetroHarbor Hospital Taichung Taiwan Graduate Institute of Biomedical Sciences National Chung Hsing University Taichung Taiwan Doctoral Program in Biotechnology Industrial Innovation and Management National Chung Hsing University Taichung Taiwan Department of Life Sciences National Chung Hsing University Taichung Taiwan Department of Obstetrics and Gynecology Taichung Veterans General Hospital Taichung Taiwan Doctoral Program in Translational Medicine National Chung Hsing University Taichung Taiwan Department of Nephrology Taichung Tzu Chi Hospital Buddhist Tzu chi Medical Foundation Taichung Taiwan School of Medicine Tzu Chi University Hualein Taiwan Department of Artificial Intelligence and Data Science National Chung Hsing University Taichung Taiwan Master Program in Precision Health Rong Hsing Research Center for Translational Medicine The iEGG and Animal Biotechnology Research Center National Chung Hsing University Taichung Taiwan Department of Medical Laboratory Science and Biotechnology Asia University Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan

Bladder cancer is one of the most common human malignancies worldwide. Aberrant activation of signal transducer and activator of transcription 3 (STAT3) is crucial to driving malignant progression and predicting poor prognosis of multiple human cancers, including bladder cancer, making STAT3 a promising target of cancer therapeutics. Cryptotanshinone (CTS) is an anticancer ingredient of Danshen (Salvia miltiorrhiza), a top-graded Chinese medicinal herb. However, whether CTS targets STAT3 to exert its cytotoxic effect on human bladder cancer remains unknown. Herein, we demonstrated that CTS is cytotoxic to multiple human urinary bladder transitional cell carcinoma (TCC) cell lines while sparing normal human urothelial cells. CTS provoked apoptosis-dependent bladder TCC cytotoxicity, as apoptosis blockage by z-VAD-fmk markedly rescued the clonogenicity of CTS-treated cells. Besides, CTS was found to suppress constitutive and interleukin 6-inducible activation of STAT3, evidenced by the downregulation of STAT3 tyrosine 705 phosphorylation and BCL2, a recognized STAT3 transcriptional target. Notably, ectopic expression of a dominant-active STAT3 mutant (STAT3-C) or BCL-2 alleviated CTS-induced apoptosis and clonogenicity inhibition, thus confirming STAT3 blockade as a pivotal mechanism of CTS's cytotoxic action on bladder TCC cells. Lastly, immunoblotting revealed that CTS lowered the levels of active JAK2, an upstream kinase that mediates STAT3 tyrosine 705 phosphorylation. Altogether, we conclude that the blockade of the JAK2/STAT3/BCL-2 antiapoptotic signaling axis is a vital mechanism whereby CTS provokes bladder cancer cytotoxicity. The current evidence implicates CTS's potential to be translated into a bladder cancer therapeutic agent. © 2024 Wiley Periodicals LLC.

关键词： Cell death

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Psychosis Multiscale Functional Network Connectivity Subspace Encodes Sex Specificity and Identifies Sex Biotypes

Psychosis Multiscale Functional Network Connectivity Subspac...

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IEEE International Symposium on Biomedical Imaging

作者： Adithya Ram Ballem Prerana Bajracharya Pablo André S-Camazón Jiayu Chen Vince D. Calhoun Armin Iraji Georgia State University Atlanta USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University Georgia Institute of Technology Emory University) Atlanta USA Institute of Psychiatry and Mental Health Hospital General Universitario Gregorio Marañón IiSGM CIBERSAM ISCIII School of Medicine Universidad Complutense Madrid Spain

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Psychosis is a functionally disruptive condition, however little is known about the varying connectivity between different sexes and the extent of its variability. Resting-state fMRI (rsfMRI) data from the BSNIP consortium involving 626 controls (CON) and 1127 psychosis (PSY) participants (873 Females and 719 Males) were analyzed. Multiscale functional network connectivity (msFNC) for each subject was extracted by applying multivariate objective optimization ICA with Neuromark 2.2 data-driven reference template. The principal component space of PSY msFNC was then identified, within which sex specificity was uncovered. Our analysis revealed 4 statistically significant sex biotypes, 2 female dominant and 2 male dominant ones with unique connectivity characteristics. Psychosis dysconnectivity along cerebellar-subcortical regions was more prominent in males than females. These biotype findings highlight the manifestations of psychosis across different sexes with varying connectivity deficits relative to CON.

关键词： Functional magnetic resonance imaging Aerospace electronics Reliability Replicability Optimization Biomedical imaging

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Molecular epidemiology and phylogenetic analysis of influenza viruses A (H3N2) and B/ Victoria during the COVID-19 pandemic in Guangdong, China

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Infectious Diseases of Poverty 2024年第4期13卷 99-100页

作者： Zhiqi Zeng Yong Liu Wenxiang Jin Jingyi Liang Jinbin Chen Ruihan Chen Qianying Li Wenda Guan Lixi Liang Qiubao Wu Yuanfang Lai Xiaoyan Deng Zhengshi Lin Chitin Hon Zifeng Yang State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory Disease Guangzhou Institute of Respiratory Health the First Affiliated Hospital of Guangzhou Medical University Guangzhou Guangdong 510180 P.R. China Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory Medicine Guangzhou Medical University Guangzhou People’s Republic of China Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovative Engineering Macau University of Science and Technology Macau SAR China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Taipa Macau China Guangzhou Laboratory Guangzhou China Kingmed Virology Diagnostic and Translational Center Guangzhou Kingmed Center for Clinical Laboratory Co. Ltd. Guangzhou China

Background Non-pharmaceutical measures and travel restrictions have halted the spread of coronavirus disease 2019(COVID-19)and ***,with COVID-19 restrictions lifted,an unanticipated outbreak of the influ-enza B/Nictoria virus in late 2021 and another influenza H3N2 outbreak in mid-2022 occurred in Guangdong,southern *** mechanism underlying this phenomenon remains *** better prepare for potential influenza out-breaks during COVID-19 pandemic,we studied the molecular epidemiology and phylogenetics of influenza A(H3N2)and BNictoria that circulated during the COVID-19 pandemic in this *** From January 1,2018 to December 31,2022,we collected throat swabs from 173,401 patients in Guang-dong who had acute respiratory tract *** viruses in the samples were tested using reverse tran-scription-polymerase chain reaction,followed by subtype identification and sequencing of hemagglutinin(HA)and neuraminidase(NA)*** and genetic diversity analyses were performed on both genes from 403 samples.A rigorous molecular clock was aligned with the phylogenetic tree to measure the rate of viral evolution and the root-to-tip distance within strains in different years was assessed using regression curve models to determine the *** During the early period of COVID-19 control,various influenza viruses were nearly undetectable in respiratory *** control measures were relaxed in January 2020,the influenza infection rate peaked at 4.94%(39/789)in December 2021,with the influenza B/Victoria accounting for 87.18%(34/39)of the total influenza *** months later,the influenza infection rate again increased and peaked at 11.34%(255/2248)in June 2022;influenza A/H3N2 accounted for 94.51%(241/255)of the total influenza cases in autumn *** diverse geographic distribution of HA genes of B/Nictoria and A/H3N2 had drastically reduced,and most strains originated from *** rate of B/Victoria HA e

关键词： Insidious transmission Influenza COVID-19 control Genetic diversity Evolution

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Revealing Alzheimer's Disease Dementia Patterns in [18F]Florbetapir PET with Independent Component Analysis

Revealing Alzheimer's Disease Dementia Patterns in [18F]Flor...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Nigar Khasayeva Cyrus Eierud Kyle M. Jensen Enrico Premi Barbara Borroni Vince D. Calhoun Armin Iraji Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Atlanta GA USA Stroke Unit ASST Spedali Civili Spedali Civili Hospital Brescia Italy Department of Clinical and Experimental Sciences University of Brescia Italy Department of Continuity of Care and Frailty ASST Spedali Civili Brescia Italy

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This study investigates Alzheimer’s Disease (AD) dementia through [18F]Florbetapir ([18F]FBP) Positron Emission Tomography (PET) imaging. We employ Independent Component Analysis (ICA) to identify shared latent patterns across controls and individuals with Dementia. The dataset comprises PET brain images from 440 participants from the Alzheimer’s Disease neuroimaging Initiative (ADNI). After visual inspection, nine independent components (IC) were selected, including visual, salience, default mode, cerebellum, left and right temporal, motor, frontal, and subcortical/brainstem. A Generalized Linear Model (GLM) analysis was performed on the IC weights to evaluate group differences. Salience, default mode, left and right temporal, and frontal components displayed a significant group effect with increased weights in the AD dementia group. Notably, the salience and frontal components demonstrated a significant interaction effect of diagnosis with age. This study emphasizes the potential of ICA in conjunction with [18F]FBP PET imaging to provide valuable insights into the neurobiology of AD dementia.

关键词： neuroimaging Visualization Imaging Independent component analysis Positrons Inspection Motors Positron emission tomography Alzheimer's disease Integrated circuit modeling

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Multiscale neuroimaging Features for the Identification of Medication Class and Non-Responders in Mood Disorder Treatment

Multiscale Neuroimaging Features for the Identification of M...

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IEEE International Symposium on Biomedical Imaging

作者： Bradley T. Baker Mustafa S. Salman Zening Fu Armin Iraji Elizabeth Osuch H. Jeremy Bockholt Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Atlanta GA USA Georgia Institute of Technology Atlanta GA USA Lawson Health Research Institute London Health Sciences Centre FEMAP London Canada Schulich School of Medicine and Dentistry Western University London Canada Emory University Atlanta GA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

In the clinical treatment of mood disorders, the complex behavioral symptoms presented by patients and variability of patient response to particular medication classes can create difficulties in providing fast and reliable treatment when standard diagnostic and prescription methods are used. Increasingly, the incorporation of physiological information such as neuroimaging scans and derivatives into the clinical process promises to alleviate some of the uncertainty surrounding this process. Particularly, if neural features can help to identify patients who may not respond to standard courses of antidepressants or mood stabilizers, clinicians may elect to avoid lengthy and side-effect-laden treatments and seek out a different, more effective course that might otherwise not have been under consideration. Previously, approaches for the derivation of relevant neuroimaging features work at only one scale in the data, potentially limiting the depth of information available for clinical decision support. In this work, we show that the utilization of multi spatial scale neuroimaging features particularly resting state functional networks and functional network connectivity measures - provide a rich and robust basis for the identification of relevant medication class and non-responders in the treatment of mood disorders. We demonstrate that the generated features, along with a novel approach for fast and automated feature selection, can support high accuracy rates in the identification of medication class and non-responders as well as the identification of novel, multi-scale biomarkers.

关键词： neuroimaging Uncertainty Limiting Mood Biomarkers Feature extraction Physiology

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CGDM-GAN: An Adversarial Network Approach with Self-supervised Learning for Site Effect Removal

CGDM-GAN: An Adversarial Network Approach with Self-supervis...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xiangxiang Cui Dongmei Zhi Weizheng Yan Vince D. Calhoun Chuanjun Zhuo Jing Sui The State Key Lab of Cognitive Neuroscience and Learning Beijing Normal University Beijing China National Institute on Alcohol Abuse and Alcoholism Lab of Neuroimaging National Institutes of Health Bethesda United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Atlanta United States The Department of Psychiatric- Neuroimaging-Genetics and Morbidity Laboratory (PNGCLab) Tianjin Mental Health Center Nankai University Tianjin China IDG/McGovern Institute for Brain Research State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Imaging data collected from different sites is difficult to pool together due to unwarranted variations introduced by different acquisition protocols or scanners. data harmonization is an effective way to mitigate site-specific bias while preserving the intrinsic image properties, thereby increasing the sample size and enhancing the generalization of models. Although various harmonization methods exist, their performance on specific tasks is often unsatisfactory. Here, we proposed a novel approach, CGDM-GAN, by combining the advantages of generative models, maximum discrepancy theory, and gradient discrepancy minimization with self-supervised learning to harmonize site effects and improve cross-site classification performance. The proposed CGDM-GAN was successfully conducted on synthetic dataset, and further validated on in-house and ABCD datasets, outperforming three data harmonization methods, including ComBat, CycleGAN, and MCD-GAN, suggesting its potential for removing site effects and improving cross-site neuroimaging classification.

关键词： neuroimaging Training Protocols Imaging Self-supervised learning Minimization Reproducibility of results data models Engineering in medicine and biology Synthetic data

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Capturing Spatial Dynamics Using Time-Resolved Referenced-Informed Network Estimation Techniques

Capturing Spatial Dynamics Using Time-Resolved Referenced-In...

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IEEE International Symposium on Biomedical Imaging

作者： Armin Iraji Jiayu Chen Ashkan Faghiri Zening Fu Jingyu Liu Juan R. Bustillo Tulay Adali Mukesh Dhamala Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Department of Psychiatry and Behavioral Sciences University of New Mexico Albuquerque NM USA Department of CSEE University of Maryland Baltimore MD USA Department of Physics and Astronomy Georgia State University Atlanta GA USA

We recently showed that the brain is spatially dynamic, i.e., the spatial patterns of brain networks evolve over time. Yet, most studies, even those studying brain dynamics, omit this information, leading to incorrect inferences. Moreover, spatial dynamics carry unique information about networks hidden from existing spatially static ***, estimating networks in a time-resolved manner is challenging because (1) resting-state fMRI (rsfMRI) has a low signal-to-noise ratio (SNR), (2) relatively short time segments have insufficient information to characterize the spatiotemporal patterns stand-alone, and (3) identification of correspondence within and between-subject is suboptimal and *** by the group-inference framework, we addressed these limitations by adopting a referenced-informed estimation technique in a time-resolved manner to capture time-varying brain networks and their spatial functional network connectivity (spFNC). Our approach detects schizophrenia alterations in dynamic spFNC. Interestingly these changes in low-dimensional global brain dynamics are also manifested in high-dimensional (voxel-level) space.

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