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Biomedical subjects

Mohan Li

Publications and source records attributed to Mohan Li.

2 recordsLinked to original sources

A systematic review and meta-analysis of the late positive potential and internalizing psychopathology.

The present study leveraged the Hierarchical Taxonomy of Psychopathology (HiTOP) framework to conduct a systematic meta-analysis to determine the association between the late positive potential (LPP) index of emotional reactivity and internalizing psychopathology. PRISMA guidelines were followed. Articles were identified through PubMed, APA PsycInfo, and Web of Science online platforms in May 2025. Included articles examined associations between the LPP to positive and/or negative stimuli and internalizing psychopathology. Risk of bias and publication bias were assessed. Results were examined for individual disorders, distress and fear subfactors, and the internalizing spectrum using two approaches: standard analyses that examined aggregate effects and hierarchical analyses that examined direct and indirect relationships. We conducted moderator analyses for sample, task design, LPP quantification, and psychopathology measurement. We included 63 studies across 5,360 participants (Mage = 19.65, SD = 11.1; 58.7% female). In standard meta-analyses, depression was associated with a smaller LPP to positive stimuli (r = -.06, 95% confidence interval [CI; -.12, -.003]). Specific phobia was associated with a larger LPP to negative stimuli (r = .21, 95% CI [.02, .37]). Distress was associated with a smaller LPP to both positive (r = -.12) and negative (r = -.11) stimuli when measured via clinical interview, and fear was associated with a larger LPP to negative stimuli (r = .10, 95% CI [.03, .16]). Hierarchical analyses indicated that the depression results were specific to the disorder, whereas the fear disorder-level results were due to the higher order fear subfactor. The LPP demonstrates discriminant relationships with distress and fear disorders and subfactors. Results were largely robust against methodological factors. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Humans

Bridging Organ-on-a-Chip and Omics: A Multi-Dimensional Frontier in Biomedical Research.

Organ-on-a-Chip (OOC) technology offers a powerful platform for replicating human tissue-specific microenvironments, thereby narrowing the translational gap between conventional biomedical models and actual human physiology. Concurrently, omics technologies deliver comprehensive molecular-level insights into biological systems. This review highlights the transformative potential of integrating OOC platforms with high-throughput omics methodologies. We systematically examine the classification, structural configurations, and engineering principles underlying OOC systems, alongside the defining attributes of key omics domains-genomics, transcriptomics, proteomics, and metabolomics. The convergence of dynamic OOC models with advanced omics technologies enables high-resolution, multi-dimensional analyses across numerous biomedical applications, including drug metabolism, disease mechanisms, environmental toxicity assessments, and host-microbiome interactions. This interdisciplinary integration is driving a paradigm shift in precision and translational medicine. However, several challenges remain to be addressed, such as the development of whole-organ mimetics, adaptation of sample collection techniques, and real-time artificial intelligence-based integration of biosensor data with multi-omics datasets. Addressing these hurdles will be vital for unlocking the full potential of this technological synergy in biomedical science.

Multiomics