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Jiaqi Zhou

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Open access Jul 2026

Integrated Multi-Omics Identifies Core Molecular Targets in Cerebral Venous Sinus Thrombosis-Induced Brain Injury

Background: Cerebral venous sinus thrombosis (CVST) is a critical cause of brain injury and intracranial hypertension. However, its underlying molecular mechanisms remain poorly understood, limiting the development of targeted therapies. This study aims to systematically identify key molecular targets and signaling pathways involved in CVST-induced brain lesions using multi-omics approaches in a modified rat model of CVST. Methods: An optimized rat CVST model was established. Cortical tissues were collected from Sham-operated, 2-day post-CVST, and 7-day post-CVST groups for transcriptomic, proteomic, and single-cell transcriptomic sequencing. Bioinformatics analyses were performed to identify differentially expressed genes/proteins, followed by functional enrichment, protein–protein interaction network construction, and hub-gene screening. Further investigations included drug enrichment analysis, molecular docking, and molecular dynamics, as well as the prediction of competing endogenous RNA networks, transcription factor analysis, and expression profiling of potential edema-related therapeutic targets. Results: Multi-omics analyses revealed dynamic changes in gene and protein expression in the brain after CVST, along with associated pathways involved in immune inflammatory responses and tissue repair. Integrative analysis identified 12 core genes (Cd44, Cd40, Sdc1, Myd88, Icam1, Stat3, Jak2, Ptgs2, Aldh1a1, Hspb1, Pxdn, and Casp3). Single-cell RNA sequencing validated their expression and delineated cell-type specificity. Molecular docking hinted at the high binding potential of glucocorticoids such as dexamethasone and methylprednisolone to several core targets (JAK2, PTGS2, and CD44), with all docked complexes showing binding energies below −8.2 kcal/mol. Further molecular dynamics simulations indicated that methylprednisolone forms a stable complex with CD44, driven primarily by van der Waals and electrostatic interactions. Additionally, dynamic levels of several potential edema-related targets (Kcnn4, Piezo1, Trpv4, and Atp1a2) were observed. Conclusions: In summary, by applying integrated multi-omics profiling to a modified rat model, this study systematically mapped the molecular landscape of CVST-induced brain injury. A number of candidate targets and signaling pathways emerged from our analysis, along with several compounds of potential therapeutic interest. Collectively, these results provide a basis for further investigation into the mechanisms underlying CVST and for the design of novel treatment approaches.

Xiaohong Qin, Haoran Lu, Zhibiao Chen et al. · 0 citations
Preprint Aug 2026

Socialized Division and Collaboration: Rethinking Class-Incremental Learning under Optimization Conflicts

Class-incremental learning is commonly instantiated as a single-model paradigm, where a unified model sequentially adapts to an unbounded stream of sessions. While effective under mild distributional shifts, this formulation becomes strained when successive sessions induce incompatible optimization directions, leading to destructive interference and catastrophic forgetting. We argue that such forgetting reflects a structural limitation of enforcing heterogeneous learning dynamics within a single parameter space. Motivated by social solidarity theory, we propose Socialized Division and Collaboration (SDC) as a reformulation of continual learning that decomposes session learning across specialized models in response to optimization conflicts, while enabling coordinated collaboration. To support this formulation with a principled allocation mechanism, we introduce an energy-based session-model compatibility criterion grounded in Helmholtz free energy, which guides adaptive session allocation and model evolution under conflicting objectives. This framework integrates session assignment, model evolution, and collaborative inference into a unified pipeline, offering an alternative to monolithic continual learning formulations and highlighting a broader design principle for learning under persistent optimization conflicts.

Xinjie Yao, Zhihe Fan, Yunqi Zhu et al. · 0 citations
Preprint Aug 2026

Towards a new paradigm of scientific discovery with socialized artificial intelligence

Bridging Literature, Agents, and Zero-gap Experimentation (BLAZE), a paradigm of socialized scientific intelligence, makes discovery more traceable, reproducible, and cumulative while preserving human creativity, judgment, and responsibility.

Xinjie Yao, Xingxin Xu, Xiyuan Gao et al. · 0 citations