Exosome-related genes influence the progression of stroke through neuroinflammatory responses
Bibliographic record
Abstract
Ischemic stroke (IS) ranks among the top causes of mortality and disability globally. Exosomes exert a crucial effect on maintaining a complex regulatory balance with neuroinflammation in IS. Hence, this research aimed to elucidate the roles of exosome-related genes IS. We integrated data from five IS-related datasets from the Gene Expression Omnibus (GEO) database and exosome-related genes from ExoCarta. The least absolute shrinkage and selection operator regression and random forest models were performed to detect feature genes. Search Tool for the Retrieval of Interacting Genes and Cytoscape were employed to recognize the hub genes. Enrichment analyses were conducted to examine biological processes. CIBERSORT and MCPcounter were applied to assess immune infiltration, and Principal Component Analysis was utilized to explore the associations of feature genes and hub genes with immune cells. After identified different cell types, we analyzed differentiation, developmental trajectory, and interactions of the cell populations. Middle cerebral artery occlusion models were conducted on mice, followed by quantitative polymerase chain reaction to assess the expression levels of each hub gene. We identified 13 feature genes and 10 hub genes. Through qPCR, LGALS3, CD36, TLR2, ICAM1, and CD14 were significantly upregulated after Middle Cerebral Artery Occlusion surgery. Hub genes were significantly involved in inflammatory responses, as well as chemokine signaling and JAK-STAT signaling. Immuno-infiltration analysis revealed significant differences in immune cell populations between IS and controls. Additionally, neutrophils and monocytes/macrophages were positively correlated with CD14 and LGALS3, respectively. Single-cell analysis revealed 19 cell subpopulations with detailed pseudo-time trajectory predictions, highlighting the developmental importance of MG2 microglial cells. In conclusion, our results illuminate exosomal genes, including LGALS3 and CD14, participate in the progression of IS through neuroinflammation, as well as highlight potential therapeutics to mitigate IS injury.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".