Identification of critical genes associated with spinal cord injury based on the gene expression profile of spinal cord tissues from trkB.T1 knockout mice
Bibliographic record
Abstract
The present study aimed to identify the genes and underlying mechanisms critical to the pathology of spinal cord injury (SCI). Gene expression profiles of spinal cord tissues of trkB.T1 knockout (KO) mice following SCI were accessible from the Gene Expression Omnibus database. Compared with trkB.T1 wild type (WT) mice, the differentially expressed genes (DEGs) in trkB.T1 KO mice following injury at different time points were screened out. The significant DEGs were subjected to function, co‑expression and protein‑protein interaction (PPI) network analyses. A total of 664 DEGs in the sham group and SCI groups at days 1, 3, and 7 following injury were identified. Construction of a Venn diagram revealed the overlap of several DEGs in trkB.T1 KO mice under different conditions. In total, four modules (Magenta, Purple, Brown and Blue) in a co‑expression network were found to be significant. Protein tyrosine phosphatase, receptor type C (PTPRC), coagulation factor II, thrombin (F2), and plasminogen (PLG) were the most significant nodes in the PPI network. 'Fc γ R‑mediated phagocytosis' and 'complement and coagulation cascades' were the significant pathways enriched by genes in the PPI and co‑expression networks. The results of the present study identified PTPRC, F2 and PLG as potential targets for SCI treatment, which may further improve the general understanding of SCI pathology.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".