Identification of critical mitochondrial hub gene for facial nerve regeneration
Bibliographic record
Abstract
Mitochondria play a critical role in nerve regeneration, yet the impact of gene expression changes related to mitochondria in facial nerve regeneration remains unknown. To address this knowledge gap, we analyzed the expression profile of the facial motor nucleus (FMN) using data obtained from the Gene Expression Omnibus (GEO) database (GSE162977). By comparing different time points in the data, we identified differentially expressed genes (DEGs). Additionally, we collected mitochondria-related genes from the Gene Ontology (GO) database and intersected them with the DEGs, resulting in the identification of mitochondria-related DEGs (MIT-DEGs). To gain further insights, we performed functional enrichment and pathway analysis of the MIT-DEGs. To explore the interactions among these MIT-DEGs, we constructed a protein-protein interaction (PPI) network using the STRING database and identified hub genes using the Degree algorithm of Cytoscape software. To validate the relevance of these genes to nerve regeneration, we established a rat facial nerve injury (FNI) model and conducted a series of experiments. Through these experiments, we confirmed three MIT-DEGs (Myc, Lyn, and Cdk1) associated with facial nerve regeneration. Our findings provide valuable insights into the transcriptional changes of mitochondria-related genes in the FMN following FNI, which can contribute to the development of new treatment strategies for FNI.
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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) | 0.003 | 0.001 |
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".