Ketogenic diet-mediated steroid metabolism reprogramming improves the immune microenvironment and myelin growth of spinal cord injury rats through gene analysis and co-expression network analysis
Bibliographic record
Abstract
Abstract Background The ketogenic diet has been widely used in the treatment of various nervous system and metabolic-related diseases. Our previous research found that a ketogenic diet exerts a protective effect and promotes functional recovery after spinal cord injury. However, the mechanism of treatment is still unclear. In this study, different dietary feeding methods were used to detect myelin expression and gene level changes between different groups.Result We established 15 RNA-Seq cDNA libraries and divided them into 4 groups (SCI_KD, SCI_SD, Sham_KD and Sham_SD), and a total of 32,883 genes were detected by RNA-seq. First, KEGG pathway enrichment of upregulated differentially expressed genes (DEGs) in the SCI_KD and SCI_SD groups and GSEA analysis of the two groups found that a ketogenic diet significantly improved the steroid anabolic pathway in rats with spinal cord injury. Through cluster analysis, PPI analysis and visualization of iPath metabolic pathways, Sqle, Sc5d, Cyp51, Dhcr24, Msmo1, Hsd17b7, and Fdft1 changed significantly in the pathway. Second, through WGCNA analysis, all samples are placed in a gene network to analyse the correlation between gene modules and phenotypes. After module analysis, GO function and KEGG analysis showed that rats fed a ketogenic diet showed significantly reduced immune-related pathways, including those associated with immune and infectious diseases.Conclusion A ketogenic diet may improve the immune microenvironment and myelin growth in rats with spinal cord injury through reprogramming of steroid metabolism.
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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) | 0.001 | 0.001 |
| 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".