Peripheral microRNA alteration and pathway signaling after mild traumatic brain injur
Bibliographic record
Abstract
Discovering novel diagnostic biomarkers and signatures for traumatic brain injury (TBI) represents a major challenge in the brain trauma research. Detailed analysis of post-concussive molecular pathways based on experimental data could provide a new insight into the pathophysiological sequelae and mapping of recovery mechanisms involved in TBI. MicroRNAs (miRNAs) detectable in peripheral body fluids after TBI are promising carriers of this missing knowledge. In order to define the signature of peripheral miRNAs signaling associated with mild TBI (mTBI), we performed a comprehensive meta-analysis of miRNA profiles in mTBI patients using multiple curated pathway databases. Using a bioinformatic pipeline with integrated data analysis we identified a set of genes that are connected to deregulated circulating miRNAs following the mTBI. Identified genes belong to specific pathways of MAPK, TGF-β, WNT, TLR2/4, PI3K/AKT, insulin, and growth factor signaling. Since the enriched pathways markedly overlap among the various biological fluids, signaling associated with mTBI that is concomitantly reflected in serum, plasma and saliva is robust and unique. Furthermore, we identified a network of 33 validated interacting proteins and their regulatory miRNAs that link the post-mTBI signaling in peripheral fluids with neurodegeneration-associated interaction pathways. Presented data provide a comprehensive insight into molecular events following mTBI, and the top predicted genes represent a group of novel candidate targets to be validated in connection with mTBI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".