microRNA let-7i-5p mediates the relationship between muscle fat infiltration and neck pain disability following motor vehicle collision: a preliminary study
Bibliographic record
Abstract
Persistent neck-pain disability (PNPD) is common following traumatic stress exposures such as motor vehicle collision (MVC). Substantial literature indicates that fat infiltration into neck muscle (MFI) is associated with post-MVC PNPD. However, little is known about the molecular mediators underlying this association. In the current study, we assessed whether microRNA expression signatures predict PNPD and whether microRNA mediate the relationship between neck MFI and PNPD. A nested cohort of 43 individuals from a longitudinal study of MVC survivors, who provided blood (PAXgene RNA) and underwent magnetic resonance imaging (MRI), were included in the current study. Peritraumatic microRNA expression levels were quantified via small RNA sequencing, neck MFI via MRI, and PNPD via the Neck Disability Index two-weeks, three-months, and twelve-months following MVC. Repeated measures regression models were used to assess the relationship between microRNA and PNPD and to perform mediation analyses. Seventeen microRNA predicted PNPD following MVC. One microRNA, let-7i-5p, mediated the relationship between neck MFI and PNPD. Peritraumatic blood-based microRNA expression levels predict PNPD following MVC and let-7i-5p might contribute to the underlying effects of neck MFI on persistent disability. In conclusion, additional studies are needed to validate this finding.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".