Decreases in Dorsal Cervical Spinal Cord White Matter Tract Integrity Are Associated with Elevated Levels of Serum MicroRNA Biomarkers in NCAA Division I Collegiate Football Players
Bibliographic record
Abstract
This prospective, controlled, observational cohort study assessed the performance of a novel panel of serum microRNA (miRNA) biomarkers relative to findings on cervical spinal cord magnetic resonance imaging (MRI) in collegiate football players. There were 44 participants included in the study: 30 non-athlete control subjects and 14 male collegiate football athletes participating in a Division I Football Bowl Subdivision of the National Collegiate Athletic Association. Diffuse tensor MRI and blood samples were acquired within the week before the athletic season began and within the week after the last game of the season. All miRNAs were significantly higher in athletes regardless of their fractional anisotropy (FA) values (p < 0.001), even those considered to be in the “normal” range of FA for white and gray matter integrity in the cervical spinal cord. miRNA biomarkers were most significantly correlated with FA of the white matter (WM) tracts of the dorsal (posterior) spinal cord; particularly, the fasciculus gracilis, fasciculus cuneatus, lateral corticospinal tract, rubrospinal tract, lateral reticulospinal tract, spinal lemniscus, and spinothalamic and -reticular tracts. Areas under the curve for miRNA biomarkers predicting lower FA of WM dorsal (posterior) cervical spinal tracts, therefore lower white matter integrity (connectivity), were miR-505* = 0.75 (0.54–0.96), miR-30d = 0.74 (0.52–0.95), and miR-92a = 0.75 (0.53–0.98). Should these findings be replicated in a larger cohort of athletes, these markers could potentially serve as measures of neuroimaging abnormalities in athletes at risk for concussion and subconcussive injuries to the cervical spinal cord.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".