Age and Sex Affect the Chronic Trajectory of Plasma Neurofilament Light and Glial Fibrillary Acidic Protein Levels in a Murine Thoracic Contusion Spinal Cord Injury Model
Bibliographic record
Abstract
Background: Mouse models of traumatic spinal cord injury (SCI) are used to understand pathophysiology and test potential interventions. Experimental injury parameters, deficits on functional tasks, and histology are used to assess severity and recovery. Blood biomarkers may be a promising additional metric to assess severity and detect efficacy of interventions, but they have not been examined previously in mouse spinal cord injury (SCI). Objectives: To examine blood biomarkers in mouse SCI. Methods: We measured plasma levels of neurofilament light (NfL) and glial fibrillary acidic protein (GFAP) longitudinally following a thoracic contusion SCI in adolescent (3 month old) and aged (18 month old) male and female C57Bl6/J mice. Biomarkers were also assessed in comparably aged uninjured animals. Results: Three-month-old animals exhibited elevated plasma NfL and GFAP 1 month after injury. NfL levels decreased from 1 to 2 months post injury but remained elevated from baseline, while GFAP levels remained high. Adolescent males exhibited higher NfL levels than females post injury. In aged animals, NfL was comparably elevated at 1 and 2 months post injury. In aged females, GFAP was elevated at 1 and 2 months after injury, while levels in males did not increase from baseline until 2 months after injury. Values from uninjured animals show plasma NfL and GFAP increase with age in absence of injury. Conclusion: In a mouse SCI model, plasma NfL and GFAP are elevated chronically after injury. Sex and age at injury may affect biomarker trajectories, which may indicate underlying pathology relevant to treatment and recovery. Establishing the trajectory of NfL and GFAP after experimental injury may help to standardize injury paradigms, assess recovery, and detect efficacy of interventions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".