Telomere length and advanced diffusion MRI as biomarkers for repetitive mild traumatic brain injury in adolescent rats
Bibliographic record
Abstract
Mild traumatic brain injuries (mTBI) are of worldwide concern in adolescents of both sexes, and repeated mTBI (RmTBI) may have serious long-term neurological consequences. As such, the study of RmTBI and discovery of objective biomarkers that can help guide medical decisions is an important undertaking. Diffusion-weighted MRI (DWI), which provides markers of axonal injury, and telomere length (TL) are two clinically relevant biomarkers that have been implicated in a number of neurological conditions, and may also be affected by RmTBI. Therefore, this study utilized the lateral impact injury model of RmTBI to investigate changes in diffusion MRI and TL, and how these changes relate to each other. Adolescent male and female rats received either three mTBIs or three sham injuries. The first injury was given on postnatal day 30 (P30), with the repeated injuries separated by four days each. Seven days after the final injury, a sample of ear tissue was collected for TL analysis. Rats were then euthanized and whole brains were collected and fixated for MRI analyses that included diffusion and high-resolution structural sequences. Compared to the sham-injured group, RmTBI rats had significantly shorter TL at seven days post-injury. Analysis of advanced DWI measures found that RmTBI rats had abnormalities in the corpus callosum and cortex at seven days post-injury. Notably, many of the DWI changes were correlated with TL. These findings demonstrate that TL and DWI measurements are changed by RmTBI and may represent clinically applicable biomarkers for this.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".