Chronic Hyperglycemia before Spinal Cord Injury Increases Inflammatory Reaction and Astrogliosis after Injury: Human and Rat Studies
Bibliographic record
Abstract
Traumatic spinal cord injury (SCI) can cause permanent disabilities that seriously reduce quality of life. We evaluated the effects of chronic hyperglycemia before SCI on inflammatory markers and functional recovery after SCI in human patients and a rat model. In the human study, multivariate logistical regression analysis revealed that hemoglobin A1c (HbA1c) values, reflecting average plasma glucose concentration over a 3 month period, at admission were a significant risk factor for poor functional recovery. Moreover, patients with chronic hyperglycemia (HbA1c ≥ 6.5%) had high concentrations of inflammatory biomarkers (interleukin [IL]-6 and IL-8) of cerebrospinal fluid after SCI. Consistent with patient findings, chronic hyperglycemia before SCI in rats was associated with increased inflammatory responses and oxygen-free radicals in the spinal cord and blood, thus resulting in poor functional recovery and histological outcomes. Tight glucose control before SCI decreased the harmful effects of hyperglycemia after SCI in both human and rat studies. Our findings suggest that chronic hyperglycemia before SCI may be a significant prognostic factor with a negative impact on functional and histological outcomes, highlighting the importance of tight glucose control before SCI.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".