Do alterations in temperature regulation contribute to protein‐energy malnutrition‐induced increase in reactive gliosis after global ischemia?
Bibliographic record
Abstract
Protein‐energy malnutrition (PEM) impairs functional outcome after global ischemia, and this is associated with an increase in reactive gliosis. Since temperature is a key determinant of brain damage following an ischemic insult, the objective of this study was to determine if PEM modifies post‐ischemic temperature regulation. Bio‐electrical sensor transmitters were implanted into the peritoneal cavity of male, Sprague‐Dawley rats (31d) for continuous monitoring of core temperature. Rats were randomized for 7d to modified AIN‐93G control diet (CON; 18% protein) or PEM (2% protein). Animals were exposed to global ischemia (I) induced by 10 min bilateral carotid artery occlusion and hypotension (38 ± 0.2 mm Hg) or sham surgery (S) with tympanic temperature maintained at 37.4 ± 0.2°C. Physiological values were as follows: pH 7.29–7.41, pO 2 119–144 mm Hg, pCO 2 34–51 mm Hg, blood glucose 3.8–6.7 mmol/L. Preliminary results indicate mean (± SEM) core and daily temperature fluctuation from 5d of postsurgical monitoring as follows: CON‐S: 37.4 ± 0.04°C, 2.0 ± 0.1°C (n=3); PEM‐S: 37.4 ± 0.1°C, 2.6 ± 0.1°C (n=3); CON‐I: 37.7 ± 0.1°C, 1.9 ± 0.2 °C (n=3); PEM‐I: 37.5 ± 0.2°C, 2.3 ± 0.3°C (n=2). These data suggest that although PEM does not cause marked hypothermia or hyperthermia during the postsurgical period, it reduces thermoregulatory function in both sham and ischemic animals. Funded by the Heart and Stroke Foundation of SK.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".