Effect of ketamine on the physiological responses to combined hypoglycemic and psychophysical stress
Bibliographic record
Abstract
There is evidence that hypoglycemic stress can interact with other stressors, and that ketamine can mitigate the impact of these stressors on behavior and physiology. The current study in male Sprague-Dawley rats investigated whether pre-treatment with 0, 10, or 20 mg/kg ketamine could modulate the interaction between hypoglycemia induced by 0 or 300 mg/kg 2-deoxy-D-glucose (2-DG) and the psychophysical stress of forced swimming (FSS; 6 sessions, 10 min/session) on serum concentrations of corticosterone (CORT) and the pro-inflammatory cytokine, tumor necrosis factor (TNF)-α. It was found that 2-DG enhanced the CORT response to an initial session of FSS, and this effect dissipated after multiple sessions. More importantly, animals displayed significantly higher levels of CORT and lower levels of TNF-α in response to a drug-free test swim conducted 1 week after exposure to the combined stressors, and these responses were not observed in rats that were pre-treated with ketamine. Overall, these findings indicate that ketamine has the potential to reduce the negative impact of interacting stressors on the biological reactivity of the hypothalamic-pituitary-adrenal axis and the immune system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".