Physiological and pharmacological inductors of HSP70 enhance the antioxidative defense mechanisms of the liver and pancreas in diabetic rats
Bibliographic record
Abstract
Heat preconditioning (HP) is a powerful adaptive and protective phenomenon and the heat stress proteins (HSPs) it produces are an important determinant for the development of diabetic complications. Aspirin has been reported to modulate heat shock response in different organisms through increased induction of HSPs and is also known to exert antioxidative and radical scavenging effects in diabetes. We estimated the effect of physiological (heat stress: 45 min at 41 ± 0.5 °C) and pharmacological (aspirin treatment) induction of HSP70 on several parameters of oxidative state in the pancreas and liver of diabetic rats. Diabetes increased HSP70 level and decreased poly(ADP) ribose polymerase (PARP), glutathione (GSH), and glutathione peroxidase (GPx) activities in the pancreas. In the liver, there was reduction of HSP70 level, GSH concentration, and CAT activity, while GPx and GR activity were enhanced. HP of diabetic rats caused an additional increase of HSP70, GSH, and antioxidant enzymes in both organs. Pre-treatment of HP-diabetic animals with aspirin led to an additional increase of PARP and HSP70. Both HP and aspirin, as physiological and pharmacological inductors of HSP70, respectively, enhanced the antioxidative defense mechanisms of the liver and pancreas in diabetic rats.
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) | 0.000 | 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".