Heat shock proteins: in vivo heat treatments reveal adipose tissue depot-specific effects
Bibliographic record
Abstract
Heat treatments (HT) and the induction of heat shock proteins (HSPs) improve whole body and skeletal muscle insulin sensitivity while decreasing white adipose tissue (WAT) mass. However, HSPs in WAT have been understudied. The purpose of the present study was to examine patterns of HSP expression in WAT depots, and to examine the effects of a single in vivo HT on WAT metabolism. Male Wistar rats received HT (41°C, 20 min) or sham treatment (37°C), and 24 h later subcutaneous, epididymal, and retroperitoneal WAT depots (SCAT, eWAT, and rpWAT, respectively) were removed for ex vivo experiments and Western blotting. SCAT, eWAT, and rpWAT from a subset of rats were also cultured separately and received a single in vitro HT or sham treatment. HSP72 and HSP25 expression was greatest in more metabolically active WAT depots (i.e., eWAT and rpWAT) compared with the SCAT. Following HT, HSP72 increased in all depots with the greatest induction occurring in the SCAT. In addition, HSP25 increased in the rpWAT and eWAT, while HSP60 increased in the rpWAT only in vivo. Free fatty acid (FFA) release from WAT explants was increased following HT in the rpWAT only, and fatty acid reesterification was decreased in the rpWAT but increased in the SCAT following HT. HT increased insulin responsiveness in eWAT, but not in SCAT or rpWAT. Differences in HSP expression and induction patterns following HT further support the growing body of literature differentiating distinct WAT depots in health and disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".