Heat shock protein 90 does not contribute to cutaneous vasodilatation in older adults during heat stress
Bibliographic record
Abstract
OBJECTIVES: Heat shock protein 90 (HSP90) contributes to cutaneous vasodilatation during exercise in the heat through nitric oxide (NO) synthase (NOS)-dependent mechanisms in young adults. We hypothesized that similar responses would be observed in older middle-aged adults. METHODS: In nineteen habitually active older middle-aged (56 ± 5 years) men (n = 9) and women (n = 10), cutaneous vascular conductance (CVC) was measured at four forearm skin sites continuously treated with (a) lactated Ringers solution (Control), (b) 10 mmol/L L-NAME (NOS inhibitor), (c) 178 μmol/L geldanamycin (HSP90 inhibitor), or (d) 10 mmol/L L-NAME and 178 μmol/L geldanamycin combined. Participants rested in an upright semi-recumbent position in the heat (35°C) for 70 minutes, followed by a 50-minute bout of moderate-intensity cycling (~55% peak oxygen uptake) and a 30-minute recovery period in the heat. RESULTS: In both men and women, we observed no significant effects of HSP90 inhibition on CVC throughout rest, exercise, and recovery in the heat (all P > 0.27). Conversely, NOS inhibition and dual NOS and HSP90 inhibition attenuated CVC relative to Control throughout the protocol (all P ≤ 0.05). CONCLUSIONS: While NOS mediates cutaneous vasodilatation during rest, exercise, and recovery in the heat, HSP90 does not measurably influence this response in habitually active older middle-aged men or women under these conditions.
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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) | 0.000 | 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 | 0.000 |
| 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".