Hsp70 distribution patterns in rat skeletal muscle and vasculature post high and low intensity exercise
Bibliographic record
Abstract
Contracting skeletal muscle works as an integrated unit where increased muscle blood flow, myofiber (MF) activation and elevated temperature represent stresses which result in induction of the cytoprotective heat shock protein 70 (Hsp70) in blood vessels (BV) and MF. The effect of exercise intensity on Hsp70 expression within, and relationships between, these structures has yet to be examined. Tested hypotheses were 1) with increasing exercise intensity, more BVs, progressing from large to small would express Hsp70 2) MFs expressing Hsp70 would be located adjacent to Hsp70 expressing BVs. Male rats served as controls or were run for 1h on a treadmill (2% incline) at 15m/min (LoEx) or 30m/min (HiEx). 24 hr post‐exercise the white portion of the vastus muscle was harvested for localization of Hsp70 (n=10/group). The ratio of BVs expressing Hsp70/total vessels was greater in HiEx vs LoEx as a consequence of increased expression in small vessels (P<0.05). Hsp70 integrated density was also greater in large vs small BVs (P<0.05). Finally, the ratio of Hsp70 expressing MFs/total MFs was higher around smaller vs larger BVs (P<0.05). These observations demonstrate that HiEx produces a greater and more extensive Hsp70 response than LoEx in the vasculature, possibly as a result of vessel recruitment. The greater Hsp70 response in MF surrounding small BVs suggests an intensity‐dependent linkage of MF recruitment with blood flow.
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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 | 0.001 | 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 | 0.000 |
| 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".