Expression of Myosin Heavy-Chain Isoforms in the Respiratory Muscles Following Inspiratory Resistive Breathing
Bibliographic record
Abstract
We investigated the effect of inspiratory resistive breathing (IRB) on the expression of the genes encoding fast and slow isoforms of myosin heavy chain (MyHC) in respiratory muscles. Eleven mongrel dogs were studied for baseline MyHC messenger RNA (mRNA) expression, seven of which were also used to study the effects of IRB. For this latter objective, awake and spontaneously breathing animals were subjected to 2 h of IRB (80 cm H(2)O/L/s) per day for four consecutive days. mRNA expression was assessed in the diaphragm, external intercostal muscle, and a limb muscle, using both slot- blot and in situ hybridizations with isoform-specific probes. A current semiquantitative scoring method (from 0 to 4) was used to quantify the in situ mRNA expression levels, and slot-blot data were analyzed with densitometry. Prior to IRB, slow- and fast-MyHC mRNA expression was moderate, similar, and homogeneous throughout the different regions of the diaphragm, with scores of 1.50 +/- 0.54 (mean +/- SD) for slow and 2.13 +/- 0.35 for fast mRNAs in the costal region of the diaphragm, and of 1.81 +/- 0.37 for slow and 2. 13 +/- 0.64 for fast mRNAs in the crural region of the diaphragm. Although expression of fast-MyHC mRNA remained unchanged after IRB, the relative expression of the mRNA for the slow isoform increased in costal (+30%), crural (+12%), and external intercostal (+27%) muscles. MyHC mRNA expression did not change in limb muscles. We conclude that breathing with a moderate inspiratory resistance for a short period induces the expression of slow MyHC in respiratory muscles.
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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 | 0.001 |
| 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".