Differential Cytokine Gene Expression in the Diaphragm in Response to Strenuous Resistive Breathing
Bibliographic record
Abstract
Strenuous resistive breathing induces plasma cytokines that do not originate from circulating monocytes. We hypothesized that cytokine production is induced inside the diaphragm in response to resistive loading. Anesthetized, tracheostomized, spontaneously breathing Sprague-Dawley rats were subjected to 1, 3, or 6 hours of inspiratory resistive loading, corresponding to 45-50% of the maximum inspiratory pressure. Unloaded sham-operated rats breathing spontaneously served as control animals. The diaphragm and the gastrocnemius muscles were excised at the end of the loading period, and messenger ribonucleic acid expression of tumor necrosis factor-alpha, tumor necrosis factor-beta, interleukin (IL)-1alpha, IL-1beta, IL-2, IL-3, IL-4, IL-5, IL-6, IL-10, IFN-gamma, and two housekeeping genes was analyzed using multiprobe RNase protection assay. IL-6, IL-1beta, and, to lesser extents, tumor necrosis factor-alpha, IL-10, IFN-gamma, and IL-4 were significantly increased in a time-dependent fashion in the diaphragms but not the gastrocnemius of loaded animals or in the diaphragm of control animals. Elevation of protein levels of IL-6 and IL-1beta in the diaphragm of loaded animals was confirmed with immunoblotting. Immunostaining revealed IL-6 protein localization inside diaphragmatic muscle fibers. We conclude that increased ventilatory muscle activity during resistive loading induces differential elevation of proinflammatory and antiinflammatory cytokine gene expression in the ventilatory 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 | 0.000 |
| 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".