Rotten eggs’ H <sub>2</sub> S acting on human airway smooth muscle mechanics
Bibliographic record
Abstract
The well‐known and reported mechanisms of action of small molecular gases on the vasculature point to a possible therapeutic agent for patients suffering from hypertension and/or stroke. Hydrogen sulphide (H 2 S) is a new gas in this class. H 2 S relaxes vascular smooth muscle via the K ATP channel in several species. Trachealis of dog and rat express ATP‐sensitive potassium channels. Here, we explored the impact of two agents capable of generating H 2 S (Na 2 S and GYY4137) on human airway smooth muscle (ASM). ASM cells were prepared from human bronchi obtained from lungs unsuitable for transplantation, and dynamic changes in stiffness were measured as an indicator of contraction and relaxation of isolated human ASM cells, using Magnetic Twisting Cytometry (MTC). Acute exposure to both Na 2 S and GYY4137 decreased cell stiffness in a dose‐dependent fashion, with maximal relaxation at 10mM and 5mM, respectively. In addition, cells exposed to varying doses of GYY4137 for 24 hours exhibited dose‐dependent decreases in cell stiffness that were substantially greater than acute responses (21% relaxation at 300s vs. 50% relaxation at 24h). These findings demonstrate that smooth muscle in the airway react quite similarly to that in the vasculature to H 2 S and suggest, thereby, this class of small gas molecules can be exploited for treating constricted human airways. We are currently validating the effects of H 2 S on human lung slices, as well as exploring for the presence of H 2 S synthesizing enzymes (cystathionine β‐synthase and cystathionine γ‐lyase) in human ASM and their mechanistic role using animal models. NIH Funding: HL107361 , HL 050712–13
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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) | 0.005 | 0.001 |
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".