Different effect of <scp>l</scp>-NAME treatment on susceptibility to decompression sickness in male and female rats
Bibliographic record
Abstract
Vascular bubble formation results from supersaturation during inadequate decompression contributes to endothelial injuries, which form the basis for the development of decompression sickness (DCS). Risk factors for DCS include increased age, weight-fat mass, decreased maximal oxygen uptake, chronic diseases, dehydration, and nitric oxide (NO) bioavailability. Production of NO is often affected by diving and its expression-activity varies between the genders. Little is known about the influence of sex on the risk of DCS. To study this relationship we used an animal model of Nω-nitro-l-arginine methyl ester (l-NAME) to induce decreased NO production. Male and female rats with diverse ages and weights were divided into 2 groups: treated with l-NAME (in tap water; 0.05 mg·mL(-1) for 7 days) and a control group. To control the distribution of nitrogen among tissues, 2 different compression-decompression protocols were used. Results showed that l-NAME was significantly associated with increased DCS in female rats (p = 0.039) only. Weight was significant for both sexes (p = 0.01). The protocol with the highest estimated tissue pressures in the slower compartments was 2.6 times more likely to produce DCS than the protocol with the highest estimated tissue pressures in faster compartments. The outcome of this study had significantly different susceptibility to DCS after l-NAME treatment between the sexes, while l-NAME per se had no effect on the likelihood of DCS. The analysis also showed that for the appearance of DCS, the most significant factors were type of protocol and weight.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".