Tirofiban, a Glycoprotein IIb/IIIa Antagonist, Has a Protective Effect on Decompression Sickness in Rats: Is the Crosstalk Between Platelet and Leukocytes Essential?
Bibliographic record
Abstract
In its severest forms, decompression sickness may extend systemically and/or induce severe neurological deficits, including paralysis or even death. It seems that the sterile and ischemic inflammatory phenomena are consecutive to the reaction of the bubbles with the organism and that the blood platelet activation plays a determinant role in the development of decompression sickness. According to the hypotheses commonly put forward, the bubbles could either activate the platelets by direct contact or be the cause of abrasion of the vascular epithelium, which would expose the basal plate glycogen and then prompt the platelets to activate. The purpose of this study is to confirm anti-platelet drugs specific to GPIIb/IIIa integrin could prevent decompression sickness, using a rat model. There is a significant difference concerning the incidence of the drug on the clinical status of the rats (p=0.016), with a better clinical outcome for rats treated with tirofiban (TIR) compared with the control rats (p=0.027), even if the three anti-GPIIb/IIIa agents used have limited respiratory distress. Tirofiban limited the decrease in platelet counts following the hyperbaric exposure. Tirofiban help to prevent from DCS. Tirofiban is specific to GPIIb/IIIa whereas eptifibatide and abciximab could inhibit αVβ3 and αMβ2 involved in communication with the immune system. While inhibiting GPIIb/IIIa could highlight a platelet-dependent inflammatory pathway that improves DCS outcomes, we wonder whether inhibiting the αVβ3 and αMβ2 communications is not a wrong approach for limiting mortality in DCS.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".