The Method and Results of a Treatment Targeting SARS-CoV-2-Activated Inflammasomes
Bibliographic record
Abstract
Abstract Background Clinicians considered dapsone administration to treat SARS-CoV-2 inflammasome. Dapsone is helpful in the molecular regulation of Nod-like receptor family pyrin domain-containing 3 (NLRP3).Objective To study the targeting of NLRP3 itself or up-/downstream factors of the NLRP3 inflammasome by dapsone must be responsible for its observed preventive effects, functioning as a competitor.Methods This is a randomized controlled trial (RCT). We set out to use objective criteria of improvement, such as A. a reduction in the FIO2 requirement and B. a decrease in the progression of hypoxia. We treated the patients with standard COVID-19 acute respiratory distress syndrome (ARDS) treatment with dapsone. The RCT results were analyzed.Results ARDS progression was blocked in 17 of 19 total patients at the first period. The 44 (trial 22/ control 22) subjects were analyzed during the second period. The chi-square statistic is 5.1836. The p-value is .02280. (RR 0.21, OR 0.1) Fisher's exact test statistic value is 0.0433. (The result is significant at p < .05) (RR 0.15, OR 0) It is significant at the ARDS onset stage.Conclusion There was a significant difference in dapsone treatment results in the ARDS-onset group. We confirmed that dapsone clinically treated the onset of ARDS by targeting SARS-CoV-2-activated inflammasomes. Like chemically reacting substances, inflammasome and dapsone compete, proving that it is effective in early ARDS. ClinicalTrials.gov Identifier NCT04918914
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".