The Method and Results of a Treatment Targeting SARS-CoV-2-Activated Inflammasomes
Bibliographic record
Abstract
Abstract Background Clinicians in critical care medicine considered dapsone administration to treat SARS-CoV-2 inflammasome. Dapsone is useful 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 case series with or without intervention; a cross-sectional study. 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 ARDS treatment with dapsone 100 mg to target NLRP3 inflammasomes. Results The 22 cases were treated with standard COVID-19 therapy with dapsone (trial group), and the 22 cases were the control group. The comparison was made assuming that only decreased FIO2 was influential in the trial and control groups, which applied to only the ARDS onset stage. The chi-square statistic is 5.1836. The p-value is .02280. Fisher’s exact test statistic value is 0.0433. (The result is significant at p < .05) Furthermore, the ARDS-onset mortality rates were 0% (with dapsone) and 40% (without dapsone). 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 are competing, proving that it is only effective in treating early ARDS.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.005 | 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".