Thymoquinone Targeting T Helper 2 Cytokines in Animal Models of Asthma: A Systematic Review.
Bibliographic record
Abstract
Background: L. It has therapeutic properties in allergic diseases, such as the antihistamine effect on the airways of patients with asthma and inhibition of inflammatory changes. This systematic review was conducted to investigate the effect of TQ on T helper 2 (Th2) cytokines, including IL-4, IL-5, and IL-13, in the treatment of animal models of asthma. Materials and Methods: A comprehensive article search was conducted using Web of Science, Scopus, and PubMed to find articles published until 2022 regarding the efficacy of TQ in treating animal models of asthma. We found 399 articles in Scopus, 927 in Web of Science, and 790 in PubMed, from a total number of 2116 articles. After deleting duplicate articles, we read the remaining 1126 titles and abstracts. Finally, 37 articles were selected for full reading. After excluding papers without full text, duplicates, letters, case studies, and those whose topic did not meet the criteria of this study, 8 articles remained. In the manual search, we did not find any deviating articles from the systematic search. Results: Our results showed that TQ had a significant effect on the reduction of Th2 cytokines, including IL-4, IL-5, and IL-13, in animal models of asthma. Conclusion: Current evidence shows the anti-inflammatory effects of TQ on Th2 cytokines, but its association with the reduction of Th2 cytokines in animal models of asthma needs further studies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".