Association between IL-25, IL-33 and atopic dermatitis: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective: To evaluate the association between IL-25, IL-33 and AD more generally. Methods: Databases, including PubMed, Web of Science, EMBASE, Scopus, CNKI and Sinomed were searched. Based on the criteria, publications were collected. The evaluation of study quality was through Newcastle-Ottawa Scale (NOS). Fixed or random effect model was selected according to the between-study heterogeneity to evaluate the association. The analysis procedure and the construction plots were using Review Manager 5.3 software. Results: Six studies were included. A total of 282 subjects were included from four studies to analyze the association between IL-25 and AD. The level of IL-25 was significantly elevated in AD patients, comparing with the control subjects (SMD = 0.89, 95% CI: 0.64, 1.14, p < 0.05). For IL-33, a total of 247 subjects were included from two studies, and the level of IL-33 was also significantly elevated in AD patients comparing to the control subjects (SMD = 0.49, 95% CI: 0.19, 0.80, p < 0.05). Conclusions: The serum levels of IL-25, IL-33 are elevated in AD patients of this study. The IL-25 and IL-33 are significantly associated with the risk of AD. Further studies with larger samples, in multiple countries and focused on different age groups are need.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".