Serum Cytokines in Erythema Nodosum Leprosum Versus Non-Reactional Leprosy: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background. Erythema nodosum leprosum is an immune-mediated complication of leprosy whose underlying mechanism has not yet been fully elucidated, making management difficult.Objectives. To determine the serum cytokine profile of ENL compared to non-reactional leprosy states. Methods. An open literature search was performed using MEDLINE, Cochrane Library, TRIP and HERDIN electronic databases using the keywords ("cytokines" or “inflammatory mediators”) and (“erythema nodosum leprosum” or “ENL”) and (“leprosy” or “lepra”). Studies were selected by two independent review authors. Risk of bias was assessed using the Newcastle-Ottawa Scale and statistical analysis was performed using RevMan 5.3 software. Results. Eight cross-sectional studies with 197 participants were included. Meta-analysis showed that both serum IL-17 and serum IFN-γ were significantly decreased (Z 2.39, p = 0.02 and Z 2.74, p = 0.01, respectively) in ENL compared to non-reactional states. However, for IL-1β, IL-6, IL-10, IL-22, TNF-α and TGF-β, no significant differences were found between the two groups. Conlusion. ENL appears to be an exacerbation of the Th2 cytokine response seen in the lepromatous pole of leprosy. However, despite pooling of data, sample sizes remain small resulting in significant heterogeneity. Future studies involving large sample sizes and investigating a wider range of cytokines are encouraged.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".