Drug- and Virus- or Bacteria-induced Exanthems: The Role of Immunohistochemical Staining for Cytokines in Differential Diagnosis
Bibliographic record
Abstract
BACKGROUND: The differential clinical diagnosis between drug-induced exanthema (DIE) and virus- or bacteria-induced exanthema (VBIE) is frequently not easy because the serologic analysis for virus and bacteria and skin tests are not always exhaustive. In these cases, only the oral challenge test is nullifying. OBJECTIVES: This study wants to identify 1 or more structural changes and/or cytokine markers that might be helpful in discriminating the etiology and the possible correlation with the clinical features, type of the involved drug, blood and skin eosinophilia, and time of skin biopsy. METHODS: Involved non-sun-exposed skin biopsy specimens were obtained from 36 patients with DIE and 30 patients with VBIE. Blood investigations, skin tests, and oral rechallenge tests were carried out in all subjects. The histopathologic features and the immunohistochemical expression of a cytokine panel [fatty acid synthase-ligand, granzyme B, interleukin (IL) 2, IL-4, IL-5, IL-10, IL-13, interferon γ, perforin, tumor necrosis factor α] were analyzed. CONCLUSIONS: Finally, DIE and VBIE have distinct skin cytokine profile (IL-5 alone or in combination with granzyme B and perforin in DIEs was statistically more frequent than in VBIEs, mainly when skin biopsy was carried out within 2 days from clinical onset), which might be helpful in discriminating the etiology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".