Does immune dysregulation contribute towards development of hypopigmentation in Indian post kala‐azar dermal leishmaniasis?
Bibliographic record
Abstract
Abstract Post kala‐azar dermal leishmaniasis (PKDL), a sequel of apparently cured visceral leishmaniasis (VL) presents with papulonodular (polymorphic) or hypopigmented lesions (macular) and is the proposed disease reservoir. As hypopigmentation appears consistently in PKDL, especially the macular form, this study aimed to delineate immune factors that singly or in combination could contribute towards this hypopigmentation. At lesional sites, the presence of melanocytes and CD8+ T‐cells was assessed by immunohistochemistry and mRNA expression of melanogenic markers (tyrosinase, tyrosinase‐related protein‐1 and MITF) by droplet digital PCR, while plasma levels of cytokines and chemokines were measured by a multiplex assay. In comparison with skin from healthy individuals, macular PKDL demonstrated a near total absence of Melan‐A+ cells at dermal sites, while the polymorphic cases demonstrated a 3.2‐fold decrease, along with a dramatic reduction in the expression of key enzymes related to the melanogenesis signalling pathway in both forms. The levels of circulating IFN‐γ, IL‐6, IL‐2, IL‐1β, TNF‐α and IFN‐γ‐inducible chemokines (CXCL9/10/11) were elevated and was accompanied by an increased lesional infiltration of CD8+ T‐cells. The proportion of CD8+ T‐cells correlated strongly with plasma levels of IFN‐γ (r = 0.8), IL‐6 (r = 0.9, p < 0.05), IL‐2 (r = 0.7), TNF‐α (r = 0.9, p < 0.05) and IL‐1β (r = 0.7), as also with CXCL9 (r = 0.5) and CXCL10 (r = 0.6). Taken together, the absence/reduction in Melan‐A suggested hypopigmentation in PKDL was associated with the destruction of melanocytes, following the impairment of the melanogenesis pathway. Furthermore, the presence of CD8+ T‐cells and an enhanced IFN‐γ‐associated immune milieu suggested the generation of a pro‐inflammatory landscape that facilitated melanocyte dysfunction/destruction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".