Cytokine Gene Expression in Healing and Non‐Healing Cases of Cutaneous Leishmaniasis in Response to <i>In vitro</i> Stimulation with Recombinant gp63 Using Semi‐Quantitative RT–PCR
Bibliographic record
Abstract
Objectives of this study were to test the cytokine gene expression in peripheral blood mononuclear cells (PBMCs) from cases with nonhealing and healing cutaneous leishmaniasis (CL) in response to in vitro stimulation of recombinant gp63 (rgp63) and soluble Leishmania antigen (SLA). Healing and nonhealing cases are, respectively, defined as recovered from disease and refractory to various treatments. To evaluate the type of immunological response, mRNA transcription level for interleukin (IL)-4, IL-10, IL-12 and interferon (IFN)-gamma were determined using semiquantitative reverse transcription-polymerase chain reaction (RT-PCR) technique in PBMCs of these volunteers. The results clearly demonstrated a high level of IL-4 expression in nonhealing cases of CL and a low expression level of transcripts for IFN-gamma and IL-12. In contrast, a high level of IFN-gamma and IL-12 expression and a low level of IL-4 and IL-10 expression were detected in the healing cases. These findings not only support the balance of Th1/Th2 cytokines in the inducing predominant profile in healing and nonhealing cases, but it may also show the potential of rgp63 as a proper immunogen which might induce protective responses.
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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 | 0.000 | 0.000 |
| 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".