Identification of asymptomatic Leishmania infection in patients undergoing kidney transplant using multiple tests
Bibliographic record
Abstract
OBJECTIVES: In immunocompromised patients, asymptomatic Leishmania infection can reactivate, and evolve to severe disease. To date, no test is considered the gold standard for the identification of asymptomatic Leishmania infection. A combination of methods was employed to screen for Leishmania infection in patients undergoing kidney transplant (KT). METHODS: We employed polymerase chain reaction for the detection of parasitic DNA in peripheral blood, Western blot to identify serum immunoglobulin G and whole blood assay to detect cytokines/chemokines after stimulation of whole blood with parasitic antigen. RESULTS: One-hundred twenty patients residing in Italy were included in the study at the time of KT. Each patient that tested positive to at least one test was considered as Leishmania positive. Fifty out of 120 patients (42%) tested positive for one or more tests. The detection of specific cell-mediated response (32/111, 29%) was the most common marker of Leishmania infection, followed by a positive serology (24/120, 20%). Four patients (3%) harbored parasitic DNA in the blood. CONCLUSION: Our findings underline the high prevalence of asymptomatic Leishmania infection in patients undergoing KT in Italy, who are potentially at-risk for parasite reactivation and can benefit from an increased vigilance. Understanding the clinical relevance of these findings deserves further studies.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".