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Record W2775643792 · doi:10.1371/journal.pntd.0006052

Drug resistance and treatment failure in leishmaniasis: A 21st century challenge

2017· review· en· W2775643792 on OpenAlexafffund
Alicia Ponte‐Sucre, Francisco Gamarro, Jean‐Claude Dujardin, Michael P. Barrett, Rogelio López‐Vélez, Raquel García‐Hernández, Andrew W. Pountain, Roy Mwenechanya, Barbara Papadopoulou

Bibliographic record

VenuePLoS neglected tropical diseases · 2017
Typereview
Languageen
FieldMedicine
TopicResearch on Leishmaniasis Studies
Canadian institutionsUniversité Laval
FundersWellcome TrustCanadian Institutes of Health ResearchAlexander von Humboldt-Stiftung
KeywordsMiltefosineVisceral leishmaniasisLeishmaniasisDrug resistanceMedicineLeishmaniaLeishmania donovaniDrugParomomycinImmunologyIntensive care medicinePharmacologyBiologyAntibioticsMicrobiologyParasite hosting

Abstract

fetched live from OpenAlex

Reevaluation of treatment guidelines for Old and New World leishmaniasis is urgently needed on a global basis because treatment failure is an increasing problem. Drug resistance is a fundamental determinant of treatment failure, although other factors also contribute to this phenomenon, including the global HIV/AIDS epidemic with its accompanying impact on the immune system. Pentavalent antimonials have been used successfully worldwide for the treatment of leishmaniasis since the first half of the 20th century, but the last 10 to 20 years have witnessed an increase in clinical resistance, e.g., in North Bihar in India. In this review, we discuss the meaning of "resistance" related to leishmaniasis and discuss its molecular epidemiology, particularly for Leishmania donovani that causes visceral leishmaniasis. We also discuss how resistance can affect drug combination therapies. Molecular mechanisms known to contribute to resistance to antimonials, amphotericin B, and miltefosine are also outlined.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.076
GPT teacher head0.345
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations976
Published2017
Admission routes2
Has abstractyes

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