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Record W2888130386 · doi:10.1371/journal.pntd.0006512

Cysteine proteases in protozoan parasites

2018· review· en· W2888130386 on OpenAlexafffund
Jair L. Siqueira-Neto, Anjan Debnath, Laura‐Isobel McCall, Jean Bernatchez, Momar Ndao, Sharon L. Reed, Philip J. Rosenthal

Bibliographic record

VenuePLoS neglected tropical diseases · 2018
Typereview
Languageen
FieldMedicine
TopicResearch on Leishmaniasis Studies
Canadian institutionsMcGill University Health Centre
FundersNational Center for Advancing Translational SciencesNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchMcGill University Health CentrePublic Health AgencyNational Institutes of HealthPublic Health Agency of CanadaMedicines for Malaria VentureMcGill University
KeywordsProteasesTrypanosoma cruziBiologyMicrobiologyTrypanosoma bruceiTrypanosomaImmune systemVirologyParasite hostingBiochemistryImmunologyEnzymeGene

Abstract

fetched live from OpenAlex

Cysteine proteases (CPs) play key roles in the pathogenesis of protozoan parasites, including cell/tissue penetration, hydrolysis of host or parasite proteins, autophagy, and evasion or modulation of the host immune response, making them attractive chemotherapeutic and vaccine targets. This review highlights current knowledge on clan CA cysteine proteases, the best-characterized group of cysteine proteases, from 7 protozoan organisms causing human diseases with significant impact: Entamoeba histolytica, Leishmania species (sp.), Trypanosoma brucei, T. cruzi, Cryptosporidium sp., Plasmodium sp., and Toxoplasma gondii. Clan CA proteases from three organisms (T. brucei, T. cruzi, and Plasmodium sp.) are well characterized as druggable targets based on in vitro and in vivo models. A number of candidate inhibitors are under development. CPs from these organisms and from other protozoan parasites should be further characterized to improve our understanding of their biological functions and identify novel targets for chemotherapy.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.075
GPT teacher head0.376
Teacher spread0.301 · 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

Citations164
Published2018
Admission routes2
Has abstractyes

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