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Record W2789830696 · doi:10.1128/aac.02309-17

Inactivation of Plasmepsins 2 and 3 Sensitizes Plasmodium falciparum to the Antimalarial Drug Piperaquine

2018· article· en· W2789830696 on OpenAlexafffund
Angana Mukherjee, Dominic Gagnon, Dyann F. Wirth, Dave Richard

Bibliographic record

VenueAntimicrobial Agents and Chemotherapy · 2018
Typearticle
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsPiperaquinePlasmodium falciparumArtemisininBiologyMalariaContext (archaeology)Drug resistanceGeneticsDihydroartemisininVirologyImmunology

Abstract

fetched live from OpenAlex

ABSTRACT Dihydroartemisinin-piperaquine (DHA-PPQ), the current frontline artemisinin combination therapy used to treat Plasmodium falciparum malaria in multiple Southeast Asian countries, is now increasingly failing in Cambodia, where artemisinin resistance is nearly fixed, which suggests that PPQ resistance has emerged and is spreading rapidly in the Greater Mekong Subregion. Recent reports have shown that amplification of the genes encoding plasmepsins 2 and 3 is a molecular marker of PPQ resistance; however, whether these enzymes play a role in the mechanism of resistance is currently unknown. We show here that inactivating the genes encoding plasmepsin 2 or 3 individually in P. falciparum reference strain 3D7 results in hypersusceptibility to PPQ. Interestingly, no significant differences in the susceptibility to other antimalarials were observed, which suggests specific roles of plasmepsins 2 and 3 in PPQ susceptibility. The piperaquine hyper-sensitivity of the plasmepsin-2-and-3-inactivated lines provides direct evidence that these enzymes modulate parasite susceptibility to PPQ in the context of a single copy of PfMDR1 and independent of Kelch13 mutations conferring artemisinin resistance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.272
Teacher spread0.260 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations54
Published2018
Admission routes2
Has abstractyes

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