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Record W4414888936 · doi:10.3390/pathogens14101012

Protein Profiling of Wild-Caught Phlebotomus papatasi in Morocco: First Observation of Nematodes in Moroccan Population of Sandflies

2025· article· en· W4414888936 on OpenAlexafffund
Mohamed Daoudi, Myriam Beaulieu, George Dong, Momar Ndao, Samia Boussaa, Mohamed Hafidi, Ali Boumezzough, Martin Olivier

Bibliographic record

VenuePathogens · 2025
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsParasite hostingPopulationProteomicsPsychodidaeParasitologyProteomeLeishmaniasis

Abstract

fetched live from OpenAlex

Phlebotomine-borne diseases, transmitted by sand flies, cause significant public health burdens worldwide. In Morocco, Phlebotomus papatasi is a primary vector for Leishmania major and phleboviruses. Despite extensive research in other countries, entomopathogenic parasite investigations in P. papatasi have not been conducted in Morocco until now. This study performed proteomic analysis of female P. papatasi collected from four Moroccan localities using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Our analysis revealed that Phlebotomus papatasi peptides were the most abundant, with 884 peptides identified. Additionally, we detected 732 peptides from nematodes, 86 from Leishmania major, 79 from L. infantum, eight from L. tropica, and two peptides associated with phleboviruses. Microscopic examination of 1752 sand flies confirmed P. sergenti female infected with Tetranematidae, Didilia spp. in Imintanout (Z2). This study provides the first report of nematodes in sand flies in Africa and represents the first application of proteomics to identify pathogens carried by P. papatasi. These findings highlight remarkable proteomic differences among localities and generate critical data for understanding parasite-vector interactions.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.291
Teacher spread0.267 · 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 designObservational
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

Citations0
Published2025
Admission routes2
Has abstractyes

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