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Record W4283017142 · doi:10.1093/infdis/jiac245

Factors Associated With Human IgG Antibody Response to <i>Anopheles albimanus</i> Salivary Gland Extract, Artibonite Department, Haiti, 2017

2022· article· en· W4283017142 on OpenAlexaff
Alicia Jaramillo-Underwood, Daniel Impoinvil, Alice Sutcliff, Karen E. S. Hamre, Vena Joseph, Lotus L. van den Hoogen, Jean Frantz Lemoine, Ruth A. Ashton, Michelle Chang, Alexandre Existe, Jacques Boncy, Chris Drakeley, Gillian Stresman, Thomas Druetz, Thomas P. Eisele, Eric Rogier

Bibliographic record

VenueThe Journal of Infectious Diseases · 2022
Typearticle
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsUniversité de Montréal
FundersNational Institutes of HealthBill and Melinda Gates Foundation
KeywordsAnopheles albimanusSerologyMalariaAntigenImmunologyAntibodyMultiplexPopulationPlasmodium falciparumMedicineLogistic regressionBiologyAnophelesInternal medicineEnvironmental healthBioinformatics

Abstract

fetched live from OpenAlex

Serological data can provide estimates of human exposure to both malaria vector and parasite based on antibody responses. A multiplex bead-based assay was developed to simultaneously detect IgG to Anopheles albimanus salivary gland extract (SGE) and 23 Plasmodium falciparum antigens among 4185 participants enrolled in Artibonite department, Haiti in 2017. Logistic regression adjusted for participant- and site-level covariates and found children under 5 years and 6-15 years old had 3.7- and 5.4-fold increase in odds, respectively, of high anti-SGE IgG compared to participants >15 years. Seropositivity to P. falciparum CSP, Rh2_2030, and SEA-1 antigens was significantly associated with high IgG response against SGE, and participant enrolment at elevations under 200 m was associated with higher anti-SGE IgG levels. The ability to approximate population exposure to malaria vectors through SGE serology data is very dependent by age categories, and SGE antigens can be easily integrated into a multiplex serological assay.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.145
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.308
Teacher spread0.285 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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