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Record W1983966554 · doi:10.1089/aid.2012.0049

Understanding Factors That Modulate HIV Infection at the Female Genital Tract Mucosae for the Rationale Design of Microbicides

2012· article· en· W1983966554 on OpenAlexaff
Héla Saïdi, Mohammad-Ali Jenabian, Laurent Bélec

Bibliographic record

VenueAIDS Research and Human Retroviruses · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMicrobicideMicrobicides for sexually transmitted diseasesVaginal microbicideImmunologyContext (archaeology)MedicineVaginaChemokineBiologyHuman immunodeficiency virus (HIV)VirologyImmune systemPopulation

Abstract

fetched live from OpenAlex

Women are now becoming the pivot of the epidemiological spread of HIV infection worldwide, especially in developing countries. Therefore, research to develop an efficient microbicide is now a priority for the prevention of HIV-1 acquisition in exposed women. However, recent disappointing failures in microbicide clinical trials revealed major gaps in basic and applied knowledge that hinder the development of effective microbicide formulations. Indeed, the inhibitory power of microbicide molecules may be affected by several physiological and immunological factors present in male and female genital tracts. Furthermore, mucosal crossing of HIV-1 to increase the ability to reach the submucosal target cells (macrophages, lymphocytes, and dendritic cells) may be modulated by supraepithelial factors such as seminal complement components (opsonized HIV-1), by epithelial factors released in the submucosal microenvironment such as antimicrobial soluble factors, cytokines, and chemokines, and by potent intraepithelial and submucosal innate immunity. The design of vaginal microbicide formulations should take into account an understanding of the intimate mechanisms involved in the crossing of HIV through the female genital mucosae, in the context of a mixture of both male and female genital fluids.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.419
GPT teacher head0.396
Teacher spread0.023 · 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.

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

Citations7
Published2012
Admission routes1
Has abstractyes

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