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Record W2147372130 · doi:10.1155/2012/131243

The Microbiological Context of HIV Resistance: Vaginal Microbiota and Mucosal Inflammation at the Viral Point of Entry

2012· article· en· W2147372130 on OpenAlexafffund
John J. Schellenberg, Francis A. Plummer

Bibliographic record

VenueInternational Journal of Inflammation · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsCanadian Science Centre for Human and Animal HealthUniversity of Manitoba
FundersCanadian Institutes of Health ResearchNational Institutes of HealthBill and Melinda Gates Foundation
KeywordsImmunologyContext (archaeology)Immune systemBacterial vaginosisBiologyHuman immunodeficiency virus (HIV)InflammationTransmission (telecommunications)VirologyMicrobiology

Abstract

fetched live from OpenAlex

Immune activation is increasingly recognized as a critical element of HIV infection and pathogenesis, causing expansion of virus founder populations at the mucosal port of entry and eventual exhaustion of cellular immune effectors. HIV susceptibility is well known to be influenced by concurrent sexually transmitted infections; however, the role of commensal vaginal microbiota is poorly characterized. Bacterial vaginosis (BV) is a risk factor for HIV acquisition in studies worldwide; however, the etiology of BV remains enigmatic, and the mechanisms by which BV increases HIV susceptibility are not fully defined. A model of how vaginal microbiota influences HIV transmission is considered in the context of a well-established cohort of HIV-exposed seronegative (HESN) commercial sex workers (CSW) in Nairobi, Kenya, many of whom have increased levels of anti-inflammatory factors in vaginal secretions and reduced peripheral immune activation (immune quiescence). Elucidation of the relationship between complex microbial communities and inflammatory mucosal responses underlying HIV infection should be a priority for future prevention-focussed research.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.275
Teacher spread0.264 · 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

Citations30
Published2012
Admission routes2
Has abstractyes

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