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Record W2387894936 · doi:10.1093/ofid/ofw073

Immunological Signaling During Herpes Simplex Virus-2 and Cytomegalovirus Vaginal Shedding After Initiation of Antiretroviral Treatment

2016· article· en· W2387894936 on OpenAlexaff
Martha Nason, Eshan U. Patel, Allison Kirkpatrick, Jessica L. Prodger, Kamnoosh Shahabi, Aaron A.R. Tobian, Sara Gianella, Sarah Kalibbala, Paschal Ssebbowa, Rupert Kaul, Ronald H. Gray, Thomas C. Quinn, David Serwadda, Steven J. Reynolds, Andrew D. Redd

Bibliographic record

VenueOpen Forum Infectious Diseases · 2016
Typearticle
Languageen
FieldMedicine
TopicHerpesvirus Infections and Treatments
Canadian institutionsUniversity of Toronto
FundersNational Institute of Allergy and Infectious DiseasesJohns Hopkins Bloomberg School of Public HealthNational Institutes of HealthDoris Duke Charitable Foundation
KeywordsMedicineCytomegalovirusHerpes simplex virusVirologyViral sheddingHuman immunodeficiency virus (HIV)ImmunologyAntiretroviral treatmentAntiretroviral therapyVirusHerpesviridaeViral loadViral disease

Abstract

fetched live from OpenAlex

Vaginal proinflammatory cytokine expression during herpes virus reactivation was examined in human immunodeficiency virus-infected women before and after initiation of antiretroviral therapy (ART). Vaginal swabs were screened for levels of cytokines interleukin (IL)-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, tumor necrosis factor (TNF)-α, and interferon-γ. The relative risk (RR) of herpes simplex virus-2 or cytomegalovirus (CMV) shedding being associated with cytokine levels above the median were estimated. Herpes simplex virus-2 shedding was significantly associated with higher levels of IL-6 (RR = 1.4, P = .003) and TNF-α (RR = 1.3, P = .010), whereas CMV shedding was associated with higher IL-6 (RR = 1.3, P = .006) and IL-2 (RR = 1.4, P = .01). The association of viral shedding with higher IL-6 levels suggests that herpes virus reactivation may be playing a role in immune activation after ART initiation.

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 score0.154
Threshold uncertainty score0.658

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)0.0010.000

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 head0.015
GPT teacher head0.290
Teacher spread0.276 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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