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Record W4213374180 · doi:10.1097/id9.0000000000000047

Fungal Translocation Marker in People Living with HIV Needing Treatment for Onychomycosis: A Protocol for the Prospective Pilot Study

2022· article· en· W4213374180 on OpenAlexaff
Yaling Chen, Jing Ouyang, Stéphane Isnard, Cecilia T. Costiniuk, Jiangyu Yan, Xin Zhou, Jean‐Pierre Routy, Yaokai Chen

Bibliographic record

VenueInfectious Diseases & Immunity · 2022
Typearticle
Languageen
FieldMedicine
TopicAntifungal resistance and susceptibility
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsTerbinafineDiscontinuationMedicineHuman immunodeficiency virus (HIV)MicrobiomeRegimenSalivaInternal medicineAntifungalImmunologyBiologyBioinformaticsDermatology

Abstract

fetched live from OpenAlex

Abstract Increased microbial translocation and chronic immune activation are two critical problems for people living with HIV (PLWH) in the antiretroviral therapy (ART) era. Compared with numerous studies on bacterial microbiomic communities, there are only a limited number of studies focusing on fungal microbiomic composition and products in PLWH. This study protocol is used to evaluate the changes in bacterial and fungal microbiome populations induced by terbinafine treatment, which is an antifungal agent widely used amongst PLWH. Twenty-two PLWH on a stable ART regimen for more than six months, who require treatment for onychomycosis, will be recruited. The participants will be followed-up for a 12-week treatment period (oral terbinafine 250 mg daily) and another 12-weeks of terbinafine discontinuation. Plasma and fecal samples will be collected before and after terbinafine treatment, and for 12weeks after the discontinuation of terbinafine. Plasma gut injury and microbial translocation biomarker assays, in addition to testing for gut microbiome composition, will be undertaken. With this pilot study, we will perform formal sample size calculations and test study feasibility for a possible full-scale study.

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)0.000
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 score0.222
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.029
GPT teacher head0.320
Teacher spread0.291 · 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
Published2022
Admission routes1
Has abstractyes

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