MétaCan
Menu
Back to cohort
Record W4205415987 · doi:10.3389/fimmu.2021.808722

Pulmonary Immune Dysregulation and Viral Persistence During HIV Infection

2022· review· en· W4205415987 on OpenAlexafffund
Yulia Alexandrova, Cecilia T. Costiniuk, Mohammad‐Ali Jenabian

Bibliographic record

VenueFrontiers in Immunology · 2022
Typereview
Languageen
FieldMedicine
TopicPneumocystis jirovecii pneumonia detection and treatment
Canadian institutionsMcGill UniversityMcGill University Health CentreUniversité du Québec à Montréal
FundersCanadian Institutes of Health Research
KeywordsImmune dysregulationImmunologyImmune systemLungMedicineMicrobiomeInflammationCOPDPopulationDiseaseDysbiosisBiologyGut floraBioinformaticsPathologyInternal medicine

Abstract

fetched live from OpenAlex

Despite the success of antiretroviral therapy (ART), people living with HIV continue to suffer from high burdens of respiratory infections, lung cancers and chronic lung disease at a higher rate than the general population. The lung mucosa, a previously neglected HIV reservoir site, is of particular importance in this phenomenon. Because ART does not eliminate the virus, residual levels of HIV that remain in deep tissues lead to chronic immune activation and pulmonary inflammatory pathologies. In turn, continuous pulmonary and systemic inflammation cause immune cell exhaustion and pulmonary immune dysregulation, creating a pro-inflammatory environment ideal for HIV reservoir persistence. Moreover, smoking, gut and lung dysbiosis and co-infections further fuel the vicious cycle of residual viral replication which, in turn, contributes to inflammation and immune cell proliferation, further maintaining the HIV reservoir. Herein, we discuss the recent evidence supporting the notion that the lungs serve as an HIV viral reservoir. We will explore how smoking, changes in the microbiome, and common co-infections seen in PLWH contribute to HIV persistence, pulmonary immune dysregulation, and high rates of infectious and non-infectious lung disease among these individuals.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.021
GPT teacher head0.259
Teacher spread0.238 · 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 designOther design
Domainnot available
GenreReview

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

Citations62
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in ImmunologySame topicPneumocystis jirovecii pneumonia detection and treatmentFrench-language works237,207