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Restriction Factors in HIV-1 Disease Progression

2015· review· en· W2339684523 on OpenAlexafffund
Natacha Mérindol, Lionel Berthoux

Bibliographic record

VenueCurrent HIV Research · 2015
Typereview
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersCanadian Institutes of Health Research
KeywordsAPOBEC3GTetherinSAMHD1DiseaseContext (archaeology)ImmunologyBiologyInterferonHuman immunodeficiency virus (HIV)PhenotypeVirologyMedicineViral replicationGeneGeneticsVirusReverse transcriptaseViral envelopeInternal medicineRNA

Abstract

fetched live from OpenAlex

About 35 million people worldwide were living with HIV-1 at the end of 2013 and over 25 million have already died of AIDS. AIDS patients show high variability in the speed of disease progression in the absence of treatment. While certain immunological traits have been shown to correlate with accelerated or slowed progression in some subjects, including slow progressors, factors controlling HIV-1 replication and disease kinetics remain largely enigmatic. The importance of T lymphocytes and of protective HLA-alleles is undeniable, but not sufficient to explain every attenuated phenotype. A thorough understanding of HIV-1 infection control in these patient subsets may help the development of novel strategies for treatment and prevention. Restriction factors are type I interferon-induced specialized cellular proteins that block viruses at different steps of their life cycle. TRIM5α, Mx2/MxB, TRIM22/Staf50, SAMHD1, p21/CDKN1, tetherin/BST2/CD137, APOBEC3G and APOBEC3F have all been proposed to inhibit HIV-1, often with gene variant- or cellular context-specificity. Recent evidence highlights their possible implication in AIDS disease progression. In this review, we depict their restrictive activity against HIV-1 and recapitulate the latest data on their potential role in vivo, in both normal and slow progressors.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.900
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.021

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 head0.274
GPT teacher head0.502
Teacher spread0.228 · 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 designNot applicable
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

Citations37
Published2015
Admission routes2
Has abstractyes

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