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Record W2901444941 · doi:10.1172/jci125264

Blocking HIV-1 replication: are Fc–Fcγ receptor interactions required?

2018· letter· en· W2901444941 on OpenAlexafffund
Donald N. Forthal, Andrés Finzi

Bibliographic record

VenueJournal of Clinical Investigation · 2018
Typeletter
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health Research
KeywordsReceptorViral replicationBlocking (statistics)Simian immunodeficiency virusIn vivoVirologyHuman immunodeficiency virus (HIV)Replication (statistics)ImmunologyFc receptorBiologyVirusAntibodyComputer scienceGenetics

Abstract

fetched live from OpenAlex

Interactions between IgG Fc and its receptors (FcγRs) have been shown to augment broadly neutralizing Ab-mediated (BnAb-mediated) protection from simian-human immunodeficiency virus (SHIV) challenge. In the current issue of the JCI, Parsons and collaborators compared the BnAb PGT121 with a version engineered to have impaired FcγR binding for their ability to protect macaques from an intravenous challenge with SHIV-infected cells as well as to treat already infected animals. Unexpectedly, and in contrast to previous studies, both versions of the Ab were equally able to prevent infection and decrease viral loads in infected animals. Thus, FcγR engagement does not always improve the in vivo antiviral activity of BnAbs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0150.008
Insufficient payload (model declined to judge)0.0040.003

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 head0.219
GPT teacher head0.463
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2018
Admission routes2
Has abstractyes

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