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Record W2970203651 · doi:10.3390/vaccines7030103

Understudied Factors Influencing Fc-Mediated Immune Responses against Viral Infections

2019· review· en· W2970203651 on OpenAlexafffund
Sai Priya Anand, Andrés Finzi

Bibliographic record

VenueVaccines · 2019
Typereview
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsUniversité de MontréalMcGill UniversityCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health Research
KeywordsAntibodyAntibody-dependent cell-mediated cytotoxicityBiologyEffectorImmune systemVirologyInnate immune systemImmunologyViral replicationCell biologyVirusMonoclonal antibody

Abstract

fetched live from OpenAlex

Antibodies play a crucial role in host defense against viruses, both by preventing infection and by controlling viral replication. Besides their capacity to neutralize viruses, antibodies also exert their antiviral effects by crystallizable fragment (Fc)-mediated effector mechanisms. This involves a bridge between innate and adaptive immune systems, wherein antibodies form immune complexes that drive numerous innate immune effector functions, including antibody-dependent cellular cytotoxicity, antibody-dependent complement-mediated lysis, and antibody-dependent phagocytosis. Here, we review certain mechanisms that modulate these antibody-mediated effector functions against virally infected cells, such as viral glycoprotein shedding, viral glycoprotein internalization, antibody cooperativity, and antibody glycosylation. These mechanisms can either protect viral replication or enhance infected cell clearance. Here we discuss the importance of these understudied factors in modulating Fc-mediated effector functions.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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 score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.117
GPT teacher head0.395
Teacher spread0.279 · 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

Citations19
Published2019
Admission routes2
Has abstractyes

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