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Record W2010411786 · doi:10.1111/cei.12532

7<sup>th</sup>International Immunoglobulin Conference: Mechanisms of Action

2014· article· en· W2010411786 on OpenAlexaff
Milan Bašta, Donald R. Branch

Bibliographic record

VenueClinical & Experimental Immunology · 2014
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsCanadian Blood ServicesUniversity of Toronto
Fundersnot available
KeywordsAntibodyImmunologyReceptorFragment crystallizable regionImmunoglobulin Fc FragmentsGlycosylationImmunoglobulin GEffectorNeonatal Fc receptorComplement systemFc receptorMechanism of actionBiologyMedicineGeneticsIn vitro

Abstract

fetched live from OpenAlex

The mechanism of action by which therapeutic administration of intravenous immunoglobulin (IVIg) is able to provide a beneficial effect in autoimmune and inflammatory diseases is not yet fully understood, but current research is providing some answers. Signalling via receptors that interact with immunoglobulin (Ig) is crucial, and genetic polymorphisms of the Fc receptors have clear links to disease and also appear to influence the outcome of IVIg treatment. Glycosylation of the IgG, Fc- or Fab-fragments has a role in enhancing or blocking the pro- and anti-inflammatory effector functions. In addition, and independently of Fc receptors and glycosylation, Fc fragment and the constant domain of the Fab fragment contain binding sites for activated complement fragments that mediate complement-scavenging based immunomodulation. Although IgG Fc sialylation may not be critical for IVIg activity, research in some diseases suggests that it is associated with improved clinical outcomes. Therefore, further investigation of how IgG and IgA receptor expression and regulation affects the outcome of IVIg treatment may further clarify the mechanisms behind IVIg, and provide valuable guidance for future treatment paradigms.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.100
GPT teacher head0.447
Teacher spread0.347 · 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 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

Citations8
Published2014
Admission routes1
Has abstractyes

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