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Record W3195676492 · doi:10.1111/sji.13098

Beginnings of coinhibition

2021· review· en· W3195676492 on OpenAlexaff
Nicholas R. StC. Sinclair

Bibliographic record

VenueScandinavian Journal of Immunology · 2021
Typereview
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsWestern University
Fundersnot available
KeywordsAntibodyAntigenImmune systemGerminal centerImmunologyReceptorMonoclonal antibodyBiologyCell biologyNeuroscienceB cellGenetics

Abstract

fetched live from OpenAlex

Costimulatory and coinhibitory mechanisms appear to be involved throughout immune responses to control their specificity and level. Many mechanisms operate; therefore, various theoretical models should be considered complementary rather than competing. One such coinhibitory model, pictured in 1971, involves the crosslinking of antigen receptors with inhibitory Fc receptors by antigen/antibody complexes. This model was prompted by observations that the Fc portion of antibody was required for potent suppression of immune responses by antibody. The signal via the antigen receptor wakes up T or B cells, providing specificity, while costimulators and coinhibitors stimulate or inhibit these awoken cells. The recent observations that administration of monoclonal anti-SARS-CoV-2 spike antibodies in early COVID-19 patients inhibits the induction of clinically damaging autoimmune antibodies suggest they may provide negative Fc signals that are blocked in COVID-19 patients. Furthermore, the reduced ability of SARS-CoV-2 antigen to localize to germinal centres in COVID-19 patients also suggests a block in binding of the Fc of antibody bound to antigen on FcγRIIb of follicular dendritic cells. The distinction between self and foreign is made not only at the beginning of immune responses but also throughout, and involves multiple mechanisms and models. There are past beginnings (history of models) and current and future beginnings for solving serious clinical problems (such as COVID-19) and different types of models used for understanding the complexities of fundamental immunology.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
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)0.0000.000

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 head0.062
GPT teacher head0.394
Teacher spread0.332 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2021
Admission routes1
Has abstractyes

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