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Record W4396675913 · doi:10.1101/2024.05.05.592601

The role of VISTA engagement in limiting neutrophil-mediated inflammation

2024· preprint· en· W4396675913 on OpenAlexaff
Elizabeth C. Nowak, Jiannan Li, Mohamed ElTanbouly, Wilson L. Davis, Petra Sergent, Lindsay Mendyka, J. Louise Lines, Nicole C. Smits, Rodwell Mabaera, Shibani Rajanna, Catherine Carrière, Brent H. Koehn, Bruce R. Blazar, Christopher M. Burns, Randolph J. Noelle, Sladjana Skopelja‐Gardner

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsResearch & Development Corporation
Fundersnot available
KeywordsLimitingInflammationImmunologyMedicineEngineering

Abstract

fetched live from OpenAlex

Abstract A growing body of evidence suggests that VISTA, an immune checkpoint inhibitory receptor, plays a central role in the regulation of innate immunity in the settings of inflammatory diseases and cancer. Neutrophils are among the cells that have the highest membrane density of surface VISTA. Targeting VISTA on neutrophils with an agonist antibody resulted in a striking reduction in their LPS-induced peripheral accumulation. Fc receptor engagement was required for anti-VISTA antibody to mediate its effects on neutrophils. Concomitant with reduced peripheral neutrophil cell numbers, anti-VISTA antibody treatment increased neutrophil cell death in the liver. In a murine model of neutrophil-mediated arthritis, agonist anti-VISTA antibody treatment ameliorated disease severity, which was associated with reduced myeloperoxidase activity in the joints. These studies add to a growing spectrum of negative regulatory functions that VISTA performs in controlling inflammation through the innate and adaptive arms of the immune system that has implications for translation into the clinic.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicNeutrophil, Myeloperoxidase and Oxidative MechanismsFrench-language works237,207