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Record W4306250043 · doi:10.1101/2022.10.13.512127

SARS-CoV-2 immune complex triggers human monocyte necroptosis

2022· preprint· en· W4306250043 on OpenAlexaff
Leonardo Duarte Santos, Krist Helen Antunes, Gisele Cassão, João Ismael Gonçalves, Bruno Lopes Abbadi, Cristiano Valim Bizarro, Luiz Augusto Basso, Pablo Machado, Ana Paula Duarte de Souza, Bárbara N. Porto

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of ManitobaResearch Manitoba
Fundersnot available
KeywordsNecroptosisMonocyteRIPK1Immune systemSykBiologyProgrammed cell deathPathogenesisImmunologyTyrosine kinaseCell biologyApoptosisSignal transductionBiochemistry

Abstract

fetched live from OpenAlex

Abstract We analyzed the ability of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) itself and SARS-CoV-2-IgG immune complexes to trigger human monocyte necroptosis. SARS-CoV-2 was able to induce monocyte necroptosis dependently of MLKL activation. Necroptosis-associated proteins (RIPK1, RIPK3 and MLKL) were involved in SARS-CoV-2 N1 gene expression in monocytes. SARS-CoV-2 immune complexes promoted monocyte necroptosis in a RIPK3- and MLKL-dependent manner, and Syk tyrosine kinase was necessary for SARS-CoV-2 immune complex-induced monocyte necroptosis, indicating the involvement of Fcγ receptors on necroptosis. Finally, we provide evidence that elevated LDH levels as a marker of lytic cell death are associated with COVID-19 pathogenesis.

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 score0.002
Threshold uncertainty score0.007

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.039
GPT teacher head0.267
Teacher spread0.228 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicPhagocytosis and Immune RegulationFrench-language works237,207