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Record W4200270895 · doi:10.1002/jmv.27554

The emerging roles of the CDK/cyclin complexes in antiviral innate immunity

2021· review· en· W4200270895 on OpenAlexaff
Chunfu Zheng, Yan‐Dong Tang

Bibliographic record

VenueJournal of Medical Virology · 2021
Typereview
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCyclin-dependent kinaseBiologyCell biologyInnate immune systemCyclinKinaseCell cycleImmunologyCellImmune systemBiochemistry

Abstract

fetched live from OpenAlex

More than 20 members of the human cyclin-dependent kinases (CDKs) family share the feature of being activated by cyclins. CDKs have been involved in diverse biological processes, such as cell cycle, transcription, DNA damage response, and apoptosis. If CDKs are not properly regulated, they can cause diseases like cancer. CDKs are Ser/Thr kinases that work with cyclins to control cell cycle progression. Various CDK-cyclin complexes phosphorylate particular target proteins and drive different cell cycle stages. Accumulating evidence demonstrated that CDKs play an essential role in the cell cycle; however, their roles in antiviral innate immunity are just emerging. This minireview summarizes how CDKs play their roles in antiviral innate immunity. Our goal is to draw attention to the involvement of CDKs in antiviral innate immunity, whether as separate entities or as components of CDK/cyclin complexes that have gotten less attention in the past.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.031
GPT teacher head0.353
Teacher spread0.321 · 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 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

Citations29
Published2021
Admission routes1
Has abstractyes

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