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Record W4290730329 · doi:10.3389/fimmu.2022.985622

When liquid-liquid phase separation meets viral infections

2022· review· en· W4290730329 on OpenAlexaff
Wenqiang Wei, Lu Bai, Bing Yan, Weiquan Meng, Hongju Wang, Jingbo Zhai, Fusheng Si, Chunfu Zheng

Bibliographic record

VenueFrontiers in Immunology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsOrganelleImmune systemCytoplasmCell biologyBiologyMechanism (biology)Function (biology)ChemistryImmunologyPhysics

Abstract

fetched live from OpenAlex

Eukaryotic cells have both membranous and membraneless organelles. While the formation mechanism of membranous organelles is well understood, the formation mechanism of membraneless organelles remains unknown. Many biomolecules in the cytoplasm transition from the liquid phase to the agglutinated phase are known as liquid-liquid phase separation (LLPS). The biomolecular agglomerates' physical properties enable them to function as dynamic compartments that respond to external pressures and stimuli. Scientists have gradually recognized the importance of phase separation during viral infections. LLPS provides a powerful new framework for understanding the viral life cycle from viral replication to evasion of host immune surveillance. As a result, this review focuses on the progress of LLPS research in viral infection and immune regulation to provide clues for antiviral therapeutic strategies.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.950
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.023
GPT teacher head0.348
Teacher spread0.325 · 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 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

Citations46
Published2022
Admission routes1
Has abstractyes

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