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Record W4386099544 · doi:10.3389/fcell.2023.1220672

Background and roles: myosin in autoimmune diseases

2023· review· en· W4386099544 on OpenAlexaff
Longsheng Fu, Yonghui Zou, Boyang Yu, Daojun Hong, Teng Guan, Jinfang Hu, Yi Xu, Yaoqi Wu, Junping Kou, Yanni Lv

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2023
Typereview
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsUniversity of Manitoba
FundersJiangxi Provincial Department of Science and TechnologyEducation Department of Jiangxi ProvinceNational Natural Science Foundation of China
KeywordsMyosinAutoimmune diseaseImmune systemMyositisImmunologyDiseasePathologicalMultiple sclerosisMechanism (biology)PathogenesisBiologyMedicineAntibodyPathologyCell biology

Abstract

fetched live from OpenAlex

The myosin superfamily is a group of molecular motors. Autoimmune diseases are characterized by dysregulation or deficiency of the immune tolerance mechanism, resulting in an immune response to the human body itself. The link between myosin and autoimmune diseases is much more complex than scientists had hoped. Myosin itself immunization can induce experimental autoimmune diseases of animals, and myosins were abnormally expressed in a number of autoimmune diseases. Additionally, myosin takes part in the pathological process of multiple sclerosis, Alzheimer's disease, Parkinson's disease, autoimmune myocarditis, myositis, hemopathy, inclusion body diseases, etc. However, research on myosin and its involvement in the occurrence and development of diseases is still in its infancy, and the underlying pathological mechanisms are not well understood. We can reasonably predict that myosin might play a role in new treatments of autoimmune diseases.

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)0.001
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.003

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 head0.058
GPT teacher head0.348
Teacher spread0.291 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Cell and Developmental BiologySame topicViral Infections and Immunology ResearchFrench-language works237,207