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MAVS: The next STING in cancers and other diseases

2024· review· en· W4405942046 on OpenAlexafffund
Xichen Wang, Qingwen Wang, Chunfu Zheng, Leisheng Wang

Bibliographic record

VenueCritical Reviews in Oncology/Hematology · 2024
Typereview
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsStingMedicineIntensive care medicineEngineering

Abstract

fetched live from OpenAlex

The mitochondrial antiviral signaling protein (MAVS) is a pivotal adaptor in the antiviral innate immune signaling pathway and plays a crucial role in the activation of antiviral defences. This comprehensive review delves into the multifaceted functions of MAVS, spanning from its integral role in the RIG-I-like receptor (RLR) pathway to its emerging roles in tumor biology and autoimmune diseases. We discuss the structural and functional aspects of MAVS, its activation mechanisms, and the intricate regulatory networks that govern its activity. The potential of MAVS as a therapeutic target has been explored, highlighting its promise in personalized cancer therapy and developing combination treatment strategies. Additionally, we compare it with the STING signaling pathway and discuss the synergistic potential of targeting both pathways in immunotherapy. Our review underscores the importance of MAVS in maintaining immune homeostasis and its implications for a broad spectrum of diseases, offering new avenues for therapeutic intervention.

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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.125
GPT teacher head0.450
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; both teacher heads agree on what is shown here.

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

Citations13
Published2024
Admission routes2
Has abstractyes

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