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Record W4399296264 · doi:10.1111/febs.17195

The new kids on the block: <scp>RNA</scp>‐binding proteins regulate autophagy in disease

2024· review· en· W4399296264 on OpenAlexfundno aff
Shai Dushnitzky, Hasan Ishtayeh, Avraham Ashkenazi

Bibliographic record

VenueFEBS Journal · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsnot available
FundersAzrieli Foundation
KeywordsAutophagyCell biologyBiologyRNA-binding proteinOrganelleCytoplasmExpansiveRNAStress granuleFunction (biology)Messenger RNATranslation (biology)BiochemistryGene

Abstract

fetched live from OpenAlex

Mammalian autophagy is a highly regulated and conserved cellular homeostatic process. Its existence allows the degradation of self-components to mediate cell survival in different stress conditions. Autophagy is involved in the regulation of cellular metabolic needs, protecting the cell or tissue from starvation through the degradation and recycling of cytoplasmic materials and organelles to basic molecular building blocks. It also plays a critical role in eliminating damaged or harmful proteins, organelles, and intracellular pathogens. Thus, a deterioration of the process may result in pathological conditions, such as aging-associated disorders and cancer. Understanding the crucial role of autophagy in maintaining the normal physiological function of cells, tissue, or organs has led to copious and expansive research regarding the regulation of this process. So far, most of the research has revolved around transcriptional and post-translational regulation. Here, we discuss the regulation of autophagy-related (ATG) mRNA transcripts by RNA-binding proteins (RBPs). This analysis focuses on how RBPs modulate autophagy in disease. A deeper understanding of the involvement of RBPs in autophagy can facilitate further research and treatment of a variety of human 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 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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.819
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.314
Teacher spread0.284 · 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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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