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Record W4229058985 · doi:10.33612/diss.208570309

Unravelling novel functions of the MTOR network across subcellular compartments

2022· dissertation· en· W4229058985 on OpenAlexfundno aff
Marti Cadena Sandoval

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA regulation and disease
Canadian institutionsnot available
FundersRijksuniversiteit GroningenDeutsche ForschungsgemeinschaftEuropean CommissionMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsPI3K/AKT/mTOR pathwayLysosomeSubcellular localizationCell biologyBiologyNucleusNeuroscienceComputational biologySignal transductionChemistryBiochemistryCytoplasm

Abstract

fetched live from OpenAlex

The mechanistic target of rapamycin complex 1 (mTORC1) kinase is a master regulator of metabolism and aging.A complex signaling network converges on mTORC1 and integrates growth factor, nutrient and stress signals.Aging is a dynamic process characterized by declining cellular survival, renewal, and fertility.Stressors elicited by aging hallmarks such as mitochondrial malfunction, loss of proteostasis, genomic instability and telomere shortening impinge on mTORC1 thereby contributing to age-related processes.Stress granules (SGs) constitute a cytoplasmic non-membranous compartment formed by RNAprotein aggregates, which control RNA metabolism, signaling and survival under stress.Increasing evidence reveals complex crosstalk between the mTORC1 network and SGs.In this review, we cover stressors elicited by aging hallmarks that impinge on mTORC1 and SGs.We discuss their interplay, and we highlight possible links in the context of aging and age-related 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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.277
Teacher spread0.263 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2022
Admission routes1
Has abstractyes

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