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Autophagy and heat: a potential role for heat therapy to improve autophagic function in health and disease

2020· review· en· W3096636232 on OpenAlexafffund
James J. McCormick, Karol Dokładny, Pope Moseley, Glen P. Kenny

Bibliographic record

VenueJournal of Applied Physiology · 2020
Typereview
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health ResearchNovo Nordisk FondenGovernment of Canada
KeywordsAutophagyMechanism (biology)Cell biologyFunction (biology)DiseaseBiologyMedicineNeuroscienceBioinformaticsApoptosisPathologyBiochemistry

Abstract

fetched live from OpenAlex

Autophagy is a crucial cell survival mechanism that involves the degradation and recycling of old or damaged organelles and proteins to maintain cellular homeostasis. Impairments in autophagy are central to the pathogenesis of many conditions including metabolic and neurodegenerative disorders, cardiovascular and pulmonary diseases, diabetes, and aging. Although various pharmacological agents may be able to stimulate autophagic function, to our knowledge, few interventions exist that have been deemed safe and effective in humans. An emerging body of evidence suggests that targeting the autophagic pathway via passive heating (heat therapy) may stimulate autophagic function. Therefore, the primary focus of the present review is to analyze the mechanisms in which passive heating induces autophagy as defined by in vitro and in vivo (animal and human) models. Our secondary focus is to examine the implications of utilizing passive heating to restore dysfunctional autophagy in chronic disease and aging. Finally, we discuss potential therapeutic strategies to implement passive heating to stimulate autophagic function in humans.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.313
Teacher spread0.297 · 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 designOther design
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

Citations44
Published2020
Admission routes2
Has abstractyes

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