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Record W4315433619 · doi:10.1016/j.tins.2022.12.001

Mitochondrial signalling and homeostasis: from cell biology to neurological disease

2023· review· en· W4315433619 on OpenAlexafffund
Jack J. Collier, Monika Oláhová, Thomas G. McWilliams, Robert W. Taylor

Bibliographic record

VenueTrends in Neurosciences · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersMedical Research CouncilNovo NordiskNovo Nordisk FondenAcademy of FinlandLily FoundationSigrid Juséliuksen SäätiöNational Institute for Health and Care ResearchSydäntutkimussäätiöWellcome TrustMcGill UniversityHelsingin Yliopisto
KeywordsNeuroscienceHomeostasisMitochondrionDiseaseBiologyPsychologyCell biologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Efforts to understand how mitochondrial dysfunction contributes to neurodegeneration have primarily focussed on the role of mitochondria in neuronal energy metabolism. However, progress in understanding the etiological nature of emerging mitochondrial functions has yielded new ideas about the mitochondrial basis of neurological disease. Studies aimed at deciphering how mitochondria signal through interorganellar contacts, vesicular trafficking, and metabolic transmission have revealed that mitochondrial regulation of immunometabolism, cell death, organelle dynamics, and neuroimmune interplay are critical determinants of neural health. Moreover, the homeostatic mechanisms that exist to protect mitochondrial health through turnover via nanoscale proteostasis and lysosomal degradation have become integrated within mitochondrial signalling pathways to support metabolic plasticity and stress responses in the nervous system. This review highlights how these distinct mitochondrial pathways converge to influence neurological health and contribute to disease pathology.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.077
GPT teacher head0.347
Teacher spread0.270 · 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 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

Citations125
Published2023
Admission routes2
Has abstractyes

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