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Record W2307646011 · doi:10.1093/brain/aww081

Emerging therapies for mitochondrial disorders

2016· review· en· W2307646011 on OpenAlexaff
Helen Nightingale, Gerald Pfeffer, David Bargiela, Rita Horváth, Patrick F. Chinnery

Bibliographic record

VenueBrain · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsUniversity of Calgary
FundersMedical Research CouncilNational Institute for Health and Care ResearchWellcome Trust
KeywordsMitochondrial diseaseMitochondrial DNABiologyBioinformaticsMedicineNeuroscienceClinical trialComputational biologyGenetics

Abstract

fetched live from OpenAlex

Mitochondrial disorders are a diverse group of debilitating conditions resulting from nuclear and mitochondrial DNA mutations that affect multiple organs, often including the central and peripheral nervous system. Despite major advances in our understanding of the molecular mechanisms, effective treatments have not been forthcoming. For over five decades patients have been treated with different vitamins, co-factors and nutritional supplements, but with no proven benefit. There is therefore a clear need for a new approach. Several new strategies have been proposed acting at the molecular or cellular level. Whilst many show promise in vitro, the clinical potential of some is questionable. Here we critically appraise the most promising preclinical developments, placing the greatest emphasis on diseases caused by mitochondrial DNA mutations. With new animal and cellular models, longitudinal deep phenotyping in large patient cohorts, and growing interest from the pharmaceutical industry, the field is poised to make a breakthrough.

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 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 score0.905
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.022
GPT teacher head0.320
Teacher spread0.298 · 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

Citations68
Published2016
Admission routes1
Has abstractyes

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