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Record W2883229971 · doi:10.1073/pnas.1806296115

Elesclomol restores mitochondrial function in genetic models of copper deficiency

2018· article· en· W2883229971 on OpenAlexafffund
Shivatheja Soma, Andrew J. Latimer, Haarin Chun, Alison C. Vicary, Shrishiv Timbalia, Aren Boulet, Jennifer J. Rahn, Sherine S.L. Chan, Scot C. Leary, Byung‐Eun Kim, Jonathan D. Gitlin, Vishal M. Gohil

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsUniversity of Saskatchewan
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of General Medical SciencesGovernment of CanadaCanadian Institutes of Health ResearchWelch Foundation
KeywordsMitochondrionZebrafishCytochrome c oxidaseCopperCopper deficiencyBiologyMutationCell biologyChemistryGeneBiochemistry

Abstract

fetched live from OpenAlex

Significance Inherited pathogenic mutations in genes required for copper delivery to cytochrome c oxidase (CcO) perturb mitochondrial energy metabolism and result in fatal mitochondrial disease. A prior attempt to treat human patients with these mutations by direct copper supplementation was not successful, possibly because of inefficient copper delivery to the mitochondria. We performed a targeted search to identify compounds that can efficiently transport copper across biological membranes and identified elesclomol (ES), an investigational anticancer drug, as the most efficient copper delivery agent. ES rescues CcO function in yeast, zebrafish, and mammalian models of copper deficiency by increasing cellular and mitochondrial copper content. Thus, our study offers a possibility of repurposing this anticancer drug for the treatment of disorders of copper metabolism.

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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.060
GPT teacher head0.337
Teacher spread0.278 · 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

Citations104
Published2018
Admission routes2
Has abstractyes

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