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Record W2981101234

Role of Mitochondrial Markers in Improved Detection and Risk-Stratification in Barrett's Esophagus Patients.

2019· article· en· W2981101234 on OpenAlexaff
Udhayvir Singh Grewal, Mehraj S Randhawa, Aryan Mehta

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsWestern University
Fundersnot available
KeywordsMitochondrial DNABarrett's esophagusMitochondrionEsophagusHeteroplasmyOxidative stressEsophageal adenocarcinomaRisk stratificationPopulationPathogenesisEsophageal cancerBiologyBioinformaticsMedicineCancerCancer researchAdenocarcinomaPathologyGeneticsInternal medicineGeneBiochemistryEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

Barrett's esophagus (BE) is the only known precursor of esophageal adenocarcinoma (EAC) and is amenable to treatment. However, more than 90 percent of EAC patients are never diagnosed with antecedent BE. Identification of molecular markers for BE is needed to improve detection of BE through efficient non-endoscopic methods that are cost-effective, sensitive and can be used to cater to a larger group of the population at risk. Alterations in mitochondria and mitochondrial DNA have been shown to be associated with various cancers, including esophageal cancer. Mitochondrial response to oxidative stress, alterations in mitochondrial metabolism, changes in mitochondrial membrane potential and mitochondrial genetic mutations have been found to be associated with BE pathogenesis. This mini-review focuses on the role of mitochondria in the pathogenesis of BE and EAC and the prospects of using that knowledge to develop effective strategies for the improved detection and risk-stratification in BE patients.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.003
GPT teacher head0.178
Teacher spread0.175 · 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 designObservational
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

Citations3
Published2019
Admission routes1
Has abstractyes

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Same venuePubMed→Same topicCancer, Hypoxia, and Metabolism→French-language works237,207→