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Record W2017207034 · doi:10.1517/17530059.1.2.169

The mitochondrial genome: a biosensor for early cancer detection?

2007· article· en· W2017207034 on OpenAlexaff
Ryan Parr, John P. Jakupciak, Mark A. Birch‐Machin, Gabriel D. Dakubo

Bibliographic record

VenueExpert Opinion on Medical Diagnostics · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsPyrogenesis (Canada)
Fundersnot available
KeywordsMitochondrial DNAMitochondrionCancerBiologyGenomeHuman mitochondrial geneticsMutationCancer researchCancer cellHeteroplasmyDNAJA3GeneticsGenemitochondrial fusion

Abstract

fetched live from OpenAlex

Mutations in the mitochondrial genome have been reported as biomarkers for the detection of cancer. Hallmarks of cancer development include the accumulation of genetic alterations in the mitochondrial and nuclear genomes. Damage to mitochondria affects energy metabolism, generation of reactive oxygen species, apoptosis, cell growth and other processes that contribute to the neoplastic process. Furthermore, mitochondrial DNA mutations occur frequently in cancer. Little work has been done to link a pathway between mitochondrial mutations and cancer etiology. Volumes of work have been reported on the association of mitochondrial mutations and almost all types of cancer including the use of body fluids for early detection. This review examines the measurement of mitochondrial mutations for the application of detecting human tumor tissue.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0060.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.017
GPT teacher head0.323
Teacher spread0.306 · 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 designTheoretical or conceptual
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

Citations2
Published2007
Admission routes1
Has abstractyes

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