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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 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)0.004
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: Methods · Consensus signal: none
Teacher disagreement score0.753
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.017
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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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