Mitochondrial DNA as a potential tool for early cancer detection
Bibliographic record
Abstract
The recent surge in mitochondrial research has been driven by the identification of mitochondria-associated diseases and the role of mitochondria in apoptosis. Both of these aspects have identified mitochondrial analysis as a vital component of medical research. Moreover, mitochondria have been implicated in the process of carcinogenesis because of their vital role in energy production, nuclear-cytoplasmic signal integration and control of metabolic pathways. Interestingly, at some point during neoplastic transformation, there is an increase in reactive oxygen species, which damage the mitochondrial genome. This accelerates the somatic mutation rate of mitochondrial DNA. It has been proposed that these mutations may serve as an early indication of potential cancer development and may represent a means for tracking tumour progression. The purpose of this review is to explore the potential utility that these mutations may afford for the identification and monitoring of neoplasia and malignant transformation where appropriate body fluids or non-invasive tissue access is available for mitochondrial DNA recovery. Specifically, prostate, breast, colorectal, skin and lung cancers are discussed.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".