A Critical Review on the Effect of Docosahexaenoic Acid (DHA) on Cancer Cell Cycle Progression
Bibliographic record
Abstract
Globally, there were 14.1 million new cancer diagnoses and 8.2 million cancer deaths in 2012. For many cancers, conventional therapies are limited in their successes and an improved understanding of disease progression is needed in conjunction with exploration of alternative therapies. The long chain polyunsaturated fatty acid, docosahexaenoic acid (DHA), has been shown to enhance many cellular responses that reduce cancer cell viability and decrease proliferation both in vitro and in vivo. A small number of studies suggest that DHA improves chemotherapy outcomes in cancer patients. It is readily incorporated into cancer cell membranes and, as a result there has been considerable research regarding cell membrane initiated events. For example, DHA has been shown to mediate the induction of apoptosis/reduction of proliferation in vitro and in vivo. However, there is limited research into the effect of DHA on cell cycle regulation in cancer cells and the mechanism(s) by which DHA acts are not fully understood. The purpose of the current review is to provide a critical examination of the literature investigating the ability of DHA to stall progression during different cell cycle phases in cancer cells, as well as the consequences that these changes may have on tumour growth, independently and in conjunction with chemotherapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".