An investigation into the effect of dietary lipids and lovastatin on the multi-step process of colon carcinogenesis and associated cellular and molecular events
Bibliographic record
Abstract
An Investigation into the Effect of Dietary Lipids and Lovastatin on the Multi-Step Process of Colon Carcinogenesis and Associated Cellular and Molecular EventsColorectal Cancer (CRC) is the second leading cause of cancer deaths in Canadal.Diets high in saturated and unsaturated lipid promote CRC2 via different mechanisms3, including differential regulation of cholesterol biosl'nthesis.The rate-limiting enzyme of cholesterol biosynthesis, 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCAR), produces an essential intermediate for p2lK-*' membrane association; p21*'*' plays a critical role in cell signal transduction regulating cell growth and differentiation.p21K-*' mutations have been found in over 50%o of colonic tumors.Lovastatin (LOV) decreases CRC mortality rates and is known to inhibit HMGCAR4. Inhibition of HMGCAR function disrupts p2l*-* membrane association and functionleading to disruption of downstream protein regulating growth signals.Our objectives were to determine: (1) whether LOV would retard CRC development, (2) if the different stages of CRC growth and their responsiveness to LOV is effected by dietary lipid, and (3) the influence of dietary lipids and LOV on the expression of HMGCAR, p2lK-ot, LDLr, as well as specific signal transduction and cell cycle related molecules involved in cellular growth (ERK-1/2, CDI) and apoptosis (caspase-3).MaleF344 rats were injected with azoxymethane (lsmg/kg body weight/week for two weeks) and fed a low fat corn oil (LFC) diet for 12 weeks.They were divided into two more groups receiving a high fat corn oil (HFC) diet or beef tallow (HFB) diet for an additional nine weeks, after which a subset of rats from the HFC and HFB groups were treated with LOV (20 mgn
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".