Targeting carbohydrate metabolism in colorectal cancer - synergy between DNA-damaging agents, cannabinoids, and intermittent serum starvation
Bibliographic record
Abstract
Chemotherapy is a therapy of choice for many cancers. However, it is often inefficient for long-term patient survival and is usually accompanied by multiple adverse effects. The adverse effects are mainly associated with toxicity to normal cells, frequently resulting in immune system depression, nausea, loss of appetite and metabolic changes. In this respect, the combination of chemotherapy with cannabinoids, especially non-psychoactive, such as cannabidiol, cannabinol and other minor cannabinoids, as well as terpenes, may become very useful. This is especially pertinent because the mechanisms of anticancer effects of cannabinoids on cancer cells are often different from conventional chemotherapeutics. In addition, cannabinoids help alleviate chemotherapy-induced adverse effects, regulate sleep and appetite, and are shown to have analgesic properties. Another component for achieving potential anti-cancer synergism is regulating nutrient availability and metabolism by calorie restriction and intermittent fasting in cancer cells. As tumours require a lot of energy to grow and because glucose is constantly available, malignant cells often opt to use glucose as a primary source of ATP production through substrate-level phosphorylation (fermentation) rather than through oxidative phosphorylation. Thus, periodic depletion of cancer cells of primary fuel, glucose, could result in a strong synergy in killing cancer cells by chemo- and possibly radiotherapy when combined with cannabinoids. This commentary will discuss what is known about such combinatorial treatments, including potential mechanisms and future protocols.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".