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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".