Targeting mitochondrial and oxidative stress vulnerability of cancer cells to induce apoptosis using natural compounds and extracts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Treatment of malignant/metastatic cancers with current chemotherapies targeting general non-selective targets such as DNA replication/repair and tubulins has limited success and causes severe side effects and patients suffer with miserable quality of life. These treatment regiments cannot be given for longer duration due to the toxicity, therefore the possibility of relapse is almost certain. Cancerous cells maintain rapid growth and use different energy metabolism and face higher oxidative stress. Potentially all malignant cells could be differentially targeted for cell death by targeting these vulnerabilities. Indeed, we have demonstrated that natural compound pancratistatin selectively targets cancer cell mitochondria to induce apoptosis without affecting non-cancerous cells while compounds like piperlongumine and synthetic analogues of curcumins selectively kill cancer cells by inducing oxidative stress. Most importantly, some of the natural extracts including dandelion root, long pepper, lemon grass and white tea extract also trigger cancer cell death by inducing oxidative stress and mitochondrial depolarization selectively in cancer cells. The dandelion root extract (DRE) has progressed to phase I/II clinical trial for cancer in Canada. Gene expression profiling studies indicates that DRE displays extreme selectivity towards cancer cells in inducing cell death, while protecting the non-cancerous cells. These findings open a new window of opportunity to develop new therapeutic regiments that are extremely selective to cancer cells and thus should be free of side effects and these natural extract can be taken for long duration to prevent relapse of the disease.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it