Camel Milk Triggers Apoptotic Signaling Pathways in Human Hepatoma HepG2 and Breast Cancer MCF7 Cell Lines through Transcriptional Mechanism
Bibliographic record
Abstract
Few published studies have reported the use of crude camel milk in the treatment of stomach infections, tuberculosis and cancer. Yet, little research was conducted on the effect of camel milk on the apoptosis and oxidative stress associated with human cancer. The present study investigated the effect and the underlying mechanisms of camel milk on the proliferation of human cancer cells using an in vitro model of human hepatoma (HepG2) and human breast (MCF7) cancer cells. Our results showed that camel milk, but not bovine milk, significantly inhibited HepG2 and MCF7 cells proliferation through the activation of caspase-3 mRNA and activity levels, and the induction of death receptors in both cell lines. In addition, Camel milk enhanced the expression of oxidative stress markers, heme oxygenase-1 and reactive oxygen species production in both cells. Mechanistically, the increase in caspase-3 mRNA levels by camel milk was completely blocked by the transcriptional inhibitor, actinomycin D; implying that camel milk increased de novo RNA synthesis. Furthermore, Inhibition of the mitogen activated protein kinases differentially modulated the camel milk-induced caspase-3 mRNA levels. Taken together, camel milk inhibited HepG2 and MCF7 cells survival and proliferation through the activation of both the extrinsic and intrinsic apoptotic pathways.
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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".