Mechanical Stress Promotes Cisplatin-Induced Hepatocellular Carcinoma Cell Death
Bibliographic record
Abstract
Cisplatin (CisPt) is a commonly used platinum-based chemotherapeutic agent. Its efficacy is limited due to drug resistance and multiple side effects, thereby warranting a new approach to improving the pharmacological effect of CisPt. A newly developed mathematical hypothesis suggested that mechanical loading, when coupled with a chemotherapeutic drug such as CisPt and immune cells, would boost tumor cell death. The current study investigated the aforementioned mathematical hypothesis by exposing human hepatocellular liver carcinoma (HepG2) cells to CisPt, peripheral blood mononuclear cells, and mechanical stress individually and in combination. HepG2 cells were also treated with a mixture of CisPt and carnosine with and without mechanical stress to examine one possible mechanism employed by mechanical stress to enhance CisPt effects. Carnosine is a dipeptide that reportedly sequesters platinum-based drugs away from their pharmacological target-site. Mechanical stress was achieved using an orbital shaker that produced 300 rpm with a horizontal circular motion. Our results demonstrated that mechanical stress promoted CisPt-induced death of HepG2 cells (~35% more cell death). Moreover, results showed that CisPt-induced death was compromised when CisPt was left to mix with carnosine 24 hours preceding treatment. Mechanical stress, however, ameliorated cell death (20% more cell death).
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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".