Electron transfer-based combination therapy of cisplatin with tetramethyl- <i>p</i> -phenylenediamine for ovarian, cervical, and lung cancers
Bibliographic record
Abstract
The platinum-based chemotherapy is the standard treatment for several types of cancer. However, cancer cells often become refractory with time and most patients with serious cancers die of drug resistance. Recently, we have discovered a unique dissociative electron-transfer mechanism of action of cisplatin, the first and most widely used platinum-based anticancer drug. Here, we show that the combination of cisplatin with an exemplary biological electron donor, N,N,N',N'-tetramethyl-p-phenylenediamine (TMPD), may overcome the resistance of cancer cells to cisplatin. Our steady-state absorption and fluorescence spectroscopic measurements confirm the effective dissociative electron-transfer reaction between TMPD and cisplatin. More significantly, we found that the combination of 100 μM TMPD with cisplatin enhances double-strand breaks of plasmid DNA by a factor of approximately 3.5 and dramatically reduces the viability of cisplatin-sensitive human cervical (HeLa) cancer cells and highly cisplatin-resistant human ovarian (NIH:OVCAR-3) and lung (A549) cancer cells. Furthermore, this combination enhances apoptosis and DNA fragmentation by factors of 2-5 compared with cisplatin alone. These results demonstrate that this combination treatment not only results in a strong synergetic effect, but also makes resistant cancer cells sensitive to cisplatin. Because cisplatin is the cornerstone agent for the treatment of a variety of human cancers (including testicular, ovarian, cervical, bladder, head/neck, and lung cancers), our results show both the potential to improve platinum-based chemotherapy of various human cancers and the promise of femtomedicine as an emerging frontier in advancing cancer therapy.
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 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 | 0.001 |
| 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".