Synergistic Antiproliferative Effect of Linagliptin-Metformin Combination on the Growth of Hela Cancer Cell Line
Bibliographic record
Abstract
This in-vitro study explores the cytotoxic properties of the linagliptin-metformin combination on cervical cancer cells and examines the synergistic interaction between the two drugs. An MTT assay was used to explore the anti-cancer effects of the linagliptin-metformin mixture on a cervical cancer cell line (HeLa cell line) across 24 and 72-hour incubation periods. The concentrations of metformin, linagliptin, and their combination ranged from 0.1 to 1000 µg/ml. while the concentrations in the mixture were kept at fifty percentage of the individually used drug. The study included an estimated combination index value (CI) and the dosage reduction index (DRI) to ascertain the possibility of a synergistic effect between combined drugs and mixture safety. study finding exhibited that all studied drugs- metformin, linagliptin, and their combined mixture- inhibited the growth of cervical cancer cells with a superior efficacy of the mixture over individual drugs. Inhibition patterns of the drugs were directly proportional to the drug's concentration and the incubation time. The combination index finding revealed that the mixture's cytotoxic effect of metformin and linagliptin was synergistic. The dose reduction index value revealed that lower drug concentrations were required in the combination mixture than when used individually indicating a greater cytotoxic potential of the mixture. The study findings of MTT, CI, and DRI indicate that the mixture is an effective, safer, and promising anticancer therapy for cervical cancer. Conclusion: This study explores the cytotoxic potential of metformin and linagliptin individually and in combination. The greater cytotoxic potential of the drugs in combination highlights their lower effective concentrations, paving the way for further research on using these drugs for effective cancer treatment.
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 | 0.002 | 0.001 |
| 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) | 0.000 | 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".