The improved antitumor efficacy of continuous intratumoral chemotherapy with cisplatin-loaded implants for the treatment of sarcoma 180 tumor-bearing mice
Bibliographic record
Abstract
Cisplatin is the most commonly used antitumor drug in the chemotherapy of a variety of malignancies. However, the severe side effects and drug resistance limit its clinical application. The aim of this study was to develop PLGA-based cisplatin-loaded implants and evaluate the antitumor efficacy of continuous intratumoral chemotherapy with the implants. The cisplatin-loaded implants were prepared by the direct compression method and characterized regarding drug content, micromorphology, in vitro and in vivo drug release profiles. Furthermore, the antitumor activity of the implants was conducted in sarcoma 180 tumor-bearing mice. The SEM images showed smooth surface of the implants and the mean drug content of the tested implants was (37.7% ± 0.5%, w/w). Both in vitro and in vivo release profiles of the implants were characterized by initial burst release followed by the sustained-release of cisplatin. Intratumoral implantation of the cisplatin-loaded implants could effectively inhibit the tumor growth. Additionally, intratumoral chemotherapy with the implants significantly reduced the systemic toxicity compared with intravenous injection of cisplatin. It is worth noting that an increase in the dose of the implants led to a higher tumor suppression rate without additional systemic toxicity. These results demonstrated that cisplatin-loaded implants enhanced the antitumor efficacy and reduced the dose-related side effects in sarcoma 180 tumor-bearing mice.
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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) | 0.000 | 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 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".