Recent advances in MOF-based nanoplatforms generating reactive species for chemodynamic therapy
Bibliographic record
Abstract
Still today, cancer remains a threat to human health. Possible common treatments to cure this disease include chemotherapy (CT), radiotherapy (RT), photothermal therapy (PTT), and surgical resection, which give unreasonable results because of their limited efficiency and also lead to side-effects. Hence, different strategies are now being exploited to not only enhance the efficiency of these traditional therapeutic methods or treat the tumor cells but also curtail the side effects. A latest method with authentic proof of chemodynamic therapy (CDT) utilizing the Fenton reaction is now gaining importance. This approach, which is developed based on the high level of hydrogen peroxide (H2O2) in a tumor microenvironment (TME), can be used to catalyze the Fenton reaction to generate cancer cell-killing reactive oxygen species (ROS). The selection of materials is extremely important and nanomaterials offer the most likely method to facilitate CDT. Among various materials, metal-organic frameworks (MOFs) which have been extensively applied in medical areas are regarded as a promising material and possess potential for the next generation of nanotechnology. This review focuses on summarizing the use of MOFs in CDT and their synergetic therapeutics as well as the challenges, obstacles, and development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".