Therapeutic Potential of Nanoscale Metal–Organic Frameworks in Hepatocellular Carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) represents the predominant type of primary liver cancer and remains a major global health concern. Current therapeutic strategies-such as surgical resection, radiation, and chemotherapy-provide clinical benefits but are frequently accompanied by considerable adverse effects. Consequently, identifying alternative treatment modalities and developing strategies that allow the use of lower drug doses without compromising therapeutic outcomes are essential goals in HCC management. Among emerging nanoscale platforms, metal-organic frameworks (MOFs) have attracted exceptional interest as promising candidates for targeted drug delivery in cancer therapy. Their inherent characteristics, including highly ordered porosity, large surface area, tunable cavities, adjustable chemical functionality, and remarkable drug-loading capacity, set them apart from conventional porous nanomaterials. Owing to their hierarchical architecture, MOFs are especially suitable for multimodal and synergistic anti-cancer treatments. MOF-based systems have demonstrated the ability to reinforce the performance of several therapeutic modalities, including photodynamic therapy (PDT), photothermal therapy (PTT), chemodynamic therapy (CDT), and sonodynamic therapy (SDT), while also serving as efficient carriers for targeted drug release. Their structural versatility further enables improved drug stability, enhanced solubility, and controlled-release behavior. This review provides an overview of recent progress in MOF-enabled therapeutic strategies and discusses their potential applications in the treatment of HCC.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".