The green synthesis and applications of biological metal–organic frameworks for targeted drug delivery and tumor treatments
Bibliographic record
Abstract
Biological metal-organic frameworks (bio-MOFs) constitute a growing subclass of MOFs composed of metals and bio-ligands derived from biology, such as nucleobases, peptides, saccharides, and amino acids. Bio-ligands are more abundant than other traditional organic ligands, providing multiple coordination sites for MOFs. However, bio-MOFs are typically prepared using hazardous or harmful solvents or reagents, as well as laborious processes that do not conform to environmentally friendly standards. To improve biocompatibility and biosafety, eco-friendly synthesis and functionalization techniques should be employed with mild conditions and safer materials, aiming to reduce or avoid the use of toxic and hazardous chemical agents. Recently, bio-MOF applications have gained importance in some research areas, including imaging, tumor therapy, and targeted drug delivery, owing to their flexibility, low steric hindrances, low toxicity, remarkable biocompatibility, surface property refining, and degradability. This has led to an exponential increase in research on these materials. This paper provides a comprehensive review of updated strategies for the synthesis of environmentally friendly bio-MOFs, as well as an examination of the current progress and accomplishments in green-synthesized bio-MOFs for drug delivery aims and tumor treatments. In conclusion, we consider the challenges of applying bio-MOFs for biomedical applications and clarify the possible research orientation that can lead to highly efficient therapeutic outcomes.
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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) | 0.001 | 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".