Metal–Drug Coordination Nanoparticles and Hydrogels for Enhanced Delivery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drug delivery is a key component of nanomedicine, and conventional delivery relies on the adsorption or encapsulation of drug molecules to a nanomaterial. Many delivery vehicles contain metal ions, such as metal-organic frameworks, metal oxides, transition metal dichalcogenides, MXene, and noble metal nanoparticles. These materials have a high metal content and pose potential long-term toxicity concerns leading to difficulties for clinical approval. In this review, recent developments are summarized in the use of drug molecules as ligands for metal coordination forming various nanomaterials and soft materials. In these cases, the drug-to-metal ratio is much higher than conventional adsorption-based strategies. The drug molecules are divided into small-molecule drugs, nucleic acids, and proteins. The formed hybrid materials mainly include nanoparticles and hydrogels, upon which targeting ligands can be grafted to improve efficacy and further decrease toxicity. The application of these materials for addressing cancer, viral infection, bacterial infection inflammatory bowel disease, and bone diseases is reviewed. In the end, some future directions are discussed from fundamental research, materials science, and medicine.
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.
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) | 0.000 | 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 | 0.001 |
| 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 it