<p>Loading Amlodipine on Diamond Nanoparticles: A Novel Drug Delivery System</p>
Bibliographic record
Abstract
BACKGROUND: Diamond nanoparticles (Nanodiamond) are biocompatible drug delivery platforms with outstanding surface properties. Their passage into the brain has been confirmed previously. Thus, nanodiamond could provide a drug delivery system to shuttle several drugs through the blood-brain barrier (BBB) which represents a real challenge for the effective delivery of several drugs into the brain. Amlodipine is a calcium channel blocker that cannot pass through BBB and may elicit neuroprotective effects to reverse calcium-induced excitotoxicity and mitochondrial dysfunction that underlie several neurologic disorders including Alzheimer's disease and stroke. AIM: The study aimed to investigate the loading of amlodipine on nanodiamond particles. METHODS: Nanodiamond particles were oxidized in a strong oxidizing acidic mixture of sulfuric and nitric acids. Adsorption of amlodipine on nanodiamond particles was achieved in alkaline pH using various concentrations of sodium hydroxide. The loaded amlodipine was determined by high-performance liquid chromatography and confirmed by Fourier transform infrared (FTIR) spectroscopy and transmission electron microscopy. RESULTS: The highest percentage (41%) of loaded amlodipine onto nanodiamond particles was achieved in alkaline medium using 2 mM NaOH at a corresponding pH of 8.5. Also, characteristic FTIR bands of amlodipine and nanodiamond were shown obviously in the nanodiamond-amlodipine conjugates. Moreover, the successful loading of amlodipine on diamond nanoparticles was confirmed by transmission electron microscopy. CONCLUSION: The present study demonstrates the successful loading of amlodipine onto nanodiamond particles. These findings offer a potential for applying diamond nanoparticles as a drug delivery system to shuttle amlodipine into the brain and open the door to deliver other similar drugs into the brain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".