An Improved Method for Magnetic Nanocarrier Drug Delivery across the Cell Membrane
Bibliographic record
Abstract
One of the crucial issues in the pharmacological field is developing new drug delivery systems. The main concern is to develop new methods for improving the drug delivery efficiencies such as low disruptions, precise control of the target of delivery and drug sustainability. Nowadays, there are many various methods for drug delivery systems. Carbon-based nanocarriers are a new efficient tool for translocating drug into the defined area or cells inside the body. These nanocarriers can be functionalized with proteins, peptides and used to transport their freight to cells or defined areas. Since functionalized carbon-based nanocarriers show low toxicity and high biocompatibility, they are used in many nanobiotechnology fields. In this study, different shapes of nanocarrier are investigated, and the suitable magnetic field, which is applied using MRI for the delivery of the nanocarrier, is proposed. In this research, based on the force required to cross the membrane and MD simulations, the optimal magnetic field profile is designed. This optimal magnetic force field is derived from the mathematical model of the system and magnetic particle dynamics inside the nanocarrier. The results of this paper illustrate the effects of the nanocarrier's shapes on the percentage of success in crossing the membrane and the optimal required magnetic field.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".