Introduction for Design of Nanoparticle Based Drug Delivery Systems
Bibliographic record
Abstract
Conventional drug delivery systems contain numerous limitations such as limited targeting, low therapeutic indices, poor water solubility, and the induction of drug resistances. In order to overcome the drawbacks of conventional pathway of drug delivery, nanoparticle delivery systems are therefore designed and used as the drug carriers. Nanoparticle based drug delivery systems have been rapidly growing and are being applied to various sections of biomedicine. Drug nanocarriers based on dendrimers, liposomes, self-assembling peptides, watersoluble polymers, and block copolymer micelles are the most extensively studied types of drug delivery systems and some of them are being used in clinical therapy. In particular for cancer therapy, antineoplastic drugs are taking advantage of nanoparticulate drug carriers to improve the cure efficacy. Nanoparticle based drug carriers are capable of improving the therapeutic effectiveness of the drugs by using active targeting for the site-specific delivery, passive targeting mechanisms such as enhanced permeability and retention (EPR), de novo synthesis and uptake of low density liposome in cancer cells or by being water-soluble to improve the suboptimal pharmacokinetics in limited water-soluble delivery methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.012 |
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".