Nanoencapsulation of Antitumor and Antituberculosis Drug Preparations with Biocompatible Polymers
Bibliographic record
Abstract
Controlled release of drugs at the locus of the targeted disease is one of the most challenging research areas in the pharmaceutical field. Nowadays novel drug delivery systems on the basis of polymers are attracting great attention since they can improve therapeutic efficiency of potent drug preparations decreasing the risk of side effects. By developing colloidal drug delivery systems such as liposomes/vesicles and polymeric nanoparticles and nanocapsules the pharmacokinetics of the drug can be changed and thus the therapeutic efficiency of the drug can be increased. Nanoparticles with their special characteristics such as small particle size, large surface area and high capacity of carrying biologically active substances offer a number of advantages compared to other colloidal drug delivery systems [1, 2]. Controlled drug release systems are constructed on the basis of natural and biocompatible synthetic polymers. Among the most promising biocompatible polymers human serum albumin (HSA), polyalkyl cyanoacrylates (PACA) and poly-D,L-lactic acid (PLA) are of great importance. Nanoparticles on their basis have been proven to be efficient in treatment of serious and long-termed diseases such as tumors, tuberculosis and bacterial infections [3-126]. Therefore this article is aimed to give a brief review on the research works devoted to the synthesis and investigation of polymeric nanoparticles and nanocapsules based on PACA, HSA and PLA for the past three decades.
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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".