Recent Advances in Combating Acne with Novel Drug Delivery Systems:A Review
Bibliographic record
Abstract
Abstract: Acne vulgaris is an inflammatory disorder of the skin that occurs when hair follicles get clogged with sebum and dead skin cells resulting in pustules, papules, or nodules. This condi-tion affects a large number of people throughout the world. Over time, various conventional ther-apies like topical, systemic and hormonal treatments have been prescribed by doctors to patients for managing acne. Amongst these, topical therapy is the first-line therapy dominated by retinoid and antibiotic drugs. However, conventional topical treatments have many drawbacks concerning physicochemical instability, poor ability to penetrate the skin, and wide-ranging side effects. Novel drug delivery systems are the formulations that were first devised to overcome the issues faced by traditional methods. Researchers have developed novel therapies for topical use, such as niosomes, transfersomes, solid lipid nanoparticles, microsponges, microemulsion, microspheres, nanocochleates, and liposomes, which can be used to target acne. These nanoparticulate systems aim to overcome the disadvantages of conventional treatment and provide higher safety and effi-cacy, along with an increase in the stability of the formulation. Many comparative studies have been conducted between the novel and conventional methods, which give us a better understand-ing of the safety and efficacy of drugs present in the formulation and prove that novel drug deliv-ery systems are more favorable than their conventional counterparts. This review article provides a brief insight into the conventional methods, discusses in depth the earlier mentioned novel ther-apies that can be used successfully in treating acne, and provides patent information on the drug delivery systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".