Nanotechnology meets atopic dermatitis: Current solutions, challenges and future prospects. Insights and implications from a systematic review of the literature
Bibliographic record
Abstract
Atopic dermatitis is a chronic, relapsing, non-contiguous, exudative eczema/dermatitis, which represents a complex, multi-factorial disorder, due to an impairment of the stratum corneum barrier. Currently available drugs have a low skin bioavailability and may give rise to severe adverse events. Nanotechnologies, including nano-particles, liposomes, nano-gels, nano-mixtures, nano-emulsions and other nano-carriers, offer unprecedented solutions to these issues, enabling: i) the management of different clinical forms of atopic dermatitis, especially the recalcitrant ones, i) a better bio-availability and trans-dermal drug targeted delivery at the inflammation site, ii) dose control, iii) significant improvements both in clinical symptoms and immune responses, iv) with less adverse events being reported and a better safety profile. However, some nano-sized structures could amplify and even worsen symptoms in particularly susceptible individuals. Furthermore, most studies included in the present systematic review have been conducted in-vitro or in-vivo, with few randomized controlled clinical trials (RCTs). Future investigations should adopt this design in order to enable scholars achieving robust findings and evidence. Therefore, given the above-mentioned shortcomings, further research in the field is urgently warranted.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".