Nanotechnology meets atopic dermatitis: Current solutions, challenges and future prospects. Insights and implications from a systematic review of the literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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) | 0.000 | 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 it