Transcutaneous penetration of a single-chain variable fragment (scFv) compared to a full-size antibody: potential tool for atopic dermatitis (AD) treatment
Bibliographic record
Abstract
Currently, several biologics are used for the treatment of cutaneous pathologies such as atopic dermatitis (AD), psoriasis or skin cancers. The main administration routes are subcutaneous and intravenous injections. However, little is known about antibody penetration through the skin. The aim was to study the transcutaneous penetration of a reduced-size antibody as a single-chain variable fragment (scFv) compared to a whole antibody (Ab) and to determine its capacity to neutralize an inflammatory cytokine involved in AD such as human interleukin-4 (hIL-4). Transcutaneous penetration was evaluated by ex vivo studies on tape-stripped pig ear skin. ScFv and Ab visualization through the skin was measured by Raman microspectroscopy. In addition, hIL-4 neutralization was studied in vitro using HEK-Blue™ IL-4/IL-13 cells and normal human keratinocytes (NHKs). After 24 h of application, analysis by Raman microspectroscopy showed that scFv penetrated into the upper dermis while Ab remained on the stratum corneum. In addition, the anti-hIL4 scFv showed very efficient and dose-dependent hIL-4 neutralization. Thus, scFv penetrates through to the upper papillary dermis while Ab mostly remains on the surface, the anti-hIL4 scFv also neutralizes its target effectively suggesting its potential use as topical therapy for AD.
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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".