An Engineered Infected Epidermis Model for In Vitro Study of the Skin’s Pro-Inflammatory Response
Bibliographic record
Abstract
Wound infection is a major clinical challenge that can significantly delay the healing process, can create pain, and requires prolonged hospital stays. Pre-clinical research to evaluate new drugs normally involves animals. However, ethical concerns, cost, and the challenges associated with interspecies variation remain major obstacles. Tissue engineering enables the development of in vitro human skin models for drug testing. However, existing engineered skin models are representative of healthy human skin and its normal functions. This paper presents a functional infected epidermis model that consists of a multilayer epidermis structure formed at an air-liquid interface on a hydrogel matrix and a three-dimensionally (3D) printed vascular-like network. The function of the engineered epidermis is evaluated by the expression of the terminal differentiation marker, filaggrin, and the barrier function of the epidermis model using the electrical resistance and permeability across the epidermal layer. The results showed that the multilayer structure enhances the electrical resistance by 40% and decreased the drug permeation by 16.9% in the epidermis model compared to the monolayer cell culture on gelatin. We infect the model with Escherichia coli to study the inflammatory response of keratinocytes by measuring the expression level of pro-inflammatory cytokines (interleukin 1 beta and tumor necrosis factor alpha). After 24 h of exposure to Escherichia coli, the level of IL-1β and TNF-α in control samples were 125 ± 78 and 920 ± 187 pg/mL respectively, while in infected samples, they were 1429 ± 101 and 2155.5 ± 279 pg/mL respectively. However, in ciprofloxacin-treated samples the levels of IL-1β and TNF-α without significant difference with respect to the control reached to 246 ± 87 and 1141.5 ± 97 pg/mL respectively. The robust fabrication procedure and functionality of this model suggest that the model has great potential for modeling wound infections and drug testing.
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".