Liver–Skin Microphysiological System for Evaluating Topical Drug Delivery-Induced Liver Injury
Bibliographic record
Abstract
Drug-induced liver injury is the main cause of acute liver failure and has become a major health problem. Nowadays, topical and transdermal drug delivery has appeared as a popular approach to deliver drugs, and its risk of inducing hepatotoxicity requires careful evaluation before approval for application to humans. To date, hepatotoxicity resulting from topical administration has rarely been studied due to the lack of effective research models. Here, a highly biomimetic liver-skin microphysiological system (LS-MPS) was developed, providing a dynamic and physiologically similar microenvironment to mimic in vivo liver, skin, and cyclic flow between them. The three-dimensional (3D) liver microtissue and 3D skin microtissue were successfully established on the LS-MPS. The skin microtissue showed high cell viability and physiologically similar dermal and epidermal layers. More importantly, the barrier and permeability functions of skin were maintained for long-term culture. The 3D liver microtissue showed high cell viability, biomimetic cell arrangement, and active metabolic function. In addition, the LS-MPS was applied to evaluate the topical delivery of drug-induced liver injury, demonstrating its potential in drug safety testing. Thus, this study provides a promising platform to assess complex drug toxicity, which contributes to the development of new drugs.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".