Hydrodynamically generated multilayer skin spheroids enable in vitro screening of biologically active ingredients and toxicity tests
Bibliographic record
Abstract
Human tissues often have a multilayer structure, with each layer performing a distinct physiological task. Reconstructing layered tissue structures with their respective functions is crucial for disease modeling, screening biologically active ingredients, and performing toxicology tests; however, multicellular spheroids used for these purposes generally lack a well-defined multilayer architecture. Here, to recapitulate a multilayer structure of the skin, we developed a hydrodynamically mediated approach to the generation of large arrays of fibroblast spheroids (a dermal core) that were engulfed with an epidermal layer of keratinocytes. These spheroids expressed biomarkers of the epidermis, epidermal-dermal junction, and dermis, and exhibited skin-like barrier properties. Screening of the synergistic effect of vitamins and peptides on protein synthesis by the spheroids and evaluation of skin toxicity with chemical agents showed a correlation with clinical results or existing standards. This approach offers enhanced control over spatial cell distribution in spheroids for advanced in vitro models of multilayer tissues.
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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 | 0.001 |
| 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".