Development of In-vitro Skin Models for Screening Bioactive Compounds and Modelling Diseases
Bibliographic record
Abstract
There is an increasing demand for in vitro skin models to replace animal testing for disease modeling and drug screening. The construction of reliable in vitro skin models relies on the development of biomimetic materials and high-throughput screening techniques. This thesis focuses on advancing in vitro skin models through the development of biomaterials and the design of microfluidic (MF) devices. Two fibrous hydrogels derived from cellulose nanocrystals (CNCs) and gelatin, named EKGel and EKGelMA, were developed as biomimetic scaffolds for cell culture. EKGel was formed by crosslinking aldehyde-functionalized CNCs (a-CNCs) and gelatin. By varying EKGel’s composition, I recapitulated fibrosis-associated changes in the mechanical and structural properties of the extracellular matrix (ECM). By using EKGelMA, a gel consisting of a-CNCs and gelatin methacryloyl, I achieved a broader range of hydrogel stiffness and decoupled the hydrogel’s mechanical properties from its structural properties by controlling the extent of intrafibrillar crosslinking. The compositions of EKGelMA were then optimized using machine learning (ML) to mimic the viscoelastic properties of ECMs in various tissues. Through multi-objective Bayesian optimization, I identified hydrogel compositions with the elastic modulus and stress relaxation behavior mimicking the ECMs of healthy and scarred skin, as well as benign and malignant breast tumors. The ML algorithm also efficiently delineated the competing relationship between the elastic modulus and stress relaxation in EKGelMA. To recapitulate the structural anisotropy of the skin ECM, an extrusion-based printing approach was introduced. The hydrogel precursors, composed of aldehyde-functionalized cellulose nanofibers (a-CNFs) and gelatin, were extruded through a MF or nozzle printhead. This process resulted in the shear-induced alignment of a-CNFs, leading to the formation of structural anisotropy in the hydrogel. The influence of this structural anisotropy on the orientation of dermal fibroblasts on or in the hydrogels was investigated. Skin spheroids were cultured in EKGel as a miniaturized skin model. A MF platform was developed to grow large arrays of human skin spheroids within three days. The rapid formation of dermal fibroblast spheroids or multilayer skin spheroids enabled high-throughput screening of active skincare ingredients and the evaluation of the toxicity of different chemical agents for skin.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".