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Hydrogels are proving to be very versatile as wound healing devices. In addition to their capabilities of providing a moist cellular environment and adaptive mechanical properties mimicking the extracellular matrix, they allow the incorporation of small molecules, which have potential impacts on cellular behaviour, in their nanostructures. This strategy can allow for specific targeting of the different stages of wound healing namely hemostasis, inflammation, and proliferative and remodelling phases. The latter include interlinked processes such as angiogenesis, collagen synthesis, growth factor release, collagen maturation and re-epithelialization. In this review, we attempt to match the mechanisms of action of natural molecules/extracts to the different stages of wound healing so that they can be used in a novel approach of multiphase-directed tissue regeneration using loaded hydrogel scaffolds.

The engineering of polymeric scaffolds for tissue regeneration has known a phenomenal growth during the past decades as materials scientists seek to understand cell biology and cell–material behaviour. Statistical methods are being applied to physico-chemical properties of polymeric scaffolds for tissue engineering (TE) to guide through the complexity of experimental conditions. We have attempted using experimental in vitro data and physico-chemical data of electrospun polymeric scaffolds, tested for skin TE, to model scaffold performance using machine learning (ML) approach. Fibre diameter, pore diameter, water contact angle and Young's modulus were used to find a correlation with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay of L929 fibroblasts cells on the scaffolds after 7 days. Six supervised learning algorithms were trained on the data using Seaborn/Scikit-learn Python libraries. After hyperparameter tuning, random forest regression yielded the highest accuracy of 62.74%. The predictive model was also correlated with in vivo data. This is a first preliminary study on ML methods for the prediction of cell–material interactions on nanofibrous scaffolds.