Sugar-cane bagasse cellulose-based scaffolds promote multi-cellular interactions, angiogenesis and reduce inflammation for skin tissue regeneration.

In a previous article, we reported on the physico-chemical properties of cellulose-based scaffolds derived from sugar-cane bagasse and their preliminary in vitro assessment. In view of skin tissue regeneration, we here present our findings of an extensive in vitro testing of these scaffolds using key cells involved in the wound healing cascade namely fibroblasts, keratinocytes, endothelial cells and macrophages either singly or in various combinations to mimic in vivo conditions. Inflammation was quantified using TNF-α. In vivo biocompatibility as well as wound healing potential of the scaffolds was demonstrated using Wistar rats. Finally, we discuss the effect of curcumin-loaded scaffolds on inflammation and angiogenesis in vitro and in vivo. Nanosilica extracted from sugar-cane bagasse ash was also loaded in the scaffolds and its effect on biological response was assessed.

Correlating in vitro performance with physico-chemical characteristics of nanofibrous scaffolds for skin tissue engineering using supervised machine learning algorithms.

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.

Sugar-cane bagasse derived cellulose enhances performance of polylactide and polydioxanone electrospun scaffold for tissue engineering.

Bagasse is a waste product of sugar extraction from sugar-cane with approximately 30% cellulose content. Cellulose was successfully extracted from sugar-cane bagasse using a modified mercerization-bleaching approach with a 40% yield. Extracted cellulose was converted to cellulose acetate for enhanced electrospinnability and blended with poly-l-Lactide or polydioxanone before solution electrospinning. Physico-chemical evaluation of the electrospun mats showed variable miscibility of blends. In vitro cell studies with L929 mouse fibroblast cells was quite conclusive as regards the biocompatibility of the blended mats with proliferative behavior of cells, extracellular matrix deposition and characteristic features of healthy cellular response. MTT assay indicated that the cellulose blended mats induced higher cell densities than the controls. Cellulose content influenced parameters such as fiber diameter, porosity and cell-matrix interaction of mats impacting on cell growth and behavior. Preliminary assessment of biomineralization potential of the mats by SEM showed nano-hydroxyapatite deposits on the electrospun fibers.