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Development of new and effective scaffold continues to be an area of intense research in skin tissue engineering. The objective of this study was to study the effect of copper nanoparticles over physico-chemical properties of the chitosan and gelatin composite scaffolds for skin tissue engineering. The copper-doped scaffolds were prepared using freeze-drying method. Chitosan and gelatin were taken in varied composition with 0.01%, 0.02%, and 0.03% Cu nanoparticles. The physico-chemical properties of the copper nanoparticles and the scaffolds were analyzed using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy. Porosity of the scaffolds was measured by liquid displacement method and hemocompatibility was tested using goat blood. SEM micrographs of the scaffolds displayed the interconnected pores which ranged between 25 and 40 µm. This average pore size was later enhanced to 95 µm after the addition of copper nanoparticles. Cell viability assay was performed to ensure the growth and proliferation of the skin cells over the scaffolds. FTIR, EDS, and XRD analysis of scaffolds confirmed the presence of copper in the chitosan-based scaffolds. Porosity measurement showed the interconnectivity between pores which ranged between 65 and 88% as required for skin tissue engineering application. The degradation study of the scaffolds was done which depicted that, after the addition of copper nanoparticles with 0.03%, degradation rate was decreased. SEM and cytocompatibility assay on all scaffolds showed the cell adhesion and proliferation on the scaffolds which was not affected after addition of copper nanoparticles. Oxidative stress evaluation was done to study the effect of copper nanoparticles on the cells which showed that there was no such production of ROS in the scaffolds. Hence, scaffolds prepared after doping of copper nanoparticles show suitable physico-chemical and biological properties for skin tissue engineering application.

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Unpredictable weather vagaries in the Asian tropics often increase the risk of a series of abiotic stresses in maize-growing areas, hindering the efforts to reach the projected demands. Breeding climate-resilient maize hybrids with a cross-tolerance to drought and waterlogging is necessary yet challenging because of the presence of genotype-by-environment interaction (GEI) and the lack of an efficient multi-trait-based selection technique. The present study aimed at estimating the variance components, genetic parameters, inter-trait relations, and expected selection gains (SGs) across the soil moisture regimes through genotype selection obtained based on the novel multi-trait genotype–ideotype distance index (MGIDI) for a set of 75 tropical pre-released maize hybrids. Twelve traits including grain yield and other secondary characteristics for experimental maize hybrids were studied at two locations. Positive and negative SGs were estimated across moisture regimes, including drought, waterlogging, and optimal moisture conditions. Hybrid, moisture condition, and hybrid-by-moisture condition interaction effects were significant (p ≤ 0.001) for most of the traits studied. Eleven genotypes were selected in each moisture condition through MGIDI by assuming 15% selection intensity where two hybrids, viz., ZH161289 and ZH161303, were found to be common across all the moisture regimes, indicating their moisture stress resilience, a unique potential for broader adaptation in rainfed stress-vulnerable ecologies. The selected hybrids showed desired genetic gains such as positive gains for grain yield (almost 11% in optimal and drought; 22% in waterlogging) and negative gains in flowering traits. The view on strengths and weaknesses as depicted by the MGIDI assists the breeders to develop maize hybrids with desired traits, such as grain yield and other yield contributors under specific stress conditions. The MGIDI would be a robust and easy-to-handle multi-trait selection process under various test environments with minimal multicollinearity issues. It was found to be a powerful tool in developing better selection strategies and optimizing the breeding scheme, thus contributing to the development of climate-resilient maize hybrids.

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