| 1 | Gelatin methacrylate hydrogel scaffold carrying resveratrol-loaded solid lipid nanoparticles for enhancement of osteogenic differentiation of BMSCs and effective bone regeneration显示文摘Critical-sized bone defects caused by traumatic fractures,tumour resection and congenital malformation are unlikely to heal spontaneously.Bone tissue engineering is a promising strategy aimed at developing in vitro replacements for bone transplantation and overcoming the limitations of natural bone grafts.In this study,we developed an innovative bone engineering scaffold based on gelatin methacrylate(GelMA)hydrogel,obtained via a two-step procedure:first,solid lipid nanoparticles(SLNs)were loaded with resveratrol(Res),a drug that can promote osteogenic differentiation and bone formation;these particles were then encapsulated at different concentrations(0.01%,0.02%,0.04%and 0.08%)in GelMA to obtain the final Res-SLNs/GelMA scaffolds.The effects of these scaffolds on osteogenic differentiation of bone marrow mesenchymal stem cells(BMSCs)and bone regeneration in rat cranial defects were evaluated using various characterization assays.Our in vitro and in vivo investigations demonstrated that the different Res-SLNs/GelMA scaffolds improved the osteogenic differentiation of BMSCs,with the ideally slow and steady release of Res;the optimal scaffold was 0.02 Res-SLNs/GelMA.Therefore,the 0.02 Res-SLNs/GelMA hydrogel is an appropriate release system for Res with good biocompatibility,osteoconduction and osteoinduction,thereby showing potential for application in bone tissue engineering. | Bangguo Wei Wenrui Wang Xiangyu Liu Chenxi Xu Yanan Wang Ziqi Wang Jinnuo Xu Jianzhong Guan Pinghui Zhou Yingji Mao | 2021 | Regenerative Biomaterials2021，8，5： | 2 |
| 3 | Machine learning-based spectral and spatial analysis of hyper-and multi-spectral leaf images for Dutch elm disease detection and resistance screening显示文摘Diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest management.The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees.In this study,Dutch elm disease(DED;caused by Ophiostoma novo-ulmi,)and American elm(Ulmus americana)was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper-and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection.Hyper-and multi-spectral images were collected from leaves of American elm geno-types with varied disease susceptibilities after mock-inoculation and inoculation with O.novo-ulmi under green-house conditions.Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes.Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED.Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees.In addition,spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes.Though further studies are needed to assess applications in other pathosystems,hyper-and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees. | Xing Wei Jinnuo Zhang Anna O.Conrad Charles E.Flower Cornelia C.Pinchot Nancy Hayes-Plazolles Ziling Chen Zhihang Song Songlin Fei Jian Jin | 2023 | Artificial Intelligence in Agriculture2023，，4： | 0 |
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