Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks.

Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors' intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets.

Numerical Study of Customized Artificial Cornea Shape by Hydrogel Biomaterials on Imaging and Wavefront Aberration.

The blindness caused by cornea diseases has exacerbated many patients all over the world. The disadvantages of using donor corneas may cause challenges to recovering eye sight. Developing artificial corneas with biocompatibility may provide another option to recover blindness.

p-GaN/n-ZnO nanorods: the use of graphene nanosheets composites to increase charge separation in self-powered visible-blind UV photodetectors.

ZnO-based heterojunctions have found applications as self-powered ultraviolet photodetectors (PDs). However, high doping levels are not compatible with high mobility for metallic doped ZnO-based PDs so further development has been inhibited. This study demonstrates a method to increase the open-circuit voltage (V ) that allows keeping a sufficiently high level of mobility of ZnO, using a ZnO nanorod/GaN heterojunction that incorporates graphene nanosheets as the active layer.

Discovery of a new series of imidazo[1,2-a]pyridine compounds as selective c-Met inhibitors.

Aim: Aberrant c-Met activation plays a critical role in cancer formation, progression and dissemination, as well as in development of resistance to anticancer drugs. Therefore, c-Met has emerged as an attractive target for cancer therapy. The aim of this study was to develop new c-Met inhibitors and elaborate the structure-activity relationships of identified inhibitors.

Energetic factors determining the binding of type I inhibitors to c-Met kinase: experimental studies and quantum mechanical calculations.

Aim: To decipher the molecular interactions between c-Met and its type I inhibitors and to facilitate the design of novel c-Met inhibitors.

Methods: Based on the prototype model inhibitor 1, four ligands with subtle differences in the fused aromatic rings were synthesized. Quantum chemistry was employed to calculate the binding free energy for each ligand.