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 oc) 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. These hybrid PDs have triple the value for V oc of PDs that have only pure ZnO and better exhibit photo-response characteristics. The results of surface Kelvin probe microscopy and x-ray photoelectron spectrometer show that the complex defects that occur because Zn interstitials form a shallow donor in ZnO are mainly responsible for the increase in the value of V oc. Using this functional nanostructure as an active layer represents a new method for the manufacture of high-performance self-powered PDs.

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. METHODS: Based on the predicted binding modes of Compounds 5 and 14 in docking studies, a new series of c-Met inhibitor-harboring 3-((1H-pyrrolo[3,2-c]pyridin-1-yl)sulfonyl)imidazo[1,2-a]pyridine scaffolds was discovered. Potent inhibitors were identified through extensive optimizations combined with enzymatic and cellular assays. A promising compound was further investigated in regard to its selectivity, its effects on c-Met signaling, cell proliferation and cell scattering in vitro. RESULTS: The most potent Compound 31 inhibited c-Met kinase activity with an IC50 value of 12.8 nmol/L, which was >78-fold higher than those of a panel of 16 different tyrosine kinases. Compound 31 (8, 40, 200 nmol/L) dose-dependently inhibited the phosphorylation of c-Met and its key downstream Akt and ERK signaling cascades in c-Met aberrant human EBC-1 cancer cells. In 12 human cancer cell lines harboring different background levels of c-Met expression/activation, Compound 31 potently inhibited c-Met-driven cell proliferation. Furthermore, Compound 31 dose-dependently impaired c-Met-mediated cell scattering of MDCK cells. CONCLUSION: This series of c-Met inhibitors is a promising lead for development of novel anticancer drugs.

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. Symmetry-adapted perturbation theory (SAPT) was used to decompose the binding energy into several fundamental forces to elucidate the determinant factors. RESULTS: Binding free energies calculated from quantum chemistry were correlated well with experimental data. SAPT calculations showed that the predominant driving force for binding was derived from a sandwich π-π interaction with Tyr-1230. Arg-1208 was the differentiating factor, interacting with the 6-position of the fused aromatic ring system through the backbone carbonyl with a force pattern similar to hydrogen bonding. Therefore, a hydrogen atom must be attached at the 6-position, and changing the carbon atom to nitrogen caused unfavorable electrostatic interactions. CONCLUSION: The theoretical studies have elucidated the determinant factors involved in the binding of type I inhibitors to c-Met.

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. The developed models were applied to prospective validations of recruiting experienced blood donors during two COVID-19 pandemics, while the routine method was used as a control. Overall, a total of 95,476 recruitments via SMS and their donation results were enrolled in our modelling study. The strongest predictor features for the donation of experienced donors were blood donation interval, age, and donation frequency. Among the seven baseline models, the eXtreme Gradient Boosting (XGBoost) and Support vector machine models (SVM) achieved the best performance: mean (95%CI) with the highest AUC: 0.809 (0.806-0.811), accuracy: 0.815 (0.812-0.818), precision: 0.840 (0.835-0.845), and F1 score of XGBoost: 0.843 (0.840-0.845) and recall of SVM: 0.991 (0.988-0.994). The hit rate of the XGBoost model alone and the combined XGBoost and SVM models were 1.25 and 1.80 times higher than that of the conventional method as a control in 2 recruitments respectively, and the hit rate of the high willingness to donate group was 1.96 times higher than that of the low willingness to donate group. Our results suggested that the machine learning models could predict and determine the experienced donors with a strong willingness to donate blood by a ranking score based on personalized donation data and demographical details, significantly improve the recruitment rate of blood donors and help blood agencies to maintain the blood supply in emergencies.

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. The techniques of making individual artificial corneas that fit the biometric parameters for each person can be used to help these patients effectively. In this study, artificial corneas with different shapes (spherical, aspherical, and biconic shapes) are designed and they could be made by two different hydrogel polymers that form an interpenetrating polymer network for their excellent mechanical strength. Two designed cases for the artificial corneas are considered in the simulations: to optimize the artificial cornea for patients who still wear glasses and to assume that the patient does not wear glasses after transplanting with the optimized artificial cornea. The results show that the artificial corneas can efficiently decrease the imaging blur. Increasing asphericity of the current designed artificial corneas can be helpful for the imaging corrections. The differences in the optical performance of the optimized artificial corneas by using different materials are small. It is found that the optimized artificial cornea can reduce the high order aberrations for the second case.