TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

Synthesis,structure, CO oxidation,and H2 production activities of CaCu3−xMnxTi4−xMnxO12 (x = 0, 0.5, and 1.0)

Synthesis of nanocrystalline pristine and Mn-doped calcium copper titanate quadruple perovskites, CaCu_3?xMn_xTi_4?xMn_xO₁₂ (x = 0, 0.5, and 1.0) by modified citrate solution combustion method has been reported. Powder X-ray diffraction patterns attest the phase purity of the perovskite materials. Average particle sizes of all the materials obtained from the Scherrer's formula are in the range of 55–70 nm. The specific surface areas for all the perovskites obtained from BET isotherms are found to be low as expected for the condensed oxide systems and fall in the range of 13–17 m² g^?1. Transmission electron microscopy studies show a reduction in particle size of CaCu₃Ti₄O₁₂ with increase in Mn doping. Ca and Ti are present in +2 and +4 oxidation states in all the materials as demonstrated by X-ray photoelectron spectroscopy analyses. Cu²⁺ gets reduced in CaCu₃Ti₄O₁₂ with higher Mn content. Mn is observed to be present only in +3 oxidation state. All the materials have been examined to be active in CO oxidation as well as H₂ production from methanol steam reforming. CaCu₃Ti₄O₁₂ with ~14 at.% Mn is found to show best catalytic activities among these materials. A comprehensive analysis of the catalytic activities of these perovskites toward CO oxidation and H₂ production from MSR reveal the cooperative activity of copper-manganese in the doped perovskites and it is more effective at lower manganese content.     

Fabrication and characterizations of Cr3+-doped ZnGa2O4 transparent ceramics with persistent luminescence

0.5 at.% Cr:ZnGa₂O₄ precursor was synthesized by the co-precipitation method with nitrates as raw materials, using ammonium carbonate as the precipitant. Low-agglomerated Cr:ZnGa₂O₄ powders with an average particle size of 43 nm were obtained by calcining the precursor at 900℃ for 4 h. Using the powders as starting materials, 0.5 at.% Cr:ZnGa₂O₄ ceramics with an average grain size of about 515 nm were prepared by presintering at 1150℃ for 5 h in air and HIP post-treatment at 1100℃ for 3 h under 200 MPa Ar. The in-line transmittance of 0.5 at.% Cr:ZnGa₂O₄ ceramics with a thickness of 1.3 mm reaches 59.5% at the wavelength of 700 nm. The Cr:ZnGa₂O₄ ceramics can be effectively excited by visible light and produce persistent luminescence at 700 nm. For Cr:ZnGa₂O₄ transparent ceramics, the brightness of afterglow was larger than 0.32 mcd/m² after 30 min, which is far superior to that of Cr:ZnGa₂O₄ persistent luminescence powders.     

径向不耦合装药爆破效果的数值研究

为了研究径向不耦合条件下爆破效果的影响因素,利用AUTODYN-2D对比不耦合系数、不耦合介质、炸药种类对爆破效果的影响规律。结果表明,与耦合装药相比,径向不耦合装药条件下炮孔压力明显降低。对比水介质和空气介质下的爆破效果可知,水介质不耦合条件下,炸药爆炸能量的传递效率更高。炮孔压力与炸药种类密切相关,TNT产生的炮孔压力最大,但作用时间较短,而ANFO和乳化炸药的作用时间较长。     

2-Phenyloxazole-4-carboxamide as a Scaffold for Selective Inhibition of Human Monoamine Oxidase B

A series of 2-phenyloxazoles bearing an amide group at position 4 were designed and synthesized for evaluation as potential inhibitors of human recombinant monoamine oxidases (hrMAOs). Results of kinetics experiments demonstrated that all compounds behave as competitive MAO inhibitors, with good selectivity toward the MAO-B isoform. The most potent and selective derivatives are characterized by inhibition constant (K_i) values in the sub-micromolar range and a good selectivity index (K_{i MAO-A}/K_{i MAO-B}>50). Some derivatives were also found to be able to inhibit MAO activity in nerve growth factor (NGF)-differentiated PC12 cells, taken as a model of neuronal cells. In particular, 2-(2-hydroxyphenyl)-N-phenyloxazole-4-carboxamide (compound 4 a ) may be a promising new scaffold, exerting the highest selectivity and inhibitory effect toward MAOs in NGF-differentiated PC12 cell lysates, without compromising cell viability. Molecular docking analysis allowed a rationalization of the experimentally observed binding affinity and selectivity.     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

The influence of volume fraction of amorphous phase on corrosion resistance of Mg67Zn 29Ca 4 alloy

The aim of this study was to investigate the structure and corrosion resistance of amorphous, amorphous‐crystalline, and crystalline Mg₆₇Zn₂₉Ca₄ alloy for biodegradable applications. This paper presents a preparation method and results of the structural characterization and corrosion resistance analysis of the material. Samples were prepared in the form of 3 mm diameter rods. The structure of the alloy was examined with the use of X‐ray diffractometry and scanning electron microscopy. The thermal properties of the samples were examined with differential scanning calorimetry (DSC). Results of DSC analysis were used to determine heat treatment temperatures, allowing to obtain different fractures of crystalline phase in the material. Corrosion resistance of heat‐treated samples was investigated by immersion tests and electrochemical measurements performed in the simulated body fluid. The X‐ray diffraction results confirmed that the prepared Mg₆₇Zn₂₉Ca₄ alloy's structure is fully amorphous. After heat treatment, samples with different fractions of amorphous phase in the structure were obtained. Immersion tests of the samples showed that the structure significantly influenced corrosion resistance in examined materials. It should be pointed out, that certain amounts of crystalline phase in amorphous matrix can greatly improve the corrosion resistance of Mg₆₇Zn₂₉Ca₄ alloy.