Synthesis of Benzophenones and in vitro Evaluation of Their Anticancer Potential in Breast and Prostate Cancer Cells

Breast and prostate cancers are frequently treated with chemotherapy. Several novel chemicals are being reported for this purpose, particularly synthetic and natural benzophenones. This work reports the synthesis of substituted 2-hydroxybenzophenones through 1,4-conjugate addition/intramolecular cycloaddition/dehydration of nitromethane on key intermediate chromones. Structures were extensively studied by means of 2D NMR spectroscopy and single-crystal XRD. Their cytotoxicity was evaluated in vitro in two breast cancer cell lines (MDA-MB-231 and T47-D) and one prostate cancer cell line (PC3). The most potent compound exhibited good cytotoxic effects against the three cancer cell lines (IC₅₀ values ranging from 12.09 to 26.49 μm ) and induced cell-cycle retardation only on prostate cancer cells, which suggested that it might exert cell-type-specific effects.     

Characterization of Aluminum Foam Impact Response

Strength of Materials - The paper addresses the investigation of high-strain rate compressive behavior of Al foams subjected to impact at the intermediate striking velocity ranged from 40 to...     

Hardening of Rods by High-Frequency Current

Russian Engineering Research - Improvement in the wear resistance of plunger-pump rods is considered. Hardening by high-frequency current is proposed.     

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 Concept of a New Generation of Electrocardiogram Simulators

Measurement Techniques - The article is devoted to the issues of conceptual development of a new generation of electrocardiogram imitators. A mathematical model is proposed for simulating ECG...     

Enhanced linear magneto-resistance near the Dirac point in topological insulator Bi2(Te1−xSex)3 nanowires

Nano Research - We report the composition and back-gate voltage tuned transport properties of ternary compound Bi2(Te1−xSex)3 nanowires synthesized by chemical vapor deposition (CVD). It is...     

Biocatalytic Alkylation Cascades: Recent Advances and Future Opportunities for Late-Stage Functionalization

This Concept article describes the latest developments in the emerging area of late-stage biocatalytic alkylation. Central to these developments is the ability to efficiently prepare S-adenosyl methionine (SAM) cofactor analogues and couple this with enzymatic alkyl transfer. Recent developments in the enzymatic synthesis of SAM cofactor analogues are summarized first, followed by their application as alkyl transfer agents catalyzed by methyltransferases (MTases). Second, innovative methods to regenerate SAM cofactors by enzymatic cascades is reported. Finally, future opportunities towards establishing a generalized platform for late-stage alkylation are described.     

Potentiometric sensors with plastized poly(vinyl chloride) membranes selective to penicillin antibiotics: Properties and applications

Potentiometric sensors with plasticized poly(vinyl chloride) membranes based on β-lactam–tetraalkylammonium ion associates sensitive to penicillin antibiotics are proposed. The physicochemical characteristics (solubility product constants and dissociation constants) of active membrane components and the electrode, transport, and selective properties of the membranes of liquidand solid-contact sensors have been studied. The quantitative characteristics of membrane transport (penetrability, ion flux, and transport rate) have been evaluated. The main charge carriers in the membranes and at the membrane/solution interface have been determined from the membrane transport characteristics. The potentiometric sensors are shown to be applicable to the determination of penicillin antibiotics in biological fluids (blood serum and oral fluid) from patients with urinary tract infection.