Skeletal Muscle Health and Cognitive Function: A Narrative Review

Sarcopenia is the loss of skeletal muscle mass and function with advancing age. It involves both complex genetic and modifiable risk factors, such as lack of exercise, malnutrition and reduced neurological drive. Cognitive decline refers to diminished or impaired mental and/or intellectual functioning. Contracting skeletal muscle is a major source of neurotrophic factors, including brain-derived neurotrophic factor, which regulate synapses in the brain. Furthermore, skeletal muscle activity has important immune and redox effects that modify brain function and reduce muscle catabolism. The identification of common risk factors and underlying mechanisms for sarcopenia and cognition may allow the development of targeted interventions that slow or reverse sarcopenia and also certain forms of cognitive decline. However, the links between cognition and skeletal muscle have not been elucidated fully. This review provides a critical appraisal of the literature on the relationship between skeletal muscle health and cognition. The literature suggests that sarcopenia and cognitive decline share pathophysiological pathways. Ageing plays a role in both skeletal muscle deterioration and cognitive decline. Furthermore, lifestyle risk factors, such as physical inactivity, poor diet and smoking, are common to both disorders, so their potential role in the muscle–brain relationship warrants investigation.     

Protection of an Object on a Movable Base Using Anticipating Control under the Worst Disturbances

Journal of Computer and Systems Sciences International - The problem of constructing a guaranteeing anticipating control for a shock isolator that protects an object on a movable base from impacts...     

Dreissenid veliger dynamics along a nearshore to offshore transect in Lake Michigan

Dreissenid mussel veligers compose a substantial component of pelagic biomass in the Great Lakes, yet their dynamics are poorly understood. To evaluate seasonal, spatial, and inter-annual variation in veliger density, we used a 64-μm mesh plankton net (2008, 2013–2016) and a 153-μm mesh plankton net (2007–2016) to collect dreissenid veligers at nearshore (15–25?m depth), transitional (45?m) and offshore (93–110?m) sites in southeast Lake Michigan during March–December. We also evaluated trends in density of recently settled mussels relative to veliger abundance and the density of the standing stock of adult mussels. Veliger density peaked during both summer and fall at all sites, but peak densities in summer were generally higher nearshore, whereas peak densities in the fall were generally higher offshore. The density of veligers in the 153-μm net was overall 28% of that in the 64-μm net, but there was high variability in this comparison among months. Smaller veligers were much more abundant in the 64-μm net, but there was little difference in the size distribution and abundance between nets for the 210–300?μm size classes. Thus, the 153-μm net could still be a useful tool for assessing density trends of larger veligers just prior to their settlement. Newly settled mussels (≤2?mm) were most abundant in summer or fall at the nearshore and offshore sites but were nearly absent at the transitional site despite the high density of veligers there. Factors other than veliger density must play an important role in mussel recruitment.     

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.