Frailty among Older Adults and Its Distribution in England.

BACKGROUND: Information on the spatial distribution of the frail population is crucial to inform service planning in health and social care. OBJECTIVES: To estimate small-area frailty prevalence among older adults using survey data. To assess whether prevalence differs between urban, rural, coastal and inland areas of England. DESIGN: Using data from the English Longitudinal Study of Ageing (ELSA), ordinal logistic regression was used to predict the probability of frailty, according to age, sex and area deprivation. Probabilities were applied to demographic and economic information in 2020 population projections to estimate the district-level prevalence of frailty. RESULTS: The prevalence of frailty in adults aged 50+ (2020) in England was estimated to be 8.1 [95% CI 7.3-8.8]%. We found substantial geographic variation, with the prevalence of frailty varying by a factor of 4.0 [3.5-4.4] between the most and least frail areas. A higher prevalence of frailty was found for urban than rural areas, and coastal than inland areas. There are widespread geographic inequalities in healthy ageing in England, with older people in urban and coastal areas disproportionately frail relative to those in rural and inland areas. CONCLUSIONS: Interventions aimed at reducing inequalities in healthy ageing should be targeted at urban and coastal areas, where the greatest benefit may be achieved.

Application of Intelligent Paradigm through Neural Networks for Numerical Solution of Multiorder Fractional Differential Equations.

In this study, the intelligent computational strength of neural networks (NNs) based on the backpropagated Levenberg-Marquardt (BLM) algorithm is utilized to investigate the numerical solution of nonlinear multiorder fractional differential equations (FDEs). The reference data set for the design of the BLM-NN algorithm for different examples of FDEs are generated by using the exact solutions. To obtain the numerical solutions, multiple operations based on training, validation, and testing on the reference data set are carried out by the design scheme for various orders of FDEs. The approximate solutions by the BLM-NN algorithm are compared with analytical solutions and performance based on mean square error (MSE), error histogram (EH), regression, and curve fitting. This further validates the accuracy, robustness, and efficiency of the proposed algorithm.

LC-QTOF-MS/MS Based Molecular Networking Approach for the Isolation of α-Glucosidase Inhibitors and Virucidal Agents from Coccinia grandis (L.) Voigt.

Coccinia grandis or ivy gourd is an edible plant. Its leaves and fruits are used as vegetable in many countries. Many works on antidiabetic activity of a crude extract of C. grandis, i.e., in vitro, in vivo, and clinical trials studies, have been reported. Profiles of the antidiabetic compounds were previously proposed by using LC-MS or GC-MS. However, the compounds responsible for antidiabetic activity have rarely been isolated and characterized by analysis of 1D and 2D NMR data. In the present work, UHPLC-ESI-QTOF-MS/MS analysis and GNPS molecular networking were used to guide the isolation of α-glucosidase inhibitors from an extract of C. grandis leaves. Seven flavonoid glycosides including rutin (1), kaempferol 3-O-rutinoside (2) or nicotiflorin, kaempferol 3-O-robinobioside (3), quercetin 3-O-robinobioside (4), quercetin 3-O-ß-D-apiofuranosyl-(1→2)-[α-L-rhamnopyranosyl-(1→6)]-ß-D-glucopyranoside (5) or CTN-986, kaempferol 3-O-ß-D-api-furanosyl-(1→2)-[α-L-rhamnopyranosyl-(1→6)]-ß-D-glucopyranoside (6), and kaempferol 3-O-ß-D-apiofuranosyl-(1→2)-[α-L-rhamnopyranosyl-(1→6)]-ß-D-galactopyranoside (7) were isolated from C. grandis leaves. This is the first report of glycosides containing apiose sugar in the genus Coccinia. These glycosides exhibited remarkable α-glucosidase inhibitory activity, being 4.4-10.3 times more potent than acarbose. Moreover, they also displayed virucidal activity against influenza A virus H1N1, as revealed by the ASTM E1053-20 method.

Application of Euler Neural Networks with Soft Computing Paradigm to Solve Nonlinear Problems Arising in Heat Transfer.

In this study, a novel application of neurocomputing technique is presented for solving nonlinear heat transfer and natural convection porous fin problems arising in almost all areas of engineering and technology, especially in mechanical engineering. The mathematical models of the problems are exploited by the intelligent strength of Euler polynomials based Euler neural networks (ENN's), optimized with a generalized normal distribution optimization (GNDO) algorithm and Interior point algorithm (IPA). In this scheme, ENN's based differential equation models are constructed in an unsupervised manner, in which the neurons are trained by GNDO as an effective global search technique and IPA, which enhances the local search convergence. Moreover, a temperature distribution of heat transfer and natural convection porous fin are investigated by using an ENN-GNDO-IPA algorithm under the influence of variations in specific heat, thermal conductivity, internal heat generation, and heat transfer rate, respectively. A large number of executions are performed on the proposed technique for different cases to determine the reliability and effectiveness through various performance indicators including Nash-Sutcliffe efficiency (NSE), error in Nash-Sutcliffe efficiency (ENSE), mean absolute error (MAE), and Thiel's inequality coefficient (TIC). Extensive graphical and statistical analysis shows the dominance of the proposed algorithm with state-of-the-art algorithms and numerical solver RK-4.