Assessing Association Between Circulating Bilirubin Levels and the Risk of Frailty: An Observational and Mendelian Randomization Study.

Background: Bilirubin is a by-product of haemoglobin breakdown and has been reported to be a potent antioxidant recently. While elevated levels of bilirubin have been linked to a reduced risk of various diseases, their role remains unknown in frailty. This study aims to explore the relationship between serum bilirubin levels and the risk of frailty.

Identification of lncRNA in circulating exosomes as potential biomarkers for MCI among the elderly.

Background: The abnormal expression of lncRNA in elderly patients with mild cognitive impairment (MCI), and the ability of exosomes to stably carry non-coding RNAs provide a reliable physiological basis for exosomal lncRNA in plasma as a biomarker of MCI.

Methods: This case-control study enrolled 155 patients with MCI and 155 healthy controls from a community-based population aged≥60 years. The expression profiles of lncRNA and mRNA in plasma exosomes were analyzed and validated using high-throughput RNA sequencing and qRT-PCR.

The association of cognitive function and its changes with all-cause mortality among community-dwelling older adults.

Background: The association of cognitive function, its changes, and all-cause mortality has not reached a consensus, and the independence of the association between changes in cognitive function and mortality remains unclear. The purpose of this study was to evaluate the longitudinal association between baseline cognitive function and cognitive changes over 1 year with subsequent all-cause mortality among the older adults aged 60 and above.

Methods: A prospective cohort study utilizing the Community Older Adults Health Survey data.

A new domain adaption residual separable convolutional neural network model for cross-domain remaining useful life prediction.

In order to realize the remaining useful life (RUL) prediction of mechanical equipment under different operating conditions, a domain adaption residual separable convolutional neural network (DRSCN) model is proposed in this paper. In the DRSCN model, instead of the traditional convolutional layer, a residual separable convolutional module is developed to improve the feature extraction ability of the model. Moreover, a multi-kernel maximum mean discrepancy metric function and an adversarial learning mechanism are embedded in the DRSCN model to enhance its ability to resist domain shifts, thus improving the cross-domain RUL prediction accuracy of the model.

Dual Neural Network Method for Solving Multiple Definite Integrals.

This study, which examines a calculation method on the basis of a dual neural network for solving multiple definite integrals, addresses the problems of inefficiency, inaccuracy, and difficulty in finding solutions. First, the method offers a dual neural network method to construct a primitive function of the integral problem; it can approximate the primitive function of any given integrand with any precision. On this basis, a neural network calculation method that can solve multiple definite integrals whose upper and lower bounds are arbitrarily given is obtained with repeated applications of the dual neural network to construction of the primitive function.