The Associations of Dietary Inflammatory Potential With Musculoskeletal Health in Chinese Community-Dwelling Older People: The Mr. OS and Ms. OS (Hong Kong) Cohort Study

Inflammation, an important contributory factor of muscle and bone aging, is potentially modulated by diet. This study examined the associations of dietary inflammatory index (DII) score with musculoskeletal parameters and related disease outcomes in 3995 community-dwelling Chinese men and women aged ≥65 years in Hong Kong. DII score at baseline was estimated from a food frequency questionnaire. Bone mineral density (BMD) and muscle mass estimated by dual-energy X-ray absorptiometry (DXA), hand grip strength, gait speed, and chair stand test were measured at baseline, year 4, and year 14. The associations of DII score with the longitudinal changes of musculoskeletal parameters, and incidence of osteoporosis, sarcopenia, and fractures were examined by using general linear model, multinomial logistic regression model, and Cox proportional hazards regression model, respectively. After multiple adjustments, each tertile increase in DII score in men was associated with 0.37 (95% confidence interval [CI], 0.10–0.64) kg loss in grip strength and 0.02 (95% CI, 0.01–0.03) m/s loss in gait speed over 4 years. In men, the highest tertile of DII was associated with a higher risk of incident fractures, with adjusted and competing death adjusted hazard ratio (HR) (95% CI) of 1.56 (1.14–2.14) and 1.40 (1.02–1.91), respectively. In women, DII score was not significantly associated with any muscle-related outcomes or incidence of fracture, but a significant association between higher DII score and risk of osteoporosis at year 14 was observed, with the highest tertile of DII score having adjusted odds ratio (OR) (95% CI) of 1.90 (1.03–3.52). In conclusion, pro-inflammatory diet consumption promoted loss of muscle strength and physical function, and increased risk of fractures in older Chinese men. Pro-inflammatory diets had no significant association with muscle related outcomes but increased the long-term risk of osteoporosis in older Chinese women. © 2022 American Society for Bone and Mineral Research (ASBMR).     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.     

Psychosocial mechanisms of a behavioral treatment for urinary incontinence of prostate cancer survivors

Abstract

Purpose: We examined underlying psychosocial processes of a behavioral treatment for urinary incontinence (UI) of prostate cancer survivors.

Design: Secondary analysis of data collected from a clinical trial.

Sample: Two hundred forty-four prostate cancer survivors who participated in a clinical trial of behavioral intervention to UI as intervention or control subjects.

Methods: The participants had a 3-month behavioral intervention or usual care and were followed up for an additional 3?months. They were assessed at baseline, 3, and 6?months. Latent growth curve models were performed to examine trajectories of each study variable and relationships among the variables.

Findings: Increasing self-efficacy and social support were significantly and independently associated with more reduction of urinary leakage frequency over time.

Implications for psychosocial oncology: Providing problem-solving skills and social support, including peer support, are essential for empowering patients to reduce UI.