The ability to pick up objects off the floor can degrade over time with elderly individuals, leading to a reduced quality of life and an increase in the risk of falling. Healthcare professionals have expressed an interest in monitoring the decline in pickup ability of a subject over extended periods of time and intervening when it becomes hazardous to the subject\'s health. The current means of evaluating pickup ability involving in-clinic patient visits is both time and financially expensive. There is a clear need for a cost-effective, remote means of pickup evaluation to ease the burden on both patients and physicians. To address these challenges, we introduce a Time-of-Pickup (ToP) solution, called ToPick, designed for the automatic assessment of pickup ability over time. The practical performance of ToPick is evident, demonstrated by a minimal median error of approximately 100 milliseconds in evaluating 20 pickup events among 10 elderly individuals. Furthermore, ToPick exhibits a high level of reliability, achieving perfect accuracy, precision, and recall scores for pickup event detection. We actualize our research findings by designing an application intended for adoption by both healthcare practitioners and elderly individuals. The app aims to reduce both time and financial costs while enabling mobile treatment for users.

暂无摘要。

Hyperuricemia and gout are risk factors of nephrolithiasis. However, it is unclear whether the ABCG2 gene contributes to the development of nephrolithiasis. We aimed to investigate the interaction between the ABCG2 rs2231142 variant and incident nephrolithiasis in the Taiwanese population.
A total of 120,267 adults aged 30-70 years were enrolled from the Taiwan Biobank data-base in this retrospective case-control study and genotyped for rs2231142. The primary outcome was the prevalence of self-reported nephrolithiasis. The odds ratio (OR) of incident nephrolithiasis was analyzed by multivariable logistic regression models with adjustment for multifactorial confounding factors. Associations of the ABCG2 rs2231142 variant with serum uric acid levels, and the incident nephrolithiasis were explored.
The frequency of rs2231142 T allele was 53%, and 8,410 participants had nephrolithiasis. The multivariable-adjusted OR (95% confidence interval) of nephrolithiasis was 1.18 (1.09-1.28) and 1.12 (1.06-1.18) for TT and GT genotypes, respectively, compared with the GG genotype (p<0.001), specifically in the male population with hyperuricemia. Higher age, male sex, hyperlipidemia, hypertension, diabetes mellitus, hyperuricemia, smoking and overweight were independent risk factors for nephrolithiasis. In contrast, regular physical exercise is a protective factor against nephrolithiasis.
ABCG2 genetic variation is a significant risk of nephrolithiasis, independent of serum uric acid levels. For rs2231142 T allele carriers, our result provides evidence for precision healthcare to tackle hyperuricemia, comorbidities, smoking, and overweight, and recommend regular physical exercise for the prevention of nephrolithiasis.

Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing interindividual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding of personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from nonhuman animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumer-facing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions.

暂无摘要。

Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing interindividual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding of personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from nonhuman animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumer-facing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions.

The explosion of data throughout the biomedical sciences provides unprecedented opportunities to learn about the dynamics of evolution and disease progression, but harnessing these large and diverse datasets remains challenging. Here, we describe a highly generalizable statistical platform to infer the dynamic pathways by which many, potentially interacting, traits are acquired or lost over time. We use HyperTraPS (hypercubic transition path sampling) to efficiently learn progression pathways from cross-sectional, longitudinal, or phylogenetically linked data, readily distinguishing multiple competing pathways, and identifying the most parsimonious mechanisms underlying given observations. This Bayesian approach allows inclusion of prior knowledge, quantifies uncertainty in pathway structure, and allows predictions, such as which symptom a patient will acquire next. We provide visualization tools for intuitive assessment of multiple, variable pathways. We apply the method to ovarian cancer progression and the evolution of multidrug resistance in tuberculosis, demonstrating its power to reveal previously undetected dynamic pathways.

It is well-recognized that there is a need for medicine to migrate to a platform of delivering preventative care based on an individual\'s genetic make-up. The US National Research Council, the National Institute of Health and the American Heart Association all support the concept of utilizing genomic information to enhance the clinical management of patients. It is believed this type of precision healthcare will revolutionize health management. This current attitude of some of the most respected institutes in healthcare sets the stage for the utilization of the haptoglobin (Hp) genotype to guide precision management in type 2 diabetics (DM). There are three main Hp genotypes: 1-1, 2-1, 2-2. The Hp genotype has been studied extensively in (DM) and from the accumulated data it is clear that Hp should be considered in all DM patients as an additional independent cardiovascular disease (CVD) risk factor. In DM patients Hp2-2 generates five times increased risk of CVD compared to Hp1-1 and three times increased risk compared to Hp2-1. Data has also shown that carrying the Hp2-2 gene in DM compared to carrying an Hp1-1 genotype can increase the risk the microvascular complications of nephropathy and retinopathy. In addition, the Hp2-2 gene enhances post percutaneous coronary intervention (PCI) complications such as, in stent restenosis and need for additional revascularization during the first-year post PCI. Studies have demonstrated significant mitigation of CVD risk in Hp2-2 DM patients with administration of vitamin E and maintaining tight glycemic control. CVD is the leading cause of death and disability in DM as well-representing a huge financial burden. As such, evaluating the Hp genotype in DM patients can enhance the predictability and management of CVD risk.

Emotional eating has been linked to ovarian hormone functioning, but no studies to-date have considered the role of brain function. This knowledge gap may stem from methodological challenges: Data are heterogeneous, violating assumptions of homogeneity made by between-subjects analyses. The primary aim of this paper is to describe an innovative within-subjects analysis that models heterogeneity and has potential for filling knowledge gaps in eating disorder research. We illustrate its utility in an application to pilot neuroimaging, hormone, and emotional eating data across the menstrual cycle.
Group iterative multiple model estimation (GIMME) is a person-specific network approach for estimating sample-, subgroup-, and individual-level connections between brain regions. To illustrate its potential for eating disorder research, we apply it to pilot data from 10 female twins (N = 5 pairs) discordant for emotional eating and/or anxiety, who provided two resting state fMRI scans and hormone assays. We then demonstrate how the multimodal data can be linked in multilevel models.
GIMME generated person-specific neural networks that contained connections common across the sample, shared between co-twins, and unique to individuals. Illustrative analyses revealed positive relations between hormones and default mode connectivity strength for control twins, but no relations for their co-twins who engage in emotional eating or who had anxiety.
This paper showcases the value of person-specific neuroimaging network analysis and its multimodal associations in the study of heterogeneous biopsychosocial phenomena, such as eating behavior.

The occurrence and progression of diseases are strongly associated with a combination of genetic, lifestyle, and environmental factors. Understanding the interplay between genetic and nongenetic components provides deep insights into disease pathogenesis and promotes personalized strategies for people healthcare. Recently, the paradigm of systems medicine, which integrates biomedical data and knowledge at multidimensional levels, is considered to be an optimal way for disease management and clinical decision-making in the era of precision medicine. In this chapter, epigenetic-mediated genetics-lifestyle-environment interactions within specific diseases and different ethnic groups are systematically discussed, and data sources, computational models, and translational platforms for systems medicine research are sequentially presented. Moreover, feasible suggestions on precision healthcare and healthy longevity are kindly proposed based on the comprehensive review of current studies.