Brief Intervention to Promote Physical Activity and Mental Well-Being in Community Adults: A Pilot Cluster Randomized Controlled Trial.

OBJECTIVES: We aimed to evaluate the feasibility and effectiveness of a brief community-based intervention to promote physical activity (PA) and the mental well-being of adults in Hong Kong. METHODS: A pilot cluster randomized controlled trial was conducted in 15 family service centers. The intervention group (N = 162, 8 centers) received two 2-h interventions uniquely combining "Sharing, Mind and Enjoyment (SME)," Zero-time Exercises (ZTEx), positive psychology, and simple family games. "Sharing" involved promoting PA among families and peers, "Mind" captured positive emotions during PA, and "Enjoyment" assessed engagement of PA. ZTEx are simple PAs to reduce sedentary behaviors and enhancing PA and fitness and require minimal time and no cost or equipment. The control group (N = 152, 7 centers) received interventions unrelated to SME. Primary outcomes were PA-related SME at a 3-month follow-up after completing the baseline questionnaire. Secondary outcomes included subjective happiness, well-being, and family-related outcomes. Participants reported self-perceived changes at 1- and 3-month. Nine focus group discussions with the participants and 4 individual in-depth interviews with community service providers were conducted. RESULTS: The retention rate at1 month was 90.1% for the intervention group and 95.4% for the control group, while at 3 months, it was 83.3% and 92.8%, respectively. The intervention group showed significantly greater positive changes in PA-related outcomes than the control group at 3-month follow-up (Cohen's d = 0.33-0.42, all P < .05). Most secondary outcomes were non-significantly different between the 2 groups. However, more than 90% of participants in the intervention group reported self-perceived positive changes at 1- and 3-month follow-ups. The qualitative data showed that ZTEx was popular with families due to its simplicity. CONCLUSIONS: Our trial showed the feasibility of implementing the brief interventions and the potential benefits for promoting physical activity in community adults. CLINICALTRIALS.GOV IDENTIFIER: NCT03332810 (date of registration: November 6, 2017).

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation.

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

Plasma metabolic fingerprints for large-scale screening and personalized risk stratification of metabolic syndrome.

Direct diagnosis and accurate assessment of metabolic syndrome (MetS) allow for prompt clinical interventions. However, traditional diagnostic strategies overlook the complex heterogeneity of MetS. Here, we perform metabolomic analysis in 13,554 participants from the natural cohort and identify 26 hub plasma metabolic fingerprints (PMFs) associated with MetS and its early identification (pre-MetS). By leveraging machine-learning algorithms, we develop robust diagnostic models for pre-MetS and MetS with convincing performance through independent validation. We utilize these PMFs to assess the relative contributions of the four major MetS risk factors in the general population, ranked as follows: hyperglycemia, hypertension, dyslipidemia, and obesity. Furthermore, we devise a personalized three-dimensional plasma metabolic risk (PMR) stratification, revealing three distinct risk patterns. In summary, our study offers effective screening tools for identifying pre-MetS and MetS patients in the general community, while defining the heterogeneous risk stratification of metabolic phenotypes in real-world settings.

Complete genomic analysis of ST117 lineage extraintestinal pathogenic Escherichia coli (ExPEC) to reveal multiple genetic determinants to drive its global transmission: ST117 E. coli as an emerging multidrug-resistant foodborne ExPEC with zoonotic potential.

Avian pathogenic Escherichia coli (APEC) is recognized as a primary source of foodborne extraintestinal pathogenic E. coli (ExPEC), which poses a significant risk of extraintestinal infections in humans. The potential of human infection with ST117 lineage APEC/ExPEC from poultry is particularly concerning. However, relatively few whole-genome studies have focused on ST117 as an emerging ExPEC lineage. In this study, the complete genomes of 11 avian ST117 isolates and the draft genomes of 20 ST117 isolates in China were sequenced to reveal the genomic islands and large plasmid composition of ST117 APEC. With reference to the extensive E. coli genomes available in public databases, large-scale comprehensive genomic analysis of the ST117 lineage APEC/ExPEC was performed to reveal the features of the ST117 pan-genome and population. The high variability of the accessory genome emphasized the diversity and dynamic traits of the ST117 pan-genome. ST117 isolates recovered from different hosts and geographic sources were randomly located on a phylogeny tree, suggesting that ST117 E. coli lacked host specificity. A time-scaled phylogeny tree showed that ST117 was a recent E. coli lineage with a relatively short evolutionary period. Further characterization of a wide diversity of ExPEC-related virulence genes, pathogenicity islands (PAIs), and resistance genes of the ST117 pan-genome provided insights into the virulence and resistance of ST117 APEC/ExPEC. The results suggested zoonotic potential of ST117 APEC/ExPEC between birds and humans. Moreover, genomic analysis showed that a pool of diverse plasmids drove the virulence and multidrug resistance of ST117 APEC/ExPEC. Several types of large plasmids were scattered across the ST117 isolates, but there was no strong plasmid-clade adaptation. Combined with the pan-genome analysis, a double polymerase chain reaction (PCR) method was designed for rapid and cost-effective detection of ST117 isolates from various avian and human APEC/ExPEC isolates. Overall, this study addressed a gap in current knowledge about the ST117 APEC/ExPEC genome, with significant implications to understand the success and spread of ST117 APEC/ExPEC.