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Background Frailty is common and associated with poorer outcomes in the elderly, but its prognostic value in acute coronary syndromes (ACS) requires clarification. We thus undertook a systematic review and meta-analysis to evaluate the relationship between frailty and poor prognosis in patients with ACS. Methods We systematically searched PubMed, Embase to find literatures which studied the prognostic value of frailty in elderly patients with ACS. Our main endpoints were the all-cause mortality, cardiovascular disease (CVD), major bleeding and readmissions. We pooled studies using random-effect generic inverse variance method, and conducted three pre-specified subgroup analyses. Results Of 1216 identified studies, 15 studies were included in our analysis. Compared with the normal group, frailty (HR = 2.65; 95%CI: 1.81–3.89, I 2  = 60.2%) and pre-frailty (HR = 1.41; 95%CI: 1.19–1.66, I 2  = 0%) were characterized by a higher risk of mortality after adjustment. Frailty also was associated with increased risk of any-type CVD, major bleeding and hospital readmissions in elderly patients with ACS. The pooled effect sizes in frail patients were 1.54 (95%CI: 1.32–1.79), 1.51 (95%CI: 1.14–1.99) and 1.51 (95%CI: 1.09–2.10). Conclusions Frailty provides quantifiable and significant prognostic value for mortality and adverse events in elderly ACS patients, helping doctors to appraise the comprehensive prognosis risk and to applicate appropriate management strategies.

Background In China, long-term care (LTC) system has been implemented in recent years to improve the quality of care for older adults. To address healthcare needs of older adults with disabilities, this study investigated the disease trajectory and medical expenditures. Methods This study included older adults aged 65 and above with disabilities, using data from China’s Long-Term Care Insurance (LTCI) program since July 2017. The participants were followed until June 2021. Diagnoses and hospitalization costs were extracted from electronic medical records and the medical insurance system. Disease trajectory networks were constructed by identifying and linking disease pairs with overlapping conditions. Medical expenditures associated with specific diseases were then calculated. Results The study included 30,003 participants with a mean age of 79.6 ± 11.1 years, 57.0% of whom were female. After a mean follow-up of 21 ± 16 months, 17,428 (58.1%) deaths occurred. The diseases with the highest hazard ratios (HRs) included septic shock (HR 3.59, 95% CI, 3.36–3.84), respiratory failure (HR 3.19, 95% CI, 3.05–3.34), sepsis (HR 2.98, 95% CI, 2.80–3.18), malnutrition (HR 2.38, 95% CI, 2.27–2.48), and decubitus ulcer (HR 2.27, 95% CI, 2.14–2.41). Disease trajectories indicated that mortality was closely associated with malnutrition related diseases (anemia, hypoproteinemia, and malnutrition), pneumonia, and organ failure (respiratory failure and heart failure). Among the top 30 diseases leading to frequent hospitalization, intracerebral hemorrhage (47,882.4 CNY), sepsis (37,978.2 CNY), and respiratory failure (25,921.1 CNY) accounted for the highest total medical costs. Conclusions The study revealed that malnutrition and infection-related diseases contributed significantly to mortality among older adults with disabilities, with the latter also driving higher medical costs. These findings could inform updates to LTCI policies by emphasizing adequate nutritional support and strengthened infection prevention measures. Trial registration chictr.org.cn, ChiCTR2100049973, retrospectively registered.

Recently, software architectures applied to physical agents have become a boost from the emerging Artificial Intelligence(AI). In these smart physical agents, simultaneously image information processing appears vital in particular. Single Image Super Resolution(SISR) serves as the foundation of the image process, presenting its prospects driven by deep learning(DL) methods. In these DL methods, convolutional layers are stacked to implement a mapping between the original low resolution(LR) image and the high resolution(HR) image. Despite improved performances, convolution neural networks(CNN) based methods with large parameters are so complex and time-consuming, which is difficult to apply in mobile devices. To tackle the above issue, we propose Distillation Information Block(DIB) and Feature Information Refined Block(FIRB). In our proposed Lightweight Refined Networks for single image Super-Resolution(LRSR), to lengthen our network with fewer parameters increased, the proposed DIB grasps more information by using a large receptive field. To enhance the information utilization, we build FIRB to refine advanced features and recover more details. Furthermore, with the compact structure, the execution time can be comparatively reduced with higher performance. We conduct extensive experiments on different datasets, which demonstrate that the proposed method performs comparatively better compared with the state-of-the-art lightweight methods.

Background Mobile health (mHealth) lifestyle interventions have showed promise in improving healthy lifestyles and reducing metabolic syndrome (MetS) risk, yet most studies adopt isolated frameworks. The 3SLIFE model—integrating the socioecological model, smart devices, and self-management strategies—provides a holistic approach to sustained behavioral change. It considers environmental influences, empowers individuals in goal-setting and engagement, and leverages smart devices for monitoring and feedback. Despite its potential, evidence on this integrated approach remains scarce. This study applies 3SLIFE to community-dwelling adults, aiming to improve healthy lifestyles. Methods In this parallel, cluster-randomized controlled trial, 20 communities in Southwestern China were randomly assigned 1:1 to either the intervention or control group. Participants in the intervention group received the 3SLIFE intervention for 6 months, while those in the control group received routine management. The healthy lifestyle score was calculated for each participant based on smoking, alcohol intake, physical activity, dietary habits, and overweight/obesity. The primary outcome was the change in the healthy lifestyle score at the end of the 6-month trial. Differences in the score between groups were estimated using generalized linear mixed-effects models in the intention-to-treat population at 3, 6, and 12 months of follow-up. Results From April to July 2023, 383 community-dwelling adults (mean age: 57.64 ± 11.32 years; 42.30% male) were recruited—190 in the intervention group and 193 in the control group. After the 6-month intervention, the increase in the healthy lifestyle score was slightly higher in the intervention group (13.22 ± 3.30 to 13.40 ± 2.88) than in the control group (13.34 ± 3.10 to 12.79 ± 3.32), with a mean difference of 0.77 (95% CI, 0.17 to 1.38). A higher proportion in the intervention group reduced at least one unhealthy lifestyle compared to the control group (31.48% vs. 19.64%, P  = 0.016). However, no significant difference in score change was observed between groups at 12-month follow-up. Conclusions This study provides evidence that the 3SLIFE intervention could modestly improve healthy lifestyles in a community-based population, but the effects were not sustained at the 12-month follow-up. A further refinement is needed to enhance the intervention’s long-term effectiveness in promoting sustainable lifestyle changes and reduce MetS risk in communities. Trial registration Chinese Clinical Trial Registry Identifier: ChiCTR2300070575.

Background Healthy lifestyles are crucial for preventing chronic diseases. Nonetheless, approximately 90% of Chinese community residents regularly engage in at least one unhealthy lifestyle. Mobile smart devices-based health interventions (mHealth) that incorporate theoretical frameworks regarding behavioral change in interaction with the environment may provide an appealing and cost-effective approach for promoting sustainable adaptations of healthier lifestyles. We designed a randomized controlled trial (RCT) to evaluate the effectiveness of a socioecological model-guided, smart device-based, and self-management-oriented lifestyles (3SLIFE) intervention, to promote healthy lifestyles among Chinese community residents. Methods This two-arm, parallel, cluster-RCT with a 6-month intervention and 6-month follow-up period foresees to randomize a total of 20 communities/villages from 4 townships in a 1:1 ratio to either intervention or control. Within these communities, a total of at least 256 community residents will be enrolled. The experimental group will receive a multi-level intervention based on the socioecological model supplemented with a multi-dimensional empowerment approach. The control group will receive information only. The primary outcome is the reduction of modifiable unhealthy lifestyles at six months, including smoking, excess alcohol consumption, physical inactivity, unbalanced diet, and overweight/obesity. A reduction by one unhealthy behavior measured with the Healthy Lifestyle Index Score (HLIS) will be considered favorable. Secondary outcomes include reduction of specific unhealthy lifestyles at 3 months, 9 months, and 12 months, and mental health outcomes such as depression measured with PHQ-9, social outcomes such as social support measured with the modified Multidimensional Scale of Perceived Social Support, clinical outcomes such as obesity, and biomedical outcomes such as the development of gut microbiota. Data will be analyzed with mixed effects generalized linear models with family and link function determined by outcome distribution and accounting for clustering of participants in communities. Discussion This study will provide evidence concerning the effect of a mHealth intervention that incorporates a behavioral change theoretical framework on cultivating and maintaining healthy lifestyles in community residents. The study will provide insights into research on and application of similar mHealth intervention strategies to promote healthy lifestyles in community populations and settings. Trial registration number ChiCTR2300070575. Date of registration: April 17, 2023. https://www.chictr.org.cn/index.aspx .

Background Evidence of the optimal blood pressure (BP) target for older adults with disability in long-term care is limited. We aim to analyze the associations of BP with mortality in older adults in long-term care setting with different levels of disability. Methods This prospective cohort study was based on the government-led long-term care programme in Chengdu, China, including 41,004 consecutive disabled adults aged ≥ 60 years. BP was measured during the baseline survey by trained medical personnel using electronic sphygmomanometers. Disability profile was assessed using the Barthel index. The association between blood pressure and mortality was analyzed with doubly robust estimation, which combined exposure model by inverse probability weighting and outcome model fitted with Cox regression. The non-linearity was examined by restricted cubic spline. The primary endpoint was all-cause mortality, and the secondary endpoints were cardiovascular and non-cardiovascular mortality. Results The associations between systolic blood pressure (SBP) and all-cause mortality were close to a U-shaped curve in mild-moderate disability group (Barthel index ≥ 40), and a reversed J-shaped in severe disability group (Barthel index < 40). In mild-moderate disability group, SBP < 135 mmHg was associated with elevated all-cause mortality risks (HR 1.21, 95% CI, 1.10–1.33), compared to SBP between 135 and 150 mmHg. In severe disability group, SBP < 150 mmHg increased all-cause mortality risks (HR 1.21, 95% CI, 1.16–1.27), compared to SBP between 150 and 170 mmHg. The associations were robust in subgroup analyses in terms of age, gender, cardiovascular comorbidity and antihypertensive treatment. Diastolic blood pressure (DBP) < 67 mmHg (HR 1.29, 95% CI, 1.18–1.42) in mild-moderate disability group and < 79 mmHg (HR 1.15, 95% CI, 1.11–1.20) in severe disability group both demonstrated an increased all-cause mortality risk. Conclusion The optimal SBP range was found to be higher in older individuals in long-term care with severe disability (150-170mmHg) compared to those with mild to moderate disability (135-150mmHg). This study provides new evidence that antihypertensive treatment should be administered cautiously in severe disability group in long-term care setting. Additionally, assessment of disability using the Barthel index can serve as a valuable tool in customizing the optimal BP management strategy. Trial registration Chinese Clinical Trial Registry (Registration Number: ChiCTR2100049973).

Recently, using deep learning(DL) in super-resolution(SR) has ac- hieved great success. These methods combine the convolutional neural network(CNN) to learn a general matrix function for an end-to-end mapping. However, as the width and depth of the network increase, there are two essential problems in the SR tasks. On the one hand, a wider and deeper network will bring better performance but increase the calculating complexity and memory consumption. On the other hand, the expanded architecture will miss the intermediate feature details in the information transmitting process. Hence, a SISR(Single Image Super-Resolution) network with wider feature information blocks(WFIB) is proposed to address these issues by making a balance between the network complexity and performance. Cascade residual block(CRB) helps the network make full use of contextual feature information. Extensive experiments verify that our network achieves better performance and with fewer parameters than the state-of-the-art methods.

Gut microbiota affects host health and disease. Large-scale cohorts have explored the interactions between the microbiota, host, and environment to reveal the disease-associated microbiota variation. A population-level gut metagenomic cohort is still rare in China. Here, we performed metagenomic sequencing on fecal samples from the CMEC Microbiome Project in Southwest China. In this study, we identified host socioeconomics, diet, lifestyle, and medical measurements that were significantly associated with microbiome function and composition. We revealed extensive novel associations between the host microbiome and common metabolic disorders. Our results provide new insight into associations of gut microbiota with metabolic disorders so as to support the translation of gut microbiome findings into potential clinical practice.

Whether the role of gut microbial features lies in the pathways from lifestyles to dyslipidemia remains unclear. In this cross-sectional study, we conducted a metagenome-wide association analysis and fecal metabolomic profiling in 994 adults from the China Multi-Ethnic cohort. A total of 26 microbial species were identified as mediators between lifestyle factors and risk for dyslipidemia. Specifically, the abundance of [Ruminococcus] gnavus mediated the associations between lifestyles and risks for dyslipidemia, elevated low-density lipoprotein cholesterol, elevated total cholesterol, and elevated triglycerides. [Ruminococcus] gnavus, Alistipes shahii, and Lachnospira eligens were replicated to be associated with dyslipidemia in an external validation cohort. The potential metabolic pathways included arachidonic acid, bile acid, and aromatic amino-acid metabolism.

Recently, numerous methods based on convolutional neural networks (CNNs) have been proposed to attain satisfactory performance in single image super-resolution (SISR). Meanwhile, diverse lightweight CNN-based networks have been proposed to achieve applicability in real-time scenarios. However, the receptive fields in lightweight networks are limited because they do not make good use of multi-scale information. In this paper, we propose a lightweight multi-scale feature integration network (MFIN) to address the above issue. Specifically, to expand the receptive fields for global features, MFIN is constructed by cascading the multi-scale feature integration blocks (MFIBs) in a serial manner. Each MFIB contains a multi-scale feature extraction module (MFEM) and a feature integration unit (FIU). To enlarge the receptive fields at a granular level, the features in MFEM are cascaded in a parallel manner. To capture the full-image dependencies, FIU incorporates the dense and pixel-wise correlations from the outputs of MFEM efficiently. The conducted experiments demonstrate that our method outperforms state-of-the-art methods in quantitative and qualitative evaluation. Notably, the experimental results on running time state that our method can achieve real-time performance.