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Obesity is closely associated with various chronic diseases.Therefore, accurate, reliable and cost-effective methods for preventing its occurrence and progression are required. In this study, we developed a visualized obesity risk prediction system based on machine learning techniques, aiming to achieve personalized comprehensive health management for obesity. The system utilized a dataset consisting of 1678 anonymized health examination records, including individual lifestyle factors, body composition, blood routine, and biochemical tests. Ten multi-classification machine learning models, including Random Forest and XGBoost, were constructed to identify non-obese individuals (BMI < 25), class 1 obese individuals (25 ≤ BMI < 30), and class 2 obese individuals (30 ≤ BMI). By evaluating the performance of each model on the test set, we selected XGBoost as the best model and built the visualized obesity risk prediction system based on it. The system exhibited good predictive performance and interpretability, directly providing users with their obesity risk levels and determining corresponding intervention priorities. In conclusion, the developed obesity risk prediction system possesses high accuracy and interactivity, aiding physicians in formulating personalized health management plans and achieving comprehensive and accurate obesity management.

As an important risk factor for many cardiovascular diseases, hypertension requires convenient and reliable methods for prevention and intervention. This study designed a visualization risk prediction system based on Machine Learning and SHAP as an auxiliary tool for personalized health management of hypertension. We used ten Machine Learning algorithms such as random forests and 1617 anonymized health check data to build ten hypertension risk prediction models. The model performance was evaluated through indicators such as accuracy, F1-score, and ROC curve. We used the best-performing model combined with the SHAP algorithm for feature importance analysis and built a visualization risk prediction system on the web page. The LightGMB model exhibited the best predictive performance, and age, alkaline phosphatase, and triglycerides were important features for predicting the risk of hypertension. Users can obtain their risk probability of hypertension and determine the focus of intervention through the visualization system built on the web page. Our research helps doctors and patients to develop personalized prevention and intervention programs for hypertension based on health check data, which has significant clinical and public health significance.

Body heat, a clean and ubiquitous energy source, is promising as a renewable resource to supply wearable electronics. Emerging tough thermogalvanic device could be a sustainable platform to convert body heat energy into electricity for powering wearable electronics if its Carnot-relative efficiency ( η r ) reaches ~5%. However, maximizing both the η r and mechanical strength of the device are mutually exclusive. Here, we develop a rational strategy to construct a flexible thermogalvanic armor (FTGA) with a η r over 8% near room temperature, yet preserving mechanical robustness. The key to our design lies in simultaneously realizing the thermosensitive-crystallization and salting-out effect in the elaborately designed ion-transport highway to boost η r and improve mechanical strength. The FTGA achieves an ultrahigh η r of 8.53%, coupling with impressive mechanical toughness of 70.65 MJ m −3 and substantial elongation (~900%) together. Our strategy holds sustainable potential for harvesting body heat and powering wearable electronics without recharging. Flexible thermogalvanic device provides a sustainable platform for body heat harvesting, but its performance is limited by low energy conversion efficiency and poor mechanical strength. The authors report a flexible thermogalvanic armor with a Carnot-relative efficiency of 8.53% and strong mechanical toughness.

Neurological gliomas, as the most common and deadly primary brain tumors, face two major therapeutic obstacles: the blockade of the blood-brain barrier (BBB) and high tumor heterogeneity. In recent years, research on novel drug delivery systems has brought hope for glioma treatment. This article elaborates on the research progress of novel drug delivery systems in glioma treatment, including various nanocarriers, targeted delivery strategies, and gene therapy drug delivery systems. It analyzes their advantages and challenges, outlooks future development directions, and aims to provide a reference for optimizing drug delivery systems for glioma therapy.

The sense of place is a multidimensional construct that evokes an emotional commitment to a specific geographic setting. It can be a crucial aspect of cultural ecosystem services. While social media has gained popularity as a tool for assessing ecosystem services, its effectiveness in capturing a sense of place, its impact on cultural ecosystem services, and its role in the landscape design process remains less certain. This study investigates the role of urban parks in shaping the sense of place by analyzing user-generated content from a specific social media platform (Twitter). We gathered tweets from 30 diverse urban parks in Chicago, covering various park types, sizes, shapes, and management styles. Our analysis reveals multiple facets of the sense of place associated with urban parks. We suggest that a sense of place is not solely rooted in the attachment to physical surroundings but also in the personal experiences individuals encounter within these spaces. Residents residing near parks tend to develop a sense of ownership and responsibility, leading to stronger emotional bonds with their environment. Urban parks foster community engagement, enhance social cohesion, and offer opportunities for nature-based experiences. Furthermore, this study underscores the significance of diverse park features, accessibility, and size in bolstering place attachment. Our research demonstrates the potential for geoinformation analysis in the geodesign process as a cost-effective and scalable approach for understanding the person–place connection.

Solar-driven interfacial desalination is an emerging approach to address global freshwater crisis while minimizing carbon emissions. A key challenge in interfacial desalination technology is maintaining long-term high efficiency with fouling-resistance and energy-saving. Here, we develop a 3D-printed concave-shaped solar evaporator and a floating freshwater collection setup, that achieve nearly 100% photothermal evaporation efficiency with a rate of 2.23 kg m − 2 h − 1 and freshwater collection rate of 1.23 kg m − 2 h − 1 under one sun illumination. This 3D concave-shaped solar evaporator design, achieved through 3D printing and double-sided surface modification, allows interfacial desalination process to occur at the bottom surface of the evaporator with superior heat transfer, ultra-effective salt-resistance and enlarged water-air interfacial area. The evaporation stability, extending well beyond traditional limitations of days or months, is realized by a decoupling design and the low-cost renewal of water-intake layer. This design allows vapor to escape downward without causing fouling problem within the top solar absorber. Furthermore, a self-floating freshwater collection setup facilitates thermal exchange with low-temperature seawater for sustainable application. Our large-scale integrated 3D printed evaporator-collector strategy demonstrates potential for portable solar-driven interfacial desalination and freshwater collection. This solar-driven interfacial desalination research integrates 3D printed modules modifications, decoupling design, anticorrosion treatment for high desalination efficiency and long-term stability. This self-floating setup utilizes natural cooling seawater to conduct thermal convection for downward freshwater collection.

The difference in the survival of obese patients and normal-weight/lean patients with diabetic MAFLD remains unclear. Therefore, we aimed to describe the long-term survival of individuals with diabetic MAFLD and overweight/obesity (OT2M), diabetic MAFLD with lean/normal weight (LT2M), MAFLD with overweight/obesity and without T2DM (OM), and MAFLD with lean/normal weight and without T2DM (LM). Using the NHANESIII database, participants with MAFLD were divided into four groups. Hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause, cardiovascular disease (CVD)-related, and cancer-related mortalities for different MAFLD subtypes were evaluated using Cox proportional hazards models. Of the 3539 participants, 1618 participants (42.61%) died during a mean follow-up period of 274.41 ± 2.35 months. LT2M and OT2M had higher risks of all-cause mortality (adjusted HR, 2.14; 95% CI 1.82–2.51; p  < 0.0001; adjusted HR, 2.24; 95% CI 1.32–3.81; p  = 0.003) and CVD-related mortality (adjusted HR, 3.25; 95% CI 1.72–6.14; p  < 0.0001; adjusted HR, 3.36; 95% CI 2.52–4.47; p  < 0.0001) than did OM. All-cause and CVD mortality rates in LT2M and OT2M patients were higher than those in OM patients. Patients with concurrent T2DM and MAFLD should be screened, regardless of the presence of obesity.

In 2023, experts from the European and American regions proposed the concepts of steatotic liver disease (SLD) and metabolic dysfunction-associated steatotic liver disease (MASLD). MASLD was proposed as a replacement for nonalcoholic fatty liver disease (NAFLD). We compared the long-term outcomes of patients with MASLD, NAFLD, and various subtypes of SLD. We conducted a retrospective study using the NHANESIII database. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality and cause-specific mortality among patients with subtypes of SLD, MASLD, and NAFLD. During a follow-up period of 31 years (median 25 years), the adjusted risks of all-cause death for patients with MASLD was 1.19 (95% CI 1.06-1.34; P = 0.006) vs. the non-SLD group. There was a moderate level of consistency between MASLD and NAFLD (Cohen's kappa coefficient of 0.62545). Advanced fibrosis was the most serious risk factor for all-cause mortality in MASLD, and high C-reactive protein concentration was the most serious risk factor for all-cause mortality in NAFLD, followed by type 2 diabetes. MASLD is associated with a higher risk of all-cause mortality, and this association is independent of patients' demographic or metabolic characteristics, despite a relatively small hazard ratio. Our research findings further support that MASLD is a pathological disease related to liver disease itself. Therefore, redefining NAFLD as MASLD may help improve our understanding of predictive factors that increase the risk of death.

Tranquility is typically associated with low noise levels and remote natural areas. Various methods for preserving potentially tranquil places have been proposed, although these typically involve setting aside places with low noise levels located in remote areas. To gain the benefits of tranquility in accessible urban areas, we need to identify the characteristics of tranquil spaces. This study focuses on the landscape-based, visual aspects of the phenomena. We investigated the role of visual context using a nationwide dataset of crowdsourced photographs from Sweden. Text mining identified personal perception and accompanying photographs identified the physical features. The photographs were characterized by time period and landscape conditions using computer vision technology. We found that waterbodies consistently enhanced tranquil views, while grass, flowers, and other dense vegetation were generally not well connected. Trees were positively correlated during daylight hours but had a negative impact at night. Dynamic objects such as people and vehicles were negatively associated, potentially due to aural considerations. Their effect was less significant during hours when noise would generally be less of a factor. This study provides insights for future research and design practices aimed at promoting tranquil experiences in urban environments and demonstrates the potential for crowdsourced data to help understand the qualities of built environments as perceived by the public.

Passive daytime radiative cooling that could provide sub-ambient cooling emerges as a promising technology to reduce household energy consumption. Nonetheless, prevailing studies are predominantly focused on surface cooling, often overlooking its adaptability to enclosed spaces with active cooling technologies. Here we present a multilayer radiative cooling film ( -MRC) with optical properties in the mid-infrared region, consisting of the nanoporous polyethylene films, the polyethylene oxide film, and silver nanowires. The top side of the -MRC functions as a conventional radiative cooling material to supply sub-ambient surface cooling, while the bottom side with low mid-infrared emissivity transfers limited heat via thermal radiation to the low-temperature enclosures. Our experiments validate that the -MRC possesses an enhanced space cooling performance in comparison to the conventional radiative cooling film. This work provides a valuable design concept for radiative cooling materials, thereby expanding their practical scenarios and contributing to reduce the carbon emission.