Iron Metabolic Biomarkers and the Mortality Risk in the General Population: A Nationwide Population-Based Cohort Study.

Context: Iron is an essential element in the human body and plays a critical role in many physiological and cellular processes. However, the association between iron status and the risk of all-cause or cause-specific mortality has not been well-investigated. And it is unclear whether the association between iron metabolic biomarkers and the risk of mortality differs between people with and without diabetes mellitus (DM). Objective: This work aimed to investigate associations between iron metabolic biomarkers and all-cause and cause-specific mortality risk in the general population, and heterogeneities in the associations among population with and without DM.. Methods: A total of 29 166 adults from the National Health and Nutrition Examination Survey (NHANES) III and NHANES 1999 to 2010 were included, with linkage to the National Death Index to December 31, 2019. Cox proportional-hazard models and Fine-Gray subdistribution hazard models were used to estimate associations between iron metabolic biomarkers and outcomes. Results: During a median follow-up of 18.83 years, 9378 deaths were observed, including 3420 cardiovascular disease (CVD) deaths and 1969 cancer deaths. A significant linear association between serum ferritin (SF) and all-cause mortality was observed among the overall population and those without DM. J-shaped associations between transferrin saturation (TSAT) and all-cause and CVD mortality were observed among all populations. In the overall population, compared to the first quartile (Q1) group, the adjusted hazard ratio (HR) (95% CI) for all-cause mortality was 1.07 (1.00-1.15), 1.05 (0.98-1.12), 1.13 (1.05-1.21) in Q2, Q3, and Q4 groups for SF, while the HR was 0.94 (0.88-0.99), 0.92 (0.86-0.97), and 0.93 (0.88-0.99) for TSAT. In individuals without DM, the adjusted HR of the Q4 of SF were 1.19 (1.03-1.37) for CVD mortality and 1.25 (1.05-1.48) for cancer mortality. In individuals with DM, the adjusted HRs of the Q4 of TSAT were 0.76 (0.62-0.93) for CVD mortality and 1.47 (1.07-2.03) for cancer mortality. Conclusion: Iron metabolism abnormalities increase mortality risk in the general population. The associations of iron status with mortality were significantly different between individuals with and without DM, which indicated tailored strategies for iron homeostasis are needed.

Healthy lifestyle, statin, and mortality in people with high CVD risk: A nationwide population-based cohort study.

Objective: To examine the joint association of healthy lifestyles and statin use with all-cause and cardiovascular mortality in high-risk individuals, and evaluate the survival benefits by life expectancy. Methods: During 2015-2021, participants aged 35-75 years were recruited by the China Health Evaluation And risk Reduction through nationwide Teamwork. Based on number of healthy lifestyles related to smoking, alcohol drinking, physical activity, and diet, we categorized them into: very healthy (3-4), healthy (2), and unhealthy (0-1). Statin use was determined by self-report taking statin in last two weeks. Results: Among the 265,209 included participants at high risk, 6979 deaths were observed, including 3236 CVD deaths during a median 3.6 years of follow-up. Individuals taking statin and with a very healthy lifestyle had the lowest risk of all-cause (HR: 0.70; 95 %CI: 0.57-0.87) and cardiovascular mortality (0.56; 0.40-0.79), compared with statin non-users with an unhealthy lifestyle. High-risk participants taking statin and with a very healthy lifestyle had the highest years of life gained (5.90 years at 35-year-old [4.14-7.67; P < 0.001]) compared with statin non-users with an unhealthy lifestyle among high-risk people. And their life expectancy was comparable with those without high risk but with a very healthy lifestyle (4.49 vs. 4.68 years). Conclusion: The combination of preventive medication and multiple healthy lifestyles was associated with lower risk of all-cause and cardiovascular mortality and largest survival benefits. Integrated strategy to improve long-term health for high-risk people was urgently needed.

Sociodemographic Factors, Leisure-Time Physical Activity and Mortality.

INTRODUCTION: Exploring sociodemographic effect modification is important to provide evidence for developing targeted recommendations and reducing health inequalities. This study evaluated how sociodemographic factors including age, sex, race/ethnicity and socioeconomic status (SES) modify the association between leisure-time physical activity (LTPA) and all-cause and major cause-specific mortality. METHODS: The study sample included 471,992 people from the 1997-2018 National Health Interview Survey (NHIS) and 41,830 people from the 1999-2018 National Health and Nutrition Examination Survey (NHANES). Data were analyzed in December 2022. Mortality data from the National Death Index were available to December 31, 2019. Sufficient LTPA was defined as at least 150 minutes of moderate and/or vigorous intensity per week. RESULTS: There were 46,289 deaths in NHIS participants and 4,617 deaths in NHANES participants during a mean follow-up of 10 years. Individuals with sufficient LTPA had lower risk of all-cause (NHIS: hazard ratio, 0.74, 95% CI: [0.74-0.74]; NHANES: 0.73 [0.68-0.79]) and cardiovascular mortality (NHIS: 0.75 [0.75-0.75]; NHANES: 0.80 [0.69-0.93]) compared with inactive participants. The subgroup analysis showed significant interactions between LTPA and all sociodemographic factors. Associations between LTPA and mortality were weaker among younger individuals, males, Hispanic adults or those of low SES, respectively. CONCLUSIONS: Sociodemographic factors significantly modified the associations between LTPA and mortality. The health benefits of sufficient LTPA were smaller in younger individuals, males, Hispanic adults or those of low SES. These findings can help identify target populations for promotion of physical activity to reduce health inequalities and the development of physical activity guidelines.

Neural Network Aided Homogenization Approach for Predicting Effective Thermal Conductivity of Composite Construction Materials.

Thermal conductivity is a fundamental material parameter involved in various infrastructure design guides around the world. This paper developed an innovative neural network (NN) aided homogenization approach for predicting the effective thermal conductivity of various composite construction materials. The 2-D meso-structures of dense graded asphalt mixture, porous asphalt mixture, and cement concrete were generated and divided into 2n × 2n square elements with specific thermal conductivity values. A two-layer feed-forward neural network with sigmoid hidden neurons and linear output neurons was built to predict the effective thermal conductivity of the 2 × 2 block. The Levenberg-Marquardt backpropagation algorithm was used to train the network. By repeatedly using the neural network, the effective thermal conductivities of 2-D meso-structures were calculated. The accuracy of the above NN aided homogenization approach was validated with experiment, and various factors affecting the effective thermal conductivity were analyzed. The analysis results show that the accuracy of the NN aided approach is acceptable with relative errors of 1.92~4.34% for the dense graded asphalt mixture, 1.10~6.85% for the porous asphalt mixture, and 1.13~3.14% for the cement concrete. The relative errors for all the materials are lower than 5% when the heterogeneous structures are divided into 512 × 512 elements. Ignoring the actual material meso-structures may lead to significant errors (134.01%) in predicting the effective thermal conductivity of materials with high heterogeneity such as porous asphalt mixture. While proper simplification is acceptable for dense construction composite materials. The effective thermal conductivity of composite cement-asphalt mixtures increases with higher saturation of grouted material. However, the improvement effect of the high-conductive cement paste on the composite cement-asphalt mixtures could be significantly reduced when the cement paste concentrates at the bottom of the mixture. Cracked aggregates and segregation of material components tend to decrease the effective thermal conductivity of construction materials. The NN aided homogenization approach presented in this paper is useful for selecting the effective thermal conductivity of construction materials.

Original Hosts, Clinical Features, Transmission Routes, and Vaccine Development for Coronavirus Disease (COVID-19).

The pandemic of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to public concern worldwide. Although a variety of hypotheses about the hosts of SARS-CoV-2 have been proposed, an exact conclusion has not yet been reached. Initial clinical manifestations associated with COVID-19 are similar to those of other acute respiratory infections, leading to misdiagnoses and resulting in the outbreak at the early stage. SARS-CoV-2 is predominantly spread by droplet transmission and close contact; the possibilities of fecal-oral, vertical, and aerosol transmission have not yet been fully confirmed or rejected. Besides, COVID-19 cases have been reported within communities, households, and nosocomial settings through contact with confirmed COVID-19 patients or asymptomatic individuals. Environmental contamination is also a major driver for the COVID-19 pandemic. Considering the absence of specific treatment for COVID-19, it is urgent to decrease the risk of transmission and take preventive measures to control the spread of the virus. In this review, we summarize the latest available data on the potential hosts, entry receptors, clinical features, and risk factors of COVID-19 and transmission routes of SARS-CoV-2, and we present the data about development of vaccines.

Recent progress in understanding 2019 novel coronavirus (SARS-CoV-2) associated with human respiratory disease: detection, mechanisms and treatment.

Viral respiratory diseases such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) always pose a severe threat to people. First identified in late December 2019, a novel coronavirus (2019-nCoV; SARS-CoV-2) has affected many provinces in China and multiple countries worldwide. The viral outbreak has aroused panic and a public-health emergency around the world, and the number of infections continues to rise. However, the causes and consequences of the pneumonia remain unknown. To effectively implement epidemic prevention, early identification and diagnosis are critical to disease control. Here we scrutinise a series of available studies by global scientists on the clinical manifestations, detection methods and treatment options for the disease caused by SARS-CoV-2, named coronavirus disease 2019 (COVID-19), and also propose potential strategies for preventing the infection.

Interfacing DNA with nanoparticles: Surface science and its applications in biosensing.

The knowledge on the mechanisms of DNA interfacing with nanoparticles holds great potential for the design, assembly and usage of DNA in biological applications. A wave of understanding and exploitation of the mechanisms in DNA-nanoparticles interfacial phenomenon has raised. Although some previous reviews have been reported, systematic and detailed reviews are rare. To achieve a better understanding of the mechanisms in the interaction between DNA and nanoparticles, here, we summarized the recent progresses on the fundamental principles regarding the DNA-nanoparticle interactions and their applications in biosensing. Special focus was put on inorganic nanoparticles such as metal nanoparticles, carbon-based materials, metal oxides and quantum dots. For each material, the surface properties, the interfacing mechanisms, and the kinetics and spatial control of DNA adsorption were summarized and discussed. We also highlighted some of the recent technologies based on DNA-NPs interactions for biomolecules detection. Finally, the challenges and future directions were discussed and proposed. This review provides a systematic understanding of the mechanisms in the interaction of DNA-nanoparticles, which, in turn, can inspire new insights for designing biosensors with improved properties.