Positive association of nap duration with risk of non-alcoholic fatty liver disease in an occupational population in Guangdong Province, China: a cross-sectional study.

BACKGROUND: A lack of sleep or disorder in sleep-wake cycles has been associated with metabolic impairments. However, few studies have investigated the association between daytime napping duration and the risk of non-alcoholic fatty liver disease. This study aimed to investigate the association of daytime napping duration with the risk of non-alcoholic fatty liver disease in a Chinese population. METHODS: This cross-sectional study analyzed data from the Health Management Center of Nanfang Hospital, Guangdong Province. A total of 3363 participants aged 20-79 years were recruited and admitted from January 20, 2018, to October 16, 2020. Non-alcoholic fatty liver disease was diagnosed using abdominal ultrasonography. The outcome was the association between daytime sleep duration and the risk of non-alcoholic fatty liver disease. RESULTS: Compared with non-nappers, long daytime nappers (≥ 60 min) were associated with a higher risk of non-alcoholic fatty liver disease in the crude model (odds ratio 2.138; 95% confidence interval 1.88-2.61, P < 0.05) and in the multivariable adjustment model (odds ratio 2.211; 95% confidence interval 1.042-4.690, P < 0.05) after adjusting for demographic, educational, and metabolic risk factors. The association was moderately enhanced with additional adjustments for night sleep duration and socioeconomic or other factors (odds ratio 2.253; 95% confidence interval 1.061-4.786, P = 0.035). CONCLUSION: In this cross-sectional study, daytime napping duration of ≥ 60 min was positively associated with the risk of non-alcoholic fatty liver disease in an occupational population of Guangdong Province after multivariable adjustment.

Breakfast consumption frequency is associated with dyslipidemia: a retrospective cohort study of a working population.

BACKGROUND: Dyslipidemia is a significant contributor to cardiovascular and cerebrovascular diseases. Research on the relationship between breakfast consumption frequency and dyslipidemia in the working population is lacking. Therefore, we aimed to investigate this relationship based on a retrospective cohort study of a large working population in China. METHODS: This retrospective cohort study used data from the physical examinations and questionnaire survey of working participants at Nanfang Hospital from January 20, 2015 to October 16, 2020. Univariate and multivariate analyses were conducted to explore the relationship between breakfast consumption frequency and dyslipidemia in this working population (n = 7644). RESULTS: The prevalence of dyslipidemia among the participants was 26.4%. The univariate logistic regression test showed that the breakfast consumption frequency was inversely correlated with dyslipidemia. After adjusting for multiple factors, such as sex, age, body mass index, hypertension, hyperuricaemia, diabetes, smoking status, alcohol consumption, education level, marital status, long-term exposure to kitchen oil fumes, attending business dinners, and sleep time, it was found that breakfast consumption remained inversely associated with dyslipidaemia. The odds ratio for daily breakfast consumption was 0.466 (95% confidence interval 0.283-0.770, P = 0.003). After adjusting for confounding factors, we found that the higher the frequency of breakfast consumption, the lower the odds ratios for hypertriglyceridaemia. CONCLUSIONS: This study demonstrated that breakfast consumption frequency was inversely correlated with dyslipidemia. The higher the frequency of breakfast, the lower the risk of hypertriglyceridaemia. This study provides a basis on which dietary suggestions for the working population and lifestyle guidance for patients with a clinical need to prevent dyslipidemia can be made.

Gestational diabetes and risk of perinatal depression in low- and middle-income countries: a meta-analysis.

Background: The relationship between gestational diabetes (GDM) and the risk of depression has been thoroughly investigated in high-income countries on their financial basis, while it is largely unexplored in low- and middle- income countries. This meta-analysis aims to assess how GDM influences the risk of perinatal depression by searching multiple electronic databases for studies measuring the odds ratios between them in low- and middle-income countries. Methods: Two independent reviewers searched multiple electronic databases for studies that investigated GDM and perinatal mental disorders on August 31, 2023. Pooled odds ratios (ORs) and confidence intervals (CIs) were calculated using the random effect model. Subgroup analyses were further conducted based on the type of study design and country income level. Results: In total, 16 observational studies met the inclusion criteria. Only the number of studies on depression (n=10) satisfied the conditions to conduct a meta-analysis, showing the relationship between mental illness and GDM has been overlooked in low- and middle-income countries. Evidence shows an elevated risk of perinatal depression in women with GDM (pooled OR 1.92; 95% CI 1.24, 2.97; 10 studies). The increased risk of perinatal depression in patients with GDM was not significantly different between cross-sectional and prospective design. Country income level is a significant factor that adversely influences the risk of perinatal depression in GDM patients. Conclusion: Our findings suggested that women with GDM are vulnerable to perinatal depressive symptoms, and a deeper understanding of potential risk factors and mechanisms may help inform strategies aimed at prevention of exposure to these complications during pregnancy.

Physician-Centered EHR Data Utilization: A Pilot Study.

The utilization of vast amounts of EHR data is crucial to the studies in medical informatics. Physicians are medical participants who directly record clinical data into EHR with their personal expertise, making their roles essential in follow-up data utilization, which current studies have yet to recognize. This paper proposes a physician-centered perspective for EHR data utilization and emphasizes the feasibility and potentiality of digging into physicians' latent decision patterns in EHR. To support our proposal, we design a physician-centered CDS approach named PhyC and test it on a real-world EHR dataset. Experiments show that PhyC performs significantly better in the auxiliary diagnosis of multiple diseases than globally learned models. Discussions on experimental results suggest that physician-centered data utilization can help to derive more objective CDS models, while more means for utilization need further exploration.

Rational design of soluble expressed human aldehyde dehydrogenase 2 with high stability and activity in pepsin and trypsin.

Acetaldehyde dehydrogenase 2 (ALDH2) is a crucial enzyme in alcohol metabolism, and oral administration of ALDH2 is a promising method for alcohol detoxification. However, recombinant ALDH2 is susceptible to hydrolysis by digestive enzymes in the gastrointestinal tract and is expressed as inactive inclusion bodies in E. coli. In this study, we performed three rounds of rational design to address these issues. Specifically, the surface digestive sites of pepsin and trypsin were replaced with other polar amino acids, while hydrophobic amino acids were incorporated to reshape the catalytic cavity of ALDH2. The resulting mutant DE2-852 exhibited a 45-fold increase in soluble expression levels, while its stability against trypsin and pepsin increased by eightfold and twofold, respectively. Its catalytic efficiency (kcat/Km) at pH 7.2 and 3.2 improved by more than four and five times, respectively, with increased Vmax and decreased Km values. The enhanced properties of DE2-852 were attributed to the D457Y mutation, which created a more compact protein structure and facilitated a faster collision between the substrate and catalytic residues. These results laid the foundation for the oral administration and mass preparation of highly active ALDH2 and offered insights into the oral application of other proteins.

Multimodal Machine Learning-Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia.

Hyperuricemia (HUA) has emerged as the second most prevalent metabolic disorder characterized by prolonged and asymptomatic period, triggering gout and metabolism-related outcomes. Early detection and prognosis prediction for HUA and gout are crucial for pre-emptive interventions. Integrating genetic and clinical data from 421287 UK Biobank and 8900 Nanfang Hospital participants, a stacked multimodal machine learning model is developed and validated to synthesize its probabilities as an in-silico quantitative marker for hyperuricemia (ISHUA). The model demonstrates satisfactory performance in detecting HUA, exhibiting area under the curves (AUCs) of 0.859, 0.836, and 0.779 within the train, internal, and external test sets, respectively. ISHUA is significantly associated with gout and metabolism-related outcomes, effectively classifying individuals into low- and high-risk groups for gout in the train (AUC, 0.815) and internal test (AUC, 0.814) sets. The high-risk group shows increased susceptibility to metabolism-related outcomes, and participants with intermediate or favorable lifestyle profiles have hazard ratios of 0.75 and 0.53 for gout compared with those with unfavorable lifestyles. Similar trends are observed for other metabolism-related outcomes. The multimodal machine learning-based ISHUA marker enables personalized risk stratification for gout and metabolism-related outcomes, and it is unveiled that lifestyle changes can ameliorate these outcomes within high-risk group, providing guidance for preventive interventions.

A Highly-Lithiophilic Mn3O4/ZnO-Modified Carbon Nanotube Film for Dendrite-Free Lithium Metal Anodes.

Lithium metal anode is deemed as a potential candidate for high energy density batteries, which has attracted increasing attention. Unfortunately, Li metal anode suffers from issues such as dendrite grown and volume expansion during cycling, which hinders its commercialization. Herein, we designed a porous and flexible self-supporting film comprising of single-walled carbon nanotube (SWCNT) modified with a highly-lithiophilic heterostructure (Mn3O4/ZnO@SWCNT) as the host material for Li metal anodes. The p-n-type heterojunction constructed by Mn3O4 and ZnO generates a built-in electric field that facilitates electron transfer and Li+ migration. Additionally, the lithiophilic Mn3O4/ZnO particles serve as the pre-implanted nucleation sites, dramatically reducing the lithium nucleation barrier due to their strong binding energy with lithium atoms. Moreover, the interwoven SWCNT conductive network effectively lowers the local current density and alleviates the tremendous volume expansion during cycling. Thanks to the aforementioned synergy, the symmetric cell composed of Mn3O4/ZnO@SWCNT-Li can stably maintain a low potential for more than 2500 h at 1 mA cm-2 and 1 mAh cm-2. Furthermore, the Li-S full battery composed of Mn3O4/ZnO@SWCNT-Li also shows excellent cycle stability. These results demonstrate that Mn3O4/ZnO@SWCNT has great potential as a dendrite-free Li metal host material.

A method for the early prediction of chronic diseases based on short sequential medical data.

Noncommunicable diseases (NCDs) have become the leading cause of death worldwide. NCDs' chronicity, hiddenness, and irreversibility make patients' disease self-awareness extremely important in disease control but hard to achieve. With an accumulation of electronic health record (EHR) data, it has become possible to predict NCDs early through machine learning approaches. However, EHR data from latent NCD patients are often irregularly sampled temporally, and the data sequences are short and imbalanced, which prevents researchers from fully and effectively using such data. Here, we outline the characteristics of typical short sequential data for NCD early prediction and emphasize the importance of using such data in machine learning schemes. We then propose a novel NCD early prediction method: the short sequential medical data-based early prediction method (SSEPM). The SSEPM network contains two stacked subnetworks for multilabel enhancement. In each subnetwork, long short-term memory (LSTM) and attention layers are implemented to extract both temporal and nontemporal embedded features. During training, with prior clinical knowledge of the NCD characteristics, a random connection (RC) process is proposed for data augmentation. Comparative experiments involving ten-fold cross-validation are performed with real-world medical data to predict 5 NCDs. The result shows that the SSEPM outperforms the state-of-the-art NCD early prediction algorithms and works well in dealing with short sequential data. The results also suggest that the direct use of short sequential data could be more effective than formatting datasets with temporal exclusion limitations.

Tea Consumption is Associated with an Increased Risk of Hyperuricemia in an Occupational Population in Guangdong, China.

Purpose: Chronic hyperuricemia leads to long-term deposition of monosodium urate crystals that may damage the joint structure and affect quality of life. Although hyperuricemia prevalence varies, most studies indicate increased cases of hyperuricemia worldwide. The relationship between hyperuricemia and tea consumption is uncertain. This cross-sectional study investigated the effect of tea consumption on the risk of hyperuricemia in the working population in Guangdong, China. Patients and Methods: Data on weight, height, blood pressure, laboratory test results, and health questionnaire responses of 7644 adults aged ≥18 years were obtained from the health examinee dataset of Nanfang Hospital. The characteristics of subjects with and without hyperuricemia were compared using t-tests or non-parametric Mann-Whitney U-tests for continuous variables and chi-square tests for categorical variables. Relationships between hyperuricemia and participant characteristics (sex, age, education level, smoking history, alcohol consumption, hypertension, body mass index, tea consumption, and other dietary factors) were examined using univariate and multivariate logistic regression models to identify independent risk factors for hyperuricemia. Results: Tea consumption was associated with a higher risk of hyperuricemia in the crude model (odds ratio [OR] 1.74, 95% confidence interval [CI] 1.48-2.05, once a month through twice a week vs never, P<0.001; OR 2.44, 95% CI 2.07-2.89, ≥3 times a week vs never, P<0.001). The adjusted OR for hyperuricemia was 1.30 (95% CI 1.08-1.56, P=0.006) in participants who consumed tea once a month through twice a week and 1.35 (95% CI 1.11-1.64, P=0.003) in those who consumed tea ≥3 times a week compared with the "never" reference group after adjusting for sociodemographic factors, anthropometric and biochemical indices, and dietary factors. This relationship remained significant in men but not women in subgroup analysis. Conclusion: Tea consumption is an independent risk factor for hyperuricemia and is more pronounced in men than women.

An Augmented Model with Inferred Blood Features for the Self-diagnosis of Metabolic Syndrome.

BACKGROUND AND OBJECTIVES: The penetration rate of physical examinations in China is substantially lower than that in developed countries. Therefore, an auxiliary approach that does not depend on hospital health checks for the diagnosis of metabolic syndrome (MetS) is needed. METHODS: In this study, we proposed an augmented method with inferred blood features that uses self-care inputs available at home for the auxiliary diagnosis of MetS. The dataset used for modeling contained data on 91,420 individuals who had at least 2 consecutive years of health checks. We trained three separate models using a regularized gradient-boosted decision tree. The first model used only home-based features; additional blood test data (including triglyceride [TG] data, fasting blood glucose data, and high-density lipoprotein cholesterol [HDL-C] data) were included in the second model. However, in the augmented approach, the blood test data were manipulated using multivariate imputation by chained equations prior to inclusion in the third model. The performance of the three models for MetS auxiliary diagnosis was then quantitatively compared. RESULTS: The results showed that the third model exhibited the highest classification accuracy for MetS in comparison with the other two models (area under the curve [AUC]: 3rd vs. 2nd vs. 1st = 0.971 vs. 0.950 vs. 0.905, p < 0.001). We further revealed that with full sets of the three measurements from earlier blood test data, the classification accuracy of MetS can be further improved (AUC: without vs. with = 0.971 vs. 0.993). However, the magnitude of improvement was not statistically significant at the 1% level of significance (p = 0.014). CONCLUSION: Our findings demonstrate the feasibility of the third model for MetS homecare applications and lend novel insights into innovative research on the health management of MetS. Further validation and implementation of our proposed model might improve quality of life and ultimately benefit the general population.

A 135-190 GHz Broadband Self-Biased Frequency Doubler using Four-Anode Schottky Diodes.

This paper describes the design and demonstration of a 135-190 GHz self-biased broadband frequency doubler based on planar Schottky diodes. Unlike traditional bias schemes, the diodes are biased in resistive mode by a self-bias resistor; thus, no additional bias voltage is needed for the doubler. The Schottky diodes in this verification are micron-scaled devices with an anode area of 6.6 µm2 and an epitaxial layer thickness of 0.26 µm. For accurate design of the doubler, the 3D-EM model of the Schottky diode is built up to extract the parasitic parameters induced by the diode package when frequency rises up to the terahertz band. In order to implement broadband working, input waveguide steps, output suspended microstrip steps, and output probe with bias filter are all used as matching elements for impedance matching. Measured results show that the doubler exhibits a 3 dB bandwidth of 34% from 135 GHz to 190 GHz, with a conversion efficiency of above 4% when supplied with 100 mW of input power. A 17.8 mW peak output power with a 10.2% efficiency was measured at 166 GHz when the input power was 174 mW. The measured results agree well with the simulated results, which indicates that the self-bias scheme for Schottky diode-based frequency multipliers is feasible and effective.

Development of a Service-Oriented Sharable Clinical Decision Support System Based on Ontology for Chronic Disease.

Clinical decision support systems (CDSSs) have been proved as an efficient way to improve health care quality. However, the inflexibility in integrating multiple clinical practice guidelines (multi-CPGs), the mass input workload of patient data, and the difficulty in system sharing become barriers of CDSSs implementation. In this paper, we proposed a framework of CDSS for chronic disease based on ontology and service-oriented architecture (SOA) to improve these defects. We used ontology for knowledge base construction on multi-CPGs integration to overcome their differences as well as reduce the input procedure of patient data by ontology reasoning. Furthermore, we built the CDSS on an SOA structure to provide flexibility in system and data sharing, such that patients could get suggestions from the same system for self-management of chronic disease. A typical case was used to validate the CDSS functions and accuracy. Two clients were developed to illustrate the SOA superiority.

CD-KES: An Ontology Based Knowledge Education System for Patients with Chronic Diseases and Its Constructing Approach.

Patients' participation plays a crucial role in the management of chronic diseases. Educating patients about their diseases allows patients to self-regulate their daily health conditions more reasonably and effectively. This study focuses on an informative way to develop daily education and guidance among patients. We provide a systematic approach to establish, process, and present the knowledge map of chronic diseases. An ontology technique is used to model clinical knowledge and rules. Rule-based inference service is constructed based on the RETE reasoning algorithm. Several considerations in semantic visualization and interaction are listed as recommendations. A prototype of Chronic Disease Knowledge Education System (CD-KES) based on this approach has been built for diabetes mellitus. With the prototype, comparative evaluations for system performances in knowledge querying and browsing are taken. The results shows the system can help patients to more easily understand medical knowledge and avoid potential negative consequences.