Prevalence and Clustering of Cardiovascular Disease Risk Factors among Adults Along the Lancang-Mekong River: A Cross-Sectional Study from Low- and Middle-Income Countries.

Background: Progress in cardiovascular health is increasingly concentrated in high-income countries, while the burden of cardiovascular disease (CVD) is high in low- and middle-income countries, a clear health inequity that must be urgently addressed. Objective: This study aims to evaluate the prevalence and clustering of CVD risk factors in the three Lancang-Mekong regions. Methods: We conducted a population-based cross-sectional survey from January 2021 to March 2023 in China, Laos, and Cambodia. We compared the prevalence and clustering of CVD risk factors-including hypertension, dyslipidemia, diabetes mellitus, overweight/obesity, current smoking status, current drinking status, inadequate vegetable and fruit intake, and insufficient physical activity-across the three regions, further stratifying the data by gender and age. Multivariate logistic regression models were performed to explore factors influencing the aggregation of CVD risk factors (≥2, ≥3, ≥4). Results: A total of 11,005 adults were included in the study. Hypertension emerged as the primary metabolic risk factor in Laos (36.8%) and Cambodia (23.5%), whereas overweight/obesity was the primary risk factor in China (37.6%). In terms of behavioral risk factors, participants in all three regions showed insufficient vegetable and fruit intake. The prevalence of individuals without CVD risk factors was 10% in China, 1.9% in Laos, and 5.2% in Cambodia. Meanwhile, the prevalence of two or more risk factors was 64.6% in China, 79.2% in Laos, and 76.0% in Cambodia. Multivariate logistic regression models revealed that the propensity for CVD risk factors clustering was higher in men and increased with age in all three countries. Conclusions: CVD risk factors and multiple clustering are pressing health threats among adults in low- and middle-income areas along the Lancang-Mekong River Basin. This study highlights the urgent need for proactive tailored strategies to control CVD risk factors.

Inference of Gene Regulatory Networks Based on Multi-view Hierarchical Hypergraphs.

Since gene regulation is a complex process in which multiple genes act simultaneously, accurately inferring gene regulatory networks (GRNs) is a long-standing challenge in systems biology. Although graph neural networks can formally describe intricate gene expression mechanisms, current GRN inference methods based on graph learning regard only transcription factor (TF)-target gene interactions as pairwise relationships, and cannot model the many-to-many high-order regulatory patterns that prevail among genes. Moreover, these methods often rely on limited prior regulatory knowledge, ignoring the structural information of GRNs in gene expression profiles. Therefore, we propose a multi-view hierarchical hypergraphs GRN (MHHGRN) inference model. Specifically, multiple heterogeneous biological information is integrated to construct multi-view hierarchical hypergraphs of TFs and target genes, using hypergraph convolution networks to model higher order complex regulatory relationships. Meanwhile, the coupled information diffusion mechanism and the cross-domain messaging mechanism facilitate the information sharing between genes to optimise gene embedding representations. Finally, a unique channel attention mechanism is used to adaptively learn feature representations from multiple views for GRN inference. Experimental results show that MHHGRN achieves better results than the baseline methods on the E. coli and S. cerevisiae benchmark datasets of the DREAM5 challenge, and it has excellent cross-species generalization, achieving comparable or better performance on scRNA-seq datasets from five mouse and two human cell lines.

A tryst of 'blood pressure control- sex- comorbidities': the odyssey of basic public health services in Yunnan in quest for truth.

BACKGROUND: The Basic Public Health Service (BPHS), a recently announced free healthcare program, aims to combat the most prevalent Noncommunicable Disease-"Hypertension" (HTN)-and its risk factors on a nationwide scale. In China, there is a rife that HTN less impacts women during their lifetime. We, therefore, aimed to evaluate the sex disparity in hypertension patients with comorbidities among south-west Chinese and the contribution of BPHS to address that concern. METHODS: We have opted for a multistage stratified random sampling method to enroll hypertensive patients of 35 years and older, divided them into BPHS and non-BPHS groups. We assessed the sex disparity in HTN patients with four major comorbidities- Dyslipidemia, Diabetes Mellitus (DM), Cardiovascular Disease (CVD), and Chronic Kidney Disease (CKD), and descriptive data were compiled. Odds ratios from logistic regression models estimated the effectiveness of BPHS in the management of HTN with comorbidities. RESULTS: Among 1521 hypertensive patients,1011(66.5%) were managed in the BPHS group. The proportion of patients who had at least one comorbidity was 70.7% (95% confidence interval [CI]: 66.3-76.8%), patients aged 65 years and older were more likely to have coexisting comorbidities. Participants who received the BPHS showed significant blood pressure (BP) control with two comorbidities (odds ratio [OR] = 2.414, 95% CI: 1.276-4.570), three or more (OR = 5.500, 95%CI: 1.174-25.756). Patients with dyslipidemia and DM also benefited from BPHS in controlling BP (OR = 2.169, 95% CI: 1.430-3.289) and (OR = 2.785, 95%CI: 1.242-6.246), respectively. In certain high-income urban survey centers, there was sex differences in the HTN management provided by BPHS, with men having better BP control rates than women. CONCLUSIONS: Perhaps this is the first study in China to succinctly show the effectiveness and sex disparity regarding "management of hypertensive comorbidities". This supports that the BPHS program plays a pivotal role in controlling BP, therefore should recommend the national healthcare system to give women a foremost priority in BPHS, especially to those from low-socioeconomic and low-scientific literacy regions.

Searching for protein variants with desired properties using deep generative models.

BACKGROUND: Protein engineering aims to improve the functional properties of existing proteins to meet people's needs. Current deep learning-based models have captured evolutionary, functional, and biochemical features contained in amino acid sequences. However, the existing generative models need to be improved when capturing the relationship between amino acid sites on longer sequences. At the same time, the distribution of protein sequences in the homologous family has a specific positional relationship in the latent space. We want to use this relationship to search for new variants directly from the vicinity of better-performing varieties. RESULTS: To improve the representation learning ability of the model for longer sequences and the similarity between the generated sequences and the original sequences, we propose a temporal variational autoencoder (T-VAE) model. T-VAE consists of an encoder and a decoder. The encoder expands the receptive field of neurons in the network structure by dilated causal convolution, thereby improving the encoding representation ability of longer sequences. The decoder decodes the sampled data into variants closely resembling the original sequence. CONCLUSION: Compared to other models, the person correlation coefficient between the predicted values of protein fitness obtained by T-VAE and the truth values was higher, and the mean absolute deviation was lower. In addition, the T-VAE model has a better representation learning ability for longer sequences when comparing the encoding of protein sequences of different lengths. These results show that our model has more advantages in representation learning for longer sequences. To verify the model's generative effect, we also calculate the sequence identity between the generated data and the input data. The sequence identity obtained by T-VAE improved by 12.9% compared to the baseline model.

Ab initio prediction of key parameters and magneto-structural correlation of tetracoordinated lanthanide single-ion magnets.

Single-molecule magnets (SMMs) have great potential in becoming revolutionary materials for micro-electronic devices. As one type of SMM and holding the performance record, lanthanide single-ion magnets (Ln-SIMs) stand at the forefront of the family. Lowering the coordination number (CN) is an important strategy to improve the performance of Ln-SIMs. Here, we report a theoretical study on a typical group of low-CN Ln-SIMs, i.e., tetracoordinated structures. Our results are consistent with those of experiments and they identify the same three best Ln-SIMs via a concise criterion, i.e., the co-existence of long τQTM and high Ueff. Compared to the record-holding dysprosocenium systems, the best SIMs here possess τQTM values that are shorter by several orders of magnitude and Ueff values that are lower by ∼1000 Kelvin (K). These are important reasons for the fact that the tetracoordinated Ln-SIMs are clearly inferior to dysprosocenium. A simple but intuitive crystal-field analysis leads to several routes to improve the performance of a given Ln-SIM, including compression of the axial bond length, widening the axial bond angle, elongation of the equatorial bond length and usage of weaker equatorial donor ligands. Although these routes are not brand-new, the most efficient option and the degree of improvement resulting from it are not known in advance. Consequently, a theoretical magneto-structural study, covering various routes, is carried out for the best Ln-SIM here and the most efficient route is shown to be widening the axial ∠O-Dy-O angle. The most optimistic case, having a ∠O-Dy-O of 180°, could have a τQTM (up to 103 s) and Ueff (∼2400 K) close to those of the record-holders. Subsequently, a blocking temperature (TB) of 64 K is predicted to be possible for it. A more practical case, with ∠O-Dy-O being 160°, could have a τQTM of up to 400 s, Ueff of around 2200 K and the possibility of a TB of 57 K. Although having an inherent precision limit, these predictions provide a guide to performance improvement, starting from an existing system.

The 'Ironclad friendship' of China-Cambodia, lays the first step in the foundation of early diagnosis and treatment of asymptomatic congenital heart Defects- A multi-national screening and intervention project, 2017-2020.

BACKGROUND: Congenital heart disease (CHD) is the leading cause of mortality in childhood worldwide. However, a large number of children with CHD are not diagnosed promptly in low- and middle-income regions, due to limited healthcare resources and lack the ability of prenatal and postnatal ultrasound examinations. The research on asymptomatic CHD in the community is still blank, resulting in a large number of children with asymptomatic CHD can not be found and treated in time. Through the China-Cambodia collaborative health care initiative, the project team conducted research, screened children's CHD through a sampling survey in China and Cambodia, collected relevant data, and retrospectively analyzed the data of all eligible patients. OBJECTIVES: The project aimed to evaluate the prevalence of asymptomatic CHD in a sample population of 3-18years old and effects on their growth status and treatment outcomes. METHODS: We examined the prevalence of 'asymptomatic CHD' among 3-18years old children and adolescents at the township/county levels in the two participating. A total of eight provinces in China and five provinces in Cambodia were analyzed from 2017 to 2020. During 1 year follow-up after treatment, the differences in heights and weights of the treated and control groups were evaluated. RESULTS: Among the 3,068,075 participants screened from 2017 to 2020, 3967 patients with asymptomatic CHD requiring treatment were identified [0.130%, 95% confidence interval (CI) 0.126 -0.134%]. The prevalence rate of CHD ranged from 0.02 to 0.88%, and was negatively related to local per capita GDP (p = 0.028). The average height of 3310 treated CHD patients were 2.23% (95% CI: -2.51%~-1.9%) lower than that of the standard group and the average weight was - 6.41% (95% CI: -7.17%~-5.65%) lower, the developmental gap widening with advancing age. One year after treatment, the relative height difference remained comparable while that, in weight was reduced by 5.68% (95% CI: 4.27% ~7.09%). CONCLUSIONS: Asymptomatic CHD now is often overlooked and is an emerging public health challenge. Early detection and treatment are essential to lower the potential burden of heart diseases in children and adolescents.

Dawning public health services dogma: An indigenous Southwest Chinese perspective in managing hypertension-with or without the "BPHS"?

Background: To alleviate the rising mortality burden due to hypertension and other non-communicable diseases, a new public health policy initiative in 2009 called the Basic Public Health Services (BPHS). Program was introduced by the Chinese government. The goal of the study is to assess the feasibility and impact of a nationwide health care service-the "BPHS". Methods: From January to December 2021, a stratified multistage random sampling method in the survey was conducted to select 6,456 people from 8 cities/districts in Yunnan Province, China, who were above the age of 35 years. 1,521 hypertensive patients were previously aware of their high blood pressure status were matched to the BPHS program database based on ID number and then further divided into BPHS group and non-BPHS (control) group. The results of the current study are based on their responses to a short structured questionnaire, a physical examination, and laboratory tests. The association between BPHS management and its effect on the control of hypertension was estimated using multivariable logistic regression models. We evaluated the accessibility and efficacy of BPHS health care services by analyzing various variables such as blood pressure, BMI, lifestyle modification, anti-hypertensive drugs taken, and cardiovascular risk factors. Results: Among the 1,521 hypertensive patients included in this study, 1,011 (66.5%) were managed by BPHS programme. The multivariable logistic regression model demonstrated that the BPHS facilitated hypertension control (OR = 1.640, 95% CI: 1.237-2.175). A higher proportion of participants receiving lifestyle guidance from the BPHS management showed lowering of total cholesterol. In comparison to the non-BPHS group, those under BPHS management adhered better to antihypertensive medications either single drug (54.3%) or in combination (17.3%) of drugs. Additionally, we also noticed that urban areas with centralized and well-established digital information management system had better hypertension treatment and control. Conclusions: Nearly two-thirds of the hypertensive patients in Yunnan Province were included in BPHS management. The impact of the national BPHS program was evident in lowering risk factors for cardiovascular diseases, promoting healthy lifestyles, lowering blood pressure, increasing medication adherence, and the better control rate of hypertension.

Graph Neural Networks and Their Current Applications in Bioinformatics.

Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of GNNs and their advances in bioinformatics is presented from multiple perspectives. We first introduce some commonly used GNN models and their basic principles. Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction, and graph generation. Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery, and biomedical imaging. Based on the analysis, we provide an outlook on the shortcomings of current studies and point out their developing prospect. Although GNNs have achieved excellent results in many biological tasks at present, they still face challenges in terms of low-quality data processing, methodology, and interpretability and have a long road ahead. We believe that GNNs are potentially an excellent method that solves various biological problems in bioinformatics research.

Reducing Effective Mass for Advancing Thermoelectrics in Sb/Bi-Doped AgCrSe2 Compounds.

Liquid-like materials have attracted increasing attention, owing to their phonon-liquid electron-crystal feature. As a typical representative, the superionic conductor AgCrSe2 is regarded as a promising thermoelectric for its intrinsic ultralow lattice thermal conductivity. The primary challenge for achieving high thermoelectric performance is to enhance the inferior electronic performance in AgCrSe2 compounds. Thus, it is very significant to manipulate band effective mass to achieve a higher power factor. In this work, the Sb/Bi elements are doped at Cr sites in Ag0.97CrSe2, i.e., Ag0.97Cr1-x(Sb/Bi)xSe2, aiming at producing a better overlap of electron orbits between different atoms for sharpening the valence band and decreasing the effective mass. In comparison to pristine AgCrSe2, a considerable improvement (>50%) in the power factor (∼387 µW m-1 K-2 at 750 K) is realized upon 3% Sb doping. The single parabolic band model clarifies that the decreased effective mass and optimized carrier concentration contribute to the enhanced electronic property. Furthermore, an ultralow lattice thermal conductivity (∼0.2 W m-1 K-1) is well-maintained for the sample with 3% Sb doping as a result of the nearly unchanged superionic conduction. Eventually, a high peak figure of merit zT (∼0.7 at 750 K) is obtained in Ag0.97Cr0.97Sb0.03Se2. The current finding provides an excellent avenue for advancing thermoelectrics in AgCrSe2 materials.

Ga-Doping-Induced Carrier Tuning and Multiphase Engineering in n-type PbTe with Enhanced Thermoelectric Performance.

P-type lead telluride (PbTe) emerged as a promising thermoelectric material for intermediate-temperature waste-heat-energy harvesting. However, n-type PbTe still confronted with a considerable challenge owing to its relatively low figure of merit ZT and conversion efficiency η, limiting widespread thermoelectric applications. Here, we report that Ga-doping in n-type PbTe can optimize carrier concentration and thus improve the power factor. Moreover, further experimental and theoretical evidence reveals that Ga-doping-induced multiphase structures with nano- to micrometer size can simultaneously modulate phonon transport, leading to dramatic reduction of lattice thermal conductivity. As a consequence, a tremendous enhancement of ZT value at 823 K reaches ∼1.3 for n-type Pb0.97Ga0.03Te. In particular, in a wide temperature range from 323 to 823 K, the average ZTave value of ∼0.9 and the calculated conversion efficiency η of ∼13% are achieved by Ga doping. The present findings demonstrate the great potential in Ga-doped PbTe thermoelectric materials through a synergetic carrier tuning and multiphase engineering strategy.

A partially function-to-topic model for protein function prediction.

BACKGROUND: Proteins are a kind of macromolecules and the main component of a cell, and thus it is the most essential and versatile material of life. The research of protein functions is of great significance in decoding the secret of life. In recent years, researchers have introduced multi-label supervised topic model such as Labeled Latent Dirichlet Allocation (Labeled-LDA) into protein function prediction, which can obtain more accurate and explanatory prediction. However, the topic-label corresponding way of Labeled-LDA is associating each label (GO term) with a corresponding topic directly, which makes the latent topics to be completely degenerated, and ignores the differences between labels and latent topics. RESULT: To achieve more accurate probabilistic modeling of function label, we propose a Partially Function-to-Topic Prediction (PFTP) model for introducing the local topics subset corresponding to each function label. Meanwhile, PFTP not only supports latent topics subset within a given function label but also a background topic corresponding to a 'fake' function label, which represents common semantic of protein function. Related definitions and the topic modeling process of PFTP are described in this paper. In a 5-fold cross validation experiment on yeast and human datasets, PFTP significantly outperforms five widely adopted methods for protein function prediction. Meanwhile, the impact of model parameters on prediction performance and the latent topics discovered by PFTP are also discussed in this paper. CONCLUSION: All of the experimental results provide evidence that PFTP is effective and have potential value for predicting protein function. Based on its ability of discovering more-refined latent sub-structure of function label, we can anticipate that PFTP is a potential method to reveal a deeper biological explanation for protein functions.

Switchable sensitizers stepwise lighting up lanthanide emissions.

Analagous to a long-ranged rocket equipped with multi-stage engines, a luminescent compound with consistent emission signals across a large range of concentrations from two stages of sensitizers can be designed. In this approach, ACQ, aggregation-caused quenching effect of sensitizers, would stimulate lanthanide emission below 10(-4) M, and then at concentrations higher than 10(-3) M, the "aggregation-induced emission" (AIE) effect of luminophores would be activated with the next set of sensitizers for lanthanide emission. Simultaneously, the concentration of the molecules could be monitored digitally by the maximal excitation wavelengths, due to the good linear relationship between the maximal excitation wavelengths and the concentrations {lg(M)}. This model, wherein molecules are assembled with two stages (both AIE and ACQ effect) of sensitizers, may provide a practicable strategy for design and construction of smart lanthanide bioprobes, which are suitable in complicated bioassay systems in which concentration is variable.