Diagnostic performance evaluation of urine HIV-1 antibody rapid test kits in a real-life routine care setting in China.

OBJECTIVES: To evaluate the diagnostic performance of urine HIV antibody rapid test kits in screening diverse populations and to analyse subjects' willingness regarding reagent types, purchase channels, acceptable prices, and self-testing. DESIGNS: Diagnostic accuracy studies PARTICIPANTS: A total of 2606 valid and eligible samples were collected in the study, including 202 samples from female sex workers (FSWs), 304 persons with injection drug use (IDU), 1000 pregnant women (PW), 100 subjects undergoing voluntary HIV counselling and testing (VCT) and 1000 students in higher education schools or colleges (STUs). Subjects should simultaneously meet the following inclusion criteria: (1) being at least 18 years old and in full civil capacity, (2) signing an informed consent form and (3) providing truthful identifying information to ensure that the subjects and their samples are unique. RESULTS: The sensitivity, specificity and area under the curve (AUC) of the urine HIV-1 antibody rapid test kits were 92.16%, 99.92% and 0.960 (95% CI: 0.952 to 0.968, p<0.001), respectively, among 2606 samples collected during on-site screenings. The kits showed good diagnostic performance in persons with IDU (AUC, 1.000; 95% CI, 1.000 to 1.000, p<0.001), PW (AUC, 0.999; 95% CI, 0.999 to 1.000, p<0.001) and FSWs (AUC, 1.000; 95% CI, 1.000 to 1.000, p<0.001). The AUC of the urine reagent kits in subjects undergoing VCT was 0.941 (95% CI: 0.876 to 0.978, p<0.001). The 'acceptable price' had the greatest influence on STUs (Pi=1.000) and PW (Pi=1.000), the 'purchase channel' had the greatest influence on subjects undergoing VCT (Pi=1.000) and persons with IDU (Pi=1.000) and the 'reagent types' had the greatest influence on FSWs (Pi=1.000). CONCLUSIONS: The rapid urine test kits showed good diagnostic validity in practical applications, despite a few cases involving misdiagnosis and underdiagnosis.

Point convolutional neural network algorithm for Ising model ground state research based on spring vibration.

The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of the springs, we propose the Spring-Ising Algorithm, a point convolutional neural network algorithm for ground state search based on the spring vibration model. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establishes the equation of motion for all spins. Spring-Ising Algorithm can be mapped on AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has shown promising results in solving the Ising model and has been tested in the recognized test benchmark K2000. The optimal results of this algorithm after 10,000 steps of iteration are 2.9% of all results. The algorithm introduces the concept of dynamic equilibrium to achieve a more detailed local search by dynamically adjusting the weight of the Ising model in the spring oscillation model. Spring-Ising Algorithm offers the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations.

[Genetic Subtypes and Pretreatment Drug Resistance in the Newly Reported Human Immunodeficiency Virus-Infected Men Aged≥50 Years Old in Guangxi].

Objective To analyze the genetic subtypes of human immunodeficiency virus (HIV) and the prevalence of pretreatment drug resistance in the newly reported HIV-infected men in Guangxi. Methods The stratified random sampling method was employed to select the newly reported HIV-infected men aged≥50 years old in 14 cities of Guangxi from January to June in 2020.The pol gene of HIV-1 was amplified by nested reverse transcription polymerase chain reaction and then sequenced.The mutation sites associated with drug resistance and the degree of drug resistance were then analyzed. Results A total of 615 HIV-infected men were included in the study.The genetic subtypes of CRF01_AE,CRF07_BC,and CRF08_BC accounted for 57.4% (353/615),17.1% (105/615),and 22.4% (138/615),respectively.The mutations associated with the resistance to nucleoside reverse transcriptase inhibitors (NRTI),non-nucleoside reverse transcriptase inhibitors (NNRTI),and protease inhibitors occurred in 8 (1.3%),18 (2.9%),and 0 patients,respectively.M184V (0.7%) and K103N (1.8%) were the mutations with the highest occurrence rates for the resistance to NRTIs and NNRTIs,respectively.Twenty-two (3.6%) patients were resistant to at least one type of inhibitors.Specifically,4 (0.7%),14 (2.3%),4 (0.7%),and 0 patients were resistant to NRTIs,NNRTIs,both NRTIs and NNRTIs,and protease inhibitors,respectively.The pretreatment resistance to NNRTIs had much higher frequency than that to NRTIs (2.9% vs.1.3%;χ2=3.929,P=0.047).The prevalence of pretreatment resistance to lamivudine,zidovudine,tenofovir,abacavir,rilpivirine,efavirenz,nevirapine,and lopinavir/ritonavir was 0.8%, 0.3%, 0.7%, 1.0%, 1.3%, 2.8%, 2.9%, and 0, respectively. Conclusions CRF01_AE,CRF07_BC,and CRF08_BC are the three major strains of HIV-infected men≥50 years old newly reported in Guangxi,2020,and the pretreatment drug resistance demonstrates low prevalence.

CST: Complex Sparse Transformer for Low-SNR Speech Enhancement.

Speech enhancement tasks for audio with a low SNR are challenging. Existing speech enhancement methods are mainly designed for high SNR audio, and they usually use RNNs to model audio sequence features, which causes the model to be unable to learn long-distance dependencies, thus limiting its performance in low-SNR speech enhancement tasks. We design a complex transformer module with sparse attention to overcome this problem. Different from the traditional transformer model, this model is extended to effectively model complex domain sequences, using the sparse attention mask balance model's attention to long-distance and nearby relations, introducing the pre-layer positional embedding module to enhance the model's perception of position information, adding the channel attention module to enable the model to dynamically adjust the weight distribution between channels according to the input audio. The experimental results show that, in the low-SNR speech enhancement tests, our models have noticeable performance improvements in speech quality and intelligibility, respectively.

Constrain Bias Addition to Train Low-Latency Spiking Neural Networks.

In recent years, a third-generation neural network, namely, spiking neural network, has received plethora of attention in the broad areas of Machine learning and Artificial Intelligence. In this paper, a novel differential-based encoding method is proposed and new spike-based learning rules for backpropagation is derived by constraining the addition of bias voltage in spiking neurons. The proposed differential encoding method can effectively exploit the correlation between the data and improve the performance of the proposed model, and the new learning rule can take complete advantage of the modulation properties of bias on the spike firing threshold. We experiment with the proposed model on the environmental sound dataset RWCP and the image dataset MNIST and Fashion-MNIST, respectively, and assign various conditions to test the learning ability and robustness of the proposed model. The experimental results demonstrate that the proposed model achieves near-optimal results with a smaller time step by maintaining the highest accuracy and robustness with less training data. Among them, in MNIST dataset, compared with the original spiking neural network with the same network structure, we achieved a 0.39% accuracy improvement.

Sex-Specific Associations of Urinary Metals with Renal Function: a Cross-sectional Study in China.

BACKGROUND: Extensive studies have revealed the link between heavy metals and CKD. Compared to single meta-elements, mixture of metals reflect real-life metals exposure scenarios and are of interest. However, the mechanism of action of metal mixture on renal function is unclear. METHODS: This study aimed to explore the potential relationship between urinary arsenic (As), cadmium (Cd), lead (Pb), manganese (Mn), and chromium (Cr) contents with estimated glomerular filtration rate (eGFR) levels in 2775 participants. The levels of metals in urine were determined by inductively coupled plasma-mass spectrometry. We used linear regression models and the Bayesian kernel machine regression (BKMR) to evaluate the association between metals and eGFR levels. RESULTS: In linear regression analysis, urinary As (ß = 2.723, 95%CI: 0.29, 5.157) and Pb (ß = 3.081, 95%CI: 1.725, 4.438) were positively associated with eGFR in the total population. In the BKMR model, a mixture of the five metals had a positive joint effect on eGFR levels, while Pb (PIP = 0.996) contributed the most to eGFR levels. Pb was positively associated with eGFR levels in the total participants and women. As was positively correlated with eGFR levels in women. Pb and eGFR levels were positively correlated when the other metals were set at 25th, 50th, and 75th percentiles. CONCLUSIONS: To the best of our knowledge, all five metals mixed exposure was positively associated with eGFR. Pb showed more important effects than the other four metals in the mixture, especially in women.

Repetitive transcranial magnetic stimulation promotes neurological functional recovery in rats with traumatic brain injury by upregulating synaptic plasticity-related proteins.

Studies have shown that repetitive transcranial magnetic stimulation (rTMS) can enhance synaptic plasticity and improve neurological dysfunction. However, the mechanism through which rTMS can improve moderate traumatic brain injury remains poorly understood. In this study, we established rat models of moderate traumatic brain injury using Feeney's weight-dropping method and treated them using rTMS. To help determine the mechanism of action, we measured levels of several important brain activity-related proteins and their mRNA. On the injured side of the brain, we found that rTMS increased the protein levels and mRNA expression of brain-derived neurotrophic factor, tropomyosin receptor kinase B, N-methyl-D-aspartic acid receptor 1, and phosphorylated cAMP response element binding protein, which are closely associated with the occurrence of long-term potentiation. rTMS also partially reversed the loss of synaptophysin after injury and promoted the remodeling of synaptic ultrastructure. These findings suggest that upregulation of synaptic plasticity-related protein expression is the mechanism through which rTMS promotes neurological function recovery after moderate traumatic brain injury.

MnO2 doped graphene nanosheets for carotid body tumor combination therapy.

Combination therapy is a cornerstone of tumor therapy, which can make up for the shortcomings of a single treatment and improve the cure rate of cancer. Near infrared induced therapy is widely applied owing to good accessibility, safety profile, and a wide range of effectiveness. Here, we use reduced nanographene oxide (rNGO) sheets with MnO2 nanoparticles as a photothermal agent to trigger further photodynamic therapy and chemotherapy. Doxorubicin (DOX, chemotherapeutic agent) and methyl blue (MB, photosensitizer) are loaded onto graphene oxide through a strong physical bond and rapidly released under high temperature. Besides, MnO2 nanoparticles can catalyze hydrogen peroxide inside of tumor and produce oxygen as a raw material for photodynamic therapy. In vitro experiments illustrated an effective ablation of PC-12 cells by rGO@MnO2/MB/Dox incubation combined with 808 nm near-infrared (NIR) laser radiation. For in vivo experiments in a model of carotid body tumor, rGO@MnO2/MB/Dox was locally injected, followed by 808 nm NIR laser irradiation. We found that the number of tumor cells was significantly reduced, the tumor volume was reduced, and there were no side effects. This may provide a new idea for the combination treatment of carotid body tumor.

Dynamic Compensation Method for Humidity Sensors Based on Temperature and Humidity Decoupling.

Currently, integrated humidity sensors with fast-response time are widely needed. The most commonly used polyimide capacitive humidity sensor has a long response time, which is difficult to meet the need for a fast response. Most studies focusing on technology and materials have a high cost and are difficult to ensure compatability with the CMOS process. The dynamic compensation method can shorten the response time by only adding digital circuits or software processing. However, conventional compensation technology is not suitable for humidity sensors due to temperature coupling. This paper proposes a new dynamic compensation method for humidity sensors based on the decoupling of temperature factors by analyzing the coupling relationship between sensor dynamic characteristics and temperature. Simulations and experiments were used to verify the proposed method. The experimental results show that the proposed method reduces the humidity response time of the sensor by 85.6%. The proposed method can effectively shorten the response time of humidity sensors.

Transformer Based Binocular Disparity Prediction with Occlusion Predict and Novel Full Connection Layers.

The depth estimation algorithm based on the convolutional neural network has many limitations and defects by constructing matching cost volume to calculate the disparity: using a limited disparity range, the authentic disparity beyond the predetermined range can not be acquired; Besides, the matching process lacks constraints on occlusion and matching uniqueness; Also, as a local feature extractor, a convolutional neural network lacks the ability of global context information perception. Aiming at the problems in the matching method of constructing matching cost volume, we propose a disparity prediction algorithm based on Transformer, which specifically comprises the Swin-SPP module for feature extraction based on Swin Transformer, Transformer disparity matching network based on self-attention and cross-attention mechanism, and occlusion prediction sub-network. In addition, we propose a double skip connection fully connected layer to solve the problems of gradient vanishing and explosion during the training process for the Transformer model, thus further enhancing inference accuracy. The proposed model in this paper achieved an EPE (Absolute error) of 0.57 and 0.61, and a 3PE (Percentage error greater than 3 px) of 1.74% and 1.56% on KITTI 2012 and KITTI 2015 datasets, respectively, with an inference time of 0.46 s and parameters as low as only 2.6 M, showing great advantages compared with other algorithms in various evaluation metrics.

Genetic network analysis of human immunodeficiency virus sexual transmission in rural Southwest China after the expansion of antiretroviral therapy: A population-based study.

Background: This study is used to analyze the genetic network of HIV sexual transmission in rural areas of Southwest China after expanding antiretroviral therapy (ART) and to investigate the factors associated with HIV sexual transmission through the genetic network. Materials and methods: This was a longitudinal genetic network study in Guangxi, China. The baseline survey and follow-up study were conducted among patients with HIV in 2015, and among those newly diagnosed from 2016 to 2018, respectively. A generalized estimating equation model was employed to explore the factors associated with HIV transmission through the genetic linkage between newly diagnosed patients with HIV (2016-2018) and those at baseline (2015-2017), respectively. Results: Of 3,259 identified HIV patient sequences, 2,714 patients were at baseline, and 545 were newly diagnosed patients with HIV at follow-up. A total of 8,691 baseline objectives were observed by repeated measurement analysis. The prevention efficacy in HIV transmission for treated HIV patients was 33% [adjusted odds ratio (AOR): 0.67, 95% confidence interval (CI): 0.48-0.93]. Stratified analyses indicated the prevention efficacy in HIV transmission for treated HIV patients with a viral load (VL) of <50 copies/ml and those treated for 4 years with a VL of <50 copies/ml to be 41 [AOR: 0.59, 95% CI: 0.43-0.82] and 65% [AOR: 0.35, 95% CI: 0.24-0.50], respectively. No significant reduction in HIV transmission occurred among treated HIV patients with VL missing or treated HIV patients on dropout. Some factors were associated with HIV transmission, including over 50 years old, men, Zhuang and other nationalities, with less than secondary schooling, working as a farmer, and heterosexual transmission. Conclusion: This study reveals the role of ART in reducing HIV transmission, and those older male farmers with less than secondary schooling are at high risk of HIV infection at a population level. Improvements to ART efficacy for patients with HIV and precision intervention on high-risk individuals during the expansion of ART are urgently required.

PassTCN-PPLL: A Password Guessing Model Based on Probability Label Learning and Temporal Convolutional Neural Network.

The frequent incidents of password leakage have increased people's attention and research on password security. Password guessing is an essential part of password cracking and password security research. The progression of deep learning technology provides a promising way to improve the efficiency of password guessing. However, the mainstream models proposed for password guessing, such as RNN (or other variants, such as LSTM, GRU), GAN and VAE still face some problems, such as the low efficiency and high repetition rate of the generated passwords. In this paper, we propose a password-guessing model based on the temporal convolutional neural network (PassTCN). To further improve the performance of the generated passwords, we propose a novel password probability label-learning method, which reconstructs labels based on the password probability distribution of the training set and deduplicates the training set when training. Experiments on the RockYou dataset showed that, when generating 108 passwords, the coverage rate of PassTCN with password probability label learning (PassTCN-PPLL) reached 12.6%, which is 87.2%, 72.6% and 42.9% higher than PassGAN (a password-guessing model based on GAN), VAEPass (a password-guessing model based on VAE) and FLA (a password-guessing model based on LSTM), respectively. The repetition rate of our model is 25.9%, which is 45.1%, 31.7% and 17.4% lower than that of PassGAN, VAEPass and FLA, respectively. The results confirm that our approach not only improves the coverage rate but also reduces the repetition rate.

A Cooperative Lightweight Translation Algorithm Combined with Sparse-ReLU.

In the field of natural language processing (NLP), machine translation algorithm based on Transformer is challenging to deploy on hardware due to a large number of parameters and low parametric sparsity of the network weights. Meanwhile, the accuracy of lightweight machine translation networks also needs to be improved. To solve this problem, we first design a new activation function, Sparse-ReLU, to improve the parametric sparsity of weights and feature maps, which facilitates hardware deployment. Secondly, we design a novel cooperative processing scheme with CNN and Transformer and use Sparse-ReLU to improve the accuracy of the translation algorithm. Experimental results show that our method, which combines Transformer and CNN with the Sparse-ReLU, achieves a 2.32% BLEU improvement in prediction accuracy and reduces the number of parameters of the model by 23%, and the sparsity of the inference model increases by more than 50%.

LPG-PCFG: An Improved Probabilistic Context- Free Grammar to Hit Low-Probability Passwords.

With the development of the Internet, information security has attracted more attention. Identity authentication based on password authentication is the first line of defense; however, the password-generation model is widely used in offline password attacks and password strength evaluation. In real attack scenarios, high-probability passwords are easy to enumerate; extremely low-probability passwords usually lack semantic structure and, so, are tough to crack by applying statistical laws in machine learning models, but these passwords with lower probability have a large search space and certain semantic information. Improving the low-probability password hit rate in this interval is of great significance for improving the efficiency of offline attacks. However, obtaining a low-probability password is difficult under the current password-generation model. To solve this problem, we propose a low-probability generator-probabilistic context-free grammar (LPG-PCFG) based on PCFG. LPG-PCFG directionally increases the probability of low-probability passwords in the models' distribution, which is designed to obtain a degeneration distribution that is friendly for generating low-probability passwords. By using the control variable method to fine-tune the degeneration of LPG-PCFG, we obtained the optimal combination of degeneration parameters. Compared with the non-degeneration PCFG model, LPG-PCFG generates a larger number of hits. When generating 107 and 108 times, the number of hits to low-probability passwords increases by 50.4% and 42.0%, respectively.

Adherence to dietary guide for elderly adults and health risks of older adults in ethnic minority areas in China: a cross-sectional study.

BACKGROUND: The impact of dietary guidelines on health in ethnic minority regions needs to be further explored because of multiple sociocultural factors. Therefore, this study was conducted to analyze the association between adherence to dietary guidelines and health risks in an elderly population in an ethnic minority region. METHODS: A cross-sectional survey was conducted among 836 older adults in ethnic minority areas. They were asked to describe their daily dietary intake levels through a semi-quantitative food frequency questionnaire. The closeness coefficient for each study subject was calculated by using the technique for order preference by similarity to an ideal solution (TOPSIS), which measures the adherence to Dietary Guide for Elderly Adults (DGEA). Regression models were used to analyze the association between adherence and health risks. RESULTS: The daily food of the elderly in this area comprised cereals and vegetables. They had low intake of milk, dairy products, and water and high intake of salt. The closeness coefficient for the total population was 0.51, and the adherence of this population to dietary guidelines for the elderly was low. In both the crude model and the models adjusted for covariates, the closeness coefficient was not significantly associated with clinical indicators and health outcomes (p > 0.05). CONCLUSIONS: No association was found between adherence to large sample-based dietary guidelines and clinical indicators or health outcomes in ethnic minority populations. The applicability of dietary guidelines to ethnic minority areas and whether they yield the expected health benefits require further study.

Associations between multiple heavy metals exposure and glycated hemoglobin in a Chinese population.

BACKGROUND: Heavy metals may play an important role as environmental risk factors in diabetes mellitus. This study aimed to explore the association of HbA1c with As, Cd, Cu, Ni, Pb, and Zn in single-metal exposure and multi-metal co-exposure models. METHODS: A cross-sectional study involving 3472 participants was conducted. Plasma concentrations of heavy metals were determined by inductively coupled plasma mass spectrometry. We estimated the association of each metal with HbA1c by linear regression. Potential heterogeneities by sex, age, and smoking were investigated, and metal mixtures and interactions were assessed by the Bayesian kernel machine regression (BKMR). RESULTS: In linear regression, Cu (ß = 0.324, p < 0.05) and Ni (ß = -0.19, p < 0.05) showed significant association with HbA1c in all participants. In BKMR analyses, all exposure-response relationships were approximately linear. Cu was significantly and positively associated with HbA1c levels in overall participants, women, participants aged 60 years old and above, and nonsmokers. Ni was significantly and negatively associated with HbA1c levels in overall participants. We did not observe the overall effect of plasma metal mixtures on HbA1c or the interaction effect of the metals on HbA1c. CONCLUSION: Cu was positively correlated with HbA1c, whereas Ni was negatively correlated with HbA1c, when evaluated individually or in a metal mixture. Additional studies are necessary to confirm these correlations and to control for exposure to different metals in the general population.

Prevalence and spatial heterogeneity of Trichomonas vaginalis infection among the female population and association with climate in Guangxi Zhuang autonomous region, Southern China.

BACKGROUND: Two cervical cancer screening (CCS) projects have been ongoing for years in Guangxi Zhuang autonomous region (Guangxi), and some Trichomonas vaginalis infection (TVI) cases have been found as an opportunistic finding. This study aimed to identify the high-risk population and expound the spatial epidemiological features of TVI in Guangxi. METHODS: This study was based on CCS from 2012 to 2019. Adjusted odds ratio (AOR), and spatial analyses were used to identify the high-risk subgroups, as well as to depict the spatial epidemiological feature and its relationship with meteorological factors. RESULTS: The infection rate of TVI was 0.38% in 873,880 samples. Significant association with a high risk of TVI was found in the following: females aged 40-49 years (aOR=4.464; 95% CI, 3.359-5.932; p<0.001), aged 50-59 years (aOR=3.169; 95% CI, 2.370-4.237; p<0.001), from urban (aOR=1.577; 95% CI, 1.471-1.691; p<0.001), from minority areas (aOR=1.183; 95% CI, 1.060-1.320; p=0.003), areas with GPD <41,500 CNY (aOR=1.191; 95% CI, 1.106-1.282; p<0.001), and inland areas (aOR=1.520; 95% CI, 1.339-1.726; p<0.001). Counties with higher infection rate were concentrated in northwest Guangxi's mountainous area (Z-score=3.9656, p<0.001), in the upper reaches of the Hongshui River and Yu River, and with a significant spatial autocorrelation (Moran's I=0.581, p=0.002). Spatial error model showed significantly negative regressions among temperature (B=-0.295, p=0.002), annual temperature range (B=-0.295, p=0.002), and TVI spatial distribution. CONCLUSION: The spatial clustering and disparity of TVI in northwest Guangxi warrant further study, and meteorological conditions may play an important role in TVI in northwest Guangxi.

Populations at high risk of cervical cancer in Guangxi Province: Findings from two screening projects in a minority area of South China.

OBJECTIVE: To analyse the positive rates of low-grade (LSIL) and high-grade (HSIL) squamous intraepithelial lesions, and cervical cancer (CC), and identify groups at high risk for CC in Guangxi. SETTING: CC screening options in Guangxi, which is the only minority autonomous area in South China, include the National Cervical Cancer Screening Project (NCCSP) and physical examination (PE). METHODS: This study was based on PE and NCCSP sample data obtained from 2012 to 2019. We calculated the positive rates of LSIL, HSIL, and CC; analysed the adjusted odds ratio (aOR) and 95% confidence intervals (CI) of the variables in multivariate logistic regression; and subsequently identified groups at high risk for CC. RESULTS: The positive rates of LSIL, HSIL, and CC for the total of 873,880 samples were 1.89%, 0.60%, and 0.03%, respectively. Females over 64 years of age (vs. 50-64; aOR = 2.05; 95% CI, 1.71-2.46; P < 0.001) and those from urban (vs. rural; aOR = 1.66; 95% CI, 1.57-1.76; P < 0.001), minority (vs. non-minority; aOR = 1.24; 95% CI, 1.13-1.35; P < 0.001), and coastal (vs. inland; aOR = 1.15; 95% CI, 1.06-1.25; P = 0.001) areas were associated with a high risk of HSIL. Females over 64 (vs. 50-64; aOR = 4.37; 95% CI, 2.88-6.63; P < 0.001) and those from urban (vs. rural; aOR = 3.05; 95% CI, 2.36-3.95; P < 0.001) areas were significantly associated with a high risk of CC. CONCLUSION: Females from urban areas in Guangxi are at high risk for CC. Public health strategies should focus on high-risk populations.

Geographical distinctions of longevity indicators and their correlation with climatic factors in the area where most Chinese Yao are distributed.

Longevity research is a hot topic in the health field. Considerable research focuses on longevity phenomenon in Bama Yao Autonomous County, which has a typical karst landform and is located in Southwest China. This study aims to illustrate the spatial feature of longevity indicators in other Yao areas, to analyze the correlation between climatic factors and longevity indicators, and to provide new clues and targets for further longevity studies. We collect and integrate population, climate, and terrain data into a spatial database. The main analysis methods include spatial autocorrelation, high/low clustering, and multiscale geographically weighted regression (MGWR). Two longevity clusters are identified in Guijiang River Basin (longevity index (LI%): 2.49 ± 0.63) and Liujiang River Basin (LI%: 2.13 ± 0.60). The spatial distribution of longevity indicators is autocorrelative (Moran's I = 0.652, p < 0.001) and clustered significantly (Z score = 4.268, p < 0.001). MGWR shows that the atmospheric pressure significantly affects the spatial distribution of LI% (estimate value (EV) = - 0.566, p = 0.012), centenarity index (CI%) (EV = - 0.425, p = 0.007), UC (EV = - 0.502, p = 0.006), and CH (EV = - 0.497, p = 0.007). Rainfall significantly affects the spatial distribution of LI% (EV = 0.300, p = 0.003) and CI% (EV = - 0.191, p = 0.016). The spatial distribution of the main longevity indicators shows significant heterogeneity and autocorrelation, and they cluster in the Guijiang River and Liujiang River basins. Atmospheric pressure and rainfall may contribute to the longevity phenomenon through complex mechanisms. The longevity phenomenon in the Yao nationality in Guijiang River Basin requires further study to improve our understanding of the health effect of meteorological, environmental, and social conditions on longevity.

Associations between Apolipoprotein E Gene Polymorphism, Diet and Dyslipidemia in a Yao Minority Area, China.

OBJECTIVE: This study aimed to assess the effects of Apolipoprotein E (APOE) gene polymorphisms, dietary nutrient intake, and their interactions on the prevalence of dyslipidemia in a Yao minority area, China. METHODS: rs429358, rs7412, rs7259620, and rs405509 of the APOE gene were genotyped in 1014 adults aged ≥30 years by using the MassArray system. Dietary nutrients intake data were ascertained from a 109-item food frequency questionnaire (FFQ). RESULTS: Multifactorial logistics regression analysis showed that dyslipidemia was associated with rs7412-CT/TT genotype (OR: 0.540, Bonferroni correction confidence interval: 0.336-0.869), high fat intake (OR: 1.644, 95% CI: 1.128-2.395), high saturated fatty acid (SFA) intake (OR: 1.668, 95% CI: 1.146-2.428), medium carbohydrate intake (OR: 0.634, 95% CI: 0.461-0.873) and high carbohydrate intake (OR: 0.591, 95% CI: 0.406-0.858). The results of logistics regression multiplication model showed that medium carbohydrate intake in the carriers of the rs7412 minor alleles was associated with a low risk of dyslipidemia (OR: 0.433, Bonferroni correction confidence interval: 0.199-0.941). CONCLUSIONS: Genetic variations at the rs7412, dietary fat, SFA, and carbohydrate intake and the interaction between APOE gene polymorphisms and carbohydrate intake are associated with dyslipidemia in Yao nationality people.