OBJECTIVE: To investigate the distribution of traditional Chinese medicine (TCM) syndrome types of and influencing factors on ED with hyperuricemia. METHODS: Based on the clinical data on 271 cases of ED with hyperuricemia admitted to our Department of Andrology, we studied the characteristics of syndrome elements, summarized the TCM syndrome types, and investigated the influencing factors on the distribution of the syndrome types by factor analysis and cluster analysis. RESULTS: By factor analysis of the data collected on TCM symptoms, 12 common factors and 15 syndrome type elements were identified, including disease type syndrome elements dampness, phlegm, heat, qi stagnation, blood stasis, qi deficiency, blood deficiency, yin deficiency, yang deficiency and essence deficiency, and disease-location syndrome elements kidney, liver, spleen, limbs and joints. Common factor cluster analysis revealed the main TCM syndrome types kidney deficiency damp-heat syndrome, spleen and kidney deficiency syndrome, liver depression and kidney deficiency syndrome, kidney deficiency and blood stasis syndrome, and the main influencing factors on the distribution of syndrome types including uric acid, systolic blood pressure, urea, obesity and so on. CONCLUSION The main TCM syndrome types of ED with hyperuricemia include kidney deficiency damp-heat syndrome, spleen and kidney deficiency syndrome, liver depression and kidney deficiency syndrome, kidney deficiency and blood stasis syndrome, and the related influencing factors can be used as an objective basis for the differentiation of TCM syndromes.
Humans ; Medicine, Chinese Traditional ; Hyperuricemia/complications* ; Male ; Cluster Analysis

OBJECTIVE: Human brucellosis is a serious public health concern in the Xilingol League, Inner Mongolia; however, the epidemic trends are unclear. METHOD: In this study, Joinpoint regression analysis and spatiotemporal analysis were applied to investigate the epidemic evolution of human brucellosis. RESULT: From 2004 to 2023, a total of 35,747 cases were reported, with an annual average of 1787.35 cases and an annual average incidence rate of 176.04/100,000. The incidence increased from 173.96/100,000 in 2004 to 500.71/100,000 in 2009 and fluctuated to 61.43/100,000 in 2023. Three epidemic join points were observed in which the disease experienced an alternative rise and fall, peaking in 2009 (APC = 21.73, P > 0.001) and 2020 (APC = 21.51, P > 0.001). The disease showed a persistent decline trend in lentitude (AAPC = -5.30, P > 0.001), suggesting challenges in disease control and a higher risk of rebound. The most cases were reported in Xilinhot City ( n = 4,777), followed by 4,391 in Sonid Left Banner, and 4,324 in Abaga Banner. Spatiotemporal analysis revealed two high clusters (CI and CII) from 2005 to 2012, the high cluster encompassing eight counties and shifting from north to south. CONCLUSION The present analysis highlights that human brucellosis has decreased significantly in the Xilingol League, but the epidemic is still severe; further implementation of a strict control program is necessary.
China/epidemiology* ; Humans ; Brucellosis/epidemiology* ; Epidemics ; Spatio-Temporal Analysis ; Incidence ; Cluster Analysis

The elucidation of resources pertaining to the Chimonanthus praecox varieties and the establishment of a fingerprint serve as crucial underpinnings for advancing scientific inquiry and industrial progress in relation to C. praecox. Employing the SSR molecular marker technology, an exploration of the genetic diversity of 175 C. praecox varieties (lines) in the Yanling region was conducted, and an analysis of the genetic diversity among these varieties was carried out using the UPDM clustering method in NTSYSpc 2.1 software. We analyzed the genetic structure of 175 germplasm using Structure v2.3.3 software based on a Bayesian model. General linear model (GLM) association was utilized to analyze traits and markers. The genetic diversity analysis revealed a mean number of alleles (Na) of 6.857, a mean expected heterozygosity (He) of 0.496 3, a mean observed heterozygosity (Ho) of 0.503 7, a mean genetic diversity index of Nei՚s of 0.494 9, and a mean Shannon information index of 0.995 8. These results suggest that the C. praecox population in Yanling exhibits a rich genetic diversity. Additionally, the population structure and the UPDM clustering were examined. In the GLM model, a total of fifteen marker loci exhibited significant (P < 0.05) association with eight phenotypic traits, with the explained phenotypic variation ranging from 14.90% to 36.03%. The construction of fingerprints for C. praecox varieties (lines) was accomplished by utilizing eleven primer pairs with the highest polymorphic information content, resulting in the analysis of 175 SSR markers. The present study offers a thorough examination of the genetic diversity and SSR molecular markers of C. praecox in Yanling, and establishes a fundamental germplasm repository of C. praecox, thereby furnishing theoretical underpinnings for the selection and cultivation of novel and superior C. praecox varieties, varietal identification, and resource preservation and exploitation.
Bayes Theorem ; Biomarkers ; Phenotype ; Cluster Analysis ; Genetic Variation

Objective: To analyze the spatial and temporal distribution characteristics of seasonal A(H3N2) influenza [influenza A(H3N2)] in China and to provide a reference for scientific prevention and control. Methods: The influenza A(H3N2) surveillance data in 2014-2019 was derived from China Influenza Surveillance Information System. A line chart described the epidemic trend analyzed and plotted. Spatial autocorrelation analysis was conducted using ArcGIS 10.7, and spatiotemporal scanning analysis was conducted using SaTScan 10.1. Results: A total of 2 603 209 influenza-like case sample specimens were detected from March 31, 2014, to March 31, 2019, and the influenza A(H3N2) positive rate was 5.96%(155 259/2 603 209). The positive rate of influenza A(H3N2) was statistically significant in the north and southern provinces in each surveillance year (all P<0.05). The high incidence seasons of influenza A (H3N2) were in winter in northern provinces and summer or winter in southern provinces. Influenza A (H3N2) clustered in 31 provinces in 2014-2015 and 2016-2017. High-high clusters were distributed in eight provinces, including Beijing, Tianjin, Hebei, Shandong, Shanxi, Henan, Shaanxi, and Ningxia Hui Autonomous Region in 2014-2015, and high-high clusters were distributed in five provinces including Shanxi, Shandong, Henan, Anhui, and Shanghai in 2016-2017. Spatiotemporal scanning analysis from 2014 to 2019 showed that Shandong and its surrounding twelve provinces clustered from November 2016 to February 2017 (RR=3.59, LLR=9 875.74, P<0.001). Conclusion: Influenza A (H3N2) has high incidence seasons with northern provinces in winter and southern provinces in summer or winter and obvious spatial and temporal clustering characteristics in China from 2014-2019.
Humans ; Influenza, Human/epidemiology* ; China/epidemiology* ; Influenza A Virus, H3N2 Subtype ; Seasons ; Cluster Analysis

Objective: To analyze the trend of epidemiological characteristics and spatiotemporal distribution of pulmonary tuberculosis (PTB) among smear-positive or other types of students in Guizhou Province from 2011 to 2020, and to provide a reference for improving prevention and control measures. Methods: Data were collected from the Chinese Information System's Notifiable Disease and Tuberculosis Management Information System for disease control and prevention, the Joinpoint 4.9.1.0 software was used to analyze the trend of registration rate; the ArcGIS 10.6 software was used to construct a ring map and to perform spatial autocorrelation analysis; the SaTScan 9.7 software was used for spatial-temporal scan statistics. Results: A total of 32 682 student PTB cases were reported in Guizhou Province from 2011 to 2020, including 5 949 (18.20%) smear-positive cases. Most cases occurred from high school students of 16 to 18 years old (43.99%, 14 376/32 682); the annual average registered rate was 36.22/100 000, the highest in 2018 (52.90/100 000), and the registration rate showed an increasing trend. Meanwhile, a similar trend of registration rate was observed among smear-positive or other types of students. The spatialtemporal heterogeneity was found that the "high-high" clustering patterns of smear-positive or other types were aggregated in Bijie City. Six spatialtemporal clusters with statistically significant (all P<0.001) were detected among smear-positive or other cases, respectively. Conclusions: Upward trend with spatial- temporal clusters of PTB cases reported in students from Guizhou Province from 2011 to 2020. Surveillance should be strengthened for high school students, and regular screening should be conducted in high-risk areas to control the source of infection and reduce the risk of transmission.
Humans ; Adolescent ; Tuberculosis, Pulmonary/epidemiology* ; Asian People ; Cluster Analysis ; Software ; Students

Mining data from traditional Chinese medicine(TCM) prescriptions is one of the important methods for inheriting the experience of famous doctors and developing new drugs. However, current research work has problems such as to be optimized research plans and non-standard statistics. The main problems and corresponding solutions summarized by the research mainly include four aspects.(1)The research plan design needs to consider the efficacy and quality of individual cases.(2)The significance of the difference in confidence order of association rules needs to be further considered, and the lift should not be ignored.(3)The clustering analysis steps are complex. The selection of clustering variables should comprehensively consider factors such as the frequency of TCM, network topology parameters, and practical application significance. The selection of distance calculation and clustering methods should be improved based on the characteristics of TCM clinical data. Jaccard distance and its improvement plan should be given attention in the future. A single, unexplained clustering result should not be presented, but the final clustering plan should be selected based on a comprehensive consideration of TCM clinical characteristics and objective evaluation indicators for clustering.(4)When calculating correlation coefficients, algorithms that are only suitable for continuous variables should not be applied to binary variables. This article explained the connotations of the above problems based on the characteristics of TCM clinical research and statistical principles and proposed corresponding suggestions to provide important references for future data mining research work.
Humans ; Medicine, Chinese Traditional ; Prescriptions ; Data Mining ; Cluster Analysis ; Physicians ; Drugs, Chinese Herbal/therapeutic use*

Single-cell transcriptome sequencing (scRNA-seq) can resolve the expression characteristics of cells in tissues with single-cell precision, enabling researchers to quantify cellular heterogeneity within populations with higher resolution, revealing potentially heterogeneous cell populations and the dynamics of complex tissues. However, the presence of a large number of technical zeros in scRNA-seq data will have an impact on downstream analysis of cell clustering, differential genes, cell annotation, and pseudotime, hindering the discovery of meaningful biological signals. The main idea to solve this problem is to make use of the potential correlation between cells and genes, and to impute the technical zeros through the observed data. Based on this, this paper reviewed the basic methods of imputing technical zeros in the scRNA-seq data and discussed the advantages and disadvantages of the existing methods. Finally, recommendations and perspectives on the use and development of the method were provided.
Cluster Analysis ; Transcriptome

OBJECTIVE: To analyze the spatial distribution of the prevalence of cognitive dysfunction and its risk factors in Chinese population aged 45 years and above to provide evidence for formulating regional prevention and control strategies. METHODS: The study subjects with complete cognitive function data were selected from the follow-up data of the China Health and Retirement Longitudinal Study (CHARLS) Phase IV. ArcGis 10.4 software was used for spatial analysis of the prevalence of cognitive dysfunction in the population aged 45 years and above for each province based on the geographic information system (GIS) technology. RESULTS: In 2018, the overall prevalence of cognitive dysfunction was 33.59% (5951/17716) in individuals aged 45 and above in China. Global spatial autocorrelation analysis indicated a spatial clustering and a positive autocorrelation (P < 0.001) of the prevalence of cognitive dysfunction in the study subjects, with a Moran's I value of 0.333085. The results of local spatial autocorrelation analysis showed that the southwestern region of China was the main aggregation area of patients with cognitive dysfunction. Geographically weighted regression analysis suggested that a male gender, an advanced age, and illiteracy were the major risk factors for cognitive dysfunction (P < 0.05). These 3 risk factors showed a spatial distribution heterogeneity with greater impact in the northern, western, and northwestern regions of China, respectively. CONCLUSION The prevalence of cognitive dysfunction is relatively high in individuals aged 45 years and above in China. A male gender, an advanced age, and illiteracy are the major risk factors for cognitive dysfunction and show different spatial distribution patterns, with the northern, western and northwestern regions of China as the key areas for prevention and control, where the prevention and control measures should be designed based on local conditions.
Humans ; Male ; China/epidemiology* ; Cluster Analysis ; Cognitive Dysfunction/epidemiology* ; East Asian People ; Longitudinal Studies ; Risk Factors ; Middle Aged

OBJECTIVE: To investigate the incidence trend and spatial clustering characteristics of scarlet fever in China from 2016 to 2020 to provide evidence for development of regional disease prevention and control strategies. METHODS: The incidence data of scarlet fever in 31 provinces and municipalities in mainland China from 2016 to 2020 were obtained from the Chinese Health Statistics Yearbook and the Public Health Science Data Center led by the Chinese Center for Disease Control and Prevention.The three-dimensional spatial trend map of scarlet fever incidence in China was drawn using ArcGIS to determine the regional trend of scarlet fever incidence.GeoDa spatial autocorrelation analysis was used to explore the spatial aggregation of scarlet fever in China in recent years. RESULTS: From 2016 to 2020, a total of 310 816 cases of scarlet fever were reported in 31 provinces, municipalities directly under the central government and autonomous regions, with an average annual incidence of 4.48/100 000.The reported incidence decreased from 4.32/100 000 in 2016 to 1.18/100 000 in 2020(Z=103.47, P < 0.001).The incidence of scarlet fever in China showed an obvious regional clustering from 2016 to 2019(Moran's I>0, P < 0.05), but was randomly distributed in 2020(Moran's I>0, P=0.16).The incidence of scarlet fever showed a U-shaped distribution in eastern and western regions of China, and increased gradually from the southern to northern regions.Inner Mongolia Autonomous Region and Hebei and Gansu provinces had the High-high (H-H) clusters of scarlet fever in China. CONCLUSION Scarlet fever still has a high incidence in China with an obvious spatial clustering.For the northern regions of China with H-H clusters of scarlet fever, the allocation of health resources and public health education dynamics should be strengthened, and local scarlet fever prevention and control policies should be made to contain the hotspots of scarlet fever.
Humans ; Incidence ; Scarlet Fever/epidemiology* ; China/epidemiology* ; Spatial Analysis ; Cluster Analysis ; Spatio-Temporal Analysis

From 2015 to 2019, the annual average incidence rate of scarlet fever was 7.80/100 000 in Yantai City, which showed an increasing trend since 2017 (χ²_trend=233.59, P<0.001). The peak period of this disease was from April to July and November to January of the next year. The ratio of male to female was 1.49∶1, with a higher prevalence among cases aged 3 to 9 years (2 357/2 552, 92.36%). Children in kindergartens, primary and middle school students, and scattered children were the high risk population, with the incidence rate of 159.86/100 000, 25.57/100 000 and 26.77/100 000, respectively. The global spatial auto-correlation analysis showed that the global Moran's I index of the reported incidence rate of scarlet fever in Yantai from 2015 to 2019 was 0.28, 0.29, 0.44, 0.48, and 0.22, respectively (all P values<0.05), suggesting that the incidence rate of scarlet fever in Yantai from 2015 to 2019 was spatial clustering. The local spatial auto-correlation analysis showed that the "high-high" clustering areas were mainly located in Laizhou City, Zhifu District, Haiyang City, Fushan District and Kaifa District, while the "low-high" clustering areas were mainly located in Haiyang City and Fushan District.
Child ; Humans ; Male ; Female ; Scarlet Fever/epidemiology* ; Spatial Analysis ; Cities/epidemiology* ; Seasons ; Risk Factors ; Incidence ; Cluster Analysis ; China/epidemiology*