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

BACKGROUND: Large disparities exist in liver cancer burden trends across countries but are poorly understood. We aimed to investigate the global trajectories of liver cancer burden, explore the driving forces, and predict future trends. METHODS: Data on the liver cancer burden in 204 countries and territories from 1990 to 2019 were extracted from the Global Burden of Disease Study. The age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR) trajectories were defined using growth mixture models. Five major risk factors contributing to changes in the ASIR or ASMR and socioeconomic determinants were explored using the identified trajectories. A Bayesian age-period-cohort model was used to predict future trends through 2035. RESULTS: Three trajectories of liver cancer burden were identified: increasing, stable, and decreasing groups. Almost half of the American countries were classified in the decreasing group (48.6% for ASIR and ASMR), and the increasing group was the most common in the European region (ASIR, 49.1%; ASMR, 37.7%). In the decreasing group, the decrease of liver cancer due to hepatitis B contributed 63.4% and 60.4% of the total decreases in ASIR and ASMR, respectively. The increase of liver cancer due to alcohol use, hepatitis C, and hepatitis B contributed the most to the increase in the increasing group (30.8%, 31.1%, and 24.2% for ASIR; 33.7%, 30.2%, and 22.2% for ASMR, respectively). The increasing group was associated with a higher sociodemographic index, gross domestic product per capita, health expenditure per capita, and universal health coverage (all P <0.05). Significant variations in disease burden are predicted to continue through 2035, with a disproportionate burden in the decreasing group. CONCLUSION Global disparities were observed in liver cancer burden trajectories. Hepatitis B, alcohol use, and hepatitis C were identified as driving forces in different regions.
Humans ; Bayes Theorem ; Liver Neoplasms ; Risk Factors ; Hepatitis C/complications* ; Hepatitis B ; Hepacivirus ; Incidence

OBJECTIVE: This study aimed to examine the trends in stroke mortality among young and middle-aged adults in China. METHODS: Data were obtained from the China national vital registration system. Significant changes in mortality were assessed by Joinpoint regression. Age-period-cohort analysis was used to explain the reasons for the changes. Future mortality and counts were predicted by the Bayesian age-period-cohort model. RESULTS: Between 2002 and 2019, a total of 6,253,951 stroke mortality in young and middle-aged adults were recorded. The age-adjusted mortality rates (AAMRs) of women showed a downward trend. The annual percent changes (APC) were -3.5% (-5.2%, -1.7%) for urban women and -2.8% (-3.7%, -1.9%) for rural women. By contrast, the AAMRs per 100,000 for rural men aged 25-44 years continued to rise from 9.40 to 15.46. The AAMRS for urban men aged 25-44 years and urban and rural men aged 45-64 years did not change significantly. Between 2020 and 2030, the projected stroke deaths are 1,423,584 in men and 401,712 in women. CONCLUSION Significant sex and age disparities in the trends of stroke mortality among young and middle-aged adults were identified in China. Targeted health policy measures are needed to address the burden of stroke in the young generation, especially for rural men, with a focus on the prevention and management of high risk factors.
Middle Aged ; Male ; Adult ; Humans ; Female ; Bayes Theorem ; Urban Population ; Stroke/epidemiology* ; Cohort Studies ; China/epidemiology* ; Mortality

OBJECTIVE: The mode of human immunodeficiency virus (HIV) transmission via injection drug use (IDU) still exists, and the recent shift in IDU-related transmission of HIV infection is largely unknown. The purpose of this study was to analyze the spatiotemporal sources and dynamics of HIV-1 transmission through IDU in Guangxi. METHODS: We performed a molecular epidemiological investigation of infections across Guangxi from 2009 to 2019. Phylogenetic and Bayesian time-geographic analyses of HIV-1 sequences were performed to confirm the characteristics of transmission between IDUs in combination with epidemiological data. RESULTS: Among the 535 subjects, CRF08_BC (57.4%), CRF01_AE (28.4%), and CRF07_BC (10.7%) were the top 3 HIV strains; 72.6% of infections were linked to other provinces in the transmission network; 93.6% of sequence-transmitted strains were locally endemic, with the rest coming from other provinces, predominantly Guangdong and Yunnan; 92.1% of the HIV transmission among people who inject drugs tended to be transmitted between HIV-positive IDUs. CONCLUSION HIV recombinants were high diversity, and circulating local strains were the transmission sources among IDUs in Guangxi. However, there were still cases of IDUs linked to other provinces. Coverage of traditional prevention strategies should be expanded, and inter-provincial collaboration between Guangxi, Yunnan, and Guangdong provinces should be strengthened.
Humans ; HIV-1/genetics* ; HIV Infections ; Drug Users ; Phylogeny ; Bayes Theorem ; China/epidemiology* ; Genotype

Humans ; Bayes Theorem ; Age Factors ; Age Distribution ; Mouth Neoplasms/epidemiology* ; China/epidemiology* ; Mortality

OBJECTIVE: We aimed to assess the feasibility and superiority of machine learning (ML) methods to predict the risk of Major Adverse Cardiovascular Events (MACEs) in chest pain patients with NSTE-ACS. METHODS: Enrolled chest pain patients were from two centers, Beijing Anzhen Emergency Chest Pain Center Beijing Bo'ai Hospital, China Rehabilitation Research Center. Five classifiers were used to develop ML models. Accuracy, Precision, Recall, F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring system. Ultimately, ML model constructed by Naïve Bayes was employed to predict the occurrence of MACEs. RESULTS: According to learning metrics, ML models constructed by different classifiers were superior over HEART (History, ECG, Age, Risk factors, & Troponin) scoring system when predicting acute myocardial infarction (AMI) and all-cause death. However, according to ROC curves and AUC, ML model constructed by different classifiers performed better than HEART scoring system only in prediction for AMI. Among the five ML algorithms, Linear support vector machine (SVC), Naïve Bayes and Logistic regression classifiers stood out with all Accuracy, Precision, Recall and F-Measure from 0.8 to 1.0 for predicting any event, AMI, revascularization and all-cause death ( vs. HEART ≤ 0.78), with AUC from 0.88 to 0.98 for predicting any event, AMI and revascularization ( vs. HEART ≤ 0.85). ML model developed by Naïve Bayes predicted that suspected acute coronary syndrome (ACS), abnormal electrocardiogram (ECG), elevated hs-cTn I, sex and smoking were risk factors of MACEs. CONCLUSION Compared with HEART risk scoring system, the superiority of ML method was demonstrated when employing Linear SVC classifier, Naïve Bayes and Logistic. ML method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.
Humans ; Acute Coronary Syndrome/epidemiology* ; Bayes Theorem ; Feasibility Studies ; Risk Assessment/methods* ; Chest Pain/etiology* ; Myocardial Infarction/diagnosis*

In Zherong county, Fujian province, the black spot of Pseudostellaria heterophylla often breaks out in the rainy season from April to June every year. As one of the main leaf diseases of P. heterophylla, black spot seriously affects the yield and quality of the medicinal material. To identify and characterize the pathogens causing black spot, we isolated the pathogens, identified them as a species of Alternaria according to Koch's postulates, and then tested their pathogenicity and biological characteristics. The results showed that the pathogens causing P. heterophylla black spot were A. gaisen, as evidenced by the similar colony morphology, spore characteristics, sporulation phenotype, and the same clade with A. gaisen on the phylogenetic tree(the maximum likelihood support rate of 100% and the Bayesian posterior probability of 1.00) built based on the tandem sequences of ITS, tef1, gapdh, endoPG, Alta1, OPA10-2, and KOG1077. The optimum conditions for mycelial growth of the pathogen were 25 ℃, pH 5-8, and 24 h dark culture. The lethal conditions for mycelia and spores were both treatment at 50 ℃ for 10 min. We reported for the first time the A. gaisen-caused black spot of P. heterophylla. The results could provide a theoretical basis for the diagnosis and control of P. heterophylla leaf spot diseases.
Bayes Theorem ; Phylogeny ; Caryophyllaceae ; Alternaria ; Mycelium

The odor fingerprint of Pollygonati Rhizoma samples with different mildewing degrees was analyzed and the relationship between the odor variation and the mildewing degree was explored. A fast discriminant model was established according to the response intensity of electronic nose. The α-FOX3000 electronic nose was applied to analyze the odor fingerprint of Pollygonati Rhizoma samples with different mildewing degrees and the radar map was used to analyze the main contributors among the volatile organic compounds. The feature data were processed and analyzed by partial least squares discriminant analysis(PLS-DA), K-nearest neighbor(KNN), sequential minimal optimization(SMO), random forest(RF) and naive Bayes(NB), respectively. According to the radar map of the electronic nose, the response values of three sensors, namely T70/2, T30/1, and P10/2, increased with the mildewing, indicating that the Pollygonati Rhizoma produced alkanes and aromatic compounds after the mildewing. According to PLS-DA model, Pollygonati Rhizoma samples of three mildewing degrees could be well distinguished in three areas. Afterwards, the variable importance analysis of the sensors was carried out and then five sensors that contributed a lot to the classification were screened out: T70/2, T30/1, PA/2, P10/1 and P40/1. The classification accuracy of all the four models(KNN, SMO, RF, and NB) was above 90%, and KNN was most accurate(accuracy: 97.2%). Different volatile organic compounds were produced after the mildewing of Pollygonati Rhizoma, and they could be detected by electronic nose, which laid a foundation for the establishment of a rapid discrimination model for mildewed Pollygonati Rhizoma. This paper shed lights on further research on change pattern and quick detection of volatile organic compounds in moldy Chinese herbal medicines.
Electronic Nose ; Odorants/analysis* ; Volatile Organic Compounds/analysis* ; Bayes Theorem ; Drugs, Chinese Herbal/analysis* ; Discriminant Analysis

This study aimed to systematically evaluate the efficacy and safety of different Chinese patent medicines in the treatment of idiopathic membranous nephropathy. The relevant randomized controlled trial(RCT) was retrieved from PubMed, EMbase, Cochrane Library, CNKI, SinoMed, Wanfang, and VIP with the time interval from database inception to December 2022. The Cochrane risk of bias assessment tool was employed to evaluate the quality of the included RCT, and Stata 15.0 and GEMTC to perform the Bayesian network Meta-analysis. Finally, 51 RCTs were included, involving 9 Chinese patent medicines and 3 591 patients. The results of network Meta-analysis showed that in terms of the total effective rate and the increase in plasma albumin, the top three interventions were Zhengqing Fengtongning Sustained Release Tablets + conventional western medicine, Bailing Capsules + conventional western medicine, and Tripterygium Glycosides Tablets + conventional western medicine. In terms of reducing 24-hour urine total protein, the top three interventions were Zhengqing Fengtongning Sustained Release Tablets + conventional western medicine, Shenfukang Capsules +conventional western medicine, and Huangkui Capsules + conventional western medicine. In terms of reducing serum creatinine, the top three interventions were Shenfukang Capsules + conventional western medicine, Bailing Capsules + conventional western medicine, and Zhengqing Fengtongning Sustained Release Tablets + conventional western medicine. In terms of safety, Chinese patent medicines combined with conventional western medicine had fewer adverse reactions than the control group. The results suggest that Chinese patent medicines combined with conventional western medicine can improve the therapeutic effect on idiopathic membranous nephropathy, and differentiated medications can be adopted according to the specific symptoms of patients in clinical treatment. Further validation needs to be carried out in the future with multi-center, large-sample, and high-quality RCT.
Humans ; Nonprescription Drugs/therapeutic use* ; Network Meta-Analysis ; Glomerulonephritis, Membranous/drug therapy* ; Bayes Theorem ; Capsules ; Delayed-Action Preparations ; Drugs, Chinese Herbal/adverse effects* ; Tablets

The method of using deep learning technology to realize automatic sleep staging needs a lot of data support, and its computational complexity is also high. In this paper, an automatic sleep staging method based on power spectral density (PSD) and random forest is proposed. Firstly, the PSDs of six characteristic waves (K complex wave, δ wave, θ wave, α wave, spindle wave, β wave) in electroencephalogram (EEG) signals were extracted as the classification features, and then five sleep states (W, N1, N2, N3, REM) were automatically classified by random forest classifier. The whole night sleep EEG data of healthy subjects in the Sleep-EDF database were used as experimental data. The effects of using different EEG signals (Fpz-Cz single channel, Pz-Oz single channel, Fpz-Cz + Pz-Oz dual channel), different classifiers (random forest, adaptive boost, gradient boost, Gaussian naïve Bayes, decision tree, K-nearest neighbor), and different training and test set divisions (2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, single subject) on the classification effect were compared. The experimental results showed that the effect was the best when the input was Pz-Oz single-channel EEG signal and the random forest classifier was used, no matter how the training set and test set were transformed, the classification accuracy was above 90.79%. The overall classification accuracy, macro average F1 value, and Kappa coefficient could reach 91.94%, 73.2% and 0.845 respectively at the highest, which proved that this method was effective and not susceptible to data volume, and had good stability. Compared with the existing research, our method is more accurate and simpler, and is suitable for automation.
Humans ; Random Forest ; Bayes Theorem ; Sleep Stages ; Sleep ; Electroencephalography/methods*