OBJECTIVE: To investigate the feasibility of constructing the risk index of Echinococcus infection based on the classification of echinococcosis lesions, so as to provide insights into the management of echinococcosis.
METHODS: The imaging data of echinococcosis cases were collected from epidemiological surveys of echinococcosis in China from 2012 to 2016, and the detection of incident echinococcosis cases was captured from the annual echinococcosis prevention and control reports across provinces (autonomous regions) and Xinjiang Production and Construction Corps in China from 2017 to 2022. After echinococcosis lesions were classified, a risk index of Echinococcus infection was constructed based on the principle of discrete distribution marginal probability and multi-group classification data tests. The correlation between the risk index of Echinococcus infection and the detection of incident echinococcosis cases was evaluated in the provinces (autonomous regions and corps) from 2017 to 2022, and the correlations between the short and medium-term risk indices and between the medium and long-term risk indices of Echinococcus infection were examined using a univariate linear regression model.
RESULTS: A total of 4 014 echinococcosis cases in China from 2012 to 2016 were included in this study. The short-, medium- and long-term risk indices of E. granulosus infection varied in echinococcosis-endemic provinces (autonomous regions and corps) of China (χ2 = 4.12 to 708.65, all P values < 0.05), with high short- (0.058), medium- (0.137) and long-term risk indices (0.104) in Tibet Autonomous Region, and the short-, medium- and long-term risk indices of E. multilocularis infection varied in echinococcosis-endemic provinces (autonomous regions and corps) of China (χ2 = 6.74 to 122.60, all P values < 0.05), with a high short-term risk index in Sichuan Province (0.016) and high medium- (0.009) and long-term risk indices in Qinghai Province (0.018). There were no significant correlations between the risk index of E. granulosus infection and the detection of incident cystic echinococcosis cases during the study period (t = -0.518 to 2.265, all P values > 0.05), and strong correlations were found between the risk indices of E. multilocularis infection and the detection of incident alveolar echinococcosis cases (including mixed type) in 2018, 2020, 2021, 2022, during the period from 2017 through 2020, from 2017 through 2021, from 2017 through 2022 (all r values > 0.7, t = 2.521 to 3.692, all P values < 0.05). Linear regression models were established between the risk index of E. multilocular infection and the detection of alveolar echinococcosis cases (including mixed type), and the models were all statistically significant (b = 0.214 to 2.168, t = 2.458 to 3.692, F = 6.044 to 13.629, all P values < 0.05). The regression coefficients for the correlations between the medium- and short-term, and between the long- and medium-term risk indices of E. granulosus infection were 2.339 and 0.765, and the regression coefficients for the correlations between the medium- and short-term, and between the long- and medium-term risk indices of E. multilocular infection were 0.280 and 1.842, with statistical significance seen in both the regression coefficients and regression models (t = 16.479 to 197.304, F = 271.570 to 38 928.860, all P values < 0.05).
CONCLUSIONS: The risk index of Echinococcus infection has been successfully established based on the classification of echinococcosis lesions, which may provide insights into the prevention and control, prediction, diagnosis and treatment, and classified management of echinococcosis.
［摘要］ 目的 分析基于棘球蚴病病灶分型构建棘球蚴感染风险指数的可行性, 从而为棘球蚴病防控提供参考。方法 收集2012—2016年我国棘球蚴病流行病学调查中棘球蚴病病例病灶影像学资料及2017—2022年我国棘球蚴病防治 工作年报中各流行省 (自治区) 及新疆生产建设兵团棘球蚴病新发现病例检出率数据。对棘球蚴病病灶进行分型后, 参 考离散分布边际概率原理和多分组分类数据检验方法构建棘球蚴感染风险指数。对该指数与2017—2022年各流行省 (自治区) 和新疆生产建设兵团棘球蚴病新发现病例检出率数据进行相关性分析, 建立单因素线性回归模型分析近期与 中期、中期与远期棘球蚴感染风险指数间关系。结果 本研究累计纳入2012—2016年我国棘球蚴病病例4 014例。我 国各棘球蚴病流行省 (自治区) 、新疆生产建设兵团间细粒棘球蚴近期、中期和远期感染风险指数均不相同 (χ2 = 4.12 ~ 708.65, P 均< 0.05), 其中西藏自治区近期 (0.058) 、中期 (0.137) 和远期 (0.104) 细粒棘球蚴感染风险指数均较高; 多房棘 球蚴近期、中期和远期感染风险指数均不相同 (χ2 = 6.74 ~ 122.60, P 均< 0.05), 其中四川省近期感染风险指数 (0.016) 较 高, 青海省中期 (0.009) 、远期 (0.018) 感染风险指数较高。细粒棘球蚴感染风险指数与新发现病例检出率相关性均无统 计学意义 (t = -0.518 ~ 2.265, P 均> 0.05); 多房棘球蚴各期感染风险指数与2018、2020、2021、2022、2017—2020、2017—2021、2017—2022年泡型 (含混合型) 棘球蚴病新发现病例检出率均呈强相关 (r 均> 0.7, t = 2.521 ~ 3.692, P 均< 0.05) 。 对多房棘球蚴各期感染风险指数与泡型 (含混合型) 棘球蚴病新发现病例检出率建立线性回归模型, 均有统计学意义 (b = 0.214 ~ 2.168, t = 2.458 ~ 3.692, F = 6.044 ~ 13.629, P 均< 0.05) 。中期与近期、远期与中期细粒棘球蚴感染风险指数 间回归系数分别为2.339和0.765, 中期与近期、远期与中期多房棘球蚴感染风险指数间回归系数分别为0.280和1.842, 回归系数与回归模型均有统计学意义 (t = 16.479 ~ 197.304, F = 271.570 ~ 38 928.860, P均< 0.05) 。结论 成功建立了 一种基于棘球蚴病病灶分型的棘球蚴感染风险指数, 可望为棘球蚴病防控、预测、诊疗和分类管理提供参考。.

UNASSIGNED: Scalable PTSD screening strategies must be brief, accurate and capable of administration by a non-specialized workforce.
UNASSIGNED: We used PTSD as determined by the structured clinical interview as our gold standard and considered predictors sets of (a) Posttraumatic Stress Checklist-5 (PCL-5), (b) Primary Care PTSD Screen for the DSM-5 (PC-PTSD) and, (c) PCL-5 and PC-PTSD questions to identify the optimal items for PTSD screening for public sector settings in Kenya. A logistic regression model using LASSO was fit by minimizing the average squared error in the validation data. Area under the receiver operating characteristic curve (AUROC) measured discrimination performance.
UNASSIGNED: Penalized regression analysis suggested a screening tool that sums the Likert scale values of two PCL-5 questions-intrusive thoughts of the stressful experience (#1) and insomnia (#21). This had an AUROC of 0.85 (using hold-out test data) for predicting PTSD as evaluated by the MINI, which outperformed the PC-PTSD. The AUROC was similar in subgroups defined by age, sex, and number of categories of trauma experienced (all AUROCs>0.83) except those with no trauma history- AUROC was 0.78.
UNASSIGNED: In some East African settings, a 2-item PTSD screening tool may outperform longer screeners and is easily scaled by a non-specialist workforce.

UNASSIGNED: Patients with lupus nephritis experience disease symptoms and side effects from treatment. Although self-management behaviors are important in patients with this disease, there is limited research on the factors influencing these behaviors.
UNASSIGNED: This study aimed to examine the factors influencing self-management behaviors in patients with lupus nephritis.
UNASSIGNED: This cross-sectional study was conducted in 240 patients with lupus nephritis at a university hospital in Thailand between August 2019 and December 2020 using a random sampling method. Data were collected using a demographic and clinical characteristic questionnaire, Self-Management Behavior Questionnaire, Self-efficacy for Managing Chronic Disease: A 6-item Scale, Knowledge about Lupus Nephritis Questionnaire, Family Support Scale, Social Networks in Adult Life Questionnaire, and Memorial Symptom Assessment Scale for Lupus Nephritis. Descriptive statistics and multiple linear regression analyses were employed.
UNASSIGNED: The participants reported a moderate level of self-management behaviors. Multiple regression analyses revealed that disease duration, income, symptoms, self-efficacy, knowledge, family support, social networks, and classes of lupus nephritis significantly explained 21% of the variance in self-management behaviors (R2 = 0.21; F(8,231) = 7.73; p <0.001). Family support (β = 0.32, p <0.001) and symptoms (β = -0.23, p <0.001) were significant determinants of self-management behaviors in patients with lupus nephritis.
UNASSIGNED: The findings provide valuable insight for nurses to better understand the factors influencing self-management behaviors in patients with lupus nephritis. Patients with low family support and high symptom severity may face difficulty in performing self-management behaviors. Nurses should pay more attention to these patients and provide family-based interventions to optimize self-management behaviors in this population.

UNASSIGNED: treatment of severe burn injury generally requires enormous human and material resources including specialized intensive care, staged surgery, and continued restoration. This contributes to the enormous burden on patients and their families. The cost of burn treatment is influenced by many factors including the demographic and clinical characteristics of the patient. This study aimed to determine the costs of burn care and its associated predictive factors in Korle-Bu Teaching Hospital, Ghana.
UNASSIGNED: an analytical cross-sectional study was conducted among 65 consenting adult patients on admission at the Burns Centre of the Korle-Bu Teaching Hospital. Demographic and clinical characteristics of patients as well as the direct cost of burns treatment were obtained. Multiple regression analysis was done to determine the predictors of the direct cost of burn care.
UNASSIGNED: a total of sixty-five (65) participants were enrolled in the study with a male-to-female ratio of 1.4: 1 and a mean age of 35.9 ± 14.6 years. Nearly 85% sustained between 10-30% total body surface area burns whilst only 6.2% (4) had burns more than 30% of total body surface area. The mean total cost of burns treatment was GHS 22,333.15 (USD 3,897.58). Surgical treatment, wound dressing and medication charges accounted for 45.6%, 27.5% and 9.8% of the total cost of burn respectively.
UNASSIGNED: the direct costs of burn treatment were substantially high and were predicted by the percentage of total body surface area burn and length of hospital stay.

Gene expression is dynamic and varies at different stages of processes. The identification of gene profiles with temporal-specific expression patterns can provide valuable insights into ongoing biological processes, such as the cell cycle, cell development, circadian rhythms, or responses to external stimuli such as drug treatments or viral infections. However, currently, no database defines, identifies or archives gene profiles with temporal-specific expression patterns. Here, using a high-throughput regression analysis approach, eight linear and nonlinear parametric models were fitted to gene expression profiles from time-series experiments to identify eight types of gene profiles with temporal-specific expression patterns. We curated 2684 time-series transcriptome datasets and identified 2644,370 gene profiles exhibiting temporal-specific expression patterns. The results were stored in the database GeTeSEPdb (gene profiles with temporal-specific expression patterns database, http://www.inbirg.com/GeTeSEPdb/). Moreover, we implemented an online tool to identify gene profiles with temporal-specific expression patterns from user-submitted data. In summary, GeTeSEPdb is a comprehensive web service that can be used to identify and analyse gene profiles with temporal-specific expression patterns. This approach facilitates the exploration of transcriptional changes and temporal patterns of responses. We firmly believe that GeTeSEPdb will become a valuable resource for biologists and bioinformaticians.

Risk prediction models fitted using maximum likelihood estimation (MLE) are often overfitted resulting in predictions that are too extreme and a calibration slope (CS) less than 1. Penalized methods, such as Ridge and Lasso, have been suggested as a solution to this problem as they tend to shrink regression coefficients toward zero, resulting in predictions closer to the average. The amount of shrinkage is regulated by a tuning parameter, λ , $\\lambda ,$ commonly selected via cross-validation (\"standard tuning\"). Though penalized methods have been found to improve calibration on average, they often over-shrink and exhibit large variability in the selected λ $\\lambda $ and hence the CS. This is a problem, particularly for small sample sizes, but also when using sample sizes recommended to control overfitting. We consider whether these problems are partly due to selecting λ $\\lambda $ using cross-validation with \"training\" datasets of reduced size compared to the original development sample, resulting in an over-estimation of λ $\\lambda $ and, hence, excessive shrinkage. We propose a modified cross-validation tuning method (\"modified tuning\"), which estimates λ $\\lambda $ from a pseudo-development dataset obtained via bootstrapping from the original dataset, albeit of larger size, such that the resulting cross-validation training datasets are of the same size as the original dataset. Modified tuning can be easily implemented in standard software and is closely related to bootstrap selection of the tuning parameter (\"bootstrap tuning\"). We evaluated modified and bootstrap tuning for Ridge and Lasso in simulated and real data using recommended sample sizes, and sizes slightly lower and higher. They substantially improved the selection of λ $\\lambda $ , resulting in improved CS compared to the standard tuning method. They also improved predictions compared to MLE.

Heat capacity data of many crystalline solids can be described in a physically sound manner by Debye-Einstein integrals in the temperature range from 0K to 300K. The parameters of the Debye-Einstein approach are either obtained by a Markov chain Monte Carlo (MCMC) global optimization method or by a Levenberg-Marquardt (LM) local optimization routine. In the case of the MCMC approach the model parameters and the coefficients of a function describing the residuals of the measurement points are simultaneously optimized. Thereby, the Bayesian credible interval for the heat capacity function is obtained. Although both regression tools (LM and MCMC) are completely different approaches, not only the values of the Debye-Einstein parameters, but also their standard errors appear to be similar. The calculated model parameters and their associated standard errors are then used to derive the enthalpy, entropy and Gibbs energy as functions of temperature. By direct insertion of the MCMC parameters of all 4·105 computer runs the distributions of the integral quantities enthalpy, entropy and Gibbs energy are determined.

Soil Organic Carbon (SOC) is crucial for determining soil fertility and environmental quality. The problem with traditional SOC chemical analysis methods is that they are time-consuming and resource-intensive. In recent years, visible-near infrared (Vis-NIR) spectroscopy has been employed as an alternative method for SOC determination. However, when applied on a larger scale, the prediction accuracy of soil properties decreases due to the heterogeneity of samples. Therefore, this study compared and analyzed the performance of partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and gaussian process regression (GPR) in predicting SOC. On this basis, a GPR model based on a hybrid kernel function (HKF-GPR) was proposed for SOC prediction. This hybrid kernel function was designed according to the properties of single kernel functions and the characteristics of soil spectral data. Results indicate that in large soil spectral databases, the GPR model outperforms other models in estimating SOC. The HKF-GPR model achieved the best SOC estimation accuracy, with an R2 of 0.7671, RMSE of 5.2934 g/kg, RPD of 2.0721, and RPIQ of 2.5789. Compared to other regression models, the HKF-GPR model proposed in this paper offers broader applicability and superior performance, enabling SOC estimation in large soil spectral libraries.

Well-known continuous distributions such as Beta and Kumaraswamy distribution are useful for modeling the datasets which are based on unit interval [0,1]. But every distribution is not always useful for all types of data sets, rather it depends on the shapes of data as well. In this research, a three-parameter new distribution named bounded exponentiated Weibull (BEW) distribution is defined to model the data set with the support of unit interval [0,1]. Some fundamental distributional properties for the BEW distribution have been investigated. For modeling dependence between measures in a dataset, a bivariate extension of the BEW distribution is developed, and graphical shapes for the bivariate BEW distribution have been shown. Several estimation methods have been discussed to estimate the parameters of the BEW distribution and to check the performance of the estimator, a Monte Carlo simulation study has been done. Afterward, the applications of the BEW distribution are illustrated using COVID-19 data sets. The proposed distribution shows a better fit than many well-known distributions. Lastly, a quantile regression model from bounded exponentiated Weibull distribution is developed, and its graphical shapes for the probability density function (PDF) and hazard function have been shown.

UNASSIGNED: Wuhan is located in the hinterland of China, in the east of Hubei Province, at the intersection of the Yangtze River and Hanshui River. It is a national historical and cultural city, an important industrial, scientific, and educational base, and a key transportation hub. There are many schools in Wuhan, with nearly a thousand of all kinds. The number of students is ~2.2 million, accounting for nearly one-fifth of the resident population; college or university students account for ~60% of the total student population. The geographical location of these colleges is relatively concentrated, and the population density is relatively high, making it prone to tuberculosis cluster epidemic.
UNASSIGNED: This study analyzed the epidemiological characteristics and influencing factors of tuberculosis aggregation in schools in Wuhan, China, during 2017-2022 to provide the basis for the scientific development of tuberculosis prevention and control strategies and measures in schools.
UNASSIGNED: This study adopted the methods of descriptive epidemiology to analyze the epidemic characteristics of tuberculosis aggregation in schools in Wuhan from January 2017 to December 2022, collecting the relevant data on tuberculosis prevention and control in all kinds of schools in the city using Questionnaire Star, an application of the China network questionnaire survey, and analyze the influencing factors of tuberculosis aggregation by using multifactor logistic regression analysis.
UNASSIGNED: From 2017 to 2022, 54 outbreaks of pulmonary tuberculosis aggregation in schools were reported in Wuhan, which involved 37 different schools, including 32 colleges or universities and five senior high schools; 176 cases were reported, among which 73 were positive for pathogens and 18 were rifampicin or izoniazid resistant. The median duration of a single cluster epidemic was 46 (26,368) days. Universities were more prone to cluster outbreaks than middle schools (χ2 = 105.160, P = 0.001), and the incidence rate among male students was higher than that of female students in cluster epidemics (χ2 = 12.970, P = 0.001). The multivariate logistic regression analysis results showed that boarding in school (OR = 7.60) is the risk factor for a tuberculosis cluster epidemic in schools. The small number of students (OR = 0.50), the location of the school in the city (OR = 0.60), carry out physical examinations for freshmen (OR = 0.44), carry out illness absence and cause tracking (OR = 0.05), dormitories and classrooms are regularly ventilated with open windows (OR = 0.16), strict implement the management of sick student\'s suspension from school (OR = 0.36), and seeking timely medical consultation (OR = 0.32) were the protective factors for a tuberculosis cluster epidemic in schools.
UNASSIGNED: We successfully identified the epidemiological characteristics and influencing factors of tuberculosis aggregation in schools in Wuhan. The results revealed the influence and status of various factors and indicated ways for schools to improve their TB prevention and control measures in their daily activities. These measures can effectively help curb the cluster epidemic of tuberculosis in schools.