UNASSIGNED: Patients with schizophrenia often exhibit poor life skills, posing significant clinical challenges. Life skills comprise cognitive functions crucial for planning daily activities, including divergent thinking. However, the cognitive deficits contributing to these diminished skills among patients with schizophrenia are underexplored. This study introduces a modified Tinkertoy Test (m-TTT) to investigate the correlation between life skills, divergent thinking, and psychological assessment tools in patients with schizophrenia.
UNASSIGNED: Fifty-two patients with schizophrenia, alongside a control group, matched for sex, age, and education, were evaluated using psychological assessment tools. For the patient group, the Life Skills Profile (LSP) and Positive and Negative Syndrome Scale were administered to measure functional abilities and psychiatric symptoms, respectively. Additionally, duration of disease and antipsychotic daily dosage levels were assessed exclusively in the patient group. Both groups were evaluated with the m-TTT, Idea Fluency Test (IFT), Design Fluency Test (DFT), and Brief Assessment of Cognition in Schizophrenia (BACS) to comprehensively assess cognitive functions. A stepwise multiple regression model was conducted to identify significant correlates of LSP total score among the patient group.
UNASSIGNED: The schizophrenia group scored notably lower than the neurotypical controls on the m-TTT, IFT, DFT, and BACS. Our stepwise multiple regression analysis highlighted that the LSP total score was significantly correlated with the total m-TTT score and presence of negative symptoms.
UNASSIGNED: Divergent thinking could be a crucial factor in the life skills of individuals with schizophrenia. Rehabilitation programs based on this cognitive function might enhance their daily living capabilities.

This paper introduces a novel approach to modeling malaria incidence in Nigeria by integrating clustering strategies with regression modeling and leveraging meteorological data. By decomposing the datasets into multiple subsets using clustering techniques, we increase the number of explanatory variables and elucidate the role of weather in predicting different ranges of incidence data. Our clustering-integrated regression models, accompanied by optimal barriers, provide insights into the complex relationship between malaria incidence and well-established influencing weather factors such as rainfall and temperature.We explore two models. The first model incorporates lagged incidence and individual-specific effects. The second model focuses solely on weather components. Selection of a model depends on decision-makers priorities. The model one is recommended for higher predictive accuracy. Moreover, our findings reveal significant variability in malaria incidence, specific to certain geographic clusters and beyond what can be explained by observed weather variables alone.Notably, rainfall and temperature exhibit varying marginal effects across incidence clusters, indicating their differential impact on malaria transmission. High rainfall correlates with lower incidence, possibly due to its role in flushing mosquito breeding sites. On the other hand, temperature could not predict high-incidence cases, suggesting that other factors other than temperature contribute to high cases.Our study addresses the demand for comprehensive modeling of malaria incidence, particularly in regions like Nigeria where the disease remains prevalent. By integrating clustering techniques with regression analysis, we offer a nuanced understanding of how predetermined weather factors influence malaria transmission. This approach aids public health authorities in implementing targeted interventions. Our research underscores the importance of considering local contextual factors in malaria control efforts and highlights the potential of weather-based forecasting for proactive disease management.

Imbalances in electrolyte concentrations can have severe consequences, but accurate and accessible measurements could improve patient outcomes. The current measurement method based on blood tests is accurate but invasive and time-consuming and is often unavailable for example in remote locations or an ambulance setting. In this paper, we explore the use of deep neural networks (DNNs) for regression tasks to accurately predict continuous electrolyte concentrations from electrocardiograms (ECGs), a quick and widely adopted tool. We analyze our DNN models on a novel dataset of over 290,000 ECGs across four major electrolytes and compare their performance with traditional machine learning models. For improved understanding, we also study the full spectrum from continuous predictions to a binary classification of extreme concentration levels. Finally, we investigate probabilistic regression approaches and explore uncertainty estimates for enhanced clinical usefulness. Our results show that DNNs outperform traditional models but model performance varies significantly across different electrolytes. While discretization leads to good classification performance, it does not address the original problem of continuous concentration level prediction. Probabilistic regression has practical potential, but our uncertainty estimates are not perfectly calibrated. Our study is therefore a first step towards developing an accurate and reliable ECG-based method for electrolyte concentration level prediction-a method with high potential impact within multiple clinical scenarios.

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.