Quality of life and its related-influencing factors in patients with cervical cancer based on the scale QLICP-CE(V2.0).

BACKGROUND: Quality of life research can guide clinical workers to adopt more targeted treatment and intervention measures, so as to achieve the purpose of improving patients' quality of life. The objective of this study was to evaluate health-related quality of life in Chinese patients with cervical cancer and to explore its influencing factors. METHODS: A total of 186 patients with cervical cancer were investigated by using the QLICP-CE (V2.0) scale (Quality of Life Instruments for Cancer Patients-Cervical Cancer) developed by our group in China. The data were analyzed by t-test, one-way ANOVA, univariate analysis, and multivariate linear regression. RESULTS: The total score of quality of life scale for cervical cancer patients was (62.58 ± 12.69), Univariate analysis of objective clinical indexes showed that creatinine concentration was a negative influence factor in the psychological domain, potassium ion concentration was a negative influence factor in the common symptoms and side effect domain, erythrocyte content was a positive influence factor physical domain and common general domain. Multiple linear regression results suggested that clinical staging was the influencing factor of common symptom and side effect domain, common general module and total score of scale. Marital status has different degrees of influence on the psychological, social, and common general domains. The level of education also influenced scores in the social domain. CONCLUSION: The total score of quality of life in patients with cervical cancer who received active treatment was acceptable. Marital status, clinical staging, and educational level are the factors that affect the quality of life of patients with cervical cancer. At the same time, potassium ion concentration, red blood cell count and creatinine concentration also have important effects on quality of life in patients with cervical cancer. Therefore, it is very important to give personalized treatment and nursing to patients based on various factors.

Reliability of the color measurement of resin composites using images obtained using a stereoscopic loupe.

This study assessed the reliability of a color measurement method using images obtained from a charge-coupled device (CCD) camera and a stereoscopic loupe. Disc-shaped specimens were created using the composite Filtek Z350 XT (shades DA1, DA2, DA3, and DA4) (n = 3). CIELAB color coordinates of the specimens were measured using the spectrophotometer SP60 over white and black backgrounds. Images of the same specimens were taken using a CCD camera attached to a stereoscopic loupe. The color of the image was measured (red-green-blue [RGB]) using an image processing software and converted to CIELAB coordinates. For each color coordinate, data from images were adjusted using linear regressions predicting those values from SP60. The whiteness index for dentistry (WID) and translucency parameter (TP00) of the specimens as well as the color differences (ΔE00) among pairwise shades were calculated. Data were analyzed via repeated-measures analysis of variance and Tukey's post hoc test (α = 0.05). Images obtained using the loupe tended to be darker and redder than the actual color. Data adjustment resulted in similar WID, ΔE00, and TP00 values to those observed for the spectrophotometer. Differences were observed only for the WID of shade DA3 and ΔE00 for comparing DA1 and DA3 over the black background. However, these differences were not clinically relevant. The use of adjusted data from images taken using a stereoscopic loupe is considered a feasible method for color measurement.

Salivary immunoglobulin levels and periodontal indices in Brazilian children with and without type 1 diabetes.

This cross-sectional study evaluated the association between salivary immunoglobulins, plaque index, and gingival index in Brazilian children with and without type 1 diabetes mellitus (DM1). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist for the reporting of observational studies was followed. The DM1 group had 38 children, and an equal number of volunteers matched by sex and age were recruited as controls. Clinical examination was performed for plaque index and gingival index determination. Non-stimulated whole saliva was collected. Concentrations of IgA, IgG, and IgM were determined by ELISA test. Data were tested by the Kolmogorov-Smirnov, Mann-Whitney, and Spearman tests and a multiple linear regression model (p<0.05) was performed. Gingival index was higher in the Control (DM1: 0.16±0.17; Control: 0.24±0.23, p=0.040). In DM1, there was a correlation between IgA and age (rho=0.371, p=0.024), IgM and IgG (rho=0.459, p=0.007), and IgM and gingival index (rho=0.394, p=0.014). In DM1, multiple linear regression showed that age (p=0.041; ß=0.363), gingival index (p=0.041; ß=0.398), and plaque index (p=0.008; ß=-0.506) were good predictors of IgA levels in saliva. Thus, IgA was the only researched immunoglobulin that was directly associated with plaque and gingival indices in Brazilian children with DM1, but not in control subjects.

Diving into a pool of data: Using principal component analysis to optimize performance prediction in women's short-course swimming.

This study aimed to optimise performance prediction in short-course swimming through Principal Component Analyses (PCA) and multiple regression. All women's freestyle races at the European Short-Course Swimming Championships were analysed. Established performance metrics were obtained including start, free-swimming, and turn performance metrics. PCA were conducted to reduce redundant variables, and a multiple linear regression was performed where the criterion was swimming time. A practical tool, the Potential Predictor, was developed from regression equations to facilitate performance prediction. Bland and Altman analyses with 95% limits of agreement (95% LOA) were used to assess agreement between predicted and actual swimming performance. There was a very strong agreement between predicted and actual swimming performance. The mean bias for all race distances was less than 0.1s with wider LOAs for the 800 m (95% LOA -7.6 to + 7.7s) but tighter LOAs for the other races (95% LOAs -0.6 to + 0.6s). Free-Swimming Speed (FSS) and turn performance were identified as Key Performance Indicators (KPIs) in the longer distance races (200 m, 400 m, 800 m). Start performance emerged as a KPI in sprint races (50 m and 100 m). The successful implementation of PCA and multiple regression provides coaches with a valuable tool to uncover individual potential and empowers data-driven decision-making in athlete training.

Is There an Association Between Inflammation and Serum-Vitamin D? - Results of a Retrospective Analysis of Hospitalized Geriatric Patients.

Purpose: Vitamin D deficiency is a common finding in geriatric patients. The ESPEN micronutrient guideline states that vitamin D serum levels significantly decrease in the presence of inflammation and should be interpreted with caution. This is of great interest for hospital care and would imply a significant change to the current approach to hospitalized patients with suspected vitamin D deficiency. Patients and methods: To evaluate the association of vitamin D and inflammation, we reanalyzed the data set of serum 25(OH)D-Levels of 687 consecutive geriatric hospitalized patients of a previously published study. Results: We found that vitamin D deficiency (<20 ng/dl) was prevalent in 78.0% and vitamin D insufficiency (20-30 ng/dl) in 9.9% of patients. Sperman's correlation showed a significant but very weak correlation (R = -0.100, P < 0.01) of serum vitamin D and C-reactive protein. However, linear regression with the inclusion of age and gender revealed no significant association (beta-coefficient -0.070; p=0.067). Conclusion: In this study, we could not confirm a significant and clinically relevant association between serum vitamin D levels and inflammation, contrasting with a previous study. However, longitudinal studies need to be performed to draw a final conclusion.

An evaluation of computational methods for aggregate data meta-analyses of diagnostic test accuracy studies.

BACKGROUND: A Generalized Linear Mixed Model (GLMM) is recommended to meta-analyze diagnostic test accuracy studies (DTAs) based on aggregate or individual participant data. Since a GLMM does not have a closed-form likelihood function or parameter solutions, computational methods are conventionally used to approximate the likelihoods and obtain parameter estimates. The most commonly used computational methods are the Iteratively Reweighted Least Squares (IRLS), the Laplace approximation (LA), and the Adaptive Gauss-Hermite quadrature (AGHQ). Despite being widely used, it has not been clear how these computational methods compare and perform in the context of an aggregate data meta-analysis (ADMA) of DTAs. METHODS: We compared and evaluated the performance of three commonly used computational methods for GLMM - the IRLS, the LA, and the AGHQ, via a comprehensive simulation study and real-life data examples, in the context of an ADMA of DTAs. By varying several parameters in our simulations, we assessed the performance of the three methods in terms of bias, root mean squared error, confidence interval (CI) width, coverage of the 95% CI, convergence rate, and computational speed. RESULTS: For most of the scenarios, especially when the meta-analytic data were not sparse (i.e., there were no or negligible studies with perfect diagnosis), the three computational methods were comparable for the estimation of sensitivity and specificity. However, the LA had the largest bias and root mean squared error for pooled sensitivity and specificity when the meta-analytic data were sparse. Moreover, the AGHQ took a longer computational time to converge relative to the other two methods, although it had the best convergence rate. CONCLUSIONS: We recommend practitioners and researchers carefully choose an appropriate computational algorithm when fitting a GLMM to an ADMA of DTAs. We do not recommend the LA for sparse meta-analytic data sets. However, either the AGHQ or the IRLS can be used regardless of the characteristics of the meta-analytic data.

Factors related to the low-risk perception of driving after cannabis use.

INTRODUCTION: Modifying risk perceptions related to driving after cannabis use (DACU) could deter individuals from enacting this behavior, as low-risk perception is associated with DACU engagement. This study identified sociodemographic characteristics, substance use, other driving behaviors, peer norms, and psychological characteristics that are associated with lower risk perception regarding DACU. METHODS: Canadian drivers aged 17-35 who have used cannabis in the past year (n = 1,467) completed an online questionnaire. A multivariate linear regression model allowed for identifying variables associated with the low-risk perception of DACU (i.e. believing it to be safe as one's driving ability is not impaired by cannabis or by being high). RESULTS: Lower risk perception of DACU was associated with identifying as male, weekly to daily cannabis use, engagement in DACU, general risky driving behaviors, being a passenger of a driver who engages in DACU, number of friends who engage in DACU, and peer approval of DACU. Having driven under the influence of alcohol, living in urban areas, having received traffic tickets in the past three years, and declaring past-week irritability and cognitive problems were associated with holding a higher risk perception related to DACU. DISCUSSION: Road education and prevention programs should target attitudes and perceptions regarding risks shaped by sociocultural norms and past risky driving experiences. They need to reach out more specifically to drivers with the identified characteristics associated with the low-risk perception of DACU. These interventions can potentially help reduce the rate of individuals who engage in this behavior.

Bilirubin Elevation During Hospitalization Post Radiofrequency Catheter Ablation of Persistent Atrial Fibrillation: Variation Trend, Related Factors, and Relevance to 1-Year Recurrence.

Background: The role of total bilirubin (TBIL) in cardiovascular disease has been increasingly recognized in recent decades. Studies have shown a correlation between total bilirubin levels and the prognosis of patients after heart surgery. This study aimed to investigate the clinical significance of bilirubin elevation in persistent atrial fibrillation (PAF) patients who received radiofrequency catheter ablation (RFCA). Methods and Results: A total of 184 patients with PAF who received RFCA were retrospectively studied. Laboratory examinations and demographic data were analyzed to identify independent predictors of TBIL elevation. The relationship between TBIL and prognosis was further investigated. Our results indicated that TBIL increased significantly after RFCA. Multiple linear regression analysis showed that TBIL elevation owned a negative correlation with the percentile of low voltage areas (LVAs) in left atria (ß=-0.490, P<0.001). In contrast, a positive correlation was observed with the white blood cell (WBC) ratio (ß=0.153, P=0.042) and left atrial diameter (LAD) (ß=0.232, P=0.025). It was found that postoperative TBIL levels increased and then gradually decreased to baseline within 5 days without intervention. The bilirubin ratio <1.211 indicated the possibility of 1-year AF recurrence after ablation with a predictive value of 0.743 (specificity = 75.00%, sensitivity = 66.67%). Conclusion: Bilirubin elevation post PAF RFCA was a common phenomenon and was associated with 1-year recurrence of AF in PAF patients after RFCA.

Relationship between triglyceride-glucose index and blood eosinophils among asthmatic individuals in the USA.

BACKGROUND: Presently, the majority of investigations primarily evaluate the correlation between triglyceride-glucose index (TyGI) with lung diseases, such as asthma. However, they did not delve into the correlation between TyGI and inflammatory responses related to the disease. Few studies have explored the association between TyGI and blood eosinophil count (BEOC). Thus, National Health and Nutrition Examination Survey (NHANES) data were used in this study to evaluate the correlation between TyGI and BEOC in individuals with asthma. METHODS: This study investigated 3902 individuals with asthma. Linear regression analysis was performed to investigate the association between TyGI and BEOC in patients with asthma. Subsequently, the GAM and threshold effect models were used to validate the presence of either a nonlinear or linear association between TyGI and BEOC. Finally, stratified analyses were conducted to ascertain the correlations between different subgroups. RESULTS: Four linear regression models confirmed a positive linear correlation between TyGI and BEOC in patients with asthma. In Model D, which controlled for all covariates, BEOC increased by 12.44 cells/uL for every extra unit of TyGI. The GAM and threshold effect models further verified the positive linear correlation between TyGI and BEOC. The XGBoost model indicated that the six most significant variables influencing BEOC, in order of relative importance, were age, cholesterol level, body mass index (BMI), poverty-to-income ratio (PIR), BNEUC, and TyGI. CONCLUSIONS: In patients with asthma, the study discovered a linear positive correlation between TyGI and BEOC. This indicates a potential connection between TyGI and alterations in the immune status of individuals with asthma, which may help detect abnormalities in a timely manner and provide a reference for clinical decision-making. This study offers fresh insights for the future exploration of the management and treatment of asthma.

The Michaelis-Menten Reaction at Low Substrate Concentrations: Pseudo-First-Order Kinetics and Conditions for Timescale Separation.

We demonstrate that the Michaelis-Menten reaction mechanism can be accurately approximated by a linear system when the initial substrate concentration is low. This leads to pseudo-first-order kinetics, simplifying mathematical calculations and experimental analysis. Our proof utilizes a monotonicity property of the system and Kamke's comparison theorem. This linear approximation yields a closed-form solution, enabling accurate modeling and estimation of reaction rate constants even without timescale separation. Building on prior work, we establish that the sufficient condition for the validity of this approximation is s 0 ≪ K , where K = k 2 / k 1 is the Van Slyke-Cullen constant. This condition is independent of the initial enzyme concentration. Further, we investigate timescale separation within the linear system, identifying necessary and sufficient conditions and deriving the corresponding reduced one-dimensional equations.

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models.

Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or "niches" followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).

Simulation study to evaluate when Plasmode simulation is superior to parametric simulation in estimating the mean squared error of the least squares estimator in linear regression.

Simulation is a crucial tool for the evaluation and comparison of statistical methods. How to design fair and neutral simulation studies is therefore of great interest for both researchers developing new methods and practitioners confronted with the choice of the most suitable method. The term simulation usually refers to parametric simulation, that is, computer experiments using artificial data made up of pseudo-random numbers. Plasmode simulation, that is, computer experiments using the combination of resampling feature data from a real-life dataset and generating the target variable with a known user-selected outcome-generating model, is an alternative that is often claimed to produce more realistic data. We compare parametric and Plasmode simulation for the example of estimating the mean squared error (MSE) of the least squares estimator (LSE) in linear regression. If the true underlying data-generating process (DGP) and the outcome-generating model (OGM) were known, parametric simulation would obviously be the best choice in terms of estimating the MSE well. However, in reality, both are usually unknown, so researchers have to make assumptions: in Plasmode simulation studies for the OGM, in parametric simulation for both DGP and OGM. Most likely, these assumptions do not exactly reflect the truth. Here, we aim to find out how assumptions deviating from the true DGP and the true OGM affect the performance of parametric and Plasmode simulations in the context of MSE estimation for the LSE and in which situations which simulation type is preferable. Our results suggest that the preferable simulation method depends on many factors, including the number of features, and on how and to what extent the assumptions of a parametric simulation differ from the true DGP. Also, the resampling strategy used for Plasmode influences the results. In particular, subsampling with a small sampling proportion can be recommended.

Identifying Risk Indicators of Cardiovascular Disease in Fasa Cohort Study (FACS): An Application of Generalized Linear Mixed-Model Tree.

BACKGROUND: Today, cardiovascular disease (CVD) is the most important cause of death around the world. In this study, our main aim was to predict CVD using some of the most important indicators of this disease and present a tree-based statistical framework for detecting CVD patients according to these indicators. METHODS: We used data from the baseline phase of the Fasa Cohort Study (FACS). The outcome variable was the presence of CVD. The ordinary Tree and generalized linear mixed models (GLMM) were fitted to the data and their predictive power for detecting CVD was compared with the obtained results from the GLMM tree. Statistical analysis was performed using the RStudio software. RESULTS: Data of 9499 participants aged 35‒70 years were analyzed. The results of the multivariable mixed-effects logistic regression model revealed that participants' age, total cholesterol, marital status, smoking status, glucose, history of cardiac disease or myocardial infarction (MI) in first- and second-degree relatives, and presence of other diseases (like hypertension, depression, chronic headaches, and thyroid disease) were significantly related to the presence of CVD (P<0.05). Fitting the ordinary tree, GLMM, and GLMM tree resulted in area under the curve (AUC) values of 0.58 (0.56, 0.61), 0.81 (0.77, 0.84), and 0.80 (0.76, 0.83), respectively, among the study population. In addition, the tree model had the best specificity at 81% but the lowest sensitivity at 65% compared to the other models. CONCLUSION: Given the superior performance of the GLMM tree compared with the standard tree and the lack of significant difference with the GLMM, using this model is suggested due to its simpler interpretation and fewer assumptions. Using updated statistical models for more accurate CVD prediction can result in more precise frameworks to aid in proactive patient detection planning.

Factors influencing the posterior cruciate ligament buckling phenomenon-a multiple linear regression analysis of bony and soft tissue structures of the knee joint.

PURPOSE: To determine whether posterior cruciate ligament (PCL) buckling (angular change) is associated with anterior cruciate ligament (ACL) status (intact or ruptured), meniscal bone angle (MBA), anterior tibial translation (ATT), body weight, femoral-tibial rotation (FTR), posterior tibial slope (PTS), PCL length and femoral-tibial distance (FTD) and to identify the factors that have the greatest influence. METHODS: All enrolled participants were scanned with a 3.0 T, 8-channel coil MRI system (Magnetom Verio; Siemens). Bone and soft tissue parameters were measured by MIMICS software for each subject and each measured parameter was correlated with PCL buckling phenomena. The correlated and statistically significant parameters were then analyzed by multiple linear regression to determine the magnitude of the effect of the different parameters on the PCL buckling phenomenon. RESULTS: A total of 116 subjects (50 ACL ruptured and 66 age, weight and height matched volunteers with uninjured knees) were enrolled. Among all measured parameters, there were 8 parameters that correlated with PCL angle (PCLA), of which ACL status had the strongest correlation with PCLA (r = - 0.67, p = < 0.001); and 7 parameters that correlated with PCL-posterior femoral cortex angle (PCL-PCA), of which ATT had the strongest correlation with PCL-PCA (r = 0.69, p = < 0.001). PCLIA was not significantly correlated with any of the measured parameters. Multiple linear regression analyses revealed four parameters can explain PCLA, of which ACL status had the strongest effect on PCLA (absolute value of standardized coefficient Beta was 0.508). Three parameters can explain PCL-PCA, of which ATT had the strongest effect on PCLIA (r = 0.69, p = < 0.001), ATT has the greatest effect on PCL-PCA (absolute value of normalized coefficient Beta is 0.523). CONCLUSIONS: PCLA may be a simple and easily reproducible and important supplement for the diagnosis of ACL injury; PCL-PCA is a simple and easily reproducible and important complementary tool for the detection of ATT. The use of PCLA is more recommended to aid in the diagnosis of ACL injury.

Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design.

Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays, a computational model to predict warhead reactivities could be useful for several aspects of the covalent inhibitor design process. Studies have shown correlations between covalent warhead reactivities and quantum mechanic (QM) properties that describe important aspects of the covalent reaction mechanism. However, the models from these studies are often linear regression equations and can have limitations associated with their usage. Applications of machine learning (ML) models to predict covalent warhead reactivities with QM descriptors are not extensively seen in the literature. This study uses QM descriptors, calculated at different levels of theory, to train ML models to predict reactivities of covalent acrylamide warheads. The QM/ML models are compared with linear regression models built upon the same QM descriptors and with ML models trained on structure-based features like Morgan fingerprints and RDKit descriptors. Experiments show that the QM/ML models outperform the linear regression models and the structure-based ML models, and literature test sets demonstrate the power of the QM/ML models to predict reactivities of unseen acrylamide warhead scaffolds. Ultimately, these QM/ML models are effective, computationally feasible tools that can expedite the design of new covalent inhibitors.

Sarcopenia prevalence using handgrip strength or chair stand performance in adults living with type 2 diabetes mellitus.

BACKGROUND: The updated European Working Group on Sarcopenia in Older People (EWGSOP2) recommends handgrip strength (HGS) and the chair stand test (CST) to assess muscle strength, with the CST being a convenient proxy for lower limb strength. However, adiposity may differentially influence these strength criteria and produce discrepant sarcopenia prevalence. OBJECTIVE: To determine the prevalence of sarcopenia using HGS or the CST, and to investigate the associations between these strength criteria and adiposity in adults with type 2 diabetes mellitus. METHODS: The EWGSOP2 definition was used to assess the prevalence of probable (low muscle strength), confirmed (plus low muscle mass) and severe (plus poor physical performance) sarcopenia. Linear regression models were used to study the association between different measures of muscle strength and adiposity. RESULTS: We used data from 732 adults with type 2 diabetes mellitus (35.7% female, aged 64 ± 8 years, body mass index 30.7 ± 5.0 kg/m2). Using the CST compared with HGS produced a higher prevalence of probable (31.7% vs. 7.1%), confirmed (5.6% vs. 1.6%) and severe (1.0% vs. 0.3%) sarcopenia, with poor agreement between strength criteria to identify probable sarcopenia. CST performance, but not HGS, was significantly associated with all measures of adiposity in unadjusted and adjusted models. CONCLUSIONS: Higher levels of adiposity may impact CST performance, but not HGS, resulting in a higher prevalence of sarcopenia in adults with type 2 diabetes mellitus. Consideration should be paid to the most appropriate measure of muscle function in this population.

Sex differences in 3- to 5-year-old children's motor competence: A pooled cross-sectional analysis of 6241 children.

There is some, albeit inconsistent, evidence supporting sex differences in preschoolers' motor competence (MC), with these observations not uniform when analyzed by age, and cultural groups. Thus, this study examined sex differences across ages in 3- to 5-year-old children's MC. A cross-country pooled sample of 6241 children aged 3-5 years (49.6% girls) was assessed for MC using the Test of Gross Motor Development-2nd/3rd edition, and children were categorized into groups of age in months. Multiple linear regression models and predictive margins were calculated to explore how sex and age in months affect scores of MC (i.e., locomotor and ball skills), with adjustments for country and BMI. The Chow's Test was used to test for the presence of a structural break in the data. Significant differences in favor of girls were seen at 57-59 and 66-68 months of age for locomotor skills; boys performed better in ball skills in all age periods, except for 42-44 and 45-47 months of age. The higher marginal effects were observed for the period between 45-47 and 48-50 months for locomotor skills (F = 30.21; and F = 25.90 for girls and boys, respectively), and ball skills (F = 19.01; and F = 42.11 for girls and boys, respectively). A significantly positive break point was seen at 45-47 months, highlighting the age interval where children's MC drastically improved. The identification of this breakpoint provides an evidence-based metric for when we might expect MC to rapidly increase, and an indicator of early delay when change does not occur at that age.

Hierarchical modelling of variance components makes analysis of resolvable incomplete block designs more efficient.

The standard approach to variance component estimation in linear mixed models for alpha designs is the residual maximum likelihood (REML) method. One drawback of the REML method in the context of incomplete block designs is that the block variance may be estimated as zero, which can compromise the recovery of inter-block information and hence reduce the accuracy of treatment effects estimation. Due to the development of statistical and computational methods, there is an increasing interest in adopting hierarchical approaches to analysis. In order to increase the precision of the analysis of individual trials laid out as alpha designs, we here make a proposal to create an objectively informed prior distribution for variance components for replicates, blocks and plots, based on the results of previous (historical) trials. We propose different modelling approaches for the prior distributions and evaluate the effectiveness of the hierarchical approach compared to the REML method, which is classically used for analysing individual trials in two-stage approaches for multi-environment trials.

Factors associated with the co-utilization of oral rehydration solution and zinc for treating diarrhea among under-five children in 35 sub-saharan Africa countries: a generalized linear mixed effect modeling with robust error variance.

INTRODUCTION: Even though childhood diarrhea is treated with a simple treatment solution, it continues to be one of the leading causes of under-five child mortality and malnutrition globally. In resource-limited settings such as Sub-Saharan Africa (SSA), the combination of oral rehydration salts (ORS) and zinc is regarded as an effective treatment for diarrhea; however, its utilization is very low. The purpose of this study was to determine the proportion and associated factors of co-utilization of ORS and zinc among under-five children with diarrhea in SSA. METHODS: The proportion and associated factors of co-utilization of ORS and zinc among under-five children with diarrhea in SSA were determined using secondary data analysis of recent Demographic and Health Surveys (DHS) of 35 SSA countries. The study included a total of 44,341 under-five children with diarrhea in weighted samples. A generalized linear mixed-effects model with robust error variance was used. For the variables included in the final model, adjusted prevalence ratios (aPR) with 95% confidence intervals (CI) were estimated. A model with the lowest deviance value were considered as the best-fitted model. RESULT: The pooled proportion of co-utilization of ORS and zinc for the treatment of diarrhea among under five children in SSA countries was 43.58% with a 95% CI (43.15%, 44.01%). Sex of the child, maternal age, residence, maternal educational and employment status, wealth index, media exposure, perceived distance to health facility and insurance coverage were statistically significant determinants of ORS and Zinc co-utilization for treating diarrhea among under five children in SSA. CONCLUSION: Only less than half of under-five children with diarrhea in SSA were treated with a combination of ORS and zinc. Thus, strengthening information dissemination through mass media, and community-level health education programs are important to scale up the utilization of the recommended combination treatment. Furthermore, increasing health insurance coverage, and establishing strategies to address the community with difficulty in accessing health facilities is also crucial in improving the use of the treatment.

Machine learning assisted 5-part tooth segmentation method for CBCT-based dental age estimation in adults.

BACKGROUND: The utilization of segmentation method using volumetric data in adults dental age estimation (DAE) from cone-beam computed tomography (CBCT) was further expanded by using current 5-Part Tooth Segmentation (SG) method. Additionally, supervised machine learning modelling -namely support vector regression (SVR) with linear and polynomial kernel, and regression tree - was tested and compared with the multiple linear regression model. MATERIAL AND METHODS: CBCT scans from 99 patients aged between 20 to 59.99 was collected. Eighty eligible teeth including maxillary canine, lateral incisor, and central incisor were used in this study. Enamel to dentine volume ratio, pulp to dentine volume ratio, lower tooth volume ratio, and sex was utilized as independent variable to predict chronological age. RESULTS: No multicollinearity was detected in the models. The best performing model comes from maxillary lateral incisor using SVR with polynomial kernel ( = 0.73). The lowest error rate achieved by the model was given also by maxillary lateral incisor, with 4.86 years of mean average error and 6.05 years of root means squared error. However, demands a complex approach to segment the enamel volume in the crown section and a lengthier labour time of 45 minutes per tooth.