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Background and Aim: Endoscopic ultrasound shear wave elastography (EUS-SWE) can facilitate an objective evaluation of pancreatic fibrosis. Although it is primarily applied in evaluating chronic pancreatitis, its efficacy in assessing early chronic pancreatitis (ECP) remains underinvestigated. This study evaluated the diagnostic accuracy of EUS-SWE for assessing ECP diagnosed using the Japanese diagnostic criteria 2019. Methods: In total, 657 patients underwent EUS-SWE. Propensity score matching was used, and the participants were classified into the ECP and normal groups. ECP was diagnosed using the Japanese diagnostic criteria 2019. Pancreatic stiffness was assessed based on velocity (Vs) on EUS-SWE, and the optimal Vs cutoff value for ECP diagnosis was determined. A practical shear wave Vs value of ≥50% was considered significant. Results: Each group included 22 patients. The ECP group had higher pancreatic stiffness than the normal group (2.31 ± 0.67 m/s vs. 1.59 ± 0.40 m/s, p < 0.001). The Vs cutoff value for the diagnostic accuracy of ECP, as determined using the receiver operating characteristic curve, was 2.24m/s, with an area under the curve of 0.82 (95% confidence interval: 0.69-0.94). A high Vs was strongly correlated with the number of EUS findings (rs = 0.626, p < 0.001). Multiple regression analysis revealed that a history of acute pancreatitis and ≥2 EUS findings were independent predictors of a high Vs. Conclusions: There is a strong correlation between EUS-SWE findings and the Japanese diagnostic criteria 2019 for ECP. Hence, EUS-SWE can be an objective and invaluable diagnostic tool for ECP diagnosis.

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OBJECTIVE: This study sought to determine the risk-adjusted (controlled for patient characteristics) trend in pressure injury (PI) incidence in Switzerland. METHOD: A secondary data analysis was conducted. The data originated from the national PI quality measurement based on a multicentre cross-sectional design with repeated annual measurements. Descriptive statistics, a Cochran-Armitage trend test and logistic (multilevel) regression modelling were applied. RESULTS: The analysis sample comprised 123,715 patients from 230 hospitals over 10 survey years (2011-2019 and 2022). The incidence of descriptive PI varied in Switzerland between 3.7% and 5.6% over the survey years. No linear trend could be found when patient characteristics were not considered. A non-linear trend was detected when controlling for patient characteristics and the time effect (repeated measurement). This was also reflected in the plotted risk-adjusted incidence, which revealed a decrease followed by a levelling off. When only considering the incidence of PIs rated category 2 and higher, there was also a non-linear decreasing trend when controlling for patient characteristics and time effects. If the incidence per survey year were estimated on the basis of patient characteristics, an increase in the incidence would have been expected. CONCLUSION: Although patients' risk of developing a PI increased between 2011-2022, the incidence of PIs in Switzerland first decreased and then levelled off. The results indicated that care quality in Swiss hospitals has improved regarding PIs. Nevertheless, in view of demographic trends and increasing staff shortages, it is important to continue to monitor PI incidence and to invest in PI prevention.

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Background: Previous studies comparing the efficacy and safety of different treatment regimens for lupus nephritis are scarce. Moreover, confounding factors such as the duration of follow-up were hardly adjusted in those studies, potentially compromising the results and their extents to clinical settings. Objective: To rigorously investigate the efficacy and safety of biologics in patients with lupus nephritis using Bayesian network meta-regression analyses that adjust for the follow-up period, in order to provide more robust evidence for clinicians. Methods: Databases comprising PubMed, Embase, MedlinePlus, Cochrane Library, Google Scholars, and Scopus were retrieved for eligible articles from inception to February 29, 2024. The primary endpoint was the complete response rate, the secondary endpoint was the partial response rate, the tertiary endpoints were the adverse events, and infection-related adverse events. Napierian Logarithm of hazard ratio (lnHR) and the standard error of lnHR (selnHR) were generated for dichotomous variants by STATA 18.0 MP and then put into Rstudio 4.3.2 to conduct Bayesian network meta-analysis as well as network meta-regression analysis to yield hazard ratio (HR) as pairwise effect size. Results: Ten studies involving 2138 patients and 11 treatment regimens were ultimately included. In the original analysis, for the primary endpoint, compared to the control group, obinutuzumab (22.6 months), abatacept-30mg (20.5 months), abatacept-10mg (17.8 months), and belimumab (23.3 months) demonstrated significant superiority (HR ranged from 1.6 to 2.5), more ever, their significance regarding relative efficacy was correlated with follow up period, namely "time window" (shown in parentheses above). For the secondary endpoint, compared to the control group, obinutuzumab and abatacept-30mg showed conspicuous preponderance (HR ranged from 1.6 to 2.4), "time window" was also detected in abatacept-30mg (20.5 months), whereas obinutuzumab remained consistently obviously effective regardless of the follow-up period (shown in parentheses above). For the tertiary endpoint, there were no differences among active regimens and control. Conclusions: Considering the efficacy and safety and "time window" phenomenon, we recommend obinutuzumab as the preferred treatment for LN. Certainly, more rigorous head-to-head clinical trials are warranted to validate those findings.

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Machine learning techniques were applied systematically to the spectral data of near-infrared (NIR) spectroscopy to find out the sudan dye I adulterants in turmeric powders. Turmeric powder is one of the most commonly used spice and a simple target for adulteration. Pure turmeric powder was prepared at the laboratory and spiked with sudan dye I adulterants. The spectral data of these adulterated mixtures were obtained by NIR spectrometer and investigated accordingly. The concentrations of the adulterants were 1%, 5%, 10%, 15%, 20%, 25%, 30% (w/w) respectively. Exploratory data analysis was done for the visualization of the adulterant classes by principal component analysis (PCA). Optimization of the pre-processing and wavelength selection was done by cross-validation techniques using a partial least squares regression (PLSR) model. For quantitative analysis four different regression techniques were applied namely ensemble tree regression (ENTR), support vector regression (SVR), principal component regression (PCR), and PLSR, and a comparative analysis was done. The best method was found to be PLSR. The accuracy of the PLSR analysis was determined with the coefficients of determination (R2) of greater than 0.97 and with root mean square error (RMSE) of less than 0.93 respectively. For the verification of the robustness of the model, the Figure of merit (FOM) of the model was derived with the help of the Net analyte signal (NAS) theory. The current study established that the NIR spectroscopy can be applied to detect and quantify the amount of sudan dye I adulterants added to the turmeric powders with satisfactory accuracy. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-024-05971-9.

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Background: The progression of chronic hepatitis B (CHB) to liver fibrosis and even cirrhosis is often unknown to patients, but noninvasive markers capable of effectively identifying advanced liver fibrosis remains absent. Objective: Based on the results of liver biopsy, we aimed to construct a new nomogram to validate the stage of liver fibrosis in CHB patients by the basic information of CHB patients and routine laboratory tests. Methods: Patients with CHB diagnosed for the first time in the First Affiliated Hospital of Anhui Medical University from 2010 to 2018 were selected, and their basic information, laboratory tests and liver biopsy information were collected. Eventually, 974 patients were enrolled in the study, while all patients were randomized into a training cohort (n = 732) and an internal validation cohort (n = 242) according to a 3:1 ratio. In the training cohort, least absolute shrinkage and selection operator (Lasso) regression were used for predictor variable screening, and binary logistic regression analysis was used to build the diagnostic model, which was ultimately presented as a nomogram. The predictive accuracy of the nomograms was analyzed by running operating characteristic curve (ROC) to calculate area under curve (AUC), and the calibration was evaluated. Decision curve analysis (DCA) was used to determine patient benefit. In addition, we validated the built models with internal as well as external cohort (n = 771), respectively. Results: Ultimately, the training cohort, the internal validation cohort, and the external validation cohort contained sample sizes of 188, 53, and 149, respectively, for advanced liver fibrosis. Gender, albumin (Alb), globulin (Glb), platelets (PLT), alkaline phosphatase (AKP), glutamyl transpeptidase (GGT), and prothrombin time (PT) were screened as independent predictors. Compared with the aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), and King's score, the model in the training cohort (AUC = 0.834, 95% CI 0.800-0.868, p < 0.05) and internal validation cohort (AUC = 0.804, 95% CI 0.742-0.866, p < 0.05) showed the best discrimination and the best predictive performance. In addition, DCA showed that the clinical benefit of the nomogram was superior to the APRI, FIB-4 and King's scores in all cohorts. Conclusion: This study constructed a validated nomogram model with predictors screened from clinical variables which could be easily used for the diagnosis of advanced liver fibrosis in CHB patients.

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To investigate the potential of computed tomography (CT) images of median palatine suture (MP) for adult age estimation in the Northern and Southwestern Chinese populations. A total of 1110 cranial CT scans from individuals aged 10-79 years, including 557 northern Chinese and 553 southwestern Chinese, were collected for analysis. After volume reformation and multiplanar reconstruction, a total of 20 slices of median palatine suture were selected from each individual. The closure of sutures was analyzed into four stages, and the cumulative scores of 20 slices were recorded as the suture closure score (SCS). The correlations between SCS and age were compared among the two Chinese populations residing in diverse geographic regions. Regression models were established for age estimation. The estimation accuracy was evaluated based on the test set. The mean absolute error (MAE) and the correlation between predicted age and chronological age were calculated to evaluate estimation accuracy. The SCS of MP exhibited a significant correlation with age (0.613, northern male; 0.678, southwestern male; 0.730, northern female; 0.704, Southwestern female; 0.662, total). Furthermore, there were statistically significant differences in SCS among different regions and sex groups (p < 0.001). The cubic regression model had the highest R2 value in all subjects, especially among Northern females and Southwestern males, while the power and quadratic regression models showed the highest R2 value in Northern males and Southwestern females, respectively. In the test set, the Northern cohort demonstrated a lower MAE (9.06 ± 7.32 years, males; 9.17 ± 5.28 years, females) compared to the Southwestern cohort (9.19 ± 7.49 years, male; 10.61 ± 6.83 years, female). Additionally, it was observed that males exhibited a lower MAE than females in both regional groups. This study demonstrated the potential utility of CT images of the MP for age estimation in Chinese populations, emphasizing the significance of incorporating regional and sex factors within this context.

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Autism Spectrum Disorder (ASD) is a multifactorial disorder involving genetic and environmental factors leading to pathophysiologic symptoms and comorbidities including neurodevelopmental disorders, anxiety, immune dysregulation, and gastrointestinal (GI) abnormalities. Abnormal intestinal permeability has been reported among ASD patients and it is well established that disturbances in eating patterns may cause gut microbiome imbalance (i.e., dysbiosis). Therefore, studies focusing on the potential relationship between gut microbiota and ASD are emerging. We compared the intestinal bacteriome and mycobiome of a cohort of ASD subjects with their non-ASD siblings. Differences between ASD and non-ASD subjects include a significant decrease at the phylum level in Cyanobacteria (0.015% vs. 0.074%, p < 0.0003), and a significant decrease at the genus level in Bacteroides (28.3% vs. 36.8%, p < 0.03). Species-level analysis showed a significant decrease in Faecalibacterium prausnitzii, Prevotella copri, Bacteroides fragilis, and Akkermansia municiphila. Mycobiome analysis showed an increase in the fungal Ascomycota phylum (98.3% vs. 94%, p < 0.047) and an increase in Candida albicans (27.1% vs. 13.2%, p < 0.055). Multivariate analysis showed that organisms from the genus Delftia were predictive of an increased odds ratio of ASD, whereas decreases at the phylum level in Cyanobacteria and at the genus level in Azospirillum were associated with an increased odds ratio of ASD. We screened 24 probiotic organisms to identify strains that could alter the growth patterns of organisms identified as elevated within ASD subject samples. In a preliminary in vivo preclinical test, we challenged wild-type Balb/c mice with Delftia acidovorans (increased in ASD subjects) by oral gavage and compared changes in behavioral patterns to sham-treated controls. An in vitro biofilm assay was used to determine the ability of potentially beneficial microorganisms to alter the biofilm-forming patterns of Delftia acidovorans, as well as their ability to break down fiber. Downregulation of cyanobacteria (generally beneficial for inflammation and wound healing) combined with an increase in biofilm-forming species such as D. acidovorans suggests that ASD-related GI symptoms may result from decreases in beneficial organisms with a concomitant increase in potential pathogens, and that beneficial probiotics can be identified that counteract these changes.

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This study hypothesizes that advanced machine learning (ML) models can more accurately predict certain critical water quality parameters in marine environments compared to conventional regression techniques. We specifically evaluated the spatio-temporal distribution of Chlorophyll-a (Chl-a) and Secchi Disk Depth (SDD) in the Gulf of Izmit using in-situ measurements and Sentinel-2 satellite imagery from October 2021 and 2022. Among the models tested, the Support Vector Regression (SVR) model showed better predictive performance, achieving the lowest RMSE for SDD (1.11-1.70 m) and Chl-a (1.16-4.97 mg/m3) and the lowest MAE for SDD (0.86-1.43 m) and Chl-a (1.03-3.17 mg/m3). Additionally, the study observed a shift from hypertrophic to eutrophic Chl-a conditions and from mesotrophic-eutrophic to oligotrophic SDD conditions between 2021 and 2022, aligning with SVR model predictions and in-situ observations. These findings underscore the potential of ML models to enhance the accuracy of water quality monitoring and management in marine ecosystems.

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Background: Limited data are available regarding the association between serum transferrin saturation (TSAT) levels and heart failure (HF). Methods: We utilized data from National Health and Nutrition Examination Survey (NHANES) 2017- 2020.03 for analysis. Data on TAST, HF and covariates were extracted and analyzed. Weighted logistic regression and subgroup analysis were used to explore the independent association between TSAT and HF. Furthermore, interaction tests were also carried out to evaluate the strata differences. We subsequently assessed whether there was a non-linear relationship between the 2 using Restricted cubic spline (RCS) and threshold effect models. Result: A total of 282 (3.87%) participants were identified to have HF. Among the total population, participants with HF had significantly lower TSAT levels compared to those without HF (24.63 vs. 27.95, P = 0.001). After fully adjusting for potential confounders, weighted multiple logistic regression models revealed a 2.6% reduced in the risk of HF when each unit of TSAT increased. There was also a negative association between elevated TSAT and developed risk of HF in the quartile groups (Q1 OR:1.00; Q2 OR: 0.924 [95%CI:0.593,1.440]; Q3 OR: 0.515 [95%CI:0.298,0.891]; Q4 OR:0.411 [95%CI:0.201,0.839]). The subgroup analysis results remained consistent across strata, with a strong negative correlation between TSAT and HF. Interaction tests showed no dependence on gender, age, Body Mass Index, race, diabetes, hypertension, hyperlipidemia, ratio of family income to poverty and education for this negative association between TSAT and HF (all p for interaction >0.05). The RCS and threshold effect models indicated a linear negative correlation between TSAT and HF, which was more pronounced when TSAT under 40%. Conclusion: Overall, these findings suggest a consistent and negative association between TSAT levels and the presence of HF among middle-aged and older adults in the United States.

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The study aimed to ascertain a relationship between agricultural status, socioeconomic factors, and nutrition of farm families. The study was conducted in selected villages in the West Garo Hills district of Meghalaya, using Stratified Random Sampling (St. RS). Using pretested interview schedules, we collected primary data from respondents in 2020 and 2021, focusing on socioeconomic variables, body mass index, and income from agriculture and related sectors. The data was analysed using correlation analyses and separate combined regression estimates for each year and month were obtained. Results from the study indicate that agricultural income significantly influenced nutritional status (p < 0.05) and household income growth was also found significant. The region's agricultural production of cereals, pulses, and vegetables was insufficient, as was the production of meat and meat products, milk, and milk products. Hence, expenditure towards purchasing the above food groups from the market was found to be significant (p < 0.05). Therefore, the markets near the mainland especially in the hilly region play a crucial role in the nutritional pathway of rural farm families.

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We advise a novel measure to determine the unknown number of clusters underlying a designated sample through implementation of the parametric logistic regression model. The regression analysis is carried out to estimate the probabilities of inclusion for every individual member from data, irrespective of its parent distribution, to each of the clusters under existence. The proposed one is shown to be superior to its well-known rivals by means of both synthetic and real-world data sets, while designed to significantly reduce the computational burden serving our desired purpose.

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This study investigates the influence of accounting conservatism on corporate innovation investment through the lens of information asymmetry theory. While existing literature acknowledges the importance of accounting conservatism in corporate decision-making, there remains a gap in understanding how it specifically affects innovation investment, particularly in varied market environments and regulatory contexts. Specifically, current research often overlooks the heterogeneity of the impact of accounting conservatism on innovation investment under different market environments and regulatory frameworks. Additionally, there is a lack of specialized studies on the unique group of Chinese listed companies. This study fills this gap by empirically analyzing data from Chinese A-share listed companies, revealing a negative correlation between accounting conservatism and corporate innovation investment. Through empirical analysis of the financial reports and research and development (R&D) investment data of Chinese A-share listed companies from 2015 to 2022, this study finds a significant negative correlation between accounting conservatism and corporate innovation investment. Specifically, as accounting conservatism increases, corporate investment in R&D shows a decreasing trend, with a correlation coefficient of -0.364. This result is further validated by hierarchical regression analysis, where the regression coefficient is -0.465, indicating that accounting conservatism has a significant inhibitory effect on corporate innovation investment. This study is pioneering in its examination of the relationship between accounting conservatism and corporate innovation investment within the unique market environment of China, taking into account its distinctive characteristics and rapidly evolving technological industry background. To quantify accounting conservatism, the research employs the C-Score and G-Score models, while employing a range of indicators to measure corporate innovation investment, including proportions of R&D expenditure, number of new products or services, patent applications, total R&D personnel, capital investments, and progress in innovation projects. This comprehensive evaluation method enhances the accuracy and reliability of the study. The contribution of this study is significant as it offers a fresh perspective on how accounting conservatism influences corporate innovation investment. By providing empirical data support, it assists investors and corporate managers in making informed financial decisions and shaping innovation strategies. Through hierarchical regression analysis, the study substantiates the detrimental impact of accounting conservatism on corporate innovation investment, thereby establishing new theoretical and practical foundations for further research and application in related fields.

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Background: Drug-induced thrombocytopenia is a rare adverse reaction of drug therapy and usually underdiagnosed. Cefoperazone/sulbactam is a compound preparation composed of the third generation of cephalosporin and ß-lactamase inhibitor, of which thrombocytopenia is an uncommon but serious adverse reaction. However, the existing literature on cefoperazone/sulbactam-induced thrombocytopenia remains limited, and the specific risk factors associated with this adverse effect have not been thoroughly elucidated. Consequently, this study aims to investigate the clinical characteristics and identify the risk factors for thrombocytopenia in adult patients undergoing cefoperazone/sulbactam therapy. Methods: In this retrospective study, we reviewed patients treated with cefoperazone/sulbactam at Beijing Hospital between January 2017 and June 2023. Patients were categorized into two groups based on the presence or absence of thrombocytopenia: the thrombocytopenia group and the non-thrombocytopenia group. We collected data on demographic features, clinical characteristics, laboratory parameters, treatments, and outcomes. Subsequently, univariate and multivariate logistic regression analyses were performed to identify potential risk factors for cefoperazone/sulbactam-induced thrombocytopenia. Results: In total, 6489 patients were included in this study, and 2.4% (155/6489) developed thrombocytopenia. The results of multivariate analysis showed that cefoperazone/sulbactam therapy duration (d) >14, PLT (109/L) <200, daily dose of cefoperazone/sulbactam (g) ≥6, TBil (µmoL/L) >21, AST (U/L) >35, and use of non-invasive ventilator were risk factors for cefoperazone/sulbactam-induced thrombocytopenia. Conclusion: Despite the low incidence (2.4%), cefoperazone/sulbactam could cause serious thrombocytopenia sometimes accompanied with hemorrhage. In clinical therapy, clinicians should be vigilant in monitoring platelet count, especially for patients with risk factors of cefoperazone/sulbactam-induced thrombocytopenia.

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While key aroma and taste compounds of Keemun Congou black teas (KCBT) form during aeration and thermal stages, it is still unknown whether these processing stages also produce non-volatile color-contributing metabolites. Through integrating metabolomics with correlation and ridge regression analyses, 190 metabolites were identified as marker compounds that reclassified 15 KCBT samples collected from five processing stages into four groups. Meanwhile, the results of quantification and heatmap analysis showed that the concentrations of theaflavins and theasinensins significantly increased, as catechin decreased, after rolling, while flavonoid aglycones and polyunsaturated fatty acids increased throughout drying. Regression analysis between marker compound levels and total color difference values (∆E) revealed that the major color contributors were 3,5-dicaffeoylquinic acid, glucosyl-dehydrodigallic acid, theacitrin A, kaempferol-O-robinobioside, and (-)-epigallocatechin, with regression coefficients (absolute value) exceeding 4 × 10-2. Overall, the present study confirmed that rolling and drying were the two vital stages responsible for the color formation of KCBT.

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BACKGROUND: The severity of COVID-19 outbreaks is disproportionate across settings (e.g., long-term care facilities (LTCF), schools) across Canada. Few studies have examined factors associated with outbreak severity to inform prevention and response. Our study objective was to assess how outbreak severity, as measured using outbreak intensity and defined as number of outbreak-associated cases divided by outbreak duration, differed by setting and factors known to influence SARS-CoV-2 transmission. METHODS: We described outbreak intensity trends in 2021 using data from the Canadian COVID-19 Outbreak Surveillance System from seven provinces/territories, representing 93% of the Canadian population. A negative binomial fixed-effects model was used to assess for associations between the outcome, outbreak intensity, and characteristics of outbreaks: setting type, median age of cases, number at risk, and vaccination coverage of at least 1 dose. Also included were variables previously reported to influence SARS-CoV-2 transmission: stringency of non-pharmaceutical interventions (NPI) and the predominant SARS-CoV-2 variant detected by surveillance. RESULTS: The longest outbreaks occurred in LTCF (mean = 25.4 days) and correctional facilities (mean = 20.6 days) which also reported the largest outbreaks (mean = 29.6 cases per outbreak). Model results indicated that outbreak intensity was highest in correctional facilities. Relative to correctional facilities (referent), the second highest adjusted intensity ratio was in childcare centres (intensity ratio = 0.58 [95% CI: 0.51-0.66]), followed by LTCF (0.56 [95% CI: 0.51-0.66]). Schools had the lowest adjusted intensity ratio (0.46 [95% CI: 0.40-0.53]) despite having the highest proportion of outbreaks (37.5%). An increase in outbreak intensity was associated with increases in median age, the number at risk, and stringency of NPI. Greater vaccination coverage with at least 1 dose was associated with reduced outbreak intensity. CONCLUSION: Descriptive and multivariable model results indicated that in Canada during 2021, outbreak intensity was greatest in closed congregate living facilities: correctional facilities and LTCF. Findings from this study support the importance of vaccination in reducing outbreak intensity when vaccines are effective against infection with circulating variants, which is especially important for closed congregate living facilities where NPIs are more challenging to implement.

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Cryptocurrency is an attempt to create an alternative to centralized financial systems using blockchain technology. However, our understanding of the psychological mechanisms that drive cryptocurrency adoption is limited. This study examines the role of basic human values in three stages of cryptocurrency adoption-awareness, intention to buy, and ownership-using the Theory of Planned Behavior (TPB). Logistic regression analysis was conducted on a quota sample of 714 German adults, and the results showed that openness-to-change values increased the likelihood of cryptocurrency awareness, while self-enhancement values increased the likelihood of intention to buy and ownership. These findings were consistent even after controlling for demographic characteristics, attitudinal beliefs, and perceived behavioral control, which are important factors in the TPB. The results suggest that basic human values may influence an individual's decision to adopt cryptocurrency, but the transition from awareness to ownership may be influenced by socio-economic opportunities available to interested individuals.

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BACKGROUND: Restorative solutions designed for edentulous patients such as dentures and their accompanying denture adhesives operate in the complex and dynamic environment represented by human oral physiology. Developing material models accounting for the viscoelastic behavior of denture adhesives can facilitate their further optimization within that unique physiological environment. This study aims to statistically quantify the degree of significance of three physiological variables - namely: temperature, adhesive swelling, and pH - on denture adhesive mechanical behavior. Further, based on these statistical significance estimations, a previously-developed viscoelastic material modelling approach for such denture adhesives is further expanded and developed to capture these variables' effects on mechanical behavior. METHODS: In this study a comparable version of Denture adhesive Corega Comfort was analysed rheologically using the steady state frequency sweep tests. The experimentally derived rheological storage and loss modulus values for the selected physiological variables were statistically analyzed using multi parameter linear regression analysis and the Pearson's coefficient technique to understand the significance of each individual parameter on the relaxation spectrum of the denture adhesive. Subsequently, the parameters are incorporated into a viscoelastic material model based on Prony series discretization and time-temperature superposition, and the mathematical relationship for the loss modulus is deduced. RESULTS: The results of this study clearly indicated that the variation in both the storage and loss modulus values can be accurately predicted using the oral cavity physiological parameters of temperature, swelling ratio, and pH with an adjusted R2 value of 0.85. The R2 value from the multi-parameter regression analysis indicated that the predictor variables can estimate the loss and storage modulus with a reasonable accuracy for at least 85% of the rheologically determined continuous relaxation spectrum with a confidence level of 98%. The Pearson's coefficient for the independent variables indicated that temperature and swelling have a strong influence on the loss modulus, whereas pH had a weak influence. Based on statistical analysis, these mathematical relationships were further developed in this study. CONCLUSIONS: This multi-parameter viscoelastic material model is intended to facilitate future detailed numerical investigations performed with implementation of denture adhesives using the finite element method.

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BACKGROUND: Collaboration between parents and school nurses is important for effective healthcare in schools. This study focuses on the competency of school nurses, which encompasses their knowledge and self-efficacy in diabetes care, and investigates how these factors, along with workload, influence healthcare partnerships in schools. However, it is unknown whether school nurses' knowledge and self-efficacy about diabetes care, as well as their workload, affect school healthcare partnerships concerning children with type 1 diabetes. AIM: This study aimed to investigate the impact of school nurses' self-efficacy, knowledge, attitude, and role overload on healthcare partnerships with parents of children with type 1 diabetes in schools. DESIGN: A cross-sectional, descriptive design. SETTING AND PARTICIPANTS: Between December 2023 and January 2024 in South Korea, 142 elementary- and middle-school nurses participated in this study. METHODS: School healthcare partnership, self-efficacy in diabetes education, knowledge of and attitude toward school healthcare for type 1 diabetes, and the role-overload scale were utilized in the analysis. Data were analyzed using multiple regression. RESULTS: Knowledge of school healthcare (ß = 0.34, p < .001) and attitude toward it (ß = 0.29 p = .001) for type 1 diabetes, as well as the grade level of the current employing school (ß = -0.15, p = .039) were predictors of school healthcare partnerships. These three variables explained 30.3 % of the total variance in school healthcare partnerships (F = 21.44, p < .001). CONCLUSIONS: Knowledge of school healthcare and attitudes toward it for type 1 diabetes were identified as factors in school nurses' school healthcare partnerships. Therefore, interventions to strengthen school nurses' competencies should be developed to improve school healthcare partnerships.

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Background: Significant atrioventricular valve dysfunction can be associated with mortality or need for transplant in functionally univentricular heart patients undergoing staged palliation. The purposes of this study are to characterize the impact of concomitant atrioventricular valve intervention on outcomes at each stage of single ventricle palliation and to identify risk factors associated with poor outcomes in these patients. Methods: The Society of Thoracic Surgeons Congenital Heart Surgery Database was queried for functionally univentricular heart patients undergoing single ventricle palliation from 2013 through 2022. Separate analyses were performed on cohorts corresponding to each stage of palliation (1: initial palliation; 2: superior cavopulmonary anastomosis; 3: Fontan procedure). Bivariate analysis of demographics, diagnoses, comorbidities, preoperative risk factors, operative characteristics, and outcomes with and without concomitant atrioventricular valve intervention was performed. Multiple logistic regression was used to identify predictors associated with operative mortality or major morbidity. Results: Concomitant atrioventricular valve intervention was associated with an increased risk of operative mortality or major morbidity for each cohort (cohort 1: 62% vs 46%, P < .001; cohort 2: 37% vs 19%, P < .001; cohort 3: 22% vs 14%, P < .001). Black race in cohort 1 (odds ratio [OR] 3.151, 95% CI 1.181-9.649, P = .03) and preterm birth in cohort 2 (OR 1.776, 95% CI 1.049-3.005, P = .032) were notable predictors of worse morbidity or mortality. Conclusions: Concomitant atrioventricular valve intervention is a risk factor for operative mortality or major morbidity at each stage of single ventricle palliation. Several risk factors are associated with these outcomes and may be useful in guiding decision-making.

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Life expectancy is one of the primary population health indicators and in turn increases in life expectancy indicate improvements in population health and human welfare. Therefore, one of the ultimate goals of the countries is to increase the life expectancy. This article studies the effect of education and income inequalities, ICT indicators, CO2 emissions, and real GDP per capita on life expectancy in the new EU members for the period of 2010-2022 by employing fixed effects regression. The coefficients of panel regression uncover that education and income inequalities and CO2 emissions negatively impact life expectancy, but ICT indicators of internet usage and mobile cellular subscriptions and real GDP per capita positively affects the life expectancy. The findings of the panel regression analysis indicate that public policies to decrease the inequalities in education and income will make a contribution to life expectancy.

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