Diagnostic accuracy of endoscopic ultrasonographic shear wave elastography for assessing early chronic pancreatitis using the Japanese diagnostic criteria 2019.

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.

Correlation analysis of primal cuts weight, fat contents, and auction prices in Landrace × Yorkshire × Duroc pig carcasses by VCS2000.

Currently, in pork auctions in Korea, only carcass weight and backfat thickness provide information on meat quantity, while the production volume of primal cuts and fat contents remains largely unknown. This study aims to predict the production of primal cuts in pigs and investigate how these carcass traits affect pricing. Using the VCS2000, the production of shoulder blade, loin, belly, shoulder picnic, and ham was measured for gilts (17,257 pigs) and barrows (16,365 pigs) of LYD (Landrace × Yorkshire × Duroc) pigs. Single and multiple regression analysis were conducted to analyze the relationship between the primal cuts and carcass weight. The study also examined the correlation between each primal cut, backfat thickness (1st thoracic vertebra backfat thickness, grading backfat thickness, and Multi-brached muscle middle backfat thickness), pork belly fat percentage, total fat yield, and auction price. A multiple regression analysis was conducted between the carcass traits that showed a high correlation and the auction price. After conducting a single regression analysis on the primal cuts of gilt and barrow, all coefficients of determination (R2) were 0.77 or higher. In the multiple regression analysis, the R2 value was 0.98 or higher. The correlation coefficient between the carcass weights and the auction price exceeded 0.70, while the correlation coefficients between the primal cuts and the auction prices were above 0.65. In terms of fat content, the backfat thickness of gilt exhibited a correlation coefficient of 0.70, and all other items had a correlation coefficient of 0.47 or higher. The correlation coefficients between the Forequarter, Middle, and Hindquarter and the auction price were 0.62 or higher. The R2 values of the multiple regression analysis between carcass traits and auction price were 0.5 or higher for gilts and 0.4 or higher for barrows. The regression equations between carcass weight and primal cuts derived in this study exhibited high determination coefficients, suggesting that they could serve as reliable means to predict primal cut production from pig carcasses. Elucidating the correlation between primal cuts, fat contents and auction prices can provide economic indicators for pork and assist in guiding the direction of pig farming.

Predictive modelling of laser powder bed fusion of Fe-based nanocrystalline alloys based on experimental data using multiple linear regression analysis.

This study harnessed bivariate correlational analysis, multiple linear regression analysis and tree-based regression analysis to examine the relationship between laser process parameters and the final material properties (bulk density, saturation magnetization (M s ), and coercivity (H c )) of Fe-based nano-crystalline alloys fabricated via laser powder bed fusion (LPBF). A dataset comprising of 162 experimental data points served as the foundation for the investigation. Each data point encompassed five independent variables: laser power (P), laser scan speed (v), hatch spacing (h), layer thickness (t), and energy density (E), along with three dependent variables: bulk density, M s , and H c . The bivariate correlational analysis unveiled that bulk density exhibited a significant correlation with P, v, h, and E, whereas M s and H c displayed significant correlations exclusively with v and P, respectively. This divergence may stem from the strong influence of microstructure on magnetic properties, which can be impacted not only by the laser process parameters explored in this study but also by other factors such as oxygen levels within the build chamber. Furthermore, our statistical analysis revealed that bulk density increased with rising P, h, and E, while decreased with higher v. Regarding the magnetic properties, a high M s was achievable through low v, while low H c resulted from high P. It was concluded that P and v were considered as the primary laser process parameters, influencing h and t due to their control over the melt-pool size. The application of multiple linear regression analysis allowed the prediction of the bulk density by using both laser process parameters and energy density. This approach offered a valuable alternative to time-consuming and costly trial-and-error experiments, yielding a low error of less than 1 % between the mean predicted and experimental values. Although a slightly higher error of approximately 6 % was observed for M s , a clear association was established between M s and v, with lower v values corresponding to higher M s values. Additionally, a further comparison was conducted between multiple linear regression and three tree-based regression models to explore the effectiveness of these approaches.

Potential roles of IL-38, among other inflammation-related biomarkers, in predicting post-percutaneous coronary intervention cardiovascular events.

Percutaneous coronary intervention (PCI), as a relatively rapid and effective minimally invasive treatment for coronary heart disease (CHD), can effectively relieve coronary artery stenosis and restore myocardial perfusion. However, the occurrence of major adverse cardiovascular events (MACE) is a significant challenge for post PCI care. To better understand risk/benefit indicators and provide post PCI MACE prediction, 408 patients with CHD who had undergone PCI treatment from 2018 to 2021 in Tianjin Chest hospital were retrospectively studied for their clinical characteristics in relation with the MACE occurrence during a 12-month follow-up. In the study, 194 patients had MACE and 214 patients remained MACE-free. Using uni- and multivariate regression analyses, we have shown that smoking history, elevated serum C-reactive protein levels (hs-CRP), and high haemoglobin levels A1c (HbA1c) are all independent risk factors for MACE after PCI. Furthermore, we have discovered that the serum level of IL-38, one of the latest members identified in the IL-1 cytokine family, is another predictive factor and is reversely related to the occurrence of MACE. The serum level of IL-38 alone is capable of predicting non-MACE occurrence in subcategorized patients with abnormal levels of hs-CRP and/or HbA1c.

Integrated Analysis of Clinical Outcome of Mesenchymal Stem Cell-related Genes in Pan-cancer.

Background: Although the application of mesenchymal stem cells (MSCs) in engineered medicine, such as tissue regeneration, is well known, new evidence is emerging that shows that MSCs can also promote cancer progression, metastasis, and drug resistance. However, no large-scale cohort analysis of MSCs has been conducted to reveal their impact on the prognosis of cancer patients. Objectives: We propose the MSC score as a novel surrogate for poor prognosis in pan-cancer. Methods: We used single sample gene set enrichment analysis to quantify MSC-related genes into a signature score and identify the signature score as a potential independent prognostic marker for cancer using multivariate Cox regression analysis. TIDE algorithm and neural network were utilized to assess the predictive accuracy of MSC-related genes for immunotherapy. Results: MSC-related gene expression significantly differed between normal and tumor samples across the 33 cancer types. Cox regression analysis suggested the MSC score as an independent prognostic marker for kidney renal papillary cell carcinoma, mesothelioma, glioma, and stomach adenocarcinoma. The abundance of fibroblasts was also more representative of the MSC score than the stromal score. Our findings supported the combined use of the TIDE algorithm and neural network to predict the accuracy of MSC-related genes for immunotherapy. Conclusion: We comprehensively characterized the transcriptome, genome, and epigenetics of MSCs in pan-cancer and revealed the crosstalk of MSCs in the tumor microenvironment, especially with cancer-related fibroblasts. It is suggested that this may be one of the key sources of resistance to cancer immunotherapy.

Analysis of energy supply, energy policies, and the final energy end-use consumption of the residential sector in Ethiopia.

The residential sector in Ethiopia heavily relies on biomass for cooking, using inefficient cookstoves. In order to assess energy policies and decision-making for better economic development, it is essential to have final energy consumption by end-use. However, there is a lack of readily accessible data on residential energy end-use. Our study fills this gap by using data collected from surveys of 590 urban households in Ethiopia, estimating their energy end-use consumption, and analyzing their determinants. The annual final energy consumption per household is about 7.2 MWh, where 90 % is for cooking, baking, tea/coffee boiling end-uses, and only 2.3 % for lighting. The analysis reveals that income has the strongest effect on energy consumption for Injera baking and on miscellaneous end-uses, both directly and partly indirectly as a mediating variable. The study highlights the importance of end-use consumption data to plan energy efficiency, mix technology options, and make suitable policy interventions.

Time-dependent prognostic impact of circumferential resection margin in T3 thoracic esophageal squamous cell carcinoma.

Esophageal cancer presents a clinical challenge due to its high incidence and unfavorable prognosis. The prognostic role of the circumferential resection margin (CRM) remains highly controversial, potentially due to its temporal dynamics coupled with variability in follow-up durations across studies. We aimed to explore the time-dependent prognostic significance of CRM in T3 esophageal squamous cell carcinomas (ESCCs). We systematically reviewed literature from 1990 to 2023 to determine how follow-up duration influences the prognostic role of CRM in esophageal cancer. Concurrently, we performed a retrospective examination of 354 patients who underwent treatment at the National Cancer Center between 2015 and 2018. Integrating a time interaction term in the Cox regression analyses enabled us to not only identify independent risk factors affecting overall survival (OS) but also to specifically scrutinize the potential temporal variations in CRM's prognostic impact. Our literature review suggested that CRM's influence on prognosis diminishes with longer follow-up durations for both classifications, namely the Royal College of Pathologists (RCP) (ß = -0.003, P < 0.001) and the College of American Pathologists (CAP) (ß = -0.007, P < 0.001). Time-dependent multivariate Cox regression analysis emphasized the evolving nature of CRM's prognostic effect, and the inclusion of the time interaction term enhanced model accuracy. In conclusion, CRM is an independent prognostic factor for T3 thoracic ESCC patients. Its influence appears to decrease over extended follow-up periods, shedding light on the heterogeneity seen in previous studies. With the time interaction term, CRM becomes a more precise post-operative prognostic indicator for esophageal cancer.

Amorphous stabilization of BCS II drugs using mesoporous silica.

This study aimed to investigate the amorphous stabilization of BCS Class II drugs using mesoporous silica as a carrier to produce amorphous solid dispersions. Ibuprofen, fenofibrate, and budesonide were selected as model drugs to evaluate the impact of molecular weight and partition coefficient on the solid state of drug-loaded mesoporous silica (MS) particles. The model drugs were loaded into three grades of MS, SYLYSIA SY730, SYLYSIA SY430, and SYLYSIA SY350, with pore diameters of 2.5 nm, 17 nm, and 21 nm, respectively, at 1:1, 2:1, and 3:1, carrier to drug ratios, and three different loading concentrations using solvent immersion and spray drying techniques. Differential scanning calorimetry (DSC) thermograms of SY430 and SY350 samples exhibited melting point depressions indicating constricted crystallization inside the pores, whereas SY730 samples with melting points matching the pure API may be a result of surface crystallization. Powder x-ray diffraction (PXRD) diffractograms showed all crystalline samples matched the diffraction patterns of the pure API indicating no polymorphic transitions and all 3:1 ratio samples exhibited amorphous halo profiles. Response surface regression analysis and Classification and Regression Tree (CART) analysis suggest carrier to drug ratios, followed by molecular weight, have the most significant impact on the crystallinity of a drug loaded into MS particles.

Biodegradable Schiff bases: a novel approach for the management of pathogenic fungi (Sclerotium rolfsii and Rhizoctonia bataticola) and stored grain insect (Callosobruchus maculatus) in green gram (Vigna radiata).

Twenty-two eco-friendly, novel Schiff bases were synthesized from 2,4,5-trichloro aniline and characterized by using FT-IR, 1H NMR, and 13C NMR techniques. Fungicidal activity against pathogenic fungi Sclerotium rolfsii and Rhizoctonia bataticola and insecticidal activity against the stored grain insect pest Callosobruchus maculatus of the test compounds were evaluated under control condition. All of the investigated compounds, according to the study, exhibited moderate to good antifungal and insecticidal activities. The best antifungal activity against both pathogenic fungi was demonstrated by C15 and C16 whose ED50 values were recorded 11.4 and 10.4 µg/mL against R. bataticola and 10.6 and 11.9 µg/mL against S. rolfsii, respectively. They were further screened in for disease suppression against both pathogenic fungi under pot condition through different methods of applications in green gram (Vigna radiata L.) crop. The compounds C10 and C18 had the highest insecticidal activity, with LD50 values of 0.024 and 0.042 percentages, respectively. Stepwise regression analysis using root mean square error (RMSE) and correlation coefficient (R) method used to validate the quantitative structure activity relationship (QSAR) of synthesized compounds in addition to their fungicidal and insecticidal actions. To the best of our knowledge, this investigation on the 22 new Schiff bases as possible agrochemicals is the first one that has been fully reported.

Exploration of moisture activated dry granulation for the development of gastroretentive tablets aided by SeDeM diagram.

Moisture activated dry granulation (MADG) is an attractive granulation process. However, only a few works have explored modified drug release achieved by MADG, and to the best of the authors knowledge, none of them have explored gastroretention. The aim of this study was to explore the applicability of MADG process for developing gastroretentive placebo tablets, aided by SeDeM diagram. Floating and swelling capacities have been identified as critical quality attributes (CQAs). After a formulation screening step, the type and concentration of floating matrix formers and of binders were identified as the most relevant critical material attributes (CMAs) to investigate in ten formulations. A multiple linear regression analysis (MLRA) was applied against the factors that were varied to find the design space. An optimized product based on principal component analysis (PCA) results and MLRA was prepared and characterized. The granulate was also assessed by SeDeM. In conclusion, granulates lead to floating tablets with short floating lag time (<2min), long floating duration (>4h), and showing good swelling characteristics. The results obtained so far are promising enough to consider MADG as an advantageous granulation method to obtain gastroretentive tablets or even other controlled delivery systems requiring a relatively high content of absorbent materials in their composition.

Exploring healthy lifestyle with health locus of control and sociodemographic variables in Türkiye.

In this study, we aimed to explore the effects of Internal/Chance/Powerful Others Health Locus of Control (IHLC/CHLC/PHLC) on the healthy lifestyle and to assess the sensitivity of the healthy lifestyle to sociodemographic variables. To achieve this goal, we collected data by performing online and hand-delivered surveys (n = 950) with individuals aged 18 or older in Türkiye. The results showed that IHLC and PHLC had positive and significant effects on Healthy Lifestyle Index (HLI). However, the results expressed that CHLC had no negative and significant effect on HLI. Our assessment of a healthy lifestyle in terms of health locus of control (HLC) and sociodemographic variables revealed important findings, which may contribute to the development of public health strategies in several ways; for example, they can be used as a framework to conduct public health interventions that promote a healthy lifestyle.

Estimating the axial strain of circular short columns confined with CFRP under centric compressive static load using ANN and GRA techniques.

This investigation introduces advanced predictive models for estimating axial strains in Carbon Fiber-Reinforced Polymer (CFRP) confined concrete cylinders, addressing critical aspects of structural integrity in seismic environments. By synthesizing insights from a substantial dataset comprising 708 experimental observations, we harness the power of Artificial Neural Networks (ANNs) and General Regression Analysis (GRA) to refine predictive accuracy and reliability. The enhanced models developed through this research demonstrate superior performance, evidenced by an impressive R-squared value of 0.85 and a Root Mean Square Error (RMSE) of 1.42, and significantly advance our understanding of the behavior of CFRP-confined structures under load. Detailed comparisons with existing predictive models reveal our approaches' superior capacity to mimic and forecast axial strain behaviors accurately, offering essential benefits for designing and reinforcing concrete structures in earthquake-prone areas. This investigation sets a new benchmark in the field through meticulous analysis and innovative modeling, providing a robust framework for future engineering applications and research.

Comparing the DSM-5 Dimensional Trait and Triarchic Model Conceptions of Psychopathy: An External Validity Analysis.

In the DSM-5 Alternative Model of Personality Disorders (AMPD), psychopathy is marked by the presence of attention seeking, low anxiousness, and lack of social withdrawal, along with traits from the domains of Antagonism and Disinhibition. The triarchic model of psychopathy (TriPM) posits three biobehaviorally based traits underlying it: disinhibition, meanness, and boldness. The current study directly compared relations for measures of the two models with the broad dimensions of externalizing, internalizing, and positive adjustment. Participants (1,678 adults) were surveyed regarding maladaptive personality traits, clinical symptoms, and positive adjustment features. The TriPM model explained more variance than the AMPD in substance use, positive adjustment, and empathy, whereas the AMPD model explained more variance in internalizing symptoms. In addition, AMPD Antagonism and the Psychopathy Specifier diverged from TriPM Meanness and Boldness in their associations with some specific outcomes. Overall, our study provides evidence for complementarity of the two models in characterizing the multifaceted nature of psychopathy.

Spatial patterns and sociodemographic predictors of chronic obstructive pulmonary disease in Florida.

Background: Chronic obstructive pulmonary disease (COPD) is a chronic, inflammatory respiratory disease that obstructs airflow and decreases lung function and is a leading cause death globally. In the United States (US), the prevalence among adults is 6.2%, but increases with age to 12.8% among those 65 years or older. Florida has one of the largest populations of older adults in the US, accounting for 4.5 million adults 65 years or older. This makes Florida an ideal geographic location for investigating COPD as disease prevalence increases with age. Understanding the geographic disparities in COPD and potential associations between its disparities and environmental factors as well as population characteristics is useful in guiding intervention strategies. Thus, the objectives of this study are to investigate county-level geographic disparities of COPD prevalence in Florida and identify county-level socio-demographic predictors of COPD prevalence. Methods: This ecological study was performed in Florida using data obtained from the US Census Bureau, Florida Health CHARTS, and County Health Rankings and Roadmaps. County-level COPD prevalence for 2019 was age-standardized using the direct method and 2020 US population as the standard population. High-prevalence spatial clusters of COPD were identified using Tango's flexible spatial scan statistics. Predictors of county-level COPD prevalence were investigated using multivariable ordinary least squares model built using backwards elimination approach. Multicollinearity of regression coefficients was assessed using variance inflation factor. Shapiro-Wilks, Breusch Pagan, and robust Lagrange Multiplier tests were used to assess for normality, homoskedasticity, and spatial autocorrelation of model residuals, respectively. Results: County-level age-adjusted COPD prevalence ranged from 4.7% (Miami-Dade) to 16.9% (Baker and Bradford) with a median prevalence of 9.6%. A total of 6 high-prevalence clusters with prevalence ratios >1.2 were identified. The primary cluster, which was also the largest geographic cluster that included 13 counties, stretched from Nassau County in north-central Florida to Charlotte County in south-central Florida. However, cluster 2 had the highest prevalence ratio (1.68) and included 10 counties in north-central Florida. Together, the primary cluster and cluster 2 covered most of the counties in north-central Florida. Significant predictors of county-level COPD prevalence were county-level percentage of residents with asthma and the percentage of current smokers. Conclusions: There is evidence of spatial clusters of COPD prevalence in Florida. These patterns are explained, in part, by differences in distribution of some health behaviors (smoking) and co-morbidities (asthma). This information is important for guiding intervention efforts to address the condition, reduce health disparities, and improve population health.

Establishment of a nomogram model for predicting therapy complications in patients with polycythemia and deep venous thrombosis.

BACKGROUND: Patients with deep venous thrombosis (DVT) residing at high altitudes can only rely on anticoagulation therapy, missing the optimal window for surgery or thrombolysis. Concurrently, under these conditions, patient outcomes can be easily complicated by high-altitude polycythemia (HAPC), which increases the difficulty of treatment and the risk of recurrent thrombosis. To prevent reaching this point, effective screening and targeted interventions are crucial. Thus, this study analyzes and provides a reference for the clinical prediction of thrombosis recurrence in patients with lower-extremity DVT combined with HAPC. AIM: To apply the nomogram model in the evaluation of complications in patients with HAPC and DVT who underwent anticoagulation therapy. METHODS: A total of 123 patients with HAPC complicated by lower-extremity DVT were followed up for 6-12 months and divided into recurrence and non-recurrence groups according to whether they experienced recurrence of lower-extremity DVT. Clinical data and laboratory indices were compared between the groups to determine the influencing factors of thrombosis recurrence in patients with lower-extremity DVT and HAPC. This study aimed to establish and verify the value of a nomogram model for predicting the risk of thrombus recurrence. RESULTS: Logistic regression analysis showed that age, immobilization during follow-up, medication compliance, compliance with wearing elastic stockings, and peripheral blood D-dimer and fibrin degradation product levels were indepen-dent risk factors for thrombosis recurrence in patients with HAPC complicated by DVT. A Hosmer-Lemeshow goodness-of-fit test demonstrated that the nomogram model established based on the results of multivariate logistic regression analysis was effective in predicting the risk of thrombosis recurrence in patients with lower-extremity DVT complicated by HAPC (χ 2 = 0.873; P > 0.05). The consistency index of the model was 0.802 (95%CI: 0.799-0.997), indicating its good accuracy and discrimination. CONCLUSION: The column chart model for the personalized prediction of thrombotic recurrence risk has good application value in predicting thrombotic recurrence in patients with lower-limb DVT combined with HAPC after discharge.

Relationship between serum iron, zinc, calcium, and HIF-1a-comparative analysis of 2 regions and 4 ethnic groups in China.

Background: Altitude illness has serious effects on individuals who are not adequately acclimatized to high-altitude areas and may even lead to death. However, the individualized mechanisms of onset and preventive measures are not fully elucidated at present, especially the relationship between altitude illness and elements, which requires further in-depth research. Methods: Fresh serum samples were collected from individuals who underwent health examinations at the two hospitals in Xining and Sanya between November 2021 and December 2021. The blood zinc (Zn), iron (Fe), and calcium (Ca) concentrations, as well as hypoxia-inducible factor 1-alpha (HIF-1α) concentrations, were measured. This study conducted effective sample size estimation, repeated experiments, and used GraphPad Prism 9.0 and IBM SPSS version 19.0 software for comparative analysis of differences in the expression of elements and HIF-1α among different ethnic groups, altitudes, and concentration groups. Linear regression and multiple linear regression were employed to explore the relationships among elements and their correlation with HIF-1α. Results: This study included a total of 400 participants. The results from the repeated measurements indicated that the consistency of the laboratory test results was satisfactory. In terms of altitude differences, except for Fe (p = 0.767), which did not show significant variance between low and high altitude regions, Zn, Ca, and HIF-1α elements all exhibited notable differences between these areas (p < 0.0001, p = 0.004, and p < 0.0001). When grouping by the concentrations of elements and HIF-1α, the results revealed significant variations in the distribution of zinc among different levels of iron and HIF-1α (p < 0.05). The outcomes of the linear regression analysis demonstrated that calcium and zinc, iron and HIF-1α, calcium and HIF-1α, and zinc and HIF-1α displayed substantial overall explanatory power across different subgroups (p < 0.05). Finally, the results of the multiple linear regression analysis indicated that within the high-altitude population, the Li ethnic group in Sanya, and the Han ethnic group in Sanya, the multiple linear regression model with HIF-1αas the dependent variable and elements as the independent variables exhibited noteworthy overall explanatory power (p < 0.05). Conclusion: The levels of typical elements and HIF-1α in the blood differ among various altitudes and ethnic groups, and these distinctions may be linked to the occurrence and progression of high-altitude illness.

Global burden of osteoarthritis: Prevalence and temporal trends from 1990 to 2019.

OBJECTIVE: To investigate the age-standardized prevalence rate (ASPR) and temporal trends for hip, knee, hand, and other osteoarthritis (OA) at a global, continental, and national level. DESIGN: The estimates and 95% uncertainty intervals (UIs) for case number and ASPR of OA were derived from the Global Burden of Diseases Study (GBD) 2019. The joinpoint regression analysis was utilized to examine the temporal trends from 1990 to 2019. RESULTS: In 2019, the global ASPR of hip, knee, hand, and other OA was 400.95 (95% UI: 312.77-499.41), 4375.95 (95% UI: 3793.04-5004.9), 1726.38 (95% UI: 1319.91-2254.85), and 745.62 (95% UI: 570.16-939.8). As for the ASPR of hip OA, hand OA, and other OA, Europe and America had higher rates than Asia and Africa, and Asia was second only to America in knee OA ASPRs. The period 1990-2019, the ASPR at global level dropped significantly for hand OA (AAPC = -0.4%, 95% CI: -0.47 to -0.34) and increased significantly for hip OA (AAPC = 0.43%, 95% CI: 0.39-0.46), knee OA (AAPC = 0.17%, 95% CI: 0.09-0.24) and other OA (AAPC = 0.16%, 95% CI: 0.15-0.17). Different continents, countries, and periods demonstrated significant changes. CONCLUSIONS: Globally, America has the highest OA burden and Asia has a higher knee OA burden. Appropriate prevention and control measures to reduce modifiable risk factors are needed to reduce the burden of OA.

Does sleep quality differ across political parties? Results from a survey of Arizona adults.

OBJECTIVES: In this study, we explore the relationship between political party affiliation and sleep quality since the COVID-19 pandemic. METHODS: We analyze online survey data collected for a sample of adult residents of Arizona in February and March 2023 (N = 922). We fit ordered-logistic regression models to examine how party affiliation and changes to one's personal life due to the COVID-19 pandemic are associated with the self-reported frequency of sleep difficulty. RESULTS: Compared to Republicans, Democrats and Independents report significantly worse sleep quality, net of the influence of sociodemographic controls. Additionally, having experienced major changes to one's personal life due to the COVID-19 pandemic is significantly associated with more frequent trouble sleeping for Democrats and Independents, but not for Republicans. CONCLUSIONS: We document a partisan divide in sleeping patterns among adults in a swing state and highlight an underappreciated factor contributing to sleep health amidst heightened political polarization.

The impact of deep learning based- psychological capital with ideological and political education on entrepreneurial intentions.

The aim of this study is to investigate the influence of psychological capital on college students' entrepreneurial intentions. Through a combination of relevant analysis and linear regression, the primary focus is on exploring the relationship between psychological capital and its four dimensions with entrepreneurial intentions. Firstly, the items in the psychological capital questionnaire were revised to align more closely with entrepreneurial contexts. Subsequently, the average deviations and standard deviations of each dimension of psychological capital were analyzed. Then, the correlation between psychological capital and entrepreneurial intentions was examined to explore the extent of their relationship. Finally, regression analysis was conducted on both psychological capital and entrepreneurial intentions, and utilizing a recurrent neural network model, the covariant relationship between entrepreneurial psychological capital and intentions was explored. The results indicated that the average scores for entrepreneurial self-efficacy, optimism, hope, and resilience were 3.91, 4.27, 4.19, and 4.15, respectively. The average value of psychological capital was 4.13, indicating a moderately high level. The correlation analysis between psychological capital and entrepreneurial intentions yielded a result of 0.562, indicating a moderate degree of correlation. The correlation coefficients of the four dimensions with entrepreneurial intentions were 0.390, 0.494, 0.531, and 0.467, respectively. The standardized coefficients for psychological capital and its four dimensions were 0.564, 0.382, 0.510, 0.536, and 0.468, all of which were statistically significant. Overall, psychological capital exhibited better predictive power for entrepreneurial intentions than its individual dimensions. The results from the deep learning model similarly demonstrated the positive role of psychological capital in entrepreneurial intentions, though the influence of ideological and political education (IPE) factors was relatively weaker. In conclusion, both psychological capital and IPE have a promotive effect on entrepreneurial intentions. This study provides a reference for the accurate evaluation of college students' entrepreneurial intentions.

Clinical predictive value of pre-pregnancy tests for unexplained recurrent spontaneous abortion: a retrospective study.

Introduction: Early prediction and intervention are crucial for the prognosis of unexplained recurrent spontaneous abortion (uRSA). The main purpose of this study is to establish a risk prediction model for uRSA based on routine pre-pregnancy tests, in order to provide clinical physicians with indications of whether the patients are at high risk. Methods: This was a retrospective study conducted at the Prenatal Diagnosis Center of Henan Provincial People's Hospital between January 2019 and December 2022. Twelve routine pre-pregnancy tests and four basic personal information characteristics were collected. Pre-pregnancy tests include thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine thyroid (FT4), thyroxine (TT4), total triiodothyronine (TT3), peroxidase antibody (TPO-Ab), thyroid globulin antibody (TG-Ab), 25-hydroxyvitamin D [25-(OH) D], ferritin (Ferr), Homocysteine (Hcy), vitamin B12 (VitB12), folic acid (FA). Basic personal information characteristics include age, body mass index (BMI), smoking history and drinking history. Logistic regression analysis was used to establish a risk prediction model, and receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were employed to evaluate the performance of prediction model. Results: A total of 140 patients in uRSA group and 152 women in the control group were randomly split into a training set (n = 186) and a testing set (n = 106). Chi-square test results for each single characteristic indicated that, FT3 (p = 0.018), FT4 (p = 0.048), 25-(OH) D (p = 0.013) and FA (p = 0.044) were closely related to RSA. TG-Ab and TPO-Ab were also important characteristics according to clinical experience, so we established a risk prediction model for RSA based on the above six characteristics using logistic regression analysis. The prediction accuracy of the model on the testing set was 74.53%, and the area under ROC curve was 0.710. DCA curve indicated that the model had good clinical value. Conclusion: Pre-pregnancy tests such as FT3, FT4, TG-Ab, 25-(OH)D and FA were closely related to uRSA. This study successfully established a risk prediction model for RSA based on routine pre-pregnancy tests.