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.

Landau-Kleffner syndrome (LKS) in an 8-year-old girl: a case report and review of the literature.

Introduction and importance: Landau-Kleffner syndrome (LKS) is a rare epileptic encephalopathy characterized by language regression and abnormal electroencephalogram (EEG) patterns. This case report highlights the importance of early recognition and intervention in LKS, as well as the challenges in diagnosis and management due to its varied clinical manifestations. Case presentation: An 8-year-old girl presented with delayed speech, suspected hearing loss, and regression in language skills. Diagnostic tests revealed mild sensorineural hearing loss and EEG abnormalities consistent with LKS. The patient underwent speech therapy and received pharmacological treatment with valproic acid, resulting in significant improvements in language function. Clinical discussion: This case report provides insights into the typical features of LKS, including language regression and EEG abnormalities. It also highlights uncommon findings such as sensorineural hearing loss and mild intellectual delay. The multidisciplinary approach involving neurology, audiology, speech therapy, and education is crucial in the diagnosis and management of LKS. Conclusion: Early recognition and intervention, along with tailored pharmacological approaches and a multidisciplinary care approach, are essential in managing LKS. Further research is needed to better understand the pathophysiology, natural history, and optimal treatment of LKS, aiming to improve long-term outcomes for affected children and their families.

Cross-sectional study: mindset towards Coronavirus in developing countries and lessons learned: the case of Sudan.

Introduction: People's mindset towards COVID-19 in developing countries has an impact on how they perceive and react to the preventative measures taken by the governments to contain the virus. Understanding the factors influencing the mindset and identifying lessons learned amidst COVID-19 are critical to inform any future intervention strategy. Methods: This was a cross-sectional, community-based study conducted to assess the mindset changes and lessons learned post-COVID-19 in developing countries, focusing on Sudan. The study adopted a sequential mixed approach (SMA), combining qualitative and quantitative methods. The study used a structured questionnaire with 300 respondents and in-depth interviews with two experts. To identify the factors influencing the mindset of the people towards COVID-19, the study employed logistic regression. The data was analyzed using SPSS software. Results: Of the total (N = 300) respondents, 59.0% are female, 59.3% are between the ages of 20 and 39, 79.7% have a university education, 25.3% have the Coronavirus, and 42.3% has their family or relative contracted the virus. Further, only 22.7% had taken the vaccine. Reasons for vaccine hesitancy include lack of trust (29.5%), fear of side effects (24.1%), and absence of the need to travel outside the country (25.5%). When the virus first appeared, 77.3% thought it posed a health risk, while 22.7% perceived it as a hoax or conspiracy. After 3 years, 73% still regarded it as a health threat, while 27% believed it was a hoax or conspiracy. The mindset was found to be influenced by age, history of the disease, the extent of trust in foreign media coverage, and the belief in the effectiveness of the vaccination. Conclusion: Assessing the mindset towards the virus and identifying the lessons learned from the pandemic could be of vital importance to control the spread of the virus in developing countries. Making use of such lessons and influencing the mindset of the people towards positive attitudes and behaviours are required to enhance the effectiveness of the health precaution measures adopted. Further research is required on the public's mistrust of foreign media coverage and the contribution of local media to educate the public about the virus, particularly among the elderly.

Factors Affecting Elective and Non-elective Cesarean Sections: A Multinomial Regression Analysis Using Robson's Classification in a Southeast European Country.

BACKGROUND: Albania, a middle-income Southeast European country, is experiencing an increase in cesarean section rates. This study aims to analyze cesarean section practices in Albania using the Robson classification to identify patterns and provide insights into elective and non-elective cesarean trends. METHODS: This retrospective cohort study was conducted at the University Hospital of Obstetrics and Gynecology "Koco Gliozheni" in Albania, a leading tertiary hospital, from January to May 2023, involving 5,315 consecutive women who delivered during this period, including both live births and stillbirths, with a gestational age minimum of 28 weeks to align with standards of viability. We defined a function to systematically evaluate each case based on multiple criteria: parity, fetal presentation, onset of labor, previous deliveries, number of fetuses, and gestational age according to the Robson classification. Multinomial multiple regression was used to estimate the relationship between each of the above-mentioned variables and the likelihood of each type of cesarean delivery compared to normal births. RESULTS: The participants' mean age was 28.2 years (59.6% <30 years vs. 40.4% ≥30 years), while gestational age varied (12.1% before 37 weeks, the majority (72.3%) between 37 and 40 weeks, and 15.6% > 40 weeks). In elective cesarean sections, maternal age (odds ratio (OR) = 1.06) and gestational age (OR = 1.13) were associated with increased odds, with women with previous cesarean deliveries showing significantly higher odds (OR = 20.6), breech position (OR = 15.7), and multiple pregnancies elevating odds (OR = 7.3), whereas in non-elective cesarean sections, similar associations were observed with slightly different odds ratios which were maternal age (OR = 1.07), gestational age (OR = 1.16), previous cesarean delivery (OR = 6.3), breech position (OR = 8.5), and multiple pregnancies (OR = 5.1). Significant disparities in cesarean section rates were observed across various groups, with rates ranging from as low as 0.74% in Group 1 to as high as 89.24% in Group 5, and notable contributions from Group 2 with a rate of 69.95% and Group 6 with a rate of 81.29%. CONCLUSION: In conclusion, this study emphasizes the significance of factors such as maternal age, gestational age, previous cesarean deliveries, fetal presentation, number of fetuses, and multiple pregnancies in impacting the rates of elective, non-elective, and overall cesarean sections in Albania, highlighting the need for targeted strategies to improve maternal and fetal health outcomes.

Survival outcomes and prognostic factors of advanced gastrointestinal stromal tumors: in the era of multiple tyrosine kinase inhibitors.

Background: Tyrosine kinase inhibitors (TKIs) have shown great efficacy in the treatment of advanced gastrointestinal stromal tumors (GISTs), significantly prolonging the survival of patients. In the era of imatinib, a few studies reported some prognostic factors for patients with advanced GISTs, such as age, sex, performance status, diameter of the largest lesions, KIT exon mutations, and some hematological examination results. However, with the advent of more TKIs, the prognostic factors for patients with advanced GISTs have not been fully understood in the era of multiple TKIs. In this study, we aimed to identify independent prognostic factors associated with the survival of patients diagnosed with advanced GISTs. Methods: Data on clinicopathologic characteristics, treatment approaches, and survival were retrospectively collected for patients with primary unresectable or recurrent GISTs treated from January 2010 to July 2023 at the First Affiliated Hospital of Chongqing Medical University, China. Univariable and multivariable Cox proportional hazards regression models were used to identify independent prognostic factors of survival. Results: A total of 194 patients were included in the analysis. The median follow-up duration was 59.9 months (range, 2.7-141.7 months). The median overall survival (mOS) in this cohort was 76.5 months (95% confidence interval, 63.4 to 89.6 months). All patients received TKI therapy during the follow-up period, and 56.2% received two or more types of TKIs. In multivariable Cox analysis, younger age, a single lesion at enrollment, no previous use of TKIs, smaller tumor burden, good Eastern Cooperative Oncology Group performance status (ECOG PS ≤1), and lesions limited to the liver were independent prognostic factors for better survival. Conclusions: We found that a single lesion at enrollment, no previous use of TKIs, a smaller tumor burden, and lesions limited to the liver were associated with better survival. Drug resistance is a severe challenge for advanced GISTs, and several factors mentioned above may be correlated with the development of drug resistance, leading to the poor survival of patients.

The relationship between general self-efficacy and nursing practice competence for second-year nurses: Empirical quantitative research.

AIM: To examine the relationship between general self-efficacy and nursing practice competence for nurses in the second year of employment. DESIGN: A cross-sectional design was used. DATA SOURCES: The study included 596 nurses in their second year of employment at 75 medical facilities across Japan and used an online questionnaire survey for data collection. RESULTS: The covariance structure analysis showed the path from general self-efficacy (latent variable) to nursing practice competence. Positive correlations were found between all factors on both scales. Multiple regression analysis results showed that the general self-efficacy factors of 'positivity in behavior' and 'confidence in social competence' affect nursing practice competence. CONCLUSION: This study emphasizes the importance of enhancing the general self-efficacy of second-year nurses to improve their nursing practice competence. To achieve this, it suggests developing strategies from the perspective of the factors that comprise general self-efficacy. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: The findings suggest that improving general self-efficacy can enhance nursing practice competence, which could inform the development of interventions to support nurses in improving their competence. The study provides basic data for improving nurses' practice competence. IMPACT: This study is the first to establish a relationship between general self-efficacy and nursing practice competence among second-year nurses. It demonstrates the significance of general self-efficacy in enhancing nursing practice competence, particularly for second-year nurses worldwide who may be struggling with their nursing practice competence and considering leaving the profession. The findings offer practical implications for stakeholders involved in nursing education and training programs, with potential applications in professional development. REPORTING METHOD: This manuscript adheres to the STROBE guidelines for the reporting of cross-sectional studies. PATIENT OR PUBLIC CONTRIBUTION: There was no patient or public contribution.

Analysis of Microbiome Data.

The microbiome represents a hidden world of tiny organisms populating not only our surroundings but also our own bodies. By enabling comprehensive profiling of these invisible creatures, modern genomic sequencing tools have given us an unprecedented ability to characterize these populations and uncover their outsize impact on our environment and health. Statistical analysis of microbiome data is critical to infer patterns from the observed abundances. The application and development of analytical methods in this area require careful consideration of the unique aspects of microbiome profiles. We begin this review with a brief overview of microbiome data collection and processing and describe the resulting data structure. We then provide an overview of statistical methods for key tasks in microbiome data analysis, including data visualization, comparison of microbial abundance across groups, regression modeling, and network inference. We conclude with a discussion and highlight interesting future directions.

Consumer awareness and knowledge regarding use of non-steroidal anti-inflammatory drugs (NSAIDs) in a metropolitan area.

Background: Non-steroidal anti-inflammatory drugs (NSAIDs) have well-known adverse effects, and numerous studies have shown inappropriate behaviors regarding their use. The primary aim of this study was to analyze the knowledge, attitudes, and behaviors regarding the use of NSAIDs simultaneously in one of the largest and most populated areas of Italy, Naples. Methods: From 2021 December 14th to 2022 January 4th, a cross-sectional survey study was conducted among community centers, working places, and universities using a snowball sampling method. For inclusion in the study, the participants were required to be at least 18 years old and residents in the metropolitan area of Naples. Three multiple linear regression analysis (MLRA) models were developed by including variables that could potentially be associated with the following outcomes of interest: knowledge (Model I), attitudes (Model II), and behavior (Model III) regarding the use of NSAIDs. Results: Data were acquired from 1,012 questionnaires administered to subjects evenly divided by gender with an average age of 36.8 years and revealed that only 7.9% of the participants self-admittedly did not take NSAIDs, while approximately half the participants (50%) admitted to occasionally using them. The results showed a statistically significant correlation between attitudes regarding the appropriate use of NSAIDs and less knowledge. The regression analyses indicated that behaviors regarding the appropriate use of NSAIDs were statistically significant in younger respondents, non-smokers, and those without children. These interesting results showed that behaviors regarding the appropriate use of NSAIDs were significantly higher among respondents with less knowledge and more positive attitudes. Conclusion: According to the collected data and statistical analysis results, it is possible to identify factors that can greatly affect inappropriate behaviors regarding the use of NSAIDs and establish targeted prevention programs.

Machine learning models and performance dependency on 2D chemical descriptor space for retention time prediction of pharmaceuticals.

The predictive modeling of liquid chromatography methods can be an invaluable asset, potentially saving countless hours of labor while also reducing solvent consumption and waste. Tasks such as physicochemical screening and preliminary method screening systems where large amounts of chromatography data are collected from fast and routine operations are particularly well suited for both leveraging large datasets and benefiting from predictive models. Therefore, the generation of predictive models for retention time is an active area of development. However, for these predictive models to gain acceptance, researchers first must have confidence in model performance and the computational cost of building them should be minimal. In this study, a simple and cost-effective workflow for the development of machine learning models to predict retention time using only Molecular Operating Environment 2D descriptors as input for support vector regression is developed. Furthermore, we investigated the relative performance of models based on molecular descriptor space by utilizing uniform manifold approximation and projection and clustering with Gaussian mixture models to identify chemically distinct clusters. Results outlined herein demonstrate that local models trained on clusters in chemical space perform equivalently when compared to models trained on all data. Through 10-fold cross-validation on a comprehensive set containing 67,950 of our company's proprietary analytes, these models achieved coefficients of determination of 0.84 and 3 % error in terms of retention time. This promising statistical significance is found to translate from cross-validation to prospective prediction on an external test set of pharmaceutically relevant analytes. The observed equivalency of global and local modeling of large datasets is retained with METLIN's SMRT dataset, thereby confirming the wider applicability of the developed machine learning workflows for global models.

Multi-objective extensive hypothesis testing for the estimation of advanced crash frequency models.

Analyzing crash data is a complex and labor-intensive process that requires careful consideration of multiple interdependent modeling aspects, such as functional forms, transformations, likely contributing factors, correlations, and unobserved heterogeneity. Limited time, knowledge, and experience may lead to over-simplified, over-fitted, or misspecified models overlooking important insights. This paper proposes an extensive hypothesis testing framework including a multi-objective mathematical programming formulation and solution algorithms to estimate crash frequency models considering simultaneously likely contributing factors, transformations, non-linearities, and correlated random parameters. The mathematical programming formulation minimizes both in-sample fit and out-of-sample prediction. To address the complexity and non-convexity of the mathematical program, the proposed solution framework utilizes a variety of metaheuristic solution algorithms. Specifically, Harmony Search demonstrated minimal sensitivity to hyperparameters, enabling an efficient search for solutions without being influenced by the choice of hyperparameters. The effectiveness of the framework was evaluated using two real-world datasets and one synthetic dataset. Comparative analyses were performed using the two real-world datasets and the corresponding models published in literature by independent teams. The proposed framework showed its capability to pinpoint efficient model specifications, produce accurate estimates, and provide valuable insights for both researchers and practitioners. The proposed approach allows for the discovery of numerous insights while minimizing the time spent on model development. By considering a broader set of contributing factors, models with varied qualities can be generated. For instance, when applied to crash data from Queensland, the proposed approach revealed that the inclusion of medians on sharp curved roads can effectively reduce the occurrence of crashes, when applied to crash data from Washington, the simultaneous consideration of traffic volume and road curvature resulted in a notable reduction in crash variances but an increase in crash means.

Establishment and Validation of a Predictive Model for Sarcopenia Based on 2-D Ultrasound and Shear Wave Elastography in the Medial Gastrocnemius Muscle.

OBJECTIVE: To develop and validate a predictive model for sarcopenia. METHODS: A total of 240 subjects who visited our hospital between August 2021 and May 2023 were randomly divided by time of entry into a training set containing 2/3 of patients and a validation set containing 1/3 of patients. The muscle thickness (MT), echo intensity (EI), and shear wave velocity (SWV) of the medial gastrocnemius muscle were measured. Indicators that were meaningful in the univariate analysis in the training set were included in a binary logistic regression to derive a regression model, and the model was evaluated using a consistency index, calibration plot, and clinical validity curve. Diagnostic efficacy and clinical applicability were compared between the model and unifactorial indicators. RESULTS: Four meaningful variables, age, body mass index (BMI), MT, and SWV, were screened into the predictive model. The model was Logit Y = 21.292 + 0.065 × Age - 0.411 × BMI - 0.524 × MT - 3.072 × SWV. The model was well differentiated with an internally validated C-index of 0.924 and an external validation C-index of 0.914. The calibration plot predicted probabilities against actual probabilities showed excellent agreement. The specificity, sensitivity, and Youden's index of the model were 73.80%, 97.40%, and 71.20%, respectively, when using the diagnostic cut-off value of >0.279 for sarcopenia. The logistic model had higher diagnostic efficacy (p < 0.001) and higher net clinical benefit (p < 0.001) over the same threshold range compared to indicators. CONCLUSION: The logistic model of sarcopenia has been justified to have good discriminatory, calibrated, and clinical validity, and has higher diagnostic value than indicators.

Identifying interactions among factors related to death occurred at the scene of traffic accidents: Application of "logic regression" method.

Aim: Traffic accidents are caused by several interacting risk factors. This study aimed to investigate the interactions among risk factors associated with death at the accident scene (DATAS) as an indicator of the crash severity, for pedestrians, passengers, and drivers by adopting "Logic Regression" as a novel approach in the traffic field. Method: A case-control study was designed based on the police data from the Road Traffic Injury Registry in northwest of Iran during 2014-2016. For each of the pedestrians, passengers, and drivers' datasets, logic regression with "logit" link function was fitted and interactions were identified using Annealing algorithm. Model selection was performed using the cross-validation and the null model randomization procedure. Results: regarding pedestrians, "The occurrence of the accident outside a city in a situation where there was insufficient light" (OR = 6.87, P-value<0.001) and "the age over 65 years" (OR = 2.97, P-value<0.001) increased the chance of DATAS. "Accidents happening in residential inner-city areas with a light vehicle, and presence of the pedestrians in the safe zone or on the non-separate two-way road" combination lowered the chance of DATAS (OR = 0.14, P-value<0.001). For passengers, "Accidents happening in outside the city or overturn of the vehicle" combination (OR = 8.55, P-value<0.001), and "accidents happening on defective roads" (OR = 2.18, P-value<0.001) increased the odds of DATAS; When "driver was not injured or the vehicle was two-wheeled", chance of DATAS decreased for passengers (OR = 0.25, p-value<0.001). The odds of DATAS were higher for "drivers who had a head-on accident, or drove a two-wheeler vehicle, or overturned the vehicle" (OR = 4.03, P-value<0.001). "Accident on the roads other than runway or the absence of a multi-car accident or an accident in a non-residential area" (OR = 6.04, P-value<0.001), as well "the accident which occurred outside the city or on defective roads, and the drivers were male" had a higher risk of DATAS for drivers (OR = 5.40, P-value<0.001). Conclusion: By focusing on identifying interaction effects among risk factors associated with DATAS through logic regression, this study contributes to the understanding of the complex nature of traffic accidents and the potential for reducing their occurrence rate or severity. According to the results, the simultaneous presence of some risk factors such as the quality of roads, skill of drivers, physical ability of pedestrians, and compliance with traffic rules play an important role in the severity of the accident. The revealed interactions have practical significance and can play a significant role in the problem-solving process and facilitate breaking the chain of combinations among the risk factors. Therefore, practical suggestions of this study are to control at least one of the risk factors present in each of the identified combinations in order to break the combination to reduce the severity of accidents. This may have, in turn, help the policy-makers, road users, and healthcare professionals to promote road safety through prioritizing interventions focusing on effect size of simultaneous coexistence of crash severity determinants and not just the main effects of single risk factors or their simple two-way interactions.

Divergent thinking as a predictor of life skills in patients with schizophrenia: Evidence from the modified Tinkertoy Test.

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

Association of ambient air pollution and pesticide mixtures on respiratory inflammatory markers in agricultural communities.

Air pollution exposure is associated with adverse respiratory health outcomes. Evidence from occupational and community-based studies also suggests agricultural pesticides have negative health impacts on respiratory health. Although populations are exposed to multiple inhalation hazards simultaneously, multidomain mixtures (e.g. environmental and chemical pollutants of different classes) are rarely studied. We investigated the association of ambient air pollution-pesticide exposure mixtures with urinary leukotriene E4 (LTE4), a respiratory inflammation biomarker, for 75 participants in four Central California communities over two seasons. Exposures included three criteria air pollutants estimated via the Community Multiscale Air Quality model (fine particulate matter, ozone, and nitrogen dioxide) and urinary metabolites of organophosphate (OP) pesticides (total dialkyl phosphates (DAPs), total diethyl phosphates (DE), and total dimethyl phosphates (DM)). We implemented multiple linear regression models to examine associations in single pollutant models adjusted for age, sex, asthma status, occupational status, household member occupational status, temperature, and relative humidity, and evaluated whether associations changed seasonally. We then implemented Bayesian kernel machine regression (BKMR) to analyse these criteria air pollutants, DE, and DM as a mixture. Our multiple linear regression models indicated an interquartile range (IQR) increase in total DAPs was associated with an increase in urinary LTE4 in winter (ß: 0.04, 95% CI: [0.01, 0.07]). Similarly, an IQR increase in total DM was associated with an increase in urinary LTE4 in winter (ß:0.03, 95% CI: [0.004, 0.06]). Confidence intervals for all criteria air pollutant effect estimates included the null value. BKMR analysis revealed potential non-linear interactions between exposures in our air pollution-pesticide mixture, but all confidence intervals contained the null value. Our analysis demonstrated a positive association between OP pesticide metabolites and urinary LTE4 in a low asthma prevalence population and adds to the limited research on the joint effects of ambient air pollution and pesticides mixtures on respiratory health.

Caregiver burden and its sociodemographic determinants in family caregivers of patients with schizophrenia attending a psychiatric tertiary hospital in South Africa.

Background: Chronic mental illnesses such as schizophrenia affect patients' functioning, making caregiving necessary although burdensome. Aim: This study aimed to determine caregiver burden and its sociodemographic determinants in family caregivers of patients with schizophrenia attending a Psychiatric Outpatient Department (POD). Setting: Tertiary hospital in Northern Pretoria, South Africa. Methods: In this cross-sectional study conducted over 3 months, 300 consecutive family caregivers who attended the POD were administered a 22-item Zarit Burden Interview (ZBI-22), which has a score of 0-88, with higher values indicating more burden. Their sociodemographic characteristics were ascertained. Linear and ordinal logistic regression analyses were performed to identify determinants or predictors of total and severe burdens, respectively. Results: Most caregivers were aged 46.0 ± 14 years, females (62%), parents (39%), of low-income status (93.7%), had secondary education (70%), resided with the patient (87%), and helped with all troublesome activities (95.3%). The median ZBI-22 score was 19.0 (interquartile range: 13.0-30.5). The determinants of both total and severe burdens were: caregiver age ≥ 50 years adjusted odds ratio (aOR): 2.55, confidence interval (CI): 1.49-4.36; residential area farther away from the hospital aOR: 1.76, CI: 1.3-2.99; increasing months of caregiving aOR: 1.0, CI: 1.001-1.009, p = 0.006; and not having another family member that needs care aOR: 0.43, CI: 0.24-0.78. Conclusion: Having mental healthcare facilities close to residential areas and assisting caregivers aged ≥ 50 years who have multiple family members who need care may alleviate the burden. Contribution: Predicting total and severe caregiver burdens contemporaneously is effective for identifying potential burden interventions.

Immune checkpoint regulation is critically involved in canine cutaneous histiocytoma regression.

Introduction: Canine cutaneous histiocytoma (CCH) is a benign tumor frequently occurring in young dogs which is derived from Langerhans cells (LC). Distinguishing features of this tumor are its spontaneous regression following a rapid tumor growth. Impaired control of immune checkpoints during tumor development and progression is a widespread phenomenon which may result in an absent or ineffective immune response. The interaction between the inflammatory response and the expression of immune checkpoint molecules is only partially described in this tumor type. The aim of this study was to identify immune checkpoint molecules and molecules from the interferon-mediated immune response that are involved in the regression of CCH. Methods: Forty-eight CCH derived from dogs ≤ 4 years of age were assigned to one of four groups according to the severity and distribution of lymphocyte infiltration. Using immunohistochemistry and whole-slide image scans of consecutive sections the expression of programmed death protein ligand 1 (PD-L1), CD80, CD86, Survivin, forkhead box protein 3, Ki-67, cleaved caspase-3, CD3, and mx1 were investigated. RNA in-situ hybridization was performed for transcripts of mx1 and interferon-γ. Results: Neoplastic cells showed an expression of PD-L1, CD80, CD86, and Survivin. The density of CD80 expressing cells was negatively correlated with regression while the density of cleaved caspase-3 positive cells increased with regression. Mx1 transcripts and protein were predominantly localized in neoplastic cells while interferon-γ transcripts were most frequently detected in T-cells. Conclusion: The expression of the immune checkpoint molecules CD86 and PD-L1 and particularly the reduced expression of CD80 in groups 3 and 4 indicate an influence of the investigated immune checkpoints on tumor regression. In parallel an activation of the apoptotic cascade during regression is suggested. Finally, the detection of mx1 within the neoplasm pinpoints to a yet undisclosed role of anti-cellular signaling in tumor immunity.

Cycling safely: Examining the factors associated with bicycle accidents in Seoul, South Korea.

This study investigates the factors contributing to bicycle accidents, focusing on four types of bicycle lanes and other exposure and built environment characteristics of census blocks. Using Seoul as a case study, three years of bicycle accident spot data from 2018 to 2020 was collected, resulting in 1,330 bicycle accident spots and a total of 2,072 accidents. The geographically weighted Poisson regression (GWPR) model was used as a methodological approach to investigate the spatially varying relationships between the accident frequency and explanatory variables across the space, as opposed to the Poisson regression model. The results indicated that the GWPR model outperforms the global Poisson regression model in capturing unobserved spatial heterogeneity. For example, the value of deviance that determines the goodness of fit for a model was 0.244 for the Poisson regression model and 0.500 for the far better-fitting GWPR model. Further findings revealed that the factors affecting bicycle accidents have varying impacts depending on the location and distribution of accidents. For example, despite the presence of bicycle lanes, some census blocks, particularly in the northeast part of the city, still pose a risk for bicycle accidents. These findings can provide valuable insights for urban planners and policymakers in developing bicycle safety measures and regulations.

Temporal trend and driving effect of demographic transitions on embedded carbon emissions of Chinese households.

A population is regarded as the main non-economic driver of carbon emissions, causing the climatic crisis, especially in China experiencing a dramatic demographic transition. In contrast to aging, low fertility, the most remarkable feature of the Chinese population transition, has always been ignored when evaluating carbon emissions, due to the lack of long-run data. To narrow this gap, an integrated framework combining the continuous input-output tables from 1997 to 2018 with the Mann-Kendall test and vector auto-regression was presented to clarify the fluctuating trend of household embedded carbon emissions and the driving pattern of low fertility, aging, and urbanization. Our main findings showed that changes in household embedded carbon emissions have increased sharply in the last two decades. The growth of Chinese household embedded carbon emissions began to accelerate in 2001, which lagged 1 year behind the demographic indicators. Low fertility has a positive impact on households' embedded carbon emissions. More importantly, the impact of low fertility is more significant and far-reaching than that of aging. These suggest that aggressive policies for stimulating fertility and low-carbon lifestyles should be considered by policy makers.

Serum biomarkers REG1A and REG3A combined with the traditional CEA represent a novel nomogram for the screening and risk stratification of colorectal cancer.

BACKGROUND: To develop and validate a serum protein nomogram for colorectal cancer (CRC) screening. METHODS: The serum protein characteristics were extracted from an independent sample containing 30 colorectal cancer and 12 polyp tissues along with their paired samples, and different serum protein expression profiles were validated using RNA microarrays. The prediction model was developed in a training cohort that included 1345 patients clinicopathologically confirmed CRC and 518 normal participants, and data were gathered from November 2011 to January 2017. The lasso logistic regression model was employed for features selection and serum nomogram building. An internal validation cohort containing 576 CRC patients and 222 normal participants was assessed. RESULTS: Serum signatures containing 27 secreted proteins were significantly differentially expressed in polyps and CRC compared to paired normal tissue, and REG family proteins were selected as potential predictors. The C-index of the nomogram1 (based on Lasso logistic regression model) which contains REG1A, REG3A, CEA and age was 0.913 (95% CI, 0.899 to 0.928) and was well calibrated. Addition of CA199 to the nomogram failed to show incremental prognostic value, as shown in nomogram2 (based on logistic regression model). Application of the nomogram1 in the independent validation cohort had similar discrimination (C-index, 0.912 [95% CI, 0.890 to 0.934]) and good calibration. The decision curve (DCA) and clinical impact curve (ICI) analysis demonstrated that nomogram1 was clinically useful. CONCLUSIONS: This study presents a serum nomogram that included REG1A, REG3A, CEA and age, which can be convenient for screening of colorectal cancer.

A novel bounded loss framework for support vector machines.

This paper introduces a novel bounded loss framework for SVM and SVR. Specifically, using the Pinball loss as an illustration, we devise a novel bounded exponential quantile loss (Leq-loss) for both support vector machine classification and regression tasks. For Leq-loss, it not only enhances the robustness of SVM and SVR against outliers but also improves the robustness of SVM to resampling from a different perspective. Furthermore, EQSVM and EQSVR were constructed based on Leq-loss, and the influence functions and breakdown point lower bounds of their estimators are derived. It is proved that the influence functions are bounded, and the breakdown point lower bounds can reach the highest asymptotic breakdown point of 1/2. Additionally, we demonstrated the robustness of EQSVM to resampling and derived its generalization error bound based on Rademacher complexity. Due to the Leq-loss being non-convex, we can use the concave-convex procedure (CCCP) technique to transform the problem into a series of convex optimization problems and use the ClipDCD algorithm to solve these convex optimization problems. Numerous experiments have been conducted to confirm the effectiveness of the proposed EQSVM and EQSVR.