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Materialists perceive their high socioeconomic status as justice: Associations with increased political participation
Existing research has not reached a consensus on the relationship between subjective socioeconomic status (SSS) and political participation. Thus, this study investigated the psychological variables influencing the relationship between SSS and political participation. Specifically, it explored the mediating role of perceived social justice and the moderating role of materialism value. A sample of 1306 college students was conducted with the MacArthur Scale of SSS, the System Justification Scale, the Material Value Scale, and the Political Participation Behavior Scale. The results showed that: (1) There was a significant positive correlation between SSS, perceived social justice and political participation; materialism value shows a significant negative correlation with SSS, perceived social justice, and political participation. (2) Perceived social justice played a mediating role in the effect of SSS on political participation. (3) Materialism value moderated the relationship between SSS and perceived social justice. Under high materialism value, perceived social justice increases with SSS; however, under low materialism value, this effect is no longer significant. The study enriches our understanding of the underlying psychological mechanisms and marginal effects in the relationship between SSS and political participation.
Lightweight insulator target detection algorithm based on improved YOLOX
Application research of artificial intelligence software in the analysis of thyroid nodule ultrasound image characteristics
Thyroid nodule, as a common clinical endocrine disease, has become increasingly prevalent worldwide. Ultrasound, as the premier method of thyroid imaging, plays an important role in accurately diagnosing and managing thyroid nodules. However, there is a high degree of inter- and intra-observer variability in image interpretation due to the different knowledge and experience of sonographers who have huge ultrasound examination tasks everyday. Artificial intelligence based on computer-aided diagnosis technology maybe improve the accuracy and time efficiency of thyroid nodules diagnosis. This study introduced an artificial intelligence software called SW-TH01/II to evaluate ultrasound image characteristics of thyroid nodules including echogenicity, shape, border, margin, and calcification. We included 225 ultrasound images from two hospitals in Shanghai, respectively. The sonographers and software performed characteristics analysis on the same group of images. We analyzed the consistency of the two results and used the sonographers’ results as the gold standard to evaluate the accuracy of SW-TH01/II. A total of 449 images were included in the statistical analysis. For the seven indicators, the proportions of agreement between SW-TH01/II and sonographers’ analysis results were all greater than 0.8. For the echogenicity (with very hypoechoic), aspect ratio and margin, the kappa coefficient between the two methods were above 0.75 (P < 0.001). The kappa coefficients of echogenicity (echotexture and echogenicity level), border and calcification between the two methods were above 0.6 (P < 0.001). The median time it takes for software and sonographers to interpret an image were 3 (2, 3) seconds and 26.5 (21.17, 34.33) seconds, respectively, and the difference were statistically significant (z = -18.36, P < 0.001). SW-TH01/II has a high degree of accuracy and great time efficiency benefits in judging the characteristics of thyroid nodule. It can provide more objective results and improve the efficiency of ultrasound examination. SW-TH01/II can be used to assist the sonographers in characterizing the thyroid nodule ultrasound images.
2D logistic map with unit transfer function and modulus operation based pseudorandom number generation for image encryption
Prevalence and its associated factors of medical error reporting among healthcare professionals in Ethiopia: Systematic review and meta-analysis
Introduction Medical error refers to a mistake made by a healthcare professional that poses a significant threat to patient safety worldwide. Reporting these errors is crucial for reducing healthcare-related mistakes. Despite several studies on medical error reporting and its associated factors among health professionals in Ethiopia, the national prevalence and contributing factors are not well established. Methods. We conducted a systematic review and meta-analysis of cross-sectional studies assessing the prevalence of medical error reporting and its associated factors among healthcare professionals in Ethiopia. An extensive literature search was performed from April 10 to June 10, 2024, using databases such as Google Scholar, Web of Science, and PubMed, along with a manual search. The pooled prevalence was calculated using a random-effects model. Results Out of 1233 studies retrieved from databases, only 24 studies involving a total of 6,745 healthcare professionals were included in the analysis. The overall pooled prevalence of medical error reporting was 42.66% (95% CI: 33.19, 52.13; I2 = 98.79%, p < 0.01). Factors significantly associated with medical error reporting included being trained (AOR = 3.25, 95% CI: 1.79, 4.70), fear of administrative sanctions (AOR = 0.40, 95% CI: 0.04, 0.76), lack of feedback (AOR = 0.86, 95% CI: 0.16, 1.55; p < 0.02), increased work experience (AOR = 2.90, 95%CI: 1.25, 4.54), female professionals (AOR = 2.22, 95% CI: 0.10, 4.34), and higher education status (AOR = 3.20, 95%CI: 1.10, 5.30). Conclusion Medical error reporting among healthcare professionals in Ethiopia is relatively low, primarily due to inadequate training, fear of consequences, and lack of feedback. Targeted interventions such as training programs and the creation of a non-punitive error reporting environment are needed to improve reporting practices.
Efficacy and safety of Saccharomyces boulardii as adjunct therapy with Vancomycin in treating Clostridioides difficile infection: A randomized controlled trial
Ground-truth-free deep learning approach for accelerated quantitative parameter mapping with memory efficient learning
Quantitative MRI (qMRI) requires the acquisition of multiple images with parameter changes, resulting in longer measurement times than conventional imaging. Deep learning (DL) for image reconstruction has shown a significant reduction in acquisition time and improved image quality. In qMRI, where the image contrast varies between sequences, preparing large, fully-sampled (FS) datasets is challenging. Recently, methods that do not require FS data such as self-supervised learning (SSL) and zero-shot self-supervised learning (ZSSSL) have been proposed. Another challenge is the large GPU memory requirement for DL-based qMRI image reconstruction, owing to the simultaneous processing of multiple contrast images. In this context, Kellman et al. proposed memory-efficient learning (MEL) to save the GPU memory. This study evaluated SSL and ZSSSL frameworks with MEL to accelerate qMRI. Three experiments were conducted using the following sequences: 2D T2 mapping/MSME (Experiment 1), 3D T1 mapping/VFA-SPGR (Experiment 2), and 3D T2 mapping/DESS (Experiment 3). Each experiment used the undersampled k-space data under acceleration factors of 4, 8, and 12. The reconstructed maps were evaluated using quantitative metrics. In this study, we performed three qMRI reconstruction measurements and compared the performance of the SL- and GT-free learning methods, SSL and ZSSSL. Overall, the performances of SSL and ZSSSL were only slightly inferior to those of SL, even under high AF conditions. The quantitative errors in diagnostically important tissues (WM, GM, and meniscus) were small, demonstrating that SL and ZSSSL performed comparably. Additionally, by incorporating a GPU memory-saving implementation, we demonstrated that the network can operate on a GPU with a small memory (<8GB) with minimal speed reduction. This study demonstrates the effectiveness of memory-efficient GT-free learning methods using MEL to accelerate qMRI.
Effects of strength training on neuromuscular adaptations in the development of maximal strength: a systematic review and meta-analysis
Exploring the relationship between motor visual proficiency and performance metrics in elite skeet shooters: An in-depth analysis
Background Motor vision ability entails using eyesight to collect and interpret information, such as tracking moving targets, understanding spatial relationships, predicting object movement, making decisions, and taking actions. This study aimed to explore the relationship between the visual skills of elite skeet shooters and their competition performance. Methods In this cross-sectional study (n = 42), elite skeet shooters from the Chinese National Clay Target Shooting Training Team with a mean age of 25.63 ± 6.2 years and an average training years of 7.64 ± 3.43 participated. The fundamental visual ability variables were measured using the Senaptec system, and their specialized visual ability indices were measured during target viewing tasks using aSee Glasses. The relationship between visual acuity test indices and sports performance was analyzed using correlation coefficients and multiple linear regression analysis. Results The strongest positive association was observed between Perceived Range (PS) and sports performance (r = 0.486, p < 0.001), indicating that athletes with a higher perceptual range tend to perform better. Moderate positive correlations (r = 0.333 to r = 0.362, p < 0.001) were also found for Visual Clarity (VCR), Near/Far Switching (NFQSCORE), Multi-target Tracking Speed (MOTSPEED), and Go/No-Go Score (GNGSCORE), suggesting these visual skills are beneficial for performance. Conversely, a strong negative correlation was noted between Near/Far Switching Reaction Time (NFQFRT) and performance (r = −0.510, p < 0.001), highlighting that slower reaction times are detrimental. Additionally, Target Capture (TC), Depth Perception (DPP), and Eye-Hand Coordination (EHC_RT) showed moderate negative correlations (r = −0.425 to r = −0.241, p < 0.001) with performance. The regression model explained 76.7% of the variance in athletes’ specialized performance (R² = 0.752, F = 49.692, p < 0.001), with key predictors including NFQFRT, EHC_RT, PS, and several specialized visual skills. Conclusion The visual abilities of elite skeet shooters significantly affect their performance, underscoring the importance of perceptual range, reaction time, and specialized visual skills.
Root and canal configurations of maxillary first premolars in 22 countries using two classification systems: a multinational cross-sectional study
A retrospective evaluation of access equity in virtual care during the COVID-19 pandemic: A 2-year review and comparison of visits in Ontario, Canada
Background Access equity has been raised as a fundamental concern with virtual care, both as it was used during the SARS-CoV-2 pandemic as well as its future applications within health systems. These concerns have not yet been substantiated with quantifiable data. We conducted a comparison of healthcare utilization and access across all dimensions of the Ontario Marginalization Index between virtual care and in-person care in the province of Ontario. Methods We conducted a retrospective observational study using ICES databases in the Province of Ontario between March 14, 2020, and March 13,2022. We identified all virtual and in-person visits using billing codes. All visits were linked to their individual postal dissemination area for which there was census data from the Ontario Marginalization Index. Dissemination areas were divided, according to their categorization within each marginalization dimension, and visit rates were calculated for both populations. Results A total of 93,363,194 visits were included as part of the final analysis. Significant differences in virtual healthcare utilization were noted between the most and least marginalized populations within each dimension. This effect was not observed by visits for in-person care. The only exception was that racialized, and newcomer populations had higher virtual care utilization among the most marginalized. Interpretation This data is the first that uses a large retrospective dataset and seems to confirm concerns for access inequity among the most marginalized populations. These differences much be part of policy considerations for the future of virtual care use.
Quantitative reconstruction of phase states and evolution of condensate and gas in the western Kelasu Thrust Belt, Kuqa Depression, Tarim Basin
Prolonged preoperative wait time associated with elevated postoperative thirty-day mortality following intracranial tumor craniotomy in adult patients: A retrospective cohort study
Objective Prior studies have established preoperative wait time as a potential risk factor for postoperative outcomes across various clinical conditions. However, associations between wait time and short-term prognosis following intracranial tumor surgery are still largely unknown. Our study sought to investigate associations between preoperative wait time and postoperative thirty-day mortality following intracranial tumor craniotomy in adult patients. Methods This retrospective cohort study utilized data extracted from the ACS NSQIP database, comprising 18,298 adult patients who underwent intracranial tumor craniotomy between 2012 and 2015. The primary exposure and outcome were preoperative wait time and postoperative thirty-day mortality, respectively. Smooth curve fitting evaluated the linear or nonlinear association between them. The effects of exposure on outcome were evaluated using multivariate Cox proportional hazard regression models and Kaplan-Meier curves. Subgroup analyses and interaction testing were conducted to evaluate the effect modification of confounding factors. The robustness of the main results was assessed through propensity score matching and sensitivity analyses. Results Prolonged preoperative wait time was independently and linearly related to elevated thirty-day mortality (HR = 1.075, 95%CI: 1.040–1.110). The ventilator-dependent status significantly modify the relationship between wait time and mortality. The linear wait time-mortality association was observed solely in non-ventilator-dependent patients, showing an 8.3% increase in thirty-day mortality risk for each additional day of waiting (HR = 1.083, 95%CI: 1.049–1.119). Patients who waited ≥1 day had a 0.74% higher absolute risk and a 31.3% higher relative risk of thirty-day mortality compared to those who waited <1 day. The sensitivity analyses corroborated the robustness of these results. Conclusions Prolonged preoperative wait time has an independent linear association with elevated postoperative thirty-day mortality in non-ventilator-dependent adult patients undergoing intracranial tumor craniotomy. Clinicians should minimize preoperative wait time to mitigate the risk of thirty-day mortality. Nonetheless, further research is warranted to validate the results and establish causality.
Development of a small compound that regulates the function of a maltodextrin-binding protein of Streptococcus pyogenes by multifaceted screenings
Abstract Group A Streptococcus (GAS) are gram-positive bacteria that cause various symptoms. The treatment of GAS infections currently relies on antibiotics, but new treatment options are needed due to the spread of antibiotic resistance. To develop novel treatment methods that circumvent the generation of antibiotic resistance, we used virtual screening followed by several biophysical-based screening methods to identify antibacterial compounds that target SPs0871, which is a maltodextrin-binding protein that is involved in carbohydrate catabolism in GAS. We narrowed down the list of compounds in the library via multi-step screening and finally isolated a compound that bacteriostatically inhibited the growth of GAS. Together with our previous study showing that an anti-SPs0871 variable heavy domain of heavy chain antibody, which completely blocked ligand binding, did not suppress bacterial growth, our results provide guidelines for designing an antistreptococcal therapeutic.
Correction: Power analysis for personal light exposure measurements and interventions
Exploring syphilis transmission dynamics with congenital infection and disability compartments
Development and validation of nomograms including individual- and area-level variables to predict risk of fatal and non-fatal cardiovascular diseases among Russian population
Introduction Cardiovascular diseases (CVD) are the greatest threat to health worldwide and in Russia. Our study aimed to use Cox proportional hazards models to develop cardio-vascular risk scores and nomograms based on prospective data from studies conducted in Russia. Methods All materials used in this study were obtained from the epidemiological study “Epidemiology of Cardiovascular Diseases in the Regions of the Russian Federation” (ESSE-RF): ESSE-RF (2012-2014) and ESSE-RF2 (2017). A total of 18,454 individuals without CVD aged 25–64 years were included in our study. The participants were randomly divided into a training and testing set at a ratio of 7:3. The Russian deprivation index and its components (social, economic and environmental) were used as area-level predictors. To select the best potential predictive variables for our models, the random forests variable selection algorithm based on minimal depth was used. To predict three- and five-year CVD-free survival, four prognostic nomograms were developed from the results of multivariate analysis. Results The nomograms had considerable discriminative power, calibrating abilities and clinical effectiveness. The time dependent AUC was > 0.7 for the prediction of CVD-free survival in both the training and testing sets. Conclusion For the first time, the nomograms have been created that include area-level predictors (socio-economic and environmental) and lipid spectrum indicators (triglycerides, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol) and assess the probability of fatal and non-fatal cardiovascular events among the Russian population.