Pearson Correlation Coefficient Research Articles

A WRF-UCM-SOLWEIG framework for mapping thermal comfort and quantifying urban climate drivers: Advancing spatial and temporal resolutions at city scale

The capability to evaluate city-scale outdoor thermal comfort is crucial for understanding the public's experience of heat stress, especially during heat waves. Nevertheless, there remains a methodological gap in accurately mapping thermal comfort with high spatial and temporal resolutions in complex urban environment and in quantifying the contributions of various urban climate drivers, such as sky view factors and tree canopy fraction. To bridge this gap, we propose an innovative WRF-UCM-SOLWEIG framework that couples a mesoscale model with an urban microclimate model. Specifically, it integrates the Weather Research and Forecasting model coupled with the urban canopy model (WRF-UCM) and the Solar and Longwave Environmental Irradiance Geometry model (SOLWEIG). This framework is then applied to a densely populated tropical city in China, to quantify the impact of urban climate drivers on the Universal Thermal Climate Index (UTCI). The results reveal a significant diurnal variation in the impact of urban morphology. Notably, the most significant drivers affecting daytime UTCI are the impervious surface fraction and the tree canopy fraction, with maximum Pearson's correlation coefficients of 0.80 and -0.70, respectively. These findings suggest that urban heat mitigation strategies should prioritize on reducing impervious surfaces and enhancing shade management, alongside expanding urban forestry measures.

Prostate volume on computed tomography correlates well with magnetic resonance imaging measurements and is reproducible across rater training levels.

Data are lacking for the accuracy of computed tomography (CT) in measuring prostate size, which can streamline care and prevent invasive procedures. We evaluate agreement and intra/inter-observer variability in prostate sizing between CT and magnetic resonance imaging (MRI) planimetry for a wide range of gland sizes. We retrospectively reviewed 700 patients who underwent MRI fusion biopsy at a single institution and identified 89 patients that had a CT within 2years of the MRI. Six reviewers from different training levels were categorized as student, resident, or attending and each measured prostate size on CT by the prolate ellipse method. Bland-Altman analysis determined the degree of agreement between CT and MRI. Inter- and intra-observer reliability was calculated for CT. Mean CT volume was higher than MRI volume in the < 60g group (51.5g vs. 44.5g, p = 0.004), but not in the ≥ 60g group (101g vs. 100g, p = 0.458). The bias for overestimation of prostate volume by CT was 4.1g across prostate volumes, but the proportional agreement between modalities improved with size. The Pearson correlation coefficient between CT/MRI and inter/intra-rater reliability for CT increased in the ≥ 60g vs. the < 60g group for all training levels. Our data show that there is greater clinical utility for prostate size estimation by CT than previously established, particularly for larger glands where accurate size estimation may influence therapeutic decisions. In larger glands, prostate size estimation by CT is also reproducible across various training levels.

Smart material selection strategies for sustainable and cost-effective high-performance concrete production using deep learning

The creation of high-performing concrete (HPC) is greatly influenced by the selection of materials, with cost and sustainability factors playing a bigger part in contemporary building techniques. To overcome these limitations, we developed a Multi-Objective Ant Colony Adaptive Dense Convolutional Neural Network (MOAC-ADenseNet) with 5-K-Fold cross validation, a dependable and precise forecasting model for the cost-effective selection of HPC material. First, we collect a concrete material dataset for evaluating the suggested method. MOAC-ADenseNet utilized Dense convolutional neural networks and ant colony optimization for complex material data analysis, which makes it easier to choose expensive and sustainable materials for high-performance concrete manufacturing operations. The experimental findings of the suggested approach are evaluated for the relative measure such as Pearson's Linear Correlation Coefficient (R) is 0.93, the Root Mean Square Error (RMSE) is 91.38, Mean Absolute Error (MAE) of 58.15, and Mean Absolute Percentage Error (MAPE) is 8.79. The outcomes demonstrated that the material cost of HPC was correctly predicted by the MOAC-ADenseNet. The actual measured value and the MOAC-ADenseNet model predictions, following 5-K-fold cross-validation and input feature improvement, shows its effectiveness. A The MOAC-ADenseNet approach provides feasible method for enhancing material selection in HPC manufacturing accomplishing sustainability and cost-effectiveness goals.

Low CD86 expression is a predictive biomarker for clinical response to the therapeutic HPV vaccine, IGMKK16E7: Results of a post-hoc analysis

Abstract Background Although therapeutic HPV vaccines could offer a non-invasive treatment for patients with cervical intraepithelial neoplasia (CIN), none have been clinically implemented. Oral administration of the therapeutic HPV vaccine, IGMKK16E7, results in the histological regression of HPV16-positive CIN2/3 to normal (complete response: CR). Here, we investigated biomarkers that could predict CR after oral administration of IGMKK16E7. Methods Forty-two patients administered with high-dose oral IGMKK16E7 in a Phase I/II trial were included. Cervical-exfoliated cells were collected before administration. Gene expression of CD4, CD8, Foxp3, PD-1, CTLA-4, CD103, CD28, CD80, CD86, and PD-L1 in the cells were measured by quantitative RT-PCR. ROC curve analysis and Mann-Whitney tests were used to explore potential biomarkers. Pearson correlation coefficient analysis was used to correlate gene expression profiles with clinical outcome. Results The only predictive biomarker of vaccine response for which ROC curve analysis showed significant diagnostic performance with histological CR was CD86 (AUC 0.71, 95%XI 0.53-0.88, p = .020). CR patients had significantly lower CD86 expression (CD86-low) than non-CR patients (p = .035). The CR rate for CD86-low and CD86-high cases was 50% and 19%, respectively, and CD86-low cases had a significantly higher CR rate (p = .047). Compared to all patients, the CD86-low group had a 1.5-fold increase in CR rate. Gene expression of CD86 and CTLA-4 showed the strongest positive correlation with clinical outcomes in the non-CR group (p &amp;lt; .001). Conclusion Low expression of CD86 in exfoliated cervical cells can be used as a pre-treatment biomarker to predict histological CR after IGMKK16E7 use.

Emerging psychosocial factors and work overload perceptions of Mexican university teachers and students working and studying from home during the COVID-19 pandemic

IntroductionIn Mexico, academic activities during the COVID-19 pandemic were conducted from home for over 2 years. Especially during the initial months of the pandemic, the lockdown conditions necessitated a reorganization and a new understanding of social dynamics. Therefore, this study aimed to explore the perceptions of university students and teachers regarding emerging psychosocial factors that either encouraged or hindered work and/or study from home during confinement, as well as their perceptions of work overload. Furthermore, the differences between students and teachers in the studied variables were analyzed.MethodA predominantly quantitative, cross-sectional, and correlational study was conducted with 108 participants (42.6% university teachers; 57.4% graduate or postgraduate students) who filled out an online questionnaire encompassing two open-ended inductors to identify the positive and negative aspects of working or studying from home and their frequency of perceptions, the COVID-19 Work Overload from Home Scale (ESTC-COVID-19), and questions about the hours per day devoted to different activities. The open responses were categorized by two independent groups of the research team; the emerging categories were then consensually agreed upon and further transformed into dummy and continuous variables. These variables and the results of the ESTC-COVID-19 were analyzed with SPSS 19 using Pearson's correlation coefficient, the Chi-squared test, and Student's t-test. The results identified 9 positive and 10 negative emerging psychosocial factors attributed to at least 10% of the sample's open answers. In addition, work overload correlated negatively with the emerging factor of “Making better use of time” and positively with “Work, school, and/or domestic activities overload;” moreover, students perceived more work overload than teachers.DiscussionDifferences between students and teachers were observed in the following psychosocial factors: “Self-management,” “Comfort,” and “Enjoying home” (as positive factors) and “Domestic work” and “Interruptions, distractors, noise” (as negative factors), with students generally reporting more discomfort than teachers. The study analyzes these differences in relation to the demands and nature of the study and work activities undertaken by both groups, as well as the previous training of the skills and the resources required to carry them out.

EQ-5D-5L and SF-6Dv2 health utilities scores of diffuse large B-cell lymphoma patients in China

BackgroundThis study evaluates the health-related quality of life (HRQoL) of persons with diffuse large B-cell lymphoma (DLBCL) by using EQ-5D-5L and SF-6Dv2 and compares the measurement properties of the two instruments.MethodDLBCL patients were identified via a patient group and were surveyed using web-based questionnaires. Demographic information, socioeconomic status (SES), clinical characteristics, and EQ-5D-5L and SF-6Dv2 responses were collected and statistically described. The association between the EQ-5D-5L and SF-6Dv2 dimensions were analyzed using the Spearman's correlation coefficient, whereas the correlation of the utility scores was evaluated using Pearson's correlation coefficient. The agreement between the responses of the two instruments were examined using a Bland–Altman (B-A) plot. A one-way analysis of variance (ANOVA) was performed to compare the utility scores across subgroups in different clinical states (a t-test was used if there were two subgroups). In addition, the graded response model (GRM) was used to describe the discrimination ability and difficulty characteristics of the dimensions in the two instruments.ResultsIn total, 582 valid responses were collected, among which 477 respondents were associated with initial-treatment and 105 respondents were relapsed/refractory (RR) patients. The mean (standard deviation [SD]) EQ-5D-5L and SF-6Dv2 utility scores of the DLBCL patients were 0.828 (0.222) and 0.641 (0.220), respectively. The correlation between the EQ-5D-5L and SF-6Dv2 dimensions ranged from 0.299 to 0.680, and the correlation between their utility scores was 0.787. The B-A plot demonstrated an acceptable but not strong agreement between EQ-5D-5L and SF-6Dv2 utility scores. The GRM model results indicated that all dimensions of each instrument were highly discriminating overall, but EQ-5D-5L had suboptimal discriminative power among patients with good health.ConclusionBoth the EQ-5D-5L and SF-6Dv2 showed valid properties to assess the HRQoL of DLBCL patients. However, utility scores derived from the two instruments had substantial difference, thereby prohibiting the interchangeable use of utilities from the two instruments.

A Comparative Analysis of Flavonoid and Phenolic concentration from Diverse Varieties of Moringa oleifera Leaves Utilizing Methanol and Ethanol

Having its leaves abundant in nutrients such as vitamins and minerals, the moringa plant is highly regarded because of its nutritious and industrial usefulness. Despite its widespread utilisation, little is understood concerning the way various Moringa oleifera cultivars vary with regard to their phenolic and flavonoid concentration, particularly when extracting the compound utilising various solvents. To attempt to throw insight into the ways that various solvent types and environment variables affects the phytochemical compounds of Moringa oleifera leaves, this research investigation compared the efficacy of ethanol and methanol in removing phenolics as well as flavonoids from the leaves. The information gathered through this research could assist to identify the most appropriate methods for extraction that will maximize the medical and nutritional advantages associated with Moringa oleifera. New leaves of five different types of Moringa oleifera had been bought from the Winfred Thomas Agricultural Research Station in Alabama A&amp;M University. Upon being pulverised and dried, the resulting leaves had been preserved at room temperature. The leaves were immersed in a mixture of 70% ethanol as the solvent and 80% methanol all through the extraction method. The extracted samples had been then stirred, filtered, and freeze-dried. Following that, High-performance liquid chromatography was the technique employed to know the overall concentration of phenolic and flavonoid compounds. Descriptive and statistical analysis were performed using box plots, scatter plots, bar plots, and t-tests to evaluate the extent or degree to which both solvents removed phenolic and flavonoid compounds and estimated the total quantity of phenolic and flavonoids in the different cultivars. The findings from the descriptive study revealed the Nigerian variety had an increased phenolic content when extracted using methanol—roughly 822.3 µg/ml—than when removed using ethanol (80.6 µg/ml). The Indian type, on the contrary, had phenolic concentrations of 814.3 µg/ml as compared with 647.3 µg/ml in ethanol, revealing an important distinction in preference for methanol. The Nigerian cultivar possessed an increased extraction effectiveness of 1253.12 µg/ml using ethanol as opposed to methanol (1083.52 µg/ml) for flavonoids. The t-test results proved that though the difference was not statistically significant (t(28) = 0.608, p = 0.382), the average phenolic content obtained with methanol (M = 798.60, SD = 23.567) was slightly more than that obtained via ethanol (M = 790.60, SD = 45.183). In a similar vein extraction with methanol showed a smaller average flavonoid concentration (M = 1068.40, SD = 128.641) compared to the extraction with ethanol (M = 1129.40, SD = 166.018); nonetheless, this difference was not statistically significant (t(28) = 1.125, p = 0.334). The scatter diagram revealed a significant linear connection with both phenolic and flavonoid content, having a significant positive correlation (Pearson correlation coefficient = 0.95). Although some modifications depending on the variety, the research reveals that methanol and ethanol serve as suitable solvents for phenolic and flavonoid compound extraction from Moringa oleifera leaves. The usefulness of these developments in enhancing methods of extraction in manufacturing industries needs to be explored deeper.

A fault diagnosis method for analog circuits based on EEMD-PSO-SVM

Analog circuit is an crucial component of electronic equipment, and the ability to diagnose its fault state quickly and accurately is essential for ensuring the safety and reliability of these electronic equipment. This paper addresses the problems of low diagnostic accuracy and the difficulties associated with model parameter selection in traditional fault diagnosis methods, particularly when dealing with nonlinear and non-stationary fault signals. A fault diagnosis method for analog circuits is proposed, which utilizes Ensemble Empirical Mode Decomposition (EEMD) for features extraction, the Maximum Information Coefficient algorithm (MIC) for features selection, and Particle Swarm Optimization (PSO) for optimizing Support Vector Machine (SVM) classification. Firstly, EEMD is used to adaptively decompose the fault signals in the circuit to extract multi-scale fault features. Secondly, the extracted features are quantitatively evaluated using the Pearson correlation coefficient and energy value analysis, leading to the construction of a fault feature vector is constructed. On this basis, the MIC feature selection algorithm is applied to further optimize the feature vector. Finally, an efficient fault classification model is developed by optimizing the hyperparameters of the SVM model using PSO. The simulation results show that the proposed method effectively overcome the problems of model complexity and low classification accuracy caused by improper selection of wavelet basis function. The accuracy of fault diagnosis and the efficiency of model training are significantly superior to those of traditional methods.

The inertial-based gait normalcy index of dual task cost during turning quantifies gait automaticity improvement in early-stage Parkinson’s rehabilitation

BackgroundThe loss of gait automaticity is a key cause of motor deficits in Parkinson’s disease (PD) patients, even at the early stage of the disease. Action observation training (AOT) shows promise in enhancing gait automaticity. However, effective assessment methods are lacking. We aimed to propose a novel gait normalcy index based on dual task cost (NIDTC) and evaluate its validity and responsiveness for early-stage PD rehabilitation.MethodsThirty early-stage PD patients were recruited and randomly assigned to the AOT or active control (CON) group. The proposed NIDTC during straight walking and turning tasks and clinical scale scores were measured before and after 12 weeks of rehabilitation. The correlations between the NIDTCs and clinical scores were analyzed with Pearson correlation coefficient analysis to evaluate the construct validity. The rehabilitative changes were assessed using repeated-measures ANOVA, while the responsiveness of NIDTC was further compared by t tests.ResultsThe turning-based NIDTC was significantly correlated with multiple clinical scales. Significant group-time interactions were observed for the turning-based NIDTC (F = 4.669, p = 0.042), BBS (F = 6.050, p = 0.022) and PDQ-39 (F = 7.772, p = 0.011) tests. The turning-based NIDTC reflected different rehabilitation effects between the AOT and CON groups, with the largest effect size (p = 0.020, Cohen’s d = 0.933).ConclusionThe turning-based NIDTC exhibited the highest responsiveness for identifying gait automaticity improvement by providing a comprehensive representation of motor ability during dual tasks. It has great potential as a valid measure for early-stage PD diagnosis and rehabilitation assessment.Trial registration Chinese Clinical Trial Registry: ChiCTR2300067657

Automated Seminal Root Angle Measurement with Corrective Annotation

Abstract Measuring seminal root angle is an important aspect of root phenotyping, yet au- tomated methods are lacking. We introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images. To ensure our method is flexible and user- friendly we build on an established corrective annotation training method for image segmentation. We tested SeminalRootAngle on a heterogeneous dataset of 662 spring barley rhizobox images, which presented challenges in terms of image clarity and root obstruction. Validation of our new auto- mated pipeline against manual measurements yielded a Pearson correlation coefficient of 0.71. We also measure inter-annotator agreement, obtaining a Pearson correlation coefficient of 0.68, indicat- ing that our new pipeline provides similar root angle measurement accuracy to manual approaches. We use our new SeminalRootAngle tool to identify SNPs significantly associated with angle and length, shedding light on the genetic basis of root architecture

Comparison between a Novel Knee Arthrometer and Simultaneous Stress Radiography for the Diagnosis of Complete and Partial Acute Anterior Cruciate Ligament Tears.

The type of ligamentous tear and the degree of knee laxity have important guiding significancefor the diagnosis and management of anterior cruciate ligament (ACL) tears. Instrumental measurement is necessary for ACL tears since physical examination and magnetic resonance imaging (MRI) cannot provide an objective and quantitative assessment of knee laxity. This study aimed to compare the application of a novel knee arthrometer and simultaneous stress radiography in differentiating between complete and partial acute ACL tears, and further assess the correlation between the two measurements. A total of 106 patients with complete acute ACL tears and 52 patients with partial acute ACL tears were included in the study. Preoperative arthrometry and simultaneous stress radiography were performed using the Ligs arthrometer at 90, 120, and 150 N to assess side-to-side difference (SSD) in anterior knee laxity. The optimal threshold was determined using the receiver operating characteristic (ROC) curve. The area under the ROC curve (AUC) was used to assess the diagnostic value of the measurement. Pearson's correlation coefficient was used to assess the correlation between the two measurements. The optimal differential SSD thresholds in the Ligs arthrometer were 2.7 mm at 90 N, 3.8 mm at 120 N, and 4.6 mm at 150 N. Similarly, the optimal differential SSD thresholds in simultaneous stress radiography were 3.8 mm at 90 N, 5.1 mm at 120 N, and 5.6 mm at 150 N. The AUC analysis revealed that the Ligs arthrometer was fairly informative at 90 N (AUC = 0.851), 120 N (AUC = 0.878), and 150 N (AUC = 0.884), and simultaneous stress radiography was highly informative at 90 N (AUC = 0.910), 120 N (AUC = 0.925), and 150 N (AUC = 0.948). Moreover, the AUC of the combined measurements was 0.914 at 90 N, 0.931 at 120 N, and 0.951 at 150 N. A significantly strong correlation was found between the two measurements at 90 N (r = 0.743, p < 0.001), 120 N (r = 0.802, p < 0.001), and 150 N (r = 0.823, p < 0.001). The Ligs arthrometer and simultaneous stress radiography proved to be valid diagnostic tools to differentiate between complete and partial acute ACL tears, with a strong correlation between the two measurements in SSD values. Compared with single instrumental measurement, the combination of the two measurements can further improve the diagnostic value in this regard.

Optimization of multidimensional feature engineering and data partitioning strategies in heart disease prediction models

The relentless rise in heart disease incidence, a leading global cause of death, presents a significant public health challenge. Precise prediction of heart disease risk and early interventions are crucial. This study investigates the performance improvement of heart disease prediction models using machine learning and deep learning algorithms. Initially, we utilized the Heart Failure Prediction Dataset from Kaggle. After preprocessing to ensure data quality, three distinct feature engineering techniques were applied: PCA for dimensionality reduction, ET for feature selection, and Pearson's correlation coefficient for feature selection. We assessed their impact on model performance. The dataset was then partitioned into three different data split ratios—1:9, 2:8, and 3:7—to determine their specific effects on model performance. Twelve machine learning classifiers—LGBM, Adaboost, XGB, RF, DT, KNN, LR, GNB, ET, SVC, GB, and Bagging—were trained and evaluated based on five key metrics: accuracy, recall, precision, F1 score, and training time. The influence of different feature engineering methods and data partitioning ratios on model performance were systematically analyzed using paired-sample t-tests. Among the feature engineering methods compared, the Bagging classifier, when combined with feature selection via ET, exhibited superior performance. It achieved an accuracy of 97.48 % and an F1-Score of 97.48 % with a data split ratio of 1:9 between the test and training sets. With a 2:8 split, the accuracy was 94.96 % and the F1-Score was 94.95 %. For a 3:7 split, the accuracy was 94.12 % and the F1-Score was 94.11 %. Paired sample T-test results indicate that feature selection using Pearson correlation coefficient can shorten training duration, but this also leads to a decline in classifier performance. After applying PCA dimensionality reduction, compared with the control group, there was no significant difference in the training efficiency and efficacy of the classifier. However, feature selection through ET significantly reduced the training time for various classifiers while ensuring their performance.

Factor structure and psychometric properties of the Body Perception Questionnaire-Short Form (BPQ-SF); The Persian version.

Body perception is considered an important physiological marker in physical and mental disorders, therefore, its valid and reliable quantification is indeed necessary. Due to the lack of a Persian version of an instrument with validated psychometric properties for evaluating body perception, this study aimed to investigate the factorial structure, reliability, and validity of body perception questionnaire-Short Form (BPQ-SF) among Iranian adults. A total of 748 participants (mean age = 31.74; 57%female) were included in the analysis. Participants were recruited using the online survey method. Confirmatory factor analysis (CFA) was performed and concurrent validity was determined by computing Pearson's correlation coefficient between BPQ_SF, Depression Anxiety Stress Scale (DASS), and somatization subscale of Symptom-Checklist-90 (SOM). Internal consistency (using Cronbach's alpha and McDonald's omega), and composite reliability were also evaluated. The result of the CFA yielded three factors: Body Awareness, Subdiaphragmatic Reactivity, and Supradiaphragmatic Reactivity. Cronbach's alpha values for all BPQ_SF items were 0.94. Moreover, Concurrent validity between BPQ, SOM, and DASS was determined to be between 0.44 to 0.94. and had good internal consistency (McDonald's Omega range: .74-.93 Cronbach's alpha range: .76-.94). The BPQ-SF demonstrated good psychometric properties among Iranians thus can be used to reliably assess body perception.

STATISTICAL RESEARCH ON THE CORRELATION BETWEEN BMI AND GLYCOSYRED HEMOGLOBIN IN PATIENTS WITH DIABETES

Diabetes is a significant global challenge to the health and well-being of people, families and countries. The main objectives of this research are: to statistically determine the correlation between BMI and the value of glycated hemoglobin (HbA1c) and to analyze the eating habits and lifestyle of the 109 subjects who were included in this study. The research was conducted in the period from 01.06.2023-30.06.2023. by the Faculty of Food Technology and Nutrition at the University of Tetovo and VT Diet Club - Bitola. The data from the 109 respondents (35 men and 74 women) aged from 5 to 81 years. were collected by an online questionnaire. Information about minors was obtained from their parents - guardians. The following statistical methods were used: Pearson's correlation coefficient and relative numbers. 68% of the respondents suffer from diabetes type 2, and 32% of the respondents suffer from diabetes type 1. Most of the patients with diabetes (29.4%) are aged 45-54 years. The value of the correlation coefficient r = 0.04 indicates that there is a very weak correlation between BMI and the value of glycated hemoglobin. This means that the value of glycated hemoglobin is influenced by other factors such as: diet, physical activity, use of therapy with oral antidiabetics and/or insulin, etc. 41.3% of respondents are overweight (BMI = 25-29.9 kg/m2), and the same percentage of respondents are obese (BMI = 30 and more kg/m2). Crucial negative food habits practiced by the respondents are: 69% of the respondents indicated that they have 3-4 meals a day (diabetics should have 3 main meals and 2 snacks) and the same % of respondents indicated that they consume food with a high glycemic index. From this study, it can be concluded that a healthy and balanced diet and physical activity are crucial for leveling glycemia in diabetics, as well as for the prevention of complications caused by diabetes.

Investigating academic dishonesty and its relationship with moral competence and professional identity of nursing students: a cross-sectional study

BackgroundAcademic dishonesty as a fundamental challenge in adherence of ethical principles in providing care to patients has caused concern to the scientific community of nurses. In addition, positive professional identity and high moral competence are important for nurses to perform multiple care roles in high quality care of patients. The purpose of the present study is of investigating academic dishonesty and its relationship with moral competence and professional identity of master’s and PhD nursing students.MethodsThis descriptive-analytical study was conducted by census method on 65 master’s and PhD nursing students in Shiraz University of Medical Sciences, Iran in 2023. Data collection tools included 4 questionnaires (Demographic information, Academic Dishonesty Scale (DAS), Nurses’ moral competence and Professional Identity Scale for Nursing Students (PISNS)). Data collection was done within 2 months. Descriptive and inferential analyzes including independent t-test, Pearson correlation coefficient and linear regression were used to analyze the data. The collected data were analyzed by SPSS.V.22 software. P-value less than 0.05 was considered significant.ResultsThe average age of 65 participants was 31.5 ± 5.61 years. The majority of the participants in this study were women 48 (73.8%). There is a significant and negative correlation between academic dishonesty and moral competence (r=-0.260, P-value = 0.036). There is no significant correlation between academic dishonesty and professional identity (r = 0.154, P-value = 0.22). Also, a significant positive correlation was found between moral competence and professional identity (r = 0.294, P-value = 0.018).ConclusionTherefore, considering that the fact that students have a great impact on patient care, Researchers suggest that educational institutions should prevent the increase of academic dishonesty among students by establishing new rules and regulations.

Construction and validation of a risk prediction model for extrauterine growth restriction in preterm infants born at gestational age less than 34 weeks

ObjectiveThis study aimed to develop and validate a model for predicting extrauterine growth restriction (EUGR) in preterm infants born ≤34 weeks gestation.MethodsPreterm infants from Guangxi Maternal and Child Health Hospital (2019–2021) were randomly divided into training (80%) and testing (20%) sets. Collinear clinical variables were excluded using Pearson correlation coefficients. Predictive factors were identified using Lasso regression. Random forest (RF), support vector machine (SVM), and logistic regression (LR) models were then built and evaluated using the confusion matrix, area under the curve (AUC), and the F1 score. Additionally, calibration curves and decision curve analysis (DCA) were plotted to assess the performance and practical utility of the models.ResultsThe study included 387 infants, with no significant baseline differences between training (n = 310) and testing (n = 77) sets. LR identified gestational age, birth weight, premature rupture of membranes, patent ductus arteriosus, cholestasis, and neonatal sepsis as key EUGR predictors. The RF model (19 variables) demonstrated an accuracy of greater than 90% during training, and superior AUC (0.62), F1 score (0.80), and accuracy (0.72) in testing compared to other models.ConclusionsGestational age, birth weight, premature rupture of membranes, patent ductus arteriosus, cholestasis, and neonatal sepsis are significant EUGR predictors in preterm infants ≤34 weeks. The model shows promise for early EUGR prediction in clinical practice, potentially enhancing screening efficiency and accuracy, thus saving medical resources.

Associations of static and dynamic iris parameters in healthy Chinese individuals: the Handan Eye Study.

To determine the associations between the demographic factors (age and sex) and physiological dynamic iris changes and explore the associated factors for iris cross-sectional area (IA) change in healthy Chinese individuals. This cross-sectional study included individuals aged ≥40 years with an open angle and underwent anterior segment optical coherence tomography under light and dark conditions from the follow-up cohort of the Handan Eye Study. Ocular data from the right eye were analyzed. The Pearson correlation coefficient was used to assess the relationship between age and iris parameters, including iris thickness (IT), IA, and iris curvature (IC), as well as the pupil diameter (PD) in the dark, and their changes from light to dark conditions. Linear regression analysis was performed to identify the potential factors associated with IA change. The final analysis included 465 healthy individuals. PD in dark, IA change and PD change decreased with age (P < 0.001), whereas IC increased with age (P < 0.001). IT and IT change were smaller, and IC was larger in women than that in men (P = 0.021, 0.007, and 0.010, respectively). Older age (P = 0.041), larger lens thickness (P = 0.013), larger IC change (P < 0.001), and smaller PD change (P < 0.001) were significantly associated with a smaller IA change. Our study demonstrated the associations of static and dynamic iris parameters in healthy Chinese individuals. The findings provided a possible explanation for the higher prevalence of primary angle closure disease in elderly and female populations.

Investigating the mediating role of digital game addiction in the relationship between adolescents’ school belonging and resilience

ABSTRACT Objective The aim of this study is to investigate the mediating role of digital game addiction in the relationship between school belonging and psychological resilience of adolescents in middle school. Considering school belonging together with digital game addiction may help to better understand the risk and protective factors of psychological resilience and to create a comprehensive model of psychological resilience. Method The sample consisted of 316 secondary school students aged between 11 and 14 (M = 12.84), 47.5% (n = 150) female and 52.5% (n = 166) male. Data were collected using a demographic information form and three self-report scales. In addition to descriptive statis- tics, Pearson Correlation Coefficient and Bootstrap Analysis were used to analyses the data. The mediating role of digital game addiction in the relationship between school belonging and resilience was analysed using the Bootstrap method with 5000 resamples. This approach is regarded as providing more accurate results compared to the Sobel test. Results The results of the study show that there are significant positive relationships between school belonging and psychological resilience and negative relationships with digital game addiction. Also, the results of the study revealed that the mediating role of digital game addiction in the effect of school belonging on adolescents’ psychological resilience was statistically significant. Conclusion It was revealed that digital game addiction, which affects both psychological resilience and school belonging, which is a protective factor, in a very negative way, is a mediating variable whose effect should be reduced or eliminated.

Dietary intake, energy availability, and power in men collegiate gymnasts

IntroductionThe purpose was to examine the prevalence of low energy availability (LEA), explore dietary behaviors in men collegiate gymnasts (n = 14), and investigate the relationships between energy availability (EA), body composition, and plyometric performance.MethodsBody composition was measured using air displacement plethysmography. Lower- and upper-body peak power (PWRpeak) and modified reactive strength index (RSImod) were calculated from countermovement jump (CMJ) and plyometric push-up (PP) assessments. Energy expenditure was tracked over 3 days, while daily energy and macronutrient intake were recorded. EA was calculated and used to categorize athletes into LEA and non-LEA groups. Pearson correlation coefficients were used to examine relationships between EA, body composition, and performance metrics.Results85.7% of athletes (n = 12) exhibited LEA (20.98 ± 5.2 kcals/kg FFM), with non-LEA athletes (n = 2) marginally surpassing the &amp;lt;30 kcal/kg of fat-free mass (FFM) threshold (30.58 ± 0.2 kcals/kg FFM). The cohort (n = 14) consumed insufficient energy (30.5 ± 4.5 kcal/kg/day) and carbohydrates (3.7 ± 1.1 g/kg/day), resulting in LEA (22.36 ± 5.9 kcal/kg/FFM). EA was not correlated with body composition or performance metrics.DiscussionA high prevalence of LEA may exist in men gymnasts, largely due to a low relative energy and carbohydrate intake.

Effective clinical decision support implementation using a multi filter and wrapper optimisation model for Internet of Things based healthcare data

Feature Selection (FS) is essential in the Internet of Things (IoT)-based Clinical Decision Support Systems (CDSS) to improve the accuracy and efficiency of the system. With the increasing number of sensors and devices used in healthcare, the volume of data generated is vast and complex. Relevant FS from this data is crucial in reducing computational overhead, improving the system’s interpretability, and enhancing the Decision-Making System (DMS) quality. FS also aids in addressing the problems of data redundancy and noise, which can negatively impact the system’s performance. FS is critical to developing practical and dependable CDSS in IoT-based healthcare sectors. This research proposes a two-phase FS model. Phase-I employs an ensemble of five Filter Methods (FM), followed by a Pearson Correlation Method (PCM). Phase-II uses the Binary Optimized Genetic Grey Wolf Optimization Algorithm (BOGGWOA) as a Wrapper Method (WM). This recommended model integrates the most valuable features of each filter. Then, it uses the Pearson Correlation Coefficient (PCC) to get rid of features that aren’t needed, a Support Vector Machine (SVM) to guess how accurate their classification will be, and BOGGWOA as the Wrapper Method (WM) to pick the most essential features with the best CA.