The Impact of Periodontal Inflammation on the Severity of Coronary Atherosclerosis.

Introduction Through plausible biological mechanisms, periodontitis causes systemic inflammatory burden and response, thus resulting in damage far beyond the oral cavity. Studies have demonstrated periodontitis to be a significant risk factor for coronary heart disease (CHD) and stroke. The larger the quantum of periodontal inflamed tissue, the greater the chances of periodontitis eliciting bacteremia and systemic inflammatory responses. Studies have reported that periodontitis and other common oral infections play an important role in the development of atherosclerosis. Therefore, the quantity of inflamed periodontal tissue assumes significance in determining the severity of atherosclerosis. Hence, this study investigates the impact of periodontal inflamed surface area (PISA) on the severity of coronary atherosclerosis. Materials and methods In this cross-sectional study, a total of 160 patients who presented at the department of periodontics of The British University in Egypt (BUE) from 1 January 2023 to 30 September 2023 were enrolled. Patients were only enrolled if they had undergone coronary angiography within the last six months, were less than 60 years of age, shared their previous medical history and coronary angiographic report, and gave informed written consent. Data on classic coronary risk factors and periodontal inflammatory status and angiographic findings were recorded and subjected to appropriate statistical analysis. Results The results revealed that the periodontal inflamed surface area (p = 0.002) apart from age (p < 0.047) and low-density lipoprotein cholesterol (LDL-C) (p < 0.001) is a significant independent predictor of the severity of coronary atherosclerosis. Conclusions The periodontal inflamed surface area is an independent predictor of the severity of coronary atherosclerosis.

Two-minute standing endurance test for axial postural abnormalities in patients with Parkinson's disease.

BACKGROUND: Photo-based measurement methods are used to assess axial postural abnormalities (PA) in Parkinson's disease (PD). However, they capture only moments in time. We developed the 2-minute standing endurance test (2 M-SET), which specifically captures temporal changes in posture, as a novel dynamic method for measuring axial PA in patients with PD. RESEARCH QUESTION: This study aimed to verify the effectiveness and validity of the 2 M-SET for capturing temporal changes in axial PA in patients with PD. METHODS: Twenty-eight patients with PD participated. The participants attempted to maintain an upright posture for 2 minutes during three tasks: standing, stepping in place, and walking. The rate of change in postural angle was recorded at 10-second intervals. Based on the results, the 2 M-SET was developed. Therapists evaluated the 2 M-SET using the NeuroPostureApp© to measure anterior trunk flexion (ATF) angles and lateral trunk flexion (LTF) angles at 0, 10, 30, 60, and 120 seconds. To assess reliability, the congruence between the measurements obtained by the therapists and those obtained using a three-dimensional motion-analysis system was examined. For validity, we assessed whether the ATF and LTF angles measured by the therapists could accurately capture postural changes at regular intervals over time. RESULTS: The average postural changes over 2 minutes for the standing, stepping in place, and gait tasks were 59.2±83.5%, 37.6±30.7%, and 45.4±50.6%, respectively. The intraclass correlation coefficients showed high reliability, with values of 0.985 and 0.970 for the ATF and LTF angles, respectively. SIGNIFICANCE: The results of our proposed 2 M-SET method, which uses temporal photo-based measurements to assess the patient's ability to maintain an upright standing position for 2 minutes, demonstrate the potential to capture temporal changes in axial PA. DATA AVAILABILITY STATEMENT: The data supporting the findings of this study are available upon reasonable request and approval from the local ethics committee.

The effect of school size and class size on school preparedness.

The purpose of the present study was to understand students' school readiness as a function of student and teacher behaviors but also school size and class size using both linear and non-linear analytical approaches. Data came from 21,903 schools distributed across 80 countries as per the 2018 cohort of the PISA database. Results pointed to a preference for the Cusp model in that the relationship between school and class sizes with achievement proved to be best described by the non-linearity of the Cusp catastrophe model. The critical benchmarks were a school size of 801 students and a class size of 27 students for which increases beyond those thresholds were linked to nonlinearity and unpredictability in school readiness. For this reason, we suggest using the cusp catastrophe model from Nonlinear Dynamical Systems Theory (NDST) to understand more fully such complex phenomena.

Polypeptide- and Protein-Based Conjugate Nanoparticles via Aqueous Ring-Opening Polymerization-Induced Self-Assembly (ROPISA).

Protein-polymer conjugates and polymeric nanomaterials hold great promise in many applications including biomaterials, medicine, or nanoelectronics. In this work, the first polymerization-induced self-assembly (PISA) approach performed in aqueous medium enabling protein-polymer conjugates and nanoparticles entirely composed of amino acids is presented by using ring-opening polymerization (ROP). It is indeed shown that aqueous ring-opening polymerization-induced self-assembly (ROPISA) can be used with protein or peptidic macroinitiators without prior chemical modification and afford the simple preparation of nanomaterials with protein-like property, for example, to implement biomimetic thermoresponsivity in drug delivery.

The impact of reading metacognitive strategies on mathematics learning efficiency and performance: An analysis using PISA 2018 data in China.

The current study employed latent profile analysis to examine the application patterns of students' reading metacognitive strategies using PISA 2018 data in China. Subsequently, it explored the differences in students' mathematics learning efficiency and performance. The results revealed that: (1) Six types of reading metacognitive strategies application patterns were identified: "Novice - indifferent," "Veteran - average," "Novice - low evaluating," "Veteran - skilled," "Novice - mixed," and "Novice - arbitrary." (2) The primary factors that affect the classification of reading metacognitive strategies application patterns were gender, and family economic, social, and cultural statuses (ESCS). (3) Mathematics learning time could positively predict performance overall, but the mathematics learning time of "Veteran - skilled" and "Novice - mixed" students had no significant correlation with their mathematics performance. The findings suggests that educators should not blindly increase students' mathematics learning time but instead provide appropriate guidance based on their mastery patterns of reading metacognitive strategies to enhance mathematics learning efficiency and performance.

Binding Pattern and Structural Interactome of the Anticancer Drug 5-Fluorouracil: A Critical Review.

5-Fluorouracil (5-FU) stands as one of the most widely prescribed chemotherapeutics. Despite over 60 years of study, a systematic synopsis of how 5-FU binds to proteins has been lacking. Investigating the specific binding patterns of 5-FU to proteins is essential for identifying additional interacting proteins and comprehending their medical implications. In this review, an analysis of the 5-FU binding environment was conducted based on available complex structures. From the earliest complex structure in 2001 to the present, two groups of residues emerged upon 5-FU binding, classified as P- and R-type residues. These high-frequency interactive residues with 5-FU include positively charged residues Arg and Lys (P type) and ring residues Phe, Tyr, Trp, and His (R type). Due to their high occurrence, 5-FU binding modes were simplistically classified into three types, based on interactive residues (within <4 Å) with 5-FU: Type 1 (P-R type), Type 2 (P type), and Type 3 (R type). In summary, among 14 selected complex structures, 8 conform to Type 1, 2 conform to Type 2, and 4 conform to Type 3. Residues with high interaction frequencies involving the N1, N3, O4, and F5 atoms of 5-FU were also examined. Collectively, these interaction analyses offer a structural perspective on the specific binding patterns of 5-FU within protein pockets and contribute to the construction of a structural interactome delineating the associations of the anticancer drug 5-FU.

Diagnostic Classification Models for Testlets: Methods and Theory.

Diagnostic classification models (DCMs) have seen wide applications in educational and psychological measurement, especially in formative assessment. DCMs in the presence of testlets have been studied in recent literature. A key ingredient in the statistical modeling and analysis of testlet-based DCMs is the superposition of two latent structures, the attribute profile and the testlet effect. This paper extends the standard testlet DINA (T-DINA) model to accommodate the potential correlation between the two latent structures. Model identifiability is studied and a set of sufficient conditions are proposed. As a byproduct, the identifiability of the standard T-DINA is also established. The proposed model is applied to a dataset from the 2015 Programme for International Student Assessment. Comparisons are made with DINA and T-DINA, showing that there is substantial improvement in terms of the goodness of fit. Simulations are conducted to assess the performance of the new method under various settings.

Phenanthroline Derived N-Doped Carbon Dots as Robust Metal-Free Photocatalysts for PET-RAFT Polymerization and Polymerization-Induced Self-Assembly.

Metal-free organic photocatalysts for photo-mediated reversible deactivation radical polymerization (photo-RDRP) are witnessed to make increasing advancement in the precise synthesis of polymers. However, challenges still exist in the development of high-efficiency and environmentally sustainable carbon dots (CDs)-based organocatalysts. Herein, N-doped CDs derived from phenanthroline derivative (Aphen) are prepared as metal-free photocatalysts for photoinduced electron transfer reversible addition-fragmentation chain transfer (PET-RAFT) polymerization. The introduction of phenanthroline structure enhances the excited state lifetime of CDs and expands the conjugated length of their internal structure to enable the light-absorption to reach green light region, thereby enhancing photocatalytic activity. The as-designed CDs exhibit unprecedented photocatalytic capacity in photopolymerization even in large-volume reaction (100 mL) with high monomer conversion and narrow polymer dispersity (Mw/Mn < 1.20) under green light. The photocatalytic system is compatible with PET-RAFT polymerization of numerous monomers and the production of high molecular weight polyacrylate (Mn >250 000) with exquisite spatiotemporal control. Above results confirm the potential of CDs as photocatalyst, which has not been achieved with other CDs catalysts used in photo-RDRP. In addition, the construction of fluorescent polymer nanoparticles using CDs as both photocatalyst and phosphor through photoinitiated polymerization-induced self-assembly (Photo-PISA) technology is successfully demonstrated for the first time.

Lansoprazole interferes with fungal respiration and acts synergistically with amphotericin B against multidrug-resistant Candida auris.

Candida auris has emerged as a problematic fungal pathogen associated with high morbidity and mortality. Amphotericin B (AmB) is the most effective antifungal used to treat invasive fungal candidiasis, with resistance rarely observed among clinical isolates. However, C. auris possesses extraordinary resistant profiles against all available antifungal drugs, including AmB. In our pursuit of potential solutions, we screened a panel of 727 FDA-approved drugs. We identified the proton pump inhibitor lansoprazole (LNP) as a potent enhancer of AmB's activity against C. auris. LNP also potentiates the antifungal activity of AmB against other medically important species of Candida and Cryptococcus. Our investigations into the mechanism of action unveiled that LNP metabolite(s) interact with a crucial target in the mitochondrial respiratory chain (complex III, known as cytochrome bc1). This interaction increases oxidative stress within fungal cells. Our results demonstrated the critical role of an active respiratory function in the antifungal activity of LNP. Most importantly, LNP restored the efficacy of AmB in an immunocompromised mouse model, resulting in a 1.7-log (∼98%) CFU reduction in the burden of C. auris in the kidneys. Our findings strongly advocate for a comprehensive evaluation of LNP as a cytochrome bc1 inhibitor for combating drug-resistant C. auris infections.

Quantification of Mitral Regurgitation in Mitral Valve Prolapse by Three-Dimensional Vena Contracta Area: Derived Cutoff Values and Comparison With Two-Dimensional Multiparametric Approach.

BACKGROUND: Echocardiographic grading of mitral regurgitation (MR) in mitral valve prolapse (MVP) is challenging. Three-dimensional (3D) vena contracta area (VCA) has been proposed as a valuable method. However, data defining the cutoff values of severity and validation in the subset of patients with MVP are scarce. The aim of this study was to validate the 3D VCA by 3D color-Doppler transesophageal echocardiography (TEE) in patients with MVP and to define the cutoff values of severity grading. The secondary aim was to compare 3D VCA to the effective regurgitant orifice area estimation by proximal isovelocity surface area (EROA-PISA) method. METHODS: A total of 1,138 patients with at least moderate MR who underwent TEE were included. Three-dimensional VCA was measured, and the cutoff value and area under the curve (AUC) for the prediction of severe MR were estimated by receiver operating characteristic curve using a guideline-suggested multiparametric approach as the reference standard. In a subgroup of patients, 3D regurgitant volume (RV) and 3D fraction were calculated from mitral and left ventricular outflow tract stroke volumes to further validate 3D VCA against a 3D volumetric reference standard. RESULTS: The optimal 3D VCA cutoff value for predicting severe MR was 0.45 cm2 (specificity, 0.87; sensitivity, 0.90) with an AUC of 0.95 using a multiparametric approach as reference. Three-dimensional VCA had a good linear correlation with EROA-PISA (r = 0.62, P < .05) with larger values compared to EROA-PISA (0.63 cm2 vs 0.44 cm2, P < .05). A cutoff of 0.50 cm2 (AUC of 0.84; sensitivity, 0.78; specificity, 0.78) predicts an EROA-PISA of 0.40 cm2. Three-dimensional VCA had a good linear correlation with 3D RV (r = 0.56, P < .01), with an AUC of 0.86 to predict a 3D fraction >50%. CONCLUSIONS: The present study suggests 0.45 cm2 as the best cutoff value of 3D VCA to define severe MR in patients with MVP, showing an optimal agreement with the reference standard multiparametric approach and 3D RV.

The Relationship between a Competitive School Climate and School Bullying among Secondary Vocational School Students in China: A Moderated Mediation Model.

School bullying is widespread in countries around the world and has a continuous negative impact on the physical and mental health of students. However, few studies have explored the influence mechanism of a competitive school climate on school bullying among Chinese secondary vocational school students. This study aims to explore the relationship between a competitive school climate and bullying in secondary vocational schools in the Chinese context, as well as the mediating role of school belonging and the moderating role of gender. Logit regression analysis and a moderated mediation model were used to analyze 1964 secondary vocational students from China based on PISA 2018 data from Beijing, Shanghai, Zhejiang, and Jiangsu, China. (1) The detection rate of school bullying in secondary vocational schools in China is 17.8%, lower than the world average. (2) A competitive school climate is significantly and positively correlated with secondary vocational school students' exposure to school bullying. (3) A moderated mediation model suggests that school belonging is an important mechanism by which a competitive school climate influences the occurrence of school bullying, whereas gender moderates the direct effect of a competitive school climate and the indirect effect of school belonging, which mitigates the negative effects of a competitive school climate to some extent. The research results show that creating a healthy competitive climate in schools, cultivating students' sense of belonging, and facing up to gender differences are helpful to prevent school bullying in secondary vocational schools.

Adaptive and Dissipative Hierarchical Population Crowding of Synthetic Protocells through Click-PISA under Gradient Energy Inputs.

The ability of living objects to respond rapidly en masse to various stimuli or stress is an important function in response to externally applied changes in the local environment. This occurs across many length scales, for instance, bacteria swarming in response to different stimuli or stress and macromolecular crowding within cells. Currently there are few mechanisms to induce similar autonomous behaviors within populations of synthetic protocells. Herein, we report a system in which populations of individual objects behave in a coordinated manner in response to changes in the energetic environment by the emergent self-organization of large object swarms. These swarms contain protocell populations of approximately 60 000 individuals. We demonstrate the dissipative nature of the hierarchical constructs, which persist under appropriate UV stimulation. Finally, we identify the ability of the object populations to change behaviors in an adaptive population-wide response to the local energetic environment.

The effect of reading engagement on scientific literacy - an analysis based on the XGBoost method.

Scientific literacy is a key factor of personal competitiveness, and reading is the most common activity in daily learning life, and playing the influence of reading on individuals day by day is the most convenient way to improve the level of scientific literacy of all people. Reading engagement is one of the important student characteristics related to reading literacy, which is highly malleable and is jointly reflected by behavioral, cognitive, and affective engagement, and it is of theoretical and practical significance to explore the relationship between reading engagement and scientific literacy using reading engagement as an entry point. In this study, we used PISA2018 data from China to explore the relationship between reading engagement and scientific literacy with a sample of 15-year-old students in mainland China. 36 variables related to reading engagement and background variables (gender, grade, and socioeconomic and cultural status of the family) were selected from the questionnaire as the independent variables, and the score of the Scientific Literacy Assessment (SLA) was taken as the outcome variable, and supervised machine learning method, the XGBoost algorithm, to construct the model. The dataset is randomly divided into training set and test set to optimize the model, which can verify that the obtained model has good fitting degree and generalization ability. Meanwhile, global and local personalized interpretation is done by introducing the SHAP value, a cutting-edge machine model interpretation method. It is found that among the three major components of reading engagement, cognitive engagement is the more influential factor, and students with high reading cognitive engagement level are more likely to get high scores in scientific literacy assessment, which is relatively dominant in the model of this study. On the other hand, this study verifies the feasibility of the current popular machine learning model, i.e., XGBoost, in a large-scale international education assessment program, with a better model adaptability and conditions for global and local interpretation.

Socio-economic status, mastery-approach goals and learning-related outcomes: Mediation and moderation.

BACKGROUND: Socio-economic status is one of the most important factors shaping students' motivation and achievement but has seldom been explored in relation to achievement goals. AIMS: This study aimed to investigate whether mastery-approach goals explain the link between SES and key learning-related outcomes (mediation) and whether SES modifies the relationship between mastery-approach goals and these outcomes (moderation). SAMPLE: Data came from 595,444 students nested in 21,322 schools across 77 countries. METHODS: Data were analysed using multilevel-moderated mediation analyses. RESULTS: We found significant mediation and moderation. In terms of mediation, mastery-approach goals mediated the association between family SES and learning-related outcomes. However, a different pattern emerged for school SES, as students in higher SES schools had lower mastery-approach goals. In terms of moderation, we found that family SES strengthened the association between mastery-approach goals and learning-related outcomes. However, the association between mastery-approach goals and learning-related outcomes was weaker in higher SES schools. CONCLUSION: Theoretical and practical implications for the achievement goal approach to achievement motivation are discussed.

Biclustering of Log Data: Insights from a Computer-Based Complex Problem Solving Assessment.

Computer-based assessments provide the opportunity to collect a new source of behavioral data related to the problem-solving process, known as log file data. To understand the behavioral patterns that can be uncovered from these process data, many studies have employed clustering methods. In contrast to one-mode clustering algorithms, this study utilized biclustering methods, enabling simultaneous classification of test takers and features extracted from log files. By applying the biclustering algorithms to the "Ticket" task in the PISA 2012 CPS assessment, we evaluated the potential of biclustering algorithms in identifying and interpreting homogeneous biclusters from the process data. Compared with one-mode clustering algorithms, the biclustering methods could uncover clusters of individuals who are homogeneous on a subset of feature variables, holding promise for gaining fine-grained insights into students' problem-solving behavior patterns. Empirical results revealed that specific subsets of features played a crucial role in identifying biclusters. Additionally, the study explored the utilization of biclustering on both the action sequence data and timing data, and the inclusion of time-based features enhanced the understanding of students' action sequences and scores in the context of the analysis.

In vitro and clinical validation of different correction algorithms for the two-dimensional proximal isovelocity surface area method in a low-velocity flow field for quantifying tricuspid regurgitation.

Background: The 2-dimensional proximal isovelocity surface area (2D PISA) method underestimates tricuspid regurgitation (TR) severity. Previously proposed correction algorithms should be further scrutinized. Methods: Two correction algorithms were tested. One approach involves dividing the 2D PISA effective regurgitant orifice area by a constant of 0.7 (EROA0.7). Another approach involves multiplying the unadjusted EROA by Vorifice/(Vorifice - Valiasing), where Vorifice denotes the TR jet velocity, and Valiasing represents the color aliasing velocity (EROAVo-Va). In vitro validation was performed on a commercially available multifunctional valve tester with different size orifices and peak pressure gradients. A true EROA was derived through the regurgitant volume (RVol) calculated from the tester. For clinical validation, RVol was calculated as the difference between the overall stroke volume and the forward stroke volume of the right ventricle. Volumetric EROA was derived by dividing the RVol by the TR velocity-time integral (VTI). The vena contracta area (VCA) was obtained through direct planimetry with 3D echocardiography. The mean of volumetric EROA and VCA served as the reference in clinical validation. Results: Excellent correlation between the calculated EROAs and the true EROA was observed in vitro (r=0.98, r=0.97, and r=0.98 for uncorrected EROA, EROAVo-Va, and EROA0.7, respectively; all P values <0.0001). EROAVo-Va underestimated the true EROA and averaged 33% (P=0.3163), while EROA0.7 overestimated the true EROA and averaged 8% (P=0.0032). Clinically, these methods consistently exhibited a notable underestimation that varied with the reference EROA. This systematic underestimation was mitigated by both algorithms when either the VCA (biases of 19.6, 15.1, and 11.8 mm2 for uncorrected EROA, EROAVo-Va, and EROA0.7, respectively) or the volumetric EROA (biases of 10.1, 5.6, and 2.3 mm2 for uncorrected EROA, EROAVo-Va, and EROA0.7, respectively) served as the reference. Their ability to distinguish severe TR was similar, with area under the curve values of 0.905, 0.903, and 0.893 for uncorrected EROA, EROAVo-Va, and EROA0.7, respectively. No statistically significant differences were observed for diagnostic accuracy (all P values >0.05). Conclusions: Using a correction factor of 0.7 in quantifying TR provides similar accuracy when compared to other techniques. This represents a valuable clinical tool for quickly correcting the underestimation of the 2D PISA method in TR. This simple method may increase the frequency of applying the correction and earlier recognition of patients with severe TR.

What Fosters School Connectedness? The Roles of Classroom Interactions and Parental Support.

Although research has identified the impact of school connectedness on a variety of outcomes for adolescents, much less work has focused on identifying its precursors. This study examined the relative influences of classroom interactions and parental support on elements of school connectedness among a sample of 4838 students (Mage = 15.84, SD = 0.29; 49.1% female) in the United States from the Programme for International Student Assessment (PISA) 2018 data. The results showed that three domains of classroom interactions (i.e., classroom management, instructional support, and emotional support) and parental support played unique roles in predicting school connectedness (i.e., teacher support and school belonging). Specifically, classroom management positively predicted both teacher support and school belonging; instructional support, especially directed instruction, positively predicted teacher support; emotional support was unrelated to teacher support and school belonging. Parental support positively predicted school belonging, but not teacher support. Overall, these findings highlight the roles of both teachers and parents in providing developmentally appropriate support to facilitate school connectedness.

The moderating roles of resilience and social support in the relationships between bullying victimization and well-being among Chinese adolescents: Evidence from PISA 2018.

Bullying victimization can undermine adolescents' well-being. However, few studies have comprehensively investigated the contributions of various victimization forms to well-being and compared which forms were more harmful than others. Evidence on whether resilience and social support moderate such associations is also limited. Using a sample of 12,058 Chinese adolescents in the Programme for International Student Assessment (PISA) 2018, this cross-sectional study aimed to (1) investigate the associations of physical, verbal and relational victimization with well-being; (2) compare the strengths of these associations; and (3) examine the moderating roles of resilience and teacher and parent support in these associations. Results showed that three victimization forms were associated with poorer well-being. Relational and physical victimization were more harmful to most studied well-being outcomes than verbal victimization. Furthermore, resilience weakened the negative effects of physical victimization on negative affect and life satisfaction but aggravated the negative effects of verbal victimization on both outcomes and the negative effect of relational victimization on school belonging. Teacher support intensified the negative effects of physical victimization on school belonging. Parent support was not effective in regulating the victimization-well-being association. The findings underscored the detrimental effects of bullying victimization on adolescents' well-being and the potentially harmful sides of resilience and social support. Implications for bullying prevention were discussed.

Challenges and pitfalls in classification of disproportionate mitral regurgitation.

The concept of disproportionate mitral regurgitation (dispropMR) has been introduced to identify patients with functional mitral regurgitation (MR) who benefit from percutaneous treatment. We aimed to examine echocardiographic characteristics behind this entity. We retrospectively included 172 consecutive patients with reduced left ventricular ejection fraction (LVEF), and more than mild MR referred to clinically indicated echocardiography. According to the proportionality ratio (effective regurgitant orifice area (EROA)/left ventricular end-diastolic volume (LVEDV)) patients were divided into dispropMR and proportionate MR (propMR) group. Potential factors which might affect proportionality definition were analyzed. 55 patients (32%) had dispropMR. Discrepant grading of MR severity was observed when using regurgitant volume (RegVol) by proximal isovelocity surface area (PISA) method or volumetric method, with significant discordance only in dispropMR (p < 0.001). Patients with dispropMR had more frequently left ventricular foreshortened images for LVEDV calculation than patients with propMR (p = 0.003), resulting in smaller LVEDV in dispropMR group. DispropMR group had more substantial dynamic variation of regurgitant flow compared to propMR. Accordingly, EROA was consistently overestimated by standard single-point PISA method compared to serial PISA method. This was more pronounced in dispropMR (bias:10.5 ± 28.3 mm2) compared to propMR group (bias:6.4 ± 12.8 mm2). DispropMR may be found in roughly one third of clinically indicated echocardiographic studies in patients with reduced LVEF and more than mild MR. EROA overestimation due to dynamic variation of regurgitant flow and LVEDV underestimation due to LV foreshortening were more frequently found in dispropMR. Our results indicate that methodological limitations of echocardiographic MR grading could not be neglected in classifying the proportionality of MR.

Using machine learning to predict UK and Japanese secondary students' life satisfaction in PISA 2018.

BACKGROUND: Life satisfaction is a key component of students' subjective well-being due to its impact on academic achievement and lifelong health. Although previous studies have investigated life satisfaction through different lenses, few of them employed machine learning (ML) approaches. OBJECTIVE: Using ML algorithms, the current study predicts secondary students' life satisfaction from individual-level variables. METHOD: Two supervised ML models, random forest (RF) and k-nearest neighbours (KNN), were developed based on the UK data and the Japan data in PISA 2018. RESULTS: Findings show that (1) both models yielded better performance on the UK data than on the Japanese data; (2) the RF model outperformed the KNN model in predicting students' life satisfaction; (3) meaning in life, student competition, teacher support, exposure to bullying and ICT resources at home and at school played important roles in predicting students' life satisfaction. CONCLUSIONS: Theoretically, this study highlights the multi-dimensional nature of life satisfaction and identifies several key predictors. Methodologically, this study is the first to use ML to explore the predictors of life satisfaction. Practically, it serves as a reference for improving secondary students' life satisfaction.