Search Results (643)

This article examines training itineraries for early childhood education professionals in Portugal, focusing on promoting social and human development through music and the arts for infants. The training models discussed are categorized as short-term and long-term, encompassing both theory and practice through a transdisciplinary approach. Based on initiatives promoted by the Companhia de Música Teatral (CMT) and the Education and Human Development Group of the Centre for the Study of Sociology and Musical Aesthetics (CESEM) at NOVA University Lisbon, the article highlights projects such as: (i) Opus Tutti and GermInArte, developed between 2011 and 2018; (ii) the Postgraduate Course Music in Childhood: Intervention and Research, offered at the University since 2020/21, which integrates art, health, and education, promoting collaborative work between professionals; and (iii) Mil Pássaros (Thousand Birds), developed since 2020, which exemplifies the integration of environmental education and artistic practices. The theoretical basis of these training programs combines neuroscientific and educational evidence, emphasizing the importance of the first years of life for integral development. Studies, such as those by Heckman, reinforce the impact of early investment in children’s development. Edwin Gordon’s Music Learning Theory and Malloch and Trevarthen’s concept of ‘communicative musicality’ structure the design of these courses, recognizing music as a catalyst for cognitive, emotional, and social skills. The transformative role of music and the arts in educational and social contexts is emphasized, in line with the Sustainable Development Goals of the 2030 Agenda, by proposing approaches that articulate creation, intervention, and research to promote human development from childhood onwards. Full article

Background: Epidermal growth factor receptor (EGFR) mutations are presented in approximately 50% of East Asian populations with advanced non-small cell lung cancer (NSCLC). While EGFR-tyrosine kinase inhibitors (TKIs) are the standard treatment, patient outcomes are also influenced by host-related factors. This study aimed to investigate clinical and radiological factors associated with early mortality and develop a prognostic prediction model in advanced EGFR-mutated NSCLC. Methods: A retrospective cohort was conducted in patients with EGFR-mutated NSCLC treated with first line EGFR-TKIs from January 2012 to October 2022 at Chiang Mai University Hospital. Clinical data and radiologic findings at the initiation of treatment were analyzed. A multivariable flexible parametric survival model was used to determine the predictors of death at 18 months. The predicted survival probabilities at 6, 12, and 18 months were estimated, and the model performance was evaluated. Results: Among 189 patients, 84 (44.4%) died within 18 months. Significant predictors of mortality included body mass index <18.5 or ≥23, bone metastasis, neutrophil-to-lymphocyte ratio ≥ 5, albumin-to-globulin ratio < 1, and mean pulmonary artery diameter ≥ 29 mm. The model demonstrated good performance (Harrell’s C-statistic = 0.72; 95% CI: 0.66–0.78). Based on bootstrap internal validation, the optimism-corrected Harrell’s C-statistic was 0.71 (95% CI: 0.71–0.71), derived from an apparent C-statistic of 0.75 (95% CI: 0.74–0.75) and an estimated optimism of 0.04 (95% CI: 0.03–0.04). Estimated 18-month survival ranged from 87.1% in those without risk factors to 2.1% in those with all predictors. A web-based tool was developed for clinical use. Conclusions: The prognostic model developed from fundamental clinical and radiologic parameters demonstrated promising utility in predicting 18-month mortality in patients with advanced EGFR-mutated NSCLC receiving first-line EGFR-TKI therapy. Full article

Background/Objectives: Wilson’s disease (WD) is a rare autosomal recessive disorder of copper metabolism that results in hepatic, neurological, and psychiatric manifestations. Despite being described globally, data from the Middle East remains limited. This study presents the first comprehensive national cohort analysis of WD in Oman, examining clinical features, diagnostic challenges, treatment patterns, and long-term outcomes. Methods: A retrospective cohort study was conducted on 36 Omani patients diagnosed with WD between 2013 and 2020 at Sultan Qaboos University Hospital using AASLD diagnostic criteria. Clinical presentation, biochemical parameters, treatment regimens, and progression-free survival were analyzed. Results: The median age at diagnosis was 14.5 years, with a slight female predominance (55.6%). Clinical presentation varied: 25% had hepatic symptoms, 22.2% had mixed hepatic-neurological features, and 16.7% presented with neurological symptoms alone. Asymptomatic cases identified via family screening accounted for 33.3%. Diagnostic delays were most pronounced among patients presenting with neurological symptoms. A positive family history was reported in 88.9% of cases, suggesting strong familial clustering despite a low rate of consanguinity (5.6%). Regional distribution was concentrated in Ash Sharqiyah North and Muscat. Chelation therapy with trientine or penicillamine, often combined with zinc, was the mainstay of treatment. Treatment adherence was significantly associated with improved progression-free survival (p = 0.012). Conclusions: WD in Oman is marked by heterogeneous presentations, frequent diagnostic delays, and strong familial clustering. Early detection through cascade screening and sustained treatment adherence are critical for favorable outcomes. These findings support the need for national screening policies and structured long-term care models for WD in the region. Full article

Early prediction of lymph node metastasis (LNM) following neoadjuvant therapy (NAT) is crucial for timely treatment optimization in esophageal squamous cell carcinoma (ESCC). This study developed and validated a computed tomography-based radiomic model for predicting pathologically confirmed LNM status at the time of surgery in ESCC patients after NAT. A total of 469 ESCC patients from Sun Yat-sen University Cancer Center were retrospectively enrolled and randomized into a training cohort (n = 328) and a test cohort (n = 141). Three signatures were constructed: the tumor-habitat-based signature (Habitat_Rad), derived from radiomic features of three tumor subregions identified via K-means clustering; the multiple instance learning-based signature (MIL_Rad), combining features from 2.5D deep learning models; and the clinicoradiological signature (Clinic), developed through multivariate logistic regression. A combined radiomic nomogram integrating these signatures outperformed the individual models, achieving areas under the curve (AUCs) of 0.929 (95% CI, 0.901–0.957) and 0.852 (95% CI, 0.778–0.925) in the training and test cohorts, respectively. The decision curve analysis confirmed a high net clinical benefit, highlighting the nomogram’s potential for accurate LNM prediction after NAT and guiding individualized therapy. Full article

Background: The global increase in type 2 diabetes mellitus (T2DM) cases necessitates the need for early detection of metabolic changes. This study investigated variations in liver enzymes, renal markers, electrolytes, and lipid profiles among T2DM patients stratified by hemoglobin A1c (HbA1c) categories to support early identification and better management of diabetes-related complications. Methods: A retrospective observational study at King Khalid University Hospital (KKUH), Riyadh, included 621 adult patients diagnosed with T2DM categorized into four HbA1c groups: normal (<5.7%), prediabetes (5.7–6.4%), controlled diabetes (6.5–7.9%), and uncontrolled diabetes (≥8.0%). Biochemical parameters included the liver profile: alkaline phosphatase (ALP) and bilirubin, renal profile: creatinine, blood urea nitrogen (BUN), glucose, sodium, and chloride, and lipid profile: cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglycerides. Regression models identified predictors of ALP, cholesterol, and LDL. Results: ALP was higher in uncontrolled diabetes (89.0 U/L, Q1–Q3: 106.3–72.0) than in the prediabetes group (75.0 U/L, Q1–Q3: 96.8–62.3). Sodium and chloride were lower in uncontrolled diabetes (Na: 138.3 mmol/L, Q1–Q3: 140.3–136.4; Cl: 101.1 mmol/L, Q1–Q3: 102.9–99.4) compared to the normal group (Na: 139.5 mmol/L, Q1–Q3: 142.4–136.9; Cl: 103.5 mmol/L, Q1–Q3: 106.1–101.7). LDL was lower in uncontrolled diabetes (2.1 mmol/L, Q1–Q3: 2.8–1.7) than in the normal group (2.8 mmol/L, Q1–Q3: 3.7–2.2), while triglycerides were higher in patients with uncontrolled diabetes compared to the normal group (1.45 mmol/L, Q1–Q3: 2.02–1.11 vs. 1.26 mmol/L, Q1–Q3: 1.44–0.94). Regression models showed low explanatory power (R² = 2.1–7.3%), with weight, age, and sex as significant predictors of select biochemical markers. Conclusions: The study observed biochemical variations across HbA1c categories in T2DM patients, likely reflecting insulin resistance. Monitoring these markers in conjunction with HbA1c can enhance early detection and improve the management of complications. Full article

Ensuring a sustainable and healthy human environment in university dormitories is essential for students’ learning, living, and overall health and well-being. To address this need, we carried out a series of systematic field measurements of the noise levels at 30 dormitories in three representative major urban universities in a major provincial capital city in China and designed and implemented a comprehensive questionnaire and surveyed 1005 students about their perceptions of their acoustic environment. We proposed and applied a sustainability–health-oriented, multidimensional assessment framework to assess the acoustic environment of the dormitories and student responses to natural sound, technological sounds, and human-made sounds. Using the Structural Equation Modeling (SEM) approach combined with the field measurements and student surveys, we identified three categories and six factors on student health and well-being for assessing the acoustic environment of university dormitories. The field data indicated that noise levels at most of the measurement points exceeded the recommended or regulatory thresholds. Higher noise impacts were observed in early mornings and evenings, primarily due to traffic noise and indoor activities. Natural sounds (e.g., wind, birdsong, water flow) were highly valued by students for their positive effect on the students’ pleasantness and satisfaction. Conversely, human and technological sounds (traffic noise, construction noise, and indoor noise from student activities) were deemed highly disturbing. Gender differences were evident in the assessment of the acoustic environment, with male students generally reporting higher levels of the pleasantness and preference for natural sounds compared to female students. Educational backgrounds showed no significant influence on sound perceptions. The findings highlight the need for providing actionable guidelines for dormitory ecological design, such as integrating vertical greening in dormitory design, water features, and biodiversity planting to introduce natural soundscapes, in parallel with developing campus activity standards and lifestyle during noise-sensitive periods. The multidimensional assessment framework will drive a sustainable human–ecology–sound symbiosis in university dormitories, and the category and factor scales to be employed and actions to improve the level of student health and well-being, thus, providing a reference for both research and practice for sustainable cities and communities. Full article

Food addiction (FA) is an emerging psychiatric condition that presents behavioral and neurobiological similarities with other addictions, and its early identification is essential to prevent the development of more severe disorders. The aim of the present study was to determine the ability of anthropometric measures, eating habits, symptoms related to eating disorders (ED), and lifestyle features to predict the symptoms of food addiction. Methodology: A cross-sectional study was conducted in a sample of 702 university students (77.3% women; age: 22 ± 6 years). The Food Frequency Questionnaire (FFQ), the Yale Food Addiction Scale 2.0 (YFAS 2.0), the Eating Attitudes Test (EAT-26), anthropometric measurements, and a set of self-report questions on substance use, physical activity level, and other questions were administered. A total of 6.4% of participants presented symptoms compatible with food addiction, and 8.1% were at risk for ED. Additionally, 26.5% reported daily smoking, 70.6% consumed alcohol, 2.9% used illicit drugs, and 29.4% took medication; 35.3% did not engage in physical activity. Individuals with food addiction had higher BMI (p = 0.010), waist circumference (p = 0.001), and body fat (p < 0.001) values, and a higher risk of eating disorders (p = 0.010) compared to those without this condition. In the multivariate logistic model, non-dairy beverage consumption (such as coffee or alcohol), vitamin D deficiency, and waist circumference predicted food addiction symptoms (R²_Nagelkerke = 0.349). Indeed, the machine learning approaches confirmed the influence of these variables. Conclusions: The prediction models allowed an accurate prediction of FA in the university students; moreover, the individualized approach improved the identification of people with FA, involving complex dimensions of eating behavior, body composition, and potential nutritional deficits not previously studied. Full article

The Halbach array permanent magnet can improve the power density of motors. This paper uses analytical modeling to analyze and optimize the Halbach array permanent magnet synchronous motor (PMSM). Firstly, a general motor model is established to obtain the air gap flux density. Secondly, the flux linkage and back electromotive force (EMF) were calculated. The analytical results are consistent with the finite element model (FEM) results. Thirdly, the effects of slot opening, magnetization angle, and main magnetic pole width on air gap flux density and back-EMF were studied. Finally, based on the optimization results, a prototype was manufactured, and performance testing was conducted successfully. Verification of the back-EMF of the prototype shows that the relative errors between FEM and the measured values are 1.1%, and the relative errors between the analytical values and measured values are 1.6%, which verifies the accuracy of the proposed analytical modeling. The proposed analytical model is universal and can be used to quickly adjust the magnetization form, magnetization angle, and pole width without remodeling in the finite element software, which is convenient for optimizing parameters in the early stage of motor design. Full article

This article examines the theological trajectories of Watchman Nee (1903–1972) and Witness Lee (1905–1997) on sanctification, union with Christ, and deification, situating their contributions within recent reappraisals of the doctrine of theosis in the academy. Though deification was universally affirmed by the early church and retained in various forms in medieval and early Protestant theology, post-Reformation Western Christianity marginalized this theme in favor of juridical and forensic soteriological categories. Against this backdrop, Nee and Lee offer a theologically rich, biblically grounded, and experientially oriented articulation of deification that warrants greater scholarly attention. Drawing from the Keswick Holiness tradition, patristic sources, and Christian mysticism, Nee developed a soteriology that integrates justification, sanctification, and glorification within an organic model of progressive union with God. Though he does not explicitly use the term “deification”, the language he employs regarding union and participation closely mirrors classical expressions of Christian theosis. For Nee, sanctification is not merely moral improvement but the transformative increase of the divine life, culminating in conformity to Christ’s image. Lee builds upon and expands Nee’s participatory soteriology into a comprehensive theology of deification, explicitly referring to it as “the high peak of the divine revelation” in the Holy Scriptures. For Lee, humans become God “in life and nature but not in the Godhead”. By employing the phrase “not in the Godhead”, Lee upholds the Creator–creature distinction—i.e., humans never participate in the ontological Trinity or God’s incommunicable attributes. Yet, in the first portion of his description, he affirms that human beings undergo an organic, transformative process by which they become God in deeply significant ways. His framework structures sanctification as a seven-stage process, culminating in the believer’s transformation and incorporation into the Body of Christ to become a constituent of a corporate God-man. This corporate dimension—often overlooked in Western accounts—lies at the heart of Lee’s ecclesiology, which he sees as being consummated in the eschatological New Jerusalem. Ultimately, this study argues that Nee and Lee provide a coherent, non-speculative model of deification that integrates biblical exegesis, theological tradition, and practical spirituality, and thus, present a compelling alternative to individualistic and forensic soteriologies while also highlighting the need for deeper engagement across global theological discourse on sanctification, union with Christ, and the Triune God. Full article

Hydraulic tomography (HT) based on the successive linear estimator (SLE) offers the high-resolution characterization of aquifer heterogeneity but conventionally requires prolonged pumping to achieve steady-state conditions, limiting its applicability in contamination-sensitive or low-permeability settings. This study bridged theoretical and practical gaps (1) by identifying spatial periodicity (hole effect) as the mechanism underlying divergences in steady-state cross-correlation patterns between random finite element method (RFEM) and first-order analysis, modeled via an oscillatory covariance function, and (2) by validating a novel short-term sampling strategy for SLE-based HT using field experiments at the University of Göttingen test site. Utilizing early-time drawdown data, we reconstructed spatially congruent distributions of hydraulic conductivity, specific storage, and hydraulic diffusivity after rigorous wavelet denoising. The results demonstrate that the short-term sampling strategy achieves accuracy comparable to that of long-term sampling strategy in characterizing aquifer heterogeneity. Critically, by decoupling SLE from steady-state requirements, this approach minimizes groundwater disturbance and time costs, expanding HT’s feasibility to challenging environments. Full article

Background: Poststroke dysphagia is a common condition that can lead to complications such as aspiration pneumonia and malnutrition, significantly affecting the quality of life. Most patients recover their swallowing function spontaneously, but in others difficulties persist beyond six months. Can we predict this in advance? On the other hand, there have been recent attempts to use machine learning to predict disease prognosis. Therefore, this study aims to investigate whether machine learning can predict the long-term prognosis for poststroke dysphagia using early videofluoroscopic swallowing study (VFSS) data. Methods: Data from VFSSs performed within 1 month of onset and swallowing status at 6 months were collected retrospectively in patients with dysphagia who experienced their first acute stroke at a university hospital. We selected 14 factors (lip closure, bolus formation, mastication, apraxia, tongue-to-palate contact, premature bolus loss, oral transit time, triggering of pharyngeal swallow, vallecular residue, laryngeal elevation, pyriform sinus residue, coating of the pharyngeal wall, pharyngeal transit time, and aspiration) from the VFSS data, scored them, and analyzed whether they could predict the long-term prognosis using five machine learning algorithms: Random forest, CatBoost classifier, K-neighbor classifier, Light gradient boosting machine, Extreme gradient boosting. These algorithms were combined through an ensemble method to create the final model. Results: In total, we collected data from 448 patients, of which 70% were used for training and 30% for testing. The final model was evaluated using accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC), resulting in values of 0.98, 0.94, 0.84, 0.88, and 0.99, respectively. Conclusions: Machine learning models using early VFSS data have shown high accuracy and predictive power in predicting the long-term prognosis of patients with poststroke dysphagia, and they are likely to provide useful information for clinicians. Full article

A maximum entropy (ME) methodology was used to infer the Hubble constant from the temperature anisotropies in cosmic microwave background (CMB) measurements, as measured by the Planck satellite. A simple cosmological model provided physical insight and afforded robust statistical sampling of a parameter space. The parameter space included the spectral tilt and amplitude of adiabatic density fluctuations of the early universe and the present-day ratios of dark energy, matter, and baryonic matter density. A statistical temperature was estimated by applying the equipartition theorem, which uniquely specifies a posterior probability distribution. The ME analysis inferred the mean value of the Hubble constant to be about 67 km/sec/Mpc with a conservative standard deviation of approximately 4.4 km/sec/Mpc. Unlike standard Bayesian analyses that incorporate specific noise models, the ME approach treats the model error generically, thereby producing broader, but less assumption-dependent, uncertainty bounds. The inferred ME value lies within 1σ of both early-universe estimates (Planck, Dark Energy Signal Instrument (DESI)) and late-universe measurements (e.g., the Chicago Carnegie Hubble Program (CCHP)) using redshift data collected from the James Webb Space Telescope (JWST). Thus, the ME analysis does not appear to support the existence of the Hubble tension. Full article

Background/Objectives: Generating real-world data on the efficacy of multidisciplinary care in cardiometabolic risk management is essential to ensure that guidelines are both applicable and effective, especially in public healthcare settings, where organizational structures may impede healthcare professionals’ agility. This study aimed to generate data on patient follow-up and cardiometabolic risk management during the early years of a public university family medicine group in Québec (Canada) that provides multidisciplinary care to adults with cardiometabolic conditions, in order to evaluate the implementation and effectiveness of its care model. Methods: This was a retrospective longitudinal study. Patients treated at the clinic from 31 January 2020 (clinic opening) to 8 May 2024 (n = 96) were invited to consent to the use of their medical data for research. Results: A total of 52 patients consented and were included in the study. Upon entry at the clinic, >90% of patients had anthropometry and blood pressure (BP) measured, but plasma glucose and lipids were assessed among 50% and 79% of patients, respectively. A total of 36 patients completed the personalized multidisciplinary care program. No evidence of associations between the total number of appointments or appointments with the registered dietitian specifically with changes in BMI, waist circumference, and BP was found. However, each pharmaceutical intervention was associated with a −0.51 cm (95%CI: −1.03, 0.02; p = 0.06) change in waist circumference and a −1.49 mm Hg (95%CI: −2.56, −0.43, p = 0.01) change in diastolic BP. Conclusions: These data highlight the challenges of implementing a research-oriented clinic within Québec’s public healthcare system. Full article

Background: Bariatric surgeries, specifically laparoscopic sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB), are a common intervention for morbid obesity, significantly affecting food tolerance and quality of eating. Understanding these changes is crucial for improving postoperative care and long-term success. Methods: This observational study at University Hospital Mannheim involved 91 patients, aged between 18 and 65 year, who underwent SG or RYGB between 2009 and 2019. Food tolerance was assessed between 25 days and 117 months after surgery using the validated score by Suter et al. (Food Tolerance Score, FTS) and an additional score evaluating tolerance to specific food groups and quality of life. Data on body composition were collected through Bioelectrical Impedance Analysis (BIA) at follow-up visits. Statistical analyses included linear mixed models to analyze the association of food tolerance with body composition changes. Results: The FTS indicated moderate or poor food tolerance in 62.6% of patients, with no significant differences between SG and RYGB. Considering the results of the additional score, food groups such as red meat, wheat products, raw vegetables, carbon dioxide, fatty foods, convenience food, and sweets were the most poorly tolerated food groups. A total of 57 of the participants had a baseline and follow-up BIA measurement. Postoperatively, a significant reduction in body weight and BMI as well as in BIA parameters (fat mass, lean mass, body cell mass, and phase angle) was found. Quality of life improved after bariatric surgery and 76.9% rated their nutritional status as good or excellent, despite possible food intolerances. Conclusions: Bariatric surgery significantly reduces weight and alters food tolerance. Despite moderate or poor food tolerance, patients reported high satisfaction with their nutritional status and quality of life. Detailed food tolerance assessments and personalized dietary follow-ups are essential for the early detection and management of postoperative malnutrition, ensuring sustained weight loss and improved health outcomes. Full article

Rolling bearings are widely used in rotating machinery, and their health status is crucial for the safe operation of the equipment. The research on relevant fault diagnosis algorithms is a hot topic in the field. As a leading deep learning paradigm, Convolutional Neural Networks (CNNs) have demonstrated remarkable effectiveness in bearing fault diagnosis. However, conventional CNNs encounter significant limitations in accurately identifying and classifying early-stage bearing faults, primarily due to two challenges: (1) the diagnostic accuracy is highly susceptible to variations in the input signal length and segmentation strategies and (2) incipient faults are characterized by extremely low signal-to-noise ratios (SNRs), which obscure fault signatures. To address these challenges, we propose a Waveform Intersection-CNN (WI-CNN)-based intelligent diagnosis method for early faults. This approach integrates Gramian Angular Field theory to construct high-resolution fault signatures, enabling the CNN-based diagnosis of incipient bearing faults. Validation using the Case Western Reserve University dataset demonstrates an average diagnostic accuracy exceeding 98%. Furthermore, we established a custom test platform to develop a hybrid diagnosis strategy for 10 distinct fault types. Comparative studies against two conventional CNN diagnostic methods confirm that our approach delivers superior diagnostic precision, a faster iteration speed, and enhanced algorithmic robustness. The empirical findings demonstrate that the model achieves an accuracy of 99.67% during training and 98.167% in the testing phase. Crucially, the proposed method offers exceptional simplicity, computational efficiency, and practical applicability, facilitating its widespread implementation. Full article