Search Results (3,401)

Teacher preparation programmes must now ensure instructional continuity and quality across face-to-face, online, and hybrid modes, even amid health, climate, or security crises. This mixed-methods study examined which principles policymakers and teacher education directors deem essential for such resilience, and how those principles align with prior research and leadership theory. Semi-structured elite interviews (N = 25) were analyzed inductively to surface field-driven themes and deductively through two models: the ten evidence-based training principles synthesized by Hadad et al. and the six capacities of Striepe and Cunningham’s Crises Leadership Framework (CLF). Results show strong consensus on theory–practice integration, university–school partnerships, and collaborative learning, mapping chiefly to the CLF capacities of adaptive roles and stakeholder collaboration. Directors added practice-oriented priorities—authentic field immersion, formative feedback, and inclusive pedagogy—extending the crisis care and contextual influence dimensions. By contrast, policymakers uniquely stressed policy–academic co-decision-making, reinforcing complex decision-making at the system level. Reflective thinking skills and digital pedagogy, though prominent in the literature, were under-represented, signalling implementation gaps. Overall, the integrated model offers a crisis-ready blueprint for curriculum design, partnership governance, and digital capacity-building that can sustain continuity and quality in pre-service teacher education. Full article

A woman in her 40s with a history of ER/PR+, HER2-negative breast cancer presented with a seizure three years after mastectomy. Magnetic resonance imaging (MRI) revealed a right caudate head mass, which was concerning for either high-grade glioma or metastatic disease, but biopsy was deemed too high risk. Cerebrospinal fluid (CSF) tumor-derived DNA (tDNA) analysis by next-generation sequencing (NGS) was ordered, revealing a gain-of-function variant in PIK3CA, ERBB2 copy number gain, and high aneuploidy, findings consistent with breast cancer brain metastasis. Based on these results, the patient was treated with stereotactic radiosurgery (SRS) followed by trastuzumab deruxtecan, a HER2-targeted therapy. This case highlights the diagnostic and therapeutic value of CSF tDNA analysis in central nervous system (CNS) lesions when biopsy is not feasible. The report also illustrates how clonal evolution, such as acquired ERBB2 amplification, can occur in metastatic disease and influence treatment decisions. Full article

Background: Vascular closure of the femoral artery during transcatheter aortic valve implantation (TAVI) remains a critical step prone to complications, despite advancements in introducer technology. The traditional technique involves using two ProGlide^® suture closure devices (2P), but alternative approaches, such as employing a single ProGlide^® device (1P), have emerged. Aims: We sought to evaluate the efficacy and safety of the 1P strategy compared to the standard 2P closure technique during transfemoral TAVI procedures. Methods: A registry-based study was conducted at the University Hospitals of Strasbourg, France, from January 2020 to December 2023. Consecutive patients who underwent TAVI via the transfemoral approach were deemed eligible. Results: The study cohort consisted of 1303 patients, with a mean age of 81.7 years and 47% female. The 1P strategy was used in 733 cases (56.3%), while the 2P technique was employed in 570 patients (43.7%). Hemostasis was achieved in the catheterization laboratory without additional devices in 30.4% of the single-ProGlide^® pre-closing cases. Vascular complication rates were similar in both groups, at 11.3% for the 1P technique and 11.4% for the 2P technique (p = 0.964). However, vascular closure device failure was significantly less frequent in the 1P group (1.6%) compared to the 2P group (5.3%). Conclusions: The 1P strategy for pre-closing during TAVI is as effective and safe as the conventional 2P approach. The 1P method offers potential advantages in terms of simplicity and cost-effectiveness. Full article

Cycling-induced vibration significantly affects riding comfort, with road surface conditions and vehicle type identified as primary contributing factors. This study developed a vibration measurement system based on ISO 2631-1, and proposed a method for generating cycling comfort maps grounded in vibration severity levels. Field measurements on 30 campus roads in Nanchang, China, used a Mountain Bike, Shared E-bike, and Shared Bicycle. Triaxial acceleration data were collected to evaluate vibration exposure, and comfort levels were classified to produce spatially resolved maps. Results show the proposed system has strong stability and adaptability across urban environments. The maps effectively captured vibration intensity variations along road segments. Among the three vehicle types, Mountain Bikes showed the lowest vibration exposure, with approximately 90% of segments rated as comfortable. Shared E-bike exhibited moderate vibration levels, with 42% of segments deemed uncomfortable, while Shared Bicycles experienced the highest vibration, with 80% of routes potentially inducing discomfort and only 1% meeting comfort standards. This study offers a framework for objective acquisition and visualization of cycling vibration data. The developed system and mapping method provide tools for assessing vehicle vibration, guiding route selection, and offer potential value for road quality monitoring. Full article

Pancreatic ductal adenocarcinoma (PDAC) represents a malignancy characterized by one of the lowest survival rates; furthermore, at the time of diagnosis, the majority of tumors are deemed unresectable. Consequently, there exists a pressing need to investigate early signs and symptoms, as well as to implement screening protocols for patients at risk of developing PDAC. By doing so, we may enhance the potential for improved treatment outcomes in light of the typically poor prognosis associated with PDAC. A review of recent literature focused on symptoms that manifest prior to the diagnosis of PDAC has been conducted, emphasizing the underlying biological mechanisms and potential screening applications, alongside data pertaining to the influence of these symptoms on prognosis and treatment. Additionally, the roles of pre-existing pain, depression, diabetes mellitus, and paraneoplastic syndromes in treatment and outcomes were scrutinized to ascertain the feasibility of integrating these factors into clinical practice. Full article

An approximate optimal control issue for modular unmanned systems (MUSs) is presented via a cooperative differential game for solving the trajectory tracking problem. Initially, the modular unmanned system’s dynamic model is built with the joint torque feedback technique. The moment of inertia of the motor rotor has positive symmetry. Each MUS module is deemed as a participant in the cooperative differential game. Then, the MUS trajectory tracking problem is transformed into an approximate optimal control problem by means of adaptive critic design (ACD). The approximate optimal control is obtained by the critic network, approaching the joint performance index function of the system. The stability of the closed-loop system is proved through Lyapunov theory. The feasibility of the proposed control algorithm is verified by an experimental platform. Full article

Cryptocurrencies such as Bitcoin can be classified as commodities under the Commodity Exchange Act (CEA), giving the Commodity Futures Trading Commission (CFTC) jurisdiction over those cryptocurrencies deemed commodities, particularly in the context of futures trading. This paper presents a method for predicting both long- and short-term trends in selected cryptocurrencies based on the Fractal Market Hypothesis (FMH). The FMH applies the self-affine properties of fractal stochastic fields to model financial time series. After introducing the underlying theory and mathematical framework, a fundamental analysis of Bitcoin and Ethereum exchange rates against the U.S. dollar is conducted. The analysis focuses on changes in the polarity of the ‘Beta-to-Volatility’ and ‘Lyapunov-to-Volatility’ ratios as indicators of impending shifts in Bitcoin/Ethereum price trends. These signals are used to recommend long, short, or hold trading positions, with corresponding algorithms (implemented in Matlab R2023b) developed and back-tested. An optimisation of these algorithms identifies ideal parameter ranges that maximise both accuracy and profitability, thereby ensuring high confidence in the predictions. The resulting trading strategy provides actionable guidance for cryptocurrency investment and quantifies the likelihood of bull or bear market dominance. Under stable market conditions, machine learning (using the ‘TuringBot’ platform) is shown to produce reliable short-horizon estimates of future price movements and fluctuations. This reduces trading delays caused by data filtering and increases returns by identifying optimal positions within rapid ‘micro-trends’ that would otherwise remain undetected—yielding gains of up to approximately 10%. Empirical results confirm that Bitcoin and Ethereum exchanges behave as self-affine (fractal) stochastic fields with Lévy distributions, exhibiting a Hurst exponent of roughly 0.32, a fractal dimension of about 1.68, and a Lévy index near 1.22. These findings demonstrate that the Fractal Market Hypothesis and its associated indices provide a robust market model capable of generating investment returns that consistently outperform standard Buy-and-Hold strategies. Full article

Background: Patient satisfaction with diabetes technology is increasingly recognized as a key factor in therapeutic success. Patient-reported outcomes (PROs) are gaining importance in diabetes care and in the evaluation of advanced insulin delivery systems. Objectives: This study aimed to design and validate a new questionnaire, the Insulin Pump Infusion Sets Satisfaction Scale (IPISS), to assess satisfaction with insulin infusion sets among individuals with type 1 diabetes. Methods: The questionnaire was developed by our Diabetology Unit in two versions: one for patient self-reporting and one for caregivers when the patient is too young to complete it autonomously. Content validity was assessed by six healthcare professionals (three diabetologists and three nurses) based on Polit and Beck’s methodology. The Item Content Validity Index (I-CVI) was calculated for both relevance and comprehensibility and was considered satisfactory if expert agreement reached ≥83%. The Scale Content Validity Index (S-CVI) was computed as the average of I-CVIs, with a cut-off value > 90% deemed acceptable. Results: Almost all items achieved 100% positive agreement for both relevance and comprehensibility, except one item in the caregiver version, for which one rater did not provide a rating for comprehensibility (I-CVI = 83.3%). The S-CVI was 100% for relevance in both versions, 99.24% for comprehensibility in the caregiver version, and 100% in the patient version. Conclusions: The IPISS is a content-validated, self-reported tool, suitable for evaluating satisfaction with infusion sets in individuals using insulin pumps, with versions adapted for both patients and caregivers. Full article

Depressive spectrum disorders are considered among the most common in the general population. Major depressive disorder and persistent depressive disorder (or dysthymia) are the most recognized, but other depressive disorders exist with varying or no specificity. The main difference between major depressive disorder and dysthymia lies in the duration and intensity of symptoms. Improving our understanding of its etiology and pathogenesis must be a priority for health and safety. Given the complexity of the evidence in the literature, it was deemed useful to provide a comprehensive summary of the neuroanatomical dysfunctions currently identified, with particular attention to the anterior and medial cingulate cortex, dorsolateral and ventromedial prefrontal cortex, posterior parietal cortex, insula, amygdala, and hippocampus. Significant neural network alterations include hyperconnectivity of the default mode network (DMN), impairment of the executive control network (ECN), and dysfunction of the salience network (Salience Network). Neurophysiological markers reveal frontal alpha asymmetries and front-striatal metabolic alterations. Studying neural correlates is essential to deepen our understanding of the depressive spectrum and the development of personalized therapeutic interventions, including noninvasive neurostimulation techniques and target-specific pharmacological therapies, opening new avenues for translational research in neuropsychiatric settings. Full article

Jefea lantanifolia (S. Schauer) Strother is traditionally used in Hidalgo, Mexico, to manage type 2 diabetes (T2D). The aerial parts are prepared as an infusion and consumed throughout the day. This study conducted a 2 h acute experiment under both fasting and postprandial conditions to evaluate the effects of the aqueous infusion (AE), the ethanol–water extract (EWE), and their isolated constituents in hyperglycemic rats. Structures were established using conventional spectroscopic methods. The absolute configuration was determined by optical rotation and calculated electronic circular dichroism (ECD) methods. Phytochemical analysis led to the isolation of six compounds: luteolin (1); 2β-hydroxy-dimerostemma brasiolide-1-O-(3-hydroxymethacrylate) (2); homoplantaginin (3); cynarin (4); luteolin-7-O-glucoside (5); and nepitrin (6). The extract was deemed safe at a dose of 2 g/kg b. w. in acute toxicity assays. In vivo experiments showed significant reductions in blood glucose levels during fasting, with compounds 2 and 3 achieving reductions of 42% and 40%, respectively, compared to 51% with glibenclamide. Postprandially, all treatments demonstrated effective glucose-lowering activity, particularly compound 3 and the EWE. These findings support the traditional use of J. lantanifolia and highlight its phytochemicals as promising candidates for further pharmacological investigation. Long-term studies and high-dose evaluations are warranted to validate therapeutic potential and establish safety profiles. Full article

We report the study of seven commercially available rosehip oils (Rosa canina L.) using GC-MS, colorimetry (CIELab), UV-VIS, FTIR, and 3D EEM fluorescence spectroscopy, including using a smartphone spectrometer. GC-MS revealed two groups of oil samples with different chemical constituents: ω-6-dominant with 45–51% α-linolenic acid (samples S1, S2, and S5–S7) and ω-3-dominant with 47–49% α-linolenic, 7.3–19.1% oleic, 1.9–2.8% palmitic, 1.0–1.8% stearic, and 0.1–0.72% arachidic acid (S3, S4). In S1 PUFA content was found to be ~75% with ω-6/ω-3 ≈ 2:1. Favorable lipid indices of AI 0.0197–0.0302, TI 0.0208–0.0304, and h/H 33.0–50.6 were observed. The highest h/H (50.55) was observed in S5 and the lowest TI (0.0208) in S3. FTIR showed characteristic lines at ~3021, 2929/2853, 1749, and ~1370 cm⁻¹, and PCA yielded 60–80% variation and separated S1 from the rest of the samples, while the clusters grouped S5 and S6. The smartphone spectrometer also reproduced the individual differences in sample volumes ≤ 1 µL under 355–395 nm UV excitation. The non-destructive optical markers reflect the fatty acid profile and allow fast low-cost identification and quality control. An integrated control method including routine optical screening, periodic CG-MS verification, and chemometric models to trace oxidation and counterfeiting is suggested. Full article

Artificial Intelligence (AI) applications in sports dentistry have the potential to improve early detection and diagnosis. We aimed to validate the diagnostic performance of AI-assisted software in detecting dental caries, periodontitis, and tooth wear using panoramic radiographs in elite athletes. This cross-sectional validation study included secondary data from 114 elite athletes from the Sports Dentistry department at Egas Moniz Dental Clinic. The AI software’s performance was compared to clinically validated assessments. Dental caries and tooth wear were inspected clinically and confirmed radiographically. Periodontitis was registered through self-reports. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), as well as the area under the curve and respective 95% confidence intervals. Inter-rater agreement was assessed using Cohen’s kappa statistic. The AI software showed high reproducibility, with kappa values of 0.82 for caries, 0.91 for periodontitis, 0.96 for periapical lesions, and 0.76 for tooth wear. Sensitivity was highest for periodontitis (1.00; AUC = 0.84), moderate for caries (0.74; AUC = 0.69), and lower for tooth wear (0.53; AUC = 0.68). Full agreement between AI and clinical reference was achieved in 86.0% of cases. The software generated a median of 3 AI-specific suggestions per case (range: 0–16). In 21.9% of cases, AI’s interpretation of periodontal level was deemed inadequate; among these, only 2 cases were clinically confirmed as periodontitis. Of the 34 false positives for periodontitis, 32.4% were misidentified by the AI. The AI-assisted software demonstrated substantial agreement with clinical diagnosis, particularly for periodontitis and caries. The relatively high false-positive rate for periodontitis and limited sensitivity for tooth wear underscore the need for cautious clinical integration, supervision, and further model refinements. However, this software did show overall adequate performance for application in Sports Dentistry. Full article

Climate change poses significant challenges to agriculture, leading to increased crop damage owing to extreme weather conditions. Detecting and analyzing such damage is crucial for mitigating its effects on crop yield. This study proposes a novel autoencoder (AE)-based model, termed “Memory Ganomaly,” designed to detect and analyze weather-induced crop damage under conditions of significant class imbalance. The model integrates memory modules into the Ganomaly architecture, thereby enhancing its ability to identify anomalies by focusing on normal (undamaged) states. The proposed model was evaluated using apple and peach datasets, which included both damaged and undamaged images, and was compared with existing robust Convolutional neural network (CNN) models (ResNet-50, EfficientNet-B3, and ResNeXt-50) and AE models (Ganomaly and MemAE). Although these CNN models are not the latest technologies, they are still highly effective for image classification tasks and are deemed suitable for comparative analyses. The results showed that CNN and Transformer baselines achieved very high overall accuracy (94–98%) but completely failed to identify damaged samples, with precision and recall equal to zero under severe class imbalance. Few-shot learning partially alleviated this issue (up to 75.1% recall in the 20-shot setting for the apple dataset) but still lagged behind AE-based approaches in terms of accuracy and precision. In contrast, the proposed Memory Ganomaly delivered a more balanced performance across accuracy, precision, and recall (Apple: 80.32% accuracy, 79.4% precision, 79.1% recall; Peach: 81.06% accuracy, 83.23% precision, 80.3% recall), outperforming AE baselines in precision and recall while maintaining comparable accuracy. This study concludes that the Memory Ganomaly model offers a robust solution for detecting anomalies in agricultural datasets, where data imbalance is prevalent, and suggests its potential for broader applications in agricultural monitoring and beyond. While both Ganomaly and MemAE have shown promise in anomaly detection, they suffer from limitations—Ganomaly often lacks long-term pattern recall, and MemAE may miss contextual cues. Our proposed Memory Ganomaly integrates the strengths of both, leveraging contextual reconstruction with pattern recall to enhance detection of subtle weather-related anomalies under class imbalance. Full article

Purpose: This study sets out to analyze the correlation of ET-1, a vasoactive peptide, along with various cytokines and vascular factors, with clinical parameters and OCT/OCT-A measurements in glaucoma participants. Methods: Eyes of participants with cataract (n = 30) or glaucoma (n = 87) were examined with optical coherence tomography (OCT) and OCT angiography (OCT-A). Aqueous humor (AqH) from the examined eye and plasma were sampled during cataract or glaucoma surgery and analyzed by means of ELISA and Luminex assay to determine their levels of ET-1 and 35 proteins deemed relevant for regulation of the AqH outflow pathway, ocular perfusion (OP), and glucose metabolism. Results: Glaucomatous eyes are characterized by reductions in RNFL thickness and OP, reflected by reduced vessel density. Furthermore, significantly elevated peripheral ET-1 levels were detected in participants with glaucoma. In addition, significantly elevated AqH levels of MMP-2, MMP-3, ET-1, sEMMPRIN, ZAG, sLOX-1, follistatin, cortisol, endostatin, sTIE-2, and PDGF-BB were detected in the glaucomatous eyes, with correlation to reduced VD for APN, C3a, MMP-3, resistin, sTIE-2, and ZAG. Multivariable analysis showed a correlation of AqH APN levels with the reduced VD in glaucomatous eyes. Conclusions: The peripheral ET-1 level and the intraocular levels of APN, C3a, MMP-3, resistin, sTIE-2, and ZAG are associated with impaired OP in glaucoma. Furthermore, elevated intraocular levels of MMP-3, ZAG, and APN were identified as biomarkers for impaired perfusion in glaucoma. Full article

This work is framed as an application of static and small exponential random graph models for complex networks in multiple layers. This document revisits the small network and exhibits its potential. Examining the bibliography reveals considerable interest in large and dynamic complex networks. This research examines the application of small networks (50,000 population) for analyzing global commerce, conducting a comparative graph structure of the tariffs, and importing multilayer networks. The authors created and described the scenario where the readers can compare the graph models visually, at a glance. The proposed methodology represents a significant contribution, providing detailed descriptions and instructions, thereby ensuring the operational effectiveness of the application. The method is organized into five distinct blocks (Bn) and an accompanying appendix containing reproduction notes. Each block encompasses a primary task and associated sub-tasks, articulated through a hierarchical series of steps. The most challenging mathematical aspects of a small network analysis pertain to modeling and sample selection (sel_p). This document describes several modeling tasks that confirm that sel_p = 10 is the best option, including modeling the edges and the convergence and covariance model parameters, modeling the node factor by vertex names, Pearson residual distributions, goodness of fit, and more. This method establishes a foundation for addressing the intricate questions derived from the established hypotheses. It provides eight model specifications and a detailed description. Given the scope of this investigation, a historical examination of the relationships between different network actors is deemed essential, providing context for the study of actors engaged in global trade. Various analytical perspectives (six), encompassing degree analyses, diameter and edges, hubs and authority, co-citation and cliques in mutual and collapse approaches, k-core, and clustering, facilitate the identification of the specific roles played by actors within the importation network in comparison to the tariff network. This study focuses on the Latin American and Caribbean region. Full article