Search Results (47,925)

Background: Soft tissue sarcomas (STSs) histopathological classification system and the clinical and molecular-based tools that are currently employed to estimate its prognosis have several limitations, impacting prognostication and treatment. Clinically driven molecular profiling studies may cover these gaps and offer alternative tools with superior prognostication capability and enhanced precision and personalized treatment approaches identification ability. Materials and Methods: We performed DNA sequencing (DNA-seq) and RNA sequencing (RNA-seq) to portray the molecular profile of 102 samples of high-grade STS, comprising the three most common STS histotypes. Results: The analysis of RNA-seq data using unsupervised machine learning models revealed previously unknown molecular patterns, identifying four transcriptomic subtypes/clusters (TCs). This TC-based classification has a clear prognostic value (in terms of overall survival (OS) and disease-free survival (DFS)), a finding that was externally validated using independent patient cohorts. The prognostic value of this TC-based classification outperforms the prognostic accuracy of clinical-based (SARCULATOR nomograms) and molecular-based (CINSARC) prognostication tools, being one of the first molecular-based classifications capable of predicting OS in STS. The analysis of DNA-seq data from the same cohort revealed numerous and, in some cases, never documented molecular targets for precision treatment across different transcriptomic subtypes. The functional and predictive value of each genomic variant was analyzed using the Molecular Tumor Board Portal. Conclusions: This newly identified TC-based classification offers a superior prognostic value when compared with current gold-standard clinical and molecular-based prognostication tools, and identifies novel molecular targets for precision treatment, representing a cutting-edge tool for predicting prognosis and guiding treatment across different stages of STS. Full article

Background/Objectives: Unbalanced diets contribute to a rise in low-grade systemic inflammation, a risk factor for metabolic diseases. The aim of this study is to systematically review evidence from chronic intervention studies to understand the role of foods in modulating inflammatory responses in humans. Methods: A literature search was conducted on PubMed using specific keywords. Risk of bias was assessed using Cochrane guidelines. Inclusion criteria required chronic dietary intervention studies measuring cytokine levels in humans. Results: In the 75 studies selected, results revealed extremely high variability both in outcomes, study design, and participant selection criteria. Studies with fruits and vegetables showed a reduction in circulating cytokine levels and/or an increase in anti-inflammatory cytokines in 80% of studies (8/10), followed by fish (78%; 7/9), dairy (67%; 4/6), cereals (64%; 7/11), and oils (57%; 4/7). Beverages and hot beverages showed a decrease in circulating cytokines in 50% of cases (10/20 and 4/8, respectively). An increase in pro-inflammatory cytokines was observed in dietary interventions with beverages. As further findings, we also observed greater effectiveness from fruits and vegetables (87.5%; 7/8), fish (75%; 6/8), and cereals (62.5%; 5/8) when studies were conducted in subjects with pathologies or risk factors. Conclusions: Fruits and vegetables, fish, and cereals reduce systemic inflammation mainly in subjects with pathologies or risk factors. However, the limited number of studies do not allow us to draw solid conclusions on individual foods. Standardized dietary intervention trials are urgently needed to understand the role of foods in modulating inflammatory responses and to deliver findings to the general public. Full article

Background: Severe glenoid bone loss presents a major challenge in both primary and revision reverse shoulder arthroplasty (RSA). Standard implants often fail to achieve reliable fixation in these cases. Custom-made, 3D-printed glenoid components have emerged as a potential solution, offering anatomically tailored fit and fixation. This study evaluates the clinical and radiographic outcomes of custom-made glenoid implants in managing severe glenoid bone loss. Methods: A retrospective, multicenter study was conducted on 23 shoulders (11 primary and 12 revision RSAs) that received a custom-made glenoid component using the Enovis ProMade System (San Daniele del Friuli, Udine, Italy) between 2017 and 2022, with a minimum follow-up of 24 months. Preoperative planning utilized CT-based 3D modeling to design implants with patient-specific instrumentation. Clinical outcomes (ROM, pain, Constant–Murley score) and radiographic results were assessed. Statistical comparisons were made between primary and revision groups. Results: Both groups demonstrated significant improvements in shoulder mobility, pain relief, and Constant–Murley scores (all p < 0.001), with no significant differences between primary and revision groups in delta scores. Radiographically, no loosening was observed, with minimal radiolucent lines and low complication rates. Four cases of instability occurred, all in the revision group, with only one requiring conversion to hemiarthroplasty. No differences in radiographic outcomes were observed between groups. Conclusions: Custom-made glenoid implants provide a reliable solution for severe glenoid bone loss in both primary and revision RSA, yielding consistent functional improvement and implant stability. Further prospective studies with larger cohorts and long-term follow-up are warranted to confirm these findings and assess cost-effectiveness. Full article

Background: Inherited epidermolysis bullosa (EB) includes a group of rare genetic disorders affecting the skin and mucous membranes. These disorders are characterized by extreme fragility and blister formation after minimal or no trauma. Oral and systemic manifestations vary by subtype; the more severe forms often present with extensive intra-oral blistering, scarring, microstomia, vestibular obliteration, ankyloglossia, and—in some cases—oral cancer. This study aims to collect data on oral-health practices and challenges in people with EB to inform preventive strategies and dental care. Methods: An international, structured online questionnaire with 31 items was distributed to individuals with a confirmed diagnosis of EB. The survey explored clinical and oral manifestations, home-care routines (oral hygiene and diet), experiences with dental professionals, and the impact of oral health on quality of life. Results: Eighty-two questionnaires were completed. Dystrophic EB was the most often reported subtype (69.5%). Most respondents (67.1%) experienced recurrent oral blisters and/or erosions. Many reported relying exclusively on soft foods and struggling with mechanical plaque removal because of microstomia and pseudo-syndactyly. Severe oral pain hindered effective brushing in 17% of participants. Hand contractures and microstomia interfered with oral hygiene in 74% and 31% of participants, respectively. Nearly 30% sought dental care only when in pain. Among those who did not attend regular check-ups or hygiene sessions (44.6%), the most cited reason was that dental clinics were inadequately equipped or trained to manage EB. Conclusions: Because dental procedures carry significant risks for patients with EB, preventive care should begin in early childhood. Yet many patients are still insufficiently informed about essential preventive measures and lack access to dental professionals trained in EB management. Full article

Fraudulent online payment operations represent a persistent challenge in digital commerce, particularly in sectors like air travel, where credit and debit card payments dominate. This study presents a novel fraud detection framework tailored to airline ticket purchases, combining a synthetic dataset generator with a modular, customizable feature engineering process. These are two machine learning models—support vector machines (SVMs) and the light gradient boosting machine (LightGBM)—for real-time fraud detection. A synthetic dataset was generated, including a rich set of engineered features reflecting realistic user, transaction, and flight-related attributes. While both models were evaluated using classification-evaluation metrics, LightGBM outperformed SVMs in terms of overall performance with an accuracy of 94.2% and a recall of 71.3% for fraudulent cases. The main contribution of this study is the design of a reusable, customizable feature engineering framework for fraud detection in the airline sector, along with the development of a lightweight, adaptable fraud detection system for merchants, especially small and medium-sized enterprises. These findings support the use of advanced machine learning methods to enhance security in digital airline transactions. Full article

This work aimed to design a strategy for providing a collection service of urban solid waste in communities without it, using a case study in Sombrerete, Zacatecas, Mexico. The service is provided to the municipal seat and 17 of the 173 communities, resulting in a collection coverage of 10%. Information provided by the Cleaning Department of Sombrerete was collected and analyzed on the number of collection vehicles, communities served, and final waste disposal sites. Communities without urban solid waste collection and disposal services were identified. The strategy was designed to increase the collection coverage using geographic information systems, vehicle routing problem tools, and territory sectorization. Waste collection routes were developed for 11 sectors without service, and final waste disposal sites were evaluated based on environmental protection criteria of the Mexican Official Standard. The technical and economic feasibility of the strategy were analyzed. The results obtained were the design of the collection routes strategy to increase the coverage to 100% in Sombrerete. The designed strategy was feasible since it did not require the purchase of waste collection vehicles and hiring more staff. Approximately MXN 1000 (≈USD 54, EUR 47) in economic benefits were achieved weekly. Full article

Background: Bronchiectasis is a chronic respiratory condition characterized by permanent bronchial dilation, recurrent infections, and progressive lung damage. A subset of patients, known as frequent exacerbators, experience multiple exacerbations annually, leading to accelerated lung function decline, hospitalizations, and reduced quality of life. The aim of this study is to identify distinct phenotypes and treatable traits in bronchiectasis frequent exacerbators, since it could be crucial for optimizing patient management. Research question: Could clinically distinct phenotypes and treatable traits be identified among frequent exacerbators with bronchiectasis to guide personalized management strategies? Methods: We analysed a cohort of 56 bronchiectasis frequent exacerbator patients using 21 clinically relevant variables, including pulmonary function tests, radiological patterns, and microbiological data. Hierarchical clustering and k-means algorithms were applied to identify subgroups. Key outcomes included cluster-specific characteristics, treatable traits, and their implications for management. Results: Four distinct clusters were identified: 1. Mild, idiopathic bronchiectasis (Cluster 1): Predominantly mild disease (FACED), idiopathic etiology (93.3%), and cylindrical bronchiectasis with moderate obstruction (60%). 2. Rheumatological and NTM-associated bronchiectasis (Cluster 2): Patients with systemic inflammatory diseases (50%) and NTMever (50%) but minimal infections by Pseudomonas aeruginosa. 3. Mild, post-infective bronchiectasis (Cluster 3): Exclusively mild disease, mixed idiopathic and post-infective etiologies, and preserved lung function. 4. Severe, chronic infection phenotype (Cluster 4): Severe disease with high colonization rates of Pseudomonas aeruginosa (71.4%), advanced structural damage (57.1% varicose, 50% cystic bronchiectasis), and frequent exacerbations. Interpretation: This analysis highlights the heterogeneity of bronchiectasis and its frequent exacerbator phenotype. The treatable traits framework underscores the importance of aggressive infection control and management of airway inflammation in severe cases, while milder clusters may benefit from preventive strategies. These findings support the integration of precision medicine in bronchiectasis care, focusing on phenotype-specific interventions to improve outcomes. Full article

Monkeypox virus (MPXV) experienced an unprecedented global outbreak in 2022, characterized by a significant departure from historical patterns: a rapid spread of the epidemic to more than 110 non-traditional endemic countries, with more than 90,000 confirmed cases; a fundamental shift in the mode of transmission, with human-to-human transmission (especially among men who have sex with men (MSM)) becoming the dominant route (95.2%); and genetic sequencing revealing a key adaptive mutation in a novel evolutionary branch (Clade IIb) that triggered the outbreak. These features highlight the significant evolution of MPXV in terms of host adaptation, transmission efficiency, and immune escape ability. The aim of this paper is to provide insights into the viral adaptive evolutionary mechanisms driving this global outbreak, with a particular focus on the role of immune escape (e.g., novel mechanisms of M2 proteins targeting the T cell co-stimulatory pathway) in enhancing viral transmission and pathogenicity. At the same time, we systematically evaluate the cross-protective efficacy and limitations of existing vaccines (ACAM2000, JYNNEOS, and LC16), as well as recent advances in novel vaccine platforms, especially mRNA vaccines, in inducing superior immune responses. The study further reveals the constraints to outbreak control posed by grossly unequal global vaccine distribution (e.g., less than 10% coverage in high-burden regions such as Africa) and explores the urgency of optimizing stratified vaccination strategies and facilitating technology transfer to promote equitable access. The core of this paper is to elucidate the dynamic game between viral evolution and prevention and control strategies (especially vaccines). The key to addressing the long-term epidemiological challenges of MPXV in the future lies in continuously strengthening global surveillance of viral evolution (early warning of highly transmissible/pathogenic variants), accelerating the development of next-generation vaccines based on new mechanisms and platforms (e.g., multivalent mRNAs), and resolving the vaccine accessibility gap through global collaboration to build an integrated defense system of “Surveillance, Research and Development, and Equitable Vaccination,” through global collaboration to address the vaccine accessibility gap. Full article

The diversity of energy resources in distribution networks requires new strategies for planning and operation. In this context, microgrids are solutions that can integrate renewable energy sources, energy storage systems (ESSs), and demand response (DR), thereby decentralizing operations and utilizing digital technologies to create more proactive energy markets. Given the above, this work proposes a distributed optimal dispatch strategy for microgrids with multiple energy resources, with a focus on scalability. Simulations are performed using agent modeling on the Python Agent Development (PADE) platform, leveraging distributed computing resources and agent communication. A co-simulation environment, coordinated by Mosaik, synchronizes data exchange, while a plug-and-play system allows dynamic agent modification. The main contribution of the present study relies on a system integration approach, combining a multi-agent system (MAS) and Mosaik co-simulation framework with plug-and-play agent support for the very short-term (five-minute) dispatch of energy resources. Optimization algorithms, namely particle swarm optimization (PSO) and multi-agent particle swarm optimization (MAPSO), are framed as an incremental improvement tailored to this distributed architecture. Case studies show that distributed MAPSO performs better, with lower objective function values and a smaller relative standard deviation (15.6%), while distributed PSO had a higher deviation (33.9%). Although distributed MAPSO takes up to three times longer to provide a solution, with an average of 9.0 s, this timeframe is compatible with five-minute dispatch intervals. Full article

Compounds known as liquid organic hydrogen carriers (LOHCs) offer a promising pathway for storing hydrogen. Beyond the use of pure hydrocarbons, the incorporation of oxygen atoms offers a way to modify thermodynamic properties and potentially improve suitability for hydrogen storage. This study explores the effect of oxygen functionalization in aromatic ethers and lactones on the reaction equilibrium of reversible hydrogenation. To address this question, reaction enthalpies and entropies are calculated using both experimental and theoretically determined pure substance data. The equilibrium position shift in the hydrogenation of furan derivatives has been shown to follow a similar trend to that of their hydrocarbon counterparts upon the addition of aromatic rings. This shift is, however, more pronounced in the case of the furan-based systems. The effect is reflected in increasing Gibbs reaction energies during the dehydrogenation process. Both the formation of lactones and the addition of a second ring to the furan core leads to a further increase in the Gibbs reaction energy. The highest value is observed for dibenzofuran, with a Gibbs reaction energy of 36.6 kJ∙mol⁻¹ at 500 K. These findings indicate that, from a thermodynamic perspective, hydrogen release is feasible at temperatures below 500 K, which is an important feature for the potential application as a hydrogen storage system. Full article

The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway is a crucial regulator of cellular metabolism, proliferation, and survival. It is frequently dysregulated in metabolic, cardiovascular, and neoplastic disorders. Despite the advancements in multi-omics technology, existing methods often fail to provide real-time, pathway-specific insights for precision medicine and drug repurposing. We offer Agentic RAG-Driven Multi-Omics Analysis (ARMOA), an autonomous, hypothesis-driven system that integrates retrieval-augmented generation (RAG), large language models (LLMs), and agentic AI to thoroughly analyze genomic, transcriptomic, proteomic, and metabolomic data. Through the use of graph neural networks (GNNs) to model complex interactions within the PI3K/AKT pathway, ARMOA enables the discovery of novel biomarkers, probable candidates for drug repurposing, and customized therapy responses to address the complexities of PI3K/AKT dysregulation in disease states. ARMOA dynamically gathers and synthesizes knowledge from multiple sources, including KEGG, TCGA, and DrugBank, to guarantee context-aware insights. Through adaptive reasoning, it gradually enhances predictions, achieving 91% accuracy in external testing and 92% accuracy in cross-validation. Case studies in breast cancer and type 2 diabetes demonstrate that ARMOA can identify synergistic drug combinations with high clinical relevance and predict therapeutic outcomes specific to each patient. The framework’s interpretability and scalability are greatly enhanced by its use of multi-omics data fusion and real-time hypothesis creation. ARMOA provides a cutting-edge example for precision medicine by integrating multi-omics data, clinical judgment, and AI agents. Its ability to provide valuable insights on its own makes it a powerful tool for advancing biomedical research and treatment development. Full article

People with moderate to severe intellectual disabilities (M/S ID) and behaviour that challenges are still almost exclusively encountered and understood within a highly specialized professional care system context. They are almost invisible in the societal mainstream, where a wider variety of perspectives on (everyday) manners, encounters, relationships and life applies. These (and other) exclusionary dynamics render everyday relations with residents with M/S ID whose behaviours challenge still largely dependent on the interpretative frameworks and actions of professionals. Professionals are trained and socialized within highly specialized professional care system contexts, despite a growing scientific and professional awareness that behaviour that challenges is a multifaceted and contextual phenomenon. In this paper, we report on a pioneering initiative (titled Project WAVE) which aimed to cultivate a fresh and comprehensive approach to behaviours that challenge within stagnant care practices. Our goal was to foster an innovative collaborative paradigm by facilitating an extensive and enduring exchange between “insiders”—professionals of specialized care system contexts—and “outsider-researchers”—individuals socialized through alternative avenues. We present our epistemological and methodological approach, the data collection process (a multiple case-informed community of practice), and the most important lessons learned. Full article

This article presents a proposal for blending natural gas and biogas with a control system with feedback to ensure a constant mixture density. To achieve this, we propose the following: a mathematical model to determine the gas density based on its composition; a control system whose main components are a gas mixer, valves, and a natural gas storage tank to regulate the biogas density, where its inputs are gases from biomass plants and the natural gas grid; mathematical models to calculate the volume of natural gas required in the storage tank. It is assumed that the composition at the outlet of the biogas plants is measured and that there are no losses of any kind; a case study simulation is then performed. All models consider random variation in gas composition over time. The main results are as follows: (a) reduced natural gas consumption, the promotion of biogas production and use and of mixtures of lower methane compared to natural gas, and the facilitation of the pumping of the gas mixtures; (b) all the biogas produced is used; (c) different piping, sources, storage tanks, consumers, and mixer schemes, considering the concepts of cities, microgrids, smart grids, and smart cities. Full article

Background: Brain tumors can severely impair neurological function, leading to symptoms such as headaches, memory loss, motor coordination deficits, and visual disturbances. In severe cases, they may cause permanent cognitive damage or become life-threatening without early detection. Methods: To address this, we propose an interpretable deep ensemble model for tumor detection in Magnetic Resonance Imaging (MRI) by integrating pre-trained Convolutional Neural Networks—EfficientNetB7, InceptionV3, and Xception—using a soft voting ensemble to improve classification accuracy. The framework is further enhanced with a Light Gradient Boosting Machine as a meta-learner to increase prediction accuracy and robustness within a stacking architecture. Hyperparameter tuning is conducted using Optuna, and overfitting is mitigated through batch normalization, L2 weight decay, dropout, early stopping, and extensive data augmentation. Results: These regularization strategies significantly enhance the model’s generalization ability within the BR35H dataset. The framework achieves a classification accuracy of 99.83 on the MRI dataset of 3060 images. Conclusions: To improve interpretability and build clinical trust, Explainable Artificial Intelligence methods Grad-CAM++, LIME, and SHAP are employed to visualize the factors influencing model predictions, effectively highlighting tumor regions within MRI scans. This establishes a strong foundation for further advancements in radiology decision support systems. Full article

Empirical evidence highlights the critical role of effective school leadership in driving school improvement, enhancing teacher performance, and improving student outcomes. In Omani schools, where adaptive strategies are increasingly essential, the collaborative roles of principals and teachers are pivotal in achieving meaningful educational change. This qualitative study explores how principal–teacher interactions influence teacher leadership development and contribute to overall school improvement. Using a multiple-case study design across eight schools in the Muscat Governorate, in-depth interviews with eight principals and focus group discussions with twelve teachers were conducted to capture diverse perspectives across these settings. The data were analyzed using thematic analysis to identify key leadership dynamics and their implications for teacher leadership and school improvement. Findings indicate that principals who act as change agents, offer targeted support, and cultivate a collaborative culture empower teachers to take on leadership roles. Key themes include fostering professional growth, building trust, and addressing systemic challenges. These interactions enhance school culture, classroom practices, and student outcomes, ultimately contributing to sustainable school improvement. The study underscores the importance of collaborative leadership practices and calls for strategies that optimize these dynamics to advance educational outcomes. Future research should explore the broader applicability of these findings across diverse educational contexts to inform policy and practice. Full article