Search Results (19,217)

Background: Multiple sclerosis (MS) is a neuroinflammatory disease characterized by autoimmune-driven inflammation in the central nervous system that damages axons and destroys myelin. It is difficult to diagnose multiple sclerosis due to its complexity, and different people may react differently to different treatments. While the exact cause of multiple sclerosis (MS) and the reasons for its increasing prevalence remain unclear, it is widely believed that a combination of genetic predisposition and environmental influences plays a significant role. Methods: Finding biomarkers for complicated diseases like multiple sclerosis (MS) is made more promising by the emergence of network and system biology technologies. Currently, using tools like Network Analyst to apply network-based gene expression profiling provides a novel approach to finding potential medication targets followed by molecular docking and MD Simulations. Results: There were 1200 genes found to be differentially expressed, with CD44 showing the highest degree score of 15, followed by CDC42 and SNAP25 genes, each with a degree score of 14. To explore the regulatory kinases involved in the protein–protein interaction network, we utilized the X2K online tool. The present study examines the binding interactions and the dynamic stability of four ligands (Obeticholic acid, Chlordiazepoxide, Dextromethorphan, and Hyaluronic acid) in the Hyaluronan binding site of the human CD44 receptor using molecular docking and molecular dynamics (MD) simulations. Docking studies demonstrated a significant docking score for Obeticholic acid (−6.3 kcal/mol), underscoring its medicinal potential. MD simulations conducted over a 100 ns period corroborated these results, revealing negligible structural aberrations (RMSD 1.3 Å) and consistent residue flexibility (RMSF 0.7 Å). Comparative examinations of RMSD, RMSF, Rg, and β-factor indicated that Obeticholic acid exhibited enhanced stability and compactness, establishing it as the most promising choice. Conclusions: This integrated method underscores the significance of dynamic validations for dependable drug design aimed at CD44 receptor-mediated pathways. Future experimental techniques are anticipated to further hone these findings, which further advance our understanding of putative biomarkers in multiple sclerosis (MS). Full article

We quantify the transmission and absorption of 75–110 GHz radiation through ex vivo porcine skin. Millimeter waves are currently used in a range of technologies, including communication systems, fog-penetrating radar, and the detection of hidden weapons or drugs. They have also been proposed for use in non-lethal weaponry and, more recently, in targeted cancer therapies. Since pigs are often used as biological models for humans, determining how deeply millimeter waves penetrate a pig’s skin and influence the underlying tissues is essential for understanding their potential effects on humans. This experimental study aims to quantify that penetration and associated energy loss. The results show significant absorption in the skin and fat layer. Attenuation of over three orders of magnitude can be expected in penetration through a layer with a thickness of about 12 mm (−30 dB). The reflectance from the skin is similar at all frequencies. The values range from −10 to −20 dB, which probably depends on the texture of the skin. Therefore, most skin transfer loss is caused by absorption. Full article

The present research assesses the potential of rapeseed oil soapstock for producing lecithin and its application in the food industry in the context of the circular economy and bioeconomy. The theoretical part summarizes information on the types of lecithin and its production technologies and functional properties, while the empirical part combines semi-structured interviews with 30 experts and company representatives (in Latvia and abroad) and a laboratory experiment with rapeseed soapstock samples. The data provided by the experts were analyzed using descriptive statistics and thematic analysis, while the soapstock samples were tested for dry matter, lipid content, and lecithin and acid oil yield using the techniques of n-hexane Soxhlet extraction and fractionation with cold acetone. The experts’ ratings showed that rapeseed lecithin is technologically competitive with soybean and sunflower lecithin, especially to produce bread, flour confectionery, as well as oil and fat, thereby providing good emulsification capability, texture improvement, and stabilization. The highest potential for the introduction of rapeseed lecithin has been identified in the oil and fat, bread and flour confectionery segments, but wider use is currently hampered by high production costs and lower market visibility. This research demonstrated the practical possibility of isolating lecithin from rapeseed oil soapstock. The laboratory experiment revealed that it is possible to obtain lecithin from rapeseed soapstock in amounts of 1.4–5.2% of the total weight of soapstock (6.2–23.5% of dry matter), which confirmed the usability of rapeseed soapstock as a raw material for lecithin production. The results confirm that the use of rapeseed oil soapstock to produce lecithin can reduce the amount of industrial waste and increase resource efficiency, thus reducing dependence on imported soybean lecithin. Rapeseed lecithin can be found as a sustainable alternative to soybean and sunflower lecithin with potential for oil and fat and bread production. Full article

Genetic factors are key determinants in the pathophysiology of obesity, regulating energy homeostasis. Monogenic non-syndromic obesity accounts for 2–3% of obesity in both children and adults and is most often attributable to mutations in genes encoding components of the leptin–melanocortin pathway. Genetic testing is indicated in children with severe obesity before age 5, hyperphagia, a family history of obesity, and neurodevelopmental delay or organ dysfunction. Mutations associated with monogenic obesity follow autosomal recessive (LEP, LEPR, POMC, and PCSK1) or autosomal dominant (MC4R, SH2B1, SIM1, GNAS) modes of inheritance. Other gene mutations in heterozygous states (MRAP2, MC3R, SRC1, KSR2) are associated with obesity and may exhibit autosomal dominant inheritance; however, the clinical phenotype depends on the degree of genetic penetrance and interactions with other genetic and/or environmental factors. No approved targeted pharmacotherapies are currently available for autosomal dominant monogenic obesity, and the frequent detection of variants of uncertain significance often hinders timely diagnostic confirmation. The review provides a comprehensive appraisal of autosomal dominant forms of monogenic non-syndromic obesity, analyzing genetic and molecular features, clinical presentations, and therapeutic strategies. Full article

Secretory Leukocyte Protease Inhibitor (SLPI) is a conserved serine protease inhibitor expressed on mucosal surfaces, which has multiple functions including anti-protease, anti-microbial and anti-inflammatory properties. SLPI plays critical roles in tissue homeostasis and pathology. Through its anti-protease ability, SLPI safeguards tissues from excessive damage caused by proteolytic enzymes released during inflammation and contributes to extracellular matrix remodeling, thereby influencing the cellular and tumor microenvironment. Furthermore, SLPI expression is implicated in shaping the immune landscape that facilitates tumor progression, and in driving epithelial–mesenchymal transition (EMT). Consequently, it is not surprising that SLPI plays a complex and context-dependent role across various malignancies. It is overexpressed in most cancers such as colorectal, gastric, pancreatic, and breast carcinomas, and this overexpression often correlates with a more advanced and aggressive disease. Conversely, its levels are reduced in head and neck squamous cell carcinoma and hepatocellular carcinoma, where elevated expression may be associated with a more favorable prognosis. This diverse behavior underscores that SLPI function in cancer is tissue-specific and dependent on the functional or pathological state. In prostate cancer, SLPI expression exhibits a bimodal behavior: levels are reduced in the early stages of the disease compared to normal tissues but become significantly upregulated in more advanced and aggressive stages of disease, with significantly higher levels observed in patients with castration-resistant prostate cancer. Elevated SLPI levels in prostate cancer correlate with a reduced prostate-specific antigen (PSA) progression-free survival. In this review, we outline the current evidence regarding the multifaceted functions of SLPI and its expanding role in cancer, focusing primarily on the recently described molecular mechanisms and clinical significance of SLPI in prostate carcinoma. Full article

Model-Based Systems Engineering (MBSE) originated in aerospace engineering and has emerged as a promising approach in other fields for designing, analyzing, and managing complex interdisciplinary systems throughout their entire life cycle. While MBSE is applicable to various engineering domains, its applications remain closely tied to software engineering. As software becomes a critical component of physical systems, such as vehicles, appliances, and production plants, bridging the gap between software engineering and other disciplines, such as mechanical, electrical, and civil engineering, becomes essential. Despite its potential, MBSE is still in its early stages when it comes to integrating executable models of physical systems into engineering environments. The purpose of this research is to assess the present capabilities of MBSE by identifying existing missing links, thereby enabling prospective users to make well-informed decisions about its integration into organizational processes. In this analysis, it is important to have a comprehensive view of the complexity of MBSE across different disciplines to obtain an overall picture. In addition to identifying open challenges, we present three critical gaps in the MBSE practice through a comprehensive demonstration case: limited tool interoperability and model integration, modeling language limitations, and dependence on a specialized workforce. Current studies largely view MBSE as the most applicable and effective for the design phase of the system life cycle. Yet, to capture MBSE in its entirety, its principles must be applied throughout the whole system life cycle. Full article

Standalone photovoltaic systems play an important role in providing reliable renewable energy in remote areas. These systems depend heavily on battery energy storage, especially lithium iron phosphate batteries, which are known for their safety and long cycle life. However, battery degradation remains a major challenge, as high charging currents, temperature variations, and wide state-of-charge fluctuations introduce electro-thermal stress that reduces the useful lifetime of the storage system. To address this issue, this paper presents a Flexible Power Point Tracking (FPPT) strategy supported by a fuzzy-logic-based controller. In this context, battery stress refers to the combined electrochemical and thermal stress induced by high charging currents, elevated operating temperatures, and large state-of-charge (SOC) excursions, which are known to accelerate ageing mechanisms and capacity fade. Based on a review of the existing literature, most FPPT and lifetime-oriented control studies have focused on lithium-ion batteries such as NMC or LCO chemistries, while limited attention has been given to lithium iron phosphate (LiFePO₄) batteries. The goal is to limit battery stress by reducing current peaks, mitigating temperature rise, and smoothing state-of-charge variations, thereby improving battery lifetime without compromising the stability of the standalone PV system. A complete PV–battery model is developed in PLECS and tested using one-year irradiance, temperature, and load data from Perth, Australia. The results show that the FPPT–Fuzzy controller reduces current peaks, stabilises the state of charge, and lowers the thermal impact on the battery when compared with traditional MPPT. As a result, overall degradation decreases and the battery lifetime is extended by approximately 7%. These findings demonstrate that FPPT is a promising method for improving the long-term performance of renewable energy systems based on lithium iron phosphate battery storage. Full article

In electric vehicle (EV) drivetrains, lubricant films must not only mitigate friction and wear but also manage stray currents to safely dissipate stray charge and avoid micro-arcing. This study directly compares how a conventional antiwear additive (ZDDP) and a long-chain, ashless, sulfur-free phosphite ester (Duraphos AP240L) manage this balance under current-carrying boundary lubrication conditions. Reciprocating steel-on-steel tests were conducted at fixed load and speed with applied current densities of 0, 0.02, and 42.4 A/cm². Friction and four-probe electrical contact resistance (ECR) were measured in situ, and impedance of tribofilms was measured over a 1–10⁵ Hz range after friction test. In the presence of ZDDP, ECR initially increased and then decreased to a value that was as low as the initial direct contact of two solid surfaces or even lower sometimes. During the initial stage with high ECR, a well-defined impedance semicircle was observed in the Nyquist plot; after forming the tribofilm with low ECR, frequency dependence of impedance could not be measured due to the very low resistance. The decrease in ECR suggested a structural evolution of the anti-wear film on the substrate. However, post-test wear analysis indicated that the formation of this film was accompanied by tribochemical polishing of the countersurface and sometimes pitting of the substrate, which may have been due to localized electrical discharge producing trenches deeper than ~0.5 µm; in additive-free base oil, wear was dominated by ploughing with micro-cutting of the substrate. In contrast, AP240L performed better in terms of friction and wear, showing a remarkable ~30% lower coefficient of friction, while the overall cycle dependence of ECR was similar to the ZDDP case. AP240L showed negligible boundary film controlled wear producing a shallow, smooth track (depth < 0.2 µm) during the friction test, and there was no sign of electrical arc damage. These findings support long-chain, ashless, sulfur-free phosphite esters as promising candidates for EV boundary lubrication where both mechanical and electrical protection are required. Full article

Background/Objectives: Tuberculosis (TB) remains a major global health threat. Current administration methods for anti-TB drugs, including oral or intravenous, suffer from systemic side effects, low lung distribution, and poor patient compliance. Dry powder inhalers (DPIs) offer a promising alternative. This study investigates the aerodynamic performance of co-spray-dried DPIs containing rifampin or pyrazinamide and amino acids by using machine learning. Methods: Firstly, 72 formulations were prepared by varying drug-amino acid combinations, molar ratios, and spray-drying parameters. Subsequently, the aerodynamic performance of all 72 formulations was evaluated using a Next Generation Impactor, and the solid-state characterizations of optimal DPIs were carried out. Finally, four machine learning (ML) models were successfully developed and were utilized to predict the fine particle dose (FPD), FPF, MMAD, and geometric standard deviation (GSD) of DPIs based on the high-quality in-house data above. Results: Key results showed that the aerodynamic performance of DPIs was highly dependent on the specific drug-amino acid combination, with rifampin-L-lysine acetate and pyrazinamide-L-leucine formulations achieving the highest fine particle fraction (FPF, 73.37%, 87.74%) and optimal mass median aerodynamic diameter (MMAD, 2.59 µm, 1.88 µm). Notably, XGBoost (v3.1.3) exhibited the best predictive performance, with R² values ranging from 0.894 to 0.991 in the testing set for the four prediction tasks. Meanwhile, SHapley Additive exPlanations (v0.50.0) was used for model interpretability analysis. The molecular weights and LogP of the drug and amino acid were identified as two of the most important features affecting the prediction of FPD, FPF, MMAD, and GSD. Conclusions: This work demonstrates the feasibility of ML in accelerating the development of inhalable spray-dried anti-TB drugs by enabling the prediction of DPI formulations. Full article

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) and Obesity Hypoventilation Syndrome (OHS) are core components of the obesity-related respiratory disease spectrum, and their comorbidity has become a major challenge in the global public health field. This review systematically summarizes the epidemiological characteristics, pathophysiological mechanisms, diagnostic criteria, diagnostic technologies and treatment strategies of OSAHS-OHS comorbidity, with a focus on the cutting-edge progress of digital therapeutics and metabolic intervention, as well as the historical evolution and current status of clinical management. We also conduct an in-depth analysis of the unresolved controversies and practical challenges in the current clinical management of this comorbidity. OSAHS-OHS comorbid patients have a significantly higher risk of cardiovascular complications than those with a single disease, and chronic intermittent hypoxia (CIH) forms a vicious cycle with obesity through multiple pathophysiological pathways. The combination of multi-dimensional assessment tools and portable monitoring devices has improved the screening efficiency of OSAHS-OHS comorbidity, and the selection of respiratory support therapies such as continuous positive airway pressure (CPAP) and non-invasive ventilation (NIV) depends on patient phenotypes. Digital therapeutics and novel metabolic intervention drugs have shown promising clinical value in the management of this comorbidity. The multidisciplinary collaboration model is the key to improving the prognosis of comorbid patients, while current clinical management is still faced with challenges such as policy lag, ethical controversies and uneven resource allocation. Future research should focus on individualized therapeutic targets, the integration of digital technologies and the optimization of health policies to achieve precise and efficient management of OSAHS-OHS comorbidity. Full article

Background: Celiac disease (CD) is an immune-mediated, gluten-induced enteropathy with intestinal and extraintestinal manifestations. Chronic urticaria (CU) is a heterogeneous inflammatory skin disorder often considered idiopathic, but emerging evidence suggests possible autoimmune causes. Methods: We describe a pediatric case in which CU and angioedema were the sole clinical expressions of CD. We also conducted a scoping review of the literature to assess the prevalence of CD in CU patients and the therapeutic impact of a gluten-free diet (GFD). Results: The child’s CU resolved rapidly after initiating a GFD, with complete remission and normalization of anti-tissue transglutaminase at follow-up. Literature review shows that CD is significantly more common in CU patients than in the general population, and several case reports document remission of CU after GFD. However, leading guidelines for CD and CU do not currently recommend mutual screening, and pathophysiological mechanisms linking the two conditions remain incompletely understood. Conclusions: Chronic urticaria may be the sole clinical manifestation of CD. Screening for CD in patients with CU may be considered, particularly in those with autoimmune features or disease refractory to standard treatment. Initiating a GFD can lead to rapid symptom remission, reduce dependence on conventional therapies and improve quality of life. Full article

Three-leg AC-DC Vienna rectifiers are susceptible to single open-switch faults, which make DC-link voltage ripple and make three-leg input AC currents distorted and unbalanced. Thus, this paper presents a quick identification method for single open-switch faults based on three-leg fault currents and output capacitors voltage difference. Fault-leg identification depended on zero-plateaus in the three-leg fault currents, whereas fault-side identification was dependent on reconstruction variables obtained through Clark transformation and phase shifting. In order to improve the reliability of the diagnosis system, the harmonic component of capacitor voltage difference is used to realize the missed diagnosis detection and adjust the time threshold automatically. This method requires no additional hardware and is easy to implement. Experimental results verify the effectiveness of this strategy. It is shown that the fault diagnosis method proposed in this paper has the advantages of fast diagnosis speed, high accuracy and good robustness. Full article

Electric energy is an essential resource in modern society; however, most current distribution systems are centralized and dependent on fossil fuels, posing risks of shortages and a potential energy crisis. The transition to renewable sources represents a sustainable alternative, though it introduces challenges associated with intermittency and generation variability. In this context, peer-to-peer (P2P) networks and artificial intelligence (AI) emerge as strategies to promote decentralization, self-management, and efficiency in energy operation. This research proposes an AI-based knowledge discovery model to predict electricity generation and consumption in a P2P network. The study was developed in four phases: exploration of AI techniques for energy prediction; analysis of the most widely used techniques in the Knowledge Discovery in Databases (KDD) process; construction of the predictive model; and validation using real energy generation and consumption data from renewable energy sources. The LSTM–CNN and Transformer models achieved an R² greater than 80% and mean absolute errors (MAE) of less than 0.02 kWh, demonstrating high prediction accuracy. The results confirm that integrating the KDD approach with deep LSTM–CNN and Transformer architectures significantly improves energy management in P2P networks, providing a solid foundation for the development of innovative and sustainable electrical systems. Full article

Background: Electrocardiography (ECG) represents an important noninvasive screening tool for heart disease in preparticipation screening of competitive athletes. However, interpretation of pediatric ECG based on age-specific reference values remains challenging, due to considerable variation among studies, influenced by population characteristics and documentation methodology. The variability of normal values in key pediatric ECG features regarding left ventricular hypertrophy (LVH), QTc prolongation and pre-excitation detection seem to have a significant impact on the efficacy of pediatric ECG as a preparticipation screening tool. Aims and Scope of the Study: This review aims to compare contemporary pediatric ECG reference ranges for key ECG features relevant to LVH, QTc, PR and QRS duration and highlight physiological and methodological sources of observed variability. Methods: A review of the current literature was conducted using common biomedical databases for studies reporting certain quantitative ECG reference values in healthy children from infancy through adolescence regarding the above selected key features. Reported values were summarized descriptively, with emphasis on developmental trends and methodological differences among studies affecting ECG values. Results: Across 16 pediatric studies, ECG parameters demonstrated consistent age-dependent developmental patterns, despite variability in absolute values. R-wave amplitudes in left precordial leads increased from infancy through early childhood and remained stable in older children, whereas S-wave amplitudes in right precordial leads showed greater variation between studies. PR intervals and QRS duration increased progressively with age across all datasets, while QTc values remained relatively stable throughout childhood and adolescence, with minimal sex-related differences. Variability in reported reference ranges was most pronounced for amplitude-based—compared to interval duration—parameters, and was influenced by differences in population characteristics, ECG acquisition techniques, and measurement methodology. Conclusions: This review summarizes contemporary ECG reference data in healthy children for the early detection of LVH, pre-excitation and QT prolongation, which are the main objectives of ECG screening in young athletes. Full article

Machine tools are the major consumers of industrial energy, but their energy efficiency remains low, posing a serious challenge to sustainable manufacturing. The current literature predominantly focuses on isolated subsystems or specific operational phases (e.g., cutting parameters), lacking systematic evaluations of how different methodologies interact within the Life Cycle Assessment (LCA) framework. This paper provides a critical synthesis of three core methodologies—modeling methods, system parameter optimization, and machine learning (ML)—across the design/production, usage, and recycling stages. Unlike descriptive reviews, this study highlights the scientific contribution by defining the applicability boundaries and complementary mechanisms of these approaches. The analysis reveals that while modeling lays the theoretical basis for eco-design and remanufacturing assessments, and optimization effectively resolves multi-objective trade-offs, these static methods struggle with the dynamic complexity of real-time operations where ML excels. However, ML is identified to be constrained by high data dependency and poor generalization in heterogeneous environments. Consequently, this review shows that the ‘cross-application’ of modeling methods and machine learning to construct hybrid models is essential for addressing complex nonlinear relationships and achieving accurate energy prediction throughout the entire life cycle. Finally, future directions such as transfer learning and digital twins are proposed to overcome current generalization bottlenecks, providing a theoretical foundation for the industry’s transition from passive energy assessment to active, intelligent energy management. Full article