Search Results (62)

Asthma, the most common chronic respiratory condition in children, involves airway inflammation, hyper-responsiveness, and frequent exacerbation that worsen the airflow and inflammation. Exosomes, extracellular vesicles carrying microRNAs (miRNAs), play a key role in cell communication alongside other types of communication and are promising markers of asthma severity. This study compares exosomal miRNA and long non-coding RNA (lncRNA) profiles in boys with asthma, focusing on differences between those with normal lung functions and those with severe airflow obstruction. This study enrolled 20 boys aged 9–18 years with asthma, split into two groups based on their lung function. Ten had normal lung function (NLF; FEV1/FVC > 0.84, FEF75% > 69% predicted), while ten had severe airflow obstruction (SAO; FEV1/FVC < 0.70, FEF75 < 50% predicted). Saliva and blood samples were collected. Exosomes were isolated, quantified, and analyzed via small RNA sequencing to identify differentially expressed (DE) miRNA and lncRNA profiles. Bioinformatic tools were then used to explore potential miRNA biomarkers linked to asthma severity. SAO subjects were more likely to exhibit allergen sensitization, higher IgE levels, and more eosinophils. We identified 27 DE miRNAs in plasma and 40 DE miRNAs in saliva. Additionally, five key miRNAs were identified in both saliva and plasma which underline important pathways such as neurotrophins, T-cell receptor, and B-cell receptor signaling. We further outlined key features and functions of miRNAs and long non-coding RNAS (lncRNAs) and their interactions in children with asthma. This study identified DE miRNAs and lncRNAs in children with SAO when compared to those with NLF. Exosomal miRNAs show strong potential as non-invasive biomarkers for personalized asthma diagnosis, treatment, and monitoring. These RNA markers may also aid in tracking disease progression and response to therapy, thereby supporting the need for future studies aimed at applications in precision medicine. Full article

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is a leading cause of liver cirrhosis, with its global prevalence rising due to obesity, insulin resistance, and type 2 diabetes mellitus. While bariatric surgery remains effective for weight loss, Glucagon-Like Peptide-1 analogs such as liraglutide are emerging as promising pharmacological treatments. This study aimed to evaluate the effects of a 3-month liraglutide treatment on liver steatosis, subclinical markers, and insulin resistance in non-diabetic, obese patients with MASLD. Twenty-eight obese adults (BMI ≥ 30 kg/m²) were treated with daily subcutaneous liraglutide injections for three months. Liver steatosis was assessed using FibroScan^® (CAP score) and non-invasive indices (Hepatic Steatosis Index—HSI, and NAFLD Liver Fat Score—NLFS). Insulin resistance was measured with conventional markers (HOMA-IR, QUICKI) and triglyceride-based indices (METS-IR, TyG). Liraglutide significantly reduced liver steatosis (CAP score: 305 to 268 dB/m, p < 0.05) and improved HSI, while NLFS remained unchanged. Despite significant weight loss, traditional insulin resistance markers remained unchanged, while METS-IR and TyG improved. Liraglutide therapy improved liver steatosis and triglyceride-based insulin resistance markers in non-diabetic obese patients with MASLD. These findings support the use of liraglutide, highlighting the value of personalized approaches and alternative insulin resistance assessments in MASLD management. Full article

Structural characterization of natural products in complex herbal extracts remains a major challenge in phytochemical analysis. In this study, we present a novel post-acquisition data-processing strategy—key ion diagnostics–neutral loss filtering (KID-NLF)—combined with ultra-high-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) for systematic profiling of the medicinal plant Terminalia chebula. The strategy consists of four main steps. First, untargeted data are acquired in negative electrospray ionization (ESI⁻) mode. Second, a genus-specific diagnostic ion database is constructed by leveraging characteristic fragment ions (e.g., gallic acid, chebuloyl, and HHDP groups) and conserved substructures. Third, MS/MS data are high-resolution filtered using key ion diagnostics and neutral loss patterns (302 Da for HHDP; 320 Da for chebuloyl). Finally, structures are elucidated via detailed spectral analysis. The methanol extract of T. chebula was separated on a C18 column using a gradient of acetonitrile and 0.1% aqueous formic acid within 33 min. This separation enabled detection of 164 compounds, of which 47 were reported for the first time. Based on fragmentation pathways and diagnostic ions (e.g., m/z 169 for gallic acid, m/z 301 for ellagic acid, and neutral losses of 152, 302, and 320 Da), the compounds were classified into three major groups: gallic acid derivatives, ellagitannins (containing HHDP, chebuloyl, or neochebuloyl moieties), and triterpenoid glycosides. KID-NLF overcomes key limitations of conventional workflows—namely, isomer discrimination and detection of low-abundance compounds—by exploiting genus-specific structural signatures. This strategy demonstrates high efficiency in resolving complex polyphenolic and triterpenoid profiles and enables rapid annotation of both known and novel metabolites. This study highlights KID-NLF as a robust framework for phytochemical analysis in species with high chemical complexity. It also paves the way for applications in quality control, drug discovery, and mechanistic studies of medicinal plants. Full article

Match running speed demands vary across competitive levels of football, influenced by player position, tactical considerations, and Head Coach changes. In England, the level directly below professional football, Non-League Football (NLF), comprises full-time and part-time clubs. However, the running speed demands of professional teams at this level remain unknown. Therefore, this study aimed to investigate (1) the match running speed demands in a professional NLF team, and (2) the impact of changing the Head Coach on these physical demands. Match running speed data were collected via Polar Team Pro global positioning system (GPS) devices during 41 matches of a tier 6 NLF team, comprising 311 observations of 22 full-time outfield players. Linear mixed-effect models examined the relationship between running speed metrics and fixed effects of a Head Coach change (n = 3), player position, and match outcome, with match number as a random effect. The team average total distance (TD) was 10,479 ± 42 m, and high-speed running and sprinting were 431 ± 62 m and 99 ± 26 m, respectively. The results showed significant positional differences, with wide defenders and midfielders associated with a greater TD than central defenders and strikers. Moreover, a change in Head Coach was significantly associated with a reduced TD, and a similar downward trend was observed across other running speed metrics. The TD and positional differences observed are comparable with other football cohorts, yet HSR and sprinting distances were notably lower in professional NLF. The findings highlight NLF clubs’ challenges in transitioning to higher competitive levels and provide insights for performance and training. Further research is warranted to explore the influence of running speed demands, technical and tactical factors, and other determinants on success in NLF. Full article

Introduction: Allergic rhinitis (AR) is largely driven by IgE-induced immune cell activation, which promotes allergen-induced upper airway inflammation. The regulatory mechanisms of IgE synthesis in AR are poorly understood. Several analyses associate single nucleotide polymorphisms (SNPs) which reduce the expression of the D2HGDH gene with AR. D2HGDH encodes an enzyme that converts D-2-hydroxyglutarate (D2HG) to α-ketoglutarate (α-KG). This study aims to clarify the relationship between AR and SNPs in D2HGDH. Methods: Mice were treated with vehicle control or octyl-D2HG prior to intranasal exposure to Alternaria alternata. Draining lymph nodes (dLNs) were then evaluated for IgE-producing cells and T-cell polarization. Next, mice were exposed to intranasal Alternaria on days 0, 10, 20, and 27–30 and were treated intranasally with octyl-D2HG or vehicle control on days 20 and 27. Nasal inflammation was analyzed in nasal lavage fluid (NLF) cellularity and antigen-specific IgE production. Results: The administration of D2HG prior to Alternaria exposure suppressed IgE synthesis (p < 0.01) and Th2 cell polarization (p < 0.01) in dLNs. In a murine model of AR, D2HG administration reduced overall cellular infiltrates and eosinophils in NLF. Further, antigen-specific IgE in NLF was significantly reduced in mice treated with D2HG (p < 0.05). Conclusions: An analysis of the regulatory landscape surrounding the rs34290285 SNP demonstrates that the downregulation of D2HGDH expression reduces the risk of AR. Downregulation of D2HGDH likely results in accumulation of D2HG intracellularly, suggesting that D2HG is protective against allergic rhinitis. We show that the administration of D2HG impairs IgE production, leading to the amelioration of allergic sinonasal inflammation in a murine model of AR. These findings suggest a causal relationship between D2HGDH expression, D2HG levels, and allergic rhinitis risk. Full article

In this work, we study how diffusion-based generative models produce high-dimensional data, such as images, by relying on latent abstractions that guide the generative process. We introduce a novel theoretical framework extending Nonlinear Filtering (NLF), offering a new perspective on SDE-based generative models. Our theory is based on a new formulation of joint (state and measurement) dynamics and an information-theoretic measure of state influence on the measurement process. We show that diffusion models can be interpreted as a system of SDE, describing a non-linear filter where unobservable latent abstractions steer the dynamics of an observable measurement process. Additionally, we present an empirical study validating our theory and supporting previous findings on the emergence of latent abstractions at different generative stages. Full article

The accurate, robust, and efficient prediction of transition in viscous flows is a significant challenge in computational fluid dynamics. We present a coupled high-fidelity iterative approach that leverages the FUN3D flow solver and the LASTRAC stability code to predict transition in low-disturbance environments, initiated by the linear growth of boundary-layer instability modes. Our method integrates the ability of FUN3D to compute mixed laminar–transitional–turbulent mean flows via transition-sensitized Reynolds-Averaged Navier–Stokes equations with the ability of LASTRAC to perform linear stability analysis, all within an automated framework that requires no intermediate user involvement. Unlike conventional frameworks that rely on classical stability theory or reduced-order metamodels, our approach employs parabolized stability equations to provide more accurate and reliable estimates of disturbance growth for multiple instability mechanisms, including Tollmien–Schlichting, Kelvin–Helmholtz, and crossflow modes. By accounting for the effects of mean-flow nonparallelism as well as the surface curvature, this approach lays the foundation for improved N-factor correlations for transition onset prediction in a broad class of flows. We apply this method to three distinct flow configurations: (1) flow over a zero-pressure-gradient flat plate, (2) the NLF-0416 airfoil with both natural and separation-induced transition, and (3) a 6:1 prolate spheroid, where transition is primarily driven by crossflow instability. For two-dimensional cases, a formulated intermittency distribution is used to model the transition zone between the laminar and fully turbulent flows. The results include comparisons with experimental measurements, similar numerical approaches, and transport-equation-based models, demonstrating good agreement in surface pressure coefficients, transition onset locations, and skin-friction coefficients for all three configurations. In addition to contributing a couple of new insights into boundary-layer transition in these canonical cases, this study presents a powerful tool for transition modeling in both research and design applications in aerodynamics. Full article

Porosity prediction from seismic data is of significance in reservoir property assessment, reservoir architecture delineation, and reservoir model building. However, it is still challenging to use traditional model-driven methodology to characterize carbonate reservoirs because of the highly nonlinear mapping relationship between porosity and elastic properties. To address this issue, this study proposes an advanced spatiotemporal deep learning neural network for porosity prediction, which uses the convolutional neural network (CNN) structure to extract spatial characteristics and the bidirectional gated recurrent unit (BiGRU) network to gather temporal characteristics, guaranteeing that the model accurately captures the spatiotemporal features of well logs and seismic data. This method involves selecting sensitive elastic parameters as inputs, standardizing multiple sample sets, training the spatiotemporal network using logging data, and applying the trained model to seismic elastic attributes. In blind well tests, the CNN–BiGRU model achieves a 54% reduction in the root mean square error and a 6% correlation coefficient improvement, outperforming the baseline models and traditional nonlinear fitting (NLF). The application of the proposed method to seismic data indicates that the model yields a reasonable porosity distribution for tight carbonate reservoirs, proving the strong generalization ability of the proposed model. This method compensates for the limitations of individual deep learning models by simultaneously capturing the spatial and temporal components of data and improving the estimation accuracy, showing considerable promise for accurate reservoir parameter estimation. Full article

The development of efficient and sustainable armor systems is crucial for protecting bodies and vehicles. In this study, epoxy composites reinforced with natural lignocellulosic fibers (NLFs) from carnauba (Copernicia prunifera) were produced with 0, 10, 20, 30, and 40% fiber volume fractions. Their ballistic performance was evaluated by measuring residual velocity and absorbed energy after impact with 7.62 mm ammunition, as well as their application in a multilayer armor system (MAS). Scanning electron microscopy (SEM) was used to analyze fracture regions, and explicit dynamic simulations were performed for comparison with experimental tests. Residual velocity tests indicated a limit velocity (V_L) between 213 and 233 m/s and absorbed energy (E_abs) between 221 and 264 J, surpassing values reported for aramid fabric. All formulations showed indentation depths below the National Institute of Justice (NIJ) limit, with the 40% fiber sample achieving the lowest depth (31.2 mm). The simulation results correlated well with the experimental data, providing insight into deformation mechanisms during a level III ballistic event. These findings demonstrate the high potential of carnauba fibers in epoxy-based polymer composites, particularly as an intermediate layer in MAS, offering a sustainable alternative for ballistic protection. Full article

Background: Sports science and medicine (SSM) is integral to professional football clubs. The level below professional football in England, ‘non-league football’ (NLF), consists of full-time and part-time clubs. The existing literature has exclusively focused on SSM in professional football, with the resources and practices in NLF currently unknown. Therefore, this study explored the SSM resources and practices within NLF by investigating the perspectives of key stakeholders working within NLF coaching and SSM disciplines. Methods: Fifty participants (coaching practitioners [n = 25] and SSM practitioners [n = 25]) from NLF clubs completed an anonymous online survey comprising 31 multiple-choice and Likert-scale questions, alongside optional open-ended comments. Results: Support was mixed for SSM evidence-based practices across clubs in Tiers 5–10 within the National League System. The most common SSM resources were the training ground (n = 39), resistance training equipment (n = 15), and rehabilitation area (n = 13). Fitness testing was frequent (86%) pre-season but rare end-of-season (8%). Workload monitoring primarily consisted of the session duration (80%) and time–motion data (36%). Performance analysis of competitive matches commonly used video (74%) or post-match technical analysis (40%). Injury monitoring generally occurred ‘always’ (44%) or ‘sometimes’ (28%). Nutritional support on match days was mostly fluids (80%), with ‘no support’ reported most outside match days (54%). Conclusions: The SSM resources and practices vary considerably within NLF, influenced by individual club constraints and barriers, including financial support, access to facilities, and equipment availability. These findings may inform future SSM provisions in NLF to enhance team performances and player availability. Full article

Background: Container terminal congestion is often measured by the average turnaround time for external trucks. Reducing the average turnaround time can be resolved by controlling the yard crane operation and the arrival times of external trucks (truck appointment system). Because the truck appointment system and yard crane scheduling problem are closely interconnected, this research investigates synchronization between the approaches used in truck appointment systems and yard crane scheduling strategies. Rubber-tired gantry (RTG) operators for yard crane scheduling operations strive to reduce RTG movement time as part of the container retrieval service. However, there is a conflict between individual agent goals. While seeking to minimize truck turnaround time, RTGs may travel long distances, ultimately slowing down the RTG service. Methods: We address a method that balances individual agent goals while also considering the collective objective, thereby minimizing turnaround time. An agent-based simulation is proposed to simulate scenarios for yard crane scheduling strategies and truck appointment system approaches, which are centralized and decentralized. This study explores the combined effects of different yard scheduling strategies and truck appointment procedures on performance indicators. Various configurations of the truck appointment system and yard scheduling strategies are modeled to investigate how those factors affect the average turnaround time, yard crane utilization, and CO₂ emissions. Results: At all levels of truck arrival rates, the nearest-truck-first-served (NTFS) scenario tends to provide lower external truck turnaround times than the first-come-first-served (FCFS) and nearest-truck longest-waiting-time first-served (NLFS) scenario. Conclusions: The decentralized truck appointment system (DTAS) generally shows slightly higher efficiency in emission reduction compared with centralized truck appointment system (CTAS), especially at moderate to high truck arrival rates. The decentralized approach of the truck appointment system should be accompanied by the yard scheduling strategy to obtain better performance indicators. Full article

The traditional Mongolian medicine Erdun-Uril is a conventional combination of 29 herbs commonly used for the treatment of cerebrovascular ailments. It has the effects of reducing inflammation, counteracting oxidative stress, and averting strokes caused by persistent cerebral hypoperfusion. Prior research on Erdun-Uril has predominantly concentrated on its pharmacodynamics and mechanism of action; however, there has been a lack of systematic and comprehensive investigation into its chemical constituents. Therefore, it is crucial to establish an efficient and rapid method for evaluating the chemical constituents of Erdun-Uril. In this study, Erdun-Uril was investigated using UHPLC-Q-Exactive Orbitrap MS combined with parallel reaction monitoring for the first time. Eventually, a total of 237 compounds, including 76 flavonoids, 68 phenolic compounds, 19 alkaloids, 7 amino acids, etc., were identified based on the chromatographic retention time, bibliography data, MS/MS² information, neutral loss fragments (NLFs), and diagnostic fragment ions (DFIs). And of these, 225 were reported for the first time in this study. This new discovery of these complex components would provide a reliable theoretical basis for the development of pharmacodynamics and quality standards of the Mongolian medicine Erdun-Uril. Full article

Tongmai Sini decoction (TSD), the classical prescriptions of traditional Chinese medicine, consisting of three commonly used herbal medicines, has been widely applied for the treatment of myocardial infarction and heart failure. However, the absorbed components and their metabolism in vivo of TSD still remain unknown. In this study, a reliable and effective method using ultra-performance liquid chromatography coupled with hybrid quadrupole-Orbitrap mass spectrometry (UHPLC-Q-Exactive-MS/MS) was employed to identify prototype components and metabolites in vivo (rat plasma and urine). Combined with mass defect filtering (MDF), dynamic background subtraction (DBS), and neutral loss filtering (NLF) data-mining tools, a total of thirty-two major compounds were selected and investigated for their metabolism in vivo. As a result, a total of 82 prototype compounds were identified or tentatively characterized in vivo, including 41 alkaloids, 35 phenolic compounds, 6 saponins. Meanwhile, A total of 65 metabolites (40 alkaloids and 25 phenolic compounds) were tentatively identified. The metabolic reactions were mainly hydrogenation, demethylation, hydroxylation, hydration, methylation, deoxylation, and sulfation. These findings will be beneficial for an in-depth understanding of the pharmacological mechanism and pharmacodynamic substance basis of TSD. Full article

The Amazon rainforest, spanning multiple countries in South America, is the world’s largest equatorial expanse, housing a vast array of relatively unknown plant and animal species. Encompassing the planet’s greatest flora, the Amazon offers a tremendous variety of plants from which natural lignocellulosic fibers (NLFs) can be extracted. In this century, NLFs, which have long been utilized by indigenous populations of the Amazon, have garnered interest as potential reinforcements for composites, whether polymer- or cement-based, in various technical applications such as packaging, construction, automotive products, and ballistic armor. A comparison with synthetic materials like glass, carbon, and aramid fibers, as well as other established NLFs, highlights the cost and specific property advantages of Amazon natural fibers (ANFs). Notably, the sustainable cultivation and extraction of ANFs, as alternatives to deforestation and livestock pasture, contribute to the preservation of the Amazon rainforest. This review article provides a comprehensive examination of recent studies directly related to ANF-reinforced polymer matrix composites. The specific advantages, proposed applications, and reported challenges are highlighted, shedding light on the potential of these unique natural fibers. Full article

One of the most notable signs of an aging face is the nasolabial folds (NLFs), which often diminish emotional well-being and self-confidence. To address this concern, many people seek solutions to improve their appearance, often turning to fillers. The ULTRACOL100 device, a tissue restoration material, has been previously investigated and shown to exhibit significant efficacy in both in vitro and in vivo studies. In this research, we aim to explore the safety and effectiveness of the clinical trial of ULTRACOL100 in improving the skin in the NLF area over an 8-week observation period. Male and Female adults with nasolabial folds received two injections of ULTRACOL100, with a 4-week interval between treatments, on one side of their faces. On the other side, they received control materials (REJURAN^®, JUVELOOK^®, or HYRONT^®). The assessment of skin improvement in the nasolabial fold area for each subject took place before and four weeks after each application. Various skin parameters, such as roughness, elasticity, moisture, transparency, trans-epidermal water loss, tone, radiance, skin pore size, and skin density, were measured to evaluate the outcomes. The application of the ULTRACOL100 device significantly reduced the skin roughness, the trans-epidermal water loss, and the skin pore size and increased the skin’s elasticity and internal elasticity, as well as the skin’s moisture, transparency, skin tone, radiance, and density. This study comprehensively investigates the effectiveness and safety of the ULTRACOL100 device, comparing it with three commercial products (REJURAN^®, JUVELOOK^®, and HYRONT^®). The ULTRACOL100 device showed comparable performance in improving the appearance of the NLF area among this study subjects. Full article