Search Results (30,904)

Background: Mitral valve prolapse is a common valvular heart disorder, usually associated with a benign prognosis in the absence of significant mitral regurgitation. However, a subset of patients is at increased risk for complex ventricular arrhythmias and sudden cardiac death. Identifying these high-risk individuals remains a major clinical challenge. Case Summary: We present the case of a 71-year-old female patient with recurrent syncopal episodes, a strong family history of sudden cardiac death, and complex ventricular ectopy. Multimodality imaging revealed bileaflet mitral valve prolapse, severe mitral regurgitation, mitral annular disjunction, and the Pickelhaube sign, with no evidence of myocardial fibrosis on cardiac magnetic resonance imaging. The patient underwent minimally invasive mitral valve repair and received an implantable cardioverter-defibrillator for primary prevention of sudden cardiac death. Follow-up revealed significant reverse cardiac remodeling, marked reduction in arrhythmic burden, and restoration of mitral valve function. Family screening identified mitral annular disjunction in both of her daughters, who were asymptomatic and without arrhythmias. Discussion: Mitral annular disjunction has emerged as a potentially arrhythmogenic substrate, especially in patients with familial clustering, raising the possibility of a genetic predisposition. Risk stratification remains difficult, as no individual clinical, electrocardiographic, or imaging marker has demonstrated consistent predictive value. Surgical correction of mitral valve prolapse with associated mitral annular disjunction may lead to a reduction in arrhythmic risk and promote favorable structural remodeling. Conclusions: This case-based review emphasizes the importance of advanced imaging techniques in the identification and management of high-risk mitral valve prolapse phenotypes. Early surgical intervention and close arrhythmic surveillance may improve outcomes, although further research is necessary to define risk assessment tools and explore the genetic background of arrhythmogenic mitral valve disease. Full article

Automated Valet Parking (AVP) systems require high-precision positioning, especially in indoor environments where Global Positioning System (GPS) is unavailable. Existing methods, which use markers installed on parking lot walls or ceilings, often encounter difficulties due to marker detection failures caused by complex parking behaviors, such as infrastructure constraints or perpendicular parking. This study proposes an optimized indoor positioning system for AVP using fiducial markers recognized by front and rear vehicle cameras. To enhance accuracy and robustness, an Extended Kalman Filter (EKF) fuses vehicle kinematic data with marker pose information. Critically, to address the issue of marker occlusion by the front camera during reverse parking, a novel camera switching algorithm employing a hysteresis pattern based on vehicle position, heading, and motion direction is introduced. This ensures continuous marker visibility and stable positioning during parking maneuvers. The system’s effectiveness was validated through simulations and extensive real-vehicle experiments in a real parking space. Results demonstrate that the EKF significantly reduces positioning errors compared to kinematic prediction alone, particularly during curved driving. Furthermore, the proposed camera switching algorithm successfully overcomes the limitations of a front-only camera system, significantly improving positioning accuracy (e.g., reducing RMS error by up to 25.0% in X and 17.6% in Y during parking) and eliminating instability observed with simpler switching logic. This research contributes a cost-effective and reliable positioning solution, advancing the feasibility of AVP systems in challenging indoor environments. Full article

Gilles de la Tourette syndrome (GTS) is a neurodevelopmental disorder characterized by motor and phonic tics, often including attention deficit, hyperactivity, and obsessive–compulsive behaviours. The pathophysiology involves the dysfunction of cortico-striato-thalamo-cortical circuits, primarily implicating dopaminergic hyperactivity, but also involving multiple different neurotransmitter systems. Treatment of GTS is complex, highly individualized, and influenced by considerable variability in symptom presentation. Behavioural approaches, such as Habit Reversal Therapy (HRT), play a key role, especially in milder cases. Pharmacological therapy is largely empirical and varies across countries, influenced by drug availability and the perceived risks of certain classes of drugs, particularly dopamine receptor blocking agents. Drug options for managing tics include dopamine receptor antagonists, monoamine depleting agents, and alpha-2 agonists, all of which require close monitoring for metabolic, cardiovascular, and neurological side effects. Botulinum toxin injections represent an effective solution for focal tics that are resistant to systemic treatments. Cannabinoids and antiepileptics have limited efficacy, yet they may still offer relevant therapeutic potential in selected cases. Serotonergic drugs are useful for treating obsessive–compulsive symptoms. For patients with refractory tics, deep brain stimulation (DBS) represents an intervention of last-resort; however, DBS remains off-label and consensus on optimal targets is lacking. This narrative review draws on both the relevant literature and extensive personal clinical experience to explore the complexities of managing GTS, with a focus on evidence-based treatments for tics and associated neuropsychiatric symptoms. A therapeutic algorithm is proposed, emphasizing a “start low, go slow” approach, combining pharmacological interventions with cognitive behavioural and surgical therapies, when needed. We underscore the importance of tailoring treatments to individual patient profiles and symptom variability over time, highlighting the need for further research in GTS management. Full article

Background/Objectives: Abnormal differentiation of human skin fibroblasts into myofibroblasts contributes to fibrotic skin disorders such as hypertrophic scars, keloids, and Dupuytren’s disease. This process is characterized by increased fibroblast proliferation, enhanced differentiation into myofibroblasts, and reduced programmed cell death (apoptosis). We previously demonstrated that blue light irradiation (λ = 453 nm) significantly and dose-dependently inhibits both spontaneous and TGF-β-induced fibroblast differentiation. Methods: Because fibroblast differentiation depends on cellular energy metabolism, we investigated whether the inhibitory effect of blue light is linked to changes in the cells’ energy balance. Results: We found that blue light reduced TGF-β-induced differentiation, as shown by decreased levels of α-SMA and EDA-fibronectin, key markers of myofibroblast formation. This effect was strongly associated with almost complete inhibition of mitochondrial respiration, reduced glycolysis, a lower NAD⁺/NADH ratio, and decreased ATP production. ATP-dependent processes, including endocytosis and lysosomal activity, both essential parameters of fibroblast differentiation, were also strongly suppressed. Importantly, all these changes were fully reversible within 24 h after the last irradiation. Conclusions: Mechanistically, we propose that blue light triggers photochemical reduction in flavins in proteins of the respiratory chain and possibly the Krebs cycle, which temporarily alters cellular energy metabolism. These findings suggest that non-toxic blue light therapy (80 J/cm²) can effectively prevent factor-induced fibroblast differentiation and may serve as a standalone or supportive treatment to reduce fibrotic events such as scarring and keloid formation. Furthermore, our results indicate that targeting cellular energy metabolism, whether physically or pharmacologically, could be a promising strategy to prevent sclerotic skin disorders. Full article

This scoping review assessed the current use of wearable sensors in monitoring recovery following reverse shoulder arthroplasty (RSA). A systematic search of electronic databases was undertaken (MEDLINE, EMBASE, CINAHL, and Web of Science) between 2005 and 2024 following the PRISMA-ScR protocol. Studies were eligible if they were peer reviewed, available in full text, and reported the use of wearable sensors to evaluate shoulder motion or activity in postoperative RSA patients. Fifty-seven studies were identified, of which six met the inclusion criteria. Studies were either focused on assessing shoulder motion (n = 3) or on measuring upper limb activity counts or activity intensities (n = 3); however the calculation of output variables were different across most studies. Sensors were positioned on the operated upper arm in all studies, though sensor placement on the sternum and the wrist varied. Session durations ranged from 24 h to continuous monitoring beyond seven days. Daily wear times were most commonly during full waking hours. The large variation in wearable sensor configuration, testing protocols, and the calculation of output variables limited the comparability across studies. Standardization in sensor protocols and outcomes is required to enable the reliable wearable assessment of postoperative recovery after RSA. Full article

In precision agriculture, semantic segmentation enhances the crop yield by enabling precise disease monitoring, targeted herbicide application, and accurate crop–weed differentiation. This enhances yield; reduces the overuse of herbicides, water, and fertilizers; lowers labor costs; and promotes sustainable farming. Deep-learning-based methods are particularly effective for crop and weed segmentation, and achieve potential results. Typically, segmentation is performed using homogeneous data (the same dataset is used for training and testing). However, previous studies, such as crop and weed segmentation in a heterogeneous data environment, using heterogeneous data (i.e., different datasets for training and testing) remain inaccurate. The proposed framework uses patch-based augmented limited training data within a heterogeneous environment to resolve the problems of degraded accuracy and the use of extensive data for training. We propose an attention-driven and hierarchical feature fusion network (AHFF-Net) comprising a flow-constrained convolutional block, hierarchical multi-stage fusion block, and attention-driven feature enhancement block. These blocks independently extract diverse fine-grained features and enhance the learning capabilities of the network. AHFF-Net is also combined with an open-source large language model (LLM)-based pesticide recommendation system made by large language model Meta AI (LLaMA). Additionally, a fractal dimension estimation method is incorporated into the system that provides valuable insights into the spatial distribution characteristics of crops and weeds. We conducted experiments using three publicly available datasets: BoniRob, Crop/Weed Field Image Dataset (CWFID), and Sunflower. For each experiment, we trained on one dataset and tested on another by reversing the process of the second experiment. The highest mean intersection of union (mIOU) of 65.3% and F1 score of 78.7% were achieved when training on the BoniRob dataset and testing on CWFID. This demonstrated that our method outperforms other state-of-the-art approaches. Full article

Mercury (Hg) exposure is a major environmental risk factor, closely linked to oxidative stress and cardiovascular disease. Red blood cells (RBC), due to their high oxygen exposure and lack of repair mechanisms, are particularly sensitive to oxidative injury and are key indicators of systemic redox imbalance. This study evaluates the protective effects of polyphenolic extracts from Annurca apple, specifically flesh and peel, from both ripe and unripe fruit, on HgCl₂-exposed human RBCs. Key oxidative stress markers were measured, including ROS production, GSH levels, lipid peroxidation (TBARS), MetHb formation, SH group content, microvesicle (MV) generation, and morphological changes. Peel extracts, particularly those from ripe apples, consistently exhibited stronger antioxidant and cytoprotective effects than flesh extracts, effectively reversing Hg-induced oxidative damage and preserving RBC integrity. Notably, these extracts restored redox homeostasis and GSH levels, reduced ROS and TBARS accumulation, prevented MetHb formation, and mitigated MV release and morphological alterations. These protective effects appear to involve multifactorial mechanisms. These findings highlight the nutraceutical potential of Annurca apple extracts in counteracting heavy metal-induced oxidative stress and support their possible relevance for future studies aimed at health protection and waste valorization. Full article

Herein, we report a combined computational and experimental investigation into the recently reported universal pyrazole-based reversible addition-fragmentation chain transfer (RAFT) agents (Z−C(=S)−S−R, where Z is 3,5-dimethyl-1H-pyrazol-1-yl), which can mediate controlled radical polymerization of a broad scope of monomers without the need for an additional initiator or catalyst. The results reveal that the high molar absorption coefficient and efficient photolysis kinetics of pyrazole-based chain transfer agents (CTAs) under blue light (${\lambda }_{\max }$ = 465 nm) enable rapid radical generation, underpinning ultrafast polymerization of acrylates, acrylamides, methacrylates, and N-vinylpyrrolidone (NVP). While the efficient light absorption is attributed to structural dissimilarity between the Z group and the S–R group (which breaks the local symmetry of the C=S group), the fast photolysis originates from favorable $\pi$ electron donation from the Z group to the C=S group. Meanwhile, the $\pi$ electron donation is still weaker than in xanthates, which explains the excellent control of a wide range of monomers, except methacrylates. This work establishes design principles for next-generation CTAs for ultrafast and monomer-universal photoiniferter RAFT polymerization. Full article

Salty wheat crackers prepared from wheat white (WF) and whole grain flour (WG) were enriched with 5, 10, and 15% cricket powder (CRPW). According to the content of dietary fiber and fat, two types of wheat flour and CRPW differed in terms of darkness “100 − L*” and redness a*. The color of the baked products reflected these differences, but the darkening of the whole grain crackers was less intense; the shades of wheat–cricket 90:10 and whole grain 100:0 cracker variants were comparable. Within the WF subset, the hardness diminished insignificantly, with the reverse occurring in the WG group (from 25 to 22 N and from 31 to 35 N, respectively). The flexibility of the crackers was independent on type of wheat flour and the proportion of CRPW, as shown by a 90% confidence interval of 0.97–1.06 mm. By Principal Component Analysis, the primary role of wheat flour type in distinguishing the crackers was confirmed. As expected, the darkness “100 − L*” and the redness a* of the cracker surface could be used to predict the results of the texture breaking test and fragility in general (P = 95%). The 90:10 WF–cricket crackers and 95:5 WG–cricket crackers had similar properties, and both could be adopted in baking practice without modification. Full article

Many existing reinforced concrete (RC) frames with brick infill walls are vulnerable to earthquake damage, particularly when the walls contain window openings that reduce the lateral resistance. This study aims to examine the seismic performance of RC frames strengthened with U-shaped precast concrete (PC) wall panels. In the proposed method, the window-containing brick infill walls within the RC frames are replaced with factory-fabricated U-shaped PC wall panels, thereby converting the infill into a strong and rigid structural element while preserving the openings. The panels are anchored to the RC frame using post-installed anchors inserted through predrilled holes, allowing for rapid and secure installation with minimal on-site work. To validate the method, five full-scale, one-bay, one-story RC frames were constructed and tested under reversed cyclic lateral loading. Three frames were strengthened with U-shaped PC wall panels of varying thicknesses and large openings. Displacement-controlled cycles following ACI 374.1-05 (R7.0) were applied, with three cycles at each drift ratio stage, and no axial load was applied to the columns. Compared with the reference specimen with a U-shaped brick wall, the strengthened frames exhibited up to 3.29 times higher lateral strength, 4.39 times higher initial stiffness, and 4.33 times greater energy dissipation capacity. These findings demonstrate that the proposed strengthening technique significantly enhances seismic resistance while maintaining the architectural openings, offering a practical and efficient solution for upgrading low-rise RC buildings. Full article

Exergy efficiency is viewed as the degree of approaching reversible operation, with a value of 100 percent for a reversible process characterized by zero entropy generation or equivalently zero exergy destruction since X_destroyed = T₀S_gen. As such, exergy efficiency becomes a measure of thermodynamic perfection. There are different conceptual definitions of exergy efficiency, the most common ones being (1) the ratio of exergy output to exergy input η_ex = X_output/X_input = 1 − (X_destroyed + X_loss)/X_input, (2) the ratio of the product exergy to fuel exergy η_ex = X_product/X_fuel = 1 − (X_destroyed + X_loss)/X_fuel, and (3) the ratio of exergy recovered to exergy expended η_ex = X_recovered/X_expended = 1 − X_destroyed/X_expended. Most exergy efficiency definitions are formulated with steady-flow systems in mind, and they are generally applied to systems in steady operation such as power plants and refrigeration systems whose exergy content remains constant. If these definitions are to be used for closed and unsteady-flow systems, the terms need to be interpreted broadly to account for the exergy change of the systems as exergy input or output, as appropriate. In this paper, general exergy efficiency relations are developed for closed and unsteady-flow systems and their use is demonstrated with applications. Also, the practicality of the use of the term exergy loss X_loss is questioned, and limitations on the definition η_ex = W_act,out/W_rev,out are discussed. Full article

This paper proposes a learning-driven passivity-based control (PBC) strategy for sensorless induction motors, combining a nonlinear adaptive observer with recurrent neural networks (RNNs) to improve robustness and estimation accuracy under dynamic conditions. The main novelty is the integration of neural learning into the passivity framework, enabling real-time compensation for un-modeled dynamics and parameter uncertainties with only one gain adjustment across a broad speed range. Lyapunov-based analysis guarantees the global stability of the closed-loop system. Experiments on a 1.1 kW induction motor confirm the approach’s effectiveness over conventional observer-based and fuzzy-enhanced methods. Under torque reversal and flux variation, the proposed controller achieves a torque mean absolute error (MAE) of 0.18 Nm and flux MAE of 0.21 Wb, compared to 1.58 Nm and 0.85 Wb with classical PBC. When peak torque deviation drops from 42.52% to 30.85% of the nominal, torque symmetric mean absolute percentage error (SMAPE) improves by 7.6%, and settling time is reduced to 985 ms versus 1120 ms. These results validate the controller’s precision, adaptability, and robustness in real-world sensorless motor control. Full article

Chronic obstructive pulmonary disease (COPD) involves persistent inflammation and dysregulated lipid metabolism, with foamy macrophages playing a central role in disease progression. Exosomes—vesicles transporting microRNAs (miRNAs)—mediate intercellular communication, but their contribution to foamy macrophage-driven COPD remains unclear. This study investigates the role of exosomal miRNAs, particularly let-7, in modulating lipid metabolism and inflammation in foamy macrophages. Bone marrow-derived macrophages (BMDMs) were treated with oxidized low-density lipoprotein (oxLDL) and lipopolysaccharide (LPS) to induce foamy macrophage formation. Exosomal miRNA profiles were analyzed, and the function of let-7c-3p was assessed via transfection. Foamy macrophages released significantly more exosomes (392.7 × 10⁷ particles) than controls (284.9–302.5 × 10⁷), without differences in exosome size or molecular content. The miRNA sequencing and qRT-PCR confirmed downregulation of exosomal let-7c-3p in foamy macrophages, correlating with increased RNF8 and decreased RXR expression—markers of disrupted PPAR/RXR signaling. Pathway analysis implicated let-7c-3p in regulating PPAR/RXR, WNT/β-catenin, and pulmonary fibrosis pathways. Transfection with let-7 mimics reduced lipid accumulation (52% to 19%), suppressed RNF8, restored RXR, and lowered IL-6 and TNF-α levels, indicating strong anti-inflammatory and lipid-modulating effects. Loss of exosomal let-7c-3p aggravates lipid dysregulation and inflammation in COPD by impairing PPAR/RXR signaling. Restoring let-7 expression reverses these effects, highlighting its potential as a diagnostic biomarker and therapeutic target. Full article

Background/Objectives: Polymerase chain reaction (PCR) is ubiquitous in biological research labs, as it is a fast, flexible, and cost-effective technique to amplify a DNA region of interest. However, manual primer design can be an error-prone and time-consuming process depending on the number and composition of target sites. While Primer3 has emerged as an accessible tool to solve some of these issues, additional computational pipelines are required for appropriate scaling. Moreover, this does not replace the manual confirmation of primer specificity (i.e., the assessment of off-targets). Methods: To overcome the challenges of large-scale primer design, we fused the functionality of Primer3 and In-Silico PCR (ISPCR); this integrated pipeline, CREPE (CREate Primers and Evaluate), performs primer design and specificity analysis through a custom evaluation script for any given number of target sites at scale. Results: CREPE’s final output summarizes the lead forward and reverse primer pair for each target site, a measure of the likelihood of binding to off-targets, and additional information to aid decision-making. We provide this through a customized workflow for targeted amplicon sequencing (TAS) on a 150 bp paired-end Illumina platform. Experimental testing showed successful amplification for more than 90% of primers deemed acceptable by CREPE. Conclusions: We here provide CREPE, a software platform that allows for parallelized primer design for PCR applications and that is optimized for targeted amplicon sequencing. Full article

Remote sensing image dehazing presents substantial challenges in balancing physical fidelity with generative flexibility, particularly under complex atmospheric conditions and sensor-specific degradation patterns. Traditional physics-based methods often struggle with nonlinear haze distributions, while purely data-driven approaches tend to lack interpretability and physical consistency. To bridge this gap, we propose the Atmospheric Scattering Prior embedded Diffusion Model (ASPDiff), a novel framework that seamlessly integrates atmospheric physics into the diffusion-based generative restoration process. ASPDiff establishes a closed-loop feedback mechanism by embedding the atmospheric scattering model as a physics-driven regularization throughout both the forward degradation simulation and the reverse denoising trajectory. The framework operates through the following three synergistic components: (1) an Atmospheric Prior Estimation Module that uses the Dark Channel Prior to generate initial estimates of the transmission map and global atmospheric light, which are then refined through learnable adjustment networks; (2) a Diffusion Process with Atmospheric Prior Embedding, where the refined priors serve as conditional guidance during the reverse diffusion sampling, ensuring physical plausibility; and (3) a Haze-Aware Refinement Module that adaptively enhances structural details and compensates for residual haze via frequency-aware decomposition and spatial attention. Extensive experiments on both synthetic and real-world remote sensing datasets demonstrate that ASPDiff significantly outperforms existing methods, achieving state-of-the-art performance while maintaining strong physical interpretability. Full article