Search Results (1,163)

Introduction: Hereditary transthyretin amyloidosis (ATTRv) is a severe, multisystemic, autosomal dominant disease with variable penetrance caused by mutations in the TTR gene generating protein misfolding and accumulation of amyloid fibrils. The diagnosis is usually challenging because ATTRv may initially manifest with nonspecific multisystemic symptoms. Conversely, an early diagnosis is needed to start timely appropriate therapy. Hence, screening models have been proposed to improve ATTRv diagnosis. In this study, we propose a genetic screening model based on predefined “red flags” followed by “cascading screening” on first-degree relatives of patients who tested positive. Materials and methods: After obtaining written informed consent, genetic testing on salivary swabs was performed in individuals who met at least two major red flags for ATTRv (age > 65 years old, progressive sensory or sensorimotor neuropathy not responsive to steroids or immunomodulant therapies, recent and unexplained weight loss associated with gastrointestinal signs and symptoms, diagnosis of cardiac amyloidosis, bilateral or relapsing carpal tunnel syndrome, unexplained autonomic dysfunction) or one major flag and two minor flags (family history of neuropathy, ambulation disorders or cardiopathy, sudden cardiac death, a bedridden, wheelchaired patient without specific diagnosis excluding upper motor neuron diseases, infections, juvenile cardiac disease, ocular disorders, lumbar spine stenosis, biceps tendon rupture). Results: In the first screening phase, 29 suspected cases (individuals meeting at least two major red flags or one major red flag and two minor red flags) underwent genetic testing. One patient (3.5%) was diagnosed with hereditary transthyretin amyloidosis with polyneuropathy (ATTRv-PN), carrying the Phe64Leu mutation. Then, cascade screening allowed for early recognition of two additional individuals (two pre-symptomatic carriers) among two first-degree relatives (100%). The identified patient was a 72-year-old man who had a family history of both cardiopathy, neuropathy, and a diagnosis of juvenile cardiac disease and progressive sensorimotor neuropathy unresponsive to steroids or immunomodulant therapies. Conclusions: ATTRv is a progressive and often fatal disease that should be promptly diagnosed and treated to stop progression and reduce mortality. Systematic screening for ATTRv yielded increased recognition of the disease in our neurological clinic. A focused approach for the screening of ATTRv-PN could lead to an earlier diagnosis and identification of asymptomatic carriers, enabling timely intervention through close clinical monitoring and early treatment initiation at symptom onset. Full article

The accuracy of ultrasonic rangefinders is crucial for mobile robotic navigation systems, yet environmental factors such as temperature, humidity, atmospheric pressure, and wind conditions can influence ultrasonic speed in the air. The primary objective is to investigate how environmental factors influence the output signal of an ultrasonic emitter and to develop a method for improving the accuracy of distance measurements in both outdoor and indoor settings. The research employs a combination of theoretical modeling, statistical analysis, and experimental validation. The research employs an ultrasonic rangefinder integrated with environmental sensors (BME280, Bosch Sensortec GmbH, Kusterdingen, Germany) and wind sensors (WMT700, WINDCAP^®, Vaisala Oyj, Vantaa, Finland) to account for environmental influences. Experimental studies were conducted using a prototype ultrasonic rangefinder, and statistical analysis (Student’s t-test) was performed on collected data. The results of estimation by Student’s t-test for 256 measurements demonstrate the maximum effect of air temperature and the minimum effect of relative air humidity on a piezoelectric emitter output signal both outdoors and indoors. In addition, wind parameters affect the rangefinder’s operation. The maximum range of obstacle detection depends on the reflection coefficient of the material that covers the obstacle. The results align with theoretical expectations for highly reflective surfaces. A cascade-forward artificial neural network model was developed to refine distance estimations. This study demonstrates the importance of considering environmental factors in ultrasonic rangefinder systems for mobile robots. By integrating environmental sensors and using statistical analysis, the accuracy of distance measurements can be significantly improved. The results contribute to the development of more reliable navigation systems for mobile robots operating in diverse environments. Full article

Background: Traceback testing—identifying and offering testing to people with previous cancer diagnoses who have not received current standard genetic testing—could benefit patients and their at-risk relatives. Methods: We conducted a multisite, nonrandomized pilot implementation study of a Traceback program at three integrated United States health systems. We assessed the reach, fidelity, effectiveness, and acceptability of the program using quantitative and qualitative methods. Results: We identified 597 eligible individuals using administrative data and manual chart review. We attempted to reach everyone identified (100% fidelity). We successfully contacted 354 people, for a reach of 59% of confirmed eligible individuals. In total, 133 people completed Traceback genetic testing. Ten of these (8%) received pathogenic or likely pathogenic results;. Nine of these ten people received positive results for which cascade testing of at-risk relatives would be indicated. None of their relatives underwent cascade testing during the study period. Thirty-six received variants of uncertain significance (VUS). Traceback programs were acceptable to participants and implementers and thought to be applicable to other genetic screening conditions. The time and resources required to accurately identify Traceback-eligible individuals are likely determinants of future sustainability. Conclusions: Education about free cascade testing, reminder calls to probands, and offers to directly contact at-risk relatives did not result in cascade testing in this pilot study. However, participant and implementer discussions suggest that the potential benefits of Traceback programs and high participant acceptability are worthy of further study. Full article

Background: Longhorn beetles, a widely recognized group of Chinese traditional medicinal insects, are characterized by their notable hemostatic properties. However, the comprehensive understanding of their medicinal potential has been hindered by the limitations of current research methodologies. Methods: This study focuses on the species Glenea cantor (Fabricius), which can produce several generations per year, and introduces a novel method using microwave carbonization techniques. By employing an in vitro coagulation test, UHPLC-MS, network pharmacology, molecular docking, and molecular dynamics simulation, the hemostatic efficacy and mechanism of action of Glenea cantor charcoal medicine (GC-CM) were thoroughly studied. Results: In vitro coagulation tests showed that GC-CM significantly reduced the activated partial thromboplastin time (APTT) and prothrombin time (PT), indicating its ability to enhance the coagulation cascade and preliminarily confirming its hemostatic efficacy (p < 0.01 vs. blank control group). The analysis revealed that GC-CM comprises 453 components, including 137 bioactive components with high human utilization. After predictions via databases such as SwissTargetPrediction and deduplication, 215 targets linked to hemostatic specificity were identified. These targets regulate signaling pathways such as platelet activation, complement and coagulation cascades, and cGMP-PKG. Molecular docking demonstrated strong affinities between key targets such as SRC and PIK3R1 and compounds such as 2′,6′-dihydroxy 4′-methoxydihydrochalcone, and 1-monolinoleoyl-rac-glycerol (binding energy < −5 kcal/mol). Molecular dynamics simulations show good binding capacity between core components and targets Conclusions: The aim of this study was to elucidate the material basis and mechanism of the hemostatic efficacy of GC-CM, offering a model for exploring other insect-based medicinal resources. Full article

With the growing demand for advanced control algorithms in permanent magnet synchronous motor (PMSM) speed regulation, active disturbance rejection control (ADRC) has garnered significant attention for its simplicity and effectiveness as an alternative to traditional proportional-integral (PI) controllers. However, two key challenges limit its broader application: the lack of an intuitive equivalence analysis that highlights the advantages of ADRC over PI control and the complexity in selecting appropriate extended state observer (ESO) structures within ADRC. To address these issues, this paper develops an equivalent model of ADRC based on the structure of a generalized PI controller, offering a clearer understanding of its operational principles. The results demonstrate the relationship between ADRC and generalized PI control while highlighting ADRC’s superior capabilities. Additionally, this paper constructs a generalized model that incorporates all ADRC observer configurations, including both high-order ESO (HESO) and cascaded ESO (CESO), enabling a comprehensive analysis of ADRC with various observer structures and establishing equivalence relationships between them. The findings provide valuable insights into the efficacy and versatility of ADRC in PMSM speed regulation, supported by experimental validation on a test bench using the dSPACE DS1202 MicroLabBox. Full article

Every year, electric power systems (EPSs) experience accidents resulting in static and dynamic instability, as well as power supply disruptions. Accidents evolve along various trajectories and sometimes can exhibit a cascading effect. In this case, the sequential tripping of generating and/or electric network equipment occurs due to overloads or voltage drops at various nodes of the electric network. This leads to significant losses for industrial and commercial consumers, while also escalating social tensions within the population. This study aims to develop an algorithm for revealing the possibility of cascading failure processes in EPSs and their development trajectories. The use of the algorithm in planning and managing power flows in EPSs facilitates the identification of the boundary between the regions of admissible and inadmissible post-contingency power flows. The algorithm also enables the assessment of the impact of various topology solutions and operational measures on the development of cascading failure processes. This paper presents the results of steady-state calculation for the test schemes of an EPS incorporating 25, 36, and 40 nodes with voltage levels of 6, 35, 110, and 500 kV to illustrate the influence of topology and the non-homogeneity of network parameters on the occurrence and development of cascading failure processes. The deployment of distributed generation facilities of different capacities and FACTS devices, alongside the redistribution of power flows in the network by changing the load of power plants with different electricity generation costs, are considered topology and operational measures that enhance the survivability of the EPS. The performance of the developed algorithm was illustrated through an analysis of the process of the development of a real cascading systemic accident that occurred in the EPS. The proposed algorithm, when utilized in planning and managing power flows in an EPS, facilitates the identification of possibilities for the cascading failure processes and their development pathways to subsequently design and implement the operational measures and topological adjustments to prevent them. Full article

Colorectal cancer (CRC) remains a principal contributor to oncological mortality worldwide, with chronic inflammation serving as a fundamental driver of its pathogenesis. Protease-activated receptor-2 (PAR-2), a G-protein-coupled receptor, orchestrates inflammation-driven tumorigenesis by potentiating NF-κB and Wnt/β-catenin signaling, thereby fostering epithelial–mesenchymal transition (EMT), immune evasion, and therapeutic resistance. Despite its pathological significance, targeted modulation of PAR-2 remains an underexplored avenue in CRC therapeutics. Oleocanthal (OC), a phenolic constituent of extra virgin olive oil, is recognized for its potent anti-inflammatory and anti-cancer properties; however, its regulatory influence on PAR-2 signaling in CRC is yet to be elucidated. This study interrogates the impact of OC on PAR-2-mediated inflammatory cascades using HT-29 and Caco-2 CRC cell lines subjected to lipopolysaccharide (LPS)-induced activation of PAR-2. Expression levels of PAR-2 and TNF-α were quantified through Western blotting and RT-PCR, while ELISA assessed TNF-α secretion. Intracellular calcium flux, a pivotal modulator of PAR-2-driven oncogenic inflammation, was evaluated via Fluo-4 calcium assays. LPS markedly elevated PAR-2 expression at both mRNA and protein levels in CRC cells (p < 0.01, one-way ANOVA). OC administration (20–150 μg/mL) elicited a dose-dependent suppression of PAR-2, with maximal inhibition at 100–150 μg/mL (p < 0.001, Tukey’s post hoc test). Concomitant reductions in TNF-α transcription (p < 0.01) and secretion (p < 0.001) were observed, corroborating the anti-inflammatory efficacy of OC. Additionally, OC ameliorated LPS-induced calcium dysregulation, restoring intracellular calcium homeostasis in a concentration-dependent manner (p < 0.01). Crucially, OC exhibited selectivity for PAR-2, leaving PAR-1 expression unaltered (p > 0.05), underscoring its precision as a therapeutic agent. These findings position OC as a selective modulator of PAR-2-driven inflammation in CRC, disrupting the pro-tumorigenic microenvironment through attenuation of TNF-α secretion, calcium dysregulation, and oncogenic signaling pathways. This study furnishes mechanistic insights into OC’s potential as a nutraceutical intervention in inflammation-associated CRC. Given the variability in OC bioavailability and content in commercial olive oil, future investigations should delineate optimal dosing strategies and in vivo efficacy to advance its translational potential in CRC therapy. Full article

Gastrodia elata, Polygonatum sibiricum, and Poria cocos are traditional Chinese herbs commonly used as both medicinal and food ingredients, traditionally believed to improve liver and kidney functions, replenish vital energy (qi) and blood, and mitigate stress-induced damage. These herbs are combined in the Compound Gastrodia elata Formula (CGEF), a functional food formulation. Amidst growing interest in functional foods, this study explores the cognitive-enhancing effects of CGEF, focusing on cognitive function improvement. Cognitive impairment was induced in ICR mice via chronic sleep restriction. Behavioral assessments including the Y-maze test, object recognition test, Morris water maze test, and Passive avoidance test, were conducted to evaluate CGEF’s effects. Serum levels of inflammatory markers and oxidative stress were quantified while in rat hippocampus tissue expressions of inflammatory, apoptotic, and neuroprotective-related protein markers were analyzed by Western blotting. Neurotransmitter concentrations in both the hippocampus and prefrontal cortex were determined by LC-MS/MS. CGEF significantly alleviated cognitive impairments across all behavioral tests. The underlying mechanisms likely involve a reduction in oxidative stress and peripheral inflammatory factors, and suppression of the TLR2/MyD88/NF-κB signaling cascade in the hippocampus, thereby mitigating neuroinflammation and neuronal apoptosis. Furthermore, CGEF modulated the PI3K/AKT/GSK3β signaling pathway, potentially contributing to neuronal integrity and synaptic plasticity maintenance. CGEF also restored neurotransmitter balance and regulated tryptophan metabolism, further alleviating cognitive deficits associated with sleep disruption. These findings suggest CGEF’s potential as a functional food for reversing cognitive impairments caused by chronic sleep restriction, primarily through its anti-inflammatory and neuroprotective effects. Full article

In the field of transplantology, where medical decisions are heavily dependent on complex data analysis, the challenge of small data has become increasingly prominent. Transplantology, which focuses on the transplantation of organs and tissues, requires exceptional accuracy and precision in predicting outcomes, assessing risks, and tailoring treatment plans. However, the inherent limitations of small datasets present significant obstacles. This paper introduces an advanced input-doubling classifier designed to improve survival predictions for allogeneic bone marrow transplants. The approach utilizes two artificial intelligence tools: the first Probabilistic Neural Network generates output signals that expand the independent attributes of an augmented dataset, while the second machine learning algorithm performs the final classification. This method, based on the cascading principle, facilitates the development of novel algorithms for preparing and applying the enhanced input-doubling technique to classification tasks. The proposed method was tested on a small dataset within transplantology, focusing on binary classification. Optimal parameters for the method were identified using the Dual Annealing algorithm. Comparative analysis of the improved method against several existing approaches revealed a substantial improvement in accuracy across various performance metrics, underscoring its practical benefits Full article

The impacts of COVID-19 among men who have sex with men (MSM), who face limited access to HIV services due to stigma, discrimination, and violence, need to be assessed and quantified in terms of HIV treatment outcomes for future pandemic preparedness. This study aimed to evaluate the effects of the COVID-19 lockdown on the HIV treatment cascade among MSM in selected provinces of South Africa using routine programme data after the implementation of differentiated service delivery (DSD) models. An interrupted time series analysis was employed to observe the trends and patterns of HIV treatment outcomes among MSM in Gauteng, Mpumalanga, and KwaZulu-Natal from 1 January 2018 to 31 December 2022. Interrupted time series analysis was applied to quantify changes in the accessibility and utilisation of HIV treatment services using the R software version 4.4.1. The segmented regression models showed a decrease followed by an upward trend in all HIV treatment outcomes. After the implementation of the DSD model, significant increases in positive HIV tests (estimate = 0.001572; p < 0.001), linkage to HIV care (estimate = 0.001486; p < 0.001), ART initiations (estimate = 0.001003; p = 0.004), ART collection (estimate = 0.001748; p < 0.001), and taking viral load tests (estimate = 0.001109; p = 0.001) were observed. There was an overall increase in all HIV treatment outcomes during the COVID-19 lockdown in light of the DSD model. Full article

Cardiovascular diseases, including thrombosis, are the leading cause of mortality worldwide. The generation of monoclonal antibodies (mAb) targeting specific coagulation factors could provide more targeted and safer anticoagulant therapies. Factor V (FV) is a critical cofactor in the prothrombinase complex, which catalyzes the conversion of prothrombin to thrombin, a key enzyme in the coagulation cascade. We isolated a novel human antibody specific to FV by using phage display technology. The selection occurred by panning a large repertoire of phages expressing human antibody fragments (scFv) in parallel on the purified recombinant protein in its native form (FV) or activated by proteolytic maturation (Factor Va (FVa)). Through ELISA screening, we identified the clone with the highest binding affinity for both targets, and it was successfully converted into IgG1. The novel human mAb, called D9, was found capable of binding to Factor V with a low nM affinity both by ELISA and BLI assays, whereas its cross-reactivity with some other coagulation factors was found null or very poor. Furthermore, when tested in blood clotting tests, it was found able to prolong activated partial thromboplastin time (aPTT). Thus, D9 could become not only a potential therapeutic agent as a specific anticoagulant but also a precious tool for diagnostic and research applications. Full article

The increasing density and complexity of electromagnetic signals have brought new challenges to multi-component radar signal recognition. To address the problem of low recognition accuracy under low signal-to-noise ratios (SNR) in adapting the common recognition framework of combining time–frequency transformations (TFTs) with convolutional neural networks (CNNs), this paper proposes a new dual-component radar signal recognition framework (TFGM-RMNet) that combines a deep time–frequency generation module with a Transformer-based residual network. First, the received noisy signal is preprocessed. Then, the deep time–frequency generation module is used to learn the complete basis function to obtain various TF features of the time signal, and the corresponding time–frequency representation (TFR) is output under the supervision of high-quality images. Next, a ResNet combined with cascaded multi-head attention (MHSA) is applied to extract local and global features from the TFR. Finally, modulation format prediction is achieved through multi-label classification. The proposed framework does not require explicit TFT during testing, and the TFT process is built into TFGM to replace the traditional TFT. The classification results and ideal TFR are obtained during testing, realizing an end-to-end deep learning (DL) framework. The simulation results show that, when SNR > −8 dB, this method can achieve an average recognition accuracy close to 100%. It achieves 97% accuracy even at an SNR of −10 dB. At the same time, under low SNR, the recognition performance is better than the existing algorithms including DCNN-RAMIML, DCNN-MLL, and DCNN-MIML. Full article

This work reports the design of a 1x2 photonic digital switch controlled by an electrically induced metasurface, configurated by a rectangular array of points where the refractive index is locally changed through the application of an external bias. The device is simulated using the Beam Propagation Method (BPM) and Finite Difference Time Domain (FDTD) algorithms and the structure under evaluation is an amorphous silicon 1x2 multimode interference (MMI), joined to an arrayed Metal Oxide Semiconductor (MOS) structure Al/SiNx/a-Si:H/ITO to be used in active-matrix pixel fashion to control the output of the switch. MMI couplers, based on self-imaging multimode waveguides, are very compact integrated optical components that can perform many different splitting and recombining functions. The input–output model has been defined using a machine learning approach; a high number of images have been generated through simulations, based on the beam propagation algorithm, obtaining a large dataset for an MMI structure under different activation maps of the MOS pixels. This dataset has been used for training and testing of a machine learning algorithm for the classification of the MMI configuration in terms of binary digital output for a 1x2 switch. Also, a statistical analysis has been produced, targeting the definition of the most incident-activated pixel for each switch operation. An optimal configuration is proposed and applied to demonstrate the operation of a digital cascaded switch. This proof of concept paves the way to a more complex device class, supporting the recent advances in programmable photonic integrated circuits. Full article

Urban infrastructure systems, such as water supply and transportation networks, are highly interdependent, making them susceptible to cascading disruptions. This paper introduces a bi-level optimization framework designed to coordinate water supply network repairs while minimizing traffic impacts. The framework integrates a dynamic traffic assignment (DTA) model to evaluate the interplay between repair schedules and traffic conditions. The upper-level model generates and adjusts repair schedules, focusing on timing and location, while the lower-level model simulates the resulting traffic flow and travel time changes. Five optimization algorithms—adaptive differential evolution (ADE), genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), and ant colony optimization (ACO)—are employed to identify repair plans that reduce traffic disruptions effectively. A case study in the Yangpu District of Shanghai demonstrates that the timing and spatial distribution of repairs significantly influence traffic flow. Among the tested algorithms, ADE achieves the lowest traffic impact, whereas SA excels in computational efficiency. The results highlight the importance of strategic scheduling in mitigating traffic disruptions by optimizing repair activities and leveraging traffic rerouting. This study provides a practical framework for urban planners to improve repair scheduling and minimize disruptions, contributing to more efficient infrastructure management. Future work could incorporate real-time data for adaptive scheduling and explore broader applications of the framework. Full article

Offshore wind turbines have garnered significant attention recently due to their substantial wind energy harvesting capabilities. Pitch control plays a crucial role in maintaining the rated generator speed, particularly in offshore environments characterized by highly turbulent winds, which pose a huge challenge. Moreover, hydraulic pitch systems are favored in large-scale offshore wind turbines due to their superior power-to-weight ratio compared to electrical systems. In this study, a proportional valve-controlled hydraulic pitch system is developed along with an intelligent pitch control strategy aimed at developing rated power in offshore wind turbines. The proposed strategy utilizes a cascade configuration of an improved recurrent Elman neural network, with its parameters optimized using a customized particle swarm optimization algorithm. To assess its effectiveness, the proposed strategy is compared with two other intelligent pitch control strategies, the cascade improved Elman neural network and cascade Elman neural network, and tested in a benchmark wind turbine simulator. Results demonstrate effective power generation, with the proposed strategy yielding a 78.14% and 87.10% enhancement in the mean standard deviation of generator power error compared to the cascade improved Elman neural network and cascade Elman neural network, respectively. These findings underscore the efficacy of the proposed approach in generating rated power. Full article