Search Results (5,639)

Background: Botulinum toxin type A (BoNT/A) is widely used in both clinical and aesthetic settings to induce temporary neuromuscular paralysis by inhibiting acetylcholine release. Although generally regarded as safe and effective, complications such as iatrogenic ptosis or facial asymmetry may occur and persist for several weeks or even months, with no standardized method currently available to accelerate recovery. Objective: This article explores the hypothesis that photobiomodulation (PBM)—a non-invasive modality recognized for its neuroregenerative potential—may facilitate the reversal of BoNT/A-induced neuromuscular blockade. Discussion: PBM enhances mitochondrial activity by stimulating cytochrome c oxidase in nerve and muscle tissues, thereby increasing ATP production and modulating intracellular signaling pathways associated with neuroplasticity, cell survival, and synaptogenesis. Preclinical studies have demonstrated that PBM can upregulate neurotrophic factors (e.g., BDNF, NGF), enhance SNAP-25 expression, and promote structural remodeling of neurons in both young and aged brains. These mechanisms are biologically consistent with the regenerative processes required for recovery from BoNT/A-induced effects. While controlled clinical trials for this specific application are currently lacking, anecdotal clinical reports suggest that PBM may accelerate functional recovery in cases of BoNT/A-related complications. Conclusions: Although this approach has not yet been tested in clinical trials, we propose that photobiomodulation may hypothetically serve as a supportive strategy to promote neuromuscular recovery in patients experiencing adverse effects from BoNT/A. This hypothesis is grounded in robust preclinical evidence but requires validation through translational and clinical research. Full article

Breast cancer detection through non-invasive and accurate techniques remains a critical challenge in medical diagnostics. This study introduces a deep learning-based framework that leverages a microwave radar system equipped with an arc-shaped array of six antennas to estimate key tumor parameters, including position, size, and depth. This research begins with the evolutionary design of an ultra-wideband octagram ring patch antenna optimized for enhanced tumor detection sensitivity in directional near-field coupling scenarios. The antenna is fabricated and experimentally evaluated, with its performance validated through S-parameter measurements, far-field radiation characterization, and efficiency analysis to ensure effective signal propagation and interaction with breast tissue. Specific Absorption Rate (SAR) distributions within breast tissues are comprehensively assessed, and power adjustment strategies are implemented to comply with electromagnetic exposure safety limits. The dataset for the deep learning model comprises simulated self and mutual S-parameters capturing tumor-induced variations over a broad frequency spectrum. A core innovation of this work is the development of the Attention-Based Feature Separation (ABFS) model, which dynamically identifies optimal frequency sub-bands and disentangles discriminative features tailored to each tumor parameter. A multi-branch neural network processes these features to achieve precise tumor localization and size estimation. Compared to conventional attention mechanisms, the proposed ABFS architecture demonstrates superior prediction accuracy and interpretability. The proposed approach achieves high estimation accuracy and computational efficiency in simulation studies, underscoring the promise of integrating deep learning with conformal microwave imaging for safe, effective, and non-invasive breast cancer detection. Full article

Accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is fundamental to ensuring safe operation. However, due to the complex electrochemical processes during battery operation and the limited availability of training data, accurate estimation of the state of health remains challenging. To address this, this paper proposes a prediction framework based on dual-view voltage signal features and an improved Long Short-Term Memory (LSTM) neural network. By relying solely on readily obtainable voltage signals, the data requirement is greatly reduced; dual-view features, comprising kinetic and aggregated aspects, are extracted based on the underlying reaction mechanisms. To fully leverage the extracted feature information, Scaled Dot-Product Attention (SDPA) is employed to dynamically score all hidden states of the long short-term memory network, adaptively capturing key temporal information. The experimental results based on the NASA PCoE battery dataset indicate that, under various operating conditions, the proposed method achieves an average absolute error below 0.51% and a root mean square error not exceeding 0.58% in state-of-health estimation, demonstrating high predictive accuracy. Full article

The large-scale integration of new energy is an inevitable trend to achieve the low-carbon transformation of power systems. However, the strong randomness of wind power, photovoltaic power, and loads poses severe challenges to the safe and stable operation of systems. Existing studies demonstrate insufficient integration and handling of source-load bilateral uncertainties in wind–solar–fossil fuel storage complementary systems, resulting in difficulties in balancing economy and low-carbon performance in their energy storage configuration. To address this insufficiency, this study proposes an optimal energy storage configuration method considering source-load uncertainties. Firstly, a deterministic bi-level model is constructed: the upper level aims to minimize the comprehensive cost of the system to determine the energy storage capacity and power, and the lower level aims to minimize the system operation cost to solve the optimal scheduling scheme. Then, wind and solar output, as well as loads, are treated as fuzzy variables based on fuzzy chance constraints, and uncertainty constraints are transformed using clear equivalence class processing to establish a bi-level optimization model that considers uncertainties. A differential evolution algorithm and CPLEX are used for solving the upper and lower levels, respectively. Simulation verification in a certain region shows that the proposed method reduces comprehensive cost by 8.9%, operation cost by 10.3%, the curtailment rate of wind and solar energy by 8.92%, and carbon emissions by 3.51%, which significantly improves the economy and low-carbon performance of the system and provides a reference for the future planning and operation of energy systems. Full article

Background/Objectives: Tension-type headache (TTH) is the most prevalent form of primary headache. The etiology of TTH is not yet fully understood, although it is associated with the presence of myofascial trigger points (MTPs) in cervical and facial muscles. Dry needling (DN) therapy has emerged as an effective and safe non-pharmacological option for pain relief, but there are a lack of systematic reviews focused on its specific characteristics in TTH. The aim of this paper is to examine the characteristics and methodologies of DN in managing TTH. Methods: A scoping review was conducted with inclusion criteria considering studies that evaluated DN interventions in adults with TTH, reporting target muscles, diagnostic criteria, and technical features. The search was performed using PubMed, Embase, Scopus, and the Web of Science, resulting in the selection of seven studies after a rigorous filtering and evaluation process. Results: The included studies, primarily randomized controlled trials, involved a total of 309 participants. The most frequently treated muscles were the temporalis and trapezius. Identification of MTPs was mainly performed through manual palpation, although diagnostic criteria varied. DN interventions differed in technique. All studies included indicated favorable outcomes with improvements in headache symptoms. No serious adverse effects were reported, suggesting that the technique is safe. However, heterogeneity in protocols and diagnostic criteria limits the comparability of results. Conclusions: The evidence supports the use of DN in key muscles such as the temporalis and trapezius for managing TTH, although the diversity in methodologies and diagnostic criteria highlights the need for standardization. The safety profile of the method is favorable, but further research is necessary to define optimal protocols and improve reproducibility. Implementing objective diagnostic criteria and uniform protocols will facilitate advances in clinical practice and future research, ultimately optimizing outcomes for patients with TTH. Full article

Background and objectives: Life expectancies have increased globally, including in Romania, leading to an aging population and thus increasing the burden of chronic diseases. Over 80% of individuals over 65 have more than three chronic conditions, with many exceeding ten and often requiring multiple medications and supplements. This widespread polypharmacy raises concerns about drug interactions, side effects, and inappropriate prescribing. This review examines the impact of polypharmacy in older adult patients, focusing on the physiological changes affecting drug metabolism and the potential risks associated with excessive medication use. Special attention is given to proton pump inhibitors (PPIs), a commonly prescribed drug class with significant benefits but also risks when misused. The aging process alters drug absorption and metabolism, necessitating careful prescription evaluation. Methods: We conducted literature research on polypharmacy and PPIs usage in the older adult population and the risk associated with this practice, synthesizing 217 articles within this narrative review. Results: The overuse of medications, including PPIs, may lead to adverse effects and increased health risks. Clinical tools such as the Beers criteria, the STOPP/START Criteria, and the FORTA list offer structured guidance for optimizing pharmacological treatments while minimizing harm. Despite PPIs’ well-documented safety and efficacy, inappropriate long-term use has raised concerns in the medical community. Efforts are being made internationally to regulate their consumption and reduce the associated risks. Conclusions: Physicians across all specialties must assess the risk–benefit balance when prescribing medications to older adult patients. A personalized treatment approach, supported by evidence-based prescribing tools, is essential to ensure safe and effective pharmacotherapy. Addressing inappropriate PPI use is a priority to prevent potential health complications. Full article

Large-scale events, such as festivals and public gatherings, pose serious problems in terms of traffic congestion, slow transaction processing, and security risks to transportation planning. This study proposes a blockchain-based solution for enhancing the efficiency and security of intelligent transport systems (ITS) by utilizing state channels and rollups. Throughput is optimized, enabling transaction speeds of 800 to 3500 transactions per second (TPS) and delays of 5 to 1.5 s. Prevent data tampering, strengthen security, and enhance data integrity from 89% to 99.999%, as well as encryption efficacy from 90% to 98%. Furthermore, our system reduces congestion, optimizes vehicle movement, and shares real-time, secure data with stakeholders. Practical applications include fast and safe road toll payments, faster public transit ticketing, improved emergency response coordination, and enhanced urban mobility. The decentralized blockchain helps maintain trust among users, transportation authorities, and event organizers. Our approach extends beyond large-scale events and proposes a path toward ubiquitous, Artificial Intelligence (AI)-driven decision-making in a broader urban transit network, informing future operations in dynamic traffic optimization. This study demonstrates the potential of blockchain to create more intelligent, more secure, and scalable transportation systems, which will help reduce urban mobility inefficiencies and contribute to the development of resilient smart cities. Full article

As an efficient and low-carbon renewable energy source, hydrogen plays a strategic role in the global energy transition, particularly in the transportation sector. However, the flammable and explosive nature of hydrogen makes leakage risks in enclosed environments a core challenge for the safe promotion of hydrogen fuel cell vehicles. Catalytic combustion sensors are ideal choices due to their high sensitivity and long lifespan. Nevertheless, they face technical bottlenecks under vehicle operational conditions, such as high-power consumption caused by elevated working temperatures, slow response rates, weak anti-interference capabilities, and catalyst poisoning. This paper systematically reviews the research status of catalytic combustion hydrogen sensors for vehicle applications, summarizes technical difficulties and development strategies from the perspectives of hydrogen-sensitive material design and integration processes, and provides theoretical references and technical guidance for the development of catalytic combustion hydrogen sensors suitable for vehicle use. Full article

The urban drainage system is a significant lifeline for ensuring the safe operation of a city. In recent years, defects and diseases in drainage pipes and their ancillary facilities have occurred frequently. Aiming to provide decision-makers with comprehensive benefit evaluation support, we chose to evaluate the security, environmental, social, and economic benefits of urban drainage culverts and pipes (UDCPs). An index system of 14 first-level indicators in four dimensions was established, and the indicators contain 28 influencing factors. The index weight was obtained by combining the analytical hierarchy process and entropy weight method, and the weights assigned to the security, environmental, social, and economic benefits were 0.448, 0.222, 0.202, and 0.128, respectively. The evaluation system was developed on the basis of a geographic information system (GIS), and the topological analysis of the GIS was applied in the calculation. To process the questionnaire results, this study adopted the automatic questionnaire analysis and scoring method combining natural language processing and optical character recognition technology. The method was applied in the study area in southern China, which contains 9 catchment areas and 1356 pipes. The results show that about 5% of the pipelines need to be included in the renewal plan. For UDCP renewal, the findings provide a decision-making tool of the comprehensive analysis for the selection of engineering technologies and the evaluation of the implementation effects. Full article

Photocatalysis is considered to be a very promising method for the degradation of organic matter, because its process of degrading organic matter is safe. However, some problems such as weak absorption of visible light and electronic-hole recombination easily are obviously drawbacks. In this paper, three different morphologies of Bi₅O₇I (nanoball, nanosheet, and nanotube) were successfully prepared by solvothermal method, which was used for the degradation of Rhodamine B (RhB). Comparing the photocatalytic effect of three different morphologies and concluding that the optimal morphology was the Bi₅O₇I nanoball (97.8% RhB degradation within 100 min), which was analysed by the characterisation tests. Free radical trapping experiments were tested, which revealed that the main roles in the degradation process were singlet oxygen (¹O₂) and holes (h⁺). The degradation pathways of RhB were analyzed in detail. The photo/electrochemical parts of the three materials were analysed and explained the degradation mechanism of RhB degradation. This investigate provides a very valuable guide for the development of multiple morphologies of bismuth-based photocatalysts for removing organic dyes in aquatic environment. Full article

To investigate the crack extension mechanism in CFRP-reinforced concrete, this paper derives analytical expressions for the external load and crack opening displacement in the fracture process of CFRP concrete beams based on the crack emergence toughness criterion and the Paris displacement formula as the theoretical basis. A numerical iterative method was used to computationally simulate the fracture process of CFRP-reinforced concrete beams and to analyze the effect of different initial crack lengths on the fracture process. The research results indicate that the numerical simulation results of the crack initiation load are in good agreement with the test results, and the crack propagation curves and the test results are basically consistent before the CFRP-concrete interface peels off. The numerical results of ultimate load are lower than the test results, but it is safe for fracture prediction in actual engineering. With the increase in the initial crack length, the effect of the initial crack length on the critical effective crack propagation length is more obvious. Full article

The estimation of leakage faults in evaporation tubes of supercharged boilers is crucial for ensuring the safe and stable operation of the central steam system. However, leakage faults of evaporation tubes feature high time dependency, strong coupling among monitoring parameters, and interference from noise. Additionally, the large number of monitoring parameters (approximately 140) poses a challenge for spatiotemporal feature extraction, feature decoupling, and establishing a mapping relationship between high-dimensional monitoring parameters and leakage, rendering the precise quantitative estimation of evaporation tube leakage extremely difficult. To address these issues, this study proposes a novel deep learning framework (LSTM-CNN–attention), combining a Long Short-Term Memory (LSTM) network with a dual-pathway spatial feature extraction structure (ACNN) that includes an attention mechanism(attention) and a 1D convolutional neural network (1D-CNN) parallel pathway. This framework processes temporal embeddings (LSTM-generated) via a dual-branch ACNN—where the 1D-CNN captures local spatial features and the attention models’ global significance—yielding decoupled representations that prevent cross-modal interference. This architecture is implemented in a simulated supercharged boiler, validated with datasets encompassing three operational conditions and 15 statuses in the supercharged boiler. The framework achieves an average diagnostic accuracy (ADA) of over 99%, an average estimation accuracy (AEA) exceeding 90%, and a maximum relative estimation error (MREE) of less than 20%. Even with a signal-to-noise ratio (SNR) of −4 dB, the ADA remains above 90%, while the AEA stays over 80%. This framework establishes a strong correlation between leakage and multifaceted characteristic parameters, moving beyond traditional threshold-based diagnostics to enable the early quantitative assessment of evaporator tube leakage. Full article

Background and Objectives: Although the use of allografts in revision anterior cruciate ligament reconstruction is associated with theoretical advantages, it has historically led to poorer clinical results and lower survival rates. However, the heterogeneity of the available literature makes it difficult to elucidate the effectiveness of allographs, as most of the studies published do not make any reference to some of the key aspects related to the processing of the allograft employed. The present study analyzed the clinical results and the survival of allografts in patients undergoing revision anterior cruciate ligament reconstruction with a well-characterized, single type of allograft. Materials and Methods: This was a retrospective observational study analyzing a series of patients undergoing revision anterior cruciate ligament reconstruction with an Achilles tendon allograft with a bone block (FlexiGraft, LifeNet Health), subjected to low-dose irradiation at dry ice temperatures. Preoperative and follow-up clinical variables (IKDC, pain, hop test, and YBT scores) were recorded. Survival was analyzed using the Kaplan–Meier methodology. Results: A total of 39 patients (34 male, 5 female) were included in the study. The mean patient age was 37.3 years and mean postoperative follow-up was 78.7 months. Forty-one percent of patients were competitive athletes, and all of the patients in the sample exhibited preoperative instability. The mean allograft thickness was 9.2 mm. During surgery, 51.3% of patients required meniscus repair and 20.5% had to be treated for chondral defects. At the last follow-up visit, 92.3% of the subjects presented with IKDC grade A and 7.7% with IKDC grade B. The mean subjective IKDC score was 0.79 and mean pain intensity was 1.15 according to the VAS scale. Limb symmetry, as measured by the various hop tests and the Y balance test, were within the safety range, with 74.4% of patients succeeding in returning to their previous level of sport. Ten-year survival was estimated at 97.4%. Conclusions: Allografts obtained and processed following the current regulations governing patient selection and graft harvesting, which are additionally processed without recourse to chemical procedures and sterilized at less than 2 MRad in dry ice conditions, represent an effective and safe alternative in revision anterior cruciate ligament reconstruction. Full article

Artificial intelligence (AI) has already played a significant role in the healthcare sector, particularly in image-based medical diagnosis. Deep learning models have produced satisfactory and useful results for accurate decision-making. Among the various types of medical images, magnetic resonance imaging (MRI) is frequently utilized in deep learning applications to analyze detailed structures and organs in the body, using advanced intelligent software. However, challenges related to performance and data privacy often arise when using medical data from patients and healthcare institutions. To address these issues, new approaches have emerged, such as federated learning. This technique ensures the secure exchange of sensitive patient and institutional data. It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. Federated learning has been successfully applied in medical settings, including diagnostic applications involving medical images such as MRI data. This research introduces an analytical intelligence system based on an Internet of Medical Things (IoMT) framework that employs federated learning to provide a safe and effective diagnostic solution for brain tumor identification. By utilizing specific brain MRI datasets, the model enables multiple local capsule networks (CapsNet) to achieve improved classification results. The average accuracy rate of the CapsNet model exceeds 97%. The precision rate indicates that the CapsNet model performs well in accurately predicting true classes. Additionally, the recall findings suggest that this model is effective in detecting the target classes of meningiomas, pituitary tumors, and gliomas. The integration of these components into an analytical intelligence system that supports the work of healthcare personnel is the main contribution of this work. Evaluations have shown that this approach is effective for diagnosing brain tumors while ensuring data privacy and security. Moreover, it represents a valuable tool for enhancing the efficiency of the medical diagnostic process. Full article

Green tea, derived from the unoxidized leaves of Camellia sinensis (L.) Kuntze, is one of the least processed types of tea and is rich in antioxidants and polyphenols. Among these, catechins—particularly epigallocatechin gallate (EGCG)—play a key role in regulating cell signaling pathways associated with various chronic conditions, including cardiovascular diseases, neurodegenerative disorders, metabolic diseases, and cancer. This review presents a comprehensive analysis of recent clinical studies focused on the therapeutic benefits and potential risks of interventions involving green tea extracts or EGCG. A systematic literature survey identified 17 relevant studies, classified into five key areas related to catechin interventions: toxicity and detoxification, drug pharmacokinetics, cognitive functions, anti-inflammatory and antioxidant properties, and obesity and metabolism. Findings from these clinical studies suggest that the health benefits of green tea catechins outweigh the potential risks. The review highlights the importance of subject genotyping for enzymes involved in catechin metabolism to aid in interpreting liver injury biomarkers, the necessity of assessing drug–catechin interactions in clinical contexts, and the promising effects of topical EGCG in reducing inflammation. This analysis underscores the need for further research to refine therapeutic applications while ensuring the safe and effective use of green tea catechins. Full article