Search Results (546)

The global elimination of hepatitis C virus (HCV) has been prioritized by the World Health Organization (WHO) as a key public health target, with a deadline set for 2030. This initiative aims to significantly reduce both new infection rates and HCV-associated mortality. A major breakthrough in achieving this goal has been the development of direct-acting antiviral agents (DAAs), which offer cure rates exceeding 95%, along with excellent safety and tolerability. Nevertheless, transmission via parenteral routes continues to be the dominant pathway, particularly among high-risk groups, such as individuals who inject drugs, incarcerated populations, those exposed to unsafe medical practices, and healthcare professionals. Identifying, monitoring, and delivering tailored interventions to these groups is crucial to interrupt ongoing transmission and to reduce the burden of chronic liver disease. On a global scale, several nations have demonstrated measurable progress toward HCV elimination, with some nearing the targets set by WHO. These achievements have largely resulted from context-adapted policies that enhanced diagnostic and therapeutic access while emphasizing outreach to vulnerable communities. This review synthesizes current advancements in HCV prevention and control and proposes strategic frameworks to expedite global elimination efforts. Full article

Benzoxazine resins are gaining attention for their impressive thermal stability, low water uptake, and strong mechanical properties. In this work, two new bio-based benzoxazine monomers were developed using renewable arbutin: one combined with 3-(2-aminoethylamino) propyltrimethoxysilane (AB), and the other with furfurylamine (AF). Both were synthesized using a simple Mannich-type reaction and verified through FT-IR and ¹H-NMR spectroscopy. By blending these monomers in different ratios, copolymers with adjustable thermal, dielectric, and surface characteristics were produced. Thermal analysis showed that the materials had broad processing windows and cured effectively, while thermogravimetric testing confirmed excellent heat resistance—especially in AF-rich blends, which left behind more char. The structural changes obtained during curing process were monitored using FT-IR, and XPS verified the presence of key elements like carbon, oxygen, nitrogen, and silicon. SEM imaging revealed that AB-based materials had smoother surfaces, while AF-based ones were rougher; the copolymers fell in between. Dielectric testing showed that increasing AF content raised both permittivity and loss, and contact angle measurements confirmed that surfaces ranged from water-repellent (AB) to water-attracting (AF). Overall, these biopolymers (AB/AF copolymers) synthesized from arbutin combine environmental sustainability with customizability, making them strong candidates for use in electronics, protective coatings, and flame-resistant composite materials. Full article

Facial expression recognition (FER) is a critical research direction in artificial intelligence, which is widely used in intelligent interaction, medical diagnosis, security monitoring, and other domains. These applications highlight its considerable practical value and social significance. Face expression recognition models often need to run efficiently on mobile devices or edge devices, so the research on lightweight face expression recognition is particularly important. However, feature extraction and classification methods of lightweight convolutional neural network expression recognition algorithms mostly used at present are not specifically and fully optimized for the characteristics of facial expression images, yet fail to make full use of the feature information in face expression images. To address the lack of facial expression recognition models that are both lightweight and effectively optimized for expression-specific feature extraction, this study proposes a novel network design tailored to the characteristics of facial expressions. In this paper, we refer to the backbone architecture of MobileNet V2 network, and redesign LightExNet, a lightweight convolutional neural network based on the fusion of deep and shallow layers, attention mechanism, and joint loss function, according to the characteristics of the facial expression features. In the network architecture of LightExNet, firstly, deep and shallow features are fused in order to fully extract the shallow features in the original image, reduce the loss of information, alleviate the problem of gradient disappearance when the number of convolutional layers increases, and achieve the effect of multi-scale feature fusion. The MobileNet V2 architecture has also been streamlined to seamlessly integrate deep and shallow networks. Secondly, by combining the own characteristics of face expression features, a new channel and spatial attention mechanism is proposed to obtain the feature information of different expression regions as much as possible for encoding. Thus improve the accuracy of expression recognition effectively. Finally, the improved center loss function is superimposed to further improve the accuracy of face expression classification results, and corresponding measures are taken to significantly reduce the computational volume of the joint loss function. In this paper, LightExNet is tested on the three mainstream face expression datasets: Fer2013, CK+ and RAF-DB, respectively, and the experimental results show that LightExNet has 3.27 M Parameters and 298.27 M Flops, and the accuracy on the three datasets is 69.17%, 97.37%, and 85.97%, respectively. The comprehensive performance of LightExNet is better than the current mainstream lightweight expression recognition algorithms such as MobileNet V2, IE-DBN, Self-Cure Net, Improved MobileViT, MFN, Ada-CM, Parallel CNN(Convolutional Neural Network), etc. Experimental results confirm that LightExNet effectively improves recognition accuracy and computational efficiency while reducing energy consumption and enhancing deployment flexibility. These advantages underscore its strong potential for real-world applications in lightweight facial expression recognition. Full article

Pancreatic cancer is a highly lethal malignant tumor characterized by challenges in early diagnosis and limited therapeutic options, leading to an exceptionally low clinical cure rate. With the advent of novel cancer treatment paradigms, ferroptosis—a form of iron-dependent regulated cell death driven by lipid peroxidation—has emerged as a promising therapeutic strategy, particularly for tumors harboring RAS mutations. However, the poor bioavailability and insufficient tumor-targeting capabilities of conventional drugs constrain the efficacy of ferroptosis-based therapies. Recent advancements in nanotechnology and imaging-guided treatments offer transformative solutions through targeted drug delivery, real-time monitoring of treatment efficacy, and multimodal synergistic strategies. This article aims to elucidate the mechanisms underlying ferroptosis in pancreatic cancer and to summarize the latest identified therapeutic targets for ferroptosis in this context. Furthermore, it reviews the recent progress in nanotechnology-based ferroptosis therapy for pancreatic cancer, encompassing ferroptosis monotherapy, synergistic ferroptosis therapy, and endogenous ferroptosis therapy. Subsequently, the integration of imaging-guided nanotechnology in ferroptosis therapy is summarized. Finally, this paper discusses innovative strategies, such as stroma-targeted ferroptosis therapy, immune-ferroptosis synergy, and AI-driven nanomedicine development, offering new insights and directions for future research in pancreatic cancer treatment. Full article

Brachial plexus root avulsion [BPRA] and concomitant spinal cord injury [SCI] represent devastating injuries that come with limited hope for recovery owing to the adult spinal cord’s loss of intrinsic ability to spontaneously regenerate. BPRA/SCI is an enormous public health issue the world over, and its catastrophic impact goes beyond the patient, the family, businesses, and national health budgets, draining billions of dollars annually. The rising population and economic growth have seen the incidence of SCI surging. Genomic, transcriptomic, and proteomic studies have yielded loads of information on the various molecular events that precede, regulate, and support both regenerative and degenerative pathways post-SCI. Metabolomics, on the other hand, comes in as the search for a cure and the objective monitoring of SCI severity and prognosis remains on the horizon. Despite the large number of review articles on metabolomics and its application fields such as in cancer and diabetes research, there is no comprehensive review on metabolite profiling to study disease mechanisms, biomarkers, or neuroprotection in SCI. First, we present a short review on BPRA/SCI. Second, we discuss potential benefits of metabolomics as applied in BPRA/SCI cases. Next, a look at the analytical techniques that are used in metabolomics. Next, we present an overview of the studies that have used metabolomics to reveal SCI metabolic fingerprints and point out areas of further investigation. Finally, we discuss future research directions. Full article

To study behavioral and productive factors to detect changes that may indicate and predict clinical mastitis cure, Holstein dairy cows (n = 60), in an automatic milking system (AMS) and equipped with behavioral monitoring collar, were monitored from the diagnosis of clinical mastitis (D0) until clinical cure. The parameters collected through sensors were feeding activity, milk electrical conductivity (EC), milk yield, Mastitis Detection Index (MDi), milk flow, and number of gate passages. Clinical mastitis cases (n = 22) were monitored and divided into cured cases (n = 14) and non-cured cases within 30 days (n = 8), paired with a control case group (n = 28). Cows were assessed three times per week, and cure was determined when both clinical assessment and California Mastitis Test (CMT) results were negative in three consecutive evaluations. Mixed generalized linear regression was used to assess the relationship between parameters and clinical mastitis results. Mixed generalized logistic regression was used to create a predictive model. The average clinical cure time for cows with clinical mastitis was 11 days. Feeding activity, gate passages, milk yield, milk flow, EC, and the MDi were associated with cure. The predictive model based on data from D0 showed an Area Under the Curve of 0.89 (95% CI = 0.75–1). Sensitivity and specificity were 1 (95% CI = 1–1) and 0.63 (95% CI = 0.37–0.91), respectively. The predictive model demonstrated to have good internal sensitivity and specificity, showing promising potential for predicting clinical mastitis cure within 14 days based on data on the day of clinical mastitis diagnosis. Full article

To address the challenges of concrete construction in polar regions, this study investigates the feasibility of fabricating cement-based materials under severely low temperatures using electric-induced heating curing methods. Cement mortars incorporating fly ash (FA-CM), ground granulated blast furnace slag (GGBS-CM), and metakaolin (MK-CM) were cured at environmental temperatures of −20 °C, −40 °C, and −60 °C. The optimal carbon fiber (CF) contents were determined using the initial electric resistivity to ensure a consistent electric-induced heating curing process. The thermal profiles during curing were monitored, and mechanical strength development was systematically evaluated. Hydration characteristics were elucidated through thermogravimetric analysis (TG), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) to identify phase compositions and reaction products. Results demonstrate that electric-induced heating effectively mitigates the adverse effect caused by the ultra-low temperature constraints, with distinct differences in the strength performance and hydration kinetics among supplementary cementitious materials. MK-CM exhibited superior early strength development with strength increasing rates above 10% compared to the Ref. specimen, which was attributed to the accelerated pozzolanic reactions. Microstructural analyses further verified the macroscopic strength test results that showed that electric-induced heating curing can effectively promote the performance development even under severely cold environments with a higher hydration degree and refined micro-pore structure. This work proposes a viable strategy for polar construction applications. Full article

This study analyzed the effects of curing and maturation on the formation of shrinkage strain and destructive processes in concrete. Experimental tests were performed on commonly used concrete, class C30/37, with basalt aggregate and blast furnace cement tested: at constant temperature after water curing, at constant temperature without water curing, and under cyclically changing temperature without prior curing. Shrinkage strain was measured for 46 days with an extensometer on 150 × 150 × 600 mm specimens, and the acoustic emission (AE) method was used to monitor microcracks and processes in concrete in real time. The results were compared with the model according to EN 1992-1-1:2023. It was found that this model correctly estimates shrinkage strain for wet-curing concrete, but there are discrepancies for air-dried concrete, regardless of temperature and moisture conditions (constant/variable). Correlation coefficients between shrinkage strain increments and process increments in early-age concrete are proposed. Correlations between shrinkage strain and destructive processes occurring in concrete were confirmed. It was found that by using correlation coefficients, it is possible to estimate internal damage in relation to shrinkage strain. The results indicate the need to develop guidelines for estimating shrinkage strain in non-model environmental conditions and demonstrate the usefulness of the nondestructive AE method in diagnosing early damage, especially in concrete structures exposed to adverse service conditions. Full article

Background/Objectives: Optimal management of acute bacterial prostatitis (ABP) remains uncertain, but the use of antibiotics with good prostatic tissue penetration is critical to prevent recurrence and chronic progression. This study aimed to describe clinical characteristics and outcomes of ABP due to Escherichia coli (ABP-E.coli), compare effectiveness of sequential high-dose cefuroxime (ABP-CXM) versus ciprofloxacin (ABP-CIP), and identify risk factors for clinical failure. Methods: We conducted a retrospective study including men >18 years diagnosed with ABP-E. coli between January 2010 and November 2023 at a 400-bed hospital. Patients received oral cefuroxime (500 mg/8 h) or oral ciprofloxacin (500 mg/12 h). Outcomes over 90 days included clinical cure, recurrence and reinfection. Definitions: Clinical cure—resolution of symptoms without recurrences; recurrence—new ABP episode with the same E. coli strain; reinfection—ABP involving different microorganism or E. coli strain; clinical failure—lack of cure, recurrence, or reinfection. Results: Among 326 episodes (158 ABP-CXM, 168 ABP-CIP), ABP-CXM patients were younger (median 63.5 vs. 67.5 years, p = 0.005) and had fewer comorbidities. Clinical cure was higher in ABP-CIP (96.9% vs. 85.7%, p < 0.001). Recurrence occurred only in ABP-CXM (6.96% vs. 0%, p < 0.001), while reinfection and mortality were similar. Multivariable analysis showed ciprofloxacin was protective against clinical failure (OR: 0.16, 95% CI: 0.06–0.42, p < 0.001), while prior urinary tract infection (UTI) increased failure risk (OR: 2.87, 95% CI: 1.3–6.3). Conclusions: Ciprofloxacin was more effective than cefuroxime in treating ABP-E. coli. Patients with recent UTIs may need closer monitoring or alternative therapies. Full article

The curing process of a concrete sample has a significant influence on hydration and its strength. This means that inadequate curing conditions lead to a loss of concrete quality and negative consequences in structural engineering. In addition, different state-of-the-art (SOTA) curing surface treatments and hydration periods have a significant effect on durability. This paper introduces an innovative non-destructive method to detect the development of the hydration process under different treatment conditions. Hyperspectral imaging is a non-contact measurement technique that provides detailed information on hydration characteristics within an electromagnetic wavelength range. A comparative laboratory measurement was conducted on twelve concrete samples, subjected to three curing treatments and four curing surface treatments, over a hydration period from the 1st to the 56th day. Additionally, artificial neural networks and convolutional neural networks have achieved classification accuracies of 67.8% (hydration time), 83.3% (curing regime), and 87.6% (surface type), demonstrating the feasibility of using neural networks for hydration monitoring. In this study, the results revealed differences in near-infrared spectral signatures, representing the type of curing treatment, curing surface, and hydration time of the concrete. The dataset was classified and analyzed using neural networks. For each hydration treatment, three different models were developed to achieve better prediction performance for hyperspectral imaging analysis. This method demonstrated a high level of reliability in investigating curing surface treatments, curing treatments, and hydration time. A recommended method for using hyperspectral imaging to evaluate the cured quality of concrete will be developed in future research. Full article

This study addresses the formation, detection, and repair of cracks in concrete elements exposed to temperatures above 25 °C, where accelerated evaporation compromises their structural strength. An automated intelligent curing system with embedded sensors (DS18B20, HD-38) and Arduino controllers was developed and applied to solid slabs, columns, and concrete test specimens (1:2:3.5 mix ratio). The electronic design was simulated in Proteus and validated experimentally under tropical conditions. Data with normal distribution (p > 0.05) showed a significant correlation between internal and ambient temperature (r = 0.587; p = 0.001) and a low correlation in humidity (r = 0.143; p = 0.468), indicating hygrometric independence. The system healed cracks of 0.01 mm observed two hours after pouring the mixture, associated with an evaporation rate of 1.097 mL/s in 4 m². For 28 days, automated irrigation cycles were applied every 30 to 60 min, with a total of 1680 L, achieving a 20% reduction in water consumption compared to traditional methods. The system maintained stable thermal conditions in the concrete despite ambient temperatures of up to 33.85 °C. A critical evaporation range was identified between 11:00 and 16:00 (UTC-5). The results demonstrate the effectiveness of the embedded system in optimizing curing, water efficiency, and concrete durability. Full article

To better understand the temperature changes in face slab concrete and address challenges such as delayed curing and outdated methods in complex and variable environments, this study investigates the use of visualization and real-time feedback control in concrete construction. The conducted study systematically develops an intelligent curing control system for face slab concrete based on multi-source measured data. A tailored multi-source data acquisition scheme was proposed, supported by an IoT-based transmission framework. Cloud-based data analysis and feedback control mechanisms were implemented, along with a decoupled front-end and back-end system platform. This platform integrates essential functions such as two-way communication with gateway devices, data processing and analysis, system visualization, and intelligent curing control. In conjunction with the ongoing Maerdang concrete face rockfill dam (CFRD) project, located in a high-altitude, cold-climate region, an intelligent curing system platform for face slab concrete was developed. The platform enables three core visualization functions: (1) monitoring the pouring progress of face slab concrete, (2) the early warning and prediction of temperature exceedance, and (3) dynamic feedback and adjustment of curing measures. The research outcomes were successfully applied to the intelligent curing of the Maerdang face slab concrete, providing both theoretical insight and practical support for achieving scientific and precise curing control. Full article

Over the last three decades, composite structures have become increasingly more common in everyday life, such as in wind turbines as part of the solution to produce clean energy, and their use in the aerospace industry due to their advantages over conventional materials. Most of these advantages are dependent upon the reliability and quality of the manufacturing process to ensure that there are no defects/faults or imperfections during manufacturing. Thus, it is critical to monitor the enclosed environment of moulds during fabrication in real time. This need has caused many researchers—past and present—to create or apply many sensing technologies to achieve real-time monitoring of the manufacturing processes of composite structures to ensure that the structures can meet their requirements. A consequence of these research activities is the myriad of sensing schemes, (for example, optical, electrical, piezo, and nanomaterial schemes and the use of digital twins) available to consider, and the investigations all of them have both strengths and weaknesses for a given application, with no apparent option having a distinct advantage. This review reveals that the best possible sensing solution depends upon a large set of parameters, the geometry of the composite structure, the required specification, and budget limits, to name a few. Furthermore, challenges remain for researchers trying to find solutions, such as a sensing scheme that can directly detect wrinkles/waviness during the laying-up procedure, real-time detection of the resin flow front throughout the mould, and the monitoring of the resin curing spatially, all at a spatial resolution of ~1 cm with the required sensitivity along with the need to obtain the true interpretation of the real-time data. This review offers signposts through the variety of sensing options, with their advantages and failings, to readers from the composite and sensing community to aid in making an informed decision on the possible sensing approaches to help them meet their composite structure’s desired function and tolerances, and the challenges that remain. Full article

Purpose: The aim of this in vitro study was to evaluate the influence of different models of commercially available portable dental radiometers on the measurement of light irradiance emitted by light-emitting diode (LED) photocuring units. Materials and Methods: Eight LED photocuring units, all emitting light in a single-wavelength spectrum, were tested. Light irradiance (mW/cm²) was measured using six portable dental radiometers: four digital models (D1–D4) and two analog models (A1, A2). Digital model D1 was used as the reference (control). All measurements were conducted under standardized conditions, and each LED–radiometer combination was tested in triplicate. Data were analyzed using Sigma Plot 12.0 (Palo Alto, CA, USA) to verify the assumptions of normality and homogeneity of variances. A one-way analysis of variance (ANOVA) was used to assess the effect of the radiometer model on irradiance values, followed by Tukey’s post hoc test for multiple comparisons. The significance level was set at α < 0.05. Results: No statistically significant difference in irradiance was found between D1 (control) and D2. However, significantly lower values were recorded with A2, while D3, D4, and A1 produced significantly higher irradiance values compared to the control (p < 0.05). Conclusion: Irradiance measurements can vary significantly depending on the radiometer model used. Clinicians should be aware of this variability and are encouraged to regularly check the irradiance of the light-curing units used in daily practice, ensure their proper maintenance, and implement periodic monitoring to maintain effective clinical performance. Full article

This study presents an integrated smart construction monitoring system that combines point cloud data (PCD) from a 3D laser scanner with real-time IoT sensors and ultra-wideband (UWB) indoor positioning technology to enhance construction site safety and quality management. The system addresses the limitations of traditional BIM-based methods by leveraging high-precision PCD that accurately reflects actual site conditions. Field validation was conducted over 17 days at a residential construction site, focusing on two floors during concrete pouring. The concrete strength prediction model, based on the ASTM C1074 maturity method, achieved prediction accuracy within 1–2 MPa of measured values (e.g., predicted: 26.2 MPa vs. actual: 25.3 MPa at 14 days). The UWB-based worker localization system demonstrated a maximum positioning error of 1.44 m with 1 s update intervals, enabling real-time tracking of worker movements. Static accuracy tests showed localization errors of 0.80–0.94 m under clear line-of-sight and 1.14–1.26 m under partial non-line-of-sight. The integrated platform successfully combined PCD visualization with real-time sensor data, allowing construction managers to monitor concrete curing progress and worker safety simultaneously. Full article