Search Results (24,225)

Background: Dengue virus (DENV) infection is a significant public health concern in several tropical and subtropical regions, where early and rapid detection is crucial for effective patient management and controlling the spread of the disease. Particularly in resource-limited, rural healthcare settings where dengue is endemic, there exists a need for diagnostic methods that are both easy to perform and highly sensitive. Objective: This study focuses on the development and validation of a single-tube reverse transcription loop-mediated isothermal amplification termed TURN-RT-loop-mediated isothermal amplification (LAMP) for the detection of DENV. Methodology: The TURN-RT-LAMP assay designed in this study combines two sets of primers targeting the 5′- and 3′-UTR of DENV, with the aim to increase the sensitivity of detection. Results: Clinical validation of the TURN-RT-LAMP assay using samples collected from febrile individuals with a serological or antigenic diagnosis revealed a sensitivity of >96%. The performance of this assay was statistically compared with that of the standard diagnostic method, quantitative reverse transcription-polymerase chain reaction. Conclusions: The results support the potential of RT-LAMP as a rapid, sensitive, and specific tool for the diagnosis and surveillance of dengue, particularly suitable for field use in low-resource settings. Full article

Narrow-linewidth lasers play a crucial role in nonlinear optics, atomic physics, optical metrology, and high-speed coherent optical communications. Precise linewidth measurement is essential for assessing laser noise characteristics; however, conventional methods are often bulky, costly, and unsuitable for integrated applications. This paper presents a compact and cost-effective delay self-homodyne system for laser linewidth measurement, leveraging a field-programmable gate array (FPGA)-based data acquisition circuit. By employing fast Fourier transform (FFT) analysis, the system achieves high-precision linewidth measurement in the kHz range. Additionally, by optimizing the fiber length, the system effectively suppresses low-frequency and 1/f noise, providing an integrated and efficient solution for advanced laser characterization with enhanced performance and reduced cost. Full article

Despite multimodal therapies, the treatment of glioblastoma remains challenging. In addition to the very complex mechanisms of cancer cells, including specialized phenotypes that enable them to proliferate, invade tissues, and evade immunosurveillance, they exhibit a pronounced resistance to chemo- and radiotherapy. More advanced tumors create a hypoxic environment that supports their proliferation and survival, while robust angiogenesis ensures a constant supply of nutrients. In GBM, these structures are very pronounced and contribute to the creation and maintenance of a highly immunosuppressive microenvironment that promotes tumor growth and immune escape. In addition, the high accumulation of immunosuppressive tumor-infiltrating leukocytes and other cells, the pronounced expression of immune checkpoint molecules, and the low mutational burden, i.e., the low number of neoantigens, are hallmarks of GBM and contribute to the challenge of therapeutic approaches. Here, we review a number of mechanisms that GBM exploits to support tumor growth and potential treatments. These include new chemotherapeutics, tumor treating fields, and small molecules, including compounds targeting angiogenesis or blockers of tyrosine kinases that inhibit tumor cell proliferation and survival. In addition, we focus on immunotherapies such as immune checkpoint blockade or cell therapies, in particular vaccination with dendritic cells and CAR-T cells, which can either kill GBM cells directly or bypass immunosuppression by modulating the tumor microenvironment or boosting the patient’s own immune response. Full article

Environmental microscopy is crucial for analyzing microorganisms, but traditional optical microscopes are often expensive, bulky, and impractical for field use. AI-driven image recognition, powered by deep learning models like YOLO, enhances microscopy analysis but typically requires high computational resources. To address these challenges, we present two cost-effective pipelines integrating AI with low-cost microscopes and edge computing. Both approaches use the OpenFlexure Microscope and Raspberry Pi devices. The first performs real-time inference with a Raspberry Pi 5 and Hailo-8L accelerator, while the second captures images with a Raspberry Pi 4, transferring them to a GPU-equipped desktop for processing. Using YOLOv8, we evaluate their ability to detect phytoplankton species, including cyanobacteria and diatoms. Results show that edge computing enables accurate, efficient, and low-power microscopy analysis, demonstrating its potential for real-time environmental monitoring in resource-limited settings. Full article

The demand for drinking water is a reality that plagues modern society and will worsen in the coming decades. Factors such as climate change, population growth, and intense, often disorderly urbanization are expected to limit the availability of this essential resource for life. With this justification, several technologies involving water remediation/purification have been improved to increase energy efficiency. One key approach involves the use of residual biomass derived from biological sources as adsorbents with valuable properties. This line of research supports waste management, and the materials are easily obtainable, especially on a large scale, with low costs and negligible secondary environmental impacts. In the early 2000s, it was demonstrated that these materials possess functional groups (amino, hydroxyl, and carboxyl) that are favorable for attracting certain pollutants that are present in wastewater. Generally, the unmodified precursor material has properties that are not favorable for adsorption, such as limited adsorption capacity, low mechanical resistance, and unstable surface chemistry. Therefore, there has been a strong investment in studies aimed at developing methodologies to produce bio-based materials with high properties supported by mathematical models aimed at water purification. This critical review describes the modifications, functionalization, and production of bio-based materials aimed at remediating wastewater via the adsorption process. Their use involves the elimination of organic pollutants, water/oil separation, the removal of micropollutants, and membrane filtration. The properties of bio-based materials from biopolymers and their synthesis methodologies are analyzed, with a focus on water remediation. Finally, the challenges and future perspectives are highlighted, highlighting the relevance of this group of adsorbents in minimizing the challenges and limitations present in the field of water purification and providing new, innovative solutions. Full article

The New Zealand Kaikoura Earthquake (M_w 7.8, 14 November 2016) produced co-seismic flashes of earthquake light near the ground at midnight, 230 km north of the epicentre. Mostly, there was a white hemisphere in the atmosphere just above the ground, up to 250 m radius, the colour becoming radially increasingly dark blue. Fifteen videos were available for analysis which led to the following new or reaffirmed conclusions: (i) the blue colour is due to Rayleigh Scattering (new explanation); (ii) the light also sometimes occurs within low clouds but not as lightning—this is a new classification of earthquake light; (iii) the lithology may be greywacke, broadening previous literature emphasis on igneous sources; (iv) the light is most probably explained in our study area by seismically pressured microscopic quartz producing electric fields emerging into the atmosphere and reacting with it—mechanisms relying on particle-grinding or creation of cracks in rock are unlikely in the study area; (v) within the Wellington study area, the light is mostly independent of faults or their movement and is caused by seismic impulses which have travelled hundreds of kilometres from the epicentre—this possible independence from faults has not been clearly emphasised previously; and (vi) electrical grid problems are not the explanation. Full article

As a non-invasive modality, sonodynamic therapy (SDT) offers several advantages in cancer treatment, including deep tissue penetration and precise spatiotemporal control, resulting from the interplay between low-intensity ultrasound and sonosensitizers. Piezoelectric materials, known for their remarkable capacity of interconversion of mechanical and electrical energy, have garnered considerable attention in biomedical applications, which can serve as pivotal sonosensitizers in SDT. These materials can generate internal electric fields via ultrasound-induced mechanical deformation, which modulates the alteration of charge carriers, thereby initiating surface redox reactions to generate reactive oxygen species (ROS) and realizing the therapeutic efficacy of SDT. This review provides an in-depth exploration of piezoelectric materials utilized in SDT, with a particular emphasis on recent innovations, elucidation of underlying mechanisms, and optimization strategies for advanced biomedical piezoelectric materials. Furthermore, the incorporation of piezoelectric sonosensitizers with immunotherapy, photodynamic, chemodynamic, and chemotherapy is explored, emphasizing their potential to enhance cancer therapy outcomes. By examining the basic principles of the piezoelectric effect and its contributions to SDT, this review sheds light on the promising applications of piezoelectric materials in oncology. It also highlights future directions for improving these materials and expanding their clinical utility in tumor sonodynamic therapy. Full article

Human tear analysis is gaining increasing attention as a non-invasive tool for several applications such as proteomics and biomarker identification in various diseases, including cancer. The choice of the correct sampling method determines the result of the analysis. In this study, we developed and validated a robust method for tear protein quantification using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS). Tear samples were collected with Schirmer strips, a low-cost and practical tool for tear collection. It is the first time that internal standards have been used to enhance the analytical performance of a method based on Schirmer strips for tear sampling, overcoming the issues widely reported in the literature regarding protein extraction and data reproducibility. Non-human proteins were used for method development, ensuring improved accuracy and analytical precision. The method demonstrated excellent recovery, high sensitivity, and reproducibility. The use of Schirmer strips, combined with this advanced analytical method, highlights their potential as a reliable support for tear protein quantification and biomarker discovery. This study provides a cost-effective and reliable workflow for tear proteome analysis and contributes to the growing field of tear-based diagnostics, making it suitable for routine clinical and research applications in precision medicine. Full article

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal malignancy with increasing incidence and low survival rate, primarily due to the late detection of the disease. Radiomics has demonstrated its utility in recognizing patterns and anomalies not perceptible to the human eye. This systematic literature review aims to assess the application of radiomics in the analysis of pancreatic parenchyma images to identify early indicators predictive of PDAC. Methods: A systematic search of original research papers was performed on three databases: PubMed, Embase, and Scopus. Two reviewers applied the inclusion and exclusion criteria, and one expert solved conflicts for selecting the articles. After extraction and analysis of the data, there was a quality assessment of these articles using the Methodological Radiomics Score (METRICS) tool. The METRICS assessment was carried out by two raters, and conflicts were solved by a third reviewer. Results: Ten articles for analysis were retrieved. CT scan was the diagnostic imaging used in all the articles. All the studies were retrospective and published between 2019 and 2024. The main objective of the articles was to generate radiomics-based machine learning models able to differentiate pancreatic tumors from healthy tissue. The reported diagnostic performance of the model chosen yielded very high results, with a diagnostic accuracy between 86.5% and 99.2%. Texture and shape features were the most frequently implemented. The METRICS scoring assessment demonstrated that three articles obtained a moderate quality, five a good quality, and, finally, two articles yielded excellent quality. The lack of external validation and available model, code, and data were the major limitations according to the qualitative assessment. Conclusions: There is high heterogeneity in the research question regarding radiomics and pancreatic cancer. The principal limitations of the studies were mainly due to the nature of the trials and the considerable heterogeneity of the radiomic features reported. Nonetheless, the work in this field is promising, and further studies are still required to adopt radiomics in the early detection of PDAC. Full article

Innovation through applied research is a valuable component of decision-making, as it alters process parameters and conditions that can be detected by special devices at high speed. In the case of materials such as steel, the thermal field analysis while a component is being heat treated is essential for controlling the evolution of its microstructure and its state of internal stresses. The mathematical models proposed in the observation of the behavior of this material can be consolidated by being implemented in computational systems that can be validated with physical measurements and, once a low uncertainty in the state predictions is acquired, established as management tools. In this sense, the recording of thermal histories represents a fundamental methodology to achieve the objective of diagnosing the result of handling the material. In this regard, it is proposed to implement automated testing devices that implement computer systems for recording, processing, calculating, estimating and transferring data to transform production lines’ efficiency by optimizing or canceling continuity. A prototype for measuring thermal histories with a degree of reproducibility and mitigation of systematic errors is presented as a utility model for analyzing the thermal processing of steels. Full article

Efficiently extracting effective information from the massive experimental data from physical mechanics and accurately identifying the premonitory failure information from coal rock are key and difficult points of intelligent research on rock mechanics. In order to reveal the deterioration characteristics and the forewarning law of fractured coal rock, the digital image correlation method and the acoustic emission technology were adopted in this study to non-destructively detect the strain field, displacement field, and acoustic emission response in time and frequency domains. Additionally, by introducing the derivative functions of the multi-source information function for quantitative analysis, a comprehensive evaluation method was proposed based on the multi-source information fusion monitoring to forewarn red sandstone failure by levels during loading. The results show that obvious premonitory failure information, such as strain concentration areas, appears on red sandstone’s surface before macro-cracks can be observed. With an increase in the inclination angle of the prefabricated crack, the macroscopic failure mode gradually transforms from tensile splitting failure to tensile-shear mixed failure. Moreover, the dominant frequency signals of high frequency–low amplitude (HF–LA), intermediate frequency–low amplitude (IF–LA) and low frequency–low amplitude (LF–LA) are denser near the stress peak. The initial crack expansion time and failure limit time measured by multi-source information fusion are 20.72% and 26.71% earlier, respectively, than those measured by direct observation, suggesting that the forewarning of red sandstone failure by levels is realized with multi-source information fusion. Full article

The aim of this study was to evaluate the effect of an extremely low-frequency oscillating magnetic field on the enzymatic activities of common airborne Aspergillus sp. strains that were isolated from indoor environments. A D-optimal experimental design with three factors was applied: magnetic field density (0.5 to 2 mT), exposure time (0.5 to 2 h), and Aspergillus sp. strains (A. ellipticus, A. japonicus, A. flavus, and A. fumigatus). The response variables were exoenzymatic indexes (cellulolytic, amylolytic, proteolytic, lipolytic, and hemolytic) and pH, as a measure of organic acid production. A. ellipticus was the highest producer of organic acids, and A. japonicus was as pathogenic as A. fumigatus. Different magnetobiological effects were observed: on enzyme secretion in the remaining strains, we detected no appreciable effect (I_lip and I_prot of A. flavus), inhibition (I_lip of A. ellipticus; I_cel and I_amil of A. japonicus; I_amil and I_prot of A. fumigatus), and stimulation. Predictive quadratic models were obtained, and 2 mT for 2 h was the magnetic treatment regime that influenced the fungal enzymatic activity. These physiological changes following magnetobiological effects could be influenced during fungal sporulation and must thus be considered in aeromicrobiology studies. They can also be beneficial for obtaining industrial-use enzymes, but detrimental to the biodeterioration of different materials and human health. Full article

Accurate flux observation is crucial for the high-performance control of induction motors (IMs). Implementing a full-order flux observer algorithm in digital controllers requires discretizing the continuous-domain full-order flux observer. However, the errors introduced by discretization increase with rising rotor speed. In the field-weakening region, inappropriate discretization methods can lead to significant flux estimation errors, severely affecting the performance of model predictive control-based induction motors and potentially causing system instability. To enhance the convergence speed and stability of the observer and reduce discretization errors in the field-weakening region, this paper designs a feedback gain matrix suitable for high-speed field-weakening regions and conducts a study and summary of commonly used discretization methods. Discrete full-order flux observer models based on the forward Euler method, improved Euler method, and third-order Runge–Kutta method are designed. The discretization error, stability, and model complexity of the observers using these three discretization methods in the field-weakening region are analyzed. The experimental results demonstrate that the improved Euler method can achieve high discretization accuracy with relatively low computational complexity, making it a suitable discretization approach for full-order flux observers. Full article

Hardwood has a variety of applications and can be used for low-value products, such as firewood, or for high-value applications, achieving significantly higher prices. Therefore, assessing the quality of raw material is essential for allocating the wood to the most suitable end use. The aim of this study was to explore the use of the photogrammetry technique to determine dimensional characteristics and perform remote visual grading of round oak timber stored at a log yard. The results of the visual classification were then compared with non-destructive acoustic measurements to assess their level of agreement. Based on the point cloud obtained from photogrammetry, logs were classified into three quality groups according to the European standard for round timber grading. The diameter measurements of the logs obtained through the photogrammetry survey were comparable to those taken manually, with an average difference of 0.46 cm and a mean absolute error of 2.1 cm compared to field measurements. However, the log lengths measured from the 3D survey were, on average, 5 cm shorter than those obtained using a measuring tape. The visual classification performed on the 3D reconstruction was based on the evaluation of log size, knots, buckles, and sweep, resulting in 39%, 27%, and 24% of the pieces being grouped into the high-, medium-, and low-quality classes, respectively. Acoustic measurements, performed using both resonance and time-of-flight (ToF) methods, were highly correlated with each other and successfully distinguished the three quality classes only when sweep was excluded from the classification criteria. When curvature was also considered as a parameter for log grading, acoustic velocity only differentiated the lowest quality class from the other two. Full article

In response to the high missed detection rates and low efficiency of traditional methods in detecting tiny defects on the machining surfaces of mechanical parts, this study proposes an efficient defect detection method based on deep learning. Initially, referencing the network architectures of Resnet and Yolo, an image detection network was designed featuring a shared encoder, a classification decoder, and a localization decoder. The shared encoder is used to extract a unified feature representation; the classification decoder accomplishes efficient data classification; and the localization decoder achieves precise defect localization. Furthermore, upon acquiring high-resolution images of the machining surfaces with dimensional features, this study introduces a real-time sliding window method to perform segmented detection and classification of these images, transforming most of the target detection tasks into image classification problems, thereby further enhancing the efficiency and accuracy of defect detection and target localization. Practical results demonstrate that this method outperforms traditional approaches in terms of missed detection rates and detection efficiency, effectively addressing the challenge of detecting complex machining surface defects, and providing a high-precision, high-efficiency defect detection solution for the mechanical part machining field. Full article