Search Results (17,740)

The global spread of Mpox virus (MPXV) underscores the urgent need for rapid, field-deployable diagnostic tools, especially in low-resource settings. We evaluated a loop-mediated isothermal amplification (LAMP) assay, termed LAMPOX, developed by Coris BioConcept. The assay was tested in three formats—two liquid versions and a dried, ready-to-use version—targeting only the ORF F3L (Liquid V1) or both the ORF F3L and N4R (Liquid V2 and dried) genomic regions. Analytical sensitivity and specificity were assessed using 60 clinical samples from confirmed MPXV-positive patients. Sensitivity on clinical samples was 81.7% for Liquid V1 and 88.3% for Liquid V2. The dried LAMPOX assay demonstrated a sensitivity of 88.3% and a specificity of 100% in a panel of 112 negative controls, with most positive samples detected in under 7 min. Additionally, a simplified sample lysis protocol was developed to facilitate point-of-care use. While this method showed slightly reduced sensitivity compared to standard DNA extraction, it proved effective for samples with higher viral loads. The dried format offers key advantages, including ambient-temperature stability and minimal equipment needs, making it suitable for point-of-care testing. These findings support LAMPOX as a promising tool for rapid MPXV detection during outbreaks, especially in resource-limited settings where traditional PCR is impractical. Full article

To improve the accuracy of three-dimensional (3D) reconstruction under microscopic conditions for laser-induced breakdown spectroscopy (LIBS), this study developed a novel visual platform by integrating an industrial CCD camera with a microscope. A customized microscale calibration target was designed to calibrate intrinsic and extrinsic camera parameters accurately. Based on the pinhole imaging model, disparity maps were obtained via pixel matching to reconstruct high-precision 3D ablation morphology. A mathematical model was established to analyze how key imaging parameters—baseline distance, focal length, and depth of field—affect reconstruction accuracy in micro-imaging environments. Focusing on trace element detection in WC-Co alloy samples, the reconstructed ablation craters enabled the precise calculation of ablation volumes and revealed their correlations with laser parameters (energy, wavelength, pulse duration) and the physical-chemical properties of the samples. Multivariate regression analysis was employed to investigate how ablation morphology and plasma evolution jointly influence LIBS quantification. A nonlinear calibration model was proposed, significantly suppressing matrix effects, achieving R² = 0.987, and reducing RMSE to 0.1. This approach enhances micro-scale LIBS accuracy and provides a methodological reference for high-precision spectral analysis in environmental and materials applications. Full article

This study presents a comprehensive inventory of bryophytes in Serra dos Órgãos National Park (PARNASO), aiming to evaluate species richness, floristic composition and threatened taxa. Despite the state of Rio de Janeiro being one of the most extensively sampled regions for bryophytes in Brazil, detailed surveys of its conservation units remain scarce. Data were obtained through bibliographic review, herbarium specimen analysis, and new field collections. A total of 504 species were recorded, belonging to 202 genera and 76 families. The park harbors three locally endemic species, eight endemic to Rio de Janeiro, and sixty-nine species endemic to Brazil. Additionally, eleven species were identified as threatened, comprising seven Endangered (EN), two Critically Endangered (CR), and two Vulnerable (VU) according to the IUCN guidelines. PARNASO includes four distinct ecosystems along an altitudinal gradient: sub-montane forest (up to 500 m), montane forest (500–1500 m), upper-montane forest (1500–2000 m), and high-altitude fields (above 2000 m). Montane Forest showed the highest species richness, followed by high-altitude fields, upper-montane forest, and sub-montane forest. The findings highlight PARNASO’s importance in preserving bryophyte diversity in a highly diverse yet understudied region. This work contributes valuable baseline data to support conservation strategies and future ecological studies in Atlantic Forest remnants. Full article

The aim of this work is to evaluate the effectiveness of antimicrobial photodynamic therapy (aPDT) against oral MDR (multi-drug-resistant) Candida spp. infections related to orthodontic treatment with palatal expanders through in vitro study. Methods: PDT protocol: Curcumin + H₂O₂ was used as a photosensitizer activated by a 460 nm diode LED lamp, with an 8 mm blunt tip for 2 min in each spot of interest. In vitro simulation: A palatal expander sterile device was inserted into a custom-designed orthodontic bioreactor, realized with 10 mL of Sabouraud dextrose broth plus 10% human saliva and infected with an MDR C. albicans clinical isolate CA95 strain to reproduce an oral palatal expander infection. After 48 h of incubation at 37 °C, the device was treated with the PDT protocol. Two samples before and 5 min after the PDT process were taken and used to contaminate a Petri dish with a Sabouraud field to evaluate Candida spp. CFUs (colony-forming units). Results: A nearly 99% reduction in C. albicans colonies in the palatal expander biofilm was found after PDT. Conclusion: The data showed the effectiveness of using aPDT to treat palatal infection; however, specific patient oral micro-environment reproduction (Ph values, salivary flow, mucosal adhesion of photosensitizer) must be further analyzed. Full article

Rose hips are rich in polyphenols, making them a promising ingredient for the development of functional fruit-based beverages. This study aimed to evaluate the effect of Pulsed Electric Field (PEF) extraction treatment on rose hip (RH) pulp to enhance the extraction of polyphenols, carotenoids, and volatile compounds. Additionally, this study examined the impact of adding rose hip berries during different stages of carbohydrate fermentation on the resulting phenolic and aroma profiles. A control wort and four experimental formulations were prepared. Rose hip pulp—treated or untreated with PEF—was added either during fermentation or beforehand, and the volatiles produced were analyzed using GC-MS (in triplicate). Fermentation was carried out over 10 days at 20 °C using Saccharomyces cerevisiae and Torulaspora delbrueckii. At a 10:1 ratio, all beverage samples were subjected to physicochemical testing and HPLC analysis for polyphenols, organic acids, and carotenoids, as well as GC-MS analysis for aroma compounds. The results demonstrated that the use of PEF-treated rose hips significantly improved phenolic compound extraction. Moreover, the PEF treatment enhanced the aroma profile of the beverage, contributing to a more complex and appealing sensory experience. This research highlights the rich polyphenol content of rose hips and the potential of PEF-treated fruit as a natural ingredient to improve both the functional and sensory qualities of fruit-based beverages. Their application opens new possibilities for the development of innovative, health-promoting drinks in the brewing industry. Full article

Asphalt plant reclaimed powder is a common solid waste in road engineering. Reusing reclaimed powder as filler holds significant importance for environmental protection and resource conservation. The key factors affecting the feasibility of reclaimed powder reuse are its acidity/alkalinity and cleanliness. Traditional evaluation methods, such as the methylene blue test and plasticity index, can assess reclaimed powder properties to guide its recycling. However, these methods suffer from inefficiency, strong empirical dependence, and high variability. To address these limitations, this study proposes a rapid and precise evaluation method for reclaimed powder properties based on Fourier transform infrared spectroscopy (FTIR). To do so, five field-collected reclaimed powder samples and four artificial samples were evaluated. Scanning electron microscopy (SEM), X-ray fluorescence spectroscopy (XRF), and X-ray diffraction (XRD) were employed to characterize their microphase morphology, chemical composition, and crystal structure, respectively. Subsequently, FTIR was used to establish correlations between key acidity/alkalinity, cleanliness, and multiple characteristic peak intensities. Representative infrared characteristic peaks were selected, and a quantitative functional group index (Is) was proposed to simultaneously evaluate acidity/alkalinity and cleanliness. The results indicate that reclaimed powder primarily consists of tiny, crushed stone particles and dust, with significant variations in crystal structure and chemical composition, including calcium carbonate, silicon oxide, iron oxide, and aluminum oxide. Some samples also contained clay, which critically influenced the reclaimed powder properties. Since both filler acidity/alkalinity and cleanliness are affected by clay (silicon/carbon ratio determining acidity/alkalinity and aluminosilicate content affecting cleanliness), this study calculated four functional group indices based on FTIR absorption peaks, namely the Si-O-Si stretching vibration (1000 cm⁻¹) and the CO₃²⁻ asymmetric stretching vibration (1400 cm⁻¹). These indices were correlated with conventional testing results (XRF for acidity/alkalinity, methylene blue value, and pull-off strength for cleanliness). The results show that the Is index exhibited strong correlations (R² = 0.89 with XRF, R² = 0.80 with methylene blue value, and R² = 0.96 with pull-off strength), demonstrating its effectiveness in predicting both acidity/alkalinity and cleanliness. The developed method enhances reclaimed powder detection efficiency and facilitates high-value recycling in road engineering applications. Full article

In the earthquakes that occurred in the Pazarcık (Mw = 7.7) and Elbistan (Mw = 7.6) districts of Kahramanmaraş Province on 6 February 2023, many buildings collapsed in the Hayrullah neighborhood of the Onikişubat district. In this study, we investigated whether there was a soil amplification effect on the damage occurring in the Hayrullah neighborhood of the Onikişubat district of Kahramanmaraş Province. Firstly, borehole, SPT, MASW (multi-channel surface wave analysis), microtremor, electrical resistivity tomography (ERT), and vertical electrical sounding (VES) tests were carried out in the field to determine the engineering properties and behavior of soil. Laboratory tests were also conducted using samples obtained from bore holes and field tests. Then, an idealized soil profile was created using the laboratory and field test results, and site dynamic soil behavior analyses were performed on the extracted profile. According to The Turkish Building Code (TBC 2018), the earthquake level DD-2 design spectra of the project site were determined and the average design spectrum was created. Considering the seismicity of the project site and TBC (2018) criteria (according to site-specific faulting, distance, and average shear wave velocity), 11 earthquake ground motion sets were selected and harmonized with DD-2 spectra in short, medium, and long periods. Using scaled motions, the soil profile was excited with 22 different earthquake scenarios and the results were obtained for the equivalent and non-linear models. The analysis showed that the soft soil conditions in the area amplified ground shaking by up to 2.8 times, especially for longer periods (1.0–2.5 s). This level of amplification was consistent with the damage observed in mid- to high-rise buildings, highlighting the important role of local site effects in the structural losses seen during the Kahramanmaraş earthquakes. Full article

The a priori procedure (APP) is concerned with determining appropriate sample sizes to ensure that sample statistics to be obtained are likely to be good estimators of corresponding population parameters. Previous researchers have shown how to compute a priori confidence interval means or locations for normal and skew-normal distributions. However, two critical limitations persist in the literature: (1) While numerous skewed models have been proposed, the APP equations for location parameters have only been formally established for the basic skew-normal distributions. (2) Even within this fundamental framework, the APPs for sample size determinations in estimating locations are constructed on samples of specifically dependent observations having multivariate skew-normal distributions jointly. Our work addresses these limitations by extending a priori reasoning to the more comprehensive unified skew-normal (SUN) distribution. The SUN family not only encompasses multiple existing skew-normal models as special cases but also enables broader practical applications through its capacity to model mixed skewness patterns and diverse tail behaviors. In this paper, we establish APP equations for determining the required sample sizes and set up confidence intervals for the location parameter in the one-sample case, as well as for the difference in locations in matched pairs and two independent samples, assuming independent observations from the SUN family. This extension addresses a critical gap in the literature and offers a valuable contribution to the field. Simulation studies support the equations presented, and two applications involve real data sets for illustrations of our main results. Full article

Growing interest in the future applications of nanotechnology in medicine has led to groundbreaking developments in nanosensors. Nanosensors are excellent platforms that provide reliable solutions for continuous monitoring and real-time detection of clinical targets. Nanosensors have attracted great attention due to their remarkable sensitivity, portability, selectivity, and automated data acquisition. The exceptional nanoscale properties of nanomaterials used in the nanosensors boost their sensing potential even at minimal concentrations of analytes present in a clinical sample. Along with applications in diverse sectors, the beneficial aspects of nanosensors have been exploited in healthcare systems to utilize their applications in diagnosing, treating, and preventing diseases. Hence, in this review, we have presented an overview of the disease-prognostic applications of nanosensors in chronic diseases through a detailed literature analysis. We focused on the advances in various nanosensors in the field of major diseases such as cancer, cardiovascular diseases, diabetes mellitus, and neurodegenerative diseases along with other prevalent diseases. This review demonstrates various categories of nanosensors with different nanoparticle compositions and detection methods suitable for specific diagnostic applications in clinical settings. The chemical properties of different nanoparticles provide unique characteristics to each nanosensors for their specific applications. This will aid the detection of potential biomarkers or pathological conditions that correlate with the early detection of various diseases. The potential challenges and possible recommendations of the applications of nanosensors for disease diagnosis are also discussed. The consolidated information present in the review will help to better understand the disease-prognostic potentials of nanosensors, which can be utilized to explore new avenues in improved therapeutic interventions and treatment modalities. Full article

Background: Autologous fat grafting is increasingly used in daily clinical practice across various surgical fields, including the treatment of chronic wounds, scars, burns, and non-healing perianal fistulas. Recently, some studies have shown that non-enteric cutaneous fistulas can also benefit from adipose tissue injections, but the efficacy remains unclear. This study aims to systematically review the literature on fat grafting in the context of non-enteric cutaneous fistulas and to assess treatment outcomes. Methods: A comprehensive search of the PubMed/Medline database was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines up to January 2024 without restrictions on the time period or the language of publication. Results: Seven studies meeting the inclusion criteria were analyzed, encompassing 13 patients with non-healing cutaneous fistulas treated with injections of autologous fat. The mean age of the patients was 58 ± 3 years, of which 85% had comorbidities. Fat grafting resulted in complete healing in 92% of the cases, with a mean fistula persistence of 158 days before treatment. Treatment protocols varied among patients, including preparation of the fistulous tract, fat processing techniques, and suturing of the fistulous orifice. Conclusions: The results highlight the potential of autologous fat grafting in promoting tissue regeneration and healing of non-enteric cutaneous fistulas. Standardized protocols are essential to confirm and optimize treatment efficacy and, eventually, improve patient outcomes. Further research with a larger sample size and standardization is needed to confirm fat graft efficacy. Full article

This study investigates the spatial heterogeneity of carbon pools in young Betula sp. stands on former arable lands in the southern Moscow region, Russia. The findings could be useful for the current estimates and predictions of the carbon balance in such forest ecosystems. The research focuses on understanding the interactions between plant cover and the environment, i.e., how environmental factors such as stand density, tree diameter and height, light conditions, and soil properties affect ecosystem carbon pools. We also studied how heterogeneity in edaphic conditions affects the formation of plant cover, particularly tree regeneration and the development of ground layer vegetation. Field measurements were conducted on a permanent 50 × 50 m sampling plot divided into 5 × 5 m subplots, in order to capture variability in vegetation and soil characteristics. Key findings reveal significant differences in carbon stocks across subplots with varying stand densities and light conditions. This highlights the role of the spatial heterogeneity of soil properties and vegetation cover in carbon sequestration. The study demonstrates the feasibility of indirect estimation of carbon stocks using stand parameters (density, height, and diameter), with results that closely match direct measurements. The total ecosystem carbon stock was estimated at 80.47 t ha⁻¹, with the soil contribution exceeding that of living biomass and dead organic matter. This research emphasizes the importance of accounting for spatial heterogeneity in carbon assessments of post-agricultural ecosystems, providing a methodological framework for future studies. Full article

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management. Full article

In intelligent vehicular networks, the accuracy of semantic segmentation in road scenes is crucial for vehicle-mounted artificial intelligence to achieve environmental perception, decision support, and safety control. Although deep learning methods have made significant progress, two main challenges remain: first, the difficulty in balancing global and local features leads to blurred object boundaries and misclassification; second, conventional convolutions have limited ability to perceive irregular objects, causing information loss and affecting segmentation accuracy. To address these issues, this paper proposes a global–local collaborative attention module and a spider web convolution module. The former enhances feature representation through bidirectional feature interaction and dynamic weight allocation, reducing false positives and missed detections. The latter introduces an asymmetric sampling topology and six-directional receptive field paths to effectively improve the recognition of irregular objects. Experiments on the Cityscapes, CamVid, and BDD100K datasets, collected using vehicle-mounted cameras, demonstrate that the proposed method performs excellently across multiple evaluation metrics, including mIoU, mRecall, mPrecision, and mAccuracy. Comparative experiments with classical segmentation networks, attention mechanisms, and convolution modules validate the effectiveness of the proposed approach. The proposed method demonstrates outstanding performance in sensor-based semantic segmentation tasks and is well-suited for environmental perception systems in autonomous driving. Full article

Cell-free biosensors harness the selectivity of cellular machinery without living cells’ constraints, offering advantages in environmental monitoring, medical diagnostics, and biotechnological applications. This review examines recent advances in cell-free biosensor development, highlighting their ability to detect diverse analytes including heavy metals, organic pollutants, pathogens, and clinical biomarkers with high sensitivity and specificity. We analyze technological innovations in cell-free protein synthesis optimization, preservation strategies, and field deployment methods that have enhanced sensitivity, and practical applicability. The integration of synthetic biology approaches has enabled complex signal processing, multiplexed detection, and novel sensor designs including riboswitches, split reporter systems, and metabolic sensing modules. Emerging materials such as supported lipid bilayers, hydrogels, and artificial cells are expanding biosensor capabilities through microcompartmentalization and electronic integration. Despite significant progress, challenges remain in standardization, sample interference mitigation, and cost reduction. Future opportunities include smartphone integration, enhanced preservation methods, and hybrid sensing platforms. Cell-free biosensors hold particular promise for point-of-care diagnostics in resource-limited settings, environmental monitoring applications, and food safety testing, representing essential tools for addressing global challenges in healthcare, environmental protection, and biosecurity. Full article

Fault diagnosis is of great significance for the maintenance of rotating machinery. Deep learning is an intelligent diagnostic technique that is receiving increasing attention. To address the issues of industrial data with small samples and varying working conditions, a residual convolutional neural network based on the attention mechanism is put forward for the fault diagnosis of rotating machinery. The method incorporates channel attention and spatial attention simultaneously, implementing channel-wise recalibration for frequency-dependent feature adjustment and performing spatial context aggregation across receptive fields. Subsequently, a residual module is introduced to address the vanishing gradient problem of the model in deep network structures. In addition, LSTM is used to realize spatiotemporal feature fusion. Finally, label smoothing regularization (LSR) is proposed to balance the distributional disparities among labeled samples. The effectiveness of the method is evaluated by its application to the vibration signal data from the safe injection pump and the Case Western Reserve University (CWRU). The results show that the method has superb diagnostic accuracy and strong robustness. Full article