Search Results (101,360)

Methotrexate (MTX) is an antifolic agent used in the first line of anti-inflammatory disease treatment and some oncologic issues. The metabolism of MTX is rapid, and the MTX concentration in the blood is not significant 24 h after administration. Unlike this, methotrexate polyglutamates (MTXPGs) can be informative biomarkers of drug exposure. It is widely concluded that MTXPG retention in red blood cells (RBCs) is related to appropriate efficacy, drug exposure, and toxicity during treatment. Therefore, the mentioned biomarker may be appropriately used for the PK/PD monitoring of low-dose MTX (LDMTX) treatment. The presented review study aimed to review published studies about MTX determination in clinical practice, including pre-analytical variability, bioanalytical considerations, and clinical applications of the methods for pharmacotherapy supporting target populations. In total, 14 papers from the field of bioanalytics have been included in the main review. For each phase of an analytical process, the best practises and main findings were defined as guidelines for proper analytical method optimisation, validation, and standard operation procedure implementation in clinical practice. The presented study is the first comprehensive review of MTX and its methods of metabolite determination to account for pre-analytical, analytical, and post-analytical phases concerning the TDM process. Full article

Objective: Staphylococcus aureus (SA), including methicillin-resistant strains (MRSAs), is a major cause of skin and soft tissue infections (SSTIs) in military populations. This study investigated SSTI incidence and SA carriage in a military training site over 16 weeks using a prospective observational cohort design. Methods: Two training cohorts provided pre- and post-training self-collected swabs for bacterial carriage, and environmental swabs from accommodations, personal items, and training facilities. Hygiene awareness and practices were assessed through questionnaires. Bacteria were identified using culture, mass spectrometry (MALDI-TOF), and genomic sequencing. Results: Nasal carriage of SA increased from 19% to 49% by the end of training. SSTIs requiring treatment occurred in 16% of participants. Steam cleaning reduced but did not eliminate SA on personal bed linen. Additionally, 40% of participants had poor knowledge of antibacterial cleaning practices and wound management. Conclusions: Increased SA carriage was linked to human-to-human transmission in close-quarter military training environments. Implications for Public Health: Improved personal hygiene training, wound management education, and monitored cleaning protocols are essential to mitigate SSTI risks in communal military training environments. Full article

Background: Insulin pumps coupled with continuous glucose monitoring sensors use algorithms to analyze real-time blood glucose levels. This allows for the suspension of insulin administration before hypoglycemic thresholds are reached or for adaptive tuning in hybrid closed-loop systems. This longitudinal retrospective study aims to analyze real-world glycemic outcomes in a pediatric population transitioning to such devices. Methods: We evaluated children with type 1 diabetes mellitus (T1D) admitted to the Pediatric Diabetes Department from a major University Hospital in Bucharest, Romania, who transitioned to hybrid closed-loop or predictive low-glucose suspend system from either non-automated insulin pumps or multiple daily injections. The primary outcome was assessing the change in glycated hemoglobin (HbA1c) after initiating these devices. Secondary outcomes analyzed changes in glucose metrics from the 90 days prior to the baseline and follow-up visit. Results: 51 children were included (58.8% girls), the mean age was 10.3 ± 3.7 years, and the mean follow-up duration was 13.2 ± 4.5 months. The analyzed parameters, such as HbA1c (6.9 ± 0.7% vs. 6.7 ± 0.6%, p = 0.023), time in range (69.3 ± 11.2% vs. 76 ± 9.9%, p < 0.001), time in tight range (47.4 ± 10.9% vs. 53.7 ± 10.7%, p < 0.001), time below range (5.6 ± 2.9% vs. 3.5 ± 1.9%, p < 0.001), time above range (25 ± 11.2% vs. 20.4 ± 9.4%, p = 0.001), and coefficient of variation (37.9 ± 4.8% vs. 35.6 ± 4.6%, p = 0.001), showed significant improvements. Conclusions: The application of these sensor-integrated insulin pumps can significantly enhance metabolic control in pediatric populations, minimizing glycemic variations to mitigate complications and enrich the quality of life. Full article

The colonization of historical buildings and monuments by fungi, algae, and bacteria is a common phenomenon. This often leads to deterioration processes that cause either visual or structural harm. The Batalha Monastery in Portugal, a UNESCO World Heritage Site, currently shows significant surface changes to the stone architectural elements within both the Founder’s Chapel and the church, including a widespread pink discoloration on the walls and columns. The main goal of this study was to analyze the biological colonization and assess the influence of bacterial communities on the biodeterioration of Ançã limestone, providing valuable insights to help conservators and restorers select the best preservation strategies for the monastery. The prokaryote population was characterized using both high-throughput DNA sequencing and culture-dependent methods and several orange-pink pigment-producing bacteria were identified, for example, Bacillus, Gordonia, Serratia and Methylobacterium, as well as Halalkalicoccus, an abundant archaeal genus. The pink discoloration observed could be due to biofilms created by bacteria that produce pigments, namely carotenoids. Biocolonization tests were performed using stone mock-ups, which were prepared and inoculated with the bacteria isolated in this study. These tests were designed to replicate the natural conditions of the monastery and monitor the colonization process to understand the discoloration phenomenon. Full article

The Specola Museum is housed on the premises of the old Palermo Observatory, founded in 1790, and preserves most of the observatory’s cultural heritage. Environmental monitoring following the activation of air conditioning systems in 2018 revealed significant deviations from the historic thermo-hygrometric trends, with particularly dangerous fluctuations in relative humidity. A notable example of the impact of these changes is a 19th-century painted wooden Model of Mars, displayed in the Merz Hall since 2021. In less than two years, the Model has shown progressive damage to its paint layers. Conservation actions have been adopted to stop the deterioration process, but the risk of further deterioration phenomena involving other objects is expected to increase substantially in the absence of intervention. This paper presents the outcomes of a preliminary study on the thermo-hygrometric conditions in the Merz Hall. Based on the European Standard EN 15757: 2010 and the Italian Legislative decree of 10 May 2001, safe ranges for temperature and relative humidity have been identified for the long-term preservation of the collection. These findings will inform future climate management strategies in the museum. Full article

Xinjiang is an important region due to its unique epidemic characteristics of wheat stripe rust disease caused by Puccinia striiformis f. sp. tritici. Some previous studies on race identification were conducted in this region, but it is still unclear how temporal changes affect the dynamics, diversity, and virulence characteristics of Pst races in Xinjiang. To gain a better understanding, we compared the race data from spring and winter wheat crops of 2022 with that of 2021. Our results showed a significant change in virulence frequency in 2022. Vr10, Vr13, and Vr19 exhibited an increasing trend, with a frequency of ≥18%, while the maximum decline was observed in Vr1, Vr3, and Vr9, with a frequency of ≤−25%. It was found that Yr5 and Yr15 remained effective against Xinjiang Pst races. The race diversity increased from 0.92 (70 races out of 345 isolates) to 0.94 (90 races out of 354 isolates) in 2022, with G22G being the dominant race group. Race CYR34 became prevalent in the region in 2022, while the LvG grouped was wiped out in 2022, from both summer and winter crop seasons. HyG and SuG groups showed an overall declining trend. Overall prevalent races showed over-summering and over-wintering behaviors in Xinjiang. The number of new races occurrence frequency increased by 34% in 2022, indicating a potential change in the population structure of Pst. It is crucial to introduce newly resistant gene cultivars in this region and to establish rust-monitoring protocols to prepare for any future epidemics. Full article

Accurate estimation of pasture yield in grasslands is crucial for the sustainable utilization of pasture resources and the optimization of grassland management. This study leveraged the capabilities of machine learning techniques, supported by Google Earth Engine (GEE), to assess pasture yield in the temperate grasslands of northern China. Utilizing Landsat-8 data, band reflectances, vegetation indexes (VIs), and soil water index (SWI) were extracted from 1000 field samples across Xilingol. These data, combined with field-measured pasture yields, were employed to construct models using four machine learning algorithms: elastic net regression (Enet), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). Among the models, XGBoost demonstrated the best performance for pasture yield estimation, with a coefficient of determination (R²) of 0.94 and a precision of 76.3%. Additionally, models that incorporated multiple VIs demonstrated superior prediction accuracy compared to those using individual VI, and including soil moisture data further enhanced predictive precision. The XGBoost model was subsequently applied to map the spatial patterns of pasture yield in the Xilingol grassland for the years 2014 and 2019. The estimated average annual pasture yield in the Xilingol grassland was 1042.38 and 1013.49 kg/ha in 2014 and 2019, respectively, showing a general decreasing trend from the northeast to the southwest. This study explored the effectiveness of common machine learning algorithms in predicting pasture yield of temperate grasslands utilizing Landsat-8 data and ground sample data and provided the valuable support for long-term historical monitoring of pasture resources. The findings also highlighted the importance of predictor selection in optimizing model performance, except for the reflectance and vegetation indices characterizing vegetation canopy information, the inclusion of soil moisture information could appropriately improve the accuracy of model predictions, especially for grasslands with relatively low vegetation cover. Full article

Background: Cardiac magnetic resonance imaging (MRI) plays a crucial role in monitoring disease progression and evaluating the effectiveness of treatment interventions. Cardiac MRI allows medical practitioners to assess cardiac function accurately by providing comprehensive and quantitative information about the structure and function, hence making it an indispensable tool for monitoring the disease and treatment response. Deep learning-based segmentation enables the precise delineation of cardiac structures including the myocardium, right ventricle, and left ventricle. The accurate segmentation of these structures helps in the diagnosis of heart failure, cardiac functional response to therapies, and understanding the state of the heart functions after treatment. Objectives: The objective of this study is to develop a multiscale deep learning model to segment cardiac organs based on MRI imaging data. Good segmentation performance is difficult to achieve due to the complex nature of the cardiac structure, which includes a variety of chambers, arteries, and tissues. Furthermore, the human heart is also constantly beating, leading to motion artifacts that reduce image clarity and consistency. As a result, a multiscale method is explored to overcome various challenges in segmenting cardiac MRI images. Methods: This paper proposes DeSPPNet, a multiscale-based deep learning network. Its foundation follows encoder–decoder pair architecture that utilizes the Spatial Pyramid Pooling (SPP) layer to improve the performance of cardiac semantic segmentation. The SPP layer is designed to pool features from densely convolutional layers at different scales or sizes, which will be combined to maintain a set of spatial information. By processing features at different spatial resolutions, the multiscale densely connected layer in the form of the Pyramid Pooling Dense Module (PPDM) helps the network to capture both local and global context, preserving finer details of the cardiac structure while also capturing the broader context required to accurately segment larger cardiac structures. The PPDM is incorporated into the deeper layer of the encoder section of the deep learning network to allow it to recognize complex semantic features. Results: An analysis of multiple PPDM placement scenarios and structural variations revealed that the 3-path PPDM, positioned at the encoder layer 5, yielded optimal segmentation performance, achieving dice, intersection over union (IoU), and accuracy scores of 0.859, 0.800, and 0.993, respectively. Conclusions: Different PPDM configurations produce a different effect on the network; as such, a shallower layer placement, like encoder layer 4, retains more spatial data that need more parallel paths to gather the optimal set of multiscale features. In contrast, deeper layers contain more informative features but at a lower spatial resolution, which reduces the number of parallel paths required to provide optimal multiscale context. Full article

Background/Objectives: Locally advanced rectal cancer is treated with neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision (TME). As this approach achieves complete pathologic remissions (pCR) in approximately 30% of patients, it raises the question of whether surgery is always necessary. Non-surgical strategies, such as “watch and wait” (W&W), have shown similarly promising outcomes. However, there is an unmet need for reliable biomarkers predicting pCR. Analysis of circulating tumor DNA (ctDNA) has shown potential for monitoring treatment response and detecting minimal residual disease. We hypothesized that monitoring ctDNA changes during nCRT might facilitate the identification of individuals who achieve pCR. Methods: In the prospective single-center NEORECT trial, the plasma of forty rectal cancer patients was collected before, during, and after nCRT and before TME. Informative somatic mutations were identified in tissue biopsies by NGS and subsequently used for ctDNA quantification by dPCR. Results: The results identified three distinct ctDNA patterns: increase, decrease, and absence. Remarkably, undetectable DNA was observed in good responders, while a tenfold ctDNA increase was associated with the emergence of new metastases. Despite these insights, ctDNA alone demonstrated low specificity, with no significant correlation to pCR or long-term prognosis. A multimodal approach incorporating routinely available clinical parameters remains inadequate for accurately predicting pCR prior to TME. Conclusions: In conclusion, the NEORECT trial establishes the feasibility of ctDNA-based personalized monitoring for rectal cancer patients undergoing nCRT. However, the utility of ctDNA in enhancing pCR prediction for a W&W strategy warrants further investigation. Larger studies integrating multi-gene analyses and expanded clinical datasets are essential in the future. Full article

Monitoring animal populations is crucial for assessing the health of ecosystems. Traditional methods, which require extensive fieldwork, are increasingly being supplemented by time-lapse camera-trap imagery combined with an automatic analysis of the image data. The latter usually involves some object detector aimed at detecting relevant targets (commonly animals) in each image, followed by some postprocessing to gather activity and population data. In this paper, we show that the performance of an object detector in a single frame of a time-lapse sequence can be improved by including spatio-temporal features from the prior frames. We propose a method that leverages temporal information by integrating two additional spatial feature channels which capture stationary and non-stationary elements of the scene and consequently improve scene understanding and reduce the number of stationary false positives. The proposed technique achieves a significant improvement of 24% in mean average precision ([email protected]:0.95) over the baseline (temporal feature-free, single frame) object detector on a large dataset of breeding tropical seabirds. We envisage our method will be widely applicable to other wildlife monitoring applications that use time-lapse imaging. Full article

This paper takes a portion of the Manas River Basin in Xinjiang Province, China, as an example and proposes an improved traditional comprehensive water quality index (WQI) method using Extreme Gradient Boosting(XG−BOOST) to analyze the groundwater quality levels in the region. Additionally, XG−BOOST is used to screen the existing dataset of ten water quality indicators, including fluoride (F), chlorine (Cl), nitrate (NO), sulfate (SO), silver (Ag), aluminum (Al), iron (Fe), lead (Pb), selenium (Se), and zinc (Zn), from 246 monitoring points, in order to find the dataset that optimizes model training performance. The results show that, in the selected study area, water quality categorized as “GOOD” and “POOR” accounts for the majority, with “GOOD” covering 48.7% of the area and “POOR” covering 31.6%. Regions with water quality classified as “UNFIT” are mainly distributed in the central–eastern parts of the study area, located in parts of the Changji Hui Autonomous Prefecture. Comparatively, water quality in the western part of the study area is better than that in the eastern part, while areas with “EXCELLENT” water quality are primarily distributed in the southern parts of the study area. The optimal water quality indicator dataset consists of five indicators: Cl, NO, Pb, Se, and Zn, achieving an accuracy of 98%, RMSE = 0.1414, and R² = 0.9081. Full article

Recently, there has been increasing interest in organic carbon (OC) certification of soil as an incentive for farmers to adopt sustainable agricultural practices. In this context, this pilot project combines geochemical and geophysical methods to map the distribution of OC contents in agricultural fields, allowing us to detect variations in time and space. Here we demonstrated a relationship between soil OC contents estimated in the laboratory and the apparent electrical conductivity (ECa) measured in the field. Specifically, geochemical elemental analyses were used to evaluate the OC content and relative isotopic signature in collected soil samples from a hazelnut orchard in the Emilia–Romagna region of Northeastern Italy, while the geophysical Electromagnetic Induction (EMI) method enabled the in situ mapping of the ECa distribution in the same soil field. According to the results, geochemical and geophysical data were found to be reciprocally related, as both the organic matter and soil moisture were mainly incorporated into the fine sediments (i.e., clay) of the soil. Therefore, such a relation was used to create a map of the OC content distribution in the investigated field, which could be used to monitor the soil C sequestration on small-scale farmland and eventually develop precision agricultural services. In the future, this method could be used by farmers and regional and/or national policymakers to periodically certify the farm’s soil conditions and verify the effectiveness of carbon sequestration. These measures would enable farmers to pursue Common Agricultural Policy (CAP) incentives for the reduction of CO₂ emissions. Full article

Ecological zoning is essential for optimizing regional ecological management and improving environmental protection efficiency. While previous studies have primarily focused on the independent analysis of land use intensity (LUI) and landscape ecological risk (LER), there has been limited research on their coupled relationship. This study, conducted in the Western Jilin (WJL), introduces an innovative ecological zoning method based on the Production–Living–Ecological Space (PLES) framework, which explores the interactions between LUI and LER, filling a gap in existing research. The method employs a coupling coordination degree (CCD) model and Geographic Information System (GIS) technology to construct an LUI-ERI coupling model, which is used to delineate ecological zones. The results indicate that: (1) The PLES in the study area is predominantly production space (PS), with the largest area of transfer being production ecological space (PES) 2784.23 km², and the most significant transfer in being PS 3112.33 km². (2) Between 2000 and 2020, both LUI and LER exhibited downward trends, with opposite spatial distribution characteristics. The “middle” intensity zone and “highest” risk zone were the dominant LUI and LER types, covering approximately 46% and 45% of the total area, respectively. (3) The coupling coordination degree between LUI and LER showed a polarized trend, with an overall upward trajectory from 2000 to 2020. (4) The ecological zoning of the WJL can be categorized into an ecological core protection (ECP) zone, ecological potential governance (EPG) zone, ecological comprehensive monitoring (ECM) zone, ecological optimization (EO) zone, and ecological restoration (ER) zone, with the ecological core protection area occupying 61.63% of the total area. This study provides a novel perspective on ecological zoning and offers a systematic scientific basis for regional ecological management and spatial planning. Full article

In a complex urban environment, the impact of building demolitions by blasting on the structural integrity of nearby metro tunnels is critical. This study systematically analyzed the blasting and demolition process of a building adjacent to a metro tunnel using various monitoring methods, including blasting vibration, dynamic strain, deformation and settlement, pore water pressure, and displacement. The results indicate that the metro tunnel’s vibration response can be divided into four stages: notch blasting, notch closure, overall collapse impact, and auxiliary notch blasting. The most significant impact on the tunnel segments occurred during the building’s ground impact phase, with a peak particle velocity of 0.57 cm/s. The maximum tensile and compressive stresses induced in the tunnel segments did not exceed 0.4 MPa, well within the safety limits. Displacement and settlement changes in the tunnel structure were less than 1 mm, far below the warning threshold. Additionally, blasting vibrations significantly affected the pore water pressure in the surrounding soil. However, fluctuations caused by ground impact vibrations were minimal, and the pore water pressure quickly returned to its initial level after the blasting concluded. Throughout the process, no adverse effects on the metro tunnel structure were observed. Full article

The overload problem in semi-autogenous grinding (SAG) mills is critical in the mining industry, impacting the extraction of valuable metals and overall productivity. Overloads can lead to severe operational issues, including increased wear, reduced grinding efficiency, and unscheduled shutdowns, which result in financial losses. Various strategies have been employed to address SAG mill overload, from real-time monitoring to predictive modeling and machine learning techniques. However, existing methods often lack the integration of domain-specific knowledge, particularly in handling class imbalance within operational data, leading to limitations in predictive accuracy. This paper presents a novel approach that integrates convolutional neural networks (CNNs) with physics-informed neural networks (PINNs), embedding physical laws directly into the model’s loss function. This hybrid methodology captures the complex interactions and nonlinearities inherent in SAG mill operations and leverages domain expertise to enforce physical consistency, ensuring more robust predictions. Incorporating physics-based constraints allows the model to remain sensitive to critical overload conditions while addressing the challenge of imbalanced data. Our method demonstrates a significant enhancement in prediction accuracy through extensive experiments on real-world SAG mill operational data, achieving an F1-score of 94.5%. The results confirm the importance of integrating physics-based knowledge into machine learning models, improving predictive performance, and offering a more interpretable and reliable tool for mill operators. This work sets a new benchmark in the predictive modeling of SAG mill overloads, paving the way for more advanced, physically informed predictive maintenance strategies in the mining industry. Full article