Nowadays routine residue monitoring involves the analysis of many compounds from different classes, mainly in urine. In the past two decades, developments heavily focused on the use of mass spectrometers (MS) and faster and more sensitive MS detectors have reached the market. However, chromatographic separation (CS) was rather ignored and the cognate developments in CS were not in line. As a result, residue analysis did not improve to the extent anticipated. CS by LC x LC is a promising technique and will enable a further increase in the range of compounds and compound classes that can be detected in a single run. In the present study, a self-built LC x LC system, using a 10 port valve, was connected to a single quadrupole MS with electrospray interface. Standards containing a mixture of sulphonamides, β-agonists and (steroid) hormones, 53 compounds, in total, were analysed. Results demonstrated that these compounds were well separated and could be detected at low levels in urine, i.e. limit of detection (LOD) from 1 µg L for most β-agonists to 10 µg L for some sulphonamides and most hormones. To enhance the sensitivity, optimisation was performed on an advanced commercial LC x LC system connected to a full scan accurate MS. This ultimately resulted in a fast high throughput untargeted method, including a simple sample clean-up in a 96-well format, for the analysis of urine samples.
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http://dx.doi.org/10.1080/19440049.2018.1506160 | DOI Listing |
Vet World
November 2024
Faculty of Medicine, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Background And Aim: Zoonotic diseases caused by various blood parasites are important public health concerns that impact animals and humans worldwide. The traditional method of microscopic examination for parasite diagnosis is labor-intensive, time-consuming, and prone to variability among observers, necessitating highly skilled and experienced personnel. Therefore, an innovative approach is required to enhance the conventional method.
View Article and Find Full Text PDFBrain Behav
January 2025
Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Background: While automated methods for differential diagnosis of parkinsonian syndromes based on MRI imaging have been introduced, their implementation in clinical practice still underlies considerable challenges.
Objective: To assess whether the performance of classifiers based on imaging derived biomarkers is improved with the addition of basic clinical information and to provide a practical solution to address the insecurity of classification results due to the uncertain clinical diagnosis they are based on.
Methods: Retro- and prospectively collected data from multimodal MRI and standardized clinical datasets of 229 patients with PD (n = 167), PSP (n = 44), or MSA (n = 18) underwent multinomial classification in a benchmark study comparing the performance of nine machine learning methods.
Comput Biol Med
January 2025
Department of Creative Technologies, Air University, Islamabad, 44000, Pakistan. Electronic address:
Background And Objective: Diabetic Retinopathy (DR) is a serious diabetes complication that can cause blindness if not diagnosed in its early stages. Manual diagnosis by ophthalmologists is labor-intensive and time-consuming, particularly in overburdened healthcare systems. This highlights the need for automated, accurate, and personalized machine learning approaches for early DR detection and treatment.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam.
The field of medical image segmentation powered by deep learning has recently received substantial attention, with a significant focus on developing novel architectures and designing effective loss functions. Traditional loss functions, such as Dice loss and Cross-Entropy loss, predominantly rely on global metrics to compare predictions with labels. However, these global measures often struggle to address challenges such as occlusion and nonuni-form intensity.
View Article and Find Full Text PDFVet Res
January 2025
Veterinary Diagnostic Laboratory, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA.
Cranioventral pulmonary consolidation (CVPC) is a common lesion observed in the lungs of slaughtered pigs, often associated with Mycoplasma (M.) hyopneumoniae infection. There is a need to implement simple, fast, and valid CVPC scoring methods.
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