Due to the limitations of conventional detection methods, including safety concerns, long incubation times, and limited specificity, the development of a rapid, selective, and accurate technique for the early detection of in livestock animals is crucial to prevent the spread of the associated disease. In the present study, we introduce a magnetic nanoparticle marker-based biosensor using frequency mixing magnetic detection for point-of-care testing and quantification of DNA. Superparamagnetic nanoparticles were used as magnetically measured markers to selectively detect the target DNA hybridized with its complementary capture probes immobilized on a porous polyethylene filter. Experimental conditions like density and length of the probes, hybridization time and temperature, and magnetic binding specificity, sensitivity, and detection limit were investigated and optimized. Our sensor demonstrated a relatively fast detection time of approximately 10 min, with a detection limit of 55 copies (0.09 fM) when tested using DNA amplified from genetic material. In addition, the detection specificity was examined using gDNA from and other zoonotic bacteria that may coexist in the same niche, confirming the method's selectivity for DNA. Our proposed biosensor has the potential to be used for the early detection of bacteria in the field and can contribute to disease control measures.
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http://dx.doi.org/10.3390/ijms242417272 | DOI Listing |
Pharmazie
December 2024
Department of Pharmacology and Toxicology, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia.
: Major Depressive Disorder (MDD) is a prevalent and debilitating mental disorder that has been linked to hyperhomocysteinemia and folate deficiency. These conditions are influenced by the methylenetetrahydrofolate reductase () gene, which plays a crucial role in converting homocysteine to methionine and is essential for folate metabolism and neurotransmitter synthesis, including serotonin. : This study explored the association between and polymorphisms among Saudi MDD patients attending the Erada Complex for Mental Health and Erada Services outpatient clinic in Jeddah, Saudi Arabia.
View Article and Find Full Text PDFMed Phys
January 2025
Department of Engineering Physics, Tsinghua University, Beijing, China.
Background: X-ray grating-based dark-field imaging can sense the small angle scattering caused by object's micro-structures. This technique is sensitive to the porous microstructure of lung alveoli and has the potential to detect lung diseases at an early stage. Up to now, a human-scale dark-field CT (DF-CT) prototype has been built for lung imaging.
View Article and Find Full Text PDFJ Assist Reprod Genet
January 2025
Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Clinical Sciences, Research Group Genetics, Reproduction and Development, Centre for Medical Genetics, Laarbeeklaan 101, 1090, Brussels, Belgium.
Purpose: Primary ovarian insufficiency (POI) is an important cause of female infertility, stemming from follicle dysfunction or premature oocyte depletion. Pathogenic variants in genes such as NOBOX, GDF9, BMP15, and FSHR have been linked to POI. NOBOX, a transcription factor expressed in oocytes and granulosa cells, plays a pivotal role in folliculogenesis.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Disease, Shanghai, 200080, China.
The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
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