Tuberculosis (TB) is the second deadliest infectious disease worldwide. Current TB diagnostics utilize sputum samples, which are difficult to obtain, and sample processing is time-consuming and difficult. This study developed an integrated diagnostic platform for the rapid visual detection of Mycobacterium tuberculosis (Mtb) in breath samples at the point-of-care (POC), especially in resource-limited settings. The less pathogenic Mycobacterium smegmatis containing same gene fragment of Mtb served as the model bacterium. A novel respirator was designed to collect airborne mycobacteria in breath samples, with an efficiency of 38.7-61.5 % (10-10 CFU/mL). In our vision, patients only needed to wear a respirator for 1 h, and the collected pathogens were loaded into a microfluidic chip with direct-current electric field for lysis and nucleic acid extraction (20 μL, 3 s), then recombinase polymerase amplification (36 °C, 8 min) and lateral flow strip assay (5 min) were proceeded to enable visual test for the POC. Our platform completed the entire sample collection and diagnosis within 90 min, and the bacterial DNA amplification can be completed in 8 min by handheld, showing great patient compliance and eliminating the need for large equipment. Diagnostic systems involving signal detection with the naked eye are more suitable for the large-scale screening of TB. The proposed method detected low concentrations of bacterial DNA (5.0 aM, 18 copies/μL) with high reproducibility and specificity. Moreover, the system accurately detected low bacterial concentrations (10 CFU/mL). This platform provides the potential to improve the screening of TB and other airborne infectious diseases.
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http://dx.doi.org/10.1016/j.talanta.2024.127490 | DOI Listing |
J Cardiothorac Surg
January 2025
Department of Cardiothoracic Surgery, Aalborg University Hospital, Hobrovej 18-22, Aalborg, 9000, Denmark.
Background: The outcome of coronary artery bypass grafting (CABG) depends on several factors, including the quality of the distal anastomoses to the coronary arteries. Early graft failure may be caused by, e.g.
View Article and Find Full Text PDFBMC Ophthalmol
January 2025
Department of Tuberculosis, New District Branch of Northern Jiangsu People's Hospital of Jiangsu Province, Yangzhou, 225001, Jiangsu Province, China.
Background: This study aims to detect Mycobacterium tuberculosis complex (MTBC) DNA in intraocular fluid from clinically suspected tuberculous uveitis patients using multiplex polymerase chain reaction (PCR) and investigate the diagnostic utility of multiplex PCR for tuberculous uveitis.
Methods: Primers targeting three specific genes (MPB64, CYP141, and IS6110) within the MTBC genome were designed. Multiplex PCR was conducted using DNA from the H37Rv strain as well as DNA extracted from fluids of confirmed tuberculosis patients to assess primer specificity and method feasibility.
BMC Public Health
January 2025
Department of Urology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China.
Background: Metabolic health is closely related to testosterone levels, and the cardiometabolic index (CMI) is a novel metabolic evaluation metric that encompasses obesity and lipid metabolism. However, there is currently a lack of research on the relationship between CMI and testosterone, which is the objective of this study.
Methods: This study utilized data from the National Health and Nutrition Examination Survey (NHANES) cycles from 2011 to 2016.
Behav Res Methods
January 2025
Department Neurophysics, Philipps-Universität Marburg, Fachbereich Physik, AG Neurophysik, Karl-Von-Frisch-Straße 8a, 35043, Marburg, Lahnberge, Germany.
The analysis of eye movements is a noninvasive, reliable and fast method to detect and quantify brain (dys)function. Here, we investigated the performance of two novel eye-trackers-the Thomas Oculus Motus-research mobile (TOM-rm) and the TOM-research stationary (TOM-rs)-and compared them with the performance of a well-established video-based eye-tracker, i.e.
View Article and Find Full Text PDFSci Rep
January 2025
ADAPT Research Centre, School of Computer Science, University of Galway, Galway, Ireland.
This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on a modified UNet architecture. To improve segmentation accuracy, the model integrates attention mechanisms, such as the Convolutional Block Attention Module (CBAM) and Non-Local Attention, with advanced encoder architectures, including ResNet, DenseNet, and EfficientNet. These attention mechanisms enable the model to focus more effectively on relevant tumor areas, resulting in significant performance improvements.
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