We have developed a convenient surface-enhanced Raman scattering (SERS) platform based on vertical standing gold nanowires (v-AuNWs) which enabled the on-mask detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) related substances such as the Spike-1 protein and the corresponding pseudo-virus. The Spike-1 protein was clearly distinguished from BSA protein with an accuracy above 99 %, and the detection limit could be achieved down to 0.01 μg/mL. Notably, a similar accuracy was achieved for the pseudo-SARS-CoV-2 (pSARS-2) virus as compared to the pseudo-influenza H7N9 (pH7N9) virus. The sensing strategy and setups could be easily adapted to the real SARS-CoV-2 virus and other highly contagious viruses. It provided a promising way to screen the virus carriers by a fast evaluation of their wearing v-AuNWs integrated face-mask which was mandatory during the pandemic.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.talanta.2024.126403 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Fully automated coronary artery calcium score and risk categorization from chest CT using deep learning and multiorgan segmentation: A validation study from National Lung Screening Trial (NLST).
Int J Cardiol Heart Vasc
February 2025
Department of Radiology, Frimley Park Hospital NHS Foundation Trust, Camberley, Surrey, UK.
Background: The National Lung Screening Trial (NLST) has shown that screening with low dose CT in high-risk population was associated with reduction in lung cancer mortality. These patients are also at high risk of coronary artery disease, and we used deep learning model to automatically detect, quantify and perform risk categorisation of coronary artery calcification score (CACS) from non-ECG gated Chest CT scans.
Materials And Methods: Automated calcium quantification was performed using a neural network based on Mask regions with convolutional neural networks (R-CNN) for multiorgan segmentation.
Application of mask R-CNN for building detection in UAV remote sensing images.
Heliyon
October 2024
Wuhan University of Science and Technology, Wuhan, Hubei, 430000, China.
This study aims to tackle the challenges of low accuracy in building feature extraction and insufficient details in three-dimensional (3D) modeling faced by traditional methods, particularly in complex backgrounds. To address these issues, a method for building feature extraction based on Mask Region-Convolutional Neural Network (Mask R-CNN) is proposed. This approach combines deep learning techniques with aerial images to ensure precise and efficient automatic detection and feature extraction.
View Article and Find Full Text PDFA field investigation of 3 masks proposed as respiratory protection for wildland firefighters: a randomized controlled trial in British Columbia, Canada.
Ann Work Expo Health
November 2024
Division of Preventive Medicine, University of Alberta, Edmonton, AB, T6G 2T4, Canada.
Introduction: Wildland firefighters are exposed through the lungs and skin to particulate matter, fumes, and vapors containing polycyclic aromatic hydrocarbons (PAH). Wearing respiratory protection should reduce pulmonary exposure, but there is uncertainty about the most effective and acceptable type of mask.
Methods: Firefighters from 6 unit crews working with the British Columbia Wildfire Service were approached and those consenting were randomly allocated within each crew to a "no mask" control group or to use 1 of 3 types of masks: X, half-face respirator with P100/multi gas cartridge; Y, cloth with alpaca filter; Z mesh fabric with a carbon filter.
A method for detecting ureteral stent encrustations in medical CT images based on Mask-RCNN and 3D morphological analysis.
Front Physiol
August 2024
Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China.
Objective: To develop and validate a method for detecting ureteral stent encrustations in medical CT images based on Mask-RCNN and 3D morphological analysis.
Method: All 222 cases of ureteral stent data were obtained from the Fifth Affiliated Hospital of Sun Yat-sen University. Firstly, a neural network was used to detect the region of the ureteral stent, and the results of the coarse detection were completed and connected domain filtered based on the continuity of the ureteral stent in 3D space to obtain a 3D segmentation result.
Lesion Localization and Pathological Diagnosis of Ovine Pulmonary Adenocarcinoma Based on MASK R-CNN.
Animals (Basel)
August 2024
College of Veterinary Medicine, Inner Mongolia Agricultural University, Zhao Wu Da Road No. 306, Hohhot 010018, China.
Article Synopsis
- Ovine pulmonary adenocarcinoma (OPA) is a contagious lung tumor in sheep caused by the Jaagsiekte Sheep Retrovirus (JSRV), with histopathological diagnosis being the gold standard but challenging to interpret.
- A study developed a dataset from 54 whole slide images of OPA to train a Mask R-CNN model for improved pathological image analysis, achieving a mean average specificity of 0.573 and average sensitivity of 0.745.
- The model demonstrated high accuracy in diagnosing OPA lesions, reaching 92.8% accuracy in a separate test with 500 images, proving it to be a reliable tool for rapid diagnosis alongside expert pathologists.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!