No abstract

Frog virus 3 (FV3, genera Ranavirus, family Iridoviridae), a double-stranded DNA virus, results in irreparable damage to biodiversity and significant economic losses to aquaculture. Although the existing FV3 detection methods are of high sensitivity and specificity, the complex procedure and requirement of expensive instruments limit their practical implantation. Herein, we develop a fast, easy-to-implement, highly sensitive, and point-of-care (POC) detection system for FV3. Combining recombinase polymerase amplification (RPA) and CRISPR/Cas12a, we achieve a limit of detection (LoD) of 100 aM (60.2 copies/μL) by optimizing RPA primers and CRISPR RNAs (crRNAs). For POC detection, we build a smartphone microscopy (SPM) and achieve an LoD of 10 aM within 40 minutes. Four positive animal-derived samples with a quantitation cycle (Cq) value of quantitative PCR (qPCR) in the range of 13 to 32 are detectable by the proposed system. In addition, we deploy deep learning models for binary classification (positive or negative samples) and multiclass classification (different concentrations of FV3 and negative samples), achieving 100% and 98.75% accuracy, respectively. Without temperature regulation and expensive equipment, RPA-CRISPR/Cas12a combined with a smartphone readout and artificial intelligence (AI) assisted classification shows great potential for FV3 detection. This integrated system holds great promise for POC detection of aquatic DNA pathogens.

Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions. However, training these segmentation models requires a large number of manually annotated pixel-level labels, which are time-consuming and labor-intensive, in contrast to image-level labels that are easier to obtain. It is imperative to resolve this problem through weakly-supervised semantic segmentation models using image-level labels as supervision since it can significantly reduce human annotation efforts. Most of the advanced solutions exploit class activation mapping (CAM). However, the original CAMs rarely capture the precise boundaries of lesions. In this study, we propose the strategy of multi-scale inference to refine CAMs by reducing the detail loss in single-scale reasoning. For segmentation, we develop a novel model named Mixed-UNet, which has two parallel branches in the decoding phase. The results can be obtained after fusing the extracted features from two branches. We evaluate the designed Mixed-UNet against several prevalent deep learning-based segmentation approaches on our dataset collected from the local hospital and public datasets. The validation results demonstrate that our model surpasses available methods under the same supervision level in the segmentation of various lesions from brain imaging.