Comparative Evaluation of PCR-Based, LAMP and RPA-CRISPR/Cas12a Assays for the Rapid Detection of Diaporthe aspalathi.

Southern stem canker (SSC) of soybean, attributable to the fungal pathogen Diaporthe aspalathi, results in considerable losses of soybean in the field and has damaged production in several of the main soybean-producing countries worldwide. Early and precise identification of the causal pathogen is imperative for effective disease management. In this study, we performed an RPA-CRISPR/Cas12a, as well as LAMP, PCR and real-time PCR assays to verify and compare their sensitivity, specificity and simplicity and the practicality of the reactions. We screened crRNAs targeting a specific single-copy gene, and optimized the reagent concentrations, incubation temperatures and times for the conventional PCR, real-time PCR, LAMP, RPA and Cas12a cleavage stages for the detection of D. aspalathi. In comparison with the PCR-based assays, two thermostatic detection technologies, LAMP and RPA-CRISPR/Cas12a, led to higher specificity and sensitivity. The sensitivity of the LAMP assay could reach 0.01 ng µL-1 genomic DNA, and was 10 times more sensitive than real-time PCR (0.1 ng µL-1) and 100 times more sensitive than conventional PCR assay (1.0 ng µL-1); the reaction was completed within 1 h. The sensitivity of the RPA-CRISPR/Cas12a assay reached 0.1 ng µL-1 genomic DNA, and was 10 times more sensitive than conventional PCR (1.0 ng µL-1), with a 30 min reaction time. Furthermore, the feasibility of the two thermostatic methods was validated using infected soybean leaf and seeding samples. The rapid, visual one-pot detection assay developed could be operated by non-expert personnel without specialized equipment. This study provides a valuable diagnostic platform for the on-site detection of SSC or for use in resource-limited areas.

Spatio-Temporal Classification of Lung Ventilation Patterns using 3D EIT Images: A General Approach for Individualized Lung Function Evaluation.

The Pulmonary Function Test (PFT) is a widely utilized and rigorous classification test for evaluating lung function, serving as a comprehensive diagnostic tool for lung conditions. Meanwhile, Electrical Impedance Tomography (EIT) is a rapidly advancing clinical technique that visualizes conductivity distribution induced by ventilation. EIT provides additional spatial and temporal information on lung ventilation beyond traditional PFT. However, relying solely on conventional isolated interpretations of PFT results and EIT images overlooks the continuous dynamic aspects of lung ventilation. This study aims to classify lung ventilation patterns by extracting spatial and temporal features from the 3D EIT image series. The study uses a Variational Autoencoder (VAE) with a MultiRes block to compress the spatial distribution in a 3D image into a one-dimensional vector. These vectors are then stacked to create a feature map for the exhibition of temporal features. A simple convolutional neural network is used for classification. Data from 137 subjects were utilized for the training phase. Initially, the model underwent validation through a leave-one-out cross-validation process. During this validation, the model achieved an accuracy and sensitivity of 0.96 and 1.00, respectively, with an f1-score of 0.98 when identifying the normal subjects. To assess pipeline reliability and feasibility, we tested it on 9 newly recruited subjects, with accurate ventilation mode predictions for 8 out of 9. In addition, we included 2D EIT results for comparison and conducted ablation experiments to validate the effectiveness of the VAE. The study demonstrates the potential of using image series for lung ventilation mode classification, providing a feasible method for patient prescreening and presenting an alternative form of PFT.

Multi-task edge-recalibrated network for male pelvic multi-organ segmentation on CT images.

Automated male pelvic multi-organ segmentation on CT images is highly desired for applications, including radiotherapy planning. To further improve the performance and efficiency of existing automated segmentation methods, in this study, we propose a multi-task edge-recalibrated network (MTER-Net), which aims to overcome the challenges, including blurry boundaries, large inter-patient appearance variations, and low soft-tissue contrast. The proposed MTER-Net is equipped with the following novel components. (a) To exploit the saliency and stability of femoral heads, we employed a light-weight localization module to locate the target region and efficiently remove the complex background. (b) We add an edge stream to the regular segmentation stream to focus on processing the edge-related information, distinguish the organs with blurry boundaries, and then boost the overall segmentation performance. Between the regular segmentation stream and edge stream, we introduce an edge recalibration module at each resolution level to connect the intermediate layers and deliver the higher-level activations from the regular stream to the edge stream to denoise the irrelevant activations. (c) Finally, using a 3D Atrous Spatial Pyramid Pooling (ASPP) feature fusion module, we fuse the features at different scales in the regular stream and the predictions from the edge stream to form the final segmentation result. The proposed segmentation network was evaluated on 200 prostate cancer patient CT images with manually delineated contours of bladder, rectum, seminal vesicle, and prostate. The segmentation performance of the proposed method was quantitatively evaluated using three metrics including Dice similarity coefficient (DSC), average surface distance (ASD), and 95% surface distance (95SD). The proposed MTER-Net achieves average DSC of 86.35%, ASD of 1.09 mm, and 95SD of 3.53 mm on the four organs, which outperforms the state-of-the-art segmentation networks by a large margin. Specifically, the quantitative DSC evaluation results of the four organs are 96.49% (bladder), 86.39% (rectum), 76.38% (seminal vesicle), and 86.14% (prostate), respectively. In conclusion, we demonstrate that the proposed MTER-Net efficiently attains superior performance to state-of-the-art pelvic organ segmentation methods.