TDFFM: Transformer and Deep Forest Fusion Model for Predicting Coronavirus 3C-Like Protease Cleavage Sites.

COVID-19, caused by the highly contagious SARS-CoV-2 virus, is distinguished by its positive-sense, single-stranded RNA genome. A thorough understanding of SARS-CoV-2 pathogenesis is crucial for halting its proliferation. Notably, the 3C- like protease of the coronavirus (denoted as 3CLpro) is instrumental in the viral replication process. Precise delineation of 3CLpro cleavage sites is imperative for elucidating the transmission dynamics of SARS-CoV-2. While machine learning tools have been deployed to identify potential 3CLpro cleavage sites, these existing methods often fall short in terms of accuracy. To improve the performances of these predictions, we propose a novel analytical framework, the Transformer and Deep Forest Fusion Model (TDFFM). Within TDFFM, we utilize the AAindex and the BLOSUM62 matrix to encode protein sequences. These encoded features are subsequently input into two distinct components: a Deep Forest, which is an effective decision tree ensemble methodology, and a Transformer equipped with a Multi-Level Attention Model (TMLAM). The integration of the attention mechanism allows our model to more accurately identify positive samples, thus enhancing the overall predictive performance. Evaluation on a test set demonstrates that our TDFFM achieves an accuracy of 0.955, an AUC of 0.980, and an F1-score of 0.367, substantiating the model's superior prediction capabilities.

The use of cardiac magnetic resonance in hypertrophic cardiomyopathy over the past 10 years [2013-2023]: a CiteSpace-based bibliometric analysis.

Background: Hypertrophic cardiomyopathy (HCM) is a common genetic cardiac disorder characterized by the hypertrophy of a segment of the myocardium. Cardiac magnetic resonance (CMR) has been widely used in the assessment of HCM. However, no bibliometric assessment has been conducted on the progress of research in this field. This study thus aimed to examine the current state of research into the application of CMR in HCM and the hotspots and trends that have emerged in this field over the past decade. Methods: A systematic search was conducted on the Web of Science regarding CMR in the assessment of HCM. The databases were searched from 2013 to June 2023. CiteSpace is an application that can be used to characterize the underlying knowledge of the scientific literature in a given field. We used it to analyze the relationship between publication year and country, institution, journal, author, bibliography, and keywords in the field of CMR for the assessment of HCM. Results: A total of 1,427 articles were included in the analysis. In the assessment of HCM, the findings from the past decade have consistently demonstrated a progressive rise in the quantity of articles pertaining to CMR. The country with the largest number of publications was the United States [310], and the institution with the greatest number of publications was the University College London [45]. The analysis of keywords revealed the diagnosis and management of HCM with CMR to be the current research focus and emerging trend within this academic field. Conclusions: This study used a novel approach to visually analyze the use of CMR in HCM assessment. The current research trajectory in CMR consists of the diagnosis and management of patients with HCM. Although most studies confirmed the indispensability of CMR in the assessment of HCM, larger-scale cohorts are still needed to more comprehensively evaluate the role of CMR in the differential diagnosis, pre- and post-treatment assessment, and long-term management of patients with HCM.

Comparison of performances of conventional and deep learning-based methods in segmentation of lung vessels and registration of chest radiographs.

Conventional machine learning-based methods have been effective in assisting physicians in making accurate decisions and utilized in computer-aided diagnosis for more than 30 years. Recently, deep learning-based methods, and convolutional neural networks in particular, have rapidly become preferred options in medical image analysis because of their state-of-the-art performance. However, the performances of conventional and deep learning-based methods cannot be compared reliably because of their evaluations on different datasets. Hence, we developed both conventional and deep learning-based methods for lung vessel segmentation and chest radiograph registration, and subsequently compared their performances on the same datasets. The results strongly indicated the superiority of deep learning-based methods over their conventional counterparts.

The effect of pulmonary vessel suppression on computerized detection of nodules in chest CT scans.

PURPOSE: In chest computed tomography (CT) scans, pulmonary vessel suppression can make pulmonary nodules more evident, and therefore may increase the detectability of early lung cancer. The purpose of this study was to develop a computer-aided detection (CAD) system with a vessel suppression function and to verify the effectiveness of the vessel suppression on the performance of the pulmonary nodule CAD system. METHODS: A CAD system with a vessel suppression function capable of suppressing vessels and detecting nodules was developed. First, a convolutional neural network (CNN)-based pulmonary vessel suppression technique was employed to remove the vessels from lungs while preserving the nodules. Then, a CNN-based pulmonary nodule detector was utilized to sequentially generate nodule candidates and reduce false positives (FPs). The performance levels of CAD systems with and without the vessel suppression function were compared using 888 three-dimensional chest CT scans from the Lung Nodule Analysis 2016 (LUNA16) dataset. The pulmonary nodule detection results were quantitatively evaluated using the average sensitivity at seven predefined FP rates: 0.125, 0.25, 0.5, 1, 2, 4, and 8 FPs per scan. RESULTS: The developed pulmonary nodule CAD system improved the average sensitivity to 0.977 from 0.950 owing to the addition of the vessel suppression function. CONCLUSIONS: The vessel suppression function considerably improved the performance of the CAD system for pulmonary nodule detection. In practice, it would be embedded in CAD systems to assist radiologists in detecting pulmonary nodules in chest CT scans.