Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat to people’s health. Therefore, diagnosing lung nodules at an early stage is crucial to improving patient survival rates. Numerous computer-aided diagnosis (CAD) systems have been developed to detect and classify such nodules in their early stages. Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification. A number of review articles have considered various components of such systems, but this review focuses on segmentation and classification parts. Specifically, categorizing segmentation parts based on lung nodule type and network architectures, i.e., general neural network and multiview convolution neural network (CNN) architecture. Moreover, this work organizes related literature for classification of parts based on nodule or non-nodule and benign or malignant. The essential CT lung datasets and evaluation metrics used in the detection and diagnosis of lung nodules have been systematically summarized as well. Thus, this review provides a baseline understanding of the topic for interested readers.

Nanobodies are artificial antibodies derived from the immune system of camelid species, obtained through artificial processing and isolation of antigen-binding proteins. Their small molecular size and high specificity endow nanobodies with extensive potential applications in various fields. However, determining nanobody paratopes through experimental methods is both costly and time-consuming, while traditional computational approaches often lack sufficient accuracy. To enhance the prediction accuracy, we propose Nanotope, a structurebased model capable of efficiently and accurately predicting paratopes, utilizing graph neural networks and an antibody pretrained language model. Our approach primarily leverages sequence features obtained from the AntiBERTy antibody pretrained language model and three-dimensional spatial structure features of nanobodies as inputs. Employing one-dimensional convolution, EGConv graph convolution, and GATConv convolution, we predict the probability of nanobody paratopes with a much higher accuracy. Our method significantly improves the prediction of nanobody paratopes and requires only nanobody sequences and structural information, without the need for additional antigen information. Source code is freely available at https://github.com/WangLabforComputationalBiology/Nanotope