Lung cancer has become one of the malignant tumors with the highest morbidity and mortality rate worldwide. The early images of lung cancer are pulmonary nodules, and early detection and diagnosis can help reduce the incidence of lung cancer. However, due to the large differences in shape, size and location of pulmonary nodules in medical imaging, the detection of small nodules is very challenging. This paper proposes a new model, based on improved YOLOv5s, called YOLOv5-CASP. The proposed YOLOv5s improvements include: (i) incorporating Convolutional Block Attention Modules (CBAM) to suppress the interference features of the medical images through a channel dimension and spatial dimension, and to improve the detection accuracy of the model; (ii) optimizing the Spatial Pyramid Pooling - Fast (SPPF) module of YOLOv5s to Atrous Spatial Pyramid Pooling (ASPP) as to increase the model’s receptive field of images of different sizes and extract multi-scale contextual information for improving the detection performance on small lung nodules; and (iii) introducing a Contextual Transformer (CoT) module to optimize part of the CSPDarknet53 module of YOLOv5s in order to enhance the characteristics of the model while removing redundant operations extraction capacity. Experimental results conducted on two public datasets confirm that the proposed YOLOv5-CASP model outperforms the original YOLOv5s model and other five state-of-the-art models (Faster R-CNN, SSD, YOLOv4-Tiny, DETR-R50, Deformable DETR-R50), in terms of the mean average precision ( mAP ) and F1 score , by achieving corresponding values of 72% and 0.740 on the LUNA16 dataset, and 79.44% and 0.766 on the X-Nodule dataset.

Skin cancer is a common malignant tumor that is a major threat to the health of patients. Automated classification of skin lesions using computer algorithms is crucial for enhancing diagnostic efficiency and reducing mortality rates associated with skin cancer. Enhancing the capabilities of image classification models for skin lesions is essential to assist in accurately classifying skin diseases of patients. Aiming at this goal, a novel EFAM-Net model is proposed in this paper for the skin lesion classification task. Firstly, a newly designed Attention Residual Learning ConvNeXt (ARLC) block is used to extract low-level features such as colors and textures in images. Then, the deep blocks of the network are replaced with a newly designed Parallel ConvNeXt (PCNXt) block, allowing to capture richer and more complex features. Additionally, another newly designed Multi-scale Efficient Attention Feature Fusion (MEAFF) block enhances feature extraction at various scales to effectively capture more comprehensive features in specific layers and fuse feature maps of different scales to enhance feature reuse at the end. EFAM-Net is experimentally evaluated on the ISIC 2019 and HAM10000 public datasets, as well as on a private dataset. The obtained results show that EFAM-Net achieves top classification performance among all compared models, by achieving overall accuracy of 92.30%, 93.95%, and 94.31% on the ISIC 2019, HAM10000, and private dataset, respectively.

Brain metastasis (BM) is a major complication of lung adenocarcinoma (LAD). An investigation of the pathogenic mechanisms of BM, as well as the identification of appropriate molecular markers, is necessary. The aim of this study was to determine the expression patterns of microRNAs (miRNAs) in LAD with BM, and to investigate the biological role of these miRNAs during tumorigenesis. miRNA array profiles were used to identify BM-associated miRNAs. These miRNAs were independently validated in 155 LAD patients. Several in vivo and in vitro assays were performed to verify the effects of miRNAs on BM. We identified six miRNAs differentially expressed in patients with BM as compared to patients with BM. Of these, miR-4270 and miR-423-3p were further investigated. miR-4270 and miR-423-3p directly targeted MMP19 and P21, respectively, to influence cell viability, migration, and colony formation in vitro. miR-4270 downregulation and miR-423-3p upregulation was associated with an increased risk of BM in LAD patients. Thus, our results suggested that miR-4270 and miR-423-3p might play an important role in BM pathogenesis in LAD patients, and that these miRNAs might be useful prognostic and clinical treatment targets. Brain metastasis (BM) is a major complication of lung carcinoma. Here, we aimed to identify the key miRNAs involved in BM lung cancer. We first profiled miRNA expression in 32 tissues from NSCLC patients with BM and 55 tissues from NSCLC patients without BM. We independently validated our results in 68 additional tissues from NSCLC patients. Based on our results, we identified a panel of miRNAs that distinguish BM lung adenocarcinomas from non-BM We report here for the first time that either miR-4270 downregulation or miR-423-3p upregulation significantly increased cell proliferation, colony formation, and migration in vitro. miR-4270 and miR-423-3p increased the risk of BM in mouse models by targeting MMP19 and P21, respectively. Our results suggested that miR-4270 and miR-423-3p might be useful markers of BM in lung adenocarcinoma.

The development of various therapeutic interventions, particularly immune checkpoint inhibitor therapy, have effectively induced tumor remission for patients with advanced lung cancer. However, few cancer patients can obtain significant and long-lasting therapeutic effects for the limitation of immunological nonresponse and resistance. For this case, it’s urgent to identify new biomarkers and develop therapeutic targets for future immunotherapy. Over the past decades, tumor microenvironment (TME)-related long non-coding RNAs (lncRNAs) have gradually become well known to us. A large number of existing studies have indicated that TME-related lncRNAs are one of the major factors to realize precise diagnosis and treatment of lung cancer. Herein, this paper discusses the roles of lncRNAs in TME, and the potential application of lncRNAs as biomarkers or therapeutic targets for immunotherapy in lung cancer.

Electrocardiograms (ECG) are the primary basis for the diagnosis of cardiovascular diseases. However, due to the large volume of patients’ ECG data, manual diagnosis is time-consuming and laborious. Therefore, intelligent automatic ECG signal classification is an important technique for overcoming the shortage of medical resources. This paper proposes a novel model for inter-patient heartbeat classification, named G2-ResNeXt, which adds a two-fold grouping convolution (G2) to the original ResNeXt structure, as to achieve better automatic feature extraction and classification of ECG signals. Experiments, conducted on the MIT-BIH arrhythmia database, confirm that the proposed model outperforms all state-of-the-art models considered (except the GRNN model for one of the heartbeat classes), by achieving overall accuracy of 96.16%, and sensitivity and precision of 97.09% and 95.90%, respectively, for the ventricular ectopic heartbeats (VEB), and of 80.59% and 82.26%, respectively, for the supraventricular ectopic heartbeats (SVEB).