Enhancing treatment strategies for small bowel cancer: a clinical review of targeted therapy and immunotherapy approaches.

Small bowel cancer (SBC) is a rare and aggressive disease with a poor prognosis, necessitating the exploration of novel treatment approaches. This narrative review examines the current evidence on targeted therapy and immunotherapy for SBC, focusing on the two most common subtypes: adenocarcinoma and neuroendocrine tumor. A comprehensive search of PubMed, Scopus, and Google Scholar databases was conducted to identify relevant clinical trials and case reports published in English up to September 2023. The review includes 17 clinical trials and 10 case reports, indicating that targeted therapy and immunotherapy can have the potential to improve survival rates in patients with SBC. Notably, promising targeted medicines include bevacizumab, cetuximab, and trastuzumab, while pembrolizumab and nivolumab show potential as immunotherapies. However, it should be noted that the magnitude of the increase in survival rates with these interventions was small. Further research is needed to determine the optimal combination of targeted therapy and immunotherapy for individual patients with SBC.

Deep multi-view feature learning for detecting COVID-19 based on chest X-ray images.

AIM: COVID-19 is a pandemic infectious disease which has influenced the life and health of many communities since December 2019. Due to the rapid worldwide spread of this highly contagious disease, making its early detection with high accuracy important for breaking the chain of transition. X-ray images of COVID-19 patients, reveal specific abnormalities associated with this disease. METHODS: In this study, a multi-view feature learning method for detecting COVID-19 based on chest X-ray images is presented. This method provides a framework for exploiting the multiple types of deep features, which is able to preserve both the correlative and the complementary information, and achieve accurate detection at the classification phase. Deep features are extracted using pre-trained deep CNN models of AlexNet, GoogleNet, ResNet50, SqueezeNet, and VGG19. The learned feature representation of X-ray images are then classified using ELM. RESULTS: The experiments show that our method achieves accuracy scores of 100%, 99.82%, and 99.82% in detecting three classes of COVID-19, normal, and pneumonia, respectively. The sensitivities of three classes are 100%, 100%, and 99.45%, respectively. The specificities of three classes are 100%, 99.73%, and 100%, respectively. The precision values of three classes are 100%, 99.45%, and 100%, respectively. The F-scores of three classes are 100%, 99.73%, and 99.72%, respectively. The overall accuracy score of our method is 99.82%. CONCLUSIONS: The results demonstrate the effectiveness of our method in detecting COVID-19 cases and can therefore assist experts in early diagnosis based on X-ray images.