In recent times, magnetic resonance imaging (MRI) has emerged as a highly promising modality for diagnosing severe diseases. Its exceptional spatiotemporal resolution and ease of use have established it as an indispensable clinical diagnostic tool. Nevertheless, there are instances where MRI encounters challenges related to low contrast, necessitating the use of contrast agents (CAs). Significant efforts have been made by scientists to enhance the precision of observing diseased body parts by leveraging the synergistic potential of MRI in conjunction with other imaging techniques and thereby modifying the CAs. In this work, our focus is on elucidating the rational designing approach of CAs and optimizing their compatibility for multimodal imaging and other intelligent applications. Additionally, we emphasize the importance of incorporating various artificial intelligence tools, such as machine learning and deep learning, to explore the future prospects of disease diagnosis using MRI. We also address the limitations associated with these techniques and propose reasonable remedies, with the aim of advancing MRI as a cutting-edge diagnostic tool for the future.

Head and neck squamous cell carcinoma (HNSCC) originates from the squamous epithelium of the oral cavity, oropharynx, larynx, and hypopharynx. HNSCC in the oral cavity and larynx is strongly associated with tobacco smoking and alcohol consumption, while oropharyngeal cancer is increasingly attributed to infection by human papillomavirus (HPV), particularly HPV-16. The tumor microenvironment (TME) is a complex network of cancer cells, immune cells, stromal cells, surrounding blood vessels, and signaling molecules, and plays a critical role in tumor cell survival, invasion, and recurrence. Therefore, it is critical to elucidate the molecular basis of the interaction between tumor cells and the TME in order to develop innovative anti-cancer therapeutic strategies.

Cancer is one of the most lethal diseases in human society, and its incidence is gradually increasing. However, the current tumor treatment often meets the problem of poor efficacy and big side effects. The unique physical and chemical properties of nanomaterials can target the delivery of drugs to tumors, which can improve the therapeutic effect while reducing the damage of drugs to normal cells. This makes nanomaterials become a hot topic in the field of biomedicine. This review summarizes the recent progress of nanomaterials in tumor targeted therapy.

Based on Vickrey’s bottleneck model, we propose an activity-based bottleneck model to study traffic congestion on freeways during holidays. Based on the activity-based bottleneck model, we study the bottleneck ladder charging problem under the linear marginal activity utility and compare it with the traditional bottleneck model. Compared with the activity-based bottleneck model, the traditional bottleneck model overestimates the queue delay at the bottleneck, the level of step charging, and the start and end time of peak hours. Therefore, the activity-based step charging method disperses the traffic pressure on peak sections to a certain extent, avoiding traffic accidents caused by the transition period between charging and free.