Xiaofeng Gu

Objective: Upper respiratory tract infections are among the most prevalent respiratory diseases, imposing both financial and physical burdens on affected individuals. Roxithromycin (ROX), a primary drug for treating bacterial-induced respiratory tract infections, is typically administered orally due to its hydrophobic nature. However, the non-specific distribution resulting from oral administration reduces bioavailability and can cause side effects such as diarrhea. Methods: In this study, we prepared a thermo-sensitive in-situ gel using a facile and highly reproducible method by simply mixing two types of poloxamers with ROX. Results: The ROX can be well dissolved in the poloxamer matrix in amorphous state to give solution. Upon intranasal administration, the ROX solution undergoes a phase transition to form in-situ gel under body temperature. This gel remains in the nasal cavity for an extended period, releasing the drug directly to the site of infection and minimizing non-specific distribution. Pharmacokinetic experiments revealed that, compared to oral administration, the bioavailability of local nasal administration increased by 1.5 times, and the drug concentration in the local nasal cavity increased by 8 times. In contrast, concentrations in the liver and small intestine did not significantly differ from those following oral administration. In vivo antibacterial experiments also showed that the ROX in-situ gel has superior antibacterial efficacy and excellent biocompatibility. Conclusion: These results suggest that the thermo-sensitive ROX in-situ gel is a promising formulation for treating bacterial upper respiratory tract infections.

Nasopharyngeal carcinoma (NPC) is one of the most common head and neck cancers. Early diagnosis plays a critical role in the treatment of NPC. To aid diagnosis, deep learning methods can provide interpretable clues for identifying NPC from magnetic resonance images (MRI). To identify the optimal models, we compared the discrimination performance of hierarchical and simple layered convolutional neural networks (CNN). Retrospectively, we collected the MRI images of patients and manually built the tailored NPC image dataset. We examined the performance of the representative CNN models including shallow CNN, ResNet50, ResNet101, and EfficientNet-B7. By fine-tuning, shallow CNN, ResNet50, ResNet101, and EfficientNet-B7 achieved the precision of 72.2%, 94.4%, 92.6%, and 88.4%, displaying the superiority of deep hierarchical neural networks. Among the examined models, ResNet50 with pre-trained weights demonstrated the best classification performance over other types of CNN with accuracy, precision, and an F1-score of 0.93, 0.94, and 0.93, respectively. The fine-tuned ResNet50 achieved the highest prediction performance and can be used as a potential tool for aiding the diagnosis of NPC tumors.