ObjectiveTo compare drug clinical trials between China, the United States, Europe and Japan in terms of study type, design, completion and results publication.MethodsWe randomly selected 190 clinical trials that were registered in ClinicalTrials.gov from 2009 to 2014, and followed up to December 31st, 2019. Comparisons were made for the type of sponsor, phase, design, and completion status by the sponsor’s country.ResultsAmong all included clinical trials, trials from the United States, Europe, Japan and China accounted for 50.5%, 34.2%, 9.0% and 6.3%, respectively. Among these trials, 71.1% had been completed and 69.5% disclosed results had been published publicly prior to the end of follow-up, and differences between countries were statistically significant (P<0.05). Two-thirds of the trials in China were phase Ⅲ/Ⅳ trials; in contrast, most of the clinical trials in the United States and Europe were phase Ⅰ/Ⅱ trials. The proportion of using double-blind, randomized controlled trial design was the highest in the United States (46.9%) and the lowest in China (8.3%). Chinese sponsors were mostly hospitals/universities (58.3%), while in other countries drug trials were mostly sponsored by the industry and in Japan the proportion was as high as 94.0%.ConclusionsThe number of drug trials registered in ClinicalTrials.gov from China is small and these trials are less likely to be completed and have results published/disclosed. Pharmaceutical companies in China should pay more attention to the public registration of their clinical trials, particularly those in early phases, and improve trial design and management.
Ultraviolet radiation is a primary external factor contributing to skin photoaging, as it induces cellular deoxyribonucleic acid damage and collagen degeneration, thereby accelerating skin aging and increasing the risk of skin cancer. Currently, skin aging assessment mainly relies on dermatologists’ empirical judgment, which is inherently subjective and inefficient. To address these limitations, this study proposes a deep learning–driven intelligent skin aging classification method based on high-frequency ultrasound images. The proposed model employs efficient network version 2 (EfficientNetV2) as the backbone network and introduces the Gaussian error linear unit (GELU) activation function and layer normalization to enhance nonlinear feature representation and training stability. In addition, a global-aware temporal hierarchical network is integrated to enable efficient multi-scale feature extraction of skin tissue. A dual enhancement attention mechanism, combining parallel channel–spatial attention and squeeze-and-excitation modules, is further designed to improve the model’s sensitivity to key aging-related regions. Moreover, a multi-scale path dropout regularization strategy is adopted to effectively alleviate overfitting. Experiments conducted on a facial high-frequency ultrasound dataset collected from subjects aged 25~55 years demonstrate that the proposed method achieves an accuracy of 87.66%, a precision of 88.27%, a recall of 87.66%, an F1 score of 87.80%, and a specificity of 97.94%, consistently outperforming existing mainstream models. These results indicate that the proposed approach enables high-precision identification of skin aging levels and provides an efficient and objective auxiliary diagnostic tool for skincare, anti-aging treatment, and the prevention of photoaging-related skin diseases.