Mast cells (MCs) play important roles in multiple pathologies, including fibrosis; however, their behaviors in different extracellular matrix (ECM) environments have not been fully elucidated. Accordingly, in this study, the migration of MCs on substrates with different stiffnesses was investigated using time-lapse video microscopy. Our results showed that MCs could appear in round, spindle, and star-like shapes; spindle-shaped cells accounted for 80-90 % of the total observed cells. The migration speed of round cells was significantly lower than that of cells with other shapes. Interestingly, spindle-shaped MCs migrated in a jiggling and wiggling motion between protrusions. The persistence index of MC migration was slightly higher on stiffer substrates. Moreover, we found that there was an intermediate optimal stiffness at which the migration efficiency was the highest. These findings may help to improve our understanding of MC-induced pathologies and the roles of MC migration in the immune system.

Early and accurate diagnosis of ear deformities in newborns is crucial for an effective non-surgical correction treatment, since this commonly seen ear anomalies would affect aesthetics and cause mental problems if untreated. It is not easy even for experienced physicians to diagnose the auricular deformities of newborns and the classification of the sub-types, because of the rich bio-metric features embedded in the ear shape. Machine learning has already been introduced to analyze the auricular shape. However, there is little publicly available datasets of ear images from newborns. We released a dataset that contains quality-controlled photos of 3,852 ears from 1,926 newborns. The dataset also contains medical diagnosis of the ear shape, and the health data of each newborn and its mother. Our aim is to provide a freely accessible dataset, which would facilitate researches related with ear anatomies, such as the AI-aided detection and classification of auricular deformities and medical risk analysis.

A Janus membrane can not only heal the tympanic membrane perforation with its hydrophilic surface, but also possesses the anti-adhesion properties due to its hydrophobic surface.

Auricular deformities are common in newborns and require early diagnosis and timely intervention. Several factors highlight the necessity of a machine learning-based diagnostic solution: the high prevalence of these conditions, the narrow time window for effective non-surgical treatment, limited medical resources, and the importance of both physical and mental well-being. This study presents a novel artificial intelligence (AI) model to identify and classify common sub-types of auricle deformities, using photos taken with mobile devices. The dataset was made up of the open-source dataset named BabyEar4k, which contains 3852 auricle images with diagnosis data, and another private dataset containing 104 microtia ears added from ENT Hospital of Fudan University. All the training photos were pre-processed to 800 × 800 RGB images, with the auricles located at the centers. The dataset was divided into two parts, 3835 samples for training/validation and 120 (20 for each class) for testing, i.e., the internal test dataset. 15% of the training data were used for validation during the training process. External validation was conducted on data from three centres across China (Xinjiang N = 252, Guizhou N = 186, and Fujian N = 252). The performance of the model was evaluated by comparative analyses with human volunteers. A prospective test set was collected in Shanghai (Obstetrics & Gynecology Hospital of Fudan University, from 2023/10/17 to 2023/12/29; N = 272). Given the significant variation in the distribution of sub-types, accuracy and weighted F1-score were chosen as primary evaluation metrics. Four different backbone architectures were evaluated: ResNet50, DenseNet121, EfficientNet, and RegNet. On the internal test set, the model achieved an accuracy of 0.83-0.85 for six-class classification and 0.94-0.98 for binary classification. ResNet50 backbone had the most consistent performance. Multi-center real-world data validation demonstrated satisfactory accuracy, with a range of 0.74-0.82 for six-class classification and 0.79-0.86 for normal/abnormal classification, indicating strong generalizability. In comparative analyses with volunteers, the professionals achieved an accuracy of 0.7-0.8 in the six-class classification task, while the related fellows scored 0.45-0.65, and the laypeople scored 0.45-0.55. The developed system offers an efficient and cost-effective solution for clinical applications, including early diagnosis of newborn auricular deformities, monitoring treatment progress, and educational purposes. This study was supported by Shanghai Science and Technology Innovation Action Plan (23Y21900200, 21DZ2200700, T-Y Zhang) and Medical Engineering Fund of Fudan University (Y-Y Fu). S Wang was supported by the Shanghai Sailing Program (22YF1409300) and China Computer Federation (CCF)-Baidu Open Fund Grant (CCF-BAIDU 202316).