Many strategies have been adopted to construct in vitro myocardium models, which are of great value to both drug cardiotoxicity evaluation and cardiovascular drug development. In particular, the recent rapid development of human-induced pluripotent stem cell (hiPSC) technology and the rise of the organ-on-a-chip technique have provided great potential to achieve more physiologically relevant in vitro models. However, recapitulating the key role of the vasculature endothelial layer in drug action on myocardium in the models is still challenging. In this work, we developed an openable heart-on-a-chip system using highly purified functional hiPSC-derived cardiomyocytes (hiPSC-CMs) with an integrated vascular endothelial layer based on our previously proposed culture-patch method. The purity and functionality of the differentiated hiPSC-CMs were characterized, which were applied into the lower chamber of the sandwich-structured device to form the CM layer. The integrity and cell morphology of the endothelial layer on the culture patch as well as the influence of fluid shear force were studied, which was integrated in between the upper and lower chambers. The constructed heart-on-a-chip was finally applied for drug testing. The effect of two cardiac targeting drugs (isoproterenol and E-4031) directly on the hiPSC-CMs or after penetrating through the endothelial layer under static or dynamic conditions was evaluated. The results demonstrated the significance of a vascular layer in in vitro myocardium models for drug testing, as well as the advantage and potential of the proposed platform for cardiovascular drug evaluation with more human physiological relevance.

Despite numerous clinical trials and pre-clinical developments, the diagnosis of cracked tooth, especially in the early stages, remains a challenge. Cracked tooth syndrome is often accompanied by dramatic painful responses from occlusion and temperature stimulation, which has become one of the leading causes for tooth loss in adults. Current clinical diagnostical approaches for cracked tooth have been widely investigated based on X-rays, optical light, ultrasound wave, etc. Advances in artificial intelligence (AI) development have unlocked the possibility of detecting the crack in a more intellectual and automotive way. This may lead to the possibility of further enhancement of the diagnostic accuracy for cracked tooth disease. In this review, various medical imaging technologies for diagnosing cracked tooth are overviewed. In particular, the imaging modality, effect and the advantages of each diagnostic technique are discussed. What’s more, AI-based crack detection and classification methods, especially the convolutional neural network (CNN)-based algorithms, including image classification (AlexNet), object detection (YOLO, Faster-RCNN), semantic segmentation (U-Net, Segnet) are comprehensively reviewed. Finally, the future perspectives and challenges in the diagnosis of the cracked tooth are lighted.

Accurate and prompt detection of cracked teeth plays a critical role for human oral health. The aim of this paper is to evaluate the performance of a tooth crack segmentation model (namely, FDB-DeepLabv3+) on optical microscopic images. The FDB-DeepLabv3+ model proposed here improves feature learning by replacing the backbone with ResNet50. Feature pyramid network (FPN) is introduced to fuse muti-level features. Densely linked atrous spatial pyramid pooling (Dense ASPP) is applied to achieve denser pixel sampling and wider receptive field. Bottleneck attention module (BAM) is embedded to enhance local feature extraction. Through testing on a self-made hidden cracked tooth dataset, the proposed method outperforms four classical networks (FCN, U-Net, SegNet, DeepLabv3+) on segmentation results in terms of mean pixel accuracy (MPA) and mean intersection over union (MIoU). The network achieves an increase of 11.41% in MPA and 12.14% in MIoU compared to DeepLabv3+. Ablation experiments shows that all the modifications are beneficial. An improved network is designed for segmenting tooth surface cracks with good overall performance and robustness, which may hold significant potential in computer-aided diagnosis of cracked teeth.

Gelatin, known for its excellent biocompatibility, strong aggregative properties, and low cost, has been extensively investigated as a promising material for food packaging. Among various fabrication methods, electrospinning stands out due to its simplicity, cost-effectiveness, high process controllability, and ability to produce nanofiber membranes with enhanced properties. This review provides a comprehensive overview of the sources, properties, and applications of gelatin, along with the fundamental principles of electrospinning and its applications in food packaging. Additionally, the common types of electrospinning techniques used in food packaging are also covered. In recent years, increasing research efforts have focused on gelatin-based electrospun nanofiber membranes for food packaging applications. The functionalization of electrospinning gelatin-based nanofiber membrane was realized by incorporating various active substances or combining it with other techniques, fulfilling the new requirements of food packaging. In this review, gelatin-based electrospun nanofiber membranes for food packaging applications are overviewed, with a particular emphasis on various types of modifications for the membranes aimed at meeting diverse application demands. Finally, the future perspectives and challenges in the research of gelatin-based electrospun nanofiber membranes for food packaging are discussed.