Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.

Angiotensin-converting enzyme (ACE), a zinc metalloprotein, is a central component of the renin–angiotensin system (RAS). It degrades bradykinin and other vasoactive peptides. Angiotensin-converting-enzyme inhibitors (ACE inhibitors, ACEIs) decrease the formation of angiotensin II and increase the level of bradykinin, thus relaxing blood vessels as well as reducing blood volume, lowering blood pressure and reducing oxygen consumption by the heart, which can be used to prevent and treat cardiovascular diseases and kidney diseases. Nevertheless, ACEIs are associated with a range of adverse effects such as renal insufficiency, which limits their use. In recent years, researchers have attempted to reduce the adverse effects of ACEIs by improving the selectivity of ACEIs for structural domains based on conformational relationships, and have developed a series of novel ACEIs. In this review, we have summarized the research advances of ACE inhibitors, focusing on the development sources, design strategies and analysis of structure-activity relationships and the biological activities of ACE inhibitors.

Oral squamous cell carcinoma (OSCC) is prevalent around the world and is associated with poor prognosis. OSCC is typically diagnosed from tissue biopsy sections by pathologists who rely on their empirical experience. Deep learning models may improve the accuracy and speed of image classification, thus reducing human error and workload. Here we developed a custom-made deep learning model to assist pathologists in detecting OSCC from histopathology images. We collected and analyzed a total of 2,025 images, among which 1,925 images were included in the training set and 100 images were included in the testing set. Our model was able to automatically evaluate these images and arrive at a diagnosis with a sensitivity of 0.98, specificity of 0.92, positive predictive value of 0.924, negative predictive value of 0.978, and F1 score of 0.951. Using a subset of 100 images, we examined whether our model could improve the diagnostic performance of junior and senior pathologists. We found that junior pathologists were able to delineate OSCC in these images 6.26 min faster when assisted by the model than when working alone. When the clinicians were assisted by the model, their average F1 score improved from 0.9221 to 0.9566 in the case of junior pathologists and from 0.9361 to 0.9463 in the case of senior pathologists. Our findings indicate that deep learning can improve the accuracy and speed of OSCC diagnosis from histopathology images.

Liquid biopsy is conducted through minimally invasive or noninvasive procedures, and the resulting material can be subjected to genomic, proteomic, and lipidomic analyses for early diagnosis of cancers and other diseases. Extracellular vesicles (EVs), one kind of promising tool for liquid biopsy, are nanosized bilayer particles that are secreted by all kinds of cells and that carry cargoes such as lipids, proteins, and nucleic acids, protecting them from enzymatic degradation in the extracellular environment. In this review, we provide a comprehensive introduction to the properties and applications of EVs, including their biogenesis, contents, sample collection, isolation, and applications in diagnostics based on liquid biopsy.

Gold nanoparticles (AuNP) is a new type of metal nanomaterials used in the biomedical field. Compared with ordinary metal materials and other metal nanomaterials, AuNP can be very unique. AuNP has been proven to have good performance against a variety of pathogens, and research on its antibacterial activity and mechanism has also become a hot topic in recent years. This article summarizes the antibacterial properties and clinical applications of AuNP against different kinds of bacteria and analyzes and discusses its existing and potential antibacterial mechanisms. At the same time, we also put forward the current challenges and problems to be solved by AuNP and look forward to its future development prospects, in order to correctly and comprehensively introduce this new antibacterial method.