Deep Learning-Based Recognition of Periodontitis and Dental Caries in Dental X-ray Images.

Dental X-ray images are important and useful for dentists to diagnose dental diseases. Utilizing deep learning in dental X-ray images can help dentists quickly and accurately identify common dental diseases such as periodontitis and dental caries. This paper applies image processing and deep learning technologies to dental X-ray images to propose a simultaneous recognition method for periodontitis and dental caries. The single-tooth X-ray image is detected by the YOLOv7 object detection technique and cropped from the periapical X-ray image. Then, it is processed through contrast-limited adaptive histogram equalization to enhance the local contrast, and bilateral filtering to eliminate noise while preserving the edge. The deep learning architecture for classification comprises a pre-trained EfficientNet-B0 and fully connected layers that output two labels by the sigmoid activation function for the classification task. The average precision of tooth detection using YOLOv7 is 97.1%. For the recognition of periodontitis, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve is 98.67%, and the AUC of the precision-recall (PR) curve is 98.38%. For the recognition of dental caries, the AUC of the ROC curve is 98.31%, and the AUC of the PR curve is 97.55%. Different from the conventional deep learning-based methods for a single disease such as periodontitis or dental caries, the proposed approach can provide the recognition of both periodontitis and dental caries simultaneously. This recognition method presents good performance in the identification of periodontitis and dental caries, thus facilitating dental diagnosis.

Generation and Characterization of Single Chain Variable Fragment against Alpha-Enolase of Candida albicans.

Candida albicans (C. albicans) is an opportunistic human pathogen responsible for approximately a half of clinical candidemia. The emerging Candida spp. with resistance to azoles is a major challenge in clinic, suggesting an urgent demand for new drugs and therapeutic strategies. Alpha-enolase (Eno1) is a multifunctional protein and represents an important marker for invasive candidiasis. Thus, C. albicans Eno1 (CaEno1) is believed to be an important target for the development of therapeutic agents and antibody drugs. Recombinant CaEno1 (rCaEno1) was first used to immunize chickens. Subsequently, we used phage display technology to construct two single chain variable fragment (scFv) antibody libraries. A novel biopanning procedure was carried out to screen anti-rCaEno1 scFv antibodies, whose specificities were further characterized. The polyclonal IgY antibodies showed binding to rCaEno1 and native CaEno1. A dominant scFv (CaS1) and its properties were further characterized. CaS1 attenuated the growth of C. albicans and inhibited the binding of CaEno1 to plasminogen. Animal studies showed that CaS1 prolonged the survival rate of mice and zebrafish with candidiasis. The fungal burden in kidney and spleen, as well as level of inflammatory cytokines were significantly reduced in CaS1-treated mice. These results suggest CaS1 has potential of being immunotherapeutic drug against C. albicans infections.

Machine learning to relate PM2.5 and PM10 concentrations to outpatient visits for upper respiratory tract infections in Taiwan: A nationwide analysis.

AIM: To examine the accuracy of machine learning to relate particulate matter (PM) 2.5 and PM10 concentrations to upper respiratory tract infections (URIs). METHODS: Daily nationwide and regional outdoor PM2.5 and PM10 concentrations collected over 30 consecutive days obtained from the Taiwan Environment Protection Administration were the inputs for machine learning, using multilayer perceptron (MLP), to relate to the subsequent one-week outpatient visits for URIs. The URI data were obtained from the Centers for Disease Control datasets in Taiwan between 2009 and 2016. The testing used the middle month dataset of each season (January, April, July and October), and the training used the other months' datasets. The weekly URI cases were classified by tertile as high, moderate, and low volumes. RESULTS: Both PM concentrations and URI cases peak in winter and spring. In the nationwide data analysis, MLP machine learning can accurately relate the URI volumes of the elderly (89.05% and 88.32%, respectively) and the overall population (81.75% and 83.21%, respectively) with the PM2.5 and PM10 concentrations. In the regional data analyses, greater accuracy is found for PM2.5 than for PM10 for the elderly, particularly in the Central region (78.10% and 74.45%, respectively), whereas greater accuracy is found for PM10 than for PM2.5 for the overall population, particularly in the Northern region (73.19% and 63.04%, respectively). CONCLUSION: Short-term PM2.5 and PM10 concentrations were accurately related to the subsequent occurrence of URIs by using machine learning. Our findings suggested that the effects of PM2.5 and PM10 on URI may differ by age, and the mechanism needs further evaluation.