Periapical dental X-ray image classification using deep neural networks.

This paper studies the problem of detection of dental diseases. Dental problems affect the vast majority of the world's population. Caries, RCT (Root Canal Treatment), Abscess, Bone Loss, and missing teeth are some of the most common dental conditions that affect people of all ages all over the world. Delayed or incorrect diagnosis may result in mistreatment, affecting not only an individual's oral health but also his or her overall health, thereby making it an important research area in medicine and engineering. We propose a pipelined Deep Neural Network (DNN) approach to detect healthy and non-healthy periapical dental X-ray images. Even a minor enhancement or improvement in existing techniques can go a long way in providing significant health benefits in the medical field. This paper has made a successful attempt to contribute a different type of pipelined approach using AlexNet in this regard. The approach is trained on a large dataset of 16,000 dental X-ray images, correctly identifying healthy and non-healthy X-ray images. We use an optimized Convolutional Neural Networks and three state-of-the-art DNN models, namely Res-Net-18, ResNet-34, and AlexNet for disease classification. In our study, the AlexNet model outperforms the other models with an accuracy of 0.852. The precision, recall and F1 scores of AlexNet also surpass the other models with a score of 0.850 across all metrics. The area under ROC curve also signifies that both the false-positive rate and false-negative rate are low. We conclude that even with a big data set and raw X-ray pictures, the AlexNet model generalizes effectively to previously unseen data and can aid in the diagnosis of a variety of dental diseases.

Polymeric and lipid nanoparticles for delivery of self-amplifying RNA vaccines.

Self-amplifying RNA (saRNA) is a next-generation vaccine platform, but like all nucleic acids, requires a delivery vehicle to promote cellular uptake and protect the saRNA from degradation. To date, delivery platforms for saRNA have included lipid nanoparticles (LNP), polyplexes and cationic nanoemulsions; of these LNP are the most clinically advanced with the recent FDA approval of COVID-19 based-modified mRNA vaccines. While the effect of RNA on vaccine immunogenicity is well studied, the role of biomaterials in saRNA vaccine effectiveness is under investigated. Here, we tested saRNA formulated with either pABOL, a bioreducible polymer, or LNP, and characterized the protein expression and vaccine immunogenicity of both platforms. We observed that pABOL-formulated saRNA resulted in a higher magnitude of protein expression, but that the LNP formulations were overall more immunogenic. Furthermore, we observed that both the helper phospholipid and route of administration (intramuscular versus intranasal) of LNP impacted the vaccine immunogenicity of two model antigens (influenza hemagglutinin and SARS-CoV-2 spike protein). We observed that LNP administered intramuscularly, but not pABOL or LNP administered intranasally, resulted in increased acute interleukin-6 expression after vaccination. Overall, these results indicate that delivery systems and routes of administration may fulfill different delivery niches within the field of saRNA genetic medicines.