Advances and challenges in the development of periodontitis vaccines: A comprehensive review.

Periodontitis is a prevalent polymicrobial disease. It damages soft tissues and alveolar bone, and causes a significant public-health burden. Development of an advanced therapeutic approach and exploration of vaccines against periodontitis hold promise as potential treatment avenues. Clinical trials for a periodontitis vaccine are lacking. Therefore, it is crucial to address the urgent need for developing strategies to implement vaccines at the primary level of prevention in public health. A deep understanding of the principles and mechanisms of action of vaccines plays a crucial role in the successful development of vaccines and their clinical translation. This review aims to provide a comprehensive summary of potential directions for the development of highly efficacious periodontitis vaccines. In addition, we address the limitations of these endeavors and explore future possibilities for the development of an efficacious vaccine against periodontitis.

The Impact of Artificial Intelligence on Microbial Diagnosis.

Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.