Diagnostic Accuracy of a Point-Of-Care aMMP-8 Test for Discriminating Periodontal Health Status in Adults: Validation Trials and Updated Meta-Analysis.

Aim: To evaluate the diagnostic accuracy of an active matrix metalloproteinase-8 (aMMP-8) point-of-care oral rinse test (POC-ORT) for predicting periodontitis in treatment-naïve subjects in two independent studies and update a recent meta-analysis.

Methods: The aMMP-8 POC-ORT index test was performed in a representative population in Hong Kong, China, and a consecutive convenience sample in Shanghai, China. The reference standard was the 2017 World Workshop classification of periodontal diseases.

Association Between Chewing Capacity and Mortality Risk: The Role of Diet and Ageing.

Aim: Masticatory dysfunction due to tooth loss is a potentially modifiable risk for mortality, but the pathway behind that remains to be investigated. This prospective study aimed to examine the role of diet and ageing in the associations between chewing capacity and long-term mortality.

Methods: Data were obtained from participants (aged ≥ 20) in the National Health Nutritional and Health Survey (NHANES 1999-2010, n = 22,900).

Unveiling the GT114 family: Structural characterization of A075L, a glycosyltransferase from Paramecium bursaria chlorella virus-1 (PBCV-1).

Protein A075L is a β-xylosyltransferase that participates in producing the core of the N-glycans found in VP54, the major viral capsid protein of Paramecium bursaria chlorella virus-1 (PBCV-1). In this study, we present an X-ray crystallographic analysis of the apo form of A075L, along with its complexes with the sugar donor and with a trisaccharide acceptor. The protein structure shows a typical GT-B folding, with two Rossmann-like fold domains, in which the acceptor substrate binds to the N-terminal region, and the nucleotide-sugar donor binds to the C-terminal region.

Clinical knowledge-guided hybrid classification network for automatic periodontal disease diagnosis in X-ray image.

Accurate classification of periodontal disease through panoramic X-ray images carries immense clinical importance for effective diagnosis and treatment. Recent methodologies attempt to classify periodontal diseases from X-ray images by estimating bone loss within these images, supervised by manual radiographic annotations for segmentation or keypoint detection. However, these annotations often lack consistency with the clinical gold standard of probing measurements, potentially causing measurement inaccuracy and leading to unstable classifications.