Oral health is maintained by a healthy microbiome, which can be monitored by state-of-the art diagnostics. Therefore, this study evaluated the presence and quantity of ten oral disease-associated taxa (P. gingivalis, T. forsythia, T. denticola, F. nucleatum, C. rectus, P. intermedia, A. actinomycetemcomitans, S. mutans, S. sobrinus, oral associated Lactobacilli) in saliva and their clinical status association in 214 individuals. Upon clinical examination, study subjects were grouped into healthy, caries and periodontitis and their saliva was collected. A highly specific point-of-care compatible dual color qPCR assay was developed and used to study the above-mentioned bacteria of interest in the collected saliva. Assay performance was compared to a commercially available microbial reference test. Eight out of ten taxa that were investigated during this study were strong discriminators between the periodontitis and healthy groups: C. rectus, T. forsythia, P. gingivalis, S. mutans, F. nucleatum, T. denticola, P. intermedia and oral Lactobacilli (p < 0.05). Significant differentiation between the periodontitis and caries group microbiome was only shown for S. mutans (p < 0.05). A clear distinction between oral health and disease was enabled by the analysis of quantitative qPCR data of target taxa levels in saliva.

The RespiDisk platform for automated detection of multiple viral and bacterial respiratory tract infection pathogens.

This study investigated the potential of salivary bacterial and protein markers for evaluating the disease status in healthy individuals or patients with gingivitis or caries. Saliva samples from caries- and gingivitis-free individuals (n = 18), patients with gingivitis (n = 17), or patients with deep caries lesions (n = 38) were collected and analyzed for 44 candidate biomarkers (cytokines, chemokines, growth factors, matrix metalloproteinases, a metallopeptidase inhibitor, proteolytic enzymes, and selected oral bacteria). The resulting data were subjected to principal component analysis and used as a training set for random forest (RF) modeling. This computational analysis revealed four biomarkers (IL-4, IL-13, IL-2-RA, and eotaxin/CCL11) to be of high importance for the correct depiction of caries in 37 of 38 patients. The RF model was then used to classify 10 subjects (five caries-/gingivitis-free and five with caries), who were followed over a period of six months. The results were compared to the clinical assessments of dental specialists, revealing a high correlation between the RF prediction and the clinical classification. Due to the superior sensitivity of the RF model, there was a divergence in the prediction of two caries and four caries-/gingivitis-free subjects. These findings suggest IL-4, IL-13, IL-2-RA, and eotaxin/CCL11 as potential salivary biomarkers for identifying noninvasive caries. Furthermore, we suggest a potential association between JAK/STAT signaling and dental caries onset and progression.