SUMMARY Human papillomavirus (HPV) types 16 and 18 are causally related to a sub-set of oral cavity and oropharyngeal squamous cell cancers. However, a clear estimate of the prevalence of HPV-16/18 in oral cavity and oropharyngeal dysplasia (OOPD) is not available. This literature review and meta-analysis was conducted to provide a prevalence estimate for HPV-16/18 in OOPD. Twenty-two studies that reported prevalence of HPV-16 and/or 18 in 458 OOPD lesions were analyzed. Meta-analysis was used to evaluate the prevalence of HPV-16/18 and logistic regression was used for stratified analysis by age, gender, and histological grade. The overall prevalence of HPV-16/18 in OOPD lesions was 24.5% [95% confidence interval (CI), 16.4–36.7%)]. The individual prevalence for HPV-16 alone was 24.4%. The prevalence of HPV-16/18 in oral cavity lesions alone was 25.3% (95% CI, 14.2–45.2%). The odds of detection of HPV-16/18 in dysplastic lesions in males were twice that of females [odds ratio (OR), 2.44]. HPV-16/18 were 3 times more common in dysplastic lesions (OR, 3.29; 95% CI, 1.95–5.53%) and invasive cancers (OR, 3.43; 95% CI, 2.07–5.69%), when compared to normal biopsies. There was no significant difference in HPV-16/18 rates between dysplastic lesions and cancers or between mild, moderate or severe dysplastic lesions. This meta-analysis provides a quantification of the prevalence of HPV types 16/18 in OOPD lesions. These results also support the assumption that HPV-16/18 infection occurs during the early phase of the oral cavity and oropharyngeal carcinogenesis.

As the most common chronic disease in preschool children in the United States, early childhood caries (ECC) has a profound impact on a child’s quality of life, represents a tremendous human and economic burden to society, and disproportionately affects those living in poverty. Caries risk assessment (CRA) is a critical component of ECC management, yet the accuracy, consistency, reproducibility, and longitudinal validation of the available risk assessment techniques are lacking. Molecular and microbial biomarkers represent a potential source for accurate and reliable dental caries risk and onset. Next-generation nucleotide-sequencing technology has made it feasible to profile the composition of the oral microbiota. In the present study, 16S ribosomal RNA (rRNA) gene sequencing was applied to saliva samples that were collected at 6-mo intervals for 24 mo from a subset of 56 initially caries-free children from an ongoing cohort of 189 children, aged 1 to 3 y, over the 2-y study period; 36 children developed ECC and 20 remained caries free. Analyses from machine learning models of microbiota composition, across the study period, distinguished between affected and nonaffected groups at the time of their initial study visits with an area under the receiver operating characteristic curve (AUC) of 0.71 and discriminated ECC-converted from healthy controls at the visit immediately preceding ECC diagnosis with an AUC of 0.89, as assessed by nested cross-validation. Rothia mucilaginosa, Streptococcus sp., and Veillonella parvula were selected as important discriminatory features in all models and represent biomarkers of risk for ECC onset. These findings indicate that oral microbiota as profiled by high-throughput 16S rRNA gene sequencing is predictive of ECC onset.

Untreated tooth decays affect nearly one third of the world and is the most prevalent disease burden among children. The disease progression of tooth decay is multifactorial and involves a prolonged decrease in pH, resulting in the demineralization of tooth surfaces. Bacterial species that are capable of fermenting carbohydrates contribute to the demineralization process by the production of organic acids. The combined use of machine learning and 16s rRNA sequencing offers the potential to predict tooth decay by identifying the bacterial community that is present in an individual’s oral cavity. A few recent studies have demonstrated machine learning predictive modeling using 16s rRNA sequencing of oral samples, but they lack consideration of the multifactorial nature of tooth decay, as well as the role of fungal species within their models. Here, the oral microbiome of mother–child dyads (both healthy and caries-active) was used in combination with demographic–environmental factors and relevant fungal information to create a multifactorial machine learning model based on the LASSO-penalized logistic regression. For the children, not only were several bacterial species found to be caries-associated (Prevotella histicola, Streptococcus mutans, and Rothia muciloginosa) but also Candida detection and lower toothbrushing frequency were also caries-associated. Mothers enrolled in this study had a higher detection of S. mutans and Candida and a higher plaque index. This proof-of-concept study demonstrates the significant impact machine learning could have in prevention and diagnostic advancements for tooth decay, as well as the importance of considering fungal and demographic–environmental factors.

Acute otitis media (AOM) is the most common pediatric infection for which antibiotics are prescribed in the United States. The role of the respiratory tract microbiome in pathogenesis and immune modulation of AOM remains unexplored. We sought to compare the nasopharyngeal (NP) microbiome of children 1 to 3 weeks prior to onset of AOM vs. at onset of AOM, and the NP microbiome with the microbiome in middle ear (ME). Six children age 6 to 24 months old were studied. Nasal washes (NW) were collected at healthy visits 1 to 3 weeks prior to AOM and at onset of AOM. The middle ear fluids (MEF) were collected by tympanocentesis at onset of AOM. Samples were stored in Trizol reagents or phosphate-buffered saline (PBS) at −80°C until use. The microbiome was characterized by 16S rRNA gene sequencing. Taxonomic designations and relative abundance of bacteria were determined using the RDP classifier tool through QIIME. Cumulative sum scaling normalization was applied before determining bacterial diversity and abundance. Shannon diversity index was calculated in Microsoft excel. The relative abundance of each bacteria species was compared via Mann-Whitney U test. We found that the NW microbiome of children during healthy state or at baseline was more diverse than microbiome during AOM. At AOM, no significant difference in microbiome diversity was found between NW and MEF, although some bacteria species appear to differ in MEF than in NW. The microbiome of samples stored in PBS had significant greater diversity than samples stored in Trizol reagent.

Introduction: Early childhood caries (ECC) is a complex oral disease that is prevalent in US children. Objectives: The purpose of this 2-y prospective cohort study was to examine baseline and time-dependent risk factors for ECC onset in initially caries-free preschool children. Methods: A cohort of 189 initially caries-free children aged 1 to 3 y was recruited. At each 6-mo study visit, children were examined using the ICDAS index; salivary samples were collected to assess mutans streptococci (MS), lactobacilli, Candida species, salivary cortisol (prior and after a stressor), and salivary IgA. Diet and oral health behavior were assessed from parent report. Child and family stress exposure was assessed from measures of psychological symptoms, stressful life event exposure, family organization and violence exposure, and social support. Sociodemographic factors were also considered. A Kaplan-Meier estimator of survival function of time to ECC and a Cox proportional hazards model were used to identify predictors of ECC onset. Results: Onset of ECC was associated with high salivary MS levels at baseline (log-rank test, P < 0.0001). Cox proportional hazards regression showed that the risk of dental caries significantly increased with salivary MS in log scale over the 6-mo period (hazard ratio, 1.08; P = 0.01). Other risk factors in the model did not reach statistical significance. Conclusion: Our results provide prospective evidence that an increase in salivary MS predicts ECC onset in young, initially caries-free children, confirming that a high salivary MS count likely plays a causal role in ECC onset, independent of covariates. Knowledge Transfer Statement: These results suggest that we must focus on reducing salivary MS counts in young children and preventing or delaying MS colonization in infants and young children determined to be at risk for ECC.