The oral cavity provides an ideal environment for microorganisms, including bacteria, viruses, and fungi, to flourish. Increasing attention has been focused on the connection between the oral microbiome and both oral and systemic diseases, spurring active research into the collection and analysis of specimens for healthcare purposes. Among the various methods for analyzing the oral microbiome, saliva analysis is especially prominent. Saliva samples, which can be collected non-invasively, provide information on the systemic health and oral microbiome composition of an individual. This review was performed to evaluate the current state of the relevant research through an examination of the literature and to suggest an appropriate assay method for investigating the oral microbiome. We analyzed articles published in English in SCI(E) journals after January 1, 2000, ultimately selecting 53 articles for review. Articles were identified through keyword searches in the PubMed, Embase, Cochrane, Web of Science, and CINAHL databases. Three experienced researchers conducted full-text assessments following title and abstract screening to select appropriate papers. Subsequently, they organized and analyzed the desired data. Our review revealed that most studies utilized unstimulated saliva samples for oral microbiome analysis. Of the 53 studies examined, 29 identified relationships between the oral microbiome and various diseases, such as oral disease, Behçet disease, cancer, and oral lichen planus. However, the studies employed diverse methods of collection and analysis, which compromised the reliability and accuracy of the findings. To address the limitations caused by methodological inconsistencies, a standardized saliva assay should be established.

In the present study we conducted a regression analysis of age at menarche against Maximum Peak Velocity (MPV) of height in non-athlete Korean girls which was derived with the wavelet interpolation method, and composed linear to quartic regression polynomials to obtain the best regression polynomial. We then applied the age at menarche and age at MPV of height of athletes to the best polynomial regression evaluation, and investigated the validity of a delayed menarche evaluation that we constructed. Moreover, the relation between delayed menarche and menstrual status was examined by investigating delayed menarche and menstrual pain in individuals for the first time. The subjects were 124 second year female students at a physical education high school in the suburbs of Pusan, South Korea. A questionnaire survey of these girls was conducted, from which their date of birth, age at menarche, and athletic activities in elementary, junior high, and high school were obtained. In addition, health check records were examined retrospectively, and longitudinal growth data for height were obtained from the 1st year of elementary school (7 years old) until the second year of high school (17 years old). Next, the same survey as above was also done for second year students at a general high school in the same area, as a control group. Three hundred forty-five non-athletes for whom all data were available were selected. As the results, the third order polynomial was found to be most suitable for the regression polynomial. When it was applied to individual female Korean athletes with respect to the regression evaluation, positive scores were obtained for many athletes and an overall delay in menarche was seen. Delayed menarche was not seen, however, in archery athletes. A strong delay in menarche was thus found in Korean athletes. And it was shown that 80% had moderate or greater menstrual pain and a close relation with menstrual abnormalities, the effectiveness of the delayed menarche evaluation was further validated.