Ha EJ, et al. Korean J Radiol. 2021 Dec;22(12):2094-2123. https://doi.org/10.3348/kjr.2021.0713

Porphyromonas gingivalis , a major periodontopathogen, is involved in the pathogenesis of periodontitis. Interleukin-1β (IL-1β), a proinflammatory cytokine, regulates innate immune responses and is critical for the host defense against bacterial infection. However, excessive IL-1β is linked to periodontal destruction. IL-1β synthesis, maturation, and secretion are tightly regulated by Toll-like receptor (TLR) signaling and inflammasome activation. We found much higher levels of inflammasome components in the gingival tissues from patients with chronic periodontitis than in those from healthy controls. To investigate the molecular mechanisms by which P. gingivalis infection causes IL-1β secretion, we examined the characteristics of P. gingivalis -induced signaling in differentiated THP-1 cells. We found that P. gingivalis induces IL-1β secretion and inflammatory cell death via caspase-1 activation. We also found that P. gingivalis -induced IL-1β secretion and pyroptic cell death required both NLRP3 and AIM2 inflammasome activation. The activation of the NLRP3 inflammasome was mediated by ATP release, the P2X ₇ receptor, and lysosomal damage. In addition, we found that the priming signal via TLR2 and TLR4 activation precedes P. gingivalis -induced IL-1β release. Our study provides novel insight into the innate immune response against P. gingivalis infection which could potentially be used for the prevention and therapy of periodontitis.

MicroRNAs (miRNAs) are short RNA molecules that negatively regulate gene expression primarily by degrading target mRNA or inhibit the translation of protein product. Recently, many reports have shown the altered miRNA expression in various diseases. However, there are no reports on miRNA expression related to periodontitis. Thus, this study aimed to compare the miRNAs differentially expressed in healthy and chronic periodontitis tissues and to determine the miRNAs closely associated with chronic periodontitis. To find out the miRNAs differentially induced in healthy and chronic periodontitis tissues, miRNA microarray was carried out and the expression of miRNAs was confirmed by real-time PCR. According to miRNA microarray analyses, six miRNA genes, let-7a, let-7c, miR-130a, miR301a, miR-520d, and miR- 548a, were up-regulated more than 8 fold compared to the healthy gingiva. The expression of twenty-two miRNAs was increased more than 4 fold. Among these miRNAs, eight miRNAs which are known to be closely related to inflammation were selected. Six of these miRNA genes, miR-181b, miR-19b, miR-23a, miR-30a, miR-let7a, and miR-301a, were amplified successfully and increased much more in periodontitis gingivae than in healthy ones. In summary, this study indicate that six miRNAs up-regulated in periodontitis gingiva may play a key role in chronic periodontitis.

Ultrasound (US) elastography is a valuable imaging technique for tissue characterization. Two main types of elastography, strain and shear-wave, are commonly used to image breast tissue. The use of elastography is expected to increase, particularly with the increased use of US for breast screening. Recently, the US elastographic features of breast masses have been incorporated into the 2nd edition of the Breast Imaging Reporting and Data System (BI-RADS) US lexicon as associated findings. This review suggests practical guidelines for breast US elastography in consensus with the Korean Breast Elastography Study Group, which was formed in August 2013 to perform a multicenter prospective study on the use of elastography for US breast screening. This article is focused on the role of elastography in combination with B-mode US for the evaluation of breast masses. Practical tips for adequate data acquisition and the interpretation of elastography results are also presented.

Periodontitis is a chronic and multifactorial inflammatory disease that can lead to tooth loss. At present, the diagnosis for periodontitis is primarily based on clinical examination and radiographic parameters. Detecting the periodontal pathogens at the subgingival plaque requires skilled professionals to collect samples. Periodontal pathogens are also detected on various mucous membranes in patients with periodontitis. In this study, we characterized the oral microbiome profiles from buccal mucosa and supragingival space in a total of 272 healthy subjects as a control group, and periodontitis patients as a disease group. We identified 13 phyla, 193 genera, and 527 species and determined periodontitis-associated taxa. Porphyromonas gingivalis, Tannerella forsythia, Treponema denticolar, Filifactor alocis, Porphyromonas endodontalis, Fretibacterium fastiosum and Peptostreptococcus species were significantly increased in both the buccal mucosa and the supragingival space in periodontitis patients. The identified eight periodontitis-associated bacterial species were clinically validated in an independent cohort. We generated the prediction model based on the oral microbiome profiles using five machine learning algorithms, and validated its capability in predicting the status of patients with periodontitis. The results showed that the oral microbiome profiles from buccal mucosa and supragingival space can represent the microbial composition of subgingival plaque and further be utilized to identify potential microbial biomarkers for the diagnosis of periodontitis. Besides, bacterial community interaction network analysis found distinct patterns associated with dysbiosis in periodontitis. In summary, we have identified oral bacterial species from buccal and supragingival sites which can predict subgingival bacterial composition and can be used for early diagnosis of periodontitis. Therefore, our study provides an important basis for developing easy and noninvasive methods to diagnose and monitor periodontitis.