Background: Weissella cibaria CMU (oraCMU) has been commercially available in the market for several years as oral care probiotics. The present study aimed to evaluate the effects of oraCMU-containing tablets on periodontal health and oral microbiota. Methods: A randomised, double-blind, placebo-controlled trial was conducted in 92 adults without periodontitis (20–39 years of age). All subjects received dental scaling and root planing, and were randomly assigned to either probiotic or placebo groups. The tablets were administered once daily for 8 weeks. Periodontal clinical parameters included bleeding on probing (BOP), probing depth (PD), gingival index (GI), and plaque index (PI). In addition, microbiota in the gingival sulcus were analysed. Results: BOP improved more in the probiotic group over 8 weeks. There were statistically significant differences in BOP of the maxilla buccal and lingual sites between the groups during the intervention (P < 0.05). No significant inter-group differences in PD, GI, and PI were observed during the intervention. Oral bacteria were observed to be fewer in the probiotic group. There was a significant change in levels of Fusobacterium nucleatum at four and 8 weeks between the two groups. Besides, there were significant differences at 8 weeks in levels of Staphylococcus aureus. Conclusions: We reported an improvement in BOP and microbial environment and demonstrated the antimicrobial activity of oraCMU against F. nucleatum. Thus, its supplementation may contribute to overall oral health. Trial registration: Ethical issues approved by the Kangwon National University Institutional Review Board with a number of KWNUIRB-2018-05-003-005 and CRIS code Number of KCT0005078 were retrospectively registered on 06/02/2020. This study was conducted in the period of July to November 2018.

Altogether, 81% of Korean college students experience halitosis and concomitant psychosocial problems such as depression and lowered self-esteem, as well as poor oral-health-related quality of life. Although halitosis causes many social and psychological problems among college students, there have been no reports of improvement interventions. This study aimed to identify the effects of ingesting tablets of the oral probiotic Weissella cibaria CMU (Chonnam Medical University, Gwangju, Korea) on halitosis and examine its effects on psychosocial indicators. This was a randomized, double-blind, placebo-controlled trial. The participants were randomly assigned to the experimental group or the control group. They ingested W. cibaria CMU or the placebo, depending on which group they belonged to, before going to bed daily for eight weeks. The measured indicators were subjective halitosis, subjective oral-health status, depression, self-esteem, and oral-health-related quality of life. Measurements were at baseline and eight weeks later. The participants showed statistically significant differences in subjective halitosis and oral-health-related quality of life. For college students with halitosis, intake of the oral probiotic for eight weeks could be a useful nursing intervention for reducing halitosis and improving oral-health-related quality of life.

Oral health plays an important role in people’s quality of life as it is related to eating, talking, and smiling. In recent years, many studies have utilized artificial intelligence for oral health care. Many studies have been published on tooth identification or recognition of dental diseases using X-ray images, but studies with RGB images are rarely found. In this paper, we propose a deep convolutional neural network (CNN) model that classifies teeth with periodontal diseases from optical color images captured in front of the mouth. A novel network module with one-dimensional convolutions in parallel was proposed and compared to the conventional models including ResNet152. In results, the proposed model achieved 11.45% higher than ResNet152 model, and it was proved that the proposed structure enhanced the training performances, especially when the amount of training data was insufficient. This paper shows the possibility of utilizing optical color images for the detection of periodontal diseases, which may lead to a mobile oral healthcare system in the future.

10–20% of COVID (Corona Virus Disease)-19 cases proceed to a severe stage, and age and the presence of comorbidity increased the risk of death from COVID-19. The identification of risk factors on progression to the severity stages is essential in providing more efficient and suitable management to COVID-19 patients. However, there is insufficient study on risk factors for severity stages of COVID-19 patients. In this study, 2959 confirmed COVID-19 patients were analyzed while using national data, COVID-19 patients Clinical Epidemiological Information provided from the Korea Disease Control and Prevention Agency. The epidemiological variable, hospital room, periods from confirmation to release, initial symptom and vital signs, underlying comorbidities, and initial blood variables were used to verify the relation with progression to severity stages of COVID-19 and severe COVID-19. The chi-square test, welch test, multiple regression and logistic regression analysis were performed. The ICU (Intensive Care Unit) admission rate of patients having characteristics, such as older age, male, abnormal BMI (Body Mass Index), high heart rate, high body temperature, fever, cough, sputum, sore throat, rhinorrhea, fatigue, dyspnea, change of consciousness, diabetes mellitus, hypertension, chronic artery disease, chronic kidney disease, cancer, dementia, abnormal hemoglobin, abnormal hematocrit, abnormal lymphocyte, abnormal platelets, and abnormal white blood cell were high. The risk factors for severe COVID-19 were older age, shorter hospitalization, abnormal lymphocyte, abnormal platelets, dyspnea, change of consciousness, and dementia. Whereas, significant predictors for progression to severity stages of COVID-19 were older age, longer period from confirmation to release, higher BMI, higher body temperature, abnormal lymphocyte, abnormal platelets, fever, no sore throat, dyspnea, no headache, COPD (Chronic Obstructive Pulmonary Disease), and dementia. Therefore, classifying patients with a high risk of severe stage of COVID-19 and managing patients by considering the risk factors could be helpful in the efficient management of COVID-19 patients.

Green synthesis for synthesizing silver nanoparticles (AgNPs) has been suggested as an environmentally friendly alternative to conventional physical/chemical methods. In this study, we report the green synthesis of AgNPs using a hot-melt extrusion-processed Angelica gigas Nakai (AGN) (HME-AGN) extract as a reducing agent to increase the water solubility of the active ingredient compared to the existing AGN. The mixture of the AGN extract and AgNO₃ at about 420 nm could not confirm the formation of AgNPs. The synthesis of AgNPs was found to be most advantageous at 60 °C when the mixing ratio of the HME-AGN extract was 9:1 (AgNO₃–extract, v/v) using 3 mM AgNO₃. The physicochemical properties of the optimized AgNPs were characterized by UV–Vis spectrophotometer, dynamic light scattering (DLS), zeta potential, transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS), Fourier-transform infrared spectroscopy (FT-IR), and X-ray diffractometer (XRD). DLS showed the particle size average of 102.3 ± 1.35 nm and polydispersity index (PDI) value of 0.314 ± 0.01. The particle surface charge was −35 ± 0.79 mV, confirming the stability of the particles. The particle shape was spherical, as shown through TEM analysis, and the presence of silver ions was confirmed through the EDS results. FT-IR data showed functional groups of biomolecules of the extract involved in the synthesis of AgNPs. The face-centered cubic (FCC) lattice of AgNPs was confirmed in the XRD pattern. The AgNPs had an effective antifungal activity against Candida albicans (C. albicans) that was better than that of the HME-AGN extract. In conclusion, this study suggests that the synthesis of AgNPs was improved by using the HME-AGN extract with increased water solubility through HME. In addition, it was suggested that the synthesized AgNPs can be used as an improved antifungal agent compared with the HME-AGN extract with antifungal activity.

Automatic age estimation using panoramic dental radiographic images is an important procedure for forensics and personal oral healthcare. The accuracies of the age estimation have increased recently with the advances in deep neural networks (DNN), but DNN requires large sizes of the labeled dataset which is not always available. This study examined whether a deep neural network is able to estimate tooth ages when precise age information is not given. A deep neural network model was developed and applied to age estimation using an image augmentation technique. A total of 10,023 original images were classified according to age groups (in decades, from the 10s to the 70s). The proposed model was validated using a 10-fold cross-validation technique for precise evaluation, and the accuracies of the predicted tooth ages were calculated by varying the tolerance. The accuracies were 53.846% with a tolerance of ±5 years, 95.121% with ±15 years, and 99.581% with ±25 years, which means the probability for the estimation error to be larger than one age group is 0.419%. The results indicate that artificial intelligence has potential not only in the forensic aspect but also in the clinical aspect of oral care.