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Risk assessment models for periodontal disease provide dentists with a precise and consolidated evaluation of the prognosis of periodontitis, enabling the formulation of personalized treatment plans. Periodontal risk assessment systems have been widely applied in clinical practice and research. The application fields of periodontal risk assessment systems vary based on the distinctions between clinical periodontal parameters and risk factors. The assessment models listed below are commonly used in clinical practice, including the periodontal risk calculator (PRC), which is an individual-based periodontal risk assessment tool that collects both periodontal and systemic information for prediction; the periodontal assessment tool (PAT), which allows for quantitative differentiation of stages of periodontal disease; the periodontal risk assessment (PRA) and modified periodontal risk assessment (mPRA), which are easy to use; and the classification and regression trees (CART), which assess the periodontal prognosis based on a single affected tooth. Additionally, there are orthodontic-periodontal combined risk assessment systems and implant periapical risk assessment systems tailored for patients needing multidisciplinary treatment. This review focuses on the current application status of periodontal risk assessment systems.

ObjectivesThe objective of this study was to examine the association between the oral microbiome and pregnancy outcomes, specifically healthy or preterm low birth weight (PLBW) in individuals with and without periodontal disease (PD).Material and methodsIn this prospective clinical trial, we recruited 186 pregnant women, 17 of whom exhibited PD and delivered PLBW infants (PD-PLBW group). Of the remaining women, 155 presented PD and delivered healthy infants; 18 of these subjects with similar periodontal condition and age matched to the PD-PLBW group, and they became the PD-HD group. From the total group, 11 women exhibited healthy gingiva and had a healthy delivery (HD) and healthy infants (H-HD group), and 3 exhibited healthy gingiva and delivered PLBW infants (H-PLBW group). Periodontal parameters were recorded, and subgingival plaque and serum were collected during 26-28 gestational weeks. For the plaque samples, microbial abundance and diversity were accessed by 16S rRNA sequencing.ResultsWomen with PD showed an enrichment in the genus Porphyromonas, Treponema, and Filifactor, whereas women with healthy gingiva showed an enrichment in Streptococcus, Actinomyces, and Corynebacterium, independently of the birth status. Although no significant difference was found in the beta diversity between the 4 groups, women that had PLBW infants presented a significantly lower abundance of the genus Neisseria, independently of PD status.ConclusionLower levels of Neisseria align with preterm low birth weight in pregnant women, whereas a higher abundance of Treponema, Porphyromonas, Fretibacterium, and Filifactor and a lower abundance of Streptococcus may contribute to periodontal disease during pregnancy.Clinical relevanceThe oral commensal Neisseria have potential in the prediction of PLBW.

Recent studies revealed culturable periodontal keystone pathogens are associated with preterm low birth weight (PLBW). However, the oral microbiome is also comprised of hundreds of 'culture-difficult' or 'not-yet-culturable' bacterial species. To explore the potential role of unculturable and culturable periodontitis-related bacteria in preterm low birth weight (PLBW) delivery, we recruited 90 pregnant women in this prospective study. Periodontal parameters, including pocket probing depth, bleeding on probing, and clinical attachment level were recorded during the second trimester and following interviews on oral hygiene and lifestyle habits. Saliva and serum samples were also collected. After delivery, birth results were recorded. Real-time PCR analyses were performed to quantify the levels of periodontitis-related unculturable bacteria (Eubacterium saphenum, Fretibacterium sp. human oral taxon(HOT) 360, TM7 sp. HOT 356, and Rothia dentocariosa), and cultivable bacteria (Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola, Fusobacterium nucleatum and Prevotella intermedia) in saliva samples. In addition, ELISA analyses were used to determine the IgG titres against periodontal pathogens in serum samples. Subjects were categorized into a Healthy group (H, n = 20) and periodontitis/gingivitis group (PG, n = 70) according to their periodontal status. The brushing duration was significantly lower in the PG group compared to the H group. Twenty-two of 90 subjects delivered PLBW infants. There was no significant difference in periodontal parameters and serum IgG levels for periodontal pathogens between PLBW and healthy delivery (HD) groups. However, ordinal logistic regression analysis revealed that a higher abundance of Treponema denticola, Prevotella intermedia, Fretibacterium sp. HOT360 and lower levels of Rothia dentocariosa were significantly associated with the presence of periodontal disease during pregnancy. Moreover, the amount of Eubacterium saphenum in saliva and serum IgG against Aggregatibacter actinomycetemcomitans were negatively correlated with PLBW. Taken together, unculturable periodontitis-associated bacteria may play an important role both in the presence of periodontal inflammation during pregnancy and subsequent PLBW.

Background: While physicians are considered to be more susceptible to developing depressive symptoms, empirical data are lacking. The study aims to compare the risk of depressive symptoms between emergency physicians and the general population in China based on national data. Methods: This was a national cross-sectional study. 10 457 emergency physicians and 101 120 participants from the general population were investigated from July 2018 to August 2018 and January 2019 to February 2019, respectively. PHQ-9 was used to measure depressive symptoms, and a score ≥ 10 indicates major depression. Propensity score matching was adopted to balance the characteristics between emergency physicians and the general population. Multinomial logistic regression model was used to examine the association between occupational groups and the severity of depressive symptoms. Binary logistic regression model was performed to explore the risk factors of major depression among emergency physicians. Results: The prevalence of major depression among emergency physicians was 35.7%, whereas among the general population was 13.9%. Emergency physicians had a 3.65 times higher risk of major depression than the general population. And emergency physician was significantly associated with mild (OR: 3.12, 95% CI 2.95–3.30), moderate (OR: 4.94, 95% CI 4.60–5.30), moderately severe (OR: 9.48, 95% CI 8.61–10.44), and severe depressive symptoms (OR: 14.18, 95% CI 12.47–16.13) compared with none depressive symptoms. Even after matching, the results remained consistent. Factors associated with major depression among emergency physicians included bachelor degree or above (OR: 1.22, 95% CI 1.06–1.40), worked long years (OR: 1.26, 95% CI 1.08–1.46 for 1–5 years; OR: 1.56, 95% CI 1.32–1.84 for ≥ 6 years), experienced workplace violence (OR: 2.51, 95% CI 2.16–2.94), worked more night shifts per month (OR: 1.33, 95% CI 1.16–1.51 for 6–10 times; OR: 1.83, 95% CI 1.58–2.11 for ≥ 11 times), smoked (OR: 1.64, 95% CI 1.47–1.84), and effort-reward imbalance (OR: 4.18, 95% CI 3.62–4.85). Conclusions: Emergency physicians had a higher risk of depressive symptoms than the general population. There is a need for greater awareness of the mental health issues faced by emergency physicians. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Sarcopenia is a prominent issue among aging populations and associated with poor health outcomes. This study aimed to examine the predictive value of questionnaire and biomarker data for sarcopenia, and to further develop a user‐friendly calculator for community‐dwelling middle‐aged and older adults.We used two waves (2011 and 2013) of the China Health and Retirement Longitudinal Study (CHARLS) to predict sarcopenia, defined by the Asian Working Group for Sarcopenia 2019 criteria. We restricted the analytical sample to adults aged 45 or above (N = 2934). Five machine learning models were used to construct Q‐based (only questionnaire variables), Bio‐based (only biomarker variables), and combined (questionnaire plus biomarker variables) models. Area under the receiver operating characteristic curve (AUROC) was used for performance assessment. Temporal external validation was performed based on two datasets from CHARLS. Important predictors were identified by Shapley values and coefficients.Extreme gradient boosting (XGBoost), considering both questionnaire and biomarker characteristics, emerged as the optimal model, and its AUROC was 0.759 (95% CI: 0.747–0.771) at a decision threshold of 0.20 on the test set. Models also performed well on the external datasets. We found that cognitive function was the most important predictor in both Q‐based and combined models, and blood urea nitrogen was the most important predictor in the Bio‐based model. Other key predictors included education, haematocrit, total cholesterol, drinking, number of chronic diseases, and instrumental activities of daily living score.Our findings offer a potential for early screening and targeted prevention of sarcopenia among middle‐aged and older adults in the community setting. [ABSTRACT FROM AUTHOR]

In this work, theoretical and numerical studies of the dynamic response of a fiber–metal laminate (FML) sandwich beam under uniform blast loading are conducted. On the basis of a modified rigid-plastic material model, the analytical solutions for the maximum deflection and the structural response time of FML sandwich beams with metal foam core are obtained. Finite element analysis is carried out by using ABAQUS software, and the numerical simulations corroborate the analytical predictions effectively. The study further examines the impact of the metal volume fraction, the metal strength factor between the metal layer and the composite material layer, the foam strength factor of the metal foam core to the composite material layer, and the foam density factor on the structural response. Findings reveal that these parameters influence the dynamic response of fiber–metal laminate (FML) sandwich beams to varying degrees. The developed analytical model demonstrates its capability to accurately forecast the dynamic behavior of fiber–metal laminate (FML) sandwich beams under uniform blast loading. The theoretical model in this article is a simplified model and cannot consider details such as damage, debonding, and the influence of layer angles in experiments. It is necessary to establish a refined theoretical model that can consider the microstructure and failure of composite materials in the future. [ABSTRACT FROM AUTHOR]