New-onset atrial fibrillation (NOAF) is the most common arrhythmia in critically ill patients admitted to intensive care and is associated with poor prognosis and disease burden. Identifying high-risk individuals early is crucial. This study aims to create and validate a NOAF prediction model for critically ill patients using machine learning (ML). The data came from two non-overlapping datasets from the Medical Information Mart for Intensive Care (MIMIC), with MIMIC-IV used for training and subset of MIMIC-III used as external validation. LASSO regression was used for feature selection. Eight ML algorithms were employed to construct the prediction model. Model performance was evaluated based on identification, calibration, and clinical application. The SHapley Additive exPlanations (SHAP) method was used for visualizing model characteristics and individual case predictions. Among 16,528 MIMIC-IV patients, 1520 (9.2%) developed AF post-ICU admission. A model with 23 variables was built, with XGBoost performing best, achieving an AUC of 0.891 (0.873–0.888) in validation and 0.769 (0.756–0.782) in external validation. Key predictors included age, mechanical ventilation, urine output, sepsis, blood urea nitrogen, percutaneous arterial oxygen saturation, continuous renal replacement therapy and weight. A risk probability greater than 0.6 was defined as high risk. A friendly user interface had been developed for clinician use. We developed a ML model to predict the risk of NOAF in critically ill patients without cardiac surgery and validated its potential as a clinically reliable tool. SHAP improves the interpretability of the model, enables clinicians to better understand the causes of NOAF, helps clinicians to prevent it in advance and improves patient outcomes.

Chemical constituents of Cyperus rotundus L. and their inhibitory effects on uterine fibroids

Preferences for different housing conditions in mice were evaluated by radiotelemetry. Male C57BL/6J and ICR mice were used. Preference for bedding materials in mice was compared among three materials, wood shavings (WS), paper (CF) and cloth (AG), using the length of stay in cages as a parameter. The results indicated that mice stayed longer in a cage with AG than in cages with other bedding materials. The present study confirmed our previous results and thereby indicated that radiotelemetry is a useful method to evaluate impacts of housing conditions on animal welfare. In the second part of this study, we used radiotelemetry to evaluate color preference of the mice for cloth bedding material. In C57BL/6J mice, staying time in black cloth was significantly longer than that in white cloth. In ICR mice, staying time in white cloth was significantly longer than that in black cloth. The mice preferred the environment with the same color as their fur, which may be important for animal welfare.

Objective. Research evidence suggests a link between periodontitis (PD) and atrial fibrillation, but the nature of this link is unclear. This study aimed to systematically review and evaluate the association between PD, other oral diseases, and atrial fibrillation and the role of oral hygiene in preventing atrial fibrillation. Methods. We searched the Medline, Embase, Cochrane Library, and Web of Science databases for the clinical study of oral health and atrial fibrillation from inception to November 2022. Oral health conditions included PD and other oral inflammatory diseases, regular oral hygiene, and tooth brushing. The primary outcomes were the risk of new-onset atrial fibrillation in patients with oral disease, the effect of regular oral care on preventing atrial fibrillation, the effect of frequent tooth brushing on preventing atrial fibrillation, and the incidence of atrial fibrillation in PD patients. Results. Eight clinical trials with a total of 4,328,355 patients were included. The result of the research showed that PD and other impaired oral health may be associated with new-onset atrial fibrillation, and its severity was dose-responsive to the risk of atrial fibrillation. The incidence of atrial fibrillation in patients with severe PD was about 16.3. Moreover, PD may increase the risk of long-term arrhythmia in patients with atrial fibrillation. Regular oral care and frequent tooth brushing can reduce the incidence of atrial fibrillation. Conclusion. Regular and moderate oral hygiene, frequent tooth brushing, and prevention of PD and other oral inflammatory diseases could reduce the occurrence of atrial fibrillation. It is recommended to strengthen the popularization of oral health knowledge in the publicity related to atrial fibrillation.