Medical record data-enabled machine learning can enhance prediction of left atrial appendage thrombosis in nonvalvular atrial fibrillation.

BACKGROUND: As a major complication of non-valvular atrial fibrillation (NVAF), left atrial appendage (LAA) thrombosis is associated with cerebral ischemic strokes, as well as high morbidity. Due to insufficient incorporation of risk factors, most current scoring methods are limited to the analysis of relationships between clinical characteristics and LAA thrombosis rather than detecting potential risk. Therefore, this study proposes a clinical data-driven machine learning method to predict LAA thrombosis of NVAF. METHODS: Patients with NVAF from January 2014 to June 2022 were enrolled from Southwest Hospital. We selected 40 variables for analysis, including demographic data, medical history records, laboratory results, and the structure of LAA. Three machine learning algorithms were adopted to construct classifiers for the prediction of LAA thrombosis risk. The most important variables related to LAA thrombosis and their influences were recognized by SHapley Addictive exPlanations method. In addition, we compared our model with CHADS2 and CHADS2-VASc scoring methods. RESULTS: A total of 713 participants were recruited, including 127 patients with LAA thrombosis and 586 patients with no obvious thrombosis. The consensus models based on Random Forest and eXtreme Gradient Boosting LAA thrombosis prediction (RXTP) achieved the best accuracy of 0.865, significantly outperforming CHADS2 score and CHA2DS2-VASc score (0.757 and 0.754, respectively). The SHAP results showed that B-type natriuretic peptide, left atrial appendage width, C-reactive protein, Fibrinogen and estimated glomerular filtration rate are closely related to the risk of LAA thrombosis in nonvalvular atrial fibrillation. CONCLUSIONS: The RXTP-NVAF model is the most effective model with the greatest ROC value and recall rate. The summarized risk factors obtained from SHAP enable the optimization of the treatment strategy, thereby preventing thromboembolism events and the occurrence of cardiogenic ischemic stroke.

Effect of role overload on missed nursing care in China: The role of work addiction and leader-member exchange.

AIM: The aim was to examine the effect of role overload, work addiction and leader-member exchange on missed nursing care. DESIGN: A cross-sectional study. METHODS: Chinese registered nurses from five Sichuan province public hospitals were studied from March 2022-May 2022. The measurements were derived from a questionnaire on role overload, work addiction, missed nursing care, leader-member exchange and a sociodemographic datasheet. Descriptive statistics and inferential statistics were conducted (N = 403). RESULTS: Role overload was associated with missed nursing care, and work addiction played a mediation role. Leader-member exchange negatively predicted work addiction and played a moderating role between role overload and work addiction. The effect of role overload on work addiction was attenuated when the level of leader-member exchange was higher. Promoting leader-member exchange could mitigate how role overload undermines missed nursing care.

Enhancement of biofilm formation by subinhibitory concentrations of macrolides in icaADBC-positive and -negative clinical isolates of Staphylococcus epidermidis.

Biofilm formation in Staphylococcus epidermidis is mediated by icaADBC-dependent and -independent pathways. Subinhibitory concentrations of erythromycin, azithromycin, and clarithromycin enhanced, in a dose-dependent manner, the level of biofilm formation by 20% (21/105 isolates) by macrolide-resistant ica-positive and -negative isolates tested in vitro. The presence of ica, however, apparently produced an enhanced effect on biofilm formation. The levels of expression of the biofilm-related genes icaA, atlE, fruA, pyrR, sarA, and sigB were increased in response to erythromycin. The results likely underscore the potential clinical relevance of macrolide-induced biofilm growth.

Uptake of different species of iodine by water spinach and its effect to growth.

A hydroponic experiment has been carried out to study the influence of iodine species [iodide (I(-)), iodate (IO(-)(3)), and iodoacetic acid (CH(2)ICOO(-))] and concentrations on iodine uptake by water spinach. Results show that low levels of iodine in the nutrient solution can effectively stimulate the growth of biomass of water spinach. When iodine levels in the nutrient solution are from 0 to 1.0 mg/l, increases in iodine levels can linearly augment iodine uptake rate by the leafy vegetables from all three species of iodine, and the uptake effects are in the following order: CH(2)ICOO(-) >I(-)>IO(-)(3). In addition, linear correlation was observed between iodine content in the roots and shoots of water spinach, and their proportion is 1:1. By uptake of I(-), vitamin C (Vit C) content in water spinach increased, whereas uptake of IO(-)(3) and CH(2)ICOO(-) decreased water spinach Vit C content. Furthermore, through uptake of I(-) and IO(-)(3). The nitrate content in water spinach was increased by different degrees.