AST results were generated for 111 of these urine samples and the concordance was 90% and 87% for E. coli and K. pneumoniae, respectively.Our results showed that screening of urine samples with flow cytometry to detect positive samples and identification of uropathogens directly by MALDI-TOF MS with an accuracy of over 90% can be a suitable method particularly for Gram-negative bacteria in clinical microbiology laboratories.
  Problematic internet use (PIU) is a highly prevalent condition with severe adverse effects. The literature suggests that parent-child bonding and parental behavioral control exert protective effects against PIU. However, the most relevant studies rely on simplistic measurement of parenting, cross-sectional designs and mixed-aged samples. Our study analyzed the effect of maternal and paternal parenting on PIU by using a prospective design and a cohort sample of same-aged children.
 
  Data from 1,019 Czech 12-year-old sixth-graders who were followed until ninth grade were used. Maternal and paternal responsiveness and strictness were reported by children using the Parental Acceptance-Rejection Questionnaire (PARQ) and the Parental Control Scale (PCS). PIU was measured by the Excessive Internet Use Scale (EIUS).
 
  The self-reported PIU prevalence in nine-graders (15-year-old) was 8.1%. Parenting, reported by adolescents 18 months before PIU screening, showed significant relationships with PIU parental responsiveness was negatively and moderately associated, while maternal strictness showed a weak positive association; the authoritative parenting style in both parents decreased PIU, with a PIU probability of 3.21%, while a combination of maternal authoritarian and paternal neglectful parenting was associated with PIU probability as high as 20.9%.
 
  The self-reported prevalence of PIU in Czech adolescents was found to be high. The effects of parenting on PIU were similar to the effects of parenting on other problematic behavior among adolescents. Our findings showed the need for interventions to prevent PIU by helping parents to apply optimal parenting styles.
 The self-reported prevalence of PIU in Czech adolescents was found to be high. The effects of parenting on PIU were similar to the effects of parenting on other problematic behavior among adolescents. Our findings showed the need for interventions to prevent PIU by helping parents to apply optimal parenting styles.
  Since mid-March 2020, over 3 billion people have been confined as a result of the COVID-19 pandemic. Problematic eating behaviors are likely to be impacted by the pandemic through multiple pathways. This study examined the relationships between stress related to lockdown measures and binge eating and dietary restriction in a population of French students during the first week of confinement.
 
  A sample of undergraduate students (N = 5,738) completed an online questionnaire 7 days after lockdown measures were introduced. The survey comprised variables related to lockdown measures and the COVID-19-pandemic, mood, stress, body image, binge eating and dietary restriction during the past 7 days, as well as intent to binge eat and restrict in the following 15 days.
 
  Stress related to the lockdown was associated with greater likelihood of binge eating and dietary restriction over the past week and intentions to binge eat and restrict over the next 15 days. Greater exposure to COVID-19-related media was associated with increased eating restriction over the past week. Binge eating and restriction (past and intentions) were associated with established risk factors, including female gender, low impulse regulation, high body dissatisfaction, and having a concurrent probable eating disorder.
 
  The higher the stress related to the first week of confinement, the higher the risk of problematic eating behaviors among students, particularly those characterized by eating-related concerns. Screening for risk factors and providing targeted interventions might help decrease problematic eating behaviors among those who are most vulnerable.
 The higher the stress related to the first week of confinement, the higher the risk of problematic eating behaviors among students, particularly those characterized by eating-related concerns. Screening for risk factors and providing targeted interventions might help decrease problematic eating behaviors among those who are most vulnerable.
  COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators.  https://www.selleckchem.com/products/kpt-8602.html  Predictive algorithms could potentially ease the strain on health care systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalized, or admitted to the ICU.
 
  The aim of this study is to develop, study, and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test or require hospitalization or intensive care.
 
  Using a systematic approach to model development and optimization, we trained and compared various types of machine learning models, including logistic regression, neural networks, support vector machines, ra results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19 and, therefore, help inform care and prioritize resources.Dry eye syndrome is one of the most frequently reported eye diseases in ophthalmological practice. The diagnosis of this disease is a challenging task due to its multifactorial etiology. One of the most applied tests is the manual classification of tear film images captured with the Doane interferometer. The interference phenomena in these images can be characterized as texture patterns, which can be automatically classified into one of the following categories strong fringes, coalescing strong fringes, fine fringes, coalescing fine fringes, and debris. This work presents a method for classifying tear film images based on texture analysis using phylogenetic diversity indexes and Ripley's K function. The proposed method consists of six main steps acquisition of the image dataset; segmentation of the region of interest; feature extraction using phylogenetic diversity indexes and Ripley's K function; feature selection using Greedy Stepwise; classification using the algorithms Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Multilayer Perceptron (MLP), Random Tree (RT) and Radial Basis Function Network (RBFNet); and (6) validation of results.