Systemic oxidative stress in children with cystic fibrosis with bacterial infection including Pseudomonas Aeruginosa

IntroductionOxidative stress (OS) occurs in cystic fibrosis (CF).ObjectiveThe objective of this work is to evaluate the influence of bacterial infection on biomarkers of OS (catalase [CAT], glutathione peroxidade [GPx], reduced glutathione [GSH]), markers of oxidative damage (protein carbonyls [PC], thiobarbituric acid reactive substances [TBARS]), together with the nutritional status and lung function in children with CF.MethodsCross‐sectional study including CF group (CFG, n = 55) and control group (CG, n = 31), median age: 3.89 and 4.62 years, respectively. CFG was distributed into CFG negative bacteriology (CFGB−, n = 27) or CFG positive bacteriology (CFGB+, n = 28), and CFG negative Pseudomonas aeruginosa (CFGPa−, n = 36) or CFG positive Pseudomonas aeruginosa (CFGPa+, n = 19).ResultsCompared with CG, CFG (P = .034) and CFGB+ (P = .042) had lower body mass index‐for‐age z‐score; forced expiratory volume in the first second was lower in CFGB+ and CFGPa+ (both P < .001). After adjusting for confounders and compared with CG: CFG showed higher TBARS (P ≤ .001) and PC (P = .048), and lower CAT (P = .004) and GPx (P = .003); the increase in PC levels was observed in CFGB+ (P = .011) and CFGPa+ (P = .001) but not in CFGB− (P = .510) and CFGPa− (P = .460).ConclusionsThese results indicate a systemic OS in children with CF. The presence of bacterial infection particularly Pseudomonas aeruginosa seems to be determinant to exacerbate the oxidative damage to proteins, in which PC may be a useful biomarker of OS in CF.     

The role of novel DMEK graft shapes in facilitating intraoperative unscrolling

Graefe's Archive for Clinical and Experimental Ophthalmology - To evaluate whether the preparation of Descemet membrane endothelial keratoplasty (DMEK) grafts into various shapes affect their...     

Isolated Intracardiac Echogenic Focus on Routine Ultrasound: Implications for Practice

Ultrasound is widely used as a screening tool for fetal anomalies. An intracardiac echogenic focus (ICEF) is associated with fetal aneuploidy, particularly trisomy 21, when found with other minor abnormalities known as soft markers. However, when found in isolation, intracardiac echogenic foci are morphologic variations with little or no pathologic significance for the fetus. Ambiguity about the significance of ICEF and other soft markers and the lack of preparation prior to ultrasound can result in unnecessary worry for women and their partners. A variety of tools exist that providers can use to help pregnant women and their partners make informed decisions about ultrasound and fetal screening.     

Predictors associated with mortality of extracorporeal life support therapy for acute heart failure: single-center experience with 679 patients

BackgroundExtracorporeal life support (ECLS) therapy is increasingly used for cardiac and respiratory support postcardiotomy, refractory cardiogenic shock and cardiopulmonary resuscitation. This study aims to describe in-hospital mortality of patients requiring ECLS, identify independent predictors associated with mortality and analyze changes of mortality over time.MethodsThis retrospective study includes all adult ECLS cases at the University Hospital Zurich, a designated ECLS center in Switzerland, in the period 2007 to 2019.ResultsECLS therapy was required in 679 patients (median age 60 years, 27.5% female). In-hospital mortality was 55.5%. Cubic spline interpolation did not detect evidence for a change in mortality over the whole period of 13 years. In-hospital mortality significantly varied between ECLS indications: 70.7% (152/215) for postcardiotomy, 67.9% (108/159) for cardiopulmonary resuscitation, 47.0% (110/234) for refractory cardiogenic shock, and 9.9% (7/71) for lung transplantation and expansive thoracic surgery (P<0.001). Logistic regression modelling showed excellent discrimination in the receiver operating characteristic (ROC) area under the curve (AUC) of 0.89 [95% confidence interval (CI): 0.87–0.92] and identified significant mortality predictors: age, simplified acute physiology score (SAPS) II, as well as new liver failure and each allogenic blood transfusion unit given per day. ECLS after cardiopulmonary resuscitation was associated with significantly higher mortality compared to ECLS for refractory cardiogenic shock.ConclusionsIn-hospital mortality of patients treated with ECLS therapy is high. Outcomes have not changed significantly in the observed period. We identified age, SAPS II, new liver failure and each allogenic blood transfusion unit given per day as independent mortality predictors. Knowledge of predictors strongly associated with in-hospital mortality may affect future decisions about ECLS indications and the respective management to use this elaborate therapy more effectively.     

A Comparison of Artificial Intelligence and Human Diabetic Retinal Image Interpretation in an Urban Health System

Introduction:Artificial intelligence (AI) diabetic retinopathy (DR) software has the potential to decrease time spent by clinicians on image interpretation and expand the scope of DR screening. We performed a retrospective review to compare Eyenuk’s EyeArt software (Woodland Hills, CA) to Temple Ophthalmology optometry grading using the International Classification of Diabetic Retinopathy scale.Methods:Two hundred and sixty consecutive diabetic patients from the Temple Faculty Practice Internal Medicine clinic underwent 2-field retinal imaging. Classifications of the images by the software and optometrist were analyzed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and McNemar’s test. Ungradable images were analyzed to identify relationships with HbA1c, age, and ethnicity. Disagreements and a sample of 20% of agreements were adjudicated by a retina specialist.Results:On patient level comparison, sensitivity for the software was 100%, while specificity was 77.78%. PPV was 19.15%, and NPV was 100%. The 38 disagreements between software and optometrist occurred when the optometrist classified a patient’s images as non-referable while the software classified them as referable. Of these disagreements, a retina specialist agreed with the optometrist 57.9% the time (22/38). Of the agreements, the retina specialist agreed with both the program and the optometrist 96.7% of the time (28/29). There was a significant difference in numbers of ungradable photos in older patients (≥60) vs younger patients (<60) (p=0.003).Conclusions:The AI program showed high sensitivity with acceptable specificity for a screening algorithm. The high NPV indicates that the software is unlikely to miss DR but may refer patients unnecessarily.