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Background: Heart valve replacement surgery with mechanical or biological prostheses entails a risk of thromboembolism and bleeding complications., Objective: To determine the complications related to complementary anticoagulation therapy and the probability of risk., Methods: One-hundred and sixty-three patients who underwent heart valve replacement between 2002 and 2016 with either mechanical or biological prostheses, and who received vitamin K antagonists after hospital discharge, were studied. Anticoagulation therapy was categorized into optimal and non-optimal according to INR values prior to the development of complications. Patients with comorbidities and other risk factors for thrombosis and/or bleeding were excluded., Results: In total, 68.7 % of patients received mechanical prostheses, and 31.3 %, biological prostheses (p ≤ 0.001); 25.2 % experienced the complications that motivated the study (p ≤ 0.001), which were hemorrhagic in 48.8 %, thromboembolic in 26.8 %, and of both types in 24.4 % (relative risk = 4.229). Among the patients with complications, 95.1 % received mechanical prostheses, and 4.9 %, biological (p = 0.005); non-optimal INR was identified in 49.7 % (p ≤ 0.001)., Conclusions: Given the high risk of thromboembolic and hemorrhagic complications, valve prostheses must be carefully chosen, and care priorities should include prevention and follow-up, especially in those patients who require anticoagulation therapy., (Copyright: © 2023 Permanyer.)

Background: Leptin and insulin levels are key factors regulating fetal and neonatal energy homeostasis, development and growth. Both biomarkers are used as predictors of weight gain and obesity during infancy. There are currently no prediction algorithms for cord blood (UCB) hormone levels using Artificial Neural Networks (ANN) that have been directly trained with anthropometric maternal and neonatal data, from neonates exposed to distinct metabolic environments during pregnancy (obese with or without gestational diabetes mellitus or lean women). The aims were: 1) to develop ANN models that simulate leptin and insulin concentrations in UCB based on maternal and neonatal data (ANN perinatal model) or from only maternal data during early gestation (ANN prenatal model); 2) To evaluate the biological relevance of each parameter (maternal and neonatal anthropometric variables)., Methods: We collected maternal and neonatal anthropometric data (n = 49) in normoglycemic healthy lean, obese or obese with gestational diabetes mellitus women, as well as determined UCB leptin and insulin concentrations by ELISA. The ANN perinatal model consisted of an input layer of 12 variables (maternal and neonatal anthropometric and biochemical data from early gestation and at term) while the ANN prenatal model used only 6 variables (maternal anthropometric from early gestation) in the input layer. For both networks, the output layer contained 1 variable to UCB leptin or to UCB insulin concentration., Results: The best architectures for the ANN perinatal models estimating leptin and insulin were 12-5-1 while for the ANN prenatal models, 6-5-1 and 6-4-1 were found for leptin and insulin, respectively. ANN models presented an excellent agreement between experimental and simulated values. Interestingly, the use of only prenatal maternal anthropometric data was sufficient to estimate UCB leptin and insulin values. Maternal BMI, weight and age as well as neonatal birth were the most influential parameters for leptin while maternal morbidity was the most significant factor for insulin prediction., Conclusions: Low error percentage and short computing time makes these ANN models interesting in a translational research setting, to be applied for the prediction of neonatal leptin and insulin values from maternal anthropometric data, and possibly the on-line estimation during pregnancy.

Background/aims: Clara cell protein (cc-10) has been shown to negatively regulate inflammation, protect pulmonary surfactant from degradation in the lung, and administration of this recombinant protein improves the condition of infant respiratory distress syndrome (iRDS), a disease that occurred mainly in preterm infants. In view of the possibility that altered expression of cc-10 might regulate its protective function, we attempted to characterize this protein in infants with iRDS., Methods: Using bronchotraqueal aspirates from human infants, we analyzed cc-10 in two-dimensional gel electrophoresis (2-DE) by combining immunoprecipitation, carbonyl groups and total protein immunoblotting., Results: Seven forms of cc-10 were detected with western immunoblots in infants with iRDS while only four forms were present in neonates who needed mechanical ventilation for other reasons without any lung disease (control group). The overall levels of cc-10 in iRDS were lower and differences were seen in isoform pattern and distribution., Conclusion: Our demonstration that cc-10 is differentially expressed in infants with iRDS may point the way towards one possible mechanism that potentially involves modifications of the protein structure with its anti-inflammatory and surfactant protective function and could be detrimental for this airway disorder., (Copyright © 2012 S. Karger AG, Basel.)