Evaluación del seguimiento de niños con hallazgo de hipertransaminasemia

IntroductionAlthough changes in liver function tests can be non-specific in numerous clinical conditions, they can be the first sign of a potentially serious disease in an asymptomatic patient.Material and methodsRetrospective cohort study, performed by reviewing the records of children of a reference hospital central laboratory with alanine aminorransferase enzyme (ALT) elevation during a 6 month aleatory period.Results572 blood tests with serum ALT elevation corresponding to 403 patients had been assessed during the period studied. 98 patients were excluded for presenting abnormal liver test before the study period of comorbidity that could produce ALT elevation. The remaining 305 patients, 22.6% were diagnosed with a medical condition during the first blood test that explained the ALT elevation, although only 33.3% of them were followed up until verifying their normalization. Final study sample consists of 236 patients with abnormal liver test without apparent liver disease. Adequate follow-up was found only in 29% of them. From this group, 9 patients (13%) were diagnosed with liver disease. The rest of the sample were not properly monitored. In patients with higher serum ALT levels, follow-up was early and more appropiate.ConclusionsIn our area, most children without apparent liver disease are no properly monitored. Therefore, an opportunity to diagnosis and treat a potential liver disease was lost in a great number of children. All children with unexplainedhypertransaminasaemia must be studied.     

Characterization and Local Emission Sources for Ammonia in an Urban Environment

Ammonia levels were evaluated in the urban environment of Madrid City, Spain. A total of 110 samplers were distributed throughout the city. Vehicle traffic density, garbage containers and sewers were identified as local emission sources of ammonia. The average ammonia concentrations were 4.66?±?2.14 µg/m³ (0.39–11.23 µg/m³ range) in the winter and 5.30?±?1.81 µg/m³ (2.33–11.08 µg/m³ range) in the summer. Spatial and seasonal variations of ammonia levels were evaluated. Hotspots were located in the south and center of Madrid City in both winter and summer seasons, with lower ammonia concentrations located in the north (winter) and in the west and east (summer). The number of representative points that were needed to establish a reliable air quality monitoring network for ammonia was determined using a combined clustering and kriging approach. The results indicated that 40 samplers were sufficient to provide a reliable estimate for Madrid City.     

Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset

ObjectiveThe lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning.Materials and MethodsWe used the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities.ResultsCases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting.ConclusionsData source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning.