Portosystemic shunt, a rare cause of neonatal cholestatic hepatitis. A case report. / Shunt portosistémico, una causa poco frecuente de hepatitis colestásica en neonatología. A propósito de un caso.

Congenital portosystemic shunt is a venous vascular abnormality that connects portal and systemic circulation, resulting in diversion of the blood flow, bypassing the hepatic passage. It is a rare malformation; its incidence varies from 1:30 000 to 1:50 000 newborns. It may be asymptomatic or present with complications in the pediatric age or, less frequently, in the neonatal age. Upon diagnosis, the need for a surgical or an intravascular intervention for closure should be defined. This decision depends on the malformation anatomical characteristics, clinical manifestations, and complications. We present the case of a 1-month-old patient referred to our center for the study of neonatal cholestatic hepatitis, with a diagnosis of extrahepatic portosystemic shunt. Intravascular closure of the defect was performed with significant subsequent improvement.

El shunt portosistémico congénito es una anomalía vascular venosa que comunica circulación portal y sistémica, por la que se deriva el flujo sanguíneo, salteando el paso hepático. Es una entidad poco frecuente, cuya incidencia varía entre 1/30 000 y 1/50 000 recién nacidos. Puede cursar de forma asintomática o presentarse con complicaciones en la edad pediátrica o, menos frecuente, en la edad neonatal. Ante el diagnóstico, se deberá definir la necesidad de intervención quirúrgica o intravascular para el cierre. Esta decisión depende de las características anatómicas de la malformación, de las manifestaciones clínicas y complicaciones presentes. Se presenta el caso de un paciente de un mes de vida derivado a nuestro centro para estudio de hepatitis colestásica neonatal, con diagnóstico de shunt portosistémico extrahepático. Se realizó cierre intravascular de la lesión con mejoría significativa posterior.

Shunt portosistémico, una causa poco frecuente de hepatitis colestásica en neonatología. A propósito de un caso / Portosystemic shunt, a rare cause of neonatal cholestatic hepatitis. A case report

El shunt portosistémico congénito es una anomalía vascular venosa que comunica circulación portal y sistémica, por la que se deriva el flujo sanguíneo, salteando el paso hepático. Es una entidad poco frecuente, cuya incidencia varía entre 1/30 000 y 1/50 000 recién nacidos. Puede cursar de forma asintomática o presentarse con complicaciones en la edad pediátrica o, menos frecuente, en la edad neonatal. Ante el diagnóstico, se deberá definir la necesidad de intervención quirúrgica o intravascular para el cierre. Esta decisión depende de las características anatómicas de la malformación, de las manifestaciones clínicas y complicaciones presentes. Se presenta el caso de un paciente de un mes de vida derivado a nuestro centro para estudio de hepatitis colestásica neonatal, con diagnóstico de shunt portosistémico extrahepático. Se realizó cierre intravascular de la lesión con mejoría significativa posterior.

Congenital portosystemic shunt is a venous vascular abnormality that connects portal and systemic circulation, resulting in diversion of the blood flow, bypassing the hepatic passage. It is a rare malformation; its incidence varies from 1:30 000 to 1:50 000 newborns. It may be asymptomatic or present with complications in the pediatric age or, less frequently, in the neonatal age. Upon diagnosis, the need for a surgical or an intravascular intervention for closure should be defined. This decision depends on the malformation anatomical characteristics, clinical manifestations, and complications. We present the case of a 1-month-old patient referred to our center for the study of neonatal cholestatic hepatitis, with a diagnosis of extrahepatic portosystemic shunt. Intravascular closure of the defect was performed with significant subsequent improvement.

Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation.

Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.

Improving the prediction of sub-cellular locations of proteins with a particle swarm optimization-based boosting strategy.

Learning from imbalanced data sets presents an important challenge to the machine learning community. Traditional classification methods, seeking to minimize the overall error rate of the whole training set, do not perform well on imbalanced data since they assume a relatively balanced class distribution and put too much strength on the majority class. This is a common scenario when predicting sub-cellular locations of proteins since proteins belonging to certain specific locations are naturally more abundant or have been more extensively studied. In this work, a new method to learn from imbalanced data, called SwarmBoost, is proposed in order to reduce overlapping and noise of imbalanced datasets and improve prediction performances. The method combines oversampling, subsampling based on particle swarm optimization and ensemble methods. Our results show that SwarmBoost equals and in several cases outperforms other common boosting algorithms like DataBoost-Im and AdaBoost, constituting a useful tool for improving sub-cellular location predictions.