Search

Introduction: Risk factors for developing pancreatitis due to thiopurines in patients with inflammatory bowel disease (IBD) are not clearly identified. Our aim was to evaluate the predictive pharmacogenetic risk of pancreatitis in IBD patients treated with thiopurines. Methods: We conducted an observational pharmacogenetic study of acute pancreatitis events in a cohort study of IBD patients treated with thiopurines from the prospectively maintained ENEIDA registry biobank of GETECCU. Samples were obtained and the CASR, CEL, CFTR, CDLN2, CTRC, SPINK1, CPA1, and PRSS1 genes, selected based on their known association with pancreatitis, were fully sequenced. Results: Ninety-five cases and 105 controls were enrolled; a total of 57% were women. Median age at pancreatitis diagnosis was 39 years. We identified 81 benign variants (50 in cases and 67 in controls) and a total of 35 distinct rare pathogenic and unknown significance variants (10 in CEL, 21 in CFTR, 1 in CDLN2, and 3 in CPA1). None of the cases or controls carried pancreatitis-predisposing variants within the CASR, CPA1, PRSS1, and SPINK1 genes, nor a pathogenic CFTR mutation. Four different variants of unknown significance were detected in the CDLN and CPA1 genes; one of them was in the CDLN gene in a single patient with pancreatitis and 3 in the CPA1 gene in 5 controls. After the analysis of the variants detected, no significant differences were observed between cases and controls. Conclusion: In patients with IBD, genes known to cause pancreatitis seem not to be involved in thiopurine-related pancreatitis onset. [ABSTRACT FROM AUTHOR]

Objectives: To assess the effectiveness and safety of combining granulocyte‐monocyte apheresis (GMA) and vedolizumab (VDZ) in patients with refractory ulcerative colitis (UC). Methods: This retrospective, multicentre pilot study included all UC patients receiving both GMA and VDZ. We recorded data on GMA sessions, demographic characteristics, and clinical response. Effectiveness was assessed 1 and 6 months after finishing the GMA using the partial Mayo score, C‐reactive protein, and fecal calprotectin levels. Data were also compiled on VDZ intensification, use of new immunomodulators and colectomy during follow‐up. Results: Eight patients were included (mean age 46 years; 63% female; mean disease duration, 132 months; 50% E3). GMA was started after a loss of response to VDZ in all cases (25% primary nonresponse and 75% secondary loss of response). All had previously received anti‐TNF agents. VDZ was prescribed as the second‐, third‐, or fourth‐line biologic in 37%, 50%, and 13% of cases, respectively. Patients had a mean baseline partial Mayo score of 7.5 (SD 2.1) and received a median of 15 GMA sessions (range 5‐38). After a median follow‐up of 7.5 months (IQR 5‐12), partial Mayo score decreased after 1 and 6 months (P =.01 and.06, respectively). Three patients (38%) achieved steroid‐free clinical remission and five (63%) withdrew VDZ. Colectomy rate was 38%. No adverse events were observed during the combination therapy. Conclusions: This small case series suggests that combining GMA with VDZ could be a treatment option in selected cases of UC with an inadequate response to this biologic agent. [ABSTRACT FROM AUTHOR]

The term 'Big Data' has spread rapidly in the framework of Data Mining and Business Intelligence. This new scenario can be defined by means of those problems that cannot be effectively or efficiently addressed using the standard computing resources that we currently have. We must emphasize that Big Data does not just imply large volumes of data but also the necessity for scalability, i.e., to ensure a response in an acceptable elapsed time. When the scalability term is considered, usually traditional parallel-type solutions are contemplated, such as the Message Passing Interface or high performance and distributed Database Management Systems. Nowadays there is a new paradigm that has gained popularity over the latter due to the number of benefits it offers. This model is Cloud Computing, and among its main features we has to stress its elasticity in the use of computing resources and space, less management effort, and flexible costs. In this article, we provide an overview on the topic of Big Data, and how the current problem can be addressed from the perspective of Cloud Computing and its programming frameworks. In particular, we focus on those systems for large-scale analytics based on the MapReduce scheme and Hadoop, its open-source implementation. We identify several libraries and software projects that have been developed for aiding practitioners to address this new programming model. We also analyze the advantages and disadvantages of MapReduce, in contrast to the classical solutions in this field. Finally, we present a number of programming frameworks that have been proposed as an alternative to MapReduce, developed under the premise of solving the shortcomings of this model in certain scenarios and platforms. WIREs Data Mining Knowl Discov 2014, 4:380-409. doi: 10.1002/widm.1134 For further resources related to this article, please visit the . Conflict of interest: The authors have declared no conflicts of interest for this article. [ABSTRACT FROM AUTHOR]

In time series analysis remaining autocorrelation in the errors of a model implies that it is failing to properly capture the structure of time-dependence of the series under study. This can be used as a diagnostic checking tool and as an indicator of the adequacy of the model. Through the study of the errors of the model in the Lagrange Multiplier testing framework, in this paper we derive (and validate using simulated and real world examples) a hypothesis test which allows us to determine if there is some left autocorrelation in the error series. This represents a new diagnostic checking tool for fuzzy rule-based modelling of time series and is an important step towards statistically sound modelling strategy for fuzzy rule-based models. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID-19) pandemic has had an unprecedented impact on healthcare systems, prompting the need to improve the triaging of patients in the Emergency Department (ED). This could be achieved by automatic analysis of chest X-rays (CXR) using Artificial Intelligence (AI). We conducted a research project to generate and thoroughly document the development process of an intelligent system for COVID-19 diagnosis. This work aims at explaining the problem formulation, data collection and pre-processing, use of base convolutional neural networks to approach our diagnostic problem, the process of network building and how our model was validated to reach the final diagnostic system. Using publicly available datasets and a locally obtained dataset with more than 100,000 potentially eligible CXR images, we developed an intelligent diagnostic system that achieves an average performance of 93% success. Then, we implemented a web-based interface that will allow its use in real-world medical practice, with an average response time of less than 1 second. There were some limitations in the application of the diagnostic system to our local dataset which precluded obtaining high diagnostic performance. Although not all these limitations are straightforward, the most relevant ones are discussed, along with potential solutions. Further research is warranted to overcome the limitations of state-of-the-art AI systems used for the imaging diagnosis of COVID-19 in the ED. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue including one on comparison of data driven models for the validation of residentials premises, one on a bibliometric study about the research based on hybridating the fuzzy logic field, and one on a context-sensitive approach to fuzzy modeling.

Introduction: Injury prevention programs for athletes are still limited by a lack of understanding of specific risk factors that can influence injuries within different sports. The majority of studies on volleyball have not considered the movement patterns when moving in different directions or in planned and unplanned block jump-landings.Methods: This study investigated all planes mechanics between the lead and trail limb when moving in dominant and non-dominant directions, for both planned and unplanned jump-landings in thirteen semi-professional female volleyball players. Ankle, knee and hip joint kinematics, kinetics and joint stiffness were recorded.Results: Our results showed statistically significant differences between the lead limb and the trail limb in the hip flexion angles, moments and velocity; in the knee flexion angles, moments, stiffness, power and energy absorption and in the ankle dorsiflexion, power and energy absorption, showing a tendency where the lead limb has a higher injury risk than the trail limb. When considering planned versus unplanned situations, there were statistically significant differences in knee flexion angles, moments, power and energy absorption; and hip contact angle, flexion angular velocity and energy absorption, with musculoskeletal adaptations in the planned situations.Discussion: It appears that the role of the limb, either lead or trail, is more important than the limb dominance when performing directional jump-landings, with the lead limb having a higher implication on possible overuse injuries than the trail limb. Furthermore, planned movements showed a difference in strategy indicating greater implications to possible overuse injuries than in the unplanned situations which may be associated with more conscious thought about the movements.Conclusion: Coaches should consider unilateral coordination training in both landing directions for the lead and trail limb, and should adapt training to replicate the competition environment, using unplanned situations to minimize asymmetries to might reduce injury risks. [ABSTRACT FROM AUTHOR]