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Recent event‐related potential (ERP) studies have suggested that the N140cc component reflects target selection mechanisms in tactile search tasks in which the target is presented simultaneously with homogeneous distractors. To investigate if and how the attentional selection of the tactile target is affected by the presence of a singleton distractor, we presented a four‐item search array (two stimuli to the middle and index fingers of the left and right hand) which included a singleton distractor in addition to the target and two other homogenous distractors. Participants had to localize the target (top or bottom finger on either hand), while ignoring all distractors. Behavioral results revealed that the target localization was hindered by the presence of the singleton distractor as demonstrated by distractor‐presence costs for both speed and accuracy. ERP results confirmed that attention was directed to the singleton distractor when this was the only singleton item in the array as suggested by the presence of a distractor‐related N140cc component on distractor only trials. Furthermore, when target and singleton distractor were presented to opposite hands (contralateral distractor trials) the target‐related N140cc amplitude was reduced as compared to target only trials, suggesting reduced attentional resources to the target. However, when target and singleton distractor were presented to the same hand (ipsilateral distractor trials), the N140cc amplitude was comparable to that observed on target only trials. These findings suggest that the N140cc reflects the attentional selection of the target side rather than the competition between stimuli presented to the same hand. The N140cc component has been recently characterized as the electrophysiological correlate of target selection in tactile search tasks and the functional equivalent of the visual N2pc. Our results provide initial evidence that, unlike the N2pc, the N140cc components reflects primarily the allocation of attention to the side of relevant stimuli, rather than the resolution of competitive target‐distractor interactions within that side. [ABSTRACT FROM AUTHOR]

Background: The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI.Methods and Findings: We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors.Conclusions: Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies. [ABSTRACT FROM AUTHOR]

The article presents the case of a female patient who presented a cough, difficulty breathing, and a fever. She had not responded to previously prescribed antibiotics. Further examination revealed petechiae on her legs, as well as blood and protein in her urine. Blood tests, including white cell counts, glycosated hemoglobin, and electrolyte counts, were completed and analyzed. The patient was initially treated for a pulmonary-renal syndrome, and was treated for 3 days prior to being diagnosed with Hepatitis C genotype 3a. The patient was subsequently treated with corticosteroids, peginterferon and plasmapheresis. The patient remained in care for three months. Thereafter, she was sent to a rehabilitation center where she underwent antiviral therapy.