Candida Sepsis Following Transcervical Chorionic Villi Sampling

Background: The use of invasive devices and broad spectrum antibiotics has increased the rate of candidal superinfections.Candida sepsis associated with pregnancy is rare. Candida sepsis following chorionic villi sampling (CVS) has never been reported. Case: A 31-year-old pregnant woman presented with signs of sepsis one day after undergoing transcervical CVS. Blood culture and curettage material yielded C. albicans. She was treated with 400 mg of fluconazole daily for 4 weeks and completely recovered. Conclusion: Candida sepsis should be considered in the differential diagnosis of sepsis following CVS

Sentiment Analysis in Organizational Work: Toward an Ontology of People Analytics

The present paper proposes a conceptual ontology to evaluate human factors by modeling their key performance indicators and defining these indicators' explanatory factors, manifestations and diverse corresponding digital footprints. Our methodology incorporates six main human resource constructs: performance, engagement, leadership, workplace dynamics, organizational developmental support, and learning and knowledge creation. Using sentiment analysis, we introduce a potential way to evaluate several components of the proposed human factors ontology. We use the Enron email corpus as a test case, to demonstrate how digital footprints can predict such phenomena. In so doing, we hope to encourage further research applying data mining techniques to allow real time, less costly and more reliable assessments of human factor patterns and trends

Cluster Evolution Analysis of Congestive Heart Failure Patients

This study addresses the call to harness big data analytics for more accurate clinical decision making, and is rooted in the context of Congestive Heart Failure (CHF) patients. We aim at identifying CHF patients’ risk levels and disease transitions over time, and present here the clusters that emerged in three consecutive visits. The clusters are classified into five risk levels, based on the mortality rate 30, 90, 180, 365 days post discharge. The primary method was Cluster Evolution Analysis that is able to identify patients’ risk classification, cluster evolution and patients transition over time. The clustering was based on lab results, and we added comorbidities to define the cluster characteristics. A senior cardiologist evaluated the results and stated that the fine clustering allows more accurate identification of patients’ risk groups, likely to result in an improved clinical decision. For example, three high-risk clusters, identified in visit 1, included between 42 to 53 patients out of ~10,000, which could probably be overlooked otherwise. In the next stage, we will identify disease evolution and patient transition between clusters over time

Robust inter-subject audiovisual decoding in functional magnetic resonance imaging using high-dimensional regression

Major methodological advancements have been recently made in the field of neural decoding, which is concerned with the reconstruction of mental content from neuroimaging measures. However, in the absence of a large-scale examination of the validity of the decoding models across subjects and content, the extent to which these models can be generalized is not clear. This study addresses the challenge of producing generalizable decoding models, which allow the reconstruction of perceived audiovisual features from human magnetic resonance imaging (fMRI) data without prior training of the algorithm on the decoded content. We applied an adapted version of kernel ridge regression combined with temporal optimization on data acquired during film viewing (234 runs) to generate standardized brain models for sound loudness, speech presence, perceived motion, face-to-frame ratio, lightness, and color brightness. The prediction accuracies were tested on data collected from different subjects watching other movies mainly in another scanner. Substantial and significant (QFDR<0.05) correlations between the reconstructed and the original descriptors were found for the first three features (loudness, speech, and motion) in all of the 9 test movies (R¯=0.62, R¯ = 0.60, R¯ = 0.60, respectively) with high reproducibility of the predictors across subjects. The face ratio model produced significant correlations in 7 out of 8 movies (R¯=0.56). The lightness and brightness models did not show robustness (R¯=0.23, R¯ = 0). Further analysis of additional data (95 runs) indicated that loudness reconstruction veridicality can consistently reveal relevant group differences in musical experience. The findings point to the validity and generalizability of our loudness, speech, motion, and face ratio models for complex cinematic stimuli (as well as for music in the case of loudness). While future research should further validate these models using controlled stimuli and explore the feasibility of extracting more complex models via this method, the reliability of our results indicates the potential usefulness of the approach and the resulting models in basic scientific and diagnostic contexts