We will present our experience and our preliminary data about the correlation between cardiac calcification and QT interval (and QT dispersion) in uraemia. We studied 32 haemodialysis (HD) patients (age 69 ± 16 years, time on dialysis 32 ± 27 months) and 12 chronic kidney disease stage 4 (CKD-4) patients (age 66 ± 17 years, uraemia duration 38 ± 16 months). The patients were characterized by a good mineral control, as shown by serum phosphate levels (3.6 ± 1.3 mg/dl in CKD-4 and 4.3 ± 1.6 mg/dl in HD patients) and Ca × P product (46 ± 17 and 49 ± 16 mg²/dl², respectively). The parathyroid hormone levels were higher in HD than CKD-4 patients (p < 0.0001). A TC score >400 was found to be highly prevalent in both groups. Significantly more HD patients (62.5%) showed cardiac calcification than CKD-4 patients (33%; p = 0.01). The patients were matched for TC scores higher or lower than 400. The two groups differed by gender (p < 0.05), age (p = 0.026), frequency of diabetes mellitus (p < 0.01), uraemia follow-up period (p < 0.001), low-density lipoprotein cholesterol level (p = 0.009), Ca × P product (p = 0.002), parathyroid hormone level (p < 0.0001), and corrected QT dispersion (p < 0.0001). The QT interval was higher in HD and CKD-4 patients with higher TC scores (approximately 11%), but QT interval dispersion was significantly higher in patients with TC scores >400. QT dispersion showed a linear correlation with TC scores in both groups (r = 0.899 and p < 0.0001 and r = 0.901 and p < 0.0001). Male gender, age, time (months) of uraemia, low-density lipoprotein cholesterol, albumin, calcium × phosphorus product, parathyroid hormone, and TC score are important determinants of QT dispersion. Our data show that it is possible to link dysrhythmias and cardiac calcification in uraemic patients.

Each ICU should identify the SAPS 3 equation most suitable for its case mix. The SAPS II model tended to overestimate the mortality.

Feedback Evaluation is a necessary part of any institute to maintain and monitor the academic quality of the system. Traditionally, a questionnaire based system is used to evaluate the performance of teachers of an institute. Here, we propose an automatic evaluation system based on sentiment analysis, which shall be more versatile and meaningful than existing system. In our proposed system, feedback is collected in the form of running text and sentiment analysis is performed to identify important aspects along with the orientations using supervised and semi supervised machine learning technique

Purpose An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science). Design/methodology/approach The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials. Findings The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches. Originality/value The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.

No abstract