Combined Deep Learning and Traditional NLP Approaches for Fire Burst Detection Based on Twitter Posts

The current chapter introduces a procedure that aims at determining regions that are on fire, based on Twitter posts, as soon as possible. The proposed scheme utilizes a deep learning approach for analyzing the text of Twitter posts announcing fire bursts. Deep learning is becoming very popular within different text applications involving text generalization, text summarization, and extracting text information. A deep learning network is to be trained so as to distinguish valid Twitter fire-announcing posts from junk posts. Next, the posts labeled as valid by the network have undergone traditional NLP-based information extraction where the initial unstructured text is converted into a structured one, from which potential location and timestamp of the incident for further exploitation are derived. Analytic processing is then implemented in order to output aggregated reports which are used to finally detect potential geographical areas that are probably threatened by fire. So far, the part that has been implemented is the traditional NLP-based and has already derived promising results under real-world conditions’ testing. The deep learning enrichment is to be implemented and expected to build upon the performance of the existing architecture and further improve it

Comparative performance and external validation of three different scores in predicting inadequate bowel preparation among Greek inpatients undergoing colonoscopy

Background Predictive scores aim to predict bowel preparation adequacy among hospitalized patients undergoing colonoscopy. We evaluated the comparative efficacy of these scores in predicting inadequate bowel cleansing in a cohort of Greek inpatients. Methods We performed a post hoc analysis of data generated from a cohort of inpatients undergoing colonoscopy in 4 tertiary Greek centers to validate the 3 models currently available (models A, B and C). We used the Akaike information criterion to quantify the performance of each model, while Harrell's C-index, as the area under the receiver operating characteristics curve (AUC), verified the discriminative ability to predict inadequate bowel prep. Primary endpoint was the comparison of performance among models for predicting inadequate bowel cleansing. 70.7 +/- 15.4 years-were included in the analysis. Model B showed the highest performance (Harrell's C-index: AUC 77.2% vs. 72.6% and 57.5%, compared to models A and C, respectively). It also achieved higher performance for the subgroup of mobilized inpatients (Harrell's C-index: AUC 72.21% vs. 64.97% and 59.66%, compared to models A and C, respectively). Model B also performed better in predicting patients with incomplete colonoscopy due to inadequate bowel preparation (Harrell's C-index: AUC 74.23% vs. 69.07% and 52.76%, compared to models A and C, respectively).Conclusions Predictive model B outperforms its comparators in the prediction of inpatients with inadequate bowel preparation. This model is particularly advantageous when used to evaluate mobilized inpatients

NAFLD and HBV interplay - related mechanisms underlying liver disease progression

Non-alcoholic fatty liver disease (NAFLD) and Hepatitis B virus infection (HBV) constitute common chronic liver diseases with worldwide distribution. NAFLD burden is expected to grow in the coming decade, especially in western countries, considering the increased incidence of diabetes and obesity. Despite the organized HBV vaccinations and use of anti-viral therapies globally, HBV infection remains endemic and challenging public health issue. As both NAFLD and HBV have been associated with the development of progressive fibrosis, cirrhosis and hepatocellular carcinoma (HCC), the co-occurrence of both diseases has gained great research and clinical interest. The causative relationship between NAFLD and HBV infection has not been elucidated so far. Dysregulated fatty acid metabolism and lipotoxicity in NAFLD disease seems to initiate activation of signaling pathways that enhance pro-inflammatory responses and disrupt hepatocyte cell homeostasis, promoting progression of NAFLD disease to NASH, fibrosis and HCC and can affect HBV replication and immune encountering of HBV virus, which may further have impact on liver disease progression. Chronic HBV infection is suggested to have an influence on metabolic changes, which could lead to NAFLD development and the HBV-induced inflammatory responses and molecular pathways may constitute an aggravating factor in hepatic steatosis development. The observed altered immune homeostasis in both HBV infection and NAFLD could be associated with progression to HCC development. Elucidation of the possible mechanisms beyond HBV chronic infection and NAFLD diseases, which could lead to advanced liver disease or increase the risk for severe complications, in the case of HBV-NAFLD co-existence is of high clinical significance in the context of designing effective therapeutic targets

The “Elpis” Registry on Percutaneous Coronary Interventions: A Three-Year Experience

The advent of percutaneous coronary intervention (PCI) transformed the treatment of obstructive coronary artery disease (CAD) by creating a less invasive revascularization option to coronary-artery bypass grafting (CABG).1 Although, randomized controlled clinical trials (RCTs) are the gold standard in medical research, there is not always the possibility to conduct properly designed RCTs. The gap between evidence from RCTs and clinical practice can be filled by epidemiological studies and properly designed registries.2 The results of the Hellenic Heart Registry on Percutaneous Coronary Interventions (HHR-PCI), a national registry of patients with stable angina or acute coronary syndromes who underwent PCI, were only recently published.3 The purpose of the current study is to report the experience of a newly formed Catheterization laboratory at a tertiary hospital of Athens and to compare its findings to those reported by the HHR-PCI... (excerpt

Specific Threats to Human Rights Protection from the Digital Reality : International Responses and Recommendations to Core Threats from the Digitalised World

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Net clinical benefit of PFO closure versus medical treatment in patients with cryptogenic stroke: A systematic review and meta-analysis

Background: The ideal treatment for patent foramen ovale (PFO) in patients with cryptogenic stroke remains controversial and is being evaluated. The objective of this study was to evaluate the net clinical benefit (NCB) between PFO closure and medical treatment. Methods: We searched three electronic databases from inception until January 2022. The primary outcomes were the NCB-1, defined as the cumulative incidence of stroke, major bleeding, atrial fibrillation/flutter, and serious procedural or device complications; the NCB-2 and NCB-3 were defined as NCB1 but using a weighted factor of 0.5 and 0.25 for atrial fibrillation/flutter events, respectively. We also evaluated each component outcome of NCB as a secondary outcome. Risk ratios (RR) and 95% confidence intervals (CI) of each outcome were calculated (random-effects model). Results: Our analysis included six RCTs (n = 3750 patients). The rates of NCB-1, NCB-2, and NCB-3 were not different between PFO closure and medical treatment. The heterogeneity between trials was low to moderate. Stroke showed a significant relative decrease of 44% (95% CI, 21-60%), favoring the PFO closure arm. Atrial fibrillation/flutter increased by 4.04 times (95% CI, 1.57-8.89) in the PFO closure compared with the medical treatment group. In a meta-regression analysis, the reduction in NCB-1 with PFO closure increased as the proportion of patients treated with the Amplatzer device increased (p = 0.02), and the reduction in NCB-1, NCB-2, and NCB-3 with PFO closure increased as the proportion of patients treated with substantial PFO size increased (p = 0.03). Conclusion: The NCB between PFO closure and medical treatment was not different, suggesting individualized treatment to maximize benefit