| 1. |
- Björnson, Elias, 1988, et al.
(författare)
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Mediating role of atherogenic lipoproteins in the relationship between liver fat and coronary artery calcification
- 2023
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Non-alcoholic fatty liver disease (NAFLD) is associated with increased secretion of apoB-containing lipoproteins and increased risk of coronary heart disease (CHD). ApoB-containing lipoproteins include low-density lipoproteins (LDLs) and triglyceride-rich lipoproteins (TRLs); and since both LDLs and TRLs are causally related to CHD, they may mediate a portion of the increased risk of atherosclerosis seen in people with NAFLD. In a cohort of 4161 middle aged men and women, we performed mediation analysis in order to quantify the mediating effect of apoB-containing lipoproteins in the relationship between liver fat and atherosclerosis-as measured by coronary artery calcium score (CACS). We found plasma apoB to mediate 17.6% (95% CI 11-24) of the association between liver fat and CACS. Plasma triglycerides and TRL-cholesterol (both proximate measures of TRL particles) mediated 22.3% (95% CI 11-34) and 21.6% (95% CI 10-33) of the association respectively; whereas LDL-cholesterol mediated 5.4% (95% CI 2.0-9.4). In multivariable models, the mediating effect of TRL-cholesterol and plasma triglycerides showed, again, a higher degree of mediation than LDL-cholesterol, corroborating the results seen in the univariable models. In summary, we find around 20% of the association between liver fat and CACS to be mediated by apoB-containing lipoproteins. In addition, we find that TRLs mediate the majority of this effect whereas LDLs mediate a smaller effect. These results explain part of the observed CAD-risk burden for people with NAFLD and further suggest that TRL-lowering may be particularly beneficial to mitigate NAFLD-associated coronary artery disease risk.
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| 2. |
- Rousopoulou, Vaia, et al.
(författare)
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Cognitive analytics platform with AI solutions for anomaly detection
- 2022
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Ingår i: Computers in industry (Print). - : Elsevier BV. - 0166-3615 .- 1872-6194. ; 134, s. 103555-103555
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Tidskriftsartikel (refereegranskat)abstract
- This work presents a cognitive analytics platform for anomaly detection which is capable to handle, analyze and exploit resourcefully machine data from a shop-floor of factory, so as to support the emerging and growing needs of manufacturing industry. The introduced system contributes to industrial digitization and creation of smart factories by providing a generic platform which is a complete solution supporting standards-based factory connectivity, data management, various AI models training and comparisons, live predictions and real-time visualizations. The proposed system is built on a micro-service architecture, in order to be extendable and adaptive over time, and contains three core modules, the Data Acquisition, the Knowledge Management and the Predictive maintenance, which contribute to machine failure prediction and activate predictive maintenance procedures, to efficient production schemes and decision making, to monitor anomalies and handle unforeseen conditions, to predict future behaviours on time series etc. The proposed platform utilizes continuous re-training mechanisms enabling a self learning approach for the delivery of AI solutions, usable also for various production data, guaranteeing the quality of results without continuous monitoring and human-resources allocation for AI models’ retraining. This cognitive platform is supported by machine learning techniques and deep learning architectures in order to achieve the desired performance in the management of factory processes and needs. All the information generated by the proposed platform is provided to the end user through a user interface that utilizes advanced visualization techniques.
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