Author(s):  
Milan Sebok ◽  
Matej Kubis ◽  
Miroslav Gutten ◽  
Matej Kucera ◽  
Tomasz N. Koltunowicz ◽  
...  

2020 ◽  
Vol 1 (3) ◽  
pp. 1-20
Author(s):  
Arpan Man Sainju ◽  
Zhe Jiang
Keyword(s):  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrea Piolatto ◽  
Paola Berchialla ◽  
Sarah Allegra ◽  
Silvia De Francia ◽  
Giovanni Battista Ferrero ◽  
...  

AbstractDeferasirox (DFX) is the newest among three different chelators available to treat iron overload in iron-loading anaemias, firstly released as Dispersible Tablets (DT) and more recently replaced by Film-Coated Tablets (FCT). In this retrospective observational study, pharmacokinetics, pharmacodynamics, and safety features of DFX treatment were analyzed in 74 patients that took both formulations subsequently under clinical practice conditions. Bioavailability of DFX FCT compared to DT resulted higher than expected [Cmax: 99.5 (FCT) and 69.7 (DT) μMol/L; AUC: 1278 (FCT) and 846 (DT), P < 0.0001]. DFX FCT was also superior in scalability among doses. After one year of treatment for each formulation, no differences were observed between the treatments in the overall iron overload levels; however, DFX FCT but not DT showed a significant dose–response correlation [Spearman r (dose-serum ferritin variation): − 0.54, P < 0.0001]. Despite being administered at different dosages, the long-term safety profile was not different between formulations: a significant increase in renal impairment risk was observed for both treatments and it was reversible under strict monitoring (P < 0.002). Altogether, these data constitute a comprehensive comparison of DFX formulations in thalassaemia and other iron-loading anaemias, confirming the effectiveness and safety characteristics of DFX and its applicability for treatment tailoring.


RSC Advances ◽  
2021 ◽  
Vol 11 (23) ◽  
pp. 14036-14046
Author(s):  
Binxuan Xie ◽  
Shimou Chen ◽  
Yong Chen ◽  
Lili Liu

The SGPE can achieve high performance and high safety features simultaneously.


2021 ◽  
Vol 11 (8) ◽  
pp. 3531
Author(s):  
Hesham M. Eraqi ◽  
Karim Soliman ◽  
Dalia Said ◽  
Omar R. Elezaby ◽  
Mohamed N. Moustafa ◽  
...  

Extensive research efforts have been devoted to identify and improve roadway features that impact safety. Maintaining roadway safety features relies on costly manual operations of regular road surveying and data analysis. This paper introduces an automatic roadway safety features detection approach, which harnesses the potential of artificial intelligence (AI) computer vision to make the process more efficient and less costly. Given a front-facing camera and a global positioning system (GPS) sensor, the proposed system automatically evaluates ten roadway safety features. The system is composed of an oriented (or rotated) object detection model, which solves an orientation encoding discontinuity problem to improve detection accuracy, and a rule-based roadway safety evaluation module. To train and validate the proposed model, a fully-annotated dataset for roadway safety features extraction was collected covering 473 km of roads. The proposed method baseline results are found encouraging when compared to the state-of-the-art models. Different oriented object detection strategies are presented and discussed, and the developed model resulted in improving the mean average precision (mAP) by 16.9% when compared with the literature. The roadway safety feature average prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. The introduced model can pervasively enable/disable autonomous driving (AD) based on safety features of the road; and empower connected vehicles (CV) to send and receive estimated safety features, alerting drivers about black spots or relatively less-safe segments or roads.


CNS Drugs ◽  
2021 ◽  
Vol 35 (2) ◽  
pp. 177-213
Author(s):  
Mikail Nourredine ◽  
Lucie Jurek ◽  
Bernard Angerville ◽  
Yannick Longuet ◽  
Julia de Ternay ◽  
...  

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