Emergency extraction device design

Author(s):  
Nicholas Reisweber
2021 ◽  
Vol 1885 (4) ◽  
pp. 042028
Author(s):  
Tianyin Geng ◽  
Kailang Chen ◽  
Xin Huang

2020 ◽  
Vol 140 (4) ◽  
pp. 437-442
Author(s):  
Hiroki Naito ◽  
Shunya Okamoto ◽  
Yoshiaki Ukita

2019 ◽  
Vol 20 (5) ◽  
pp. 390-400 ◽  
Author(s):  
Nabil N. AL-Hashimi ◽  
Amjad H. El-Sheikh ◽  
Rania F. Qawariq ◽  
Majed H. Shtaiwi ◽  
Rowan AlEjielat

Background: The efficient analytical method for the analysis of nonsteroidal antiinflammatory drugs (NSAIDs) in a biological fluid is important for determining the toxicological aspects of such long-term used therapies. Methods: In the present work, multi-walled carbon nanotubes reinforced into a hollow fiber by chitosan sol-gel assisted-solid/ liquid phase microextraction (MWCNTs-HF-CA-SPME) method followed by the high-performance liquid chromatography-diode array detection (HPLC–DAD) was developed for the determination of three NSAIDs, ketoprofen, diclofenac, and ibuprofen in human urine samples. MWCNTs with various dimensions were characterized by various analytical techniques. The extraction device was prepared by immobilizing the MWCNTs in the pores of 2.5 cm microtube via chitosan sol-gel assisted technology while the lumen of the microtube was filled with few microliters of 1-octanol with two ends sealed. The extraction device was operated by direct immersion in the sample solution. Results: The main factors influencing the extraction efficiency of the selected NSAIDs have been examined. The method showed good linearity R2 ≥ 0.997 with RSDs from 1.1 to 12.3%. The limits of detection (LODs) were 2.633, 2.035 and 2.386 µg L-1, for ketoprofen, diclofenac, and ibuprofen, respectively. The developed method demonstrated a satisfactory result for the determination of selected drugs in patient urine samples and comparable results against reference methods. Conclusion: The method is simple, sensitive and can be considered as an alternative for clinical laboratory analysis of selected drugs.


Author(s):  
M. Peirlinck ◽  
F. Sahli Costabal ◽  
J. Yao ◽  
J. M. Guccione ◽  
S. Tripathy ◽  
...  

AbstractPrecision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.


2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Batyrbek Alimkhanuly ◽  
Joon Sohn ◽  
Ik-Joon Chang ◽  
Seunghyun Lee

AbstractRecent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual device design, optimized interconnects, and careful material selection are key factors determining the overall computation performance. In this work, the impact of replacing conventional devices with microfabricated, graphene-based VRRAM is investigated for circuit and algorithmic levels. By exploiting a sub-nm thin 2D material, the VRRAM array demonstrates an improved read/write margins and read inaccuracy level for the weighted-sum procedure. Moreover, energy consumption is significantly reduced in array programming operations. Finally, an XNOR logic-inspired architecture designed to integrate 1-bit ternary precision synaptic weights into graphene-based VRRAM is introduced. Simulations on VRRAM with metal and graphene word-planes demonstrate 83.5 and 94.1% recognition accuracy, respectively, denoting the importance of material innovation in neuromorphic computing.


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