Low-cost voltage amplifier for biological signal acquisition through generic micro-electrode array

Author(s):  
Jose A. Fontanilla ◽  
Jesus D. Urbano ◽  
A. Luque
2012 ◽  
Vol 47 (5) ◽  
pp. 1209-1220 ◽  
Author(s):  
Jing Guo ◽  
Jie Yuan ◽  
Jiageng Huang ◽  
Jessica Ka-Yan Law ◽  
Chi-Kong Yeung ◽  
...  

Author(s):  
Jing Guo ◽  
Jiageng Huang ◽  
Jie Yuan ◽  
Jessica Ka-Yan Law ◽  
Chi-Kong Yeung ◽  
...  

Author(s):  
Martin Schuettler ◽  
Christian Henle ◽  
Juan Ordonez ◽  
Gregg J. Suaning ◽  
Nigel H. Lovell ◽  
...  

2021 ◽  
Vol 118 (45) ◽  
pp. e2110817118
Author(s):  
Dengning Xia ◽  
Rui Jin ◽  
Gaurav Byagathvalli ◽  
Huan Yu ◽  
Ling Ye ◽  
...  

Vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other pathogens with pandemic potential requires safe, protective, inexpensive, and easily accessible vaccines that can be developed and manufactured rapidly at a large scale. DNA vaccines can achieve these criteria, but induction of strong immune responses has often required bulky, expensive electroporation devices. Here, we report an ultra-low-cost (<1 USD), handheld (<50 g) electroporation system utilizing a microneedle electrode array (“ePatch”) for DNA vaccination against SARS-CoV-2. The low cost and small size are achieved by combining a thumb-operated piezoelectric pulser derived from a common household stove lighter that emits microsecond, bipolar, oscillatory electric pulses and a microneedle electrode array that targets delivery of high electric field strength pulses to the skin’s epidermis. Antibody responses against SARS-CoV-2 induced by this electroporation system in mice were strong and enabled at least 10-fold dose sparing compared to conventional intramuscular or intradermal injection of the DNA vaccine. Vaccination was well tolerated with mild, transient effects on the skin. This ePatch system is easily portable, without any battery or other power source supply, offering an attractive, inexpensive approach for rapid and accessible DNA vaccination to combat COVID-19, as well as other epidemics.


BioTechniques ◽  
2008 ◽  
Vol 45 (4) ◽  
pp. 451-456 ◽  
Author(s):  
Michael Serra ◽  
Amy Chan ◽  
Maya Dubey ◽  
Thomas B. Shea

2019 ◽  
Vol 99 ◽  
pp. 106595
Author(s):  
Michael Trujillo ◽  
Shuhei Noji ◽  
Yasuoka Satoko ◽  
Gary Cheng ◽  
Ikurou Suzuki

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Marcin Grochowina ◽  
Lucyna Leniowska ◽  
Agnieszka Gala-Błądzińska

Abstract Pattern recognition and automatic decision support methods provide significant advantages in the area of health protection. The aim of this work is to develop a low-cost tool for monitoring arteriovenous fistula (AVF) with the use of phono-angiography method. This article presents a developed and diagnostic device that implements classification algorithms to identify 38 patients with end stage renal disease, chronically hemodialysed using an AVF, at risk of vascular access stenosis. We report on the design, fabrication, and preliminary testing of a prototype device for non-invasive diagnosis which is very important for hemodialysed patients. The system includes three sub-modules: AVF signal acquisition, information processing and classification and a unit for presenting results. This is a non-invasive and inexpensive procedure for evaluating the sound pattern of bruit produced by AVF. With a special kind of head which has a greater sensitivity than conventional stethoscope, a sound signal from fistula was recorded. The proces of signal acquisition was performed by a dedicated software, written specifically for the purpose of our study. From the obtained phono-angiogram, 23 features were isolated for vectors used in a decision-making algorithm, including 6 features based on the waveform of time domain, and 17 features based on the frequency spectrum. Final definition of the feature vector composition was obtained by using several selection methods: the feature-class correlation, forward search, Principal Component Analysis and Joined-Pairs method. The supervised machine learning technique was then applied to develop the best classification model.


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