Single Osteoblast Chemical Sensor via Non-invasive Bio-Electronic Interface

2003 ◽  
Vol 782 ◽  
Author(s):  
Mo Yang ◽  
Xuan Zhang ◽  
Bonnie Kohr ◽  
Andre Morgan ◽  
Cengiz S Ozkan
Keyword(s):  
Cellular Response ◽  
Microelectrode Array ◽  
Chemical Sensor ◽  
Fast Fourier Transformation ◽  
Chemical Agent ◽  
Extracellular Potential ◽  
Membrane Excitability ◽  
Non Invasive ◽  
Chemical Agents ◽  
Signature Pattern

ABSTRACTThe broad-spectrum sensitivity of cell based biosensors offers the capability for detecting known and unknown chemical/biological agents. One cellular parameter that is often measured is the extracellular potential of electrically active cells. Membrane excitability in osteoblasts plays a key role in modulating the electrical activity in the presence of chemical agents. However, the complexity of this signal makes interpretation of the cellular response to a chemical agent difficult to interpret. By analyzing shifts in the signal's power spectrum, it is possible to determine a frequency spectrum also known as Signature Pattern Vectors (SPV) specific to a chemical. We used a 5x5 multiple microelectrode array system to spatially position osteoblast cells, by using a gradient AC field. Fast Fourier Transformation (FFT) analyses were used to extract information pertaining to the frequency of firing from the extracellular potential.

Single Cell Based Microelectrode Array Biosensors

2003 ◽  
Vol 773 ◽  
Author(s):  
Mo Yang ◽  
Shalini Prasad ◽  
Xuan Zhang ◽  
Mihrimah Ozkan ◽  
Cengiz S. Ozkan
Keyword(s):  
Electrical Activity ◽  
Single Cell ◽  
Cellular Response ◽  
Microelectrode Array ◽  
Fast Fourier Transformation ◽  
Chemical Agent ◽  
Extracellular Potential ◽  
Membrane Excitability ◽  
Specific Chemical ◽  
Chemical Agents

AbstractExtracellular potential is an important parameter which indicates the electrical activity of live cells. Membrane excitability in osteoblasts plays a key role in modulating the electrical activity in the presence of chemical agents. The complexity of cell signal makes interpretation of the cellular response to a chemical agent very difficult. By analyzing shifts in the signal power spectrum, it is possible to determine a frequency spectrum also known as Signature Pattern Vectors (SPV) specific to a chemical. It is also essential to characterize single cell sensitivity and response time for specific chemical agents for developing detect-to-warn biosensors. We used a 4x4 multiple Pt microelectrode array to spatially position single osteoblast cells, by using a gradient AC field. Fast Fourier Transformation (FFT) and Wavelet Transformation (WT) analyses were used to extract information pertaining to the frequency of firing from the extracellular potential.

A novel microfluidic microelectrode chip for a significantly enhanced monitoring of NPY-receptor activation in live mode

Lab on a Chip ◽  
2017 ◽  
Vol 17 (24) ◽  
pp. 4294-4302 ◽  
Author(s):  
Franziska D. Zitzmann ◽  
Heinz-Georg Jahnke ◽  
Felix Nitschke ◽  
Annette G. Beck-Sickinger ◽  
Bernd Abel ◽  
...  
Keyword(s):  
Real Time ◽  
Microfluidic Chip ◽  
Microelectrode Array ◽  
Fem Simulation ◽  
Receptor Activation ◽  
Living Cells ◽  
Real Time Monitoring ◽  
Non Invasive ◽  
Npy Receptor ◽  
Simulation Based

We present a FEM simulation based step-by-step development of a microelectrode array integrated into a microfluidic chip for the non-invasive real-time monitoring of living cells.

Transfer of Drugs and Other Chemicals Into Human Milk

PEDIATRICS ◽  
1989 ◽  
Vol 84 (5) ◽  
pp. 924-936
Author(s):  
Keyword(s):  
Drug Therapy ◽  
Human Milk ◽  
Breast Feeding ◽  
Drug Exposure ◽  
Chemical Agent ◽  
Blood Concentrations ◽  
Lactating Women ◽  
Chemical Agents ◽  
New Information ◽  
Oral Analgesia

Since the first publication of this statement, much new information has been published concerning the transfer of drugs and chemicals into human milk. This information, in addition to other research published before 1983, makes a revision of the previous statement necessary. In this revision, lists of the pharmacologic or chemical agents transferred into human milk and their possible effects on the infant or on lactation, if known, are provided (Tables 1 to 7). The fact that a pharmacologic or chemical agent does not appear in the Tables is not meant to imply that it is not transferred into human milk or that it does not have an effect on the infant but indicates that there are no reports in the literature. These tables should assist the physician in counseling a nursing mother regarding breast-feeding when the mother has a condition for which a drug is medically indicated. The following questions should be considered when prescribing drug therapy to lactating women. (1) Is the drug therapy really necessary? Consultation between the pediatrician and the mother's physician can be most useful. (2) Use the safest drug; for example, acetaminophen rather than aspirin for oral analgesia. (3) If there is a possibility that a drug may present a risk to the infant (eg, phenytoin, phenobarbital), consideration should be given to measurement of blood concentrations in the nursing infant. (4) Drug exposure to the nursing infant may be minimized by having the mother take the medication just after completing a breast-feeding and/or just before the infant has his or her lengthy sleep periods.

Abstract 211: Automated and Non-invasive Estimation of QT Interval from Microscopy Images of Stem Cell-Derived Cardiomyocytes

2014 ◽  
Vol 115 (suppl_1) ◽  
Author(s):  
Mahnaz Maddah ◽  
Kevin Loewke
Keyword(s):  
Drug Development ◽  
Qt Interval ◽  
Cellular Response ◽  
Cardiovascular Health ◽  
New Drugs ◽  
Patient Specific ◽  
Current Standard ◽  
Drug Induced ◽  
Safety Testing ◽  
Non Invasive

A promising application of induced pluripotent stem cells (iPSCs) is the generation of patient-specific cardiomyocytes (CMs), which can be used for drug development and safety testing related to cardiovascular health. iPSC-derived CMs can be used for preclinical testing of new drugs that may cause drug-induced arrhythmia or long QT syndrome, as well as post-market safety testing of existing drugs. The measurement of QT interval for iPSC-derived CMs is commonly analyzed using electrophysiological potentials captured by a micro-electrode array (MEA). While such systems are the current standard for characterization, they can be expensive and low-throughput, require high cell plating density, and due to the direct contact between cells and electrodes, may cause undesirable cellular response. Here, we present a new method to non-invasively measure the QT-interval in iPSC-derived CMs using video microscopy and computer vision analysis. Our algorithms can reliably and automatically extract beating signal characteristics such as frequency, irregularity, and duration through image analysis of cardiomyocyte motion. Through a correlative study with MEA, we demonstrate that a non-invasive measurement of QT interval can be derived from the duration of visible cellular motion that occurs during contraction and relaxation. We also show that our system can accurately characterize the cellular response from the addition of compounds known to modulate beating frequency and irregularity. Our measurement technique is robust, automated, and requires no physical or chemical contact with the cells, making it ideal for cardiovascular drug development and cardiotoxicity testing.

Design of a Biologically-Inspired Chemical Sensor

2013 ◽  
Author(s):  
Jacquelyn K. S. Nagel
Keyword(s):  
Liver Disease ◽  
Design Process ◽  
Chemical Sensor ◽  
Current Sensor ◽  
Biologically Inspired ◽  
Invasive Approach ◽  
Biological Inspiration ◽  
Non Invasive ◽  
Functional Representation ◽  
System Information

Sensors are an integral part of many engineered products and systems. Biological inspiration has the potential to improve current sensor designs as well as inspire innovative ones. Mimicking nature offers more than just the observable aspects that conjure up engineering solutions performing similar functions, but also less obvious strategic and sustainable aspects. This paper presents the design of an innovative, biologically-inspired chemical sensor that performs “up-front” processing through mechanical filtering. Functional representation and abstraction were used to place the biological system information in an engineering context, and facilitate the bioinspired design process. Inspiration from the physiology (function) of the guard cell coupled with the morphology (form) and physiology of tropomyosin resulted in multiple concept variants for the chemical sensor. The chemical sensor conceptual designs are provided along with detailed descriptions. Applications of the sensor design include environmental monitoring of harmful gases, and a non-invasive approach to detect illnesses including diabetes, liver disease, and cancer on the breath.

Study of Selectively Permeable Coatings to Textiles

2011 ◽  
Vol 1312 ◽  
Author(s):  
Joseph G. Sargent ◽  
Jun S. Lee ◽  
Emmanuelle Reynaud ◽  
Michael D. Gilbert ◽  
James M. Sloan
Keyword(s):  
Electron Microscopy ◽  
Industrial Applications ◽  
Chemical Agent ◽  
Coating Materials ◽  
Water Permeation ◽  
Chemical Agents ◽  
Transmission Electron ◽  
Coated Fabric ◽  
Coating Uniformity ◽  
Electron Microscopy Analysis

ABSTRACTBreathable barrier textiles for both chemical agent and moisture are being actively developed for military and industrial applications. An ideal approach is to coat textiles with a semi-permeable film that allows the transport of water while still serving as a barrier for chemical agents. Sulfonated poly (styrene-block-isobutylene-block-styrene) (SIBS) copolymer spontaneously phase separates upon drying from solution to produce a nanostructured film with the controlled barrier functionality for water permeation and repelling of chemical agents.The objective of this research is to investigate coating uniformity and phase morphology of SIBS coating materials fabricated by novel solvent combinations. Scanning electron microscopy analysis is used for the assessment of the coating uniformity as well as the level of adhesion between the polymer coating and fabric substrate. Transmission electron microscopy is used to characterize the phase separation morphology of the SIBS copolymer coating. The mechanical behavior of the coated fabric is determined through tensile and shear tests and is compared to the bare fabric behavior. The goal of this study is to relate the processing conditions of the final nanostructured block copolymer coated fabric produced on industrial scale.

scholarly journals Spectroscopic analysis of myoglobin and cytochrome c dynamics in isolated cardiomyocytes during hypoxia and reoxygenation

2015 ◽  
Vol 12 (105) ◽  
pp. 20141339 ◽  
Author(s):  
A. Almohammedi ◽  
S. M. Kapetanaki ◽  
B. R. Wood ◽  
E. L. Raven ◽  
N. M. Storey ◽  
...  
Keyword(s):  
Dissolved Oxygen ◽  
Cellular Response ◽  
Ex Vivo ◽  
Reductase Activity ◽  
Sodium Dithionite ◽  
Raman Microspectroscopy ◽  
Chemical Agent ◽  
Rat Cardiomyocytes ◽  
Hypoxic Conditions ◽  
In Cells

Raman microspectroscopy was applied to monitor the intracellular redox state of myoglobin and cytochrome c from isolated adult rat cardiomyocytes during hypoxia and reoxygenation. The nitrite reductase activity of myoglobin leads to the production of nitric oxide in cells under hypoxic conditions, which is linked to the inhibition of mitochondrial respiration. In this work, the subsequent reoxygenation of cells after hypoxia is shown to lead to increased levels of oxygen-bound myoglobin relative to the initial levels observed under normoxic conditions. Increased levels of reduced cytochrome c in ex vivo cells are also observed during hypoxia and reoxygenation by Raman microspectroscopy. The cellular response to reoxygenation differed dramatically depending on the method used in the preceding step to create hypoxic conditions in the cell suspension, where a chemical agent, sodium dithionite, leads to reduction of cytochromes in addition to removal of dissolved oxygen, and bubbling-N 2 gas leads to displacement of dissolved oxygen only. These results have an impact on the assessment of experimental simulations of hypoxia in cells. The spectroscopic technique employed in this work will be used in the future as an analytical method to monitor the effects of varying levels of oxygen and nutrients supplied to cardiomyocytes during either the preconditioning of cells or the reperfusion of ischaemic tissue.

GaN-based micro chemical sensor nodes for early warning chemical agents

2007 ◽  
Author(s):  
K.-A. Son ◽  
B. Yang ◽  
N. Prokopuk ◽  
J. S. Moon ◽  
A. Liao ◽  
...  
Keyword(s):  
Early Warning ◽  
Chemical Sensor ◽  
Sensor Nodes ◽  
Chemical Agents

scholarly journals Machine Learning Analysis of Volatolomic Profiles in Breath Can Identify Non-invasive Biomarkers of Liver Disease

2021 ◽  
Author(s):  
Jonathan Thomas ◽  
Joanna Roopkumar ◽  
Tushar Patel
Keyword(s):  
Machine Learning ◽  
Portal Hypertension ◽  
Liver Disease ◽  
Hepatic Metabolism ◽  
Molecular Feature ◽  
Non Invasive ◽  
Molecular Features ◽  
Signature Pattern ◽  
Ion Mobility Spectroscopy ◽  
Learning Analysis

Abstract Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds (VOC) could provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of combining global VOC (volatolomic) profiles from breath analysis and machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate volatolomic profiles. Samples were collected from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88-92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes without identifying specific compounds. These studies will pave the way in developing non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.

scholarly journals Machine learning analysis of volatolomic profiles in breath can identify non-invasive biomarkers of liver disease: A pilot study

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260098
Author(s):  
Jonathan N. Thomas ◽  
Joanna Roopkumar ◽  
Tushar Patel
Keyword(s):  
Machine Learning ◽  
Portal Hypertension ◽  
Liver Disease ◽  
Hepatic Metabolism ◽  
Supervised Machine Learning ◽  
Molecular Feature ◽  
Non Invasive ◽  
Molecular Features ◽  
Signature Pattern ◽  
Ion Mobility Spectroscopy

Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds could therefore provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of using global volatolomic profiles from breath analysis in combination with supervised machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate breathomic profiles. A standardized collection protocol and analysis pipeline was used to collect samples from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88–92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes using global breath output. These studies will pave the way for the development of non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.

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