scholarly journals Specificity of Biomarker Detection in Microfluidic Sensors

2020 ◽  
Author(s):  
Praneet Prakash ◽  
Manoj Varma
Keyword(s):  
Early Stage ◽  
Point Of Care ◽  
Biomarker Detection ◽  
Reaction Parameter ◽  
Reaction Parameters ◽  
Specific Detection ◽  
Early Detection Of Disease ◽  
Low Concentrations ◽  
Microfluidic Sensors ◽  
Weak Target

<p>The microfluidics based point-of-care (POC) sensing devices offer unmatched possibilities of fast and high throughput diagnosis over conventional strategies. A major challenge for the early detection of disease is the significantly lower concentration of biomarkers as compared to the interfering noise molecules. In this work, we investigate the ‘reaction parameter’ phase space to identify suitable reaction parameters to enhance biomarker detection specificity. Under similar target biomarker and noise concentration levels, we show that a target biomarker is more likely to be detected at low concentrations and weak target and noise-receptor binding kinetics. Importantly, a simulation verified time-scale based methodology is developed to guide the appropriate choice of biomarkers for specific detection. This study demonstrates the prospect of successful POC diagnostic devices during early stage of diseases such as cancer. </p>

scholarly journals Specificity of Biomarker Detection in Microfluidic Sensors

2020 ◽  
Author(s):  
Praneet Prakash ◽  
Manoj Varma
Keyword(s):  
Early Stage ◽  
Point Of Care ◽  
Biomarker Detection ◽  
Reaction Parameter ◽  
Reaction Parameters ◽  
Specific Detection ◽  
Early Detection Of Disease ◽  
Low Concentrations ◽  
Microfluidic Sensors ◽  
Weak Target

<p>The microfluidics based point-of-care (POC) sensing devices offer unmatched possibilities of fast and high throughput diagnosis over conventional strategies. A major challenge for the early detection of disease is the significantly lower concentration of biomarkers as compared to the interfering noise molecules. In this work, we investigate the ‘reaction parameter’ phase space to identify suitable reaction parameters to enhance biomarker detection specificity. Under similar target biomarker and noise concentration levels, we show that a target biomarker is more likely to be detected at low concentrations and weak target and noise-receptor binding kinetics. Importantly, a simulation verified time-scale based methodology is developed to guide the appropriate choice of biomarkers for specific detection. This study demonstrates the prospect of successful POC diagnostic devices during early stage of diseases such as cancer. </p>

scholarly journals Trends in the Design of Intensity-Based Optical Fiber Biosensors (2010–2020)

Biosensors ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 197
Author(s):  
Nerea De Acha ◽  
Abián B. Socorro-Leránoz ◽  
César Elosúa ◽  
Ignacio R. Matías
Keyword(s):  
Optical Fiber ◽  
Limit Of Detection ◽  
Early Stage ◽  
Point Of Care ◽  
Research Groups ◽  
Fiber Sensors ◽  
Environment Quality ◽  
Low Concentrations ◽  
Highly Sensitive ◽  
Fiber Dimensions

There exists an increasing interest in monitoring low concentrations of biochemical species, as they allow the early-stage detection of illnesses or the monitoring of the environment quality. Thus, both companies and research groups are focused on the development of accurate, fast and highly sensitive biosensors. Optical fiber sensors have been widely employed for these purposes because they provide several advantages for their use in point-of-care and real-time applications. In particular, this review is focused on optical fiber biosensors based on luminescence and absorption. Apart from the key parameters that determine the performance of a sensor (limit of detection, sensibility, cross-sensibility, etc.), other features are analyzed, such as the optical fiber dimensions, the sensing set ups and the fiber functionalization. The aim of this review is to have a comprehensive insight of the different aspects that must be taken into account when working with this kind of sensors.

scholarly journals Fabry Cardiomyopathy: Current Practice and Future Directions

Cells ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1532
Author(s):  
Jeffrey Yim ◽  
Olivia Yau ◽  
Darwin F. Yeung ◽  
Teresa S. M. Tsang
Keyword(s):  
Fabry Disease ◽  
Early Stage ◽  
Point Of Care ◽  
Left Ventricular ◽  
The Body ◽  
Cardiac Biomarkers ◽  
Dried Blood Spot ◽  
Point Of Care Ultrasound ◽  
Enzyme Replacement ◽  
Blood Spot

Fabry disease (FD) is an X-linked lysosomal storage disorder caused by mutations in the galactosidase A (GLA) gene that result in deficient galactosidase A enzyme and subsequent accumulation of glycosphingolipids throughout the body. The result is a multi-system disorder characterized by cutaneous, corneal, cardiac, renal, and neurological manifestations. Increased left ventricular wall thickness represents the predominant cardiac manifestation of FD. As the disease progresses, patients may develop arrhythmias, advanced conduction abnormalities, and heart failure. Cardiac biomarkers, point-of-care dried blood spot testing, and advanced imaging modalities including echocardiography with strain imaging and magnetic resonance imaging (MRI) with T1 mapping now allow us to detect Fabry cardiomyopathy much more effectively than in the past. While enzyme replacement therapy (ERT) has been the mainstay of treatment, several promising therapies are now in development, making early diagnosis of FD even more crucial. Ongoing initiatives involving artificial intelligence (AI)-empowered interpretation of echocardiographic images, point-of-care dried blood spot testing in the echocardiography laboratory, and widespread dissemination of point-of-care ultrasound devices to community practices to promote screening may lead to more timely diagnosis of FD. Fabry disease should no longer be considered a rare, untreatable disease, but one that can be effectively identified and treated at an early stage before the development of irreversible end-organ damage.

scholarly journals Learning health systems in primary care: a systematic scoping review

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Danielle M. Nash ◽  
Zohra Bhimani ◽  
Jennifer Rayner ◽  
Merrick Zwarenstein
Keyword(s):  
Primary Care ◽  
Quality Improvement ◽  
Health Systems ◽  
Scoping Review ◽  
Early Stage ◽  
Point Of Care ◽  
The United States ◽  
Stage Of Development ◽  
Integrated Health ◽  
Google Search

Abstract Background Learning health systems have been gaining traction over the past decade. The purpose of this study was to understand the spread of learning health systems in primary care, including where they have been implemented, how they are operating, and potential challenges and solutions. Methods We completed a scoping review by systematically searching OVID Medline®, Embase®, IEEE Xplore®, and reviewing specific journals from 2007 to 2020. We also completed a Google search to identify gray literature. Results We reviewed 1924 articles through our database search and 51 articles from other sources, from which we identified 21 unique learning health systems based on 62 data sources. Only one of these learning health systems was implemented exclusively in a primary care setting, where all others were integrated health systems or networks that also included other care settings. Eighteen of the 21 were in the United States. Examples of how these learning health systems were being used included real-time clinical surveillance, quality improvement initiatives, pragmatic trials at the point of care, and decision support. Many challenges and potential solutions were identified regarding data, sustainability, promoting a learning culture, prioritization processes, involvement of community, and balancing quality improvement versus research. Conclusions We identified 21 learning health systems, which all appear at an early stage of development, and only one was primary care only. We summarized and provided examples of integrated health systems and data networks that can be considered early models in the growing global movement to advance learning health systems in primary care.

scholarly journals Contagion Management at the Méditerranée Infection University Hospital Institute

2021 ◽  
Vol 10 (12) ◽  
pp. 2627
Author(s):  
Pierre-Edouard Fournier ◽  
Sophie Edouard ◽  
Nathalie Wurtz ◽  
Justine Raclot ◽  
Marion Bechet ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Early Diagnosis ◽  
Personal Protective Equipment ◽  
Early Stage ◽  
Point Of Care ◽  
University Hospital ◽  
Protective Equipment ◽  
Monitoring Point ◽  
Epidemiological Monitoring ◽  
Wide Range

The Méditerranée Infection University Hospital Institute (IHU) is located in a recent building, which includes experts on a wide range of infectious disease. The IHU strategy is to develop innovative tools, including epidemiological monitoring, point-of-care laboratories, and the ability to mass screen the population. In this study, we review the strategy and guidelines proposed by the IHU and its application to the COVID-19 pandemic and summarise the various challenges it raises. Early diagnosis enables contagious patients to be isolated and treatment to be initiated at an early stage to reduce the microbial load and contagiousness. In the context of the COVID-19 pandemic, we had to deal with a shortage of personal protective equipment and reagents and a massive influx of patients. Between 27 January 2020 and 5 January 2021, 434,925 nasopharyngeal samples were tested for the presence of SARS-CoV-2. Of them, 12,055 patients with COVID-19 were followed up in our out-patient clinic, and 1888 patients were hospitalised in the Institute. By constantly adapting our strategy to the ongoing situation, the IHU has succeeded in expanding and upgrading its equipment and improving circuits and flows to better manage infected patients.

scholarly journals Modifications of the PAMONO-Sensor Help to Size and Quantify Low Number of Individual Biological and Non-Biological Nano-Particles

2021 ◽  
Vol 6 (1) ◽  
pp. 26
Author(s):  
Rahat Morad Talukder ◽  
Al Shahriar Hossain Rakib ◽  
Julija Skolnik ◽  
Zohair Usfoor ◽  
Katharina Kaufmann ◽  
...  
Keyword(s):  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
Nano Particles ◽  
Label Free ◽  
Specific Detection ◽  
Virus Like Particles ◽  
Low Concentrations ◽  
Challenging Tasks ◽  
Image Area

In a series of recently published works, we demonstrated that the plasmon-assisted microscopy of nano-objects (PAMONO) technique can be successfully employed for the sizing and quantification of single viruses, virus-like particles, microvesicles and charged non-biological particles. This approach enables label-free, but specific detection of biological nano-vesicles. Hence, the sensor, which was built up utilizing plasmon-assisted microscopy, possesses relative versatility and it can be used as a platform for cell-based assays. However, one of the challenging tasks for such a sensor was the ability to reach a homogeneous illumination of the whole surface of the gold sensor slide. Moreover, in order to enable the detection of even relatively low concentrations of nano-particles, the focused image area had to be expanded. Both tasks were solved via modifications of previously described PAMONO-sensor set ups. Taken together, our latest findings can help to develop a research and diagnostic platform based on the principles of the surface plasmon resonance (SPR)-assisted microscopy of nano-objects.

scholarly journals Point-of-care serodiagnostic test for early-stage Lyme disease using a multiplexed paper-based immunoassay and machine learning

2019 ◽  
Author(s):  
Hyou-Arm Joung ◽  
Zachary S. Ballard ◽  
Jing Wu ◽  
Derek K. Tseng ◽  
Hailemariam Teshome ◽  
...  
Keyword(s):  
Lyme Disease ◽  
Early Stage ◽  
Point Of Care ◽  
Area Under The Curve ◽  
Diagnostic Algorithm ◽  
Cost Effective ◽  
Optimal Subset ◽  
Antigen Peptide ◽  
Vector Borne ◽  
Threshold Tuning

ABSTRACTCaused by the tick-borne spirochete, Borrelia burgdorferi, Lyme disease (LD) is the most common vector-borne infectious disease in North America and Europe. Though timely diagnosis and treatment are effective in preventing disease progression, current tests are insensitive in early-stage LD, with a sensitivity <50%. Additionally, the serological testing currently recommended by the US Center for Disease Control has high costs (>$400/test) and extended sample-to-answer timelines (>24 hours). To address these challenges, we created a cost-effective and rapid point-of-care (POC) test for early-stage LD that assays for antibodies specific to seven Borrelia antigens and a synthetic peptide in a paper-based multiplexed vertical flow assay (xVFA). We trained a deep learning-based diagnostic algorithm to select an optimal subset of antigen/peptide targets, and then blindly-tested our xVFA using human samples (N(+) = 42, N(−)= 54), achieving an area-under-the-curve (AUC), sensitivity, and specificity of 0.950, 90.5%, and 87.0% respectively, outperforming previous LD POC tests. With batch-specific standardization and threshold tuning, the specificity of our blind-testing performance improved to 96.3%, with an AUC and sensitivity of 0.963 and 85.7%, respectively.

scholarly journals Role of Machine Learning Approach for Detection and Classification of Diseases in Cotton Plant

2021 ◽  
Vol 12 (5) ◽  
pp. 810-817
Author(s):  
Sandhya N. dhage, Dr. Vijay Kumar Garg
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Cotton Plant ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Early Detection Of Disease ◽  
Cotton Crop ◽  
Machine Learning Approach ◽  
Crop Diseases

Qualitative and quantitative agricultural production leads to economic benefits which can be achieved by periodic monitoring of crop, detection and prevention of crop diseases and insects. Quality of crop production is reduced by pest infection and crop diseases. Existing measures involves manual detection of cotton diseases by farmers and experts which requires  regular monitoring and detection manifest at middle to later stage of infection which causes many disadvantages such as becoming  too late for diseases to be cured.  Lack of early detection of diseases causes the diseases to be spread in nearby crops in the field and also spraying of pesticides is done on entire field for minimizing the infection of disease. The main goal of proposed research topic is to find the solution to the agriculture problem which involves detecting disease in cotton plant at early stage and classify the disease based on symptoms. Early detection of disease at an early stage prevent it from spreading to another area and preventive measures can be taken by farmers by spraying pesticides to control its growth which helps to increase the cotton yield production. Automatic identification of the different diseases affecting cotton crop will give many benefits to the farmers so that time, money will be saved and also gives healthy life to the crop. The contribution of this paper is to present the machine learning approach used for cotton crop disease diagnosis and classification.

scholarly journals Programmable Paper-Based Microfluidic Devices for Biomarker Detections

Micromachines ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 516 ◽  
Author(s):  
Veasna Soum ◽  
Sooyong Park ◽  
Albertus Ivan Brilian ◽  
Oh-Sun Kwon ◽  
Kwanwoo Shin
Keyword(s):  
Point Of Care ◽  
High Sensitivity ◽  
Microfluidic Devices ◽  
Portable Devices ◽  
Biomarker Detection ◽  
Fluid Delivery ◽  
Wide Range ◽  
Paper Based Microfluidics ◽  
Digital Microfluidic ◽  
Detection Of Biomarkers

Recent advanced paper-based microfluidic devices provide an alternative technology for the detection of biomarkers by using affordable and portable devices for point-of-care testing (POCT). Programmable paper-based microfluidic devices enable a wide range of biomarker detection with high sensitivity and automation for single- and multi-step assays because they provide better control for manipulating fluid samples. In this review, we examine the advances in programmable microfluidics, i.e., paper-based continuous-flow microfluidic (p-CMF) devices and paper-based digital microfluidic (p-DMF) devices, for biomarker detection. First, we discuss the methods used to fabricate these two types of paper-based microfluidic devices and the strategies for programming fluid delivery and for droplet manipulation. Next, we discuss the use of these programmable paper-based devices for the single- and multi-step detection of biomarkers. Finally, we present the current limitations of paper-based microfluidics for biomarker detection and the outlook for their development.

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