scholarly journals Machine-Perception Nanosensor Platform to Detect Cancer Biomarkers

2021 ◽  
Author(s):  
Zvi Yaari ◽  
Yoona Yang ◽  
Elana Apfelbaum ◽  
Alex Settle ◽  
Quinlan Cullen ◽  
...  
Keyword(s):  
Molecular Recognition ◽  
Point Of Care ◽  
Survival Rates ◽  
Simultaneous Detection ◽  
Cancer Biomarkers ◽  
Machine Learning Algorithms ◽  
Protein Biomarkers ◽  
Recognition Elements ◽  
Biomolecular Assays ◽  
Uterine Lavage

AbstractConventional molecular recognition elements, such as antibodies, present issues for the development of biomolecular assays for use in point-of-care devices, implantable/wearables, and under-resourced settings. Additionally, antibody development and use, especially for highly multiplexed applications, can be slow and costly. We developed a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids. We demonstrated this platform in gynecologic cancers, which are often diagnosed at advanced stages, leading to low survival rates. We investigated the platform for detection in uterine lavage samples, which are enriched with cancer biomarkers compared to blood. We found that the method enables the simultaneous detection of multiple biomarkers in patient samples, with F1-scores of ~0.95 in uterine lavage samples from cancer patients. This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements.

scholarly journals Multiplexed Prostate Cancer Companion Diagnostic Devices

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5023
Author(s):  
Josephine Aidoo-Brown ◽  
Despina Moschou ◽  
Pedro Estrela
Keyword(s):  
Prostate Cancer ◽  
Point Of Care ◽  
Specific Antigen ◽  
Simultaneous Detection ◽  
Enzyme Linked Immunosorbent Assay ◽  
Protein Biomarkers ◽  
Protein Biomarker ◽  
Psa Testing ◽  
Specificity And Sensitivity ◽  
Companion Diagnostic

Prostate cancer (PCa) remains one of the most prominent forms of cancer for men. Since the early 1990s, Prostate-Specific Antigen (PSA) has been a commonly recognized PCa-associated protein biomarker. However, PSA testing has been shown to lack in specificity and sensitivity when needed to diagnose, monitor and/or treat PCa patients successfully. One enhancement could include the simultaneous detection of multiple PCa-associated protein biomarkers alongside PSA, also known as multiplexing. If conventional methods such as the enzyme-linked immunosorbent assay (ELISA) are used, multiplexed detection of such protein biomarkers can result in an increase in the required sample volume, in the complexity of the analytical procedures, and in adding to the cost. Using companion diagnostic devices such as biosensors, which can be portable and cost-effective with multiplexing capacities, may address these limitations. This review explores recent research for multiplexed PCa protein biomarker detection using optical and electrochemical biosensor platforms. Some of the novel and potential serum-based PCa protein biomarkers will be discussed in this review. In addition, this review discusses the importance of converting research protocols into multiplex point-of-care testing (xPOCT) devices to be used in near-patient settings, providing a more personalized approach to PCa patients’ diagnostic, surveillance and treatment management.

scholarly journals Microfluidics-Based Plasmonic Biosensing System Based on Patterned Plasmonic Nanostructure Arrays

Micromachines ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 826
Author(s):  
Yanting Liu ◽  
Xuming Zhang
Keyword(s):  
Plasmon Resonance ◽  
Point Of Care ◽  
Simultaneous Detection ◽  
High Sensitivity ◽  
Microfluidic Chips ◽  
Label Free ◽  
Plasmonic Nanostructure ◽  
Point Of Care Diagnostics ◽  
Surface Enhanced Raman ◽  
Nanostructure Arrays

This review aims to summarize the recent advances and progress of plasmonic biosensors based on patterned plasmonic nanostructure arrays that are integrated with microfluidic chips for various biomedical detection applications. The plasmonic biosensors have made rapid progress in miniaturization sensors with greatly enhanced performance through the continuous advances in plasmon resonance techniques such as surface plasmon resonance (SPR) and localized SPR (LSPR)-based refractive index sensing, SPR imaging (SPRi), and surface-enhanced Raman scattering (SERS). Meanwhile, microfluidic integration promotes multiplexing opportunities for the plasmonic biosensors in the simultaneous detection of multiple analytes. Particularly, different types of microfluidic-integrated plasmonic biosensor systems based on versatile patterned plasmonic nanostructured arrays were reviewed comprehensively, including their methods and relevant typical works. The microfluidics-based plasmonic biosensors provide a high-throughput platform for the biochemical molecular analysis with the advantages such as ultra-high sensitivity, label-free, and real time performance; thus, they continue to benefit the existing and emerging applications of biomedical studies, chemical analyses, and point-of-care diagnostics.

Machine Learning-Assisted Sampling of SERS Substrates Improves Data Collection Efficiency

2021 ◽  
pp. 000370282110345
Author(s):  
Tatu Rojalin ◽  
Dexter Antonio ◽  
Ambarish Kulkarni ◽  
Randy P. Carney
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Domain Knowledge ◽  
Collection Efficiency ◽  
Point Of Care ◽  
Automated Analysis ◽  
Downstream Processing ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Expert User

Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artificial intelligence methods, these efforts have largely focused on downstream processing (e.g., classification tasks) of previously collected data. While fully automated analysis pipelines are desirable, current progress is limited by cumbersome and manually intensive sample preparation and data collection steps. Specifically, a typical lab-scale SERS experiment requires the user to evaluate the quality and reliability of the measurement (i.e., the spectra) as the data are being collected. This need for expert user-intuition is a major bottleneck that limits applicability of SERS-based diagnostics for point-of-care clinical applications, where trained spectroscopists are likely unavailable. While application-agnostic numerical approaches (e.g., signal-to-noise thresholding) are useful, there is an urgent need to develop algorithms that leverage expert user intuition and domain knowledge to simplify and accelerate data collection steps. To address this challenge, in this work, we introduce a machine learning-assisted method at the acquisition stage. We tested six common algorithms to measure best performance in the context of spectral quality judgment. For adoption into future automation platforms, we developed an open-source python package tailored for rapid expert user annotation to train machine learning algorithms. We expect that this new approach to use machine learning to assist in data acquisition can serve as a useful building block for point-of-care SERS diagnostic platforms.

Abstract 17116: Incidence and Outcomes of Out-of-hospital Maternal Cardiac Arrest and Compliance With Resuscitation Guidelines

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Alan A Lipowicz ◽  
Sheldon Cheskes ◽  
Sarah H Gray ◽  
Farida Jeejeebhoy ◽  
Janice Lee ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Performance Metrics ◽  
Point Of Care ◽  
Survival Rates ◽  
Rare Event ◽  
Population Based ◽  
Childbearing Age ◽  
Child Bearing ◽  
Resuscitation Guidelines ◽  
Regional Health Authorities

Background: Published survival rates after out-of-hospital cardiac arrests (OHCA) are lower than in-hospital cardiac arrest (IHCA). Current estimates for the incidence and rate of survival for maternal cardiac arrest are published only for IHCA. There are no studies that report the incidence and outcomes of maternal OHCA. Current cardiopulmonary resuscitation guidelines contain specific maternal recommendations, although compliance with recommended benchmarks has not been reported. The objective of this study was to report maternal OHCA incidence, outcomes, and compliance with resuscitation and maternal specific guidelines. Methods: This was a population-based cohort study of consecutive maternal OHCA between May 2010 and April 2014. The denominator was estimated from the total regional population of all women of childbearing age obtained from census and age-specific pregnancy rates provided by regional health authorities. Resuscitation performance was measured against the 2010 AHA Guidelines. Results: A total of 6 maternal OHCA occurred amongst 1,085 OHCA occurring in females of child bearing age (15-49) over 4yrs; Incidence-1.85:100,000 (95% CI 1.76 to 1.95) vs. 19.4 per 100,000 (95% CI, 19.37 to 19.43). Maternal and neonatal survival to discharge was 16.7% and 33.3%, respectively. Compliance with CPR quality metrics averaged 83% with a range from 75% to 100%. Compliance with maternal-specific resuscitation guidelines averaged 46.9%, with a range from 0% to 100%. The only performance metrics with 100% compliance was intravenous line insertion above the diaphragm and prehospital activation of the maternal cardiac arrest team. Uterine displacement compliance was low at 0%. Conclusion: The incidence of maternal OHCA was 1.85:100,000, which is lower than the published estimate for maternal IHCA. Survival after OHCA for mother and for child was higher than OHCA occurring in non-pregnant adult females of child bearing age; however, the number of survivors was small (<5). Compliance rates with recommended resuscitation guidelines were high, yet compliance with maternal-specific guidelines were low suggesting targeted training and implementation optimization at the point of care is required to prepare for this rare event involving two lives.

Abstract 17060: Machine Learning and Video Recognition for Automated Detection of Fluid Status in Heart Failure Patients

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Pratik Doshi ◽  
John Tanaka ◽  
Jedrek Wosik ◽  
Natalia M Gil ◽  
Martin Bertran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Point Of Care ◽  
Machine Learning Algorithms ◽  
Right Atrial ◽  
Heart Catheterization ◽  
Heart Failure Patients ◽  
Non Invasive ◽  
Fluid Status ◽  
Video Recognition

Introduction: There is a need for innovative solutions to better screen and diagnose the 7 million patients with chronic heart failure. A key component of assessing these patients is monitoring fluid status by evaluating for the presence and height of jugular venous distension (JVD). We hypothesize that video analysis of a patient’s neck using machine learning algorithms and image recognition can identify the amount of JVD. We propose the use of high fidelity video recordings taken using a mobile device camera to determine the presence or absence of JVD, which we will use to develop a point of care testing tool for early detection of acute exacerbation of heart failure. Methods: In this feasibility study, patients in the Duke cardiac catheterization lab undergoing right heart catheterization were enrolled. RGB and infrared videos were captured of the patient’s neck to detect JVD and correlated with right atrial pressure on the heart catheterization. We designed an adaptive filter based on biological priors that enhances spatially consistent frequency anomalies and detects jugular vein distention, with implementation done on Python. Results: We captured and analyzed footage for six patients using our model. Four of these six patients shared a similar strong signal outliner within the frequency band of 95bpm – 200bpm when using a conservative threshold, indicating the presence of JVD. We did not use statistical analysis given the small nature of our cohort, but in those we detected a positive JVD signal the RA mean was 20.25 mmHg and PCWP mean was 24.3 mmHg. Conclusions: We have demonstrated the ability to evaluate for JVD via infrared video and found a relationship with RHC values. Our project is innovative because it uses video recognition and allows for novel patient interactions using a non-invasive screening technique for heart failure. This tool can become a non-invasive standard to both screen for and help manage heart failure patients.

Hands-free smartphone-based diagnostics for simultaneous detection of Zika, Chikungunya, and Dengue at point-of-care

2017 ◽  
Vol 19 (4) ◽  
Author(s):  
A. Ganguli ◽  
A. Ornob ◽  
H. Yu ◽  
G. L. Damhorst ◽  
W. Chen ◽  
...  
Keyword(s):  
Point Of Care ◽  
Simultaneous Detection

scholarly journals Wave-shaped microfluidic chip assisted point-of-care testing for accurate and rapid diagnosis of infections

2021 ◽  
Author(s):  
Binfeng Yin ◽  
Xinhua Wan ◽  
Mingzhu Yang ◽  
Changcheng Qian ◽  
A S M Muhtasim Fuad Sohan
Keyword(s):  
Microfluidic Chip ◽  
Limit Of Detection ◽  
Point Of Care ◽  
Simultaneous Detection ◽  
Accurate Diagnosis ◽  
Point Of Care Testing ◽  
Promising Alternative ◽  
Reactive Protein ◽  
Linear Relationships ◽  
Multiple Biomarkers

Abstract Background: Simultaneous and timely detection of C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6) provides effective information for the accurate diagnosis of infections. Early diagnosis and classification of infections increase the cure rate while decreasing complications, which is significant for severe infections, especially for war surgery. However, traditional methods rely on laborious operations and bulky devices. On the other hand, point-of-care (POC) methods suffer from limited robustness and accuracy. Therefore, it is of urgent demand to develop POC devices for rapid and accurate diagnosis of infections to fulfill on-site militarized requirements.Methods: We developed a wave-shaped microfluidic chip (WMC) assisted multiplexed detection platform (WMC-MDP). WMC-MDP reduces detection time and improves repeatability through premixing of the samples and reaction of the reagents. We further combined the detection platform with the streptavidin-biotin (SA-B) amplified system to enhance the sensitivity while using chemiluminescence (CL) intensity as signal readout. We realized simultaneous detection of CRP, PCT, and IL-6 on the detection platform and evaluated the sensitivity, linear range, selectivity, and repeatability. Finally, we finished detecting 15 samples from volunteers and compared the results with commercial ELISA kits.Results: Detection of CRP, PCT, and IL-6 exhibited good linear relationships between CL intensities and concentrations in the range of 1.25-40 μg/mL, 0.4-12.8 ng/mL, and 50-1600 pg/mL. The limit of detection (LOD) of CRP, PCT, and IL-6 were 0.54 μg/mL, 0.11 ng/mL, and 16.25 pg/mL, respectively. WMC-MDP is capable of good adequate selectivity and repeatability. The whole detection procedure takes only 22 minutes that meets the requirements of a POC device. Results of 15 samples from volunteers were consistent with the results detected by commercial ELISA kits.Conclusion: WMC-MDP allows simultaneous, rapid, and sensitive detection of CRP, PCT, and IL-6 with satisfactory selectivity and repeatability, requiring minimal manipulation. However, WMC-MDP takes advantage of being a microfluidic device showing the coefficients of variation less than 10% enabling WMC-MDP to be a type of POCT. Therefore, WMC-MDP provides a promising alternative to point-of-care testing (POCT) of multiple biomarkers. We believe the practical application of WMC-MDP in militarized fields will revolutionize infection diagnosis for soldiers.

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