'All In One' SARS-CoV-2 variant recognition platform: Machine learning-enabled point of care diagnostics

HIV/AIDS is an ongoing global pandemic, with an estimated 39 million infected worldwide. Early detection is anticipated to help improve outcomes and prevent further infections. Point-of-care diagnostics make HIV/AIDS diagnoses available both earlier and to a broader population. Wide-spread and automated HIV risk estimation can offer objective guidance. This supports providers in making an informed decision when considering patients with high HIV risk for HIV testing or pre-exposure prophylaxis (PrEP). We propose a novel machine learning method that allows providers to use the data from a patient's previous stays at the clinic to estimate their HIV risk. All features available in the clinical data are considered, making the set of features objective and independent of expert opinions. The proposed method builds on association rules that are derived from the data. The incidence rate ratio (IRR) is determined for each rule. Given a new patient, the mean IRR of all applicable rules is used to estimate their HIV risk. The method was tested and validated on the publicly available clinical database MIMIC-IV, which consists of around 525,000 hospital stays that included a stay at the intensive care unit or emergency department. We evaluated the method using the area under the receiver operating characteristic curve (AUC). The best performance with an AUC of 0.88 was achieved with a model consisting of 53 rules. A threshold value of 0.66 leads to a sensitivity of 98% and a specificity of 53%. The rules were grouped into drug abuse, psychological illnesses (e.g., PTSD), previously known associations (e.g., pulmonary diseases), and new associations (e.g., certain diagnostic procedures). In conclusion, we propose a novel HIV risk estimation method that builds on existing clinical data. It incorporates a wide range of features, leading to a model that is independent of expert opinions. It supports providers in making informed decisions in the point-of-care diagnostics process by estimating a patient's HIV risk.

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Plastic electronics for point-of-care diagnostics

10.1049/ic.2009.0064 ◽

2009 ◽

Cited By ~ 2

Author(s):

J.C. de Mello

Keyword(s):

Point Of Care ◽

Plastic Electronics ◽

Point Of Care Diagnostics

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COVID-19 Point-of-Care Diagnostics: Present and Future

ACS Nano ◽

10.1021/acsnano.1c02981 ◽

2021 ◽

Author(s):

Enrique Valera ◽

Aaron Jankelow ◽

Jongwon Lim ◽

Victoria Kindratenko ◽

Anurup Ganguli ◽

...

Keyword(s):

Point Of Care ◽

Point Of Care Diagnostics

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145 Point of care lung ultrasound in patient triage: integration of ultrasound into a streaming pathway for COVID-19

Emergency Medicine Journal ◽

10.1136/emj-2020-rcemabstracts.27 ◽

2020 ◽

Vol 37 (12) ◽

pp. 839.1-839

Author(s):

Dominic Craver ◽

Aminah Ahmad ◽

Anna Colclough

Keyword(s):

Point Of Care ◽

District General Hospital ◽

Training Programme ◽

Service Evaluation ◽

University Hospital ◽

Junior Doctors ◽

Point Of Care Ultrasound ◽

Face To Face ◽

Point Of Care Diagnostics ◽

Audit Data

Aims/Objectives/BackgroundRapid risk stratification of patients is vital for Emergency Department (ED) streaming during the COVID-19 pandemic. Ideally, patients should be split into red (suspected/confirmed COVID-19) and green (non COVID-19) zones in order to minimise the risk of patient-to-patient and patient-to-staff transmission. A robust yet rapid streaming system combining clinician impression with point-of-care diagnostics is therefore necessary.Point of care ultrasound (POCUS) findings in COVID-19 have been shown to correlate well with computed tomography (CT) findings, and it therefore has value as a front-door diagnostic tool. At University Hospital Lewisham (a district general hospital in south London), we recognised the value of early POCUS and its potential for use in patient streaming.Methods/DesignWe developed a training programme, ‘POCUS for COVID’ and subsequently integrated POCUS into streaming of our ED patients. The training involved Zoom lectures, a face to face practical, a 10 scan sign off process followed by a final triggered assessment. Patient outcomes were reviewed in conjunction with their scan reports.Results/ConclusionsCurrently, we have 21 ED junior doctors performing ultrasound scans independently, and all patients presenting to our department are scanned either in triage or in the ambulance. A combination of clinical judgement and scan findings are used to stream the patient to an appropriate area.Service evaluation with analysis of audit data has found our streaming to be 94% sensitive and 79% specific as an indicator of COVID 19. Further analysis is ongoing.Here we present both the structure of our training programme and our integrated streaming pathway along with preliminary analysis results.

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Point of Care Diagnostics in the Age of COVID-19

Diagnostics ◽

10.3390/diagnostics11010009 ◽

2020 ◽

Vol 11 (1) ◽

pp. 9

Author(s):

Meysam Rezaei ◽

Sajad Razavi Bazaz ◽

Sareh Zhand ◽

Nima Sayyadi ◽

Dayong Jin ◽

...

Keyword(s):

Point Of Care ◽

Diagnostic Methods ◽

Global Public Health ◽

Current Standard ◽

Healthcare Facilities ◽

Major Threat ◽

Point Of Care Diagnostics ◽

Recent Outbreak ◽

Control Virus ◽

Biosafety Level

The recent outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated serious respiratory disease, coronavirus disease 2019 (COVID-19), poses a major threat to global public health. Owing to the lack of vaccine and effective treatments, many countries have been overwhelmed with an exponential spread of the virus and surge in the number of confirmed COVID-19 cases. Current standard diagnostic methods are inadequate for widespread testing as they suffer from prolonged turn-around times (>12 h) and mostly rely on high-biosafety-level laboratories and well-trained technicians. Point-of-care (POC) tests have the potential to vastly improve healthcare in several ways, ranging from enabling earlier detection and easier monitoring of disease to reaching remote populations. In recent years, the field of POC diagnostics has improved markedly with the advent of micro- and nanotechnologies. Due to the COVID-19 pandemic, POC technologies have been rapidly innovated to address key limitations faced in existing standard diagnostic methods. This review summarizes and compares the latest available POC immunoassay, nucleic acid-based and clustered regularly interspaced short palindromic repeats- (CRISPR)-mediated tests for SARS-CoV-2 detection that we anticipate aiding healthcare facilities to control virus infection and prevent subsequent spread.

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Thoracic Point-of-Care Ultrasound: A SARS-CoV-2 Data Repository for Future Artificial Intelligence and Machine Learning

Surgical Innovation ◽

10.1177/15533506211018671 ◽

2021 ◽

pp. 155335062110186

Author(s):

Abdel-Moneim Mohamed Ali ◽

Emran El-Alali ◽

Adam S. Weltz ◽

Scott T. Rehrig

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Point Of Care ◽

Prognostic Indicators ◽

Data Repository ◽

Point Of Care Ultrasound ◽

Ultrasound Images ◽

Clinical Protocol ◽

Improved Outcomes ◽

Current Experience

Current experience suggests that artificial intelligence (AI) and machine learning (ML) may be useful in the management of hospitalized patients, including those with COVID-19. In light of the challenges faced with diagnostic and prognostic indicators in SARS-CoV-2 infection, our center has developed an international clinical protocol to collect standardized thoracic point of care ultrasound data in these patients for later AI/ML modeling. We surmise that in the future AI/ML may assist in the management of SARS-CoV-2 patients potentially leading to improved outcomes, and to that end, a corpus of curated ultrasound images and linked patient clinical metadata is an invaluable research resource.

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Pocket MUSE: an affordable, versatile and high-performance fluorescence microscope using a smartphone

Communications Biology ◽

10.1038/s42003-021-01860-5 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Yehe Liu ◽

Andrew M. Rollins ◽

Richard M. Levenson ◽

Farzad Fereidouni ◽

Michael W. Jenkins

Keyword(s):

High Performance ◽

Point Of Care ◽

Low Cost ◽

Consumer Electronics ◽

Practical Implementation ◽

The Novel ◽

Point Of Care Diagnostics ◽

Optical Module ◽

Optical Configuration ◽

User Friendly

AbstractSmartphone microscopes can be useful tools for a broad range of imaging applications. This manuscript demonstrates the first practical implementation of Microscopy with Ultraviolet Surface Excitation (MUSE) in a compact smartphone microscope called Pocket MUSE, resulting in a remarkably effective design. Fabricated with parts from consumer electronics that are readily available at low cost, the small optical module attaches directly over the rear lens in a smartphone. It enables high-quality multichannel fluorescence microscopy with submicron resolution over a 10× equivalent field of view. In addition to the novel optical configuration, Pocket MUSE is compatible with a series of simple, portable, and user-friendly sample preparation strategies that can be directly implemented for various microscopy applications for point-of-care diagnostics, at-home health monitoring, plant biology, STEM education, environmental studies, etc.

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

Micromachines ◽

10.3390/mi12070826 ◽

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.

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Triboelectric Effect Enabled Self-Powered, Point-of-Care Diagnostics: Opportunities for Developing ASSURED and REASSURED Devices

Micromachines ◽

10.3390/mi12030337 ◽

2021 ◽

Vol 12 (3) ◽

pp. 337

Author(s):

Navneet Soin ◽

Sam J. Fishlock ◽

Colin Kelsey ◽

Suzanne Smith

Keyword(s):

High Efficiency ◽

Resource Constraints ◽

Point Of Care ◽

Wearable Sensors ◽

Middle Income ◽

Physiological Signal ◽

Point Of Care Diagnostics ◽

Self Powered ◽

Triboelectric Nanogenerators ◽

Triboelectric Effect

The use of rapid point-of-care (PoC) diagnostics in conjunction with physiological signal monitoring has seen tremendous progress in their availability and uptake, particularly in low- and middle-income countries (LMICs). However, to truly overcome infrastructural and resource constraints, there is an urgent need for self-powered devices which can enable on-demand and/or continuous monitoring of patients. The past decade has seen the rapid rise of triboelectric nanogenerators (TENGs) as the choice for high-efficiency energy harvesting for developing self-powered systems as well as for use as sensors. This review provides an overview of the current state of the art of such wearable sensors and end-to-end solutions for physiological and biomarker monitoring. We further discuss the current constraints and bottlenecks of these devices and systems and provide an outlook on the development of TENG-enabled PoC/monitoring devices that could eventually meet criteria formulated specifically for use in LMICs.

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