Evidence map›Paper›PMID 27380817›Full record

ArticleAccounts of chemical research2016

Programmable Bio-nanochip Platform: A Point-of-Care Biosensor System with the Capacity To Learn.

Michael P McRae, Glennon Simmons, Jorge Wong, John T McDevitt

Open access · greenAbstract read
In one paragraph

Article in Accounts of chemical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
2.8field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 60 citations in OpenAlex.

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  16. Different Approaches to Develop Nanosensors for Diagnosis of Diseases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Michael P McRaeDepartment of Bioengineering, Rice University , Houston, Texas 77030, United States.
Glennon SimmonsDepartment of Biomaterials, Bioengineering Institute, New York University College of Dentistry , New York, New York 10010, United States.
Jorge WongDepartment of Bioengineering, Rice University , Houston, Texas 77030, United States.
John T McDevittDepartment of Bioengineering, Rice University , Houston, Texas 77030, United States.
New York University · USRice University · US

Funding

Effects of Oral Inflammation on the Levels of Salivary Biomarkers of AMIU01DE017793 · NIDCR · UNIVERSITY OF TEXAS AT AUSTIN · PI MCDEVITT, JOHN T · 2006 to 2012
$13.0M
NIDCR NIH HHS U01 DE017793
6 · The paper itself

Abstract

The combination of point-of-care (POC) medical microdevices and machine learning has the potential transform the practice of medicine. In this area, scalable lab-on-a-chip (LOC) devices have many advantages over standard laboratory methods, including faster analysis, reduced cost, lower power consumption, and higher levels of integration and automation. Despite significant advances in LOC technologies over the years, several remaining obstacles are preventing clinical implementation and market penetration of these novel medical microdevices. Similarly, while machine learning has seen explosive growth in recent years and promises to shift the practice of medicine toward data-intensive and evidence-based decision making, its uptake has been hindered due to the lack of integration between clinical measurements and disease determinations. In this Account, we describe recent developments in the programmable bio-nanochip (p-BNC) system, a biosensor platform with the capacity for learning. The p-BNC is a "platform to digitize biology" in which small quantities of patient sample generate immunofluorescent signal on agarose bead sensors that is optically extracted and converted to antigen concentrations. The platform comprises disposable microfluidic cartridges, a portable analyzer, automated data analysis software, and intuitive mobile health interfaces. The single-use cartridges are fully integrated, self-contained microfluidic devices containing aqueous buffers conveniently embedded for POC use. A novel fluid delivery method was developed to provide accurate and repeatable flow rates via actuation of the cartridge's blister packs. A portable analyzer instrument was designed to integrate fluid delivery, optical detection, image analysis, and user interface, representing a universal system for acquiring, processing, and managing clinical data while overcoming many of the challenges facing the widespread clinical adoption of LOC technologies. We demonstrate the p-BNC's flexibility through the completion of multiplex assays within the single-use disposable cartridges for three clinical applications: prostate cancer, ovarian cancer, and acute myocardial infarction. Toward the goal of creating "sensors that learn", we have developed and describe here the Cardiac ScoreCard, a clinical decision support system for a spectrum of cardiovascular disease. The Cardiac ScoreCard approach comprises a comprehensive biomarker panel and risk factor information in a predictive model capable of assessing early risk and late-stage disease progression for heart attack and heart failure patients. These marker-driven tests have the potential to radically reduce costs, decrease wait times, and introduce new options for patients needing regular health monitoring. Further, these efforts demonstrate the clinical utility of fusing data from information-rich biomarkers and the Internet of Things (IoT) using predictive analytics to generate single-index assessments for wellness/illness status. By promoting disease prevention and personalized wellness management, tools of this nature have the potential to improve health care exponentially.

Indexed as

NanotechnologyPoint-of-Care SystemsArea Under CurveBiomarkersBiosensing TechniquesCardiovascular DiseasesCell PhoneCreatine Kinase, MB FormEnzyme-Linked Immunosorbent AssayHumansLab-On-A-Chip DevicesLimit of DetectionROC CurveTroponin IBiomarkersCreatine Kinase, MB FormTroponin I

Identifiers

PMID27380817
PMCPMC6504240
OpenAlexW2463777919

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.