Evidence map›Paper›PMID 35505461›Full record

ArticleJournal of biomedical optics2022

Monte Carlo method for assessment of a multimodal insertable biosensor.

Jesse Fine, Michael J McShane, Gerard L Coté

Abstract read
In one paragraph

Article in Journal of biomedical optics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

3 authors.

Jesse FineTexas A&M University, Department of Biomedical Engineering, College Station, Texas, United States.
Michael J McShaneTexas A&M University, Department of Biomedical Engineering, College Station, Texas, United States.
Gerard L CotéTexas A&M University, Department of Biomedical Engineering, College Station, Texas, United States.

Funding

Minimally-Invasive Metabolite Monitoring Systems for in vitro and in vivo Disease ModelsR01EB024601 · NIBIB · TEXAS ENGINEERING EXPERIMENT STATION · PI MCSHANE, MIKE · 2018 to 2021
$1.3M
NIBIB NIH HHS R01 EB024601
6 · The paper itself

Abstract

significanceContinuous glucose monitors (CGMs) are increasingly utilized as a way to provide healthcare to the over 10% of Americans that have diabetes. Fully insertable and optically transduced biosensors are poised to further improve CGMs by extending the device lifetime and reducing cost. However, optical modeling of light propagation in tissue is necessary to ascertain device performance.

aimMonte Carlo modeling of photon transport through tissue was used to assess the luminescent output of a fully insertable glucose biosensor that uses a multimodal Förster resonance energy transfer competitive binding assay and a phosphorescence lifetime decay enzymatic assay. APPROACH: A Monte Carlo simulation framework of biosensor luminescence and tissue autofluorescence was built using MCmatlab. Simulations were first validated against previous research and then applied to predict the response of a biosensor in development.

resultsOur results suggest that a diode within the safety standards for light illumination on the skin, with far-red excitation, allows the luminescent biosensor to yield emission strong enough to be detectable by a common photodiode.

conclusionsThe computational model showed that the expected fluorescent power output of a near-infrared light actuated barcode was five orders of magnitude greater than a visible spectrum excited counterpart biosensor.

Indexed as

Biosensing TechniquesFluorescence Resonance Energy TransferGlucoseHumansMonte Carlo MethodPhotonsGlucosebiosensorsdiabetesMonte Carloremote health

Identifiers

PMID35505461
PMCPMC9064117

What OpenQuestion holds

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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.