Evidence map›Paper›PMID 39329654›Full record

ReviewBioengineering (Basel, Switzerland)2024

Optical Image Sensors for Smart Analytical Chemiluminescence Biosensors.

Reza Abbasi, Xinyue Hu, Alain Zhang, Isabelle Dummer, Sebastian Wachsmann-Hogiu

Abstract readReview
In one paragraph

Review in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Review
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

5 authors.

Reza AbbasiDepartment of Bioengineering, McGill University, Montreal, QC H3A 0E9, Canada.ORCID 0000-0002-3523-8444
Xinyue HuDepartment of Bioengineering, McGill University, Montreal, QC H3A 0E9, Canada.
Alain ZhangDepartment of Bioengineering, McGill University, Montreal, QC H3A 0E9, Canada.
Isabelle DummerDepartment of Bioengineering, McGill University, Montreal, QC H3A 0E9, Canada.ORCID 0009-0008-1759-2811
Sebastian Wachsmann-HogiuDepartment of Bioengineering, McGill University, Montreal, QC H3A 0E9, Canada.

Funding

Fonds de Recherche du Québec - Nature et Technologies Doctorate scholarshipMcGill University Louis HoMcGill University Faculty of Engineering MEUSMA AwardMcGill University Faculty of Engineering SURE Program for Undergraduate ResearchNatural Sciences and Engineering Research Council Discovery
6 · The paper itself

Abstract

Optical biosensors have emerged as a powerful tool in analytical biochemistry, offering high sensitivity and specificity in the detection of various biomolecules. This article explores the advancements in the integration of optical biosensors with microfluidic technologies, creating lab-on-a-chip (LOC) platforms that enable rapid, efficient, and miniaturized analysis at the point of need. These LOC platforms leverage optical phenomena such as chemiluminescence and electrochemiluminescence to achieve real-time detection and quantification of analytes, making them ideal for applications in medical diagnostics, environmental monitoring, and food safety. Various optical detectors used for detecting chemiluminescence are reviewed, including single-point detectors such as photomultiplier tubes (PMT) and avalanche photodiodes (APD), and pixelated detectors such as charge-coupled devices (CCD) and complementary metal-oxide-semiconductor (CMOS) sensors. A significant advancement discussed in this review is the integration of optical biosensors with pixelated image sensors, particularly CMOS image sensors. These sensors provide numerous advantages over traditional single-point detectors, including high-resolution imaging, spatially resolved measurements, and the ability to simultaneously detect multiple analytes. Their compact size, low power consumption, and cost-effectiveness further enhance their suitability for portable and point-of-care diagnostic devices. In the future, the integration of machine learning algorithms with these technologies promises to enhance data analysis and interpretation, driving the development of more sophisticated, efficient, and accessible diagnostic tools for diverse applications.

Indexed as

biosensorchemiluminescenceimage sensorsmachine learningoptical

Identifiers

PMID39329654
PMCPMC11428294

What OpenQuestion holds

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LicenceCC BY
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.