Evidence map›Paper›PMID 34633596›Full record

ArticleJournal of fluorescence2022

Fluorescence Nano Particle Detection in a Liquid Sample Using the Smartphone for Biomedical Application.

Anand G, Thyagarajan T, Sabitha Ramakrishnan

Abstract read
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In one paragraph

Article in Journal of fluorescence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Anand GDepartment of Instrumentation Engineering, Madras Institute of Technology Campus, Anna University, Chennai, India. g.anand777@gmail.com.ORCID http://orcid.org/0000-0001-6403-5577
Thyagarajan TDepartment of Instrumentation Engineering, Madras Institute of Technology Campus, Anna University, Chennai, India.
Sabitha RamakrishnanDepartment of Instrumentation Engineering, Madras Institute of Technology Campus, Anna University, Chennai, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we present a Smartphone-based Fluorescence Nanoparticle Detector (SPF-NPD) that can be used for identifying biological agents in biomedical applications. The experimental setup consists of an LED light source and an Eppendorf tube holder placed inside a dark chamber with an optimally located slit for aligning the camera of a smartphone. The camera acquires the fluorescence intensity variations in the target liquid sample placed in the Eppendorf tube and passes it to a dedicated android application running in the smartphone. Using the principle of fluorescence-based pathogen detection, the android application detects the pathogens and displays the results within a few seconds. Since, all smartphones are equipped with high-resolution cameras, the proposed SPF-NPD provides a simple and elegant solution for instantaneous detection of fluorescence nano particles and has a great potential for healthcare applications for live detection of pathogens. The intensity measurement in SPF-NPD algorithm uses 5-pixel method, that is, the center pixel followed by four immediate neighbor pixels, because of which, minimal sample quantity is sufficient for precise measurements. We establish the robustness of SPF-NPD through exhaustive experiments with various smartphone cameras having different resolutions ranging from 8 to 20 Megapixels. The results of the proposed SPF-NPD method are validated against those obtained from standard devices such as Perkin-Elmer Picoflor and Perkin-Elmer Enspire. The advantages of the proposed method are highlighted.

Indexed as

FluorescenceSmartphoneAlgorithmsLightNanoparticlesSolutionsSolutionsAndroidFluorescenceNanoparticlePathogensSmartphone

Identifiers

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.