Evidence map›Paper›PMID 36979540›Full record

ReviewBiosensors2023

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques.

Reshma Beeram, Kameswara Rao Vepa, Venugopal Rao Soma

Full text readReview
In one paragraph

Review in Biosensors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing 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

43 citing papers in PubMed.

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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

3 authors.

Reshma BeeramAdvanced Centre of Research in High Energy Materials (ACRHEM), DRDO Industry Academia-Centre of Excellence (DIA-COE), University of Hyderabad, Hyderabad 500046, Telangana, India.
Kameswara Rao VepaAdvanced Centre of Research in High Energy Materials (ACRHEM), DRDO Industry Academia-Centre of Excellence (DIA-COE), University of Hyderabad, Hyderabad 500046, Telangana, India.
Venugopal Rao SomaAdvanced Centre of Research in High Energy Materials (ACRHEM), DRDO Industry Academia-Centre of Excellence (DIA-COE), University of Hyderabad, Hyderabad 500046, Telangana, India.ORCID 0000-0001-5361-7256

Funding

Defence Research and Development Organisation #ERIP/ER/ 1501138/M/01/319/D(R&D)
6 · The paper itself

Abstract

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Indexed as

Biosensing TechniquesCOVID-19COVID-19 TestingHumansMachine LearningSARS-CoV-2Spectrum Analysis, Ramanbiohazardous moleculesbiomoleculesbiosensingcancerCOVID-19disease diagnosismicroorganismsplasmonicsSERS

Identifiers

PMID36979540
PMCPMC10046859

What Socratic holds

Textfull text, public
LicenceCC BY
measurements read56
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 Socratic graph.