Special Issue "Nanophotonics Systems for Translational Biosensing in Biological Fluids"

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Optical and Photonic Biosensors".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 1716

Special Issue Editors

Querrey Simpson Institute for Biointegrated Electronics, Center for Bio-Integrated Electronics, Northwestern University, Evanston, IL 60208, USA
Interests: plasmonic biosensing; point-of-care applications; implantable bio-optoelectronics systems; optogenetics; neurotechnologies
Department of Electrical Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong
Interests: nanophotonics; nanoplasmonics; biosensors; lab on a chip; translational medicine

Special Issue Information

Dear Colleagues,

Nanophotonic systems leverage light field manipulation and strong modal confinement at the nanoscale level to implement sensitive biosensing schemes. The interplay between affinity layers and the surface passivation strategies are essential to transform these conceptual devices into powerful technologies for sensitive and selective biosensors of target analytes (cancer biomarkers, cytokines, ions, proteins, exosomes, antibodies, mRNA, etc.) from a biological fluid, which is of great relevance to clinical diagnosis. Most importantly, nanophotonic biosensors achieved detection limits that surpass other non-optical methods.

The scope of this Special Issue is to amass research in nanophotonic biosensors, with an emphasis on works that demonstrate the potential translational aspect to challenging realistic environments and those that demonstrate high-level integration with supporting technologies. In this regard, we welcome submissions that involve the following topics:

  • Use of nanophotonic systems (plasmonics, photonic crystals, structured optical fibers, waveguide, etc.) for the detection of analytes in biological fluids (tear, saliva, urine, plasma, whole blood, etc.);
  • Robust fabrication methods of nanophotonic systems indicative of a strong translational potential for deployment;
  • System-level integration of nanophotonic devices with portable readout optical systems for point of care applications;
  • Biosensing with nanophotonic systems that incorporate benchmark studies with gold standards (ELISA, PCR, HPLC, etc.);
  • Integrated nanophotonics–microfluidics integration for combined on-chip sample preparation and sensing.

Dr. Abraham Vázquez-Guardado
Dr. Lip Ket Chin
Guest Editors

If you want to learn more information or need any advice, you can contact the Special Issue Editor Jessica Zhou via <[email protected]> directly.

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biosensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • nanophotonic systems
  • biosensing
  • translation nanophotonic
  • point of care
  • nanophotonics
  • clinical diagnostics
  • biological fluids
  • integrated optofluidics
  • robust nanofabrication

Published Papers (1 paper)

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The Feasibility of Early Alzheimer’s Disease Diagnosis Using a Neural Network Hybrid Platform
Biosensors 2022, 12(9), 753; https://doi.org/10.3390/bios12090753 - 13 Sep 2022
Cited by 1 | Viewed by 1202
Early diagnosis of Alzheimer’s Disease (AD) is critical for disease prevention and cure. However, currently, techniques with the required high sensitivity and specificity are lacking. Recently, with the advances and increased accessibility of data analysis tools, such as machine learning, research efforts have [...] Read more.
Early diagnosis of Alzheimer’s Disease (AD) is critical for disease prevention and cure. However, currently, techniques with the required high sensitivity and specificity are lacking. Recently, with the advances and increased accessibility of data analysis tools, such as machine learning, research efforts have increasingly focused on using these computational methods to solve this challenge. Here, we demonstrate a convolutional neural network (CNN)-based AD diagnosis approach using the surface-enhanced Raman spectroscopy (SERS) fingerprints of human cerebrospinal fluid (CSF). SERS and CNN were combined for biomarker detection to analyze disease-associated biochemical changes in the CSF. We achieved very high reproducibility in double-blind experiments for testing the feasibility of our system on human samples. We achieved an overall accuracy of 92% (100% for normal individuals and 88.9% for AD individuals) based on the clinical diagnosis. Further, we observed an excellent correlation coefficient between our test score and the Clinical Dementia Rating (CDR) score. Our findings offer a substantial indication of the feasibility of detecting AD biomarkers using the innovative combination of SERS and machine learning. We are hoping that this will serve as an incentive for future research in the field. Full article
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