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Advancement in Optical Macro-, Micro- and Nano-Sensors for Biomedical Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Nanosensors".

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 4387

Special Issue Editors


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Guest Editor
School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Dr, Singapore 637459, Singapore
Interests: biophotonics; raman spectroscopy; biomedical instrumentation and diagnosis; plasmonics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Biomedical Engineering, SSN College of Engineering, Kalavakkam, Tamilnadu, India
Interests: biophotonics; biomedical optics; biomaterials; rehabilitation engineering

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to encourage scientists and researchers to publish high quality research papers emphasizing the advancement in macro-, micro- and nano-sensor-based optical technologies, used for sensing applications in biomedical engineering and sciences. This Special Issue includes comprehensive original research and review papers that cover important aspects of state-of-the-art optical biosensor devices in macro-, micro- and nano-scale. We hope this Special Issue on various innovative aspects related to optical macro-, micro- and nano-sensors for biomedical applications will have a significant impact on improving the quality of human life and motivating dedicated researchers in exploring futuristic avenues in this domain. We welcome theoretical, experimental and/or computational works on optical macro-, micro- and nano-sensors for biomedical application. Manuscript topics may include, but are not limited to, the following:

  • Health monitoring;
  • Optical nano-sensors;
  • Optical micro-sensors;
  • Optical macro-sensors;
  • Detection of diseases/pathogens/viruses using optical biosensors;
  • Lab-on-a-chip sensors;
  • Raman based sensors;
  • Fluorescence based sensors;
  • Infrared sensors;
  • Optical fiber sensors;
  • Opto-electronic sensors;
  • Opto-mechanical sensors;
  • Optical imaging sensors;
  • Any other optical biosensors.

Dr. Clement Yuen
Dr. Pauline John
Guest Editors

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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.

Keywords

  • health monitoring
  • lab-on-a-chip sensors
  • Raman-based sensors
  • fluorescence-based sensors
  • infrared sensors
  • wearable sensors
  • opto-electronic sensors
  • opto-mechanical sensors
  • optical imaging sensors

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Published Papers (2 papers)

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Research

9 pages, 1338 KiB  
Article
Feasibility of Skin Water Content Imaging Using CMOS Sensors
by Gennadi Saiko
Sensors 2023, 23(2), 919; https://doi.org/10.3390/s23020919 - 13 Jan 2023
Cited by 5 | Viewed by 2212
Abstract
Pressure injuries (PI) result from pressure-induced damage to the skin and underlying tissues. Currently, Stage I PI are detected using visual skin assessments. However, this visual method is unable to detect skin color changes in persons with darkly pigmented skin, which results in [...] Read more.
Pressure injuries (PI) result from pressure-induced damage to the skin and underlying tissues. Currently, Stage I PI are detected using visual skin assessments. However, this visual method is unable to detect skin color changes in persons with darkly pigmented skin, which results in a higher Stage II-IV PI incidence and PI-associated mortality in persons with a darker complexion. Thus, a more objective method of early-stage PI detection is of great importance. Optical spectroscopy is a promising modality for the noncontact diagnosis and monitoring of skin water content, capable of detecting edema and Stage I PI. The scope of the current study is to assess the feasibility of imaging the water content of the skin using Si-based sensors. We have considered two primary cases: the elevated bulk water content (edema) and localized water pool (e.g., blood vessels). These two cases were analyzed using analytical models. We found that detecting the watercontent contrast associated with edema in tissues is within the reach of Si-based sensors. However, although the effect is expected to be detectable even with consumer-grade cameras, with the current state of technologies, their use in real-world conditions faces numerous technical challenges, mainly due to the narrow dynamic range. Full article
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19 pages, 2739 KiB  
Article
Digital Histopathological Discrimination of Label-Free Tumoral Tissues by Artificial Intelligence Phase-Imaging Microscopy
by José Luis Ganoza-Quintana, José Luis Arce-Diego and Félix Fanjul-Vélez
Sensors 2022, 22(23), 9295; https://doi.org/10.3390/s22239295 - 29 Nov 2022
Cited by 1 | Viewed by 1619
Abstract
Histopathology is the gold standard for disease diagnosis. The use of digital histology on fresh samples can reduce processing time and potential image artifacts, as label-free samples do not need to be fixed nor stained. This fact allows for a faster diagnosis, increasing [...] Read more.
Histopathology is the gold standard for disease diagnosis. The use of digital histology on fresh samples can reduce processing time and potential image artifacts, as label-free samples do not need to be fixed nor stained. This fact allows for a faster diagnosis, increasing the speed of the process and the impact on patient prognosis. This work proposes, implements, and validates a novel digital diagnosis procedure of fresh label-free histological samples. The procedure is based on advanced phase-imaging microscopy parameters and artificial intelligence. Fresh human histological samples of healthy and tumoral liver, kidney, ganglion, testicle and brain were collected and imaged with phase-imaging microscopy. Advanced phase parameters were calculated from the images. The statistical significance of each parameter for each tissue type was evaluated at different magnifications of 10×, 20× and 40×. Several classification algorithms based on artificial intelligence were applied and evaluated. Artificial Neural Network and Decision Tree approaches provided the best general sensibility and specificity results, with values over 90% for the majority of biological tissues at some magnifications. These results show the potential to provide a label-free automatic significant diagnosis of fresh histological samples with advanced parameters of phase-imaging microscopy. This approach can complement the present clinical procedures. Full article
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