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Special Issue "Sensors for Bioimaging"

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

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 2337

Special Issue Editors

Prof. Dr. Angel Orte
E-Mail Website
Guest Editor
Departamento de Fisicoquímica, Facultad de Farmacia, Universidad de Granada, Granada, Spain
Interests: fluorescence; spectroscopy; single-molecule biophysics; nanosensors
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. María J. Ruedas-Rama
E-Mail Website
Section Board Member
Departamento de Fisicoquímica, Facultad de Farmacia, Universidad de Granada, Granada, Spain
Interests: fluorescence sensors; quantum dots; nanosensing; FLIM; biophysics

Special Issue Information

Dear Colleagues,

One of the most active fields of multidisciplinary research involves the rational design of systems capable of qualitatively and quantitatively reporting on certain substances or events of interest. These sensing platforms are of particular interest when they tackle important societal challenges, such as biomedical or environmental applications. For this reason, varied and powerful imaging techniques are continuously being developed to provide spatial localization of the sensor. Biological and biomedical insights, obtained through bioimaging and specific sensors, are revolutionizing the understanding of physiological and pathological events at different organizational levels, from single cells to in vivo animal models and studies in human volunteers.

The field of sensors for bioimaging is scientifically exciting since it is inherently multidisciplinary. The combination of developments blurring the frontiers of physics, chemistry, biophysics, biochemistry, and biology makes the field a unique collaborative environment. Furthermore, novel imaging techniques are being developed, reaching beyond the conventional techniques. Sensors specifically designed for super-resolution fluorescence microscopy, Raman microscopy, or mass spectrometry imaging exhibit great potential for advancement in the field of bioimaging. Likewise, we would like to pay special attention to multimodal imaging approaches. Sensors capable of providing chemical specificity and high-resolution imaging by combining different techniques in a single platform are among the most powerful approaches to date.

This Special Issue aims to collect research on recent tools for sensing in bioimaging in an exciting and multidisciplinary combination that may inspire other scientists to expand and advance in the field.

Prof. Dr. Angel Orte
Prof. Dr. María J. Ruedas-Rama
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 2400 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

  • fluorescent biosensors
  • FRET sensors
  • aptamers
  • nanosensors
  • fluorescent protein sensors
  • FLIM microscopy
  • super-resolution microscopy
  • Raman microscopy
  • mass spectrometry imaging
  • NIR imaging
  • NMR imaging
  • PET imaging

Published Papers (1 paper)

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Research

Article
Automatic Detection Method for Cancer Cell Nucleus Image Based on Deep-Learning Analysis and Color Layer Signature Analysis Algorithm
Sensors 2020, 20(16), 4409; https://doi.org/10.3390/s20164409 - 07 Aug 2020
Cited by 5 | Viewed by 1994
Abstract
Exploring strategies to treat cancer has always been an aim of medical researchers. One of the available strategies is to use targeted therapy drugs to make the chromosomes in cancer cells unstable such that cell death can be induced, and the elimination of [...] Read more.
Exploring strategies to treat cancer has always been an aim of medical researchers. One of the available strategies is to use targeted therapy drugs to make the chromosomes in cancer cells unstable such that cell death can be induced, and the elimination of highly proliferative cancer cells can be achieved. Studies have reported that the mitotic defects and micronuclei in cancer cells can be used as biomarkers to evaluate the instability of the chromosomes. Researchers use these two biomarkers to assess the effects of drugs on eliminating cancer cells. However, manual work is required to count the number of cells exhibiting mitotic defects and micronuclei either directly from the viewing window of a microscope or from an image, which is tedious and creates errors. Therefore, this study aims to detect cells with mitotic defects and micronuclei by applying an approach that can automatically count the targets. This approach integrates the application of a convolutional neural network for normal cell identification and the proposed color layer signature analysis (CLSA) to spot cells with mitotic defects and micronuclei. This approach provides a method for researchers to detect colon cancer cells in an accurate and time-efficient manner, thereby decreasing errors and the processing time. The following sections will illustrate the methodology and workflow design of this study, as well as explain the practicality of the experimental comparisons and the results that were used to validate the practicality of this algorithm. Full article
(This article belongs to the Special Issue Sensors for Bioimaging)
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