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Biological Sample Analysis Techniques and Devices

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 February 2026 | Viewed by 373

Special Issue Editor


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Guest Editor
Department of Experimental and Clinical Medicine, University Magna Graecia, 88100 Catanzaro, Italy
Interests: microfluidics; micro- and nanofabrication technologies applied in the biomedical field
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past few years, part of the scientific community has been moving towards the development of innovative devices and techniques for biological sample analysis allowing unprecedented resolution and the simplification of screening protocols. The detection of traces of biological species from biological samples is of extreme importance due to the possible impact on human health. However, a great number of issues come into play before a biological sample can be used as part of a routine screening procedure. The handling and analysis of complex biological samples that require individuating a single component among a cluster of molecules is challenging and conventionally requires complicate protocols to pretreat complex samples. Screening is often translated into the detection of few molecules in diluted solutions, which are invisible to the current sensors due to the limited analysis resolution. Moreover, for some screening procedures, it is important to not affect the phenotype of a biological sample, especially during an analysis of a specific sample over a long period of time (e.g., a specific cell population). Thus, it becomes important to introduce new methodologies that are not invasive with respect to the analytes. Finally, the heterogeneity of the behavior of the human body and its response to medical treatments requires the development of tools that are compatible with personalized medicine. This is translated into the development of devices that are portable, fast, and that provide parallel high-throughput and high-content analysis with reduced costs. These devices can then become important for scientific research and clinical diagnostic applications, and the sequential handling and manipulation of cells and cell suspensions. Ways to overcome these issues could be as follows: (a) The development of microfluidic devices that integrate optical sensors. Microfluidics is an interdisciplinary discipline that focuses on the transport, manipulation, and analysis of small amounts of liquids, cells, and particles. These devices guarantee high portability, accurate control when handling samples, simplified sample pretreatment protocols, and low consumption of samples and reagents. (b) Sensing approaches that allow the monitoring of samples over a long period of time and in a label-free manner that does not affect the sample integrity in order to discover new biomarkers and unknown as well as known molecules, resolve complex mixtures, and reduce sample pretreatments, in addition to enabling high resolution analysis. This Special Issue will focus on these two aspects.

Dr. Gerardo Perozziello
Guest Editor

Manuscript Submission Information

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Keywords

  • optical sensors
  • screening techniques
  • screening devices
  • microfluidics
  • lab on a chip
  • optofluidics
  • spectroscopy techniques

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Published Papers (1 paper)

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Research

16 pages, 4169 KiB  
Article
Asymmetric Distance in K-Means Clustering Enhances Quality of Cells Raman Imaging
by Bernadette Scopacasa and Patrizio Candeloro
Appl. Sci. 2025, 15(8), 4461; https://doi.org/10.3390/app15084461 - 17 Apr 2025
Viewed by 299
Abstract
Raman microspectroscopy is a powerful, label-free technique for the biochemical characterization of cells, but its complex spectral data require advanced computational methods for meaningful interpretation. Clustering analysis is widely used in spectroscopic imaging to extract meaningful biochemical information. Traditional methods, such as K-means [...] Read more.
Raman microspectroscopy is a powerful, label-free technique for the biochemical characterization of cells, but its complex spectral data require advanced computational methods for meaningful interpretation. Clustering analysis is widely used in spectroscopic imaging to extract meaningful biochemical information. Traditional methods, such as K-means clustering with Euclidean distance, often struggle to capture subtle spectral variations, leading to suboptimal segmentation. Alternative distance metrics, including cosine and Mahalanobis distances, have been explored to enhance cluster separability, yet challenges remain in distinguishing chemically relevant features while minimizing redundancy and noise. In this study, we introduce an asymmetric metric distance matrix with a tunable eccentricity parameter to improve clustering performance in Raman hyperspectral imaging. Our results demonstrate that suitable eccentricity values enhance the identification of subcellular structures while requiring fewer clusters than Euclidean-based approaches. Compared to polar metrics, the proposed asymmetric metric achieves better stability and reduced noise, leading to more accurate segmentation. Future research could explore its application in other clustering techniques and machine learning frameworks, as well as its application in broader spectral imaging techniques where the distance metric plays a fundamental role. Full article
(This article belongs to the Special Issue Biological Sample Analysis Techniques and Devices)
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