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

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 12227

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Prof. Dr. Ralf Huss

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Institute of Pathology and Molecular Diagnostics, University Hospital Augsburg, Stenglinstrasse 2, 86156 Augsburg, Germany
Interests: digital pathology; oncology; biomarker
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Dear Colleagues,

The accelerated digitization of the biomedical space leads to an expontial growth of available data originating from many different sources. Besides information on the patient history, blood chemistry, or other conventional images, modern sensor technologies can provide a continuous flow of big data at any time and in real-time from outside and inside the human body. The still developing lab-on-a-chip technology even increases the amount and complexity of data originating from nano-scale material. Data lakes grow steadily, filled with mostly unstructed data, which are diffucult to use for standard artificial intelligence solutions. The extraction of structured data in standardized formats also from any sensor or nano device is critical to fill data warehouses and allow for the use of machine intelligence in a robust and sustainable manner. The connectivity and interoperability of sensors through concerted interfaces will yield even larger data sets accumulating real-world evidence on the fly. This facilitates the mining of complex and multi-dimensional data through artificial intelligence and machine learning. Bio-medicine requires such data strategies to develop next-generation medical devices as well as advanced therapies.

Prof. Dr. Ralf Huss
Guest Editor

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Keywords

nano-sensors
lab-on-a-chip
artificial intelligence
machine learning
data science
interoperability
data warehouse
biomedicine
clinical trials
advanced therapies

Published Papers (5 papers)

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Research

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12 pages, 5973 KiB

Open AccessArticle

Control of a Production Manipulator with the Use of BCI in Conjunction with an Industrial PLC

by Dmitrii Borkin, Andrea Nemethova, Martin Nemeth and Pavol Tanuska

Sensors 2023, 23(7), 3546; https://doi.org/10.3390/s23073546 - 28 Mar 2023

Cited by 2 | Viewed by 1352

Abstract

Research in the field of gathering and analyzing biological signals is growing. The sensors are becoming more available and more non-invasive for examining such signals, which in the past required the inconvenient acquisition of data. This was achieved mainly by the fact that biological sensors were able to be built into wearable and portable devices. The representation and analysis of EEGs (electroencephalograms) is nowadays commonly used in various application areas. The application of the use of the EEG signals to the field of automation is still an unexplored area and therefore provides opportunities for interesting research. In our research, we focused on the area of processing automation; especially the use of the EEG signals to bridge the communication between control of individual processes and a human. In this study, the real-time communication between a PLC (programmable logic controller) and BCI (brain computer interface) was investigated and described. In the future, this approach can help people with physical disabilities to control certain machines or devices and therefore it could find applicability in overcoming physical disabilities. The main contribution of the article is, that we have demonstrated the possibility of interaction between a person and a manipulator controlled by a PLC with the help of a BCI. Potentially, with the expansion of functionality, such solutions will allow a person with physical disabilities to participate in the production process. Full article

(This article belongs to the Special Issue Artificial Intelligence, Digital Sensors and Data Science in Bio-Medicine)

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14 pages, 2206 KiB

Open AccessArticle

On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks

by Ulices Que-Salinas, Dulce Martinez-Peon, Angel D. Reyes-Figueroa, Ivonne Ibarra and Christian Quintus Scheckhuber

Sensors 2022, 22(14), 5237; https://doi.org/10.3390/s22145237 - 13 Jul 2022

Viewed by 1618

Abstract

One of the hallmarks of diabetes is an increased modification of cellular proteins. The most prominent type of modification stems from the reaction of methylglyoxal with arginine and lysine residues, leading to structural and functional impairments of target proteins. For lysine glycation, several algorithms allow a prediction of occurrence; thus, making it possible to pinpoint likely targets. However, according to our knowledge, no approaches have been published for predicting the likelihood of arginine glycation. There are indications that arginine and not lysine is the most prominent target for the toxic dialdehyde. One of the reasons why there is no arginine glycation predictor is the limited availability of quantitative data. Here, we used a recently published high–quality dataset of arginine modification probabilities to employ an artificial neural network strategy. Despite the limited data availability, our results achieve an accuracy of about 75% of correctly predicting the exact value of the glycation probability of an arginine–containing peptide without setting thresholds upon whether it is decided if a given arginine is modified or not. This contribution suggests a solution for predicting arginine glycation of short peptides. Full article

(This article belongs to the Special Issue Artificial Intelligence, Digital Sensors and Data Science in Bio-Medicine)

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19 pages, 1637 KiB

Open AccessArticle

To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification

by Majed Alsanea, Abdulsalam S. Dukyil, Afnan, Bushra Riaz, Farhan Alebeisat, Muhammad Islam and Shabana Habib

Sensors 2022, 22(11), 4005; https://doi.org/10.3390/s22114005 - 25 May 2022

Cited by 11 | Viewed by 2434

Abstract

In the modern technological era, Anti-cancer peptides (ACPs) have been considered a promising cancer treatment. It’s critical to find new ACPs to ensure a better knowledge of their functioning processes and vaccine development. Thus, timely and efficient ACPs using a computational technique are highly needed because of the enormous peptide sequences generated in the post-genomic era. Recently, numerous adaptive statistical algorithms have been developed for separating ACPs and NACPs. Despite great advancements, existing approaches still have insufficient feature descriptors and learning methods, limiting predictive performance. To address this, a trustworthy framework is developed for the precise identification of ACPs. Particularly, the presented approach incorporates four hypothetical feature encoding mechanisms namely: amino acid, dipeptide, tripeptide, and an improved version of pseudo amino acid composition are applied to indicate the motif of the target class. Moreover, principal component analysis (PCA) is employed for feature pruning, while selecting optimal, deep, and highly variated features. Due to the diverse nature of learning, experiments are performed over numerous algorithms to select the optimum operating method. After investigating the empirical outcomes, the support vector machine with hybrid feature space shows better performance. The proposed framework achieved an accuracy of 97.09% and 98.25% over the benchmark and independent datasets, respectively. The comparative analysis demonstrates that our proposed model outperforms as compared to the existing methods and is beneficial in drug development, and oncology. Full article

(This article belongs to the Special Issue Artificial Intelligence, Digital Sensors and Data Science in Bio-Medicine)

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14 pages, 2496 KiB

Open AccessArticle

Muscle Mass Measurement Using Machine Learning Algorithms with Electrical Impedance Myography

by Kuo-Sheng Cheng, Ya-Ling Su, Li-Chieh Kuo, Tai-Hua Yang, Chia-Lin Lee, Wenxi Chen and Shing-Hong Liu

Sensors 2022, 22(8), 3087; https://doi.org/10.3390/s22083087 - 18 Apr 2022

Cited by 2 | Viewed by 3258

Abstract

Sarcopenia is a wild chronic disease among elderly people. Although it does not entail a life-threatening risk, it will increase the adverse risk due to the associated unsteady gait, fall, fractures, and functional disability. The import factors in diagnosing sarcopenia are muscle mass and strength. The examination of muscle mass must be carried in the clinic. However, the loss of muscle mass can be improved by rehabilitation that can be performed in non-medical environments. Electronic impedance myography (EIM) can measure some parameters of muscles that have the correlations with muscle mass and strength. The goal of this study is to use machine learning algorithms to estimate the total mass of thigh muscles (MoTM) with the parameters of EIM and body information. We explored the seven major muscles of lower limbs. The feature selection methods, including recursive feature elimination (RFE) and feature combination, were used to select the optimal features based on the ridge regression (RR) and support vector regression (SVR) models. The optimal features were the resistance of rectus femoris normalized by the thigh circumference, phase of tibialis anterior combined with the gender, and body information, height, and weight. There were 96 subjects involved in this study. The performances of estimating the MoTM used the regression coefficient (r²) and root-mean-square error (RMSE), which were 0.800 and 0.929, and 1.432 kg and 0.980 kg for RR and SVR models, respectively. Thus, the proposed method could have the potential to support people examining their muscle mass in non-medical environments. Full article

(This article belongs to the Special Issue Artificial Intelligence, Digital Sensors and Data Science in Bio-Medicine)

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Review

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26 pages, 1186 KiB

Open AccessReview

Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives

by Sharanya Manga, Neha Muthavarapu, Renisha Redij, Bhavana Baraskar, Avneet Kaur, Sunil Gaddam, Keerthy Gopalakrishnan, Rutuja Shinde, Anjali Rajagopal, Poulami Samaddar, Devanshi N. Damani, Suganti Shivaram, Shuvashis Dey, Dipankar Mitra, Sayan Roy, Kanchan Kulkarni and Shivaram P. Arunachalam

Sensors 2023, 23(12), 5744; https://doi.org/10.3390/s23125744 - 20 Jun 2023

Cited by 1 | Viewed by 2635

Abstract

The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice. Full article

(This article belongs to the Special Issue Artificial Intelligence, Digital Sensors and Data Science in Bio-Medicine)

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