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Micromachines
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Intelligent Biomedical Devices and Systems
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Intelligent Biomedical Devices and Systems

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "B：Biology and Biomedicine".

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 7791

Special Issue Editors

Dr. Shuai Na

E-Mail Website
Guest Editor
National Biomedical Imaging Center, College of Future Technology, Peking University, Beijing 100871, China
Interests: ultrasonic transducer technology; photoacoustic imaging; ultrasound imaging; neuroimaging
Dr. Feng Guo

E-Mail Website
Guest Editor
Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47405, USA
Interests: intelligent acoustofluidics; intelligent systems; microfluidics; translational medicine

Special Issue Information

Dear Colleagues,

Ultrasonic transducers and sensors are the core components of ultrasound-based instruments, including traditional ultrasound scanners, ultrasound endoscopes, focused ultrasound treatment or stimulation devices, acoustofluidic devices, sensing devices, wearable ultrasound devices, photoacoustic imaging devices, image-guided treatment devices, etc. Advances in ultrasonic transducer and sensor technology have led to an unprecedented performance of these instruments in terms of sensitivity, miniaturization, spatial resolution, temporal resolution, field of view, and cost efficiency.

This Special Issue of Micromachines covers the design, fabrication, front-end electronics, characterization, packing, system integration of all types of ultrasonic transducers, and their applications in biomedical imaging, therapy, drug delivery, cell manipulation, industrial nondestructive testing, etc. It also covers relevant developments of ultrasound and photoacoustic instruments, imaging processing, and reconstruction algorithms.

Dr. Shuai Na
Dr. Feng Guo
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. Micromachines 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 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

  • piezoelectric
  • CMUT
  • PMUT
  • polymers
  • optical ultrasound detector
  • MEMS
  • ultrasound imaging
  • photoacoustic imaging
  • ultrasonic therapy
  • drug delivery
  • acoustofluidics
  • acoustic cell manipulation
  • acoustic liquid handling
  • deep learning
  • wearable acoustic sensors

Published Papers (3 papers)

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Research

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21 pages, 15178 KiB  
Article
Optimization of a Screw Centrifugal Blood Pump Based on Random Forest and Multi-Objective Gray Wolf Optimization Algorithm
by Teng Jing, Haoran Sun, Jianan Cheng and Ling Zhou
Micromachines 2023, 14(2), 406; https://doi.org/10.3390/mi14020406 - 8 Feb 2023
Cited by 2 | Viewed by 1738
Abstract
The centrifugal blood pump is a commonly used ventricular assist device. It can replace part of the heart function, pumping blood throughout the body in order to maintain normal function. However, the high shear stress caused by the impeller rotating at high speeds [...] Read more.
The centrifugal blood pump is a commonly used ventricular assist device. It can replace part of the heart function, pumping blood throughout the body in order to maintain normal function. However, the high shear stress caused by the impeller rotating at high speeds can lead to hemolysis and, as a consequence, to stroke and other syndromes. Therefore, reducing the hemolysis level while ensuring adequate pressure generation is key to the optimization of centrifugal blood pumps. In this study, a screw centrifugal blood pump was used as the research object. In addition, pressure generation and the hemolysis level were optimized simultaneously using a coupled algorithm composed of random forest (RF) and multi-objective gray wolf optimization (MOGWO). After verifying the prediction accuracy of the algorithm, three optimized models were selected and compared with the baseline model in terms of pressure cloud, 2D streamline, SSS distribution, HI distribution, and vortex distribution. Finally, via a comprehensive evaluation, the optimized model was selected as the final optimization design, in which the pressure generation increased by 24% and the hemolysis value decreased by 48%. Full article
(This article belongs to the Special Issue Intelligent Biomedical Devices and Systems)
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11 pages, 4101 KiB  
Article
Precise Control of Glioma Cell Apoptosis Induced by Micro-Plasma-Activated Water (μ-PAW)
by Yuhan Zhang, Xiaoxia Du, Qihao Shi, Wenxiang Xiao and Hua Li
Micromachines 2022, 13(12), 2145; https://doi.org/10.3390/mi13122145 - 4 Dec 2022
Cited by 1 | Viewed by 1433
Abstract
To verify the existence of plasma with the potential to kill tumor cells, this paper designed a novel helium (He) micro-plasma jet array device and detected the concentration of typical long-lived reactive oxygen and nitrogen species (RONS) with oxidative activity generated by it. [...] Read more.
To verify the existence of plasma with the potential to kill tumor cells, this paper designed a novel helium (He) micro-plasma jet array device and detected the concentration of typical long-lived reactive oxygen and nitrogen species (RONS) with oxidative activity generated by it. The paper described a new He micro-plasma jet array device consisting of nine flexible quartz capillaries with an inner diameter of 75 μm arranged in a 3 × 3 array. Sterilized ultrapure water (up water) was first treated with the He micro-plasma jet array device to activate it to form enriched RONS micro-plasma-activated water (μ-PAW), and then μ-PAW was added to the cell culture medium (with cells) to observe the proliferation of human glioma cells. The concentration of long-lived RONS, such as nitrate (NO3−), was detected according to Beer–Lambert’s law in combination with UV spectrophotometry as well as a color development method. The MTT Cell Proliferation and Cytotoxicity Assay Kit combined with the Hoechst Staining Kit were used to assess the proliferation status of the cells. The results showed that the range of RONS concentration variation could be controlled in the order of micromoles (µmol), while plasma-induced tumor cell death is apoptosis that does not affect the surrounding environment. Full article
(This article belongs to the Special Issue Intelligent Biomedical Devices and Systems)
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Review

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30 pages, 9321 KiB  
Review
Application of Deep Learning in Histopathology Images of Breast Cancer: A Review
by Yue Zhao, Jie Zhang, Dayu Hu, Hui Qu, Ye Tian and Xiaoyu Cui
Micromachines 2022, 13(12), 2197; https://doi.org/10.3390/mi13122197 - 11 Dec 2022
Cited by 14 | Viewed by 3913
Abstract
With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification [...] Read more.
With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios. Full article
(This article belongs to the Special Issue Intelligent Biomedical Devices and Systems)
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