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Smart Nano Biomedical Devices in Advanced Healthcare

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A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 20435

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Special Issue Editor

Dr. Bharath Babu Nunna

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Guest Editor

Department of Mechanical Engineering, Weber State University, Ogden, UT 84408, USA
Interests: biosensors; 3D-bioprinting; microfluidics; fluid dynamics in bioengineering applications; bioengineered lab-on-the-chip technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The most rapidly emerging technologies of nano-engineering and artificial intelligence are making significant advancements in biomedical device development capabilities. The recent pandemic situation has reiterated the necessity of the advancement of health care technologies for the survival of mankind. The state-of-the-art technology of nano-fabrication techniques is involved in the development of 2D/3D nano-structures to achieve complex electro-mechanical designs of biomedical applications. The fascinating blend of artificial intelligence with nano-engineering provides promising solutions to the most complex problems and situations, especially in the precise automation of artificial organ fabrication (3D bioprinting), drug delivery and lab/organ on-the-chip technologies. However, there are still many technical and translational challenges that need to be addressed, including the development of the nano-bots, micro-machines and packaging of nano-devices. This Special Issue will cover the recent and innovative advances made in the development of smart nano-biomedical devices, including the nano-scale 3D printing, nano-bot development, Integration of MEMS/NEMS and technical and translational challenges, along with a broader impact. Contributions could address, for example:

Novel nano-biomedical device design and development;
Integration of artificial intelligence with nano-medical devices;
Innovative fabrication techniques of nano 3D models;
Development of organ-on-chips integrated with biosensor to validate the toxicity and drug screening.

Dr. Bharath Babu Nunna
Guest Editor

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. Bioengineering 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 2700 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

nano-fabrication
nano scale 3D printing
nano-bots
packaging/integration
biomedical devices
MEMS/NEMS
lab on the chip
organ on the chip

Published Papers (3 papers)

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Research

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16 pages, 6087 KiB

Open AccessArticle

Modeling Focused-Ultrasound Response for Non-Invasive Treatment Using Machine Learning

by Tariq Mohammad Arif, Zhiming Ji, Md Adilur Rahim and Bharath Babu Nunna

Bioengineering 2021, 8(6), 74; https://doi.org/10.3390/bioengineering8060074 - 01 Jun 2021

Cited by 3 | Viewed by 3707

Abstract

The interactions between body tissues and a focused ultrasound beam can be evaluated using various numerical models. Among these, the Rayleigh–Sommerfeld and angular spectrum methods are considered to be the most effective in terms of accuracy. However, they are computationally expensive, which is one of the underlying issues of most computational models. Typically, evaluations using these models require a significant amount of time (hours to days) if realistic scenarios such as tissue inhomogeneity or non-linearity are considered. This study aims to address this issue by developing a rapid estimation model for ultrasound therapy using a machine learning algorithm. Several machine learning models were trained on a very-large dataset (19,227 simulations), and the performance of these models were evaluated with metrics such as Root Mean Squared Error (RMSE), R-squared (R²), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The resulted random forest provides superior accuracy with an R² value of 0.997, an RMSE of 0.0123, an AIC of −82.56, and a BIC of −81.65 on an external test dataset. The results indicate the efficacy of the random forest-based model for the focused ultrasound response, and practical adoption of this approach will improve the therapeutic planning process by minimizing simulation time. Full article

(This article belongs to the Special Issue Smart Nano Biomedical Devices in Advanced Healthcare)

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Review

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27 pages, 4167 KiB

Open AccessReview

COVID-19 Biomarkers and Advanced Sensing Technologies for Point-of-Care (POC) Diagnosis

by Ernst Emmanuel Etienne, Bharath Babu Nunna, Niladri Talukder, Yudong Wang and Eon Soo Lee

Bioengineering 2021, 8(7), 98; https://doi.org/10.3390/bioengineering8070098 - 12 Jul 2021

Cited by 27 | Viewed by 5349

Abstract

COVID-19, also known as SARS-CoV-2 is a novel, respiratory virus currently plaguing humanity. Genetically, at its core, it is a single-strand positive-sense RNA virus. It is a beta-type Coronavirus and is distinct in its structure and binding mechanism compared to other types of coronaviruses. Testing for the virus remains a challenge due to the small market available for at-home detection. Currently, there are three main types of tests for biomarker detection: viral, antigen and antibody. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) remains the gold standard for viral testing. However, the lack of quantitative detection and turnaround time for results are drawbacks. This manuscript focuses on recent advances in COVID-19 detection that have lower limits of detection and faster response times than RT-PCR testing. The advancements in sensing platforms have amplified the detection levels and provided real-time results for SARS-CoV-2 spike protein detection with limits as low as 1 fg/mL in the Graphene Field Effect Transistor (FET) sensor. Additionally, using multiple biomarkers, detection levels can achieve a specificity and sensitivity level comparable to that of PCR testing. Proper biomarker selection coupled with nano sensing detection platforms are key in the widespread use of Point of Care (POC) diagnosis in COVID-19 detection. Full article

(This article belongs to the Special Issue Smart Nano Biomedical Devices in Advanced Healthcare)

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20 pages, 5735 KiB

Open AccessReview

Blood Plasma Self-Separation Technologies during the Self-Driven Flow in Microfluidic Platforms

by Yudong Wang, Bharath Babu Nunna, Niladri Talukder, Ernst Emmanuel Etienne and Eon Soo Lee

Bioengineering 2021, 8(7), 94; https://doi.org/10.3390/bioengineering8070094 - 03 Jul 2021

Cited by 19 | Viewed by 10470

Abstract

Blood plasma is the most commonly used biofluid in disease diagnostic and biomedical analysis due to it contains various biomarkers. The majority of the blood plasma separation is still handled with centrifugation, which is off-chip and time-consuming. Therefore, in the Lab-on-a-chip (LOC) field, an effective microfluidic blood plasma separation platform attracts researchers’ attention globally. Blood plasma self-separation technologies are usually divided into two categories: active self-separation and passive self-separation. Passive self-separation technologies, in contrast with active self-separation, only rely on microchannel geometry, microfluidic phenomena and hydrodynamic forces. Passive self-separation devices are driven by the capillary flow, which is generated due to the characteristics of the surface of the channel and its interaction with the fluid. Comparing to the active plasma separation techniques, passive plasma separation methods are more considered in the microfluidic platform, owing to their ease of fabrication, portable, user-friendly features. We propose an extensive review of mechanisms of passive self-separation technologies and enumerate some experimental details and devices to exploit these effects. The performances, limitations and challenges of these technologies and devices are also compared and discussed. Full article

(This article belongs to the Special Issue Smart Nano Biomedical Devices in Advanced Healthcare)

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