Journal of Medical and Biological Engineering 

About: Journal of Medical and Biological Engineering is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Medicine & Computer science. It has an ISSN identifier of 1609-0985. Over the lifetime, 1259 publications have been published receiving 13236 citations. The journal is also known as: JMBE.

Anti-fouling Coatings of Poly(dimethylsiloxane) Devices for Biological and Biomedical Applications

Abstract: Fouling initiated by nonspecific protein adsorption is a great challenge in biomedical applications, including biosensors, bioanalytical devices, and implants Poly(dimethylsiloxane) (PDMS), a popular material with many attractive properties for device fabrication in the biomedical field, suffers serious fouling problems from protein adsorption due to its hydrophobic nature, which limits the practical use of PDMS-based devices Effort has been made to develop biocompatible materials for anti-fouling coatings of PDMS In this review, typical nonfouling materials for PDMS coatings are introduced and the associated basic anti-fouling mechanisms, including the steric repulsion mechanism and the hydration layer mechanism, are described Understanding the relationships between the characteristics of coating materials and the accompanying anti-fouling mechanisms is critical for preparing PDMS coatings with desirable anti-fouling properties

Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases.

Abstract: While the spread of COVID-19 is increased, new, automatic, and reliable methods for accurate detection are essential to reduce the exposure of the medical experts to the outbreak. X-ray imaging, although limited to specific visualizations, may be helpful for the diagnosis. In this study, the problem of automatic classification of pulmonary diseases, including the recently emerged COVID-19, from X-ray images, is considered. Deep Learning has proven to be a remarkable method to extract massive high-dimensional features from medical images. Specifically, in this paper, the state-of-the-art Convolutional Neural Network called Mobile Net is employed and trained from scratch to investigate the importance of the extracted features for the classification task. A large-scale dataset of 3905 X-ray images, corresponding to 6 diseases, is utilized for training MobileNet v2, which has been proven to achieve excellent results in related tasks. Training the CNNs from scratch outperforms the other transfer learning techniques, both in distinguishing the X-rays between the seven classes and between Covid-19 and non-Covid-19. A classification accuracy between the seven classes of 87.66% is achieved. Besides, this method achieves 99.18% accuracy, 97.36% Sensitivity, and 99.42% Specificity in the detection of COVID-19. The results suggest that training CNNs from scratch may reveal vital biomarkers related but not limited to the COVID-19 disease, while the top classification accuracy suggests further examination of the X-ray imaging potential.

Novel Methods for Pulse Wave Velocity Measurement

Abstract: The great incidence of cardiovascular (CV) diseases in the world spurs the search for new solutions to enable an early detection of pathological processes and provides more precise diagnosis based in multi-parameters assessment. The pulse wave velocity (PWV) is considered one of the most important clinical parameters for evaluate the CV risk, vascular adaptation, and therapeutic efficacy. Several studies were dedicated to find the relationship between PWV measurement and pathological status in different diseases, and proved the relevance of this parameter. The commercial devices dedicate to PWV estimation make a regional assessment (measured between two vessels), however a local measurement is more precise evaluation of artery condition, taking into account the differences in the structure of arteries. Moreover, the current devices present some limitations due to the contact nature. Emerging trends in CV monitoring are moving away from more invasive technologies to non-invasive and non-contact solutions. The great challenge is to explore the new instrumental solutions that allow the PWV assessment with fewer approximations for an accurately evaluation and relatively inexpensive techniques in order to be used in the clinical routine.

Near-infrared Absorption Property of Biological Soft Tissue Constituents

Abstract: Near infrared (NIR) can penetrate relatively deep into biological soft tissues. The NIR absorption property of tissue varies with tissue constituents especially water, fat, collagen, and their combination ratio. Therefore, combination ratio of tissue constituents can be evaluated by decomposing the absorption spectrum to determine the light path length in each constituent. Standardized absorption spectra of tissue constituents are required in order to carry out decomposition. Since water, fat, and protein are the major contributors at NIR spectral region. This study is to measure their absorption spectra from standardized samples as reference for quantifying tissue constituents. Five kinds of major fatty acid found in human fat were mixed in proper ratio as a standard reference. Absorption spectrum of bovine skin gelatin and elastin in hog eye lens were used as references of protein. NIR absorption spectra were measured using a Shimadzu 3101-PC spectrophotometer. The results show that temperature has a strong effect on the absorption property of water but not on fatty acid mixture. Absorption spectrum of elastin is similar to that of dry bovine gelatin. NIR spectroscopy also can be used to characterize or identify different types of soft tissue based on their major chemical composition, such as detecting a fat plaque in a muscular tissue or a tumor in a high fat content tissue.

Review: Polymeric-Based 3D Printing for Tissue Engineering.

Abstract: Three-dimensional (3D) printing, also referred to as additive manufacturing, is a technology that allows for customized fabrication through computer-aided design. 3D printing has many advantages in the fabrication of tissue engineering scaffolds, including fast fabrication, high precision, and customized production. Suitable scaffolds can be designed and custom-made based on medical images such as those obtained from computed tomography. Many 3D printing methods have been employed for tissue engineering. There are advantages and limitations for each method. Future areas of interest and progress are the development of new 3D printing platforms, scaffold design software, and materials for tissue engineering applications.