Biomedical Engineering and Computational Biology最新文献

Background and objectives: Femurs affected by metastatic bone disease (MBD) frequently undergo surgery to prevent impending pathologic fractures due to clinician-perceived increases in fracture risk. Finite element (FE) models can provide more objective assessments of fracture risk. However, FE models of femurs with MBD have implemented strain- and strength-based estimates of fracture risk under a wide variety of loading configurations, and "physiologic" loading models typically simulate a single abductor force. Due to these variations, it is currently difficult to interpret mechanical fracture risk results across studies of femoral MBD. Our aims were to evaluate (1) differences in mechanical behavior between idealized loading configurations and those incorporating physiologic muscle forces, and (2) differences in the rankings of mechanical behavior between different loading configurations, in FE simulations to predict fracture risk in femurs with MBD.

Methods: We evaluated 9 different patient-specific FE loading simulations for a cohort of 54 MBD femurs: strain outcome simulations-physiologic (normal walking [NW], stair ascent [SA], stumbling), and joint contact only (NW contact force, excluding muscle forces); strength outcome simulations-physiologic (NW, SA), joint contact only, offset torsion, and sideways fall. Tensile principal strain and femur strength were compared between simulations using statistical analyses.

Results: Tensile principal strain was 26% higher (R ² = 0.719, P < .001) and femur strength was 4% lower (R ² = 0.984, P < .001) in simulations excluding physiologic muscle forces. Rankings of the mechanical predictions were correlated between the strain outcome simulations (ρ = 0.723 to 0.990, P < .001), and between strength outcome simulations (ρ = 0.524 to 0.984, P < .001).

Conclusions: Overall, simulations incorporating physiologic muscle forces affected local strain outcomes more than global strength outcomes. Absolute values of strain and strength computed using idealized (no muscle forces) and physiologic loading configurations should be used within the appropriate context when interpreting fracture risk in femurs with MBD.

Automated medical diagnosis has become crucial and significantly supports medical doctors. Thus, there is a demand for inventing deep learning (DL) and convolutional networks for analyzing medical images. Dermatology, in particular, is one of the domains that was recently targeted by AI specialists to introduce new DL algorithms or enhance convolutional neural network (CNN) architectures. A significantly high proportion of studies in the field are concerned with skin cancer, whereas other dermatological disorders are still limited. In this work, we examined the performance of 6 CNN architectures named VGG16, EfficientNet, InceptionV3, MobileNet, NasNet, and ResNet50 for the top 3 dermatological disorders that frequently appear in the Middle East. An Image filtering and denoising were imposed in this work to enhance image quality and increase architecture performance. Experimental results revealed that MobileNet achieved the highest performance and accuracy among the CNN architectures and can classify disorder with high performance (95.7% accuracy). Future scope will focus more on proposing a new methodology for deep-based classification. In addition, we will expand the dataset for more images that consider new disorders and variations.

Background: Assessment of paracetamol overdose in children and teenagers in the emergency department (ED) requires blood, taken 4 hours post ingestion. A commercial partner developed transdermal paracetamol measuring technology. This work aims to understand the acceptability of such a device, and potential market size.

Methods: A questionnaire study was undertaken with children and parents attending Alder Hey Children's Hospital, and healthcare professionals (HCP) involved in their care. A retrospective audit of paracetamol ingestion presenting to a paediatric ED was undertaken.

Results: One hundred forty-three questionnaires were distributed, and 139 returned (response rate 97.2%), comprising 55 children, 52 parents and 32 HCP (recruited between August-October 2019). Overall device acceptability, assessed by favourability of appearance and willingness to wear was high, at 60.0% and 81.5% respectively. Concerns raised included bulky size and weight, and concern regarding the duration younger children would tolerate wearing the device. All groups, including children, ranked accuracy of results as the most important device feature and device comfort the least important. Parents prioritised avoidance of blood tests more than children. One hundred twenty-seven children presented to ED with paracetamol ingestion (September 2017-August 2018), with 57 (44.9%) categorised as accidental overdose. Overall, 106 (83.4%) required paracetamol concentration measuring, and 25 (19.7%) of these required treatment with N-acetylcysteine. Extrapolating nationally, over 7000 children will present with accidental overdose per annum in the UK.

Conclusion: Acceptability of a non-invasive paracetamol sensor was high in all groups, provided accuracy could be assured.

Introduction: EGSnrc software package is one of the computational packages for Monte Carlo simulation in radiation therapy and has several subset codes. Directional bremsstrahlung splitting (DBS) is a technique that applies braking radiations in interactions in this software. This study aimed to evaluate the effect of this technique on the simulation time, uncertainty, particle number of phase-space data, and photon beam spectrum resulting from a medical linear accelerator (LINAC).

Materials and methods: The gantry of the accelerator, including the materials and geometries of different parts, was simulated using the BEAMnrc code (a subset code in the EGSnrc package). The phase-space data were recorded in different parts of the LINAC. The DBS values (1, 10, 100, and 1000) were changed, and their effects were evaluated on the simulation parameters and output spectra.

Results: Increasing the DBS value from 1 to 1000 resulted in an increase in the simulation time from 1.778 to 11.310 hours, and increasing the number of particles in the phase-space plane (5 590 732-180 328 382). When the DBS had been picked up from 1 to 100, the simulation uncertainty decreased by about 1.29%. In addition, the DBS increment value from 100 to 1000 leads to an increase in uncertainty and simulation time of about 0.71% and 315%, respectively.

Conclusion: Although using the DBS technique reduces the simulation time or uncertainty, increasing the DBS from a specific value, equal to 100 in our study, increases simulation uncertainties and times. Therefore, we propose considering a specific DBS value as we obtained for the Monte Carlo simulation of photon beams produced by linear accelerators.

Background: Digital Twins (DTs), virtual copies of physical entities, are a promising tool to help manage and predict outbreaks of Covid-19. By providing a detailed model of each patient, DTs can be used to determine what method of care will be most effective for that individual. The improvement in patient experience and care delivery will help to reduce demand on healthcare services and to improve hospital management.

Objectives: The aim of this study is to address 2 research questions: (1) How effective are DTs in predicting and managing infectious diseases such as Covid-19? and (2) What are the prospects and challenges associated with the use of DTs in healthcare?

Methods: The review was structured according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) framework. Titles and abstracts of references in PubMed, IEEE Xplore, Scopus, ScienceDirect and Google Scholar were searched using selected keywords (relating to digital twins, healthcare and Covid-19). The papers were screened in accordance with the inclusion and exclusion criteria so that all papers published in English relating to the use of digital twins in healthcare were included. A narrative synthesis was used to analyse the included papers.

Results: Eighteen papers met the inclusion criteria and were included in the review. None of the included papers examined the use of DTs in the context of Covid-19, or infectious disease outbreaks in general. Academic research about the applications, opportunities and challenges of DT technology in healthcare in general was found to be in early stages.

Conclusions: The review identifies a need for further research into the use of DTs in healthcare, particularly in the context of infectious disease outbreaks. Based on frameworks identified during the review, this paper presents a preliminary conceptual framework for the use of DTs for hospital management during the Covid-19 outbreak to address this research gap.

With the increasing number of molecular biology techniques, large numbers of oligonucleotides are frequently involved in individual research projects. Thus, a dedicated electronic oligonucleotide management system is expected to provide several benefits such as increased oligonucleotide traceability, facilitated sharing of oligonucleotides between laboratories, and simplified (bulk) ordering of oligonucleotides. Herein, we describe OligoPrime, an information system for oligonucleotide management, which presents a computational support for all steps in an oligonucleotide lifecycle, namely, from its ordering and storage to its application, and disposal. OligoPrime is easy to use since it is accessible via a web browser and does not require any installation from the end user's perspective. It allows filtering and search of oligonucleotides by various parameters, which include the exact location of an oligonucleotide, its sequence, and availability. The oligonucleotide database behind the system is shared among the researchers working in the same laboratory or research group. Users might have different roles which define the access permissions and range from students to researchers and primary investigators. Furthermore, OligoPrime is easy to manage and install and is based on open-source software solutions. Its code is freely available at https://github.com/OligoPrime. Moreover, an implementation of OligoPrime, which can be used for testing is available at http://oligoprime.xyz/. To our knowledge, OligoPrime is the only software solution dedicated specifically to oligonucleotide management. We strongly believe that it has a large potential to enhance the transparency of use and to simplify the management of oligonucleotides in academic laboratories and research groups.

Graphene, a relatively new two-dimensional (2D) nanomaterial, possesses unique structure (e.g. lighter, harder, and more flexible than steel) and tunable physicochemical (e.g. electronical, optical) properties with potentially wide eco-friendly and cost-effective usage in biosensing. Furthermore, graphene-related nanomaterials (e.g. graphene oxide, doped graphene, carbon nanotubes) have inculcated tremendous interest among scientists and industrials for the development of innovative biosensing platforms, such as arrays, sequencers and other nanooptical/biophotonic sensing systems (e.g. FET, FRET, CRET, GERS). Indeed, combinatorial functionalization approaches are constantly improving the overall properties of graphene, such as its sensitivity, stability, specificity, selectivity, and response for potential bioanalytical applications. These include real-time multiplex detection, tracking, qualitative, and quantitative characterization of molecules (i.e. analytes [H₂O₂, urea, nitrite, ATP or NADH]; ions [Hg²⁺, Pb²⁺, or Cu²⁺]; biomolecules (DNA, iRNA, peptides, proteins, vitamins or glucose; disease biomarkers such as genetic alterations in BRCA1, p53) and cells (cancer cells, stem cells, bacteria, or viruses). However, there is still a paucity of comparative reports that critically evaluate the relative toxicity of carbon nanoallotropes in humans. This manuscript comprehensively reviews the biosensing applications of graphene and its derivatives (i.e. GO and rGO). Prospects and challenges are also introduced.

Raman spectroscopy is a group of analytical techniques, currently applied in several research fields, including clinical diagnostics. Tissue-mimicking optical phantoms have been established as an essential intermediate stage for medical applications with their employment from spectroscopic techniques to be constantly growing. This review outlines the types of tissue phantoms currently employed in different biomedical applications of Raman spectroscopy, focusing on their composition and optical properties. It is therefore an attempt to present an informed range of options for potential use to the researchers.

The integration of nanomaterials in microfluidic devices has emerged as a new research paradigm. Microfluidic devices composed of ZnO nanowires have been developed for the collection of urine extracellular vesicles (EVs) at high efficiency and in situ extraction of various microRNAs (miRNAs). The devices can be used for diagnosing various diseases, including kidney diseases and cancers. A major research need for developing micro total analysis systems is to enhance extraction efficiency. This article presents a novel fabrication method for a herringbone-patterned microfluidic device anchored with ZnO nanowire arrays. The substrates with herringbone patterns were created by maskless photolithography. The ZnO nanowire arrays were grown on the substrates by chemical bathing. The patterned design was to introduce turbulent flows as opposed to laminar flow in traditional devices to increase the mixing and contact of the urine sample with ZnO nanowires. The device showed reduced flow rates compared with conventional planar microfluidic channels and successfully extracted urine EV-encapsulated miRNAs.

Force myography (FMG) is an appealing alternative to traditional electromyography in biomedical applications, mainly due to its simpler signal pattern and immunity to electrical interference. Most FMG sensors, however, send data to a computer for further processing, which reduces the user mobility and, thus, the chances for practical application. In this sense, this work proposes to remodel a typical optical fiber FMG sensor with smaller portable components. Moreover, all data acquisition and processing routines were migrated to a Raspberry Pi 3 Model B microprocessor, ensuring the comfort of use and portability. The sensor was successfully demonstrated for 2 input channels and 9 postures classification with an average precision and accuracy of ~99.5% and ~99.8%, respectively, using a feedforward artificial neural network of 2 hidden layers and a competitive output layer.