Biomedical Engineering and Computational Biology最新文献

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.

Classification is a common technique applied to 'omics data to build predictive models and identify potential markers of biomedical outcomes. Despite the prevalence of case-control studies, the number of classification methods available to analyze data generated by such studies is extremely limited. Conditional logistic regression is the most commonly used technique, but the associated modeling assumptions limit its ability to identify a large class of sufficiently complicated 'omic signatures. We propose a data preprocessing step which generalizes and makes any linear or nonlinear classification algorithm, even those typically not appropriate for matched design data, available to be used to model case-control data and identify relevant biomarkers in these study designs. We demonstrate on simulated case-control data that both the classification and variable selection accuracy of each method is improved after applying this processing step and that the proposed methods are comparable to or outperform existing variable selection methods. Finally, we demonstrate the impact of conditional classification algorithms on a large cohort study of children with islet autoimmunity.

Rare events such as genetic mutations or cell-cell interactions are important contributors to dynamics in complex biological systems, eg, in drug-resistant infections. Computational approaches can help analyze rare events that are difficult to study experimentally. However, analyzing the frequency and dynamics of rare events in computational models can also be challenging due to high computational resource demands, especially for high-fidelity stochastic computational models. To facilitate analysis of rare events in complex biological systems, we present a multifidelity analysis approach that uses medium-fidelity analysis (Monte Carlo simulations) and/or low-fidelity analysis (Markov chain models) to analyze high-fidelity stochastic model results. Medium-fidelity analysis can produce large numbers of possible rare event trajectories for a single high-fidelity model simulation. This allows prediction of both rare event dynamics and probability distributions at much lower frequencies than high-fidelity models. Low-fidelity analysis can calculate probability distributions for rare events over time for any frequency by updating the probabilities of the rare event state space after each discrete event of the high-fidelity model. To validate the approach, we apply multifidelity analysis to a high-fidelity model of tuberculosis disease. We validate the method against high-fidelity model results and illustrate the application of multifidelity analysis in predicting rare event trajectories, performing sensitivity analyses and extrapolating predictions to very low frequencies in complex systems. We believe that our approach will complement ongoing efforts to enable accurate prediction of rare event dynamics in high-fidelity computational models.

Background: Granular cell tumor (GCT) is a relatively uncommon tumor that may affect the skin. The tumor can develop anywhere on the body, although it is predominately seen in oral cavities and in the head and neck regions. Here, we present the results of optical coherence tomography (OCT) imaging of a large GCT located on the abdomen of a patient. We also present an analytical method to differentiate between healthy tissue and GCT tissues.

Materials and methods: A multibeam, Fourier domain, swept source OCT was used for imaging. The OCT had a central wavelength of 1305 ± 15 nm and lateral and axial resolutions of 7.5 and 10 µm, respectively. Qualitative and quantitative analyses of the tumor and healthy skin are reported.

Results: Abrupt changes in architectures of the dermal and epidermal layers in the GCT lesion were observed. These architectural changes were not observed in healthy skin.

Discussion: To quantitatively differentiate healthy skin from tumor regions, an optical attenuation coefficient analysis based on single-scattering formulation was performed. The methodology introduced here could have the capability to delineate boundaries of a tumor prior to surgical excision.