BMC biomedical engineering最新文献

{"title":"An open-access plug-in program for 3D modelling distinct material properties of cortical and trabecular bone.","authors":"Gregory R Roytman,&nbsp;Matan Cutler,&nbsp;Kenneth Milligan,&nbsp;Steven M Tommasini,&nbsp;Daniel H Wiznia","doi":"10.1186/s42490-022-00065-z","DOIUrl":"https://doi.org/10.1186/s42490-022-00065-z","url":null,"abstract":"<p><strong>Background: </strong>Finite element modelling the material behavior of bone in-silico is a powerful tool to predict the best suited surgical treatment for individual patients.</p><p><strong>Results: </strong>We demonstrate the development and use of a pre-processing plug-in program with a 3D modelling image processing software suite (Synopsys Simpleware, ScanIP) to assist with identifying, isolating, and defining cortical and trabecular bone material properties from patient specific computed tomography scans. The workflow starts by calibrating grayscale values of each constituent element with a phantom - a standardized object with defined densities. Using an established power law equation, we convert the apparent density value per voxel to a Young's Modulus. The resulting \"calibrated\" scan can be used for modeling and in-silico experimentation with Finite Element Analysis.</p><p><strong>Conclusions: </strong>This process allows for the creation of realistic and personalized simulations to inform a surgeon's decision-making. We have made this plug-in program open and accessible as a supplemental file.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"4 1","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2022-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9509591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10740442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effects of electrical stimulation on glial cell behaviour.","authors":"Christopher T Tsui,&nbsp;Preet Lal,&nbsp;Katelyn V R Fox,&nbsp;Matthew A Churchward,&nbsp;Kathryn G Todd","doi":"10.1186/s42490-022-00064-0","DOIUrl":"https://doi.org/10.1186/s42490-022-00064-0","url":null,"abstract":"<p><p>Neural interface devices interact with the central nervous system (CNS) to substitute for some sort of functional deficit and improve quality of life for persons with disabilities. Design of safe, biocompatible neural interface devices is a fast-emerging field of neuroscience research. Development of invasive implant materials designed to directly interface with brain or spinal cord tissue has focussed on mitigation of glial scar reactivity toward the implant itself, but little exists in the literature that directly documents the effects of electrical stimulation on glial cells. In this review, a survey of studies documenting such effects has been compiled and categorized based on the various types of stimulation paradigms used and their observed effects on glia. A hybrid neuroscience cell biology-engineering perspective is offered to highlight considerations that must be made in both disciplines in the development of a safe implant. To advance knowledge on how electrical stimulation affects glia, we also suggest experiments elucidating electrochemical reactions that may occur as a result of electrical stimulation and how such reactions may affect glia. Designing a biocompatible stimulation paradigm should be a forefront consideration in the development of a device with improved safety and longevity.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":" ","pages":"7"},"PeriodicalIF":0.0,"publicationDate":"2022-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40345852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biomechanical validation of additively manufactured artificial femoral bones.","authors":"F Metzner,&nbsp;C Neupetsch,&nbsp;A Carabello,&nbsp;M Pietsch,&nbsp;T Wendler,&nbsp;W-G Drossel","doi":"10.1186/s42490-022-00063-1","DOIUrl":"https://doi.org/10.1186/s42490-022-00063-1","url":null,"abstract":"<p><p>Replicating the mechanical behavior of human bones, especially cancellous bone tissue, is challenging. Typically, conventional bone models primarily consist of polyurethane foam surrounded by a solid shell. Although nearly isotropic foam components have mechanical properties similar to cancellous bone, they do not represent the anisotropy and inhomogeneity of bone architecture. To consider the architecture of bone, models were developed whose core was additively manufactured based on CT data. This core was subsequently coated with glass fiber composite. Specimens consisting of a gyroid-structure were fabricated using fused filament fabrication (FFF) techniques from different materials and various filler levels. Subsequent compression tests showed good accordance between the mechanical behavior of the printed specimens and human bone. The unidirectional fiberglass composite showed higher strength and stiffness than human cortical bone in 3-point bending tests, with comparable material behaviors being observed. During biomechanical investigation of the entire assembly, femoral prosthetic stems were inserted into both artificial and human bones under controlled conditions, while recording occurring forces and strains. All of the artificial prototypes, made of different materials, showed analogous behavior to human bone. In conclusion, it was shown that low-cost FFF technique can be used to generate valid bone models and selectively modify their properties by changing the infill.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"4 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354338/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10579198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gold-based nanoplatform for a rapid lateral flow immunochromatographic test assay for gluten detection","authors":"Arefe Momeni, M. Rostami-Nejad, R. Salarian, M. Rabiee, Elham Aghamohammadi, M. Zali, N. Rabiee, F. Tay, Pooyan Makvandi","doi":"10.1186/s42490-022-00062-2","DOIUrl":"https://doi.org/10.1186/s42490-022-00062-2","url":null,"abstract":"","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65794516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of glioma-subtypes: comparison of performance on a DL classifier using bounding box areas versus annotated tumors","authors":"Ali, Muhaddisa Barat, Gu, Irene Yu-Hua, Lidemar, Alice, Berger, Mitchel S., Widhalm, Georg, Jakola, Asgeir Store","doi":"10.1186/s42490-022-00061-3","DOIUrl":"https://doi.org/10.1186/s42490-022-00061-3","url":null,"abstract":"For brain tumors, identifying the molecular subtypes from magnetic resonance imaging (MRI) is desirable, but remains a challenging task. Recent machine learning and deep learning (DL) approaches may help the classification/prediction of tumor subtypes through MRIs. However, most of these methods require annotated data with ground truth (GT) tumor areas manually drawn by medical experts. The manual annotation is a time consuming process with high demand on medical personnel. As an alternative automatic segmentation is often used. However, it does not guarantee the quality and could lead to improper or failed segmented boundaries due to differences in MRI acquisition parameters across imaging centers, as segmentation is an ill-defined problem. Analogous to visual object tracking and classification, this paper shifts the paradigm by training a classifier using tumor bounding box areas in MR images. The aim of our study is to see whether it is possible to replace GT tumor areas by tumor bounding box areas (e.g. ellipse shaped boxes) for classification without a significant drop in performance. In patients with diffuse gliomas, training a deep learning classifier for subtype prediction by employing tumor regions of interest (ROIs) using ellipse bounding box versus manual annotated data. Experiments were conducted on two datasets (US and TCGA) consisting of multi-modality MRI scans where the US dataset contained patients with diffuse low-grade gliomas (dLGG) exclusively. Prediction rates were obtained on 2 test datasets: 69.86% for 1p/19q codeletion status on US dataset and 79.50% for IDH mutation/wild-type on TCGA dataset. Comparisons with that of using annotated GT tumor data for training showed an average of 3.0% degradation (2.92% for 1p/19q codeletion status and 3.23% for IDH genotype). Using tumor ROIs, i.e., ellipse bounding box tumor areas to replace annotated GT tumor areas for training a deep learning scheme, cause only a modest decline in performance in terms of subtype prediction. With more data that can be made available, this may be a reasonable trade-off where decline in performance may be counteracted with more data.","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138516386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The size of via holes influence the amplitude and selectivity of neural signals in Micro-ECoG arrays.","authors":"Manan Sethia,&nbsp;Mesut Sahin","doi":"10.1186/s42490-022-00060-4","DOIUrl":"https://doi.org/10.1186/s42490-022-00060-4","url":null,"abstract":"<p><strong>Background: </strong>Electrocorticography (ECoG) arrays are commonly used to record the brain activity both in animal and human subjects. There is a lack of guidelines in the literature as to how the array geometry, particularly the via holes in the substrate, affects the recorded signals. A finite element (FE) model was developed to simulate the electric field generated by neurons located at different depths in the rat brain cortex and a micro ECoG array (μECoG) was placed on the pia surface for recording the neural signal. The array design chosen was a typical array of 8 × 8 circular (100 μm in diam.) contacts with 500 μm pitch. The size of the via holes between the recording contacts was varied to see the effect.</p><p><strong>Results: </strong>The results showed that recorded signal amplitudes were reduced if the substrate was smaller than about four times the depth of the neuron in the gray matter. The signal amplitude profiles had dips around the via holes and the amplitudes were also lower at the contact sites as compared to the design without the holes; an effect that increased with the hole size. Another noteworthy result is that the spatial selectivity of the multi-contact recordings could be improved or reduced by the selection of the via hole sizes, and the effect depended on the distance between the neuron pair targeted for selective recording and its depth.</p><p><strong>Conclusions: </strong>The results suggest that the via-hole size clearly affects the recorded neural signal amplitudes and it can be leveraged as a parameter to reduce the inter-channel correlation and thus maximize the information content of neural signals with μECoG arrays.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":" ","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8935835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40310714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficacy and safety testing of a COVID-19 era emergency ventilator in a healthy rabbit lung model","authors":"White, Luke A., Maxey, Benjamin S., Solitro, Giovanni F., Takei, Hidehiro, Conrad, Steven A., Alexander, J. Steven","doi":"10.1186/s42490-022-00059-x","DOIUrl":"https://doi.org/10.1186/s42490-022-00059-x","url":null,"abstract":"The COVID-19 pandemic revealed a substantial and unmet need for low-cost, easily accessible mechanical ventilation strategies for use in medical resource-challenged areas. Internationally, several groups developed non-conventional COVID-19 era emergency ventilator strategies as a stopgap measure when conventional ventilators were unavailable. Here, we compared our FALCON emergency ventilator in a rabbit model and compared its safety and functionality to conventional mechanical ventilation. New Zealand white rabbits (n = 5) received mechanical ventilation from both the FALCON and a conventional mechanical ventilator (Engström Carestation™) for 1 h each. Airflow and pressure, blood O2 saturation, end tidal CO2, and arterial blood gas measurements were measured. Additionally, gross and histological lung samples were compared to spontaneously breathing rabbits (n = 3) to assess signs of ventilator induced lung injury. All rabbits were successfully ventilated with the FALCON. At identical ventilator settings, tidal volumes, pressures, and respiratory rates were similar between both ventilators, but the inspiratory to expiratory ratio was lower using the FALCON. End tidal CO2 was significantly higher on the FALCON, and arterial blood gas measurements demonstrated lower arterial partial pressure of O2 at 30 min and higher arterial partial pressure of CO2 at 30 and 60 min using the FALCON. However, when ventilated at higher respiratory rates, we observed a stepwise decrease in end tidal CO2. Poincaré plot analysis demonstrated small but significant increases in short-term and long-term variation of peak inspiratory pressure generation from the FALCON. Wet to dry lung weight and lung injury scoring between the mechanically ventilated and spontaneously breathing rabbits were similar. Although conventional ventilators are always preferable outside of emergency use, the FALCON ventilator safely and effectively ventilated healthy rabbits without lung injury. Emergency ventilation using accessible and inexpensive strategies like the FALCON may be useful for communities with low access to medical resources and as a backup form of emergency ventilation.","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138542599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of computational models for microtesla-level magnetic brain scanning: a novel avenue for device development.","authors":"Shane Shahrestani,&nbsp;Gabriel Zada,&nbsp;Yu-Chong Tai","doi":"10.1186/s42490-022-00058-y","DOIUrl":"https://doi.org/10.1186/s42490-022-00058-y","url":null,"abstract":"<p><strong>Background: </strong>Detection of locally increased blood concentration and perfusion is critical for assessment of functional cortical activity as well as diagnosis of conditions such as intracerebral hemorrhage (ICH). Current paradigms for assessment of regional blood concentration in the brain rely on computed tomography (CT), magnetic resonance imaging (MRI), and perfusion blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI).</p><p><strong>Results: </strong>In this study, we developed computational models to test the feasibility of novel magnetic sensors capable of detecting hemodynamic changes within the brain on a microtesla-level. We show that low-field magnetic sensors can accurately detect changes in magnetic flux density and eddy current damping signals resulting from increases in local blood concentration. These models predicted that blood volume changes as small as 1.26 mL may be resolved by the sensors, implying potential use for diagnosis of ICH and assessment of regional blood flow as a proxy for cerebral metabolism and neuronal activity. We then translated findings from our computational model to demonstrate feasibility of accurate detection of modeled ICH in a simulated human cadaver setting.</p><p><strong>Conclusions: </strong>Overall, microtesla-level magnetic scanning is feasible, safe, and has distinct advantages compared to current standards of care. Computational modeling may facilitate rapid prototype development and testing of novel medical devices with minimal risk to human participants prior to device construction and clinical trials.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"4 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10672863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Training muscle activation patterns of the lower paretic extremity using directional exertion improves mobility in persons with hemiparesis: a pilot study.","authors":"Daniel Bourbonnais,&nbsp;René Pelletier,&nbsp;Joëlle Azar,&nbsp;Camille Sille,&nbsp;Michel Goyette","doi":"10.1186/s42490-021-00057-5","DOIUrl":"https://doi.org/10.1186/s42490-021-00057-5","url":null,"abstract":"<p><strong>Background: </strong>Controlled static exertion performed in the sagittal plane on a transducer attached to the foot requires coordinated moments of force of the lower extremity. Some exertions and plantarflexion recruit muscular activation patterns similar to synergies previously identified during gait. It is currently unknown if persons with hemiparesis following stroke demonstrate similar muscular patterns, and if force feedback training utilizing static exertion results in improved mobility in this population.</p><p><strong>Methods: </strong>Electromyographic (EMG) activity of eight muscles of the lower limb were recorded using surface electrodes in healthy participants (n = 10) and in persons with hemiparesis (n = 8) during an exertion exercise (task) performed in eight directions in the sagittal plane of the foot and a plantarflexion exercise performed at 20 and 40% maximum voluntary effort (MVE). Muscle activation patterns identified during these exertion exercises were compared between groups and to synergies reported in the literature during healthy gait using cosine similarities (CS). Functional mobility was assessed in four participants with hemiparesis using GAITRite® and the Timed Up and Go (TUG) test at each session before, during and after static force feedback training. Tau statistics were used to evaluate the effect on mobility before and after training. Measures of MVE and the accuracy of directional exertion were compared before and after training using ANOVAs. Spearman Rho correlations were also calculated between changes in these parameters and changes in mobility before and after the training.</p><p><strong>Results: </strong>Muscle activation patterns during directional exertion and plantarflexion were similar for both groups of participants (CS varying from 0.845 to 0.977). Muscular patterns for some of the directional and plantarflexion were also similar to synergies recruited during gait (CS varying from 0.847 to 0.951). Directional exertion training in hemiparetic subjects resulted in improvement in MVE (p < 0.040) and task performance accuracy (p < 0.001). Hemiparetic subjects also demonstrated significant improvements in gait velocity (p < 0.032) and in the TUG test (p < 0.022) following training. Improvements in certain directional efforts were correlated with changes in gait velocity (p = 0.001).</p><p><strong>Conclusion: </strong>Static force feedback training following stroke improves strength and coordination of the lower extremity while recruiting synergies reported during gait and is associated with improved mobility.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"3 1","pages":"12"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8555217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39575079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-cell conventional pap smear image classification using pre-trained deep neural network architectures.","authors":"Mohammed Aliy Mohammed,&nbsp;Fetulhak Abdurahman,&nbsp;Yodit Abebe Ayalew","doi":"10.1186/s42490-021-00056-6","DOIUrl":"https://doi.org/10.1186/s42490-021-00056-6","url":null,"abstract":"<p><strong>Background: </strong>Automating cytology-based cervical cancer screening could alleviate the shortage of skilled pathologists in developing countries. Up until now, computer vision experts have attempted numerous semi and fully automated approaches to address the need. Yet, these days, leveraging the astonishing accuracy and reproducibility of deep neural networks has become common among computer vision experts. In this regard, the purpose of this study is to classify single-cell Pap smear (cytology) images using pre-trained deep convolutional neural network (DCNN) image classifiers. We have fine-tuned the top ten pre-trained DCNN image classifiers and evaluated them using five class single-cell Pap smear images from SIPaKMeD dataset. The pre-trained DCNN image classifiers were selected from Keras Applications based on their top 1% accuracy.</p><p><strong>Results: </strong>Our experimental result demonstrated that from the selected top-ten pre-trained DCNN image classifiers DenseNet169 outperformed with an average accuracy, precision, recall, and F1-score of 0.990, 0.974, 0.974, and 0.974, respectively. Moreover, it dashed the benchmark accuracy proposed by the creators of the dataset with 3.70%.</p><p><strong>Conclusions: </strong>Even though the size of DenseNet169 is small compared to the experimented pre-trained DCNN image classifiers, yet, it is not suitable for mobile or edge devices. Further experimentation with mobile or small-size DCNN image classifiers is required to extend the applicability of the models in real-world demands. In addition, since all experiments used the SIPaKMeD dataset, additional experiments will be needed using new datasets to enhance the generalizability of the models.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"3 1","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s42490-021-00056-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39141788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}