BioMedical Engineering OnLine最新文献

{"title":"Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer.","authors":"Leen Huang, Keying Zhou, Siyang Chen, Yanzhao Chen, Jinxin Zhang","doi":"10.1186/s12938-024-01244-w","DOIUrl":"10.1186/s12938-024-01244-w","url":null,"abstract":"<p><strong>Background: </strong>Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios.</p><p><strong>Method: </strong>To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection.</p><p><strong>Results: </strong>The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy.</p><p><strong>Conclusion: </strong>The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"50"},"PeriodicalIF":3.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143608/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141186169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special collection in association with the 2023 International Conference on aging, innovation and rehabilitation.","authors":"Babak Taati, Milos R Popovic","doi":"10.1186/s12938-024-01243-x","DOIUrl":"10.1186/s12938-024-01243-x","url":null,"abstract":"","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"49"},"PeriodicalIF":3.9,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11106936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141074613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"sEMG-based automatic characterization of swallowed materials.","authors":"Eman A Hassan, Yassin Khalifa, Ahmed A Morsy","doi":"10.1186/s12938-024-01241-z","DOIUrl":"10.1186/s12938-024-01241-z","url":null,"abstract":"<p><p>Monitoring of ingestive activities is critically important for managing the health and wellness of individuals with various health conditions, including the elderly, diabetics, and individuals seeking better weight control. Monitoring swallowing events can be an ideal surrogate for developing streamlined methods for effective monitoring and quantification of eating or drinking events. Swallowing is an essential process for maintaining life. This seemingly simple process is the result of coordinated actions of several muscles and nerves in a complex fashion. In this study, we introduce automated methods for the detection and quantification of various eating and drinking activities. Wireless surface electromyography (sEMG) was used to detect chewing and swallowing from sEMG signals obtained from the sternocleidomastoid muscle, in addition to signals obtained from a wrist-mounted IMU sensor. A total of 4675 swallows were collected from 55 participants in the study. Multiple methods were employed to estimate bolus volumes in the case of fluid intake, including regression and classification models. Among the tested models, neural networks-based regression achieved an R² of 0.88 and a root mean squared error of 0.2 (minimum bolus volume was 10 ml). Convolutional neural networks-based classification (when considering each bolus volume as a separate class) achieved an accuracy of over 99% using random cross-validation and around 66% using cross-subject validation. Multiple classification methods were also used for solid bolus type detection, including SVM and decision trees (DT), which achieved an accuracy above 99% with random validation and above 94% in cross-subject validation. Finally, regression models with both random and cross-subject validation were used for estimating the solid bolus volume with an R² value that approached 1 and root mean squared error values as low as 0.00037 (minimum solid bolus weight was 3 gm). These reported results lay the foundation for a cost-effective and non-invasive method for monitoring swallowing activities which can be extremely beneficial in managing various chronic health conditions, such as diabetes and obesity.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"48"},"PeriodicalIF":3.9,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11100060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140955729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Importance of the electrophoresis and pulse energy for siRNA-mediated gene silencing by electroporation in differentiated primary human myotubes.","authors":"Mojca Pavlin, Nives Škorja Milić, Maša Kandušer, Sergej Pirkmajer","doi":"10.1186/s12938-024-01239-7","DOIUrl":"10.1186/s12938-024-01239-7","url":null,"abstract":"<p><strong>Background: </strong>Electrotransfection is based on application of high-voltage pulses that transiently increase membrane permeability, which enables delivery of DNA and RNA in vitro and in vivo. Its advantage in applications such as gene therapy and vaccination is that it does not use viral vectors. Skeletal muscles are among the most commonly used target tissues. While siRNA delivery into undifferentiated myoblasts is very efficient, electrotransfection of siRNA into differentiated myotubes presents a challenge. Our aim was to develop efficient protocol for electroporation-based siRNA delivery in cultured primary human myotubes and to identify crucial mechanisms and parameters that would enable faster optimization of electrotransfection in various cell lines.</p><p><strong>Results: </strong>We established optimal electroporation parameters for efficient siRNA delivery in cultured myotubes and achieved efficient knock-down of HIF-1α while preserving cells viability. The results show that electropermeabilization is a crucial step for siRNA electrotransfection in myotubes. Decrease in viability was observed for higher electric energy of the pulses, conversely lower pulse energy enabled higher electrotransfection silencing yield. Experimental data together with the theoretical analysis demonstrate that siRNA electrotransfer is a complex process where electropermeabilization, electrophoresis, siRNA translocation, and viability are all functions of pulsing parameters. However, despite this complexity, we demonstrated that pulse parameters for efficient delivery of small molecule such as PI, can be used as a starting point for optimization of electroporation parameters for siRNA delivery into cells in vitro if viability is preserved.</p><p><strong>Conclusions: </strong>The optimized experimental protocol provides the basis for application of electrotransfer for silencing of various target genes in cultured human myotubes and more broadly for electrotransfection of various primary cell and cell lines. Together with the theoretical analysis our data offer new insights into mechanisms that underlie electroporation-based delivery of short RNA molecules, which can aid to faster optimisation of the pulse parameters in vitro and in vivo.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"47"},"PeriodicalIF":3.9,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11097476/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140943832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parameter subset reduction for imaging-based digital twin generation of patients with left ventricular mechanical discoordination.","authors":"Tijmen Koopsen, Nick van Osta, Tim van Loon, Roel Meiburg, Wouter Huberts, Ahmed S Beela, Feddo P Kirkels, Bas R van Klarenbosch, Arco J Teske, Maarten J Cramer, Geertruida P Bijvoet, Antonius van Stipdonk, Kevin Vernooy, Tammo Delhaas, Joost Lumens","doi":"10.1186/s12938-024-01232-0","DOIUrl":"10.1186/s12938-024-01232-0","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Integration of a patient's non-invasive imaging data in a digital twin (DT) of the heart can provide valuable insight into the myocardial disease substrates underlying left ventricular (LV) mechanical discoordination. However, when generating a DT, model parameters should be identifiable to obtain robust parameter estimations. In this study, we used the CircAdapt model of the human heart and circulation to find a subset of parameters which were identifiable from LV cavity volume and regional strain measurements of patients with different substrates of left bundle branch block (LBBB) and myocardial infarction (MI). To this end, we included seven patients with heart failure with reduced ejection fraction (HFrEF) and LBBB (study ID: 2018-0863, registration date: 2019-10-07), of which four were non-ischemic (LBBB-only) and three had previous MI (LBBB-MI), and six narrow QRS patients with MI (MI-only) (study ID: NL45241.041.13, registration date: 2013-11-12). Morris screening method (MSM) was applied first to find parameters which were important for LV volume, regional strain, and strain rate indices. Second, this parameter subset was iteratively reduced based on parameter identifiability and reproducibility. Parameter identifiability was based on the diaphony calculated from quasi-Monte Carlo simulations and reproducibility was based on the intraclass correlation coefficient ( &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;ICC&lt;/mi&gt;&lt;/mrow&gt; &lt;/math&gt; ) obtained from repeated parameter estimation using dynamic multi-swarm particle swarm optimization. Goodness-of-fit was defined as the mean squared error ( &lt;math&gt; &lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;χ&lt;/mi&gt;&lt;/mrow&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt; &lt;/math&gt; ) of LV myocardial strain, strain rate, and cavity volume.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A subset of 270 parameters remained after MSM which produced high-quality DTs of all patients ( &lt;math&gt; &lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;χ&lt;/mi&gt;&lt;/mrow&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt; &lt;/math&gt;  &lt; 1.6), but minimum parameter reproducibility was poor ( &lt;math&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;ICC&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;min&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt;  = 0.01). Iterative reduction yielded a reproducible ( &lt;math&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;ICC&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;min&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt;  = 0.83) subset of 75 parameters, including cardiac output, global LV activation duration, regional mechanical activation delay, and regional LV myocardial constitutive properties. This reduced subset produced patient-resembling DTs ( &lt;math&gt; &lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;χ&lt;/mi&gt;&lt;/mrow&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt; &lt;/math&gt;  &lt; 2.2), while septal-to-lateral wall workload imbalance was higher for the LBBB-only DTs than for the MI-only DTs (p &lt; 0.05).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;By applying sensitivity and identifiability analysis, we successfully determined a parameter subset of the CircAdapt model which can be used to generate imaging-based DTs of patients with LV mechanical discoordination. Parameters were reproducibly estimated using particle swarm optimization, and derived LV myocardial work di","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"46"},"PeriodicalIF":3.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11089736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140916044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-task learning model using RR intervals and respiratory effort to assess sleep disordered breathing","authors":"Jiali Xie, Pedro Fonseca, Johannes van Dijk, Sebastiaan Overeem, Xi Long","doi":"10.1186/s12938-024-01240-0","DOIUrl":"https://doi.org/10.1186/s12938-024-01240-0","url":null,"abstract":"Sleep-disordered breathing (SDB) affects a significant portion of the population. As such, there is a need for accessible and affordable assessment methods for diagnosis but also case-finding and long-term follow-up. Research has focused on exploiting cardiac and respiratory signals to extract proxy measures for sleep combined with SDB event detection. We introduce a novel multi-task model combining cardiac activity and respiratory effort to perform sleep–wake classification and SDB event detection in order to automatically estimate the apnea–hypopnea index (AHI) as severity indicator. The proposed multi-task model utilized both convolutional and recurrent neural networks and was formed by a shared part for common feature extraction, a task-specific part for sleep–wake classification, and a task-specific part for SDB event detection. The model was trained with RR intervals derived from electrocardiogram and respiratory effort signals. To assess performance, overnight polysomnography (PSG) recordings from 198 patients with varying degree of SDB were included, with manually annotated sleep stages and SDB events. We achieved a Cohen’s kappa of 0.70 in the sleep–wake classification task, corresponding to a Spearman’s correlation coefficient (R) of 0.830 between the estimated total sleep time (TST) and the TST obtained from PSG-based sleep scoring. Combining the sleep–wake classification and SDB detection results of the multi-task model, we obtained an R of 0.891 between the estimated and the reference AHI. For severity classification of SBD groups based on AHI, a Cohen’s kappa of 0.58 was achieved. The multi-task model performed better than a single-task model proposed in a previous study for AHI estimation, in particular for patients with a lower sleep efficiency (R of 0.861 with the multi-task model and R of 0.746 with single-task model with subjects having sleep efficiency < 60%). Assisted with automatic sleep–wake classification, our multi-task model demonstrated proficiency in estimating AHI and assessing SDB severity based on AHI in a fully automatic manner using RR intervals and respiratory effort. This shows the potential for improving SDB screening with unobtrusive sensors also for subjects with low sleep efficiency without adding additional sensors for sleep–wake detection.","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"57 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanically strained osteocyte-derived exosomes contained miR-3110-5p and miR-3058-3p and promoted osteoblastic differentiation","authors":"Yingwen Zhu, Yanan Li, Zhen Cao, Jindong Xue, Xiaoyan Wang, Tingting Hu, Biao Han, Yong Guo","doi":"10.1186/s12938-024-01237-9","DOIUrl":"https://doi.org/10.1186/s12938-024-01237-9","url":null,"abstract":"Osteocytes are critical mechanosensory cells in bone, and mechanically stimulated osteocytes produce exosomes that can induce osteogenesis. MicroRNAs (miRNAs) are important constituents of exosomes, and some miRNAs in osteocytes regulate osteogenic differentiation; previous studies have indicated that some differentially expressed miRNAs in mechanically strained osteocytes likely influence osteoblastic differentiation. Therefore, screening and selection of miRNAs that regulate osteogenic differentiation in exosomes of mechanically stimulated osteocytes are important. A mechanical tensile strain of 2500 με at 0.5 Hz 1 h per day for 3 days, elevated prostaglandin E2 (PGE2) and insulin-like growth factor-1 (IGF-1) levels and nitric oxide synthase (NOS) activity of MLO-Y4 osteocytes, and promoted osteogenic differentiation of MC3T3-E1 osteoblasts. Fourteen miRNAs differentially expressed only in MLO-Y4 osteocytes which were stimulated with mechanical tensile strain, were screened, and the miRNAs related to osteogenesis were identified. Four differentially expressed miRNAs (miR-1930-3p, miR-3110-5p, miR-3090-3p, and miR-3058-3p) were found only in mechanically strained osteocytes, and the four miRNAs, eight targeted mRNAs which were differentially expressed only in mechanically strained osteoblasts, were also identified. In addition, the mechanically strained osteocyte-derived exosomes promoted the osteoblastic differentiation of MC3T3-E1 cells in vitro, the exosomes were internalized by osteoblasts, and the up-regulated miR-3110-5p and miR-3058-3p in mechanically strained osteocytes, were both increased in the exosomes, which was verified via reverse transcription quantitative polymerase chain reaction (RT-qPCR). In osteocytes, a mechanical tensile strain of 2500 με at 0.5 Hz induced the fourteen differentially expressed miRNAs which probably were in exosomes of osteocytes and involved in osteogenesis. The mechanically strained osteocyte-derived exosomes which contained increased miR-3110-5p and miR-3058-3p (two of the 14 miRNAs), promoted osteoblastic differentiation.","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"103 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol: methodology and proof-of-concept studies","authors":"Nouman Ahmad, Hugo Dahlberg, Hanna Jönsson, Sambit Tarai, Rama Krishna Guggilla, Robin Strand, Elin Lundström, Göran Bergström, Håkan Ahlström, Joel Kullberg","doi":"10.1186/s12938-024-01235-x","DOIUrl":"https://doi.org/10.1186/s12938-024-01235-x","url":null,"abstract":"Computed tomography (CT) is an imaging modality commonly used for studies of internal body structures and very useful for detailed studies of body composition. The aim of this study was to develop and evaluate a fully automatic image registration framework for inter-subject CT slice registration. The aim was also to use the results, in a set of proof-of-concept studies, for voxel-wise statistical body composition analysis (Imiomics) of correlations between imaging and non-imaging data. The current study utilized three single-slice CT images of the liver, abdomen, and thigh from two large cohort studies, SCAPIS and IGT. The image registration method developed and evaluated used both CT images together with image-derived tissue and organ segmentation masks. To evaluate the performance of the registration method, a set of baseline 3-single-slice CT images (from 2780 subjects including 8285 slices) from the SCAPIS and IGT cohorts were registered. Vector magnitude and intensity magnitude error indicating inverse consistency were used for evaluation. Image registration results were further used for voxel-wise analysis of associations between the CT images (as represented by tissue volume from Hounsfield unit and Jacobian determinant) and various explicit measurements of various tissues, fat depots, and organs collected in both cohort studies. Our findings demonstrated that the key organs and anatomical structures were registered appropriately. The evaluation parameters of inverse consistency, such as vector magnitude and intensity magnitude error, were on average less than 3 mm and 50 Hounsfield units. The registration followed by Imiomics analysis enabled the examination of associations between various explicit measurements (liver, spleen, abdominal muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), thigh SAT, intermuscular adipose tissue (IMAT), and thigh muscle) and the voxel-wise image information. The developed and evaluated framework allows accurate image registrations of the collected three single-slice CT images and enables detailed voxel-wise studies of associations between body composition and associated diseases and risk factors.","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"124 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140576595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours","authors":"Yangchun Du, Yanju Xiao, Wenwen Guo, Jinxiu Yao, Tongliu Lan, Sijin Li, Huoyue Wen, Wenying Zhu, Guangling He, Hongyu Zheng, Haining Chen","doi":"10.1186/s12938-024-01234-y","DOIUrl":"https://doi.org/10.1186/s12938-024-01234-y","url":null,"abstract":"The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS). This study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC). The DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer–Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values. The US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"1 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140576414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strategies to enhance the ability of nerve guidance conduits to promote directional nerve growth","authors":"Ziyue Zhang, Muyuan Ma","doi":"10.1186/s12938-024-01233-z","DOIUrl":"https://doi.org/10.1186/s12938-024-01233-z","url":null,"abstract":"Severely damaged peripheral nerves will regenerate incompletely due to lack of directionality in their regeneration, leading to loss of nerve function. To address this problem, various nerve guidance conduits (NGCs) have been developed to provide guidance for nerve repair. However, their clinical application is still limited, mainly because its effect in promoting nerve repair is not as good as autologous nerve transplantation. Therefore, it is necessary to enhance the ability of NGCs to promote directional nerve growth. Strategies include preparing various directional structures on NGCs to provide contact guidance, and loading various substances on them to provide electrical stimulation or neurotrophic factor concentration gradient to provide directional physical or biological signals.","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"10 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140576903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}