BioMedical Engineering OnLine最新文献

{"title":"A review of osteoporotic vertebral fracture animal models.","authors":"Zhichao Qi, Guozhu Ye, Zhiyi Liu, Jie Zhang, Weidong Xie, Yu Li, Wenbin Yang","doi":"10.1186/s12938-025-01372-x","DOIUrl":"10.1186/s12938-025-01372-x","url":null,"abstract":"<p><strong>Introduction: </strong>Osteoporotic vertebral fractures are a common outcome of osteoporosis, imposing a substantial economic burden. The development of reliable animal models is essential for advancing research. This review examines osteoporotic vertebral fracture models across various animal species.</p><p><strong>Methods: </strong>The review compares and analyzes the different approaches used to model osteoporotic vertebral fractures in experimental animals, synthesizing the existing design protocols.</p><p><strong>Results: </strong>Rats and sheep are the primary experimental animals utilized in vertebral fracture research. The predominant approach in model design remains the creation of bone defects to simulate vertebral fractures. The spontaneous fracture model is primarily applicable to small species, such as transgenic mice. Rabbits and zebrafish are not suitable for modeling vertebral fractures due to the low cancellous bone content in their lumbar. The bone loss in the lumbar cancellous bone of the dog osteoporosis model is minimal, making it unsuitable for fracture modeling.</p><p><strong>Conclusions: </strong>The bone defect model remains the most widely used approach for osteoporotic vertebral fractures. However, the stress compression model shows promise as a potential focal point for future investigations.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"40"},"PeriodicalIF":2.9,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11977875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802274","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":"Forecasting motion trajectories of elbow and knee joints during infant crawling based on long-short-term memory (LSTM) networks.","authors":"Jieyi Mo, Qiliang Xiong, Ying Chen, Yuan Liu, Xiaoying Wu, Nong Xiao, Wensheng Hou","doi":"10.1186/s12938-025-01360-1","DOIUrl":"10.1186/s12938-025-01360-1","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Hands-and-knees crawling is a promising rehabilitation intervention for infants with motor impairments, while research on assistive crawling devices for rehabilitation training was still in its early stages. In particular, precisely generating motion trajectories is a prerequisite to controlling exoskeleton assistive devices, and deep learning-based prediction algorithms, such as Long-Short-Term Memory (LSTM) networks, have proven effective in forecasting joint trajectories of gait. Despite this, no previous studies have focused on forecasting the more variable and complex trajectories of infant crawling. Therefore, this paper aims to explore the feasibility of using LSTM networks to predict crawling trajectories, thereby advancing our understanding of how to actively control crawling rehabilitation training robots.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We collected joint trajectory data from 20 healthy infants (11 males and 9 females, aged 8-15 months) as they crawled on hands and knees. This study implemented LSTM networks to forecast bilateral elbow and knee trajectories based on corresponding joint angles. The data set comprised 58, 782 time steps, each containing 4 joint angles. We partitioned the data set into 70% for training and 30% for testing to evaluate predictive performance. We investigated a total of 24 combinations of input and output time-frames, with window sizes for input vectors ranging from 10, 15, 20, 30, 40, 50, 70, and 100 time steps, and output vectors from 5, 10, and 15 steps. Evaluation metrics included Mean Absolute Error (MAE), Mean Squared Error (MSE), and Correlation Coefficient (CC) to assess prediction accuracy.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The results indicate that across various input-output windows, the MAE for elbow joints ranged from 0.280 to 4.976°, MSE ranged from 0.203° to 59.186°, and CC ranged from 89.977% to 99.959%. For knee joints, MAE ranged from 0.277 to 4.262°, MSE from 0.229 to 53.272°, and CC from 89.454% to 99.944%. Results also show that smaller output window sizes lead to lower prediction errors. As expected, the LSTM predicting 5 output time steps has the lowest average error, while the LSTM predicting 15 time steps has the highest average error. In addition, variations in input window size had a minimal impact on average error when the output window size was fixed. Overall, the optimal performance for both elbow and knee joints was observed with input-output window sizes of 30 and 5 time steps, respectively, yielding an MAE of 0.295°, MSE of 0.260°, and CC of 99.938%.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study demonstrates the feasibility of forecasting infant crawling trajectories using LSTM networks, which could potentially integrate with exoskeleton control systems. It experimentally explores how different input and output time-frames affect prediction accuracy and sets the stage for future research focused on optimizing models and developing effective cont","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"39"},"PeriodicalIF":2.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11967147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771295","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":"Ex vivo myo-mechanical assessment of isolated rectus abdominis muscle in pregnancy-specific urinary incontinence: a cross-sectional study nested within the Diamater cohort.","authors":"Carolina N F Carvalho, David R A Reyes, Juliana F Floriano, Sofia B C V Quiroz, Rafael G Oliveira, Fernanda C B Alves, Gabriela A Garcia, Luana F Iamundo, Adriely B M Magyori, Caroline B Prudencio, Patricia S Rossignoli, Cristiane R Pedroni, Raghavendra L S Hallur, Iracema M P Calderon, Angélica M P Barbosa, Marilza V C Rudge","doi":"10.1186/s12938-025-01366-9","DOIUrl":"10.1186/s12938-025-01366-9","url":null,"abstract":"<p><strong>Background: </strong>Rectus abdominis muscle (RAM) myopathy is the underlying mechanism of pregnancy-specific urinary incontinence (PSUI), which has been shown to be a predictor of long-term urinary incontinence (UI). However, there is limited knowledge about selective ex vivo myo-mechanical assessment (MMA) of the RAM in healthy pregnant women, particularly those with PSUI.</p><p><strong>Results: </strong>Our analysis revealed no specific quantitative parameters from the window analysis or contractile response of the RAM in ex vivo MMA. A gradual and consistent decline was predominantly observed in the peak and strength parameters for both groups. The qualitative contractile response analysis of the RAM using peak and strength parameters revealed three distinct behaviors under electrical stimulation: a progressive decrease in strength, sudden muscular arrest, and asynchrony with erratic fluctuations. In-depth quantitative analysis of the RAM ex vivo did not yield substantial differences between the two groups. However, qualitative analysis proved valuable in distinguishing the groups, revealing a tendency toward the loss of the progressive peak declines observed in the control group.</p><p><strong>Conclusion: </strong>This study highlights the complexity of RAM myopathy in the context of PSUI and its potential impact on UI outcomes.</p><p><strong>Method: </strong>In this cross-sectional study nested within a cohort, we examined ex vivo myography assessments in 87 mothers, comprising 48 with PSUI and 39 without PSUI, all of whom exhibited normal glucose tolerance. General data were extracted from our database, whereas RAM samples were collected during C-section for qualitative and quantitative ex vivo myography analysis, with a focus on initial and final baseline measurements, peak responses, strength, and duration times.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"38"},"PeriodicalIF":2.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143708314","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":"New considerations in selecting donors for dental pulp stem cells: a pilot study.","authors":"Mingchang Hu, Qianqian Zhang, Jidong Xu, Linlin Xu, Xuecheng Xu, Jiajia Wang, Yu Song","doi":"10.1186/s12938-025-01367-8","DOIUrl":"10.1186/s12938-025-01367-8","url":null,"abstract":"<p><strong>Background/purpose: </strong>Tissue engineering based on stem cell therapy necessitates a substantial quantity of high-quality stem cells. However, current sources face limitations, including narrow donor pools, compromised biological properties due to cryopreservation, and cellular senescence resulting from in vitro passaging and expansion. This study examines the impact of mild periodontitis on the biological performance of dental pulp stem cells (DPSCs) to explore the potential of broadening the donor pool for these cells.</p><p><strong>Materials and methods: </strong>The experiment included two variables: age and the presence of periodontitis. DPSCs were isolated from six healthy subjects and six patients with mild periodontitis. Healthy subjects were categorized into Groups A (28-32 years) and B (52-54 years), and patients with mild periodontitis were categorized into Groups C (31-33 years) and D (50-53 years). The analyses included cell morphology, proliferation rate, multilineage differentiation capacity, apoptosis, and surface marker expression.</p><p><strong>Result: </strong>No significant differences in cell morphology, pluripotency, or senescence were observed between healthy controls and periodontitis patients across age groups. Additionally, data on proliferation, pluripotency, and senescence were not significantly different. In healthy subjects, increased age was correlated with more elongated, flattened, and broader cells, alongside greater heterogeneity and intercellular granules. The proliferation and differentiation capacities decreased, whereas the degree of apoptosis increased. Similar trends were noted in patients with periodontitis.</p><p><strong>Conclusion: </strong>The biological properties of DPSCs remain unchanged in teeth with mild periodontitis, providing valuable insights for addressing the shortage of DPSCs in tissue engineering. Teeth with mild periodontitis have the potential to be pulp stem cell donors.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"37"},"PeriodicalIF":2.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929365/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676758","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 finger on the pulse of cardiovascular health: estimating blood pressure with smartphone photoplethysmography-based pulse waveform analysis.","authors":"Ivan Shih-Chun Liu, Fangyuan Liu, Qi Zhong, Shiguang Ni","doi":"10.1186/s12938-025-01365-w","DOIUrl":"10.1186/s12938-025-01365-w","url":null,"abstract":"<p><p>Smartphone photoplethysmography (PPG) offers a cost-effective and accessible method for continuous blood pressure (BP) monitoring, but faces persistent challenges with accuracy and interpretability. This study addresses these limitations through a series of strategies. Data quality was enhanced to improve the performance of traditional statistical models, while SHapley Additive exPlanations (SHAP) analysis ensured transparency in machine learning models. Waveform features were analyzed to establish theoretical connections with BP measures, and feature engineering techniques were applied to enhance prediction accuracy and model interpretability. Bland-Altman analysis was conducted, and the results were compared against reference devices using multiple international standards to evaluate the method's feasibility. Data collected from 127 participants demonstrated strong correlations between smartphone-derived digital waveform features and those from reference BP devices. The mean absolute errors (MAE) for systolic BP (SBP), diastolic BP (DBP), and pulse pressure (PP) using multiple linear regression models were 7.75, 6.35, and 4.49 mmHg, respectively. Random forest models further improved these values to 7.34, 5.79, and 4.45 mmHg. Feature importance analysis identified key contributions from time-domain, frequency-domain, curvature-domain, and demographic features. However, Bland-Altman analysis revealed systematic biases, and the models barely meet established accuracy standards. These findings suggest that while smartphone PPG technology shows promise, significant advancements are required before it can replace traditional BP measurement devices.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"36"},"PeriodicalIF":2.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11924600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661929","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":"Analysis of preoperative ocular optical parameters in patients with cataract.","authors":"Wenqun Xi, Xinhua Liu, Zhiqiang Ren, Xiaosheng Huang, Liangnan Sun, Ye Ye, Yanhua Zhao, Zhe Zhang, Kun Zeng","doi":"10.1186/s12938-025-01364-x","DOIUrl":"10.1186/s12938-025-01364-x","url":null,"abstract":"<p><p>This study aims to evaluate the distribution of preoperative corneal parameters obtained using the Pentacam anterior segment analyzer in Chinese male and female patients with cataracts and to investigate the correlation between these parameters and related factors. Preoperative examination data of the eyes of 1,255 patients who underwent cataract surgery were retrospectively analyzed. The Pentacam AXL was used to extract preoperative corneal measurements, and the total corneal measurement data were analyzed. The average age of the patients was 52.9 ± 21.3 years. The mean simulated keratometry values and corneal curvature of total corneal refractive power were positively correlated with age (both P < 0.01). Spearman's correlation analysis revealed a positive association between age and anterior corneal spherical aberration, posterior corneal spherical aberration, and total corneal spherical aberration changes. A negative correlation was found between age and with-the-rule astigmatism, and it was positively correlated with the ratios of against-the-rule and oblique astigmatism. A significant between-eye correlation was observed regarding spherical aberration (Z40), horizontal coma (Z31), vertical coma (Z3-1), and horizontal trefoil (Z33). The corneal curvature in females was significantly steeper than that in males (P < 0.01). Corneal curvature, corneal spherical aberration, and corneal astigmatism were found to change with age. Additionally, we found physiological differences between the sexes. Individual measurements could be taken preoperatively to facilitate the development of personalized surgical plans. By identifying age- and gender-related corneal variations, this study enables more personalized cataract surgery planning, potentially improving refractive outcomes and reducing postoperative complications through tailored surgical techniques and intraocular lens selection.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"35"},"PeriodicalIF":2.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647050","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":"Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis.","authors":"Mehrsa Moannaei, Faezeh Jadidian, Tahereh Doustmohammadi, Amir Mohammad Kiapasha, Romina Bayani, Mohammadreza Rahmani, Mohammad Reza Jahanbazy, Fereshteh Sohrabivafa, Mahsa Asadi Anar, Amin Magsudy, Seyyed Kiarash Sadat Rafiei, Yaser Khakpour","doi":"10.1186/s12938-025-01336-1","DOIUrl":"10.1186/s12938-025-01336-1","url":null,"abstract":"<p><strong>Background: </strong>In recent years, artificial intelligence and machine learning algorithms have been used more extensively to diagnose diabetic retinopathy and other diseases. Still, the effectiveness of these methods has not been thoroughly investigated. This study aimed to evaluate the performance and limitations of machine learning and deep learning algorithms in detecting diabetic retinopathy.</p><p><strong>Methods: </strong>This study was conducted based on the PRISMA checklist. We searched online databases, including PubMed, Scopus, and Google Scholar, for relevant articles up to September 30, 2023. After the title, abstract, and full-text screening, data extraction and quality assessment were done for the included studies. Finally, a meta-analysis was performed.</p><p><strong>Results: </strong>We included 76 studies with a total of 1,371,517 retinal images, of which 51 were used for meta-analysis. Our meta-analysis showed a significant sensitivity and specificity with a percentage of 90.54 (95%CI [90.42, 90.66], P < 0.001) and 78.33% (95%CI [78.21, 78.45], P < 0.001). However, the AUC (area under curvature) did not statistically differ across studies, but had a significant figure of 0.94 (95% CI [- 46.71, 48.60], P = 1).</p><p><strong>Conclusions: </strong>Although machine learning and deep learning algorithms can properly diagnose diabetic retinopathy, their discriminating capacity is limited. However, they could simplify the diagnosing process. Further studies are required to improve algorithms.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"34"},"PeriodicalIF":2.9,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11909973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143633404","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":"Sex differences in electrical activity of the brain during sleep: a systematic review of electroencephalographic findings across the human lifespan.","authors":"Rhea Chapman, Sarasa Najima, Thaisa Tylinski Sant'Ana, Christy Chi Kiu Lee, Francesco Filice, Jessica Babineau, Tatyana Mollayeva","doi":"10.1186/s12938-025-01354-z","DOIUrl":"10.1186/s12938-025-01354-z","url":null,"abstract":"<p><strong>Background: </strong>With the explosion of techniques for recording electrical brain activity, our recognition of neurodiversity has expanded significantly. Yet, uncertainty exists regarding sex differences in electrical activity during sleep and whether these differences, if any, are associated with social parameters. We synthesised existing evidence applying the PROGRESS-Plus framework, which captures social parameters that may influence brain activity and function.</p><p><strong>Methods: </strong>We searched five databases from inception to December 2024, and included English language peer-reviewed research examining sex differences in electrical activity during sleep in healthy participants. We performed risk of bias assessment following recommended criteria for observational studies. We reported results on sex differences by wave frequency (delta, theta, alpha, sigma, beta, and gamma) and waveforms (spindle and sawtooth), positioning results across age-related developmental stages. We created visualizations of results linking study quality and consideration of PROGRESS-Plus parameters, which facilitated certainty assessment.</p><p><strong>Results: </strong>Of the 2,783 unique citations identified, 28 studies with a total of 3,374 participants (47% male, age range 4-5 months to 101 years) were included in data synthesis. Evidence of high certainty reported no sex differences in alpha and delta relative power among participants in middle-to-late adulthood. Findings of moderate certainty suggest no sex differences in alpha power; and theta, sigma and beta relative power; and delta density. There is evidence of moderate certainty suggesting that female participants had a steeper delta wave slope and male participants had greater normalized delta power. Evidence that female participants have higher spindle power density is of low certainty. All other findings were regarded as very low in certainty. The PROGRESS-Plus parameters were rarely integrated into the methodology of studies included in this review.</p><p><strong>Conclusion: </strong>Evidence on the topic of sex differences in sleep wave parameters is variable. It is possible that the reported results reflect unmeasured social parameters, instead of biological sex. Future research on sex differences in sleep should be discussed in relevance to functional or clinical outcomes. Development of uniform testing procedures across research settings is timely.</p><p><strong>Prospero: </strong>CRD42022327644.</p><p><strong>Funding: </strong>Canada Research Chairs (Neurological Disorders and Brain Health, CRC-2021-00074); UK Pilot Award for Global Brain Health Leaders (GBHI ALZ UK-23-971123).</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"33"},"PeriodicalIF":2.9,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11899717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603882","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":"Identification of Alzheimer's disease brain networks based on EEG phase synchronization.","authors":"Jiayi Cao, Bin Li, Xiaoou Li","doi":"10.1186/s12938-025-01361-0","DOIUrl":"10.1186/s12938-025-01361-0","url":null,"abstract":"<p><strong>Objective: </strong>Using the phase synchronization of EEG signals, two different phases, PLI and PLV, were used to construct brain network analysis and graph convolutional neural network, respectively, to achieve automatic identification of Alzheimer's disease (AD) and to assist in the early diagnosis of Alzheimer's disease.</p><p><strong>Methods: </strong>In this paper, we selected outpatients (16 AD subjects, 20 mild cognitive impairment (MCI) subjects and 21 healthy control (HC) subjects) from the outpatient clinic of Yangpu Mental Health Center in Shanghai, China, from January 2023 to December 2023, and collected resting-state EEG data. To collect resting-state EEG data, each patient was asked to sit down with eyes closed for 5 min. Firstly, the acquired EEG data were preprocessed to extract the data in the α-band at 8-13 Hz; secondly, the phase lag index (PLI) and phase-locked value (PLV) were used to construct the brain functional network, and the brain functional connectivity map was visualized by brain functional connectivity analysis. Finally, the constructed PLI and PLV were input into the graph convolutional neural network (GCN) model as node features for training and classification, respectively.</p><p><strong>Results: </strong>Healthy controls had relatively strong mean brain functional connectivity in the PLV brain network compared to AD and MCI patients. MCI patients showed lower mean brain functional connectivity in the brain network of PLI, while all three groups showed significant differences in brain functional connectivity between parietal and occipital lobes. The GCN model improved classification accuracy by more than 10% compared to using a machine learning classifier. When PLV was used as the nodal feature in the GCN model, the model achieved an average classification accuracy of 77.80% for the three groups of AD, MCI and HC, which was an improvement over the accuracy of choosing raw EEG data and PLI as the nodal feature. The performance of the model was further validated.</p><p><strong>Conclusions: </strong>The experimental results show that the GCN model can effectively identify the graph structure compared with the traditional machine learning model, the GCN-PLV model can better classify AD patients, and the alpha band is proved to be more suitable for AD resting-state EEG by tenfold cross-validation. The brain network map constructed based on PLI and PLV can further capture the local features of EEG signals and the intrinsic functional relationships between brain regions, and the combination of these two models has certain reference value for the diagnosis of AD patients.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"32"},"PeriodicalIF":2.9,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11892187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584484","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":"Advancements in osteoblast sourcing, isolation, and characterization for dental tissue regeneration: a review.","authors":"Venkata Suresh Venkataiah, Deepak Mehta, Mohammad Fareed, Mohmed Isaqali Karobari","doi":"10.1186/s12938-025-01363-y","DOIUrl":"10.1186/s12938-025-01363-y","url":null,"abstract":"<p><strong>Background: </strong>Primary osteoblasts are essential for bone formation and regeneration, making them pivotal in dental applications, including periodontal regeneration, ridge augmentation, and implant osseointegration. Sourced from various tissues like alveolar bone, calvarial bone, mandibular and maxillary bones, long bones, and bone marrow-derived stem cells (BMSCs), each type of osteoblast presents unique advantages and limitations related to yield, accessibility, and clinical relevance. Given these variables, selecting an appropriate source is crucial for experimental consistency and translational application in dentistry.</p><p><strong>Methods: </strong>This review synthesizes data from in vitro, animal, and clinical studies to provide a comprehensive overview of osteoblast sourcing, isolation, and characterization in dental research. Sources were reviewed based on yield, anatomical relevance, and accessibility, while isolation methods were compared to assess their impact on cell behavior and phenotype retention. The review evaluates methods such as enzymatic digestion, explant culture, and differentiation of BMSCs, alongside characterization techniques like morphological analysis, gene expression profiling, and mineralization assays.</p><p><strong>Results: </strong>The analysis shows that alveolar bone-derived osteoblasts offer high clinical relevance due to their anatomical similarity to oral structures but are limited by low yield and invasive collection. Calvarial and long bone osteoblasts provide higher yields, making them useful for material testing, though they lack biomechanical compatibility with oral environments. BMSCs offer a renewable source with significant regenerative potential but require precise differentiation protocols. In vitro studies contribute mechanistic insights, while animal models bridge the gap to clinical application, despite challenges in standardization and interspecies variability.</p><p><strong>Conclusion: </strong>This review highlights the importance of selecting appropriate osteoblast sources and methods for dental research to optimize outcomes in periodontal and implant-related therapies. The variability across study designs and experimental outcomes underscores the need for standardized protocols and targeted systematic reviews within specific research settings. These findings offer a framework for future osteoblast-based research and guide the effective translation of osteoblast therapies into clinical dental practice.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"31"},"PeriodicalIF":2.9,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11890725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584482","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}