BioMedical Engineering OnLine最新文献

This systematic review examines the effectiveness of physiological biometric signals in authentication and recognition systems by analyzing studies published between 2018 and 2023. Specifically, different biometric modalities (e.g., ECG, EEG, and PPG), commonly used datasets, signal processing techniques, and classification approaches are evaluated to assess their reported reliability and performance. In addition, the performance of multimodal biometric systems is compared with that of unimodal approaches. The review was conducted in accordance with the PRISMA 2020 guidelines. Relevant studies published between 2018 and 2023 were systematically retrieved from major databases, including EBSCO, PubMed, IEEE Xplore, Scopus, and Web of Science. A total of 2,064 records were initially identified, and after duplicate removal and eligibility screening, 80 articles were included in the final review. The study selection process is summarized using a PRISMA flow diagram. The reviewed studies indicate that ECG-based authentication systems report high average accuracy (98.6%), while multimodal biometric systems generally achieve accuracy levels exceeding 99%. Across modalities, deep learning-based approaches tend to outperform traditional machine learning methods. Dataset size and the choice of signal processing techniques were also found to influence reported performance outcomes. Overall, the findings suggest that biometric signal-based authentication systems demonstrate strong performance under the evaluation conditions reported in the literature. Multimodal fusion and deep learning approaches appear particularly promising, although reported results vary across datasets and protocols. Future research should prioritize larger and more diverse datasets, standardized evaluation benchmarks, and optimized signal processing pipelines to improve comparability and real-world applicability. Further studies on the integration of complementary biometric signals are also warranted.

Introduction: Dental autotransplantation (DAT) is a surgical procedure used to replace hopeless or missing teeth. The technique entails the purposeful extraction of a desired sound tooth, which is then implanted into another alveolar site of the same oral cavity.

Objectives: To analyse the survival rates and success rates of DAT in relation to donor teeth with an incomplete root development (open apex) and complete root formation (closed apex). Additionally, it attempts to evaluate the prognostic components of DAT with infection-related (inflammatory) root resorption, ankylosis, and pulpal necrosis complications.

Materials and methods: An electronic search was conducted using EBSCO MEDLINE Web of Science, Scopus and Cochrane databases from January 2014 until November 2024. The selected articles were chosen within the parameters outlined in the Materials and Statistical Methodology section. The addressed PICO question "does the stage of the donor tooth's root development affect the long-standing prognosis and clinical outcomes of dental autotransplantation?".

Results: The final 26 articles featured a total of 2837 transplanted teeth: 2192 donor teeth with an open apex and 645 donor teeth with a closed apex. The overall survival rate was 93.8% in the open apex group and 92.6% in the closed apex group. Success rate was 84.0% in the open apex group and 86.7% in the closed apex group. The rate of infection-related root resorption was 6.3% in the open apex group and 5.9% in the closed apex group. The rate of ankylosis was 4.4% in the open apex group and 6.7% in the closed apex group. The rate of pulp necrosis was 6.4% in the open apex group. No factors were identified as influencing the rate of pulp necrosis; however, the duration of follow-up was significantly associated with the rate (p = 0.057). None of the selected articles reported pulp necrosis rate in the closed apex; thus, no meta-analysis was possible.

Conclusion: DAT is a reliable treatment alternative for the replacement of lost teeth. The procedure yields low complication rates of infection-related root resorption, ankylosis, and pulp necrosis, while achieving high rates of survival and success. It can be accomplished with donor teeth that have an open or closed apex.

Background: Liver fibrosis after liver transplantation seriously affects graft survival. This study aimed to use shear wave elastography (SWE) to non-invasively evaluate the stiffness of the transplanted liver and spleen in liver transplant recipients.

Methods: Seventy-one patients who were regularly reviewed in our hospital after liver transplantation (> 2 years) were enrolled and were divided into two groups: Group B (30 cases without splenomegaly before liver transplantation) and Group C (41 cases with splenomegaly before liver transplantation). Besides, we selected 35 normal controls as Group A. All patients underwent conventional ultrasound, SWE, and serum liver fibrosis markers exams to obtain the oblique diameter of right lobe of liver (ODRL), portal vein diameter (PVD), portal vein velocity (PVV), peak systolic velocity (PSV), splenic vein diameter (SVD), splenic length, splenic thickness, liver stiffness measurement (LSM), spleen stiffness measurement (SSM) and laboratory test results. Research the correlation between SWE parameters and serum liver fibrosis markers by Pearson linear analysis.

Results: There was no difference in ODRL, PVD, PVV, and PSV among the three groups (all P > 0.05). Compared with Group A, hyaluronic acid (HA), laminin (LN), type III N-peptide collagen (PIIIP N-P), collagen type IV (IVC), and LSM were increased in Groups B and C (all P < 0.05) and were all higher in Group C than in Group B (all P < 0.05). Compared with Groups A and B, Group C was significantly higher in SVD, splenic length, splenic thickness, and SSM (all P < 0.05), whereas there was no difference in comparison of Group B with Group A (all P > 0.05). Pearson's correlation analysis showed that LSM and HA, LN, PIIIN-P, and IVC were positively correlated.

Conclusion: SWE is valuable to evaluate the stiffness of transplanted liver and spleen in recipients after liver transplantation, and preoperative splenomegaly is a factor influencing graft fibrosis after transplantation.

Alzheimer's disease (AD) poses urgent significant challenges for early detection and personalized prognostication. Deep learning (DL) is now regarded as a pivotal technology for extracting subtle imaging and non-imaging biomarkers; yet translating these advances into clinical practice demands a coherent framework. In this review, we first survey input modalities from structural and functional MRI to PET, genetic profiles, and cognitive tests and the key public cohorts that supply multimodal data. We then categorize DL architectures into three complementary pillars: (1) end-to-end classification networks for direct diagnosis; (2) multimodal fusion strategies that integrate heterogeneous biomarkers; and (3) automated segmentation pipelines for precise anatomical delineation. We also examine subtyping algorithms that uncover latent AD phenotypes via clustering and decision-tree models. In order to fill the gap between high-performance DL and real-world adoption, we detail explainable AI methods that render model decisions transparent, and we review performance benchmarks including accuracy, sensitivity/specificity, Dice and Jaccard indices to contextualize efficacy. Finally, we discuss clinical translation, covering prospective validation, workflow integration, and regulatory/privacy considerations, before outlining challenges and future directions such as data heterogeneity, interpretability-accuracy trade-offs, early/preclinical detection, and federated learning. Our roadmap highlights the interdisciplinary collaborations and technical innovations needed to deliver robust, trustworthy, and scalable DL-based tools for Alzheimer's care.

Background: The effects of conditioned medium (CM) and extracellular vesicles (EVs) derived from human Wharton's jelly mesenchymal stem cells (hWJMSCs) on in vitro maturation (IVM) of immature oocytes in both normal and polycystic ovary syndrome (PCOS)-induced mice were investigated. PCOS was induced in adult female NMRI mice by administering letrozole (90 μg/kg/day) via gavage for one week. Germinal vesicle (GV) oocytes were collected from both PCOS-induced and normal mice, while mature oocytes (MII) were obtained from superovulated normal mice to serve as controls. The experimental groups included 7 groups: Control (MII oocytes), 3 IVM groups (in vitro maturation of GV oocytes): IVM (with simple IVM media), IVM + CM, and IVM + EVs (IVM media supplemented with CM and EVs, respectively), and three PCOS groups (in vitro maturation of GV oocytes from PCOS-induced mice): PCOS IVM (with simple IVM media), PCOS IVM + CM, and PCOS IVM + EVs (IVM media supplemented with CM and EVs, respectively). After IVM was conducted in all groups, mature oocytes were harvested and assessed for maturation rate, morphology, viability, and gene expression profiles of key regulators (CDK1, CCNB1, MAP2K). Developmentally competent oocytes were selected using Brilliant Cresyl Blue staining and then subjected to in vitro maturation with or without CM or EVs supplementation. Nuclear maturation was evaluated via orcein staining, while viability was assessed using Trypan Blue. Morphometric parameters were measured using ImageJ software. Real-time PCR was utilized for the evaluation of gene expression of targeted genes.

Results: Results demonstrated that in BCB + oocytes, CM and EVs improved the mature oocytes compared to IVM. Oocytes from PCOS-induced mice exhibited reduced maturation and increased degeneration, which were rescued by CM and EV treatment. Gene expression analysis revealed downregulation of MAP2K, CCNB1, and CDK1 in IVM and PCOS IVM groups compared to the control group, while CM supplementation restored their expression. Oocyte diameter and viability were significantly enhanced in IVM + CM compared to IVM (P < 0.05).

Conclusions: These findings suggest that hWJMSC-derived secretomes, particularly CM, enhance oocyte maturation and quality, offering potential therapeutic benefits for IVM in both normal and PCOS conditions.

Epileptic spasm (ES), characterized by sudden muscle contractions and loss of consciousness, poses significant challenges in early diagnosis and treatment, especially in infants and young children. Despite advances in EEG-based seizure detection, the automatic classification of ES remains a complex task due to the variability of seizure patterns. In this study, we propose an approach for classifying ES EEG based on clinically collected data, using time-frequency domain features derived from EEG signals and machine learning models. A total of 54 time-frequency features were extracted, and three machine learning algorithms-Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)-were employed to classify the seizures. The results show that the RF model achieved the highest classification accuracy of 81.18% when fewer features were used, whereas KNN has increased performances with larger feature sets. This work highlights the potential of combining time-frequency features with machine learning for accurate seizure classification, offering a promising tool for automated monitoring and diagnosis of ES. Further research is needed to refine feature extraction methods and improve model robustness for clinical applications.

Background: Diquat (DQ) exposure can cause acute kidney failure and rhabdomyolysis. Accordingly, the current study aimed to assess how DQ toxicity affects the viability of human proximal tubular cells (HK-2) and to evaluate the potential protective effects of Tannic acid (TA) in this experimental model.

Methods: HK-2 cells were divided into groups: negative control, positive control, DQ alone, and DQ combined with TA at concentrations of 0.5, 2.5, and 5 μmol/L for 24 h. Subsequently, cell viability, reactive oxygen species (ROS) production, oxidative stress and inflammatory marker concentrations, as well as the expression of apoptosis-related genes, were evaluated in all groups.

Results: The data indicate that DQ significantly increased ROS production and elevated the levels of malondialdehyde (MDA) and interleukin-6 (IL-6), which were associated with increased expression of BCL2 associated X protein (BAX) and cleaved caspase-3 genes in HK-2 cells. Additionally, DQ exposure led to significant decreases in total antioxidant capacity (TAC), interleukin-10 (IL-10) levels, and B-cell lymphoma 2 (Bcl-2) anti-apoptotic gene expression. TA treatment inhibited oxidative stress, inflammation, and apoptosis, likely through the BAX/Bcl-2 and caspase-3/cleaved caspase-3 signaling pathways in renal cells.

Conclusion: According to the results, TA treatment at all tested concentrations enhanced the expression of anti-apoptotic genes and suppressed cell apoptosis, thereby increasing renal cell viability.

Objective: To create high-resolution, patient-specific 3D coronary artery models aimed at developing digital models and functional phantoms for the testing of cardiac catheterization devices.

Methods: Using coronary computed tomography angiography (CCTA) and optical coherence tomography (OCT), coronary artery lesions were identified and quantified. Imaging data were fused using a custom-made workflow to create highly accurate digital 3D models. For validation of the workflow, coronary artery phantoms were fabricated using additive manufacturing. An OCT was then conducted on the 3D printed phantom, and the developed workflow was applied to generate a derivative model, which was subsequently compared to the original.

Results: CCTA and OCT datasets from 15 patients were successfully collected and used to develop patient-specific 3D coronary artery models, including detailed inner shells, calcifications, outer wall structures, and side branches. Of these, 13 out of 15 3D printed phantoms were successfully validated and compared to their corresponding original model. The median vertex deviation of the derivative model was 0.15 (0.14 $-$ 0.17) mm. The median absolute stenosis difference between the derivative model and the original model was 3 (1-5)%AS.

Conclusion: We present a novel workflow to produce high-resolution patient-specific phantoms of coronary arteries.

With rapid advances in regenerative medicine and tissue engineering, poly(lactic-co-glycolic acid) (PLGA) scaffolds have garnered extensive attention owing to their excellent biocompatibility and biodegradability. Current studies primarily focus on material selection and scaffold preparation, printing techniques, and their efficacy in animal experiments and clinical applications. While several studies have demonstrated the potential of PLGA scaffolds in promoting bone regeneration, challenges remain, including insufficient mechanical properties, a mismatch between degradation rates and bone repair rates, limited long-term clinical data, and the need for improved hydrophilicity and cytocompatibility. Additionally, issues such as limited printing precision and resolution persist. Therefore, innovating material synthesis and processing technologies, as well as developing high-precision, fast-printing techniques, holds significant implications. This paper aims to analyze and summarize the application of 3D printing technology, the properties of PLGA, research on PLGA composites incorporating drugs, inorganic materials, and organic materials, as well as the design and fabrication of 3D-printed PLGA scaffolds. The aim is to review recent research progress in the use of 3D-printed PLGA scaffolds for bone defect repair, assess their potential for bone regeneration, and explore future development directions.