Biomedical Physics & Engineering Express最新文献

Differentiating between Alzheimer's disease (AD), frontotemporal dementia (FTD), and cognitively normal (CN) subjects remains a significant challenge in clinical neurodiagnosis. This study introduces an automated framework that combines electroencephalography (EEG) signal processing with graphbased deep learning (DL) to improve disease classification. The process begins with artifact suppression and a DL-driven filtering model to enhance EEG signal quality. Once filtered, the signals are segmented, and essential features are extracted to build graph representations that reflect brain connectivity patterns. These graphs are then analyzed utilizing a transformer-based graph neural network, enabling accurate classification of AD, FTD, and CN subjects. Results show that the model achieved highly competitive and well-balanced performance in both binary (AD-CN and FTD-CN) and ternary (AD-CN-FTD) classification tasks, with higher accuracy than existing EEG-based diagnostic methods, demonstrating the benefits of integrating signal filtration, graph representations, and transformer architectures. Overall, the findings suggest that this framework can serve as a reliable tool to support clinical decision-making for the early detection and differentiation of neurodegenerative disorders.

Objective: This study presents a non-invasive method for estimating the second lactate threshold (LT2) in cyclists by modeling the dynamic heart rate (HR) response to power output (PO) using discrete-time transfer function (TF) techniques. Approach: Eleven trained recreational cyclists completed an incremental step test with simultaneous HR, PO, gas exchange, and blood lactate measurements. Two TF models were developed: a time-invariant (TI) model with constant parameters and a time-variant (TV) model whose parameters adapt over time to reflect physiological changes. LT2 was estimated from deviations in model behavior and validated against laboratory-derived LT2 using the modified Dmax method. Agreement was evaluated using absolute error, Pearson correlation, Spearman rank correlation, and Q-Q plots to assess normality of model residuals. Main Results: The TV model provided markedly higher accuracy than the TI model. TV estimates showed a mean absolute error of 4%, with LT2 predicted within 10 W for 9 of 11 participants (Pearson r = 0.947; Spearman ρ = 0.954). TI estimation resulted in an average error of 11%, with only 5 participants within 10 W (Pearson r = 0.759; Spearman ρ = 0.756). Q-Q plots revealed deviations from normality in both models' error distributions, particularly for the TI model, supporting the use of rank-based correlation alongside Pearson's r. The TV model captured characteristic changes in HR-PO dynamics more reliably, especially around the transition to heavy-severe intensity. Significance: The proposed TV modeling approach offers an accurate, practical, and fully non-invasive alternative to blood lactate testing, requiring only HR and PO data typically collected by standard cycling devices. Although the method cannot estimate LT1, it holds promise for regular monitoring of LT2 in both laboratory and field settings and may broaden access to metabolic threshold assessment for athletes and coaches. .

Background: Inner-Speech (IS) based Brain-Computer Interfaces (BCIs) offer potential communication solutions for individuals with disabilities by decoding brain signals generated during speech imagination. While most IS-BCI systems rely on time-frequency EEG features, this study investigates functional connectivity-specifically, motif synchronization (MS)-to determine whether interactions between brain regions improve the discrimination of imagined words. Methods: We analyzed EEG data from the "Thinking Out Loud" dataset by Results: The model achieved an average classification accuracy of 45.8%, outperforming two of three prior studies using the same dataset while offering greater generalizability than the third (which reported higher accuracy). Conclusions: Functional connectivity features, particularly motif synchronization, show promise in IS-BCI applications by leveraging cross-regional brain interactions. This approach advances neurophysiological signal analysis and can enhance assistive technology and cognitive research. However, larger datasets are required to improve the robustness and validate scalability. .

Electrode-skin impedance plays a crucial role in electrophysiological signal acquisition, influencing signal quality and measurement reliability. We designed a reproducibility measurement setup, using a membrane with a saline solution and a three-electrode Electrochemical Impedance Spectroscopy measurement setup (range 1 Hz-20 kHz), to mimic the electrode-skin impedance. The system allowed controlled application of pressure to the working electrode (WE) and measurement of all setup parameters. With this setup, reproducible results were achieved, with a standard deviation of 5.5% of the mean impedance across three builds. Potentiostatic and impedance analyzer measurements with six types of dry electrodes produced comparable results, with an average error of 10%. The six dry electrode types exhibited impedance variations of up to a factor of 10,000 at low frequencies, depending on material and geometry. Ag/AgCl-coated electrodes exhibited an impedance reduction by a factor of 100 at 1 Hz compared to their uncoated counterparts. The proposed setup provides a standardized and reproducible approach for characterizing electrode impedance across different materials, coatings, and geometries.

Although quantitative ultrasound has crossed the threshold from research tool to routine clinical adjunct, current techniques still only interrogate tissue at the millimeter scale. Direct, micrometer-resolved insight into tissue structure, comparable to histology, remains an unmet need. The Scatterer Reconstruction (ScatRec) method, a non-stationary, deconvolution-based technique, shows promise in addressing this need. We improved the ScatRec algorithm and introduced three upgrades to improve its robustness: (i) Anisotropic total-variation, (ii) a Gaussian-noise fidelity term, and (iii) amplitude bound constraints. Additionally we bridge the gap to real work application by utilizing a spatially invariant point spread function. We then evaluated the enhanced reconstruction capabilities usingin silicoscatterer phantoms. For the first time, we analyzed the resolution limits with several two-scatterer phantoms with different scatterer distances. We tested the reconstruction quality and accuracy with phantoms containing randomly distributed scatterers and a signal-to-noise ratio (SNR) ranging from infinity to 10. Our two-scatterer phantoms showed that our proposed method at 18 MHz has an effective scatterer resolution of 38.5 μm × 156 μm in the axial and lateral directions, respectively, which is 2.6 times better than conventional B-mode. For randomly distributed scatterers, we quantified the reconstruction quality (measured by the normalized correlation coefficient, NCC) and the accuracy (indicated by the relative deviation of the effective acoustic concentration, EAC, compared to the ground truth). Compared to the original ScatRec, the NCC improved 3.7-fold, and the EAC 15.5-fold across realistic SNR of 40. Our feasibility analysis suggests thatin vivomicro-structural ultrasound for scatterer reconstruction is within reach, opening a path toward "ultrasonic histology" for diseases that are currently diagnosed only by biopsy.

Bacterial adhesion is a primary factor that induces biofilm formation on the surface of medical silicone rubber (SR) catheters. To endow the SR catheter with antibacterial adhesion behavior, a three-dimensional hydrophilic polyvinyl alcohol (PVA) fiber membrane with varying concentrations was constructed on the SR catheter surface using electrospinning technology. Utilizing scanning electron microscopy, contact angle measurements, and bacterial adhesion experiments, the structural and physical characteristics of the PVA fiber membrane composite SR catheter (PVA/SR) were explored. The results showed that, with an increase in PVA concentration (6%-10%), the average diameter of the PVA fiber membrane increased from 392.49 ± 24.35 nm to 945.04 ± 12.60 nm, and its uniformity was enhanced. PVA/SR exhibited excellent hydrophilicity with water contact angles below 95°. In comparison to conventional SR catheters, the PVA/SR catheter demonstrated a notable inhibitory effect on the adhesion ofStaphylococcus aureusandEscherichia coli, exhibiting an adhesion inhibition rate of 50%-60%, due to the hydrophilicity and physical barrier provided by PVA fiber membrane. The PVA/SR catheter exhibits excellent biocompatibility and hemocompatibility. This study provides a novel technology, theoretical basis, and experimental foundation for the development of high-performance anti-infective catheters.

Non-invasive biosensing faces major challenges due to impedance mismatch at the skin interface, which causes significant signal reflection and limits power transmission through biological tissues. In this paper, we propose and evaluate a novel, subwavelength-thick impedance-matching metasurface (MTS) designed for direct skin contact to enhance electromagnetic (EM) wave transmission in the Ka-band. Our evaluation includes controlled laboratory experiments and a first-in-human study. Using an in-house developed benchtop system, we performed transmission measurements through aqueous glucose solutions and, most importantly, through the hands of six human volunteers. Our results show that the MTS significantly enhances signal transmission into human skin tissue, yielding an average improvement of up to 5 dB in the 36-37 GHz frequency range compared to the bare-skin condition, thereby improving sensitivity for analyte detection without increasing system size or power consumption. These findings demonstrate the potential of MTS-based impedance-matching layers as practical, integrable solutions to overcome key hardware limitations in wearable biomedical sensing devices. The study represents the first human investigation of an impedance-matching MTS designed to improve microwave signal penetration for non-invasive sensing applications.

Positron emission tomography (PET) is a sensitive molecular imaging technique used extensively in cancer diagnosis, neurology, and cardiovascular disease. However, low-dose PET (LPET) imaging often results in decreased signal-to-noise ratio and loss of detail. To address this challenge, we propose ED-Mamba, a novel brain LPET image recovery network that leverages edge perception and Mamba guidance. ED-Mamba employs an edge perception module (EdPM) and an auxiliary guidance Mamba module (AGMM) to capture multi-scale information, enhance edge details, and model global dependencies. Experimental results on public brain datasets demonstrate that, compared to the current mainstream diffusion probabilistic model (DDPM), ED-Mamba increases PSNR from 25.624dB to 26.237dB (+2.39%) and SSIM from 0.963 to 0.967 (+0.42%), while maintaining a lightweight architecture with only 16.07M parameters. Furthermore, additional evaluations conducted on the patient dataset further confirm that ED-Mamba demonstrates excellent robustness and generalizability. This work highlights the potential of integrating edge perception with Mamba guidance for enhancing LPET image recovery quality. The source code is available athttps://github.com/Ethevliu/ED-Mamba.

Boron neutron capture therapy (BNCT) dose calculation often relies on fixed relative biological effectiveness (RBE) and compound biological effectiveness (CBE) values, despite their dependence on beam quality and tumor biology. We developed a microdosimetry-driven framework that predicts cell survival and RBE for BNCT by coupling PHITS lineal energy (T SED) calculations with the microdosimetric kinetic (MK) model. MK parameters (α₀, β, rd, y0) were derived for BNCT relevant cell lines (U87 glioblastoma, NB1RG skin fibroblasts, SAS human squamous carcinoma, and SCC7 murine squamous carcinoma) using low LET reference datasets curated in the PIDE database and irradiation conditions reproduced in PHITS. The derived parameters successfully reproduced in-vitro survival curves for various charged particles across different energies, and when applied to neutron fields representative of BNCT systems (Kyoto University Reactor thermal neutron beam, cyclotron based epithermal neutron source using a beryllium target, and linear accelerator system using a lithium target), the framework also reproduced measured in-vitro data. Predicted RBE at 10% survival (RBE₁₀) agreed with measurements across cell lines and beam qualities, with only a slight deviation for SCC7 under the CICS spectrum and moderate deviations for SAS due to limited and heterogeneous low-LET datasets in PIDE. This method enables spectrum and cell line specific estimation of biological effect, supporting replacement of fixed RBE/CBE with spectrum aware quantities to improve BNCT dose prescription and safety. The framework can also guide neutron-beam design by providing preliminary RBE estimates prior to construction of the moderator and beam shaping assembly. Incorporating intracellular boron microdistribution in future work is expected to refine CBE estimates and enhance biological accuracy in BNCT treatment planning. This framework provides a physics-based alternative to fixed RBE/CBE values.

The incidence of thyroid nodules is relatively high. Doctors typically distinguish the benign and malignant nodules based on ultrasound images, but this method has the risk of misdiagnosis, causing serious consequences for patients. Therefore, improving diagnostic accuracy through Computer Aided Diagnosis (CAD) is crucial. In this study, we propose a novel feature fusion network ResNet-ViT, based on ResNet18 and ViT-l-16, to predict the benign and malignant nature of thyroid nodules. This model adopts the conv layer, layer1 and layer2 of ResNet18 to extract local features, and uses ViT-l-16 without the class token to extract global features. Finally, the convolutional block is used to fuse the local features and global features. We applied ResNet-ViT model to the DDTI and TN5000 dataset and compared it with eight other popular methods, namely, ResNet18, ResNet50, Densenet121, AlexNet, ViT-l-16, Cross-ViT, Hybrid and EfficientViT. The results showed that the predictive performance of ResNet-ViT after 5-fold cross-validation is superior to that of other models. In addition, we utilized the MCB algorithm to fuse image features extracted by ResNet-ViT with clinical features, constructing a ResNet-ViT multimodal model. Experimental results demonstrated that the predictive performance of the ResNet-ViT multimodal model was significantly improved and outperformed eight other models under the same conditions. Our study indicates that the ResNet-ViT multimodal model is capable of effectively capturing both image and clinical features while exhibiting a certain degree of stability. Furthermore, comparative experiments on datasets containing varying extents of surrounding tissue revealed that incorporating some surrounding tissue aids in distinguishing between benign and malignant nodules.