Biomedical Physics & Engineering Express最新文献

The Nuclear and Energy Research Institute and the National Nuclear Energy Commission (IPEN-CNEN/SP) are developing a new Iodine-125 seed for use in brachytherapy. The manufacturing process includes cutting the titanium tube, welding the ends, and performing leak tests. Seed approval is based on a microscopic analysis of its external geometry. The dosimetric methodology follows Task Group 43 (TG-43), which adopts the Monte Carlo method as the standard simulation technique. However, TG-43 does not account for geometric variations that may occur during the manufacturing process. This study investigated two types of variation: (1) the seed width after welding, and (2) the random positions that the core may assume within the seed. A total of 100 simulations were performed for each variation. The width was measured experimentally using a caliper, after welding, while the core position was modeled using a random number generator, since it is not feasible to measure it directly. These values were used to generate 201 input files for simulations in MCNP 6.2: 100 for each investigated parameter and one for the reference geometry (IPEN-CNEN/SP seed). The results were analyzed using MATLAB through bidimensional matrices of 101 × 101 points. For each point in the matrix, the mean, standard deviation, relative difference (compared to the reference geometry), and Type A uncertainty were calculated. The main results were: a standard deviation of 0.6% and a relative difference of 11% for the seed width; and a standard deviation of 50% and a relative difference of 25% for the core position. The Type A uncertainty was below 0.3% in all cases.

Objectives.Strokes often cause long-term upper extremity impairments, yet objective, multimodal measurement tools remain scarce. Current clinical assessments rely heavily on subjective scoring based on coarse rating scales, limiting the ability to discern subtle changes in function during therapy. This study aimed to design and validate the SensorE Exoskeleton (SEE), a novel multimodal wearable device capable of simultaneously quantifying finger flexion and applied fingertip force, which is not a common feature in existing tools.Approach.SEE integrates flex and force sensors into a lightweight wearable design intended for use on normal hands as well as those on which wearing a glove would be difficult due to spasticity associated with conditions such as stroke. Static calibration tests confirmed consistent and monotonic sensor responses. Thirty non-clinical, healthy participants (mean age 25.8 ± 4.6 years; both sexes) completed three graded tasks-finger extension, contraction, and force exertion-guided by a graphical user interface (GUI) with four target levels. Participants were divided into two groups, with Group B using a modified GUI informed by feedback from Group A. SEE measurements of finger flexion and applied force were compared against a motion capture system (Leap Motion Controller) and a load cell, respectively.Main results.SEE reliably distinguished movement and force levels between targets in all tasks (p < 0.05). Flex sensor output was strongly correlated with reference motion capture data while the force output correlated well with the load cell measurements (|r|> 0.7). Mean relative errors (mean ± SE) were -0.22 ± 0.04% for flex sensors and -0.43 ± 4.37% for force sensors.Significance.SEE provides a multimodal wearable configuration for accurate, objective tracking of finger flexion and applied force, offering greater sensitivity than existing clinical assessments. These findings support its potential as a novel functional assessment tool for rehabilitation, with future validation in stroke and other patient populations.

Remote photoplethysmography (rPPG) offers a non-contact method for monitoring physiological signals using camera-based systems. The goal of this research is to estimate heart rate and spatial distributions of vascular perfusion using spatio-temporal rPPG (ST-rPPG) and to evaluate the impact of polarization, spectral filtering, and motion compensation on perfusion map quality and heart rate estimation. Two acquisition setups were used: an RGB camera with and without cross-polarization, and a monochrome camera combined with spectral filters. A motion compensation strategy was implemented that combined optical flow-based stable segment selection and temporal video stabilization to reduce motion artifacts. Four rPPG algorithms (GREEN, CHROM, POS, and G-R) were evaluated using three performance metrics: Absolute Error (AE), Signal Quality Index (SQI), and Signal-to-Noise Ratio (SNR) under cross polarized and non polarized lighting in 20 subjects to assess their suitability for perfusion mapping. GREEN and G-R method stood out giving the best results. In the second setup, nine spectral filters were tested across three anatomical regions using the GREEN method, to investigate the influence of wavelength selection on spatial perfusion signal quality. Green, orange, and blue wavelengths produced the best results in terms of AE, SQI and SNR, particularly in the palm region. Visualizations like the spatial perfusion maps, confirmed the superiority of motion-compensated, polarized, and spectrally optimized conditions for enhancing non-contact vascular perfusion assessment. Prior rPPG studies focused primarily on facial datasets or single optical factors, while this work provides the systematic evaluation of polarization, spectral filtering, and motion compensation in a unified hand-based framework, extending established rPPG methods toward high-resolution perfusion mapping.

Transcription factor (TF) and target gene interactions are pivotal in gene regulatory networks, influencing molecular biology and disease mechanisms, particularly cancer. Dysregulated TFs contribute to aberrant gene expression, driving tumor progression. While experimental methods like ChIP-seq and RNA-seq provide valuable insights, their high cost and scalability constraints necessitate computational alternatives. Machine learning offers promising solutions, yet data imbalance remains a major challenge affecting predictive accuracy. This study introduces a novel approach integrating K-means++ clustering with a data balancing strategy to enhance TF-target interaction prediction. By selecting low-frequency TFs within clusters based on an inverse information principle, our method mitigates data bias and improves model generalization. Additionally, we incorporate deep learning with random walk sampling and skip-gram embeddings to extract informative representations of heterogeneous biological networks. Experimental results using five-fold cross-validation demonstrate superior performance, achieving an average AUC of 0.9452 ± 0.0047. Our framework enhances predictive accuracy while addressing data imbalance, offering significant applications in molecular biology and biomedical research for TF-target gene discovery and therapeutic development.

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. .

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.

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.

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.