Biomedical Physics & Engineering Express最新文献

Parkinson's disease (PD) is the second most common progressive neurodegenerative disorder, characterized by both motor and non-motor symptoms. In this study, we conducted a meta-analysis of gene expression profiles from four GEO datasets (comprising 59 PD patients and 41 participants control) to identify consistently differentially expressed messenger ribonucleic acids (DEmRNAs). We identified 5,495 down-regulated and 9,850 up-regulated DEmRNAs, of which 64 and 25, respectively, were common across all datasets. Functional enrichment analysis revealed that down-regulated DEmRNAs were primarily enriched in pathways related to neurotransmitter transport, dopamine biosynthesis, and dopaminergic synapse function, while up-regulated DEmRNAs were linked to cell cycle regulation and PI3K-Akt signaling. Notably, dysregulation of key genes, including SNCA (encodingα-synuclein), SLC6A3, TUBB, TUBB3, TUBB4B, and NDUFA9, were associated with PD as well as other neurodegenerative disorders, such as Alzheimer's, Huntington's, and Prion diseases. These DEmRNAs and pathways may offer potential biomarkers and therapeutic targets for PD and related neurological disorders.

Lumbar disc herniation (LDH) is one of the most common degenerative diseases of the spine. Magnetic resonance image is the most effective way to detect LDH. The variety of shapes and blurred boundaries of diseased discs, along with the unclear classification basis of existing methods and their poor ability to differentiate between lesion types, make computer-aided diagnosis (CAD) of LDH challenging. We propose an enhanced classification of LDH through region-of-interest guidance and geometric shape features (RGGS-Net) to address these challenges. RGCG-Net establishes the connection between the segmentation of diseased lumbar disc and the classification of lesion types in LDH. A region-of-interest guided module, combined with region-of-interest supervision, is proposed to refine the features from the encoder. Weighted skip connections are used to balance the ratio between the original feature and the refined feature. Hierarchical supervision is used to reduce the training difficulty of the deep decoder and improve the final segmentation performance. Finally, the precise classification of LDH is achieved based on the geometrical features of its different types. Numerous experiments have demonstrated the effectiveness of the RGGS-Net. The classification accuracy of the RGGS-Net in the LDH classification task is 0.965. The Dice of the RGGS-Net reaches 0.957 in vertebrae and disc segmentation task.

Respiratory phase mismatch between single-photon emission computed tomography (SPECT) and computed tomography (CT) acquisition phases presents a challenge in lung perfusion scintigraphy using SPECT/CT. This study simulated lung volume and SPECT counts changes under free-breathing and breath-hold CT conditions compared to respiratory-synchronized acquisition. Chest 4D-CT images, divided into 10 respiratory phases, were used to generate lung, soft tissue, liver, and bone regions for each phase. A digital phantom was constructed via image processing using ImageJ. SPECT images were generated from these phantoms by employing the Prominence Processor to simulate projection data and reconstruct images. Simulations included a 'synchronized image,' where both SPECT and μMAP for attenuation correction were created in the same phase; a 'free-breathing image,' combining a free-breathing SPECT and μMAP; and a 'CT breath-hold image,' using phase-specific μMAPs with the free-breathing SPECT image for attenuation correction. Lung volumes and SPECT counts in the free-breathing and CT breath-hold images were compared with those in the synchronized image. By analyzing the relative errors caused by differences in the μMAPs, the study evaluated the impact of mismatch between SPECT and CT phases. Results indicated that lung volumes appeared reduced during inspiration and increased during expiration compared with synchronized images. No significant difference in the relative error was observed between the free-breathing and CT breath-hold images. Our findings revealed that in the quantitative evaluation of lung perfusion SPECT, varying the μ-map phase during free-breathing acquisition did not result in a significant improvement, suggesting that the mismatch between SPECT and CT had no statistically significant effect on quantitative accuracy. Compared with respiratory-gated SPECT, free-breathing acquisitions introduced potential errors of approximately 2.5% in lung volume measurement and 1.2% in SPECT counts. However, these errors were within acceptable tolerance limits for clinical diagnosis, indicating that free-breathing acquisition had minimal effects on diagnostic capability.

Accurate detection and segmentation of polyps during colonoscopy are of great significance for the early prevention and treatment of colorectal cancer. However, due to the considerable variations in polyp size and shape, as well as their blurred boundaries with surrounding tissues, polyps are often difficult to detect, making precise segmentation a challenging task. Although numerous deep learning (DL) based segmentation methods have been proposed in recent years and achieved certain progress, their results remain unstable and often unsatisfactory. To address these challenges, we propose PGMNet, an accurate and efficient network for polyp segmentation, which consists of a PVTv2 encoder, a Global-Local Interactive Relation Module (GLIRM), and a Multi-stage Feature Aggregation Module (MFAM). The PVTv2 encoder is capable of capturing both fine-grained details and global semantic representations, making it well-suited for complex medical image segmentation tasks. GLIRM performs multi-scale information fusion during upsampling to restore fine-grained details and global semantic context, while simultaneously introducing a bit-slice mechanism to effectively suppress noise. MFAM leverages a gating mechanism to efficiently aggregate GLIRM information from different stages, thereby improving the quality of the final predictions.Extensive experiments were conducted on five publicly available polyp datasets, and the results demonstrate that PGMNet achieved very promising performance in terms of segmentation accuracy and generalization ability. In particular, on the challenging ETIS dataset, PGMNet achieved an mDice of 82.33% and an mIoU of 74.29%, highlighting its superior performance.

A brain-computer interface (BCI) establishes a pathway for information transmission between a human (or animal) and an external device. It can be used to control devices such as prosthetic limbs and robotic arms, which in turn assist, rehabilitate, and enhance human limb function. At present, although most studies focus on brain signal acquisition, feature extraction and recognition, and further explore the use of brain signals to control external devices, the features obtained via noninvasive approaches are fewer and less robust, which makes it difficult to directly control devices with more degrees of freedom such as robotic arms. To address these issues, we propose an extended instruction set based on motor imagery that fuses eye-movement signals and electroencephalogram (EEG) signals for motion control of a dual collaborative robotic arm. The method incorporates spatio-temporal convolution and attention mechanisms for brain-signal classification. Starting from a small base of control commands, the hybrid BCI combining eye-movement signals and EEG expands the command set, enabling motion control of the dual cooperative manipulator. On the Webots simulation platform, we carried out kinematic control and three-dimensional motion simulation of a dual 6-degree-of-freedom collaborative robotic arm (UR3e). The experimental results demonstrate the feasibility of the proposed method. Our algorithm achieves an average accuracy of 83.8% with only 8.8k parameters, and the simulation results are within the expected range. The results demonstrate that the proposed extended instruction set based on motor imagery is effective not only for controlling dual collaborative robotic arms to perform grasping tasks in complex scenarios, but also for operating other multi-degree-of-freedom peripheral devices.

Objective. Endoscopic imaging is vital in Minimally Invasive Surgery (MIS), but its utility is often compromised by specular reflections that obscure important details and hinder diagnostic accuracy. Existing methods to address these reflections face limitations, particularly those relying on color-based thresholding and the underutilization of deep learning for highlight detection.Approach. To tackle these challenges, we propose the Specular Detection Median Filtering Fusion Network (SDMFFN), a novel framework designed to detect and remove specular reflections in endoscopic images. The SDMFFN employs a two-stage process: detection and removal. In the detection phase, we utilize the enhanced Specular Transformer Unet (S-TransUnet) model integrating Atrous Spatial Pyramid Pooling (ASPP), Information Bottleneck (IB) and Convolutional Block Attention Module (CBAM) to optimize multi-scale feature extraction, which helps to achieve accurate highlight detection. In the removal phase, we improve the advanced median filtering to smooth reflective areas and integrate color information for a natural restoration.Main results. Experimental results show that our proposed SDMFFN has outperformed other methods. Our method improves visual clarity and diagnostic precision, ultimately enhancing surgical outcomes and reducing the risk of misdiagnosis by delivering high-quality, reflection-free endoscopic images.Significance. The robust performance of SDMFFN suggests its adaptability to other medical imaging modalities, paving the way for broader clinical and research applications in robotic surgery, diagnostic endoscopy and telemedicine. To promote further progress in the research, we will make the code publicly available at:https://github.com/jize123457/SDMFFN.

The field excitatory postsynaptic potentials (fEPSPs) plays a crucial role in neural signal transmission and synaptic plasticity. Achieving high-precision acquisition and long-term reliable recording of neuronal fEPSPs is a key challenge. This paper presents the design of a analog front-end (AFE) system for the Schaffer-CA1 pyramidal neurons in the hippocampus, based on FPGA. The system employs a capacitance-free chopper front-end amplifier with a current-balanced architecture and a digitally controlled two-stage amplifier to achieve dynamic gain adjustment. A combination of a digital FIR filter and the filtfilt algorithm is used to implement zero-phase filtering. Experimental evaluations of long-term stability, frequency response, and dynamic response were conducted, demonstrating that the AFE can accurately acquire weak signals in the range of 160-360 μV. It achieves a high gain of 72-74 dB within the 1-300 Hz frequency band, with a theoretical gain error of less than 2.5%. Based on this system, fEPSPs acquisition experiments were conducted on synapses of Schaffer-CA1 neurons inex vivohippocampal slices. The results show that the AFE accurately captures fEPSPs and long-term potentiation (LTP) before and after induction. Compared with commercial MEA systems, the normalized amplitude difference was less than 5%, the correlation coefficient was greater than 0.82, and the normalized mean square error was less than 0.01. These results confirm that the designed AFE meets the requirements for precise acquisition and stable long-term recording of neuronal fEPSPs signals.

Objective.Intelligent computer-aided diagnosis techniques enable inspection of invisible electrocardiogram (ECG) pathological changes for early detection of latent heart diseases. This study concentrates on latent pathological changes within non-episodic ECG data, describes a cardiac dynamics based methodology for the detection of paroxysmal atrial fibrillation (PAF).Approach.Three-dimensional dominated components of routine 12-lead ECG signals are extracted without complex signal segmentation operations. Cardiac dynamics features are captured using deterministic learning algorithm and represented as the three-dimensional graphic. This kind of nonlinear dynamics representation is shown to have high discriminative power for PAF detection even before pathologic changes can be observed visibly in ECG signals. Nonlinear dynamics measures are extracted and finally fed into different machine learning methods for the PAF detection task. Suspected PAF patients undergoing Holter monitoring are studied. Cardiac dynamics measures are calcuated simultaneously with routine rest ECG examination, in which Holter monitoring results are collected as the gold standard.Main results.The proposed method yielded a sensitivity of 97%, a specificity of 91%, and an overall accuracy of 92%.Significance.Abnormal cardiac dynamics induced by PAF can be detected using cardiac dynamics features and different classification models before obvious pathological changes are present. The proposed method is expected to provide a complementary tool to the commonly used ECG examination for PAF detection, which are crucial for identifying patients at risk of latent PAF.

In the present study, electrospinning was used to create a new wound dressing consisting of hydroxyapatite nanoparticles, in which curcumin was encapsulated and prepared as a nanocomposite in gelatin and polycaprolactone solution. Physicochemical and biological properties of the prepared wound dressing were evaluated under laboratory conditions. The findings demonstrated that curcumin-HA increases the tensile strength and elongation at break while decreasing elastic modulus. In contrast, when the curcumin-HA structure was added to PCL, swelling capacity and degradation rate were significantly improved. In addition, a disk diffusion test onStaphylococcus aureusandEscherichia coliconfirmed the effectiveness of the antibacterial properties of this wound dressing. In addition, sustained release of curcumin for up to 15 days was achieved in Gel (curcumin-HA)/PCL nanofibers which could be a positive option in the performance of this wound dressing. According toin vitrocell viability tests conducted on the L929 fibroblast cell line, the (curcumin-HA)/PCL gel nanofibers not only did not have cytotoxicity but also improved the cell repair process within three days, confirming their potential for use as wound dressings.

Accurate myocardial segmentation in myocardial contrast echocardiography (MCE) images remains challenging due to the scarcity of publicly available labeled datasets and the pervasive presence of speckle noise.Currently, echocardiographers must manually delineate myocardial contours, a clinical workflow step that is both labor-intensive and prone to variability. To address these limitations, we propose SSMCE, a novel semi-supervised learning framework specifically designed for myocardial segmentation in MCE images. The proposed framework adopts a tri-model architecture comprising two structurally distinct student models and an adaptively assembled teacher model. This design inherently introduces model-level perturbations to promote output diversity, thereby reducing overfitting and improving generalization performance. In addition, a specialized loss function is designed to guide the model's self-correction behavior by increasing uncertainty in misclassified bias regions and reinforcing confidence in accurate ones, facilitating convergence. Experimental results on our self-constructed dataset demonstrate that the proposed loss function improves the primary evaluation metric by 1.75%. Furthermore, the proposed method achieves state-of-the-art performance when compared with existing approaches. The results demonstrate that SSMCE provides a robust and efficient approach for rapid myocardial detection and precise segmentation, offering significant potential to streamline clinical workflows in MCE imaging.