Biomedical Physics & Engineering Express最新文献

This study aimed to experimentally investigate the cell survival responses of tumor and normal cell lines to spatially heterogeneous carbon ion dose distributions with varying peak-to-valley dose ratios (PVDRs) and linear energy transfer (LET) conditions, and to assess the utility of equivalent uniform dose (EUD) as a quantitative metric for analyzing these responses. HSGc-C5 (tumor) and Nuli-1 (normal tissue) cell lines were irradiated using carbon ion beams with different spatial dose patterns (Grid, Frame, Half) and two PVDR levels under low LET conditions (~10 keV μm^-1). Additionally, high LET (~50 keV μm^-1) Spread Out Bragg Peak (SOBP) Grid patterns were used for the HSGc-C5 cells. Clonogenic survival assays were performed to evaluate biological response. Survival data were analyzed both as a function of delivered physical dose and EUD, calculated using an LQ model-based formulation. Tumor cells exhibited enhanced cytotoxic effects under high LET and high PVDR conditions (the dose required to reach SF = 0.1 was approximately 40% lower at PVDR = 4.0 and 10% lower at PVDR = 1.64 compared with the simulation results), whereas normal cells showed a slight sparing effect under low LET irradiation. Even at the same total dose and PVDR, different spatial dose patterns produced measurable differences in survival, underscoring the impact of spatial heterogeneity. EUD-based analysis further enabled quantitative comparison between heterogeneous and uniform dose distributions. These findings indicate that spatial dose heterogeneity and LET can be leveraged to enhance tumor control while reducing normal tissue damage in carbon ion therapy. The EUD approach may offer a practical tool for treatment plan evaluation in spatially modulated particle therapy.

This paper presents a lightweight hybrid framework that integrates a Haar-initialized Parametric Wavelet Transform (PWT) with a Convolutional Neural Network (CNN) enhanced by a multi-head Self-Attention mechanism for efficient and interpretable tumor identification from compressed Magnetic Resonance Imaging (MRI) brain image data. A Parametric Wavelet Transform (PWT) layer, initialized with Haar wavelet filters, performs compression and adaptive feature extraction from brain MRI images, enabling the model to learn optimal frequency decompositions while preserving diagnostic features. MRI images are preprocessed through this PWT layer to selectively extract and stack the approximation and diagonal detail subbands, reducing spatial redundancy and enhancing the representation of diagnostically salient structures. A custom lightweight CNN backbone extracts local features from frequency-domain representations. The integrated self-attention module captures salient features and enhances the discriminative power across wavelet-transformed inputs. Grad-CAM visualizations focussed on explaining the model's predictions and attended to tumor relevant regions. The primary contribution of the proposed model focuses on the overall performance with a classification accuracy of 95.88%, which is higher than the benchmark models of MobileNetV2 (93.1%) and MobileNetV3Small (94.80%) while preserving less trainable parameters and memory footprint. An ablation study confirms the individual contributions towards the overall model performance of PWT compression, the CNN backbone, and the self-attention module. Deploying the model on a Raspberry Pi 5 highlights the potential for real-time, point-of-care, edge-based medical imaging. This work is a pioneering integrated approach incorporating adaptive frequency-domain compression alongside attention-based refinement to produce interpretable and robust designs for embedded implementations of brain tumor classification.

Magnetic resonance guided focused ultrasound (MRgFUS) thalamotomy is an established treatment for tremor. MRgFUS utilises ultrasound to non-invasively thermally ablate or 'lesion' tremorgenic tissue. The success of treatment is contingent on accurate lesioning as assessed by tremor improvement and minimisation of adverse effects. However, coordinate planning and post-procedure lesion visualisation are difficult as the key targets, cannot be seen on standard clinical imaging. Thus, a computational tool is needed to aid target visualisation. A 3D atlas-based model was created using the Schaltenbrand-Wahren atlas. Key nuclei were manually delineated, interpolated and smoothed in 3D Slicer to create the model. Evaluation of targeting approaches across a seven-year period and patient-specific analyses of tremor treatments were performed. The anatomical position of MRgFUS lesions in the model were compared against varying clinical outcomes. The model provides an anatomical visualisation of how the change in targeting approach led to improved tremor suppression and a reduction in adverse effects for patients. This study demonstrates the successful development of a 3D atlas-based computational model of the brain target nuclei in MRgFUS thalamotomy and its clinical utility for tremor treatment analysis.

Objective. In photon beam radiotherapy, modern delivery techniques have allowed to substantially reduce the beam energy needed for the safe and efficient irradiation of deep-seated targets, with even Co-60 beams being now able to irradiate targets at any depth. The purpose of this work is to determine if for electron radiotherapy, advanced beam delivery techniques allow the use of beam energies currently available in the clinic to treat target sites usually reserved for photons or very high energy charged particles.Methods. Segmented computed tomography images from three sites, brain, lung and prostate, were used to model radiotherapy treatments in two modalities: conformal 3D and converging small field. Monte Carlo simulation was used to calculate the absorbed dose distribution in each patient for conformal 3D very-high energy plans and converging small-field, low energy (< 50 MeV) electrons. For comparison, converging small field plans for 6 MV x-ray beams were also calculated.Main results. It is shown that , for the three test cases simulated in this work, electrons with energies in the 20-25 MeV range delivered via the converging small-field modality can produce treatment plans that rival those obtained via conformal very high energy electrons in terms of target dose homogeneity and sparing of the organs at risk. Furthermore, such electron plans also compare well to those obtained with the photon beams.Significance. While the consensus has always been that to reach deeper tumors, higher electron energies, in the order of 150-200 MeV are needed, this work shows that this is not the case and, when small, circular electron fields are delivered in a converging manner, energies below 30 MeV are enough to properly irradiate tumors located at relevant radiological depths for a variety of treatment sites.

On a global scale, cancer ranks high in mortality rate. There is a need for better technology since the current treatments are insufficient. Several new cancer treatments have been developed directly from the lab to the clinic; however, the manufacturing of nanomedicine products, made possible by the rapid expansion of nanotechnology, holds enormous potential for enhancing cancer treatment approaches. The advent of nanotechnology has opened the door to the possibility of multi-functionality and very precise targeting strategies. They have the potential to enhance the pharmacodynamic and pharmacokinetic profiles of conventional treatment approaches, potentially leading to a reevaluation of the effectiveness of current anti-cancer drugs. A novel technique to enhance traditional onco-immunotherapies, recruiting nanoparticle-based delivery systems, which are adaptable carriers for a broad range of molecular payloads. The delivery of molecular payloads to the target site and their release may be well-regulated. We summarize the latest developments in nanobiotechnology for improving immunotherapies and reshaping tumour microenvironments (TMEs) in this review. The current clinical challenges that impede the real-time implementation of cancer nanomedicine are discussed, and this review study consolidates existing knowledge and recent advancements in the use of nanoparticles for cancer therapy. This provides researchers, clinicians, and students with a comprehensive understanding of the current state of the field. Finally, potential future directions are highlighted to enhance the therapeutic efficacy and facilitate the clinical translation of cancer nanomedicine.

Due to bacteria developing resistance to antibiotics, traditional antibacterial strategies face limitations. This study provides a microwave confined heating strategy for achieving gram-scale (yield: 10.8 g/batch) preparation of gallic acid-derived carbon dots (GA-CDs). Transmission electron microscopy results indicate that the GA-CDs possess a relatively small average particle size (2.92 ± 0.27 nm), which facilitates their penetration through the lipid bilayers of bacteria, thereby exhibiting superior antibacterial activity. The systematic analysis results indicate that the GA-CDs are primarily composed ofC, N, and O elements, featuring a highly carbonized graphite core, with some functional groups from the precursor retained on the core surface. Optical tests indicate that the GA- CDs have a maximum absorption wavelength at 457 nm and exhibit excellent photo-responsive reactive oxygen species performance. In addition, GA-CDs presents excellent photostability after continuous ultraviolet irradiation for 130 h. Excitation-independent tests indicate that the GA-CDs possess a stable energy level structure. Finally, experiments demonstrated that the minimum inhibitory concentration of the GA-CDs (16 μg mL^-1) is significantly lower than that of pure gallic acid (5 mg mL^-1), with a minimum bactericidal concentration of 50 μg mL^-1. This work provides a high-yield strategy for fabricating long-wavelength-absorbing, ultrasmall gallic acid derived CDs, offering a promising photodynamic approach to circumvent antibiotic resistance.

Higher levels of PEEP are suspected to induce right heart dysfunction due to increased pulmonary vascular resistance (PVR). A U-shaped correlation of PVR and lung volume has been shown in animal models, with PVR increasing with lower and higher lung volumes. This physiological study aims to investigate the relation of transpulmonary pressure and PVR. Recruited healthy subjects underwent mask continuous airway pressure (CPAP), while esophageal manometry and echocardiographic assessment of PVR were performed. Of 43 screened subjects, 20 were identified in whom echocardiographic estimation of PVR was possible. During CPAP, echocardiographic PVR was lowest when transpulmonary pressures were close to 0 mbar, and increased as transpulmonary pressures became more positive, with a positive monotonic correlation (ρ = 0.337, p = 0.012). PVR with a transpulmonary pressure of 0 mbar was similar to PVR without CPAP (1.4 WU (IQR 1.3-1.5) versus 1.2 WU (IQR 1.2-1.5), p = 0.069). Our findings suggest that PVR could be lowest when airway pressure does not exceed intrathoracic pressure. Future studies should investigate this relationship in ventilated patients. Echocardiography might be suitable to monitor PVR in the presence of sufficiently traceable tricuspid regurgitation, however validation in ventilated patients is needed to determine clinical applicability.

Objective.Accurate classification of pain levels is essential for clinical monitoring, particularly in clinical populations with limited verbal communication. This study explores the feasibility of decoding pain from EEG using explainable deep learning.Approach.EEG signals from 50 subjects exposed to low and high pain stimuli were analyzed. A 1D convolutional neural network (CNN) was trained using leave-one-subject-out (LOSO) cross-validation. To enhance interpretability, DeepSHAP was applied to identify frequency-specific contributions of EEG features to the model's decisions.Main Results.The CNN achieved a classification accuracy of 95.85%, outperforming traditional classifiers (SVM, LDA, RF, etc.) on the same dataset. Explainability analysis showed that increased beta activity (14-15 Hz) was associated with high pain, while alpha (11-12 Hz) theta and delta bands correlated with lower pain states.Significance.This work demonstrates the potential of explainable deep learning in real-time, subject-independent pain decoding. The results support the integration of XAI techniques into EEG-based brain-computer interface (BCI) systems for objective pain monitoring.

End-to-end EEG-based emotion recognition is attracting increasing attention due to its potential in human-computer interaction, mental health, and affective brain-computer interfaces (aBCIs). However, most existing methods overlook cross-frequency interactions in neural oscillations and suffer from high computational complexity, limiting their applicability in real-time or resource-constrained scenarios. To this end, we propose ACFSENet, a novel end-to-end neural architecture that integrates adaptive cross-frequency modeling with global sparse encoding. ACFSENet employs an adaptive frequency-aware mechanism to dynamically focus on subject- and task-specific local brain dynamics, thereby enhancing the flexibility of emotional representation. In addition, it incorporates a sparse attention mechanism with a temporal distillation structure to reduce computational complexity while preserving the ability to model long-range temporal dependencies. We evaluate ACFSENet using cross-block validation on three benchmark datasets: DEAP, SEED, and SEED-IV. Results demonstrate that ACFSENet outperforms state-of-the-art methods and achieves a favorable balance between recognition performance and computational efficiency.

Gastrointestinal (GI) endoscopy serves as a vital tool for assessing the GI tract and diagnosing related disorders. Recent progress in deep learning has shown significant improvements in identifying anomalies using sophisticated models and data augmentation strategies. This study introduces an enhanced approach to improve classification accuracy using 8,000 labeled endoscopic images from the Kvasir dataset, categorized into eight distinct classes. Leveraging EfficientNetB3 as the backbone, our proposed architecture eliminates the reliance on data augmentation while maintaining moderate model complexity. Our model achieves a test accuracy of 94.25%, alongside precision and recall of 94.29% and 94.24%, respectively. Furthermore, Local Interpretable Model-agnostic Explanation (LIME) saliency maps are employed to enhance interpretability by highlighting critical regions in the images that influence model predictions. To facilitate real-world usability, a user-friendly interface was developed using Gradio, enabling users to upload images, generate predictions, view confidence levels, and maintain a history of past results. This work underscores the importance of integrating high classification accuracy, interpretability, and accessibility in advancing medical imaging applications.