Biomedical Physics & Engineering Express最新文献

Scaffold systems are fundamental to regenerative endodontics, functioning as structural frameworks and delivery vehicles for bioactive cues essential to tissue regeneration. This review comprehensively examines scaffold types, functions, and translational challenges in endodontic regeneration. Scaffolds are classified into natural, synthetic, and hybrid matrices with unique mechanical and biological profiles. Advances in nanotechnology, 3D and 4D bioprinting, and smart biomaterials have significantly improved scaffold functionality. Smart scaffolds enable the controlled release of growth factors, antimicrobial agents, and gene-functionalized molecules, facilitating angiogenesis, stem cell differentiation, and infection control. Hybrid scaffolds, such as those combining collagen and gelatin methacryloyl (GelMA), provide customized degradation, biocompatibility, and mechanical strength. Innovative systems such as magnetic nanoparticle-triggered release and responsive hydrogels address vascularization and immune modulation limitations. Clinically, platelet-rich fibrin (PRF), concentrated growth factor (CGF), and decellularized extracellular matrix (dECM) have shown success in promoting root development, pulp vitality, and periapical healing. Despite these advances, obstacles remain, including regulatory hurdles, standardization of protocols, and long-term clinical validation. Integrating AI-driven scaffold design, digital twin simulations, and organ-on-chip models holds promise for personalized therapies. Establishing scaffold-based regeneration as a standard clinical approach will require harmonized practices, scalable biomaterial production, and robust clinical outcome assessments.

Deep learning methods have been widely adopted for classifying benign and malignant pulmonary nodules. However, existing models often suffer from high memory usage, computational cost, and large parameter counts. As a result, the development of lightweight classification methods for pulmonary nodules has become a major research focus. This paper proposes a lightweight classification framework specifically designed to distinguish between benign and malignant pulmonary nodules. The model contains only 119,245 parameters and occupies just 0.45 MB, offering significant advantages in terms of computational efficiency. The proposed approach integrates an attention mechanism, residual learning, and an improved DWSGhost module to construct the GAS (Ghost-Attention Separation) network. A teacher-free knowledge distillation strategy is employed to build a lightweight classification model based on GAS. Extensive experiments were conducted on three datasets-LIDC-IDRI, LungX Challenge, and Zhengzhou Ninth People's Hospital-which demonstrated the model's effectiveness in classifying pulmonary nodules. The proposed method exhibits strong competitiveness among lightweight models and achieves promising classification performance. By incorporating depthwise separable convolutions and teacher-free knowledge distillation, along with attention mechanisms and residual learning, the model achieves enhanced performance in terms of lightweight design, discriminative power, adaptability, and generalization ability.The full code is available inhttps://github.com/s1371897388-ctrl/GAS-Pulmonary-Nodule-Classification.

Early and accurate detection of pulmonary nodules in computed tomography (CT) scans is critical for reducing lung cancer mortality. While convolutional neural networks (CNNs) and Transformer-based architectures have been widely used for this task, they often suffer from insufficient global context awareness, quadratic complexity, and dependence on post-processing steps such as non-maximum suppression (NMS). This study aims to develop a novel 3D lung nodule detection framework that balances local and global contextual awareness with low computational complexity, while minimizing reliance on manual threshold tuning and redundant post-processing. We propose FCMamba, a flexible connected visual state-space model adapted from the recently introduced Mamba architecture. To enhance spatial modelling, we introduce a flexible path encoding strategy that reorders 3D feature sequences adaptively based on input relevance. In addition, a Top Query Matcher, guided by the Hungarian matching algorithm, is integrated into the training process to replace traditional NMS and enable end-to-end one-to-one nodule matching. The model is trained and evaluated using 10-fold cross-validation on the LIDC-IDRI dataset, which contains 888 CT scans. FCMamba outperforms several state-of-the-art methods, including CNN, Transformer, and hybrid models, across seven predefined false positives per scan (FPs/scan) levels. It achieves a sensitivity improvement of 2.6% to 20.3% at low FPs/scan (0.125) and delivers the highest CPM and FROC-AUC scores. The proposed method demonstrates balanced performance across nodule sizes, reduced false positives, and improved robustness, particularly in high-confidence predictions. FCMamba provides an efficient, scalable and accurate solution for 3D lung nodule detection. Its flexible spatial modeling and elimination of post-processing make it well-suited for clinical usage and adaptable to other medical imaging tasks.

Alginate microgels are attractive platforms for cell encapsulation, yet conventional gelation strategies often lead to heterogeneous crosslinking, unstable droplets, and reduced cell viability. Here, we present a paraffin oil-based flow-focusing microfluidic system that integratesin situandex situgelation to generate structurally homogeneous and monodisperse Ca-ALG microgels. Unlike conventional approaches that often suffer from unstable droplet formation or incomplete gelation, our method reliably produced uniform microgels with coefficients of variation consistently below 5% and maintained spherical morphology across a wide range of flow conditions. Scanning electron microscopy revealed a hierarchical porous architecture that supported nutrient and metabolite transport while providing structural stability. Encapsulated HEK-293 cells remained highly viable for more than two weeks, and spontaneous spheroid formation occurred within 24 h-an outcome rarely achieved in comparable systems and underscoring the functional relevance of this platform. Compared with existing microfluidic methods, this paraffin oil-driven dual gelation strategy offered superior reproducibility, droplet stability, and encapsulation efficiency. This study integrates and optimizes previously reported dual gelation strategies by employing paraffin oil in a flow-focusing device, establishing a simple, practical, and scalable solution to long-standing challenges in microgel-based encapsulation with strong potential to advance 3D culture, tissue engineering, and regenerative medicine.

Impulse-radio ultra-wideband (IR-UWB) radar technology employs short-duration impulse waves with broad bandwidth for precise detection and tracking, offering a cost-effective, non-invasive alternative for portable heart rate monitoring. Its practical design supports long-term healthcare applications without adverse effects. However, effective implementation necessitates robust signal processing techniques to minimize interference from clutter signals and breathing harmonics, enabling the extraction of the target signal from background noise and interference. This study aims to provide real-time measurements through the implementation of signal processing algorithms such as Fast Fourier Transform (FFT), autocorrelation, and peak finding with a moving average filter (MAF) to extract heartbeat signals from background noise and interference. Algorithms were tuned for range parameters and bandpass filter order, with a Kaiser window-based FIR filter (order 250) selected for testing. The FFT algorithm achieved the highest accuracy of 85.6%, while peak finding with MAF and autocorrelation attained accuracies of 78.5% and 76.6%, respectively. The FFT algorithm demonstrated superior potential for real-time heart rate monitoring and was implemented in a graphical user interface (GUI) for data visualization.

Objective. To enhance the decoding accuracy and information transfer rate of steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems and to reduce inter-subject variability for broader SSVEP-BCI applications, a dual-channel TRCA-net (DC-TRCA-net) method is proposed, based on cross-subject positive transfer. The proposed method incorporates an innovative Transfer-Accuracy-based Subject Selection (T-ASS) strategy and a deep learning network integrated with the SSVEP Domain Adaptation Network (SSVEP-DAN) to enhance SSVEP-BCI decoding performance. The T-ASS strategy constructs contribution scores by computing each subject's self-accuracy and transfer accuracy, and enables effective source subject selection while mitigating negative transfer risks. DC-TRCA-net is further developed to improve model generalization through cross-subject data augmentation. The effectiveness of the proposed method is validated on two large-scale public benchmark datasets. Experimental results demonstrate that DC-TRCA-net outperforms existing networks across both datasets, with particularly substantial performance gains observed in complex experimental scenarios.

The increasing prevalence of cardiovascular diseases (CVDs) calls for innovative diagnostic solutions that are both accurate and scalable. ElectroCardioGrams (ECGs) remain central to cardiac assessment: However, manual interpretation is time consuming and error-prone. To address this challenge, we propose a lightweight multimodal generative AI framework capable of automatically interpreting ECG images and producing structured clinical reports. The framework builds upon the SmolVLM-500M-Instruct model, fine-tuned via Quantized Low-Rank Adaptation (QLoRA) to enable efficient deployment on standard hardware. A custom multimodal ECG dataset ,comprising image report pairs curated from authoritative clinical sources and augmented to mitigate class imbalance, served as the foundation for training. The proposed architecture integrates a vision encoder, a cross-modal fusion mechanism, and a language decoder to effectively align visual ECG representations with diagnostic narratives. Experimental evaluations demonstrate significant improvements in BLEU, ROUGE-L, and BERTScore metrics through a two-phase fine-tuning strategy, highlighting the model's ability to generate clinically coherent and semantically rich reports. Overall, this work contributes a scalable, interpretable, and resource efficient AI framework for cardiac diagnostics, bridging the gap between state of the art deep learning research and real-world clinical practice.

Objective.We propose a novel method, basis vector model material indexing (BVM-MI), for predicting atomic composition and mass density from two independent basis vector model weights derived from dual-energy CT (DECT) for Monte Carlo (MC) dose planning.Approach. BVM-MI employs multiple linear regression on BVM weights and their quotient to predict elemental composition and mass density for 70 representative tissues. Predicted values were imported into the TOPAS MC code to simulate proton dose deposition to a uniform cylinder phantom composed of each tissue type. The performance of BVM-MI was compared to the conventional Hounsfield Unit material indexing method (HU-MI), which estimates elemental composition and density based on CT numbers (HU). Evaluation metrics included absolute errors in predicted elemental compositions and relative percent errors in calculated mass density and mean excitation energy. Dose distributions were assessed by quantifying absolute error in the depth of 80% maximum scored dose (R80) and relative percent errors in stopping power (SP) between MC simulations using HU-MI, BVM-MI, and benchmark compositions. Lateral dose profiles were analyzed at R80 and Bragg Peak (RBP) depths for three tissues showing the largest discrepancies in R80 depth.Main Results. BVM-MI outperformed HU-MI in elemental composition predictions, with mean root-mean-square error (RMSE) of 1.30% (soft tissue) and 0.1% (bony tissue), compared to 4.20% and 1.9% for HU-MI. R80 depth RMSEs were 0.2 mm (soft) and 0.1 mm (bony) for BVM-MI, versus 1.8 mm and 0.7 mm for HU-MI. Lateral dose profile analysis showed overall smaller dose errors for BVM-MI across core, halo, and proximal aura regions.Significance. Fully utilizing the two-parameter BVM space for material indexing significantly improved TOPAS MC dose calculations by factors of 7 to 9 in RMSE compared to the conventional HU-MI method demonstrating the potential of BVM-MI to enhance proton therapy planning, particularly for tissues with substantial elemental variability.

Millions of adults suffer from Major Depressive Disorder (MDD), globally. Applying network theory to study functional brain dynamics often use fMRI modality to identify the perturbed connectivity in depressed individuals. However, the weak temporal resolution of fMRI limits its ability to access the fast dynamics of functional connectivity (FC). Therefore, Electroencephalography (EEG), which can track functional brain dynamics every millisecond, may serve as a diagnostic marker to utilizing the dynamics of intrinsic brain networks at the sensor level. This research proposes a unique neural marker for depression detection by analyzing long-range functional neurodynamics between the default mode network (DMN) and visual network (VN) via optimal EEG nodes. While DMN abnormalities in depression are well documented, the interactions between the DMN and VN, which reflect visual imagery at rest, remain unclear. Subsequently, a novel differential graph centrality index is applied to reduce a high-dimensional feature space representing EEG temporal neurodynamics, which produced an optimized brain network for MDD detection. The proposed method achieves an exceptional classification performance with an average accuracy, f1 score, and MCC of 99.76%, 0.998, and 0.9995 for the MODMA and 99.99%, 0.999 and 0.9998 for the HUSM datasets, respectively. The findings of this study suggests that a significant decrease in connection density within the beta band (15-30 Hz) in depressed individuals exhibits disrupted long-range inter-network topology, which could serve as a reliable neural marker for depression detection and monitoring. Furthermore, weak FC links between the DMN and VN indicate disengagement between the DMN and VN, which signifies progressive cognitive decline, weak memory, and disrupted thinking at rest, often accompanied by MDD.

Objective.While peripheral mechanisms of proprioception are well understood, the cortical processing of its feedback during dynamic and complex movements remains less clear. Corticokinematic coherence (CKC), which quantifies the coupling between limb movements and sensorimotor cortex activity, offers a way to investigate this cortical processing. However, ecologically valid CKC assessment poses technical challenges. Thus, by integrating Electroencephalography (EEG) with Human Pose Estimation (HPE), this study validates the feasibility and validity of a novel methodology for measuring CKC during upper-limb movements in real-world and virtual reality (VR) settings.Approach.Nine healthy adults performed repetitive finger-tapping (1 Hz) and reaching (0.5 Hz) tasks in real and VR settings. Their execution was recorded temporally synchronized using a 64-channel EEG, optical marker-based motion capture, and monocular deep-learning-based HPE via Mediapipe. Alongside the CKC, the kinematic agreement between both systems was assessed.Main results.CKC was detected using both marker-based and HPE-based kinematics across tasks and environments, with significant coherence observed in most participants. HPE-derived CKC closely matched marker-based measurements for most joints, exhibiting strong reliability and equivalent coherence magnitudes between real and VR conditions.Significance.This study validates a noninvasive and portable EEG-HPE approach for assessing cortical proprioceptive processing in ecologically valid settings, enabling broader clinical and rehabilitation applications.