Biomedical Physics & Engineering Express最新文献

Hyperlipidemia detection involves invasive, time-consuming procedures requiring clinical laboratories and blood samples. Often asymptomatic in its early stages, hyperlipidemia significantly increases the risk of cardiovascular diseases. The objective of this study was to investigate whether hyperlipidemia produces detectable changes in pulse wave patterns and to develop a non-invasive, cost-effective diagnostic approach using deep learning techniques applied to finger pulse images. Pulse waves were recorded from 81 hyperlipidemia patients and 65 participants in the control group, with 700 single pulse wave cycles selected from each group. These waveforms were preprocessed and divided into training (70%), validation (15%), and testing (15%) subsets. Custom Convolutional Neural Network (CNN) architectures trained from scratch were developed and evaluated to identify the most effective classification model. After model selection, hyperparameter tuning was applied to enhance predictive performance. In parallel, pre-trained models such as Visual Geometry Group 16 (VGG16) were fine-tuned and optimized. The models were assessed using accuracy, precision, recall, and F1-score. The custom CNN models achieved the highest performance, with the top model reaching approximately 95%-96% for accuracy, precision, recall, and F1-score. The VGG16 models also performed well, with all metrics around 91%. Training and validation curves for both model types indicated strong learning capabilities with minimal overfitting or underfitting, showcasing their potential for generalization to unseen data. Deep learning models effectively differentiated pulse waves between individuals with hyperlipidemia and those in the control group, indicating that hyperlipidemia causes detectable changes in pulse wave patterns. This study could lead to the development of a reliable, efficient, and non-invasive device for hyperlipidemia screening.

The uncontrolled release of pharmaceuticals in traditional drug delivery systems has resulted in the development of innovative drug delivery methods based on nanotechnology and the use of tailored nanocarriers for cancer treatment. This study aimed to develop a targeted drug delivery system and photodynamic therapy (PDT) for enhanced therapeutic efficacy in lung cancer treatment. Upconversion nanoparticles (UCNPs) were synthesised via a Polyol route and surface-modified with polyethylene glycol (PEG) to improve biocompatibility. Further functionalization with folic acid (FA) facilitated targeted delivery to the human lung fibroblast cell line (MRC-5) (normal) and the human lung carcinoma cell line (A549) (lung cancer). The nanoparticles were loaded with paclitaxel (PTX), which inhibits microtubule polymerisation, forming UCNPs-FA-PTX complexes. Transmission Electron Microscopy (TEM) characterisation revealed well-dispersed nanoparticles with an average size of 22.5 ± 8.67 nm. Zeta potential analysis confirmed a shift from +24.5 mV for UCNPs to -14 mV for UCNPs-FA-PTX, indicating successful drug loading and surface modification. Dynamic Light Scattering (DLS) showed a larger particle size for drug-loaded UCNPs, with a mean diameter of 117 nm. Cell viability and apoptosis were evaluated using MTT and Flow cytometry assays. The UCNPs-FA-PTX complex demonstrated a significantly reduced A549 cell viability, with a half-maximal inhibitory concentration (IC 50) of 11.15 μg ml^-1at 72 h, compared to MRC-5 cells (IC 50 =22.8 μg ml^-1), and induced higher apoptosis in cancer cells. The study integrates PDT, using Tetraphenylporphyrin (TPP) as a dye to enhance treatment. Laser treatment (980 nm) enhanced these effects through a synergistic therapeutic approach. In contrast, UCNPs-FA and UCNPs exhibited minimal cytotoxicity, underscoring their biocompatibility.

Sparse-view low-dose computed tomography (LDCT) imaging poses difficulties in preserving image quality while reducing radiation exposure. Recent research has focused extensively on artificial intelligence (AI) to reduce artifacts in LDCT. This paper presents a unique integration based on a conditional generative adversarial network (CGAN) with metaheuristic optimization to improve the reconstruction of sparse-view computed tomography (CT) images. A Pix2Pix CGAN-based model was integrated with Particle Swarm Optimization (PSO), Differential Evolution (DE), and Cuckoo Search (CS) to improve essential hyperparameters, such as learning rate and beta values. The LDCT-P and LUNA16 datasets were used, producing seven levels of sparse-view CT images (10, 16, 32, 64, 128, 256, and 512 views) for assessment. The findings indicated a substantial improvement in image quality with an increase in the number of view projections. Pix2Pix + PSO demonstrated superior performance, with the Structural Similarity Index metric (SSIM) rising from 0.900 (10 views) to 0.972 (512 views) for abdominal CT and from 0.801 to 0.971 for lung CT, respectively. The results underscore the capability of the Pix2Pix model integrated with metaheuristic optimization to enhance sparse-view CT reconstruction. This method adeptly reconciles computing economy with image integrity, enabling improved LDCT imaging applications in clinical settings.

Nanosecond pulsed electric fields (nsPEFs) have emerged as a promising modality for cancer treatment by inducing targeted immune responses. Inin vitrostudies, commercial cuvettes with narrow 1-mm gaps are typically employed to deliver high-intensity electric fields to biological samples. However, the inherently high conductivity of the biological sample results in extremely low impedance-often only a few Ohms. Under kilovolt-level pulses, this low impedance can induce current surges of hundreds of amperes, posing risks to pulse generation equipment. This issue is further amplified in high cell-density environments. To overcome these challenges, a novel cuvette design featuring a pair of grid-patterned electrodes has been developed to enhance load impedance while preserving electric field uniformity. Numerical simulations confirm that the proposed structure improves impedance characteristics without compromising the homogeneity of the electric field. Experimental validation has been conducted using 3D-printed cuvettes based on the current-voltage measurement method, indicating a strong correlation with simulations. This proposed grid-patterned cuvette provides a reliable platform for nsPEF delivery inin vitrobiomedical research.

Auscultations are commonly used to analyze lung conditions through signal processing and classification techniques. However, the efficiency of these models is often limited by factors like signal quality, sensor performance, and dataset size. Current models rely on approximations, making it difficult to pinpoint exact causes of lung conditions. To improve accuracy and interpretability, this study proposes a composite transfer learning model with explainable AI called TLMACEA. The model first converts auscultation data into 2D spectral and spatial feature vectors, which are then processed using an ensemble convolutional neural network (CNN) to identify initial lung conditions. These results are cross verified with clinical data such as lung function tests, patient demographics, smoking history, and symptoms (e.g., cough, wheezing). The data is processed through an ensemble classification layer combining random forest, support vector machine, linear regression, and Naïve Bayes models for effective lung condition prediction. The model's performance was evaluated on over 100 patients and compared to existing models. Results showed that TLMACEA outperformed state-of-the-art models, with 8.5% higher accuracy, 6.2% better precision, 7.9% improved recall, and 10.4% lower delay. The model's ensemble classification achieved 99.5% accuracy, making it suitable for real-time clinical use. The explainable AI layer also demonstrated over 98% precision, ensuring the clinical utility of the recommendations generated.

Objective. This review aims to summarize current knowledge on the effects of various pulp capping agents on dental-derived stem cells during pulp capping procedures. Pulp capping is a biologically based treatment designed to manage minimal pulpal exposure or prevent it, thereby preserving pulp vitality and avoiding root canal therapy. The success of this approach relies heavily on dentin bridge formation, which is influenced by the behavior of dental stem cells and the type of material used. Understanding how pulp capping agents affect these stem cells and their molecular mechanisms is essential for optimizing treatment outcomes.Methods. A comprehensive literature review was conducted to evaluate the effects of various pulp capping materials on dental-derived stem cells, with a particular focus on the molecular pathways activated during pulp capping and their influence on stem cell differentiation, proliferation, and dentin bridge formation.Results. The findings indicate that pulp capping materials exert diverse effects on dental-derived stem cells, largely influenced by their composition. These materials activate specific molecular pathways that regulate stem cell fate and reparative responses. For instance, calcium hydroxide and mineral trioxide aggregate (MTA) engage distinct signaling cascades that promote odontogenic differentiation. The dynamic interaction between stem cells and pulp capping agents underscores the potential for developing targeted therapies that selectively modulate molecular pathways to enhance pulp regeneration.Conclusions. Understanding the interaction between pulp capping agents and dental-derived stem cells is essential for optimizing treatment outcomes. Future research should aim to refine both materials and clinical protocols to enhance stem cell responsiveness, thereby advancing the development of more effective and biologically driven pulp capping strategies.

As physiological artifacts commonly overlap with EEG signals in both time and frequency domains, developing an effective end-to-end EEG artifact removal method is essential for a brain-computer interface (BCI) system. An end-to-end artifact removal method based on nested generative adversarial network (GAN) is proposed, to recover the EEG signals from artifact-contaminated ones. The nested GAN consists of two components: an inner GAN operating in time-frequency domain and an outer GAN functioning in time domain. A light-weighted complex-valued restormer, designed in time-frequency domain, is employed as the generator to reconstruct the denoised EEG signal. Two metric discriminators in the inner GAN and two multi-resolution discriminators in the outer GAN are used, and gradient balance is used to address the partial learning issue during training. The performance of the nested GAN has been evaluated in the realistic EEG dataset and semi-synthetic dataset. Compared to the benchmark methods, the proposed one achieved best average performance evaluation metrics, including mean square error (MSE) = 0.098, Pearson correlation coefficient (PCC) = 0.892, relative root MSE (RRMSE) = 0.065, the percentage reduction of time domain artifacts (ηtemporal) = 71.6%, and the percentage reduction of frequency domain artifacts (ηspectral) = 76.9%. The performance of artifact removal also showed robustness across a wide range of signal-to-noise ratio (SNR) levels.The superior performance of the proposed end-to-end artifact removal method is expected to contribute to the advancement of BCI system development.

The fidelity of dose distribution prediction is paramount for radiotherapy planning. While existing deep learning-based methods have obtained noteworthy performance, most of them pursue the accurate prediction of global dose distribution but neglect local regions with sharp variations in dose, leading to inadvertent irradiation of healthy tissues. Thus, this paper proposes a dose stratification method to confront this challenge, refining neural network predictions of dose distribution in a hierarchical manner, where low-dose regions will not be overshadowed by high-dose regions in loss calculation. More specifically, the dose distribution is stratified into four subcomponents predicted individually, and the ultimate dose distribution emerges from the amalgamation of these subcomponents. Furthermore, a homogeneity index-based loss function is designed to augment the homogeneity of dose distribution, thereby mitigating collateral impact on healthy tissues. According to the experimental results on head and neck cancer cases in the OpenKBP dataset, the proposed method outperforms state-of-the-art methods for dose distribution prediction. Notably, the proposed method predicts dose distributions aligning more closely with clinically viable plans, enhancing the credibility and interpretability of artificial intelligence in the domain of radiotherapy planning.

Objective. Currently, superficial x-ray radiotherapy does not take advantage of modern treatment planning technologies. To address the problem, a treatment planning system for superficial x-ray radiotherapy has been developed using a database generated through Monte Carlo simulations. The system, called SXRTDose, can be used to optimize irradiation strategies by adjusting energy, filtration, and applicator aiming to deliver a planned dose to the target volume while minimizing radiation risk to surrounding normal tissues.Approach. TOPAS Monte Carlo code was used for establishing the dosimetric database by modeling parameters of a commercial superficial x-ray radiotherapy device and by calculating depth-dose information in a water phantom. After the radiation physics aspects have been verified, detailed Monte Carlo simulations of absorbed doses under different irradiation parameters including five skin models (representing location of the abdomen, cheek, forehead, limbs, and nose), three x-ray energies (50 kV, 70 kV, and 100 kV), corresponding filters and applicators were performed resulting in a comprehensive database. A python-based graphical user interface was developed to support the clinical application of the treatment planning system for superficial x-ray radiotherapy.Results. Compared to experimental results reported in the literature, the relative errors from water phantom simulations for the superficial x-ray radiotherapy system is acceptable. The developed treatment planning system utilizes dose-volume histograms to quantitatively evaluate the clinical applicability of various irradiation plans for skin cancer treatment. The application of the software is found to provide rapid and accurate dose guidance to clinical users in selecting optimal and alternative equipment parameters.Conclusion. The potential and feasibility of a treatment planning system for superficial x-ray radiotherapy have been evaluated, demonstrating its capability to deliver rapid, accurate, and concise dosimetry references. This enhances therapeutic guidance and treatment effectiveness, while addressing the present challenge of inadequate dosimetry support in the field of superficial radiotherapy.

Objective: Focused ultrasound (FUS) with intravenously administered microbubbles (MBs) enables different therapeutic effects, e.g. localized opening of the blood-brain barrier (BBB). Acoustic activation of MBs under FUS induces mechanical effects-primarily stable or inertial cavitation - that can reversibly disrupt endothelial tight junctions without permanent tissue damage. MB acoustic emissions are widely used as indicators of cavitation activity and, by extension, treatment efficacy and safety. While some aspects of microbubble behavior under different exposure conditions are known, the overall influence of various parameter combinations on cavitation dose remains incompletely described.Approach: This study examined how MB concentration (0.0008-0.4% V/V), peak negative pressure (61.5-2600 kPa), pulse duration (95-952 μs), and effective sonication time affect cavitation activity in a flow setup. Cavitation was quantified as a cavitation dose which was divided into three types: stable harmonic (SCD_har), ultraharmonic (SCD_ultra), and broadband (ICD) emissions.Results: SCD_harand ICD increased mostly monotonically with pressure and MB concentration, while SCD_ultrapeaked at intermediate values suggesting optimal parameters for the control of the ultrasound BBB opening procedure. Cavitation metrics showed 10% reproducibility. Critically, we found that for fixed effective sonication times, increasing the number of pulses led to significantly change the response of cavitation dose in time. To our knowledge, this relationship has not been studied before, change of pulse length was always related to effective sonication time. Our results suggests that pulse number is an important factor of how MB oscillate, introducing a potentially pivotal control parameter for therapeutic ultrasound.Significance: These findings provide new insights into MB dynamics and highlight pulse count as an underrecognized yet potentially important factor in protocol design. This perspective may inform refinements of FUS treatments, contributing to greater safety, consistency, and efficacy, and represents a step toward optimizing ultrasonic BBB opening strategies.