Biomedical Physics & Engineering Express最新文献

Tibial fractures account for approximately 15% of all fractures, typically resulting from high-energy trauma. A critical surgical approach to treat these fractures involves the fixation of the tibia using a plate with minimally invasive osteosynthesis. The selection and fixation of the implant plate are vital for stabilizing the fracture. This selection is highly dependent on the plate's stability, which is influenced by factors like the stresses generated in the plate due to the load on the bone, as well as the plate's length, thickness, and number of screw holes. Minimizing these stresses is essential to reduce the risk of implant failure, ensuring optimal stress distribution and promoting faster, more effective bone healing. In the present work, the finite element and statistical approach was used to optimize the geometrical parameters of the implant plate made of SS 316L steel and Ti6Al4V alloy. A 3D finite element model was developed for analyzing the stresses and deformation, and implant plates were manufactured to validate the results with the help of an experiment conducted on the universal testing machine. A strong correlation was observed between the experimental and predicted results, with an average error of 8.6% and 8.55% for SS316L and Ti6Al4V alloy, respectively. Further, using the signal-to-noise ratio for the minimum stress condition was applied to identify the optimum parameters of the plate. Finally, regression models were developed to predict the stresses generated in SS316L and Ti6Al4V alloy plates with different input conditions. The statistical model helps us to develop the relation between different geometrical parameters of the Tibia implant plate. As determined by the present work, the parameter most influencing is implant plate length. This outcome will be used to select the implant for a specific patient, resulting in a reduction in implant failure post-surgery.

A scaffold is a three-dimensional porous structure that is used as a template to provide structural support for cell adhesion and the formation of new cells. Metallic cellular scaffolds are a good choice as a replacement for human bones in orthopaedic implants, which enhances the quality and longevity of human life. In contrast to conventional methods that produce irregular pore distributions, 3D printing, or additive manufacturing, is characterized by high precision and controlled manufacturing processes. AM processes can precisely control the scaffold's porosity, which makes it possible to produce patient specific implants and achieve regular pore distribution. This review paper explores the potential of Ti-6Al-4V scaffolds produced via the SLM method as a bone substitute. A state-of-the-art review on the effect of design parameters, material, and surface modification on biological and mechanical properties is presented. The desired features of the human tibia and femur bones are compared to bulk and porous Ti6Al4V scaffold. Furthermore, the properties of various porous scaffolds with varying unit cell structures and design parameters are compared to find out the designs that can mimic human bone properties. Porosity up to 65% and pore size of 600 μm was found to give optimum trade-off between mechanical and biological properties. Current manufacturing constraints, biocompatibility of Ti-6Al-4V material, influence of various factors on bio-mechanical properties, and complex interrelation between design parameters are discussed herein. Finally, the most appropriate combination of design parameters that offers a good trade-off between mechanical strength and cell ingrowth are summarized.

Idiopathic Normal Pressure Hydrocephalus (iNPH) is a progressive neurologic disorder (fluid build-up in the brain) that affects 0.2%-5% of the UK population aged over 65. Mobility problems, dementia and urinary incontinence are symptoms of iNPH but often these are not properly evaluated, and patients receive the wrong diagnosis. Here, we describe the development and testing of firmware embedded in a wearable device in conjunction with a user-based software system that records and analyses a patient's gait. The movement patterns, expressed as quantitative data, allow clinicians to improve the non-invasive assessment of iNPH as well as monitor the management of patients undergoing treatment. The wearable sensor system comprises a miniature electronic unit that attaches to one ankle of the patient via a simple Velcro strap which was designed for this application. The unit monitors acceleration along three axes with a sample rate of 60 Hz and transmits the data via a Bluetooth communication link to a tablet or smart phone running the Android and the iOS operating systems. The software package extracts statistics based on stride length, stride height, distance walked and speed. Analysis confirmed that the system achieved an average accuracy of at least 98% for gait tests conducted over distances 9 m. This device has been developed to assist in the management and treatment of older adults diagnosed with iNPH.

The integration of artificial intelligence (AI) into lung cancer management offers immense potential to revolutionize diagnostic and treatment strategies. The aim is to develop a resilient AI framework capable of two critical tasks: firstly, achieving accurate and automated segmentation of lung tumors and secondly, facilitating the T classification of lung cancer according to the ninth edition of TNM staging 2024 based on PET/CT imaging. This study presents a robust AI framework for the automated segmentation of lung tumors and T classification of lung cancer using PET/CT imaging. The database includes axial DICOM CT and¹⁸FDG-PET/CT images. A modified ResNet-50 model was employed for segmentation, achieving high precision and specificity. Reconstructed 3D models of segmented slices enhance tumor boundary visualization, which is essential for treatment planning. The Pulmonary Toolkit facilitated lobe segmentation, providing critical diagnostic insights. Additionally, the segmented images were used as input for the T classification using a CNN ResNet-50 model. Our classification model demonstrated excellent performance, particularly for T1a, T2a, T2b, T3 and T4 tumors, with high precision, F1 scores, and specificity. The T stage is particularly relevant in lung cancer as it determines treatment approaches (surgery, chemotherapy and radiation therapy or supportive care) and prognosis assessment. In fact, for Tis-T2, each increase of one centimeter in tumor size results in a worse prognosis. For locally advanced tumors (T3-T4) and regardless of size, the prognosis is poorer. This AI framework marks a significant advancement in the automation of lung cancer diagnosis and staging, promising improved patient outcomes.

Cold atmospheric plasma (CAP) has been extensively utilized in medical treatment, particularly in cancer therapy. However, the underlying mechanism of CAP in skin cancer treatment remains elusive. In this study, we established a skin cancer model using CAP treatmentin vitro. Also, we established the Xenograft experiment modelin vivo. The results demonstrated that treatment with CAP induced ferroptosis, resulting in a significant reduction in the viability, migration, and invasive capacities of A431 squamous cell carcinoma, a type of skin cancer. Mechanistically, the significant production of reactive oxygen species (ROS) by CAP induces DNA damage, which then activates Ataxia-telangiectasia mutated (ATM) and p53 through acetylation, while simultaneously suppressing the expression of Solute Carrier Family 7 Member 11 (SLC7A11). Consequently, this cascade led to the down-regulation of intracellular Glutathione peroxidase 4 (GPX4), ultimately resulting in ferroptosis. CAP exhibits a favorable impact on skin cancer treatment, suggesting its potential medical application in skin cancer therapy.

Currently, Diabetes Mellitus (DM) can be life-threatening due to the dietary habits and lifestyle choices of individuals. Diabetes is characterised by elevated levels of glucose in the blood and an excess of protein in the blood. Poor eating habits and lifestyles are largely responsible for the rise in overweight, obesity, and various related conditions. This study investigated many diabetes-related risk forecasting techniques and algorithms. The eight machine learning (ML) algorithms used the diabetes dataset to test various prediction techniques, including a Support Vector Classifier, gradient-boosting, multilayer perceptron, random forest, K-nearest neighbors, logistic regression, extreme gradient boosting, and decision tree. To enhance the diabetic prediction ability of the model, we suggested using Feature Engineering (FE) and feature scaling. For our investigation, we utilized the Mendeley dataset on diabetes to assess the capacity of the model to predict diabetes. We developed a model by using Python programming and eight classification techniques. The Random Forest with 99.21%, Gradient Boosting with 99.61%, Extreme Gradient Boosting, and Decision Tree achieved the highest F1 score (99.81%), accuracy rate (99.80%), precision (99.81%), and recall (99.81%) of all classification approaches.

Facial paralysis (FP) is a condition characterized by the inability to move some or all of the muscles on one or both sides of the face. Diagnosing FP presents challenges due to the limitations of traditional methods, which are time-consuming, uncomfortable for patients, and require specialized clinicians. Additionally, more advanced tools are often uncommonly available to all healthcare providers. Early and accurate detection of FP is crucial, as timely intervention can prevent long-term complications and improve patient outcomes. To address these challenges, our research introduces Facia-Fix, a mobile application for Bell's palsy diagnosis, integrating computer vision and deep learning techniques to provide real-time analysis of facial landmarks. The classification algorithms are trained on the publicly available YouTube FP (YFP) dataset, which is labeled using the House-Brackmann (HB) method, a standardized system for assessing the severity of FP. Different deep learning models were employed to classify the FP severity, such as MobileNet, CNN, MLP, VGG16, and Vision Transformer. The MobileNet model which uses transfer learning, achieved the highest performance (Accuracy: 0.9812, Precision: 0.9753, Recall: 0.9727, F1 Score: 0.974), establishing it as the optimal choice among the evaluated models. The innovation of this approach lies in its use of advanced deep learning models to provide accurate, objective, non-invasive and real-time comprehensive quantitative assessment of FP severity. Preliminary results highlight the potential of Facia-Fix to significantly improve the diagnostic and follow-up experiences for both clinicians and patients.

Multi-drug resistance (MDR) infections are a significant global challenge, necessitating innovative and eco-friendly approaches for developing effective antimicrobial agents. This study focuses on the synthesis, characterization, and evaluation of cerium oxide nanoparticles (CeO₂NPs) for their antioxidant, anti-inflammatory, and antibacterial properties. The CeO₂NPs were synthesized using aTribulus terrestrisaqueous extract through an environmentally friendly process. Characterization techniques included UV-visible spectroscopy, Fourier Transform Infrared Spectroscopy (FT-IR), x-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Energy Dispersive x-ray (EDX) analysis. The UV-vis spectroscopy shows the presence of peak at 320 nm which confirms the formation of CeO₂NPs. The FT-IR analysis of the CeO₂NPs revealed several distinct functional groups, with peak values at 3287, 2920, 2340, 1640, 1538, 1066, 714, and 574 cm^-1. These peaks correspond to specific functional groups, including C-H stretching in alkynes and alkanes, C=C=O, C=C, alkanes, C-O-C, C-Cl, and C-Br, indicating the presence of diverse chemical bonds within the CeO₂NPs. XRD revealed that the nanoparticles were highly crystalline with a face-centered cubic structure, and SEM images showed irregularly shaped, agglomerated particles ranging from 100-150 nm. In terms of biological activity, the synthesized CeO₂NPs demonstrated significant antioxidant and anti-inflammatory properties. The nanoparticles exhibited 82.54% antioxidant activity at 100 μg ml^-1, closely matching the 83.1% activity of ascorbic acid. Additionally, the CeO₂NPs showed 65.2% anti-inflammatory activity at the same concentration, compared to 70.1% for a standard drug. Antibacterial testing revealed that the CeO₂NPs were particularly effective against multi-drug resistant strains, includingPseudomonas aeruginosa,Enterococcus faecalis, and MRSA, with moderate activity againstKlebsiella pneumoniae. These findings suggest that CeO₂NPs synthesized viaT. terrestrishave strong potential as antimicrobial agents in addressing MDR infections.

Automation is revamping our preprocessing pipelines, and accelerating the delivery of personalized digital medicine. It improves efficiency, reduces costs, and allows clinicians to treat patients without significant delays. However, the influx of multimodal data highlights the need to protect sensitive information, such as clinical data, and safeguard data fidelity. One of the neuroimaging modalities that produces large amounts of time-series data is Electroencephalography (EEG). It captures the neural dynamics in a task or resting brain state with high temporal resolution. EEG electrodes placed on the scalp acquire electrical activity from the brain. These electrical potentials attenuate as they cross multiple layers of brain tissue and fluid yielding relatively weaker signals than noise-low signal-to-noise ratio. EEG signals are further distorted by internal physiological artifacts, such as eye movements (EOG) or heartbeat (ECG), and external noise, such as line noise (50 Hz). EOG artifacts, due to their proximity to the frontal brain regions, are particularly challenging to eliminate. Therefore, a widely used EOG rejection method, independent component analysis (ICA), demands manual inspection of the marked EOG components before they are rejected from the EEG data. We underscore the inaccuracy of automatized ICA rejection and provide an auxiliary algorithm-Second Layer Inspection for EOG (SLOG) in the clinical environment. SLOG based on spatial and temporal patterns of eye movements, re-examines the already marked EOG artifacts and confirms no EEG-related activity is mistakenly eliminated in this artifact rejection step. SLOG achieved a 99% precision rate on the simulated dataset while 85% precision on the real EEG dataset. One of the primary considerations for cloud-based applications is operational costs, including computing power. Algorithms like SLOG allow us to maintain data fidelity and precision without overloading the cloud platforms and maxing out our budgets.

Background: The risk of biochemical recurrence (BCR) after radiotherapy for localized prostate cancer (PCa) varies widely within standard risk groups. There is a need for low-cost tools to more robustly predict recurrence and personalize therapy. Radiomic features from pretreatment MRI show potential as noninvasive biomarkers for BCR prediction. However, previous research has not fully combined radiomics with clinical and pathological data to predict BCR in PCa patients following radiotherapy. Purpose: This study aims to predict 5-year BCR using radiomics from pretreatment T2W MRI and clinical-pathological data in PCa patients treated with radiation therapy, and to develop a unified model compatible with both 1.5T and 3T MRI scanners. Methods: A total of 150 T2W scans and clinical parameters were preprocessed. Of these, 120 cases were used for training and validation, and 30 for testing. Four distinct machine learning models were developed: Model 1 used radiomics, Model 2 used clinical and pathological data, and Model 3 combined these using late fusion. Model 4 integrated radiomic and clinical-pathological data using early fusion. Results: Model 1 achieved an AUC of 0.73, while Model 2 had an AUC of 0.64 for predicting outcomes in 30 new test cases. Model 3, using late fusion, had an AUC of 0.69. Early fusion models showed strong potential, with Model 4 reaching an AUC of 0.84, highlighting the effectiveness of the early fusion model. Conclusions: This study is the first to use a fusion technique for predicting BCR in PCa patients following radiotherapy, utilizing pre-treatment T2W MRI images and clinical-pathological data. The methodology improves predictive accuracy by fusing radiomics with clinical-pathological information, even with a relatively small dataset, and introduces the first unified model for both 1.5T and 3T MRI images.