Biomedical Physics & Engineering Express最新文献

Plate-like structures had been thoroughly studied in literature over years to reduce the computational space from 3D to 2D. Many of these theories suffer either from satisfying the free traction condition or thickness extensibility in addition to the consistency of transverse shear strain energy. This work presents a higher order shear deformation thickness-extensible plate theory (eHSDT) for the analysis of plates. The proposed eHSDT satisfies the condition of free traction as other theories do but it also satisfies the condition of consistency of transverse shear strain energy which is neglected by many theories in the area of plates and shells. The implementation of the proposed theory in displacement-based finite element procedure requires continuity of derivatives across elements. This necessary condition was achieved using the penalty enforcement method for derivative-based nodal degrees of freedom across the standard 9-nodes Lagrange element. The theory was tested for elastic bending deformation of Polyether-ether-ketone (PEEK) which is one of the basic materials for medical implants. The theory showed good accuracy compared to experimental data of the three-points bending test. The present eHSDT was also tested for different conditions with a wide range of aspects ratios (thin to thick plates) and different boundary conditions. The accuracy of the proposed eHSDT was verified against exact solutions for these conditions which showed the advantage over other approaches and commercial finite element packages.

Objectives. This study investigates the association between cerebral blood flow (CBF) and overall survival (OS) in glioblastoma multiforme (GBM) patients receiving chemoradiation. Identifying CBF biomarkers could help predict patient response to this treatment, facilitating the development of personalized therapeutic strategies.Materials and Methods. This retrospective study analyzed CBF data from dynamic susceptibility contrast (DSC) MRI in 30 newly diagnosed GBM patients (WHO grade IV). Radiomics features were extracted from CBF maps, tested for robustness, and correlated with OS. Kaplan-Meier analysis was used to assess the predictive value of radiomic features significantly associated with OS, aiming to stratify patients into groups with distinct post-treatment survival outcomes.Results. While mean relative CBF and CBV failed to serve as independent prognostic markers for OS, the prognostic potential of radiomic features extracted from CBF maps was explored. Ten out of forty-three radiomic features with highest intraclass correlation coefficients (ICC > 0.9), were selected for characterization. While Correlation and Zone Size Variance (ZSV) features showed significant OS correlations, indicating prognostic potential, Kaplan-Meier analysis did not significantly stratify patients based on these features. Visual analysis of the graphs revealed a predominant association between the identified radiomic features and OS under two years. Focusing on this subgroup, Correlation, ZSV, and Gray-Level Nonuniformity (GLN) emerged as significant, suggesting that a lack of heterogeneity in perfusion patterns may be indicative of a poorer outcome. Kaplan-Meier analysis effectively stratified this cohort based on the features mentioned above. Receiver operating characteristic (ROC) analysis further validated their prognostic value, with ZSV demonstrating the highest sensitivity and specificity (0.75 and 0.85, respectively).Conclusion. Our findings underscored radiomics features sensitive to CBF heterogeneity as pivotal predictors for patient stratification. Our results suggest that these markers may have the potential to identify patients who are unlikely to benefit from standard chemoradiation therapy.

Objective.To investigate the potential of 3D-printable thermoplastics as tissue-equivalent materials to be used in multimodal radiotherapy end-to-end quality assurance (QA) devices.Approach.Six thermoplastics were investigated: Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), Polyethylene Terephthalate Glycol (PETG), Polymethyl Methacrylate (PMMA), High Impact Polystyrene (HIPS) and StoneFil. Measurements of mass density (ρ), Relative Electron Density (RED), in a nominal 6 MV photon beam, and Relative Stopping Power (RSP), in a 210 MeV proton pencil-beam, were performed. Average Hounsfield Units (HU) were derived from CTs acquired with two independent scanners. The calibration curves of both scanners were used to predict averageρ,RED and RSP values and compared against the experimental data. Finally, measured data ofρ,RED and RSP was compared against theoretical values estimated for the thermoplastic materials and biological tissues.Main results.Overall, goodρand RSP CT predictions were made; only PMMA and PETG showed differences >5%. The differences between experimental and CT predicted RED values were also <5% for PLA, ABS, PETG and PMMA; for HIPS and StoneFil higher differences were found (6.94% and 9.42/15.34%, respectively). Small HU variations were obtained in the CTs for all materials indicating good uniform density distribution in the samples production. ABS, PLA, PETG and PMMA showed potential equivalency for a variety of soft tissues (adipose tissue, skeletal muscle, brain and lung tissues, differences within 0.19%-8.35% for all properties). StoneFil was the closest substitute to bone, but differences were >10%. Theoretical calculations of all properties agreed with experimental values within 5% difference for most thermoplastics.Significance.Several 3D-printed thermoplastics were promising tissue-equivalent materials to be used in devices for end-to-end multimodal radiotherapy QA and may not require corrections in treatment planning systems' dose calculations. Theoretical calculations showed promise in identifying thermoplastics matching target biological tissues before experiments are performed.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are an effective tool for studying cardiac function and disease, and hold promise for screening drug effects on human tissue. Understanding alterations in motion patterns within these cells is crucial for comprehending how the administration of a drug or the onset of a disease can impact the rhythm of the human heart. However, quantifying motion accurately and efficiently from optical measurements using microscopy is currently time consuming. In this work, we present a unified framework for performing motion analysis on a sequence of microscopically obtained images of tissues consisting of hiPSC-CMs. We provide validation of our developed software using a synthetic test case and show how it can be used to extract displacements and velocities in hiPSC-CM microtissues. Finally, we show how to apply the framework to quantify the effect of an inotropic compound. The described software system is distributed as a python package that is easy to install, well tested and can be integrated into any python workflow.

Diabetic retinopathy (DR) is one of the leading causes of vision loss in adults and is one of the detrimental side effects of the mass prevalence of Diabetes Mellitus (DM). It is crucial to have an efficient screening method for early diagnosis of DR to prevent vision loss. This paper compares and analyzes the various Machine Learning (ML) techniques, from traditional ML to advanced Deep Learning models. We compared and analyzed the efficacy of Convolutional Neural Networks (CNNs), Capsule Networks (CapsNet), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), decision trees, and Random Forests. This paper also considers determining factors in the evaluation, including contrast enhancements, noise reduction, grayscaling, etc We analyze recent research studies and compare methodologies and metrics, including accuracy, precision, sensitivity, and specificity. The findings highlight the advanced performance of Deep Learning (DL) models, with CapsNet achieving a remarkable accuracy of up to 97.98% and a high precision rate, outperforming other traditional ML methods. The Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing technique substantially enhanced the model's efficiency. Each ML method's computational requirements are also considered. While most advanced deep learning methods performed better according to the metrics, they are more computationally complex, requiring more resources and data input. We also discussed how datasets like MESSIDOR could be more straightforward and contribute to highly evaluated performance and that there is a lack of consistency regarding benchmark datasets across papers in the field. Using the DL models facilitates accurate early detection for DR screening, can potentially reduce vision loss risks, and improves accessibility and cost-efficiency of eye screening. Further research is recommended to extend our findings by building models with public datasets, experimenting with ensembles of DL and traditional ML models, and considering testing high-performing models like CapsNet.

The absence of effective extracellular matrix to mimic the natural tumor microenvironment remains a significant obstacle in cancer research. Matrigel, abundant in various biological matrix components, is limited in its application due to its high cost. This has prompted researchers to explore alternative matrix substitutes. Here, we have investigated the effects of the extracellular matrix derived from pig small intestinal submucosa (ECM-SIS) in xenograft tumor modeling. Our results showed that the pig-derived ECM-SIS effectively promotes the establishment of xenograft tumor models, with a tumor formation rate comparable to that of Matrigel. Furthermore, we showed that the pig-derived ECM-SIS exhibited lower immune rejection and fewer infiltrating macrophages than Matrigel. Gene sequencing analysis demonstrated only a 0.5% difference in genes between pig-derived ECM-SIS and Matrigel during the process of tumor tissue formation. These differentially expressed genes primarily participate in cellular processes, biological regulation, and metabolic processes. These findings emphasize the potential of pig-derived ECM-SIS as a cost-effective option for tumor modeling in cancer research.