Journal of Biomedical Physics and Engineering最新文献

Recent studies offer valuable insights into viral inactivation for vaccine development. Schulze et al. have demonstrated the potential of heavy ion beam irradiation to create effective vaccines, which is particularly relevant in the context of airborne pandemics. Notably, the success in immunizing mice via intranasal administration with the inactivated influenza virus is encouraging, especially given the genetic similarities between influenza and SARS-CoV-2. However, the study raises important considerations. While heavy ion treatment shows advantages, there are concerns about viral inactivation completeness and the potential for surviving viruses, albeit at extremely low levels. Prolonged irradiation times and the risk of selective pressure leading to the evolution of resistant variants are highlighted. Biosafety concerns regarding accidental lab escape of resistant strains are crucial, emphasizing the need for caution during experiments. Moreover, limitations in Monte Carlo simulations of virus irradiation are discussed, pointing out the need for more comprehensive studies to assess the impact of secondary particles on virus inactivation under realistic irradiation conditions. Given these considerations, while the study presents a promising approach for vaccine development, further research is essential to address potential drawbacks and optimize the method for safe and effective application.

Background: Industrial radiography uses gamma or X-ray radionuclide sources to investigate the safety of industrial materials. Industrial radiation workers receive the highest occupational radiation doses.

Objective: The present study investigates the relationship between Bax and Bcl-2 gene expression variables in industrial radiation workers.

Material and methods: In this case-control study, data was collected using blood sampling from 40 workers, including two groups of non-radiation and radiation workers employed at the location. Expression levels of Bax and Bcl-2 genes were assessed in the laboratory. The environmental and absorbed doses of workers were measured using environmental and pen dosimeters.

Results: Statistical analysis showed that the radiation group's Bcl-2 gene expression level was significantly higher. Findings also demonstrated a correlation between Bcl-2 gene expression and the number of workdays. Also, the Bax gene expression did not show a significant change, and the expression ratio of Bax/Bcl-2 was insignificant in the two groups.

Conclusion: Exposure to low doses of radiation could promote an adaptive response in cells by increasing Bcl-2 gene expression.

Exposure to ionizing radiation, especially during childhood, is a well-established risk factor for thyroid cancer. Following the 1986 Chernobyl nuclear power plant accident the total number of cases of thyroid cancer registered between 1991 and 2015 in males and females who were less than 18 years old exceeded 19,000 (in Belarus and Ukraine, and in the most contaminated oblasts of the Russian Federation). However, as indicated by the United Nations Scientific Committee on the Effects of Atomic Radiation the fraction of the incidence of thyroid cancer attributable to radiation exposure among the non-evacuated residents of the contaminated regions of Belarus, Ukraine and Russia is of the order of 0.25. Apparently, the increased registration of thyroid neoplasms in the parts of these countries is a classical 'screening effect', i.e., massive diagnostic examinations of the risk-aware populations performed with modern eqipment resulting in detection of many occult neoplasms (incidentalomas). Moreover, one type of thyroid cancer previously called 'encapsulated follicular variant of papillary thyroid carcinoma' is non-invasive and instead of 'carcinoma' should now be recognized as 'noninvasive follicular thyroid neoplasm with papillary-like nuclear features.' Other potential causes of overdiagnosing of thyroid tumors include increase of the spontaneous incidence rate of this disease with age, iodine deficiency among children from Belarus, Russia and Ukraine, and/or consumption by these children of drinking water containing high levels of nitrates that likely coincides with the carcinogenic effect of radiation on the thyroid gland.

Professor John Roderick Cameron (1922-2005) stands out as a trailblazer in the field of medical physics, whose innovative work has deeply influenced radiation protection and the broader medical radiation field through sound technical judgment and insight. Best known for inventing the bone densitometry device, his pioneering efforts have reshaped modern medical practices far beyond his initial breakthroughs. Cameron's explorations extended into the realms of space biomedical science and models of terrestrial radiation, areas where his insights continue to resonate today. As the Emeritus Professor of Medical Physics at the University of Wisconsin-Madison and a founding member of the American Association of Physicists in Medicine, Cameron laid crucial groundwork for safety standards in environments with high natural radiation levels. His leadership was instrumental in advancing thermoluminescence dosimetry, radiation measurement, and image quality assurance, driving progress in both academia and clinical practices. Moreover, through establishing Medical Physics Publishing, Cameron played a pivotal role in spreading vital research and educational materials across the fields of health physics and medical physics. This commentary reflects on Cameron's far-reaching contributions, highlighting his critical work in space radiation research and terrestrial radiation models-key to the future of interplanetary travel and potential human settlement on planets like Mars. His research in areas of high background radiation, like Ramsar, Iran, has been fundamental in developing strategies for biological protection in space, which are essential for ensuring astronaut safety during long-duration space missions. We honor Professor Cameron's profound legacy, celebrating his visionary spirit and the lasting impact of his contributions on generations of scientists in radiation science.

Background: Registries are regarded as a just valuable fount of data on determining neonates suffering prematurity or low birth weight (LBW), ameliorating provided care, and developing studies.

Objective: This study aimed to probe the studies, including premature infants' registries, adapt the needed minimum data set, and provide an offered framework for premature infants' registries.

Material and methods: For this descriptive study, electronic databases including PubMed, Scopus, Web of Science, ProQuest, and Embase/Medline were searched. In addition, a review of gray literature was undertaken to identify relevant studies in English on current registries and databases. Screening of titles, abstracts, and full texts was conducted independently based on PRISMA guidelines. The basic registry information, scope, registry type, data source, the purpose of the registry, and important variables were extracted and analyzed.

Results: Fifty-six papers were qualified and contained in the process that presented 51 systems and databases linked in prematurity at the popular and government levels in 34 countries from 1963 to 2017. As a central model of the information management system and knowledge management, a prematurity registry framework was offered based on data, information, and knowledge structure.

Conclusion: To the best of our knowledge, this is a comprehensive study that has systematically reviewed prematurity-related registries. Since there are international standards to develop new registries, the proposed framework in this article can be beneficial too. This framework is essential not only to facilitate the prematurity registry design but also to help the collection of high-value clinical data necessary for the acquisition of better clinical knowledge.

Background: Docetaxel (DXL) is an antineoplastic agent for cancer treatment, the therapeutic efficiency of which is limited due to low solubility, hydrophobicity, and tissue specificity.

Objective: In this study, nano-niosomes were introduced for improving therapeutic index of DXL.

Material and methods: In this experimental study, two nano-niosomes were synthesized using Span 20® and Span 80® and a thin film hydration method with DXL loading (DXL-Span20 and DXL-Span80). Characterization, in-vitro cytotoxicity and bioavailability of the nano-niosomes was also evaluated via in-vivo experiments.

Results: DXL-Span20 and DXL-Span80 have vesicles size in a range of 84-90 nm and negative zeta potentials. DXL entrapment efficiencies were obtained as 69.6 and 74.0% for DXL-Span20 and DXL-Span80, respectively; with an in-vitro sustained release patterns. Cytotoxicity assays were performed against MDA-MB-231, Calu-6, and AsPC-1 cell lines, and the results indicated that DXL loading into nano-niosomes led to decrement in values of half-maximal inhibitory concentration (IC50) at least 2.5 times and at most 6.5 times, compared to free DXL. Moreover, the rat blood bioavailability of DXL after intraperitoneal administration and the pharmacokinetic parameters indicated higher DXL plasma level and the higher effectiveness of DXL-Span80 compared to DXL-Span20.

Conclusion: Carrying DXL by the nano-niosomes led to enhanced cytotoxicity (and lower IC50 values) and higher efficacy with enhanced pharmacokinetic parameters.

Background: The intravoxel Incoherent Motion (IVIM) model extracts perfusion map and diffusion coefficient map using diffusion-weighted imaging. The main limitation of this model is inaccuracy in the presence of noise.

Objective: This study aims to improve the accuracy of IVIM output parameters.

Material and methods: In this simulated and analytical study, the Kalman filter is applied to reject artifact and measurement noise. The proposed method purifies the diffusion coefficient from blood motion and noise, and then an artificial neural network is deployed in estimating perfusion parameters.

Results: Based on the T-test results, however, the estimated parameters of the conventional method were significantly different from actual values, those of the proposed method were not substantially different from actual. The accuracy of f and D* also was improved by using Artificial Neural Network (ANN) and their bias was minimized to 4% and 12%, respectively.

Conclusion: The proposed method outperforms the conventional method and is a promising technique, leading to reproducible and valid maps of D, f, and D*.

Background: Intraoperative Irradiation Therapy (IORT) refers to the delivery of radiation during surgery and needs the computed- thickness of the target as one of the most significant factors.

Objective: This paper aimed to compute target thickness and design a radiation pattern distributing the irradiation uniformly throughout the target.

Material and methods: The Monte Carlo code was used to simulate the experimental setup in this simulation study. The electron flux variations on an electronic board's metallic layer were studied for different thicknesses of the target tissue and validated with experimental data of the electronic board.

Results: Based on the electron number for different Poly Methyl Methacrylate (PMMA) phantom thicknesses at various energies, 6 MeV electrons are suitable to determine the target thickness. Uniformity in radiation and corresponding time for each target were investigated. The iso-dose and percentage depth dose curves show that higher energies are suitable for treatment and distribute uniform radiation throughout the target. Increasing the phantom thickness leads to rising radiation time based on the radiation time corresponding to these energies. The tissue thickness of each section is determined, and the radiation time is managed by scanning the target.

Conclusion: Calculation of the thickness of the remaining tissue and irradiation time are needed after incomplete tumor removal in IORT for various remaining tissues. The patients should be protected from overexposure to uniform irradiation of tissues since the radiation dose is prescribed and checked by an oncologist.

Background: Breast cancer requires evaluating treatment plans using dosimetric and biological parameters. Considering radiation dose distribution and tissue response, healthcare professionals can optimize treatment plans for better outcomes.

Objective: This study aimed to evaluate the effects of the different Dose Calculation Algorithms (DCAs) and Biologically Model-Related Parameters (BMRPs) on the prediction of cardiopulmonary complications due to left breast radiotherapy.

Material and methods: In this practical study, the treatment plans of 21 female patients were simulated in the Monaco Treatment Planning System (TPS) with a prescribed dose of 50 Gy in 25 fractions. Dose distribution was extracted using the three DCAs [Pencil Beam (PB), Collapsed Cone (CC), and Monte Carlo (MC)]. Cardiopulmonary complications were predicted by Normal Tissue Complication Probability (NTCP) calculations using different dosimetric and biological parameters. The Lyman-Kutcher-Burman (LKB) and Relative-Seriality (RS) models were used to calculate NTCP. The endpoint for NTCP calculation was pneumonitis, pericarditis, and late cardiac mortality. The ANOVA test was used for statistical analysis.

Results: In calculating Tumor Control Probability (TCP), a statistically significant difference was observed between the results of DCAs in the Poisson model. The PB algorithm estimated NTCP as less than others for all Pneumonia BMRPs.

Conclusion: The impact of DCAs and BMRPs differs in the estimation of TCP and NTCP. DCAs have a stronger influence on TCP calculation, providing more effective results. On the other hand, BMRPs are more effective in estimating NTCP. Consequently, parameters for radiobiological indices should be cautiously used s to ensure the appropriate consideration of both DCAs and BMRPs.

Background: Gliomas, as Central Nervous System (CNS) tumors, are greatly common with 80% of malignancy. Treatment methods for gliomas, such as surgery, radiation therapy, and chemotherapy depend on the grade, size, location, and the patient's age.

Objective: This study aimed to quantify glioma based on the radiomics analysis and classify its grade into High-grade Glioma (HGG) or Low-grade Glioma (LGG) by various machine-learning methods using contrast-enhanced brain Computerized Tomography (CT) scans.

Material and methods: This retrospective study involved acquiring and segmenting data, selecting and extracting features, classifying, analyzing, and evaluating classifiers. The study included a total of 62 patients (31 with LGG and 31 with HGG). The tumors were segmented by an experienced CT-scan technologist with 3D slicer software. A total of 14 shape features, 18 histogram-based features, and 75 texture-based features were computed. The Area Under the Curve (AUC) and Receiver Operating Characteristic Curve (ROC) were used to evaluate and compare classification models.

Results: A total of 13 out of 107 features were selected to differentiate between LGGs and HGGs and to perform various classifier algorithms with different cross-validations. The best classifier algorithm was linear-discriminant with 93.5% accuracy, 96.77% sensitivity, 90.3% specificity, and 0.98% AUC in the differentiation of LGGs and HGGs.

Conclusion: The proposed method can identify LGG and HGG with 93.5% accuracy, 96.77% sensitivity, 90.3% specificity, and 0.98% AUC, leading to the best treatment for glioma patients by using CT scans based on radiomics analysis.