Journal of Biomedical Physics & Engineering
Volume:14 Issue: 2, Mar-Apr 2024

Treatment response in High-grade Glioma (HGG) patients changes based on their genetic and biological characteristics. MiRNAs, as important regulators of drug and radiation resistance, and the Apparent Diffusion Coefficients (ADC) value of tumor can be used as a prognostic predictor for glioma. This study aimed to identify some of the pre-treatment individual patient features for predicting the treatment response in HGG patients. In this prospective study, 18 HGG patients, who were candidated for chemo-radiation treatment, participated after informed consent of the patients. The investigated features were the expression level of miR-222 and miR-205 in plasma, the ADC value of tumor, Body Mass Index (BMI), and age. Treatment response was assessed, and Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to obtain a model to predict the treatment response. Mann-Whitney U test was also applied to select the variables with a significant relationship with patients’ treatment response. The LASSO coefficients for miR-205, miR-222, tumor’s mean ADC value, BMI, and age were 3.611, -1.683, 2.468, -0.184, and -0.024, respectively. Mann-Whitney U test results showed miR-205 and tumor’s mean ADC significantly related to treatment response (P-value˂0.05).  The miR-205 expression level of the patient in plasma and tumor’s mean ADC value has the potential for prognostic predictors in HGG.

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

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.
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.
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.
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. 
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.

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.
This study aims to improve the accuracy of IVIM output parameters.
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.
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. 
The proposed method outperforms the conventional method and is a promising technique, leading to reproducible and valid maps of D, f, and D*.

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.
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.
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.
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. 
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.

Docetaxel (DXL) is an antineoplastic agent for cancer treatment, the therapeutic efficiency of which is limited due to low solubility, hydrophobicity, and tissue specificity.
In this study, nano-niosomes were introduced for improving therapeutic index of DXL.
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.
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. 
Carrying DXL by the nano-niosomes led to enhanced cytotoxicity (and lower IC50 values) and higher efficacy with enhanced pharmacokinetic parameters.

As the use of electronic devices such as mobile phones, tablets, and computers continues to rise globally, concerns have been raised about their potential impact on human health. Exposure to high energy visible (HEV) blue light, emitted from digital screens, particularly the so-called artificial light at night (ALAN), has been associated with adverse health effects, ranging from disruption of circadian rhythms to cancer. Breast cancer incidence rates are also increasing worldwide.

This study aimed at finding a correlation between breast cancer and exposure to blue light from mobile phone.

In this retrospective matched case-control study, we aimed to investigate whether exposure to blue light from mobile phone screens is associated with an increased risk of female breast cancer. We interviewed 301 breast cancer patients (cases) and 294 controls using a standard questionnaire and performed multivariate analysis, chi-square, and Fisher’s exact tests for data analysis.

Although heavy users in the case group of our study had a statistically significant higher mean 10-year cumulative exposure to digital screens compared to the control group (7089±14985 vs 4052±12515 hours, respectively, P=0.038), our study did not find a strong relationship between exposure to HEV and development of breast cancer. 

Our findings suggest that heavy exposure to HEV blue light emitted from mobile phone screens at night might constitute a risk factor for promoting the development of breast cancer, but further large-scale cohort studies are warranted.

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. 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. 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. 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.  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.

This study presents a mechanical model of a novel medical device designed to optimize the osseointegration process in upper and lower limb amputees, leading to the promotion of optimal rehabilitation. The medical device is developed to reduce the risk of implant failure, leading to re-amputation above the implant. The proposed model serves several purposes 1) to guide the osseointegration process by providing electrical endo-stimulation directly to the bone-implant contact site, using an invasive electrical stimulation system, which is implanted in the bone permanently, 2) to locally transmit stem cells after implantation, without the need for opening the skin or perforating the bone, which is particularly useful for regenerative medicine after partial healing of the implant, 3) to transmit necessary nutrients from the bone, also without opening the skin or puncturing the bone, and 4) to combat infections by locally administering drugs after implantation.