Introduction: In Generative Adversarial Networks (GANs), validation datasets are typically excluded from the adversarial optimization loop, unlike in many conventional machine learning architectures. This study evaluates GAN performance for predicting Dose Voxel Kernels (DVKs) using dedicated image-based metrics and compares these outcomes with training accuracy.Material and Methods: Density Kernels (DKs) of size 15×15×15 voxels (2.43 mm³ voxel size) were generated from homogeneous materials and CT images using ctcreate/EGSnrc. Each DK incorporated a centrally located isotropic 177Lu source, and corresponding DVKs were simulated using DOSXYZnrc/EGSnrc Monte Carlo methods. Paired DK-DVK datasets were used to train multiple GAN models. Model performance on unseen validation data comprising DKs from water, soft tissue, kidney, and bone was assessed using Structural Similarity Index Method (SSIM), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and the Relative Global Dimensionless Error of Synthesis (ERGAS).Results: Twelve GAN models with training accuracies between 96.5% and 99.26% were evaluated. Despite achieving the highest training accuracy, the 99.26% model did not exhibit the best predictive quality. Instead, the 98.4% model achieved superior performance, showing lower MSE (0.020 vs. 0.029 mGy²/MBq·s²), higher PSNR (43.25 vs. 41.35 dB), and a markedly lower ERGAS (10.96 vs. 15.75). SSIM values were consistently high (>0.99) across all models, with no statistically significant differences (p > 0.05), indicating comparable structural fidelity.Conclusion: Training accuracy alone does not reliably reflect GAN performance. Image-based similarity and error metrics provide a more comprehensive and discriminative evaluation of 177Lu DVK prediction quality.
Introduction: Current deep learning-based computer-aided diagnosis (CAD) techniques face challenges in hierarchical feature extraction and computational efficiency. Traditional convolutional neural networks (CNN) often focus on local or single-scale information, neglecting global correlations of brain atrophy and multiscale pathological features. Additionally, the parameter explosion problem in deep networks limits model's generalization ability on small and medium-sized datasets. While the introduction of attention mechanisms has significantly improved feature extraction and enhanced CNN recognition capabilities, existing attention mechanisms are mostly single-scale, focusing on feature maps at specific hierarchical levels and ignoring the correlations between features of different layers.Material and Methods: To address these issues, this study proposes a lightweight model combining a shallow feature pyramid CNN with a Dual Multi-level Attention (DMA) mechanism. Experiments using the public OASIS-1 dataset, which contains 86,437 MRI images across 4 categories, employ a focal loss function to handle class imbalance.Results: The results show that the model including DMA outperforms both the baseline CNN and the single-scale attention mechanism in terms of accuracy (ACC), sensitivity (SEN), and specificity (SPE). Specifically, compared to CNN and CNN+CBAM: ACC improved by 3.33% and 1.26%, SEN improved by 13.2% and 0.9%, and SPE improved by 1%.Conclusion: The model demonstrates significant advantages in distinguishing small-sample classes and differentiating between very mild dementia and normal controls, highlighting its superiority in fine-grained pathological discrimination.
Introduction: This study explored radiomics-based machine learning (ML) models as complementary tools to visual evaluation for classifying drug-resistant epilepsy patients and healthy controls using 18F-FDG brain Positron Emission Tomography (PET). Because visual interpretation can be subjective and variable, especially for novice readers, objective and reproducible computational methods are needed.Material and Methods: Twenty-one drug-resistant epilepsy patients and sixteen healthy controls underwent ¹⁸F-FDG brain PET imaging. From contralateral brain regions, 92 radiomics features (first-order statistics and second-order texture matrices) were extracted. Feature selection included Student’s t-test, principal component analysis, and ridge regression. Logistic regression (LR) and support vector machine (SVM) classifiers were trained and evaluated using 10-fold cross-validation and repeated 80/20 train–test splits. A permutation test (n = 1000) assessed whether differences between classifier performances were statistically significant. LR, chosen for its lower computational cost and interpretability, was used for comparison with human visual assessments.Results: Across six radiomics feature groups, LR models demonstrated strong performance, with mean accuracy of 0.94(0.05), precision 0.96(0.03), recall 0.92(0.10), specificity 0.97(0.02), and AUC 0.98(0.00). SVM models showed similarly high accuracy 0.98(0.01), precision 0.94(0.05), recall 0.96(0.03), specificity 0.98(0.01), and AUC 0.98(0.00). Novice visual assessments had moderate accuracy (0.62 and 0.67), perfect specificity, lower sensitivity (0.60 and 0.65), and AUCs of 0.80 and 0.825. The final LR model achieved a mean AUC of 0.96(0.01).Conclusion: This hybrid radiomics-visual approach improves classification accuracy in pre-surgical evaluation of drug-resistant epilepsy. By integrating quantitative radiomics with clinical interpretation, the framework reduces variability and improves reliability for less experienced clinicians.
Introduction: At radiotherapy, tumor motion is clinically tracked in real-time using external surrogates. To achieve this. A reliable correlation model is used to predict tumor coordinates based on the motion of external markers. In this work, a deep neural networks model is introduced for tumor motion tracking. Material and Methods: A motion database of 20 patients treated with the CyberKnife Synchrony System has been used to train and evaluate the model. The proposed model is based on Long-Short Term Memory neural network developed in a Python software package. The network consists of two layers, each with 40 neurons, and a fully connected layer with a linear activation function. Results: In this study, three-dimensional RMSE which is a common approach for calculating the model error is utilized. The obtained 3D RMSE of the proposed model is compared with the performance accuracy of the CyberKnife modeler. The results show a significant 15.3% reduction in three-dimensional error, indicating that our developed model has a lower error compared to the CyberKnife modeler. Conclusion: In this study, a model based on deep Long-Short Term Memory neural network is used for tumor tracking using a motion database of real patients. The reason for using this model is its robustness to remember information for a long period and its high predictive ability, which makes it promising for future clinical implementation. Unlike previously used models, this model can retain useful information from past time series and use it for training, allowing the model to outperform other models.
Introduction: Photobiomodulation (PBM) therapy relies on precise control of optical parameters such as wavelength, irradiance, and beam geometry to achieve therapeutic efficacy. Light‑emitting diodes (LEDs) are increasingly used in PBM devices due to their efficiency, cost‑effectiveness, and spectral flexibility, but their performance varies widely with device class, drive conditions, and optical configuration. This study aimed to experimentally characterize and compare the electro‑optical, thermal, and spectral properties of two commercially available classes of red LEDs—High Bright and Power—and to evaluate the effect of beam‑shaping optics on achieving PBM‑relevant irradiance at a clinically relevant distance. Material and Methods: Ten units of each LED class were tested under controlled laboratory conditions. Measurements included forward voltage–current characteristics, irradiance at 10 cm, thermal rise over time, and emission spectra (peak wavelength, full width at half maximum). The effect of integrating a 30° collimating lens with the Power LED was quantified in terms of irradiance gain. Results: At 10 cm, the High Bright LED produced sub‑therapeutic irradiance (<1 mW/cm²), whereas the Power LED achieved up to 0.72 mW/cm² without optics. The Power LED exhibited a narrower spectral bandwidth (16 nm) and higher radiant output but also a greater thermal rise (~28.6 °C in 180 s). Adding the collimating lens increased irradiance by more than thirteen‑fold across all voltages, enabling the Power LED to reach 10.64 mW/cm² at 2.3 V—within the PBM therapeutic range (10–50 mW/cm²). Unlike prior LED characterization studies that have primarily reported basic electro‑optical parameters, this work uniquely integrates irradiance, thermal stability, and spectral analysis under clinically relevant conditions and demonstrates that a single red Power LED combined with beam‑shaping optics can reliably achieve PBM‑therapeutic irradiance at a practical treatment distance. Conclusion: Power LEDs, when combined with appropriate beam‑shaping optics, can deliver PBM‑relevant irradiance at practical treatment distances using a single emitter. The methodology and findings are applicable to comparable AlGaInP red LEDs from multiple manufacturers and provide a framework for optimizing PBM device design across varying treatment distances and optical configurations.
Introduction: This study aims to analyze different dosimetric indices using various formulae in cranial Stereotactic Radiosurgery/Radiotherapy treatment planning.Material and Methods: 42 targets were constructed from 23 patients with brain metastases (≤30 cc) treated at our institution, selected for this study. The PTVs were generated using a 3.0 mm isotropic margin from the CTV. Sequential boost prescriptions of 5-15 Gy were delivered using 6 MV FFF beams with full, partial, and non-coplanar arcs. The Acuros XB algorithm with a 1.25 mm grid size was optimized to calculate the dose distribution. The Conformity Index Homogeneity Index, and Gradient Index were evaluated using a DVH with different mathematical formulae.Results: RTOG, Van’t Riet, and Paddick, and the Inverse of RTOG values were close to 1.0. Whereas Lomax & Scheib and SALT were 0.92 ± 0.06, 0.94±0.05, respectively, they achieved lower than 1.0. The results of different types of HI values achieved similar ideal values. For GI data scored, each target is in the case of multiple lesions. The effective radius and modified GI results for the dose GI are 4.61 ± 1.12 and 4.28 ± 1.24, respectively.Conclusion: This study analyzed various CI, HI, and GI definitions to assess dose distribution quality in brain SRS/SRT plans. CI, HI, and GI are valuable tools for evaluating treatment plans by quantifying conformity, dose uniformity, and dose gradient. However, these indices have limitations. Future research should investigate these correlations, linking CI, HI, and GI with local control rates and toxicity outcomes.
Introduction: While high-intensity focused ultrasound (HIFU) is widely used for non-invasive tumor ablation, current models fail to account for the cumulative energy contributions of nonlinear harmonics (up to the 256th order), significantly limiting treatment precision. This study quantifies the role of harmonic superposition (1st+2nd+…+256th order) in regulating HIFU-induced focal temperature and establishes an optimized harmonic combination to enhance clinical parameter design. Material and Methods: A coupled acousto-thermal model, validated against MRI thermometry, was developed by solving the Westervelt equation and Pennes bioheat equation to simulate nonlinear acoustic propagation and transient temperature fields in ex vivo porcine muscle under HIFU irradiation. Results: The 1st+2nd+...+128th harmonic superposition model achieved <2.2% error in focal temperature prediction across all tested power levels (80–200 W), with errors of 1.6% at 80 W (48.18 °C simulated vs. 47.96 °C measured), 1.1% at 140 W (66.0 °C vs. 65.31 °C), 1.8% at 160 W (77.78 °C vs. 76.38 °C), and 2.2% at 200 W (82.25 °C vs. 84.11 °C). Mid-order harmonics (2nd–64th) contributed 75–80% of energy deposition, while high-order harmonics (>128th, e.g., 256th at 276.48 MHz) exhibited severe attenuation (135× higher than the fundamental wave). The linear propagation model (fundamental frequency only) underestimated temperatures by 4.8-23.9%, highlighting the necessity of nonlinear harmonic inclusion. Conclusion: This work establishes a harmonically optimized framework for HIFU treatment planning, addressing a critical gap in nonlinear acoustic modeling and enabling safer, precision thermal therapy.
Introduction: Beta vulgaris has traditionally been utilized for the treatment of anemia, cancer prevention, fever management, anticoagulation, sclerosis, and bodily detoxification. Beets have been utilized for these purposes by numerous ancient tribes throughout antiquity. Material and Methods: The major purpose of this research was to investigate the impact of betalains derived from beetroot (Beta vulgaris) on the biosynthesis of silver nanoparticles (AgNPs). Results: The in vitro antibacterial effectiveness of Beta vulgaris L. pomace extract was evaluated at a dose of 100 mg/ml against clinical isolates and reference strains of Escherichia coli and Klebsiella pneumoniae. Well diffusion (50, 100 µl) and disc diffusion (15 µl) were other methods utilized. Fourier transform infrared spectroscopy, scanning tunneling microscopy (STM), and scanning electron microscopy (SEM) were employed to analyze the physical properties of the synthesized silver nanoparticles. Antimicrobial characteristics, silver nanoparticles, agar well-diffusion method. Conclusion: This distinctive green synthesis technique for generating silver nanoparticles obviates the necessity for detrimental solvents and byproducts, rendering it an optimal instrument for promoting green technology in nanotechnology.
Introduction: To determine the most effective treatment plan for tongue cancers, a comparative analysis is being conducted between Intensity-Modulated Radiation Therapy (IMRT_D) plans on the Clinac DMX system and both Volumetric Modulated Arc Therapy (VMAT_H) and IMRT_H plans for Halcyon Elite machine.Material and Methods: A retrospective study was conducted on 20 patients with tongue cancers. A total dose of 60 Gy was delivered in two phases (i.e., 50 Gy for Elective and 10 Gy for Boost). All patients were treated using VMAT plans with unflattened beams from Varian Halcyon Elite (VMAT_H). For comparison, 40 equivalent IMRT plans with unflattened and flattened beams were created for Varian Halcyon Elite and Varian Clinac DMX (IMRT_H and IMRT_D). Plan parameters such as Homogeneity, Conformity, and Gradient Indices, along with OAR doses, were compared across the three plans.Results: This study reveals that the HI of IMRT_H plans is significantly lower (P<0.05) compared to IMRT_D and VMAT_H plans. The CI and GI of IMRT_H and VMAT_H plans are significantly lower (P<0.05) than those of IMRT_D plans. VMAT_H plans and IMRT_H treatment plans achieve lower doses of OARs, showing a significant difference in statistics compared to IMRT_D plans. The Monitor Units required by VMAT_H plans are significantly lower (P<0.05) than those of IMRT_D and IMRT_H plans.Conclusion: Volumetric Modulated Arc Therapy planned with Halcyon Elite configuration is suggested for the treatment of patients with tongue cancers. VMAT_H and IMRT_H plans were similar in plan parameters and OAR sparing, but VMAT_H required fewer Monitor Units.
Introduction: Gold/gold sulfide (GGS) nanoparticles exhibit strong near-infrared (NIR) absorption, high chemical stability, low cytotoxicity, and ease of surface functionalization, enabling deep tissue penetration and targeted cancer theranostics. This study aimed to develop and evaluate 99mTc-labeled PEGylated GGS nanoconjugates for potential SPECT imaging applications.Material and Methods: GGS nanoparticles were synthesized by reacting HAuCl4 with Na₂S and stabilized with polyvinylpyrrolidone (PVP). PEGylated GGS nanoparticles were radiolabeled with 99mTc using SnCl2, and radiochemical purity and stability were assessed via ITLC in RPMI and human serum up to 24 h. Cytotoxicity and cellular uptake were evaluated in CT26 colon carcinoma cells. Biodistribution was studied in BALB/c mice at 1, 4, and 24h post-intravenous injection by gamma counting of excised organs.Results: 99mTc-GGS-PVP nanoconjugates had an average diameter of ~65.9 nm, maintained strong NIR absorption post-labeling, and achieved high radiochemical purity (97.1 ± 1.1%) with stability >85% in RPMI and >82% in human serum at 24h. Cytotoxicity was minimal, with 78% cell viability at 125 GGS. Cellular uptake increased from 1.98% at 2h to 10.0% at 24h. In vivo, the liver showed the highest uptake (51.44 ± 7.54 %ID/g at 1h), decreasing to 14.19 ± 6.54 %ID/g at 24h; spleen uptake was markedly lower (1.51 ± 0.81 %ID/g at 1h, 0.765 ± 0.28 %ID/g at 24h).Conclusion: The synthesized 99mTc-GGS-PVP nanoparticles demonstrated high stability, low toxicity, favorable biodistribution, and preserved NIR absorption properties, making them promising candidates for dual-modality cancer theranostics. Their radiolabeling efficiency and biodistribution support their use as radiotracers for SPECT imaging and potential integration into combined diagnostic and therapeutic (theranostic) platforms.
Introduction: Accurate radiation dose measurement in both target and non-target tissues is essential in modern external beam radiotherapy. In dosimetry systems, the goal is to deliver the maximum dose to the target volume while ensuring minimal exposure to the surrounding normal cells. Among various medical dosimetry systems like thermoluminescence (TL), optically stimulated luminescence (OSL), and radioluminescence (RL), the RL system offers a real-time monitoring system.Material and Methods: This study aims to characterize the Ge-doped probe in making beam profile measurements using the myDoz® RL/OSL dosimetry system and comparing its accuracy with a CC-13 ion chamber. The RL system utilized a 30-meter PMMA optical fiber with a Ge-doped optical fiber scintillator probe. Beam profiles were measured for 3 × 3 and 10 × 10 cm² field sizes at a depth of 1.5 cm in solid water, with a source-to-surface distance (SSD) of 100 cm, using a 6 MV photon beam, 400 MU/min dose rate, and a total dose of 3 Gy.Results: The RL readout mechanism enabled instantaneous dose and dose-rate readings. The results showed high consistency and close agreement with the CC-13 ion chamber, with beam profiles displaying uniform central dose, sharp penumbra, and strong symmetry. Larger fields exhibited increased flatness due to photon dispersion. Moreover, the dose distribution out-of-field may still cause low-dose exposure to normal tissues.Conclusion: To sum up, Ge-doped fibers demonstrated excellent real-time, high-resolution performance, making them promising alternatives to traditional dosimeters. Future research should focus on clinical implementation and system optimization for advanced radiotherapy methods.
Introduction: The objective of this study was to utilize Machine Learning (ML) techniques to assess the conduction of nerves located in the upper extremities, specifically the median, ulnar, and radial nerves. The study aimed to establish normal values for nerve conduction (NC) and evaluate the influence of variables such as gender, age, weight, and height on NC. Material and Methods: Electrodiagnostic tests were employed to assess the conduction of both motor and sensory nerves. ML techniques were applied to analyze the data and predict NC values. The study considered historical background and thorough medical assessments to ensure the absence of any NC agents or underlying medical conditions. Results: The investigation successfully established normal values for NC. The ML models demonstrated favorable performance in predicting NC values, considering the influence of variables such as gender, age, weight, and height. Conclusion: The study successfully established normal values for nerve conduction in the upper extremities and demonstrated the effectiveness of ML models in predicting NC values. These findings highlight the potential of ML techniques in enhancing the assessment and understanding of nerve conduction, considering various influencing factors. However, this study has limitations, including its single-center design and a relatively small female cohort, which may affect the generalizability of the results.
Introduction: Prostate cancer is a highly prevalent malignancy worldwide. Advances in radiotherapy, particularly SBRT, have enabled ultra-hypofractionated treatments that improve tumor control while reducing treatment time. This study evaluates the dosimetric accuracy and plan quality of prostate SBRT delivered using RapidArc technology on a Varian Millennium Multi Leaf Collimator (MLC) system.Material and Methods: Twenty-four patients with localized prostate adenocarcinoma received SBRT with a prescribed dose of 36.25 Gy in five fractions. Treatment planning was performed using Eclipse v15.6 with the Acuros XB algorithm, employing three 6 MV Flattening Filter Free (FFF) arcs. Planning Target Volume (PTV) coverage, Organs At Risk (OAR) doses, Paddick Conformity Index (PCI), Gradient Index (GI), and Monitor Units (MU) were analyzed. MLC performance was assessed using trajectory log files to evaluate leaf speed and positional accuracy. Pre-treatment Quality Assurance (QA) was conducted using Electronic Portal Imaging Device (EPID)-based gamma analysis.Results: The mean PTV D95 was 35.80 ± 0.46 Gy, with Dmax and Dmean of 39.80 ± 1.05 Gy and 37.10 ± 0.40 Gy, respectively. V95% averaged 99.13 ± 1.14%, confirming adequate coverage. The bladder and rectum Dmax slightly exceeded constraints, while all other OAR objectives were met. Gamma pass rates exceeded 99.6% for the 2%/2mm criterion. MLC leaf speed remained below 2.5 cm/s, ensuring delivery accuracy.Conclusion: RapidArc-based prostate SBRT demonstrated high conformity, efficient delivery, and acceptable OAR sparing. Intra-fraction CBCT improved setup accuracy, and MLC log-file analysis confirmed mechanical precision, supporting the feasibility of this approach for localized prostate cancer.
Introduction: The development of effective, eco-friendly radiation shielding materials is critical for medical and nuclear applications. This research investigates the effect of bismuth oxide (Bi₂O₃) content on the gamma-ray attenuation properties of PbO–Al₂O₃–B₂O₃–SiO₂–Bi₂O₃ glasses. Material and Methods: Six distinct glass compositions, characterized by varying mole fractions of Bi₂O₃, were meticulously selected for this study. The mass attenuation coefficients (MAC) and linear attenuation coefficients (LAC) were computed utilizing the Geant4 Monte Carlo simulation toolkit and subsequently validated against the Phy-X/PSD computational software. The half-value layer (HVL), tenth-value layer (TVL), and effective atomic number (Zeff) were derived across a gamma-ray energy spectrum ranging from 0.015 to 10 MeV. Results: Excellent agreement was observed between Geant4 and Phy-X results (relative error < 2%). The LAC increased with Bi₂O₃ content, particularly at low energies (< 0.3 MeV), where sample S-6 (highest Bi₂O₃) exhibited the highest attenuation. HVL and TVL increased with photon energy but decreased with higher Bi₂O₃ concentration, confirming enhanced shielding efficiency. S-6 displayed the lowest TVL, indicating superior performance. Zeff varied significantly with composition and energy, reaching a minimum near 2 MeV for all samples. All glasses showed shielding capabilities comparable to or better than conventional glass shields. Conclusion: Increasing Bi₂O₃ content substantially improves gamma-ray shielding in lead-bismuth borosilicate glasses, especially at low energies. These glasses are promising candidates for radiation protection applications.
Introduction: Protective agents against harmful radiation have been studied for decades. Antioxidants can protect normal tissues by scavenging free radicals generated during irradiation. Ferula asafoetida (AS), a medicinal plant with antioxidant activity, was evaluated in this study for its protective effect against radiation-induced small intestinal injury in rats.Material and Methods: Thirty-five Wistar rats were randomly divided into five groups (n=7): control, irradiated (R), irradiated+AS (R+AS), irradiated+vitamin E (R+E), and irradiated+AS+E (R+AS+E). Treatments included AS (100 mg/kg) and/or vitamin E (20 mg/kg) daily for eight days. On day six, irradiated groups received 6 Gy X-rays (6 MV, Elekta, Stockholm, Sweden). On day eight, rats were euthanized and intestine and liver tissues collected. Histopathology, malondialdehyde (MDA), and glutathione (GSH) levels were analyzed.Results: AS significantly reduced MDA and increased GSH levels in both intestine and liver. Vitamin E showed weaker effects. The combination of AS and vitamin E did not consistently enhance AS activity. Histological analysis revealed that AS reduced inflammation and atrophy, while vitamin E alone or combined with AS lowered inflammation and epithelial erosion. Neither treatment increased mucous cell counts.Conclusion: AS exerted notable antioxidant and radioprotective effects against intestinal damage in rats, indicating its potential as a natural agent for mitigating radiation-induced injury.
Introduction: The process of wound healing represents a dynamic and multifaceted phenomenon characterized by intricate cellular and molecular mechanisms aimed at the restoration of tissue integrity. The present study examined the impact of low-dose ionizing radiation, particularly alpha particles released by americium-241, on the process of wound healing in murine experimental models. Material and Methods: Twenty-four male mice were randomly divided into three groups: a control group (CG) and two experimental groups exposed to radiation for 5 minutes (IG-5) and 15 minutes (IG-15), respectively (n = 8 per group). Each mouse received two 8 mm circular full-thickness skin excisions on the dorsum. Wound healing was evaluated over 10 days using photographic analysis (quantified via ImageJ software) and histological assessment. Results: Results indicated significantly enhanced wound closure in both irradiated groups, particularly IG-15, compared to the CG. Biochemical analyses revealed elevated levels of growth factors in irradiated tissues. Histological findings showed increased collagen deposition, greater fibroblast proliferation, and reduced inflammatory cell infiltration in the experimental groups. Conclusion: These findings suggest that controlled low-dose alpha radiation, particularly in the IG-15 protocol, may beneficially modulate the wound healing process and hold potential for novel therapeutic applications.
Introduction: Gamma Knife Perfexion™ delivers 192 Cobalt-60 sources to the focal point (isocenter), and the patient is fixed using a stereotactic frame. In conformal techniques, the width of the penumbra resulting in an out-of-field dose of normal tissue adjacent to the tumor must be accurately determined. The purpose of this study was to calculate the penumbra widths of a single beam and 192 beams for different collimator sizes of the Gamma Knife Perfexion™ using the BEAMNRC/DOSXYZNRC Monte Carlo simulation code and compare the results with EBT3 film dosimetry data. Material and Methods: To investigate the physical penumbra width (80-20%), the single beam and 192 beam profiles were obtained using the DOSXYZNRC code and EBT3 films located at the isocenter point in a spherical solid water phantom with a diameter of 160 mm. Results: The results showed that the Gamma Passing Rate (GPR) value for all collimator sizes has a value above 97%. The single-beam penumbra widths obtained from simulation data for 4, 8, and 16 mm collimator sizes along the X-axis were 0.75, 0.77, and 0.87 mm, respectively. The data for 192 beams obtained from the simulation were 2.60, 4.80, and 8.70 mm along the X-axis. Conclusion: The differences between measured and simulated penumbra widths are in an acceptable range. However, for more precise measurement in the penumbra region with a high dose gradient, a Monte Carlo simulation is recommended.
Introduction: Protoporphyrin IX (PpIX) is a critical photosensitizer in photodynamic therapy (PDT) with applications in oncology and dermatology. Despite its clinical importance, comprehensive understanding of its pharmacokinetic profile remains limited. This study aimed to characterize the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of PpIX using computational approaches. Material and Methods: The molecular structure of PpIX was analyzed using two complementary computational platforms, Deep-pk and pkCSM, which utilize machine learning and deep learning algorithms trained on experimental pharmacokinetic data to predict ADMET parameters. Physicochemical properties, absorption, distribution, metabolism, excretion, and toxicity profiles were evaluated and compared between the platforms. Results: PpIX exhibited high lipophilicity (LogP>7) with moderate hydrogen bonding capacity. Both platforms predicted good intestinal absorption (63.5-98.2%) but poor oral bioavailability, explaining the preference for topical administration in clinical settings. PpIX showed moderate tissue distribution (VDss 0.63-0.77 log L/kg) and was not predicted to be a substrate for major CYP450 enzymes, suggesting metabolic stability. However, strong inhibition of CYP1A2 (probability 0.97) and transporters (OATP1B1, BCRP) indicated potential drug interactions. The predicted short half-life (<3 hours) aligned with clinical observations. Toxicity analysis revealed non-mutagenicity and cardiac safety, but conflicting hepatotoxicity predictions and potential respiratory toxicity warrant clinical monitoring. Conclusion: Computational analysis of PpIX confirmed pharmacokinetic properties supporting its clinical use but raised concerns about drug interactions and organ toxicity. These results provide a basis for optimizing PDT protocols and improving formulations. Differences between prediction methods highlight the need for experimental validation of key parameters to ensure clinical safety and effectiveness.
Introduction: The process of brain aging is a complex phenomenon that can manifest in a number of ways, including normal, pathological, and accelerated aging, each influenced by modifiable factors such as sex and lifestyle. Subtle, preclinical alterations, including iron accumulation, proteostasis disruption, and inflammation, often precede overt clinical manifestations of cognitive decline, highlighting the need for early detection and intervention.Material and Methods: This review examines the neuropathological mechanisms of age-related cognitive decline, integrating current knowledge on the interplay of genetic, environmental, and lifestyle factors. Advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI) and positron emission tomography (PET) offer powerful tools for investigating the complex biological and biochemical dynamics of the aging brain. Quantitative Susceptibility Mapping (QSM), a novel MRI technique, provides precise quantification of tissue magnetic susceptibility, enabling detailed assessment of iron deposition and myelin content, both crucial factors in age-related brain changes.Results: We explore the diagnostic potential of QSM and other advanced neuroimaging techniques for identifying early biomarkers of brain aging and predicting cognitive trajectories. This research indicates that the accumulation of non-heme iron is a primary contributor to neuronal death in brain aging. This conclusion is supported by QSM studies, which have validated the role of iron in this process.Conclusion: By integrating mechanistic understanding with practical prevention strategies, this research indicates that the accumulation of non-heme iron is a primary contributor to neuronal death in brain aging. Additionally, the literature suggests that dietary and physical activity interventions may beneficially mitigate neurodegeneration associated with aging.
Introduction: Naturally occurring ionizing radiation is present throughout the Earth's environment, both on the surface, underground, and in the air. Hot springs, renowned for their therapeutic benefits, are popular destinations for hydrotherapy worldwide. However, these hot springs often contain radon and other radioactive elements in their water, sediments, and surrounding soil, making them potential sources of radiation exposure. Despite this, no prior research has assessed the radiation risks or estimated the annual effective doses to internal organs from Hormozgan's hot springs. This study aims to measure gamma radiation levels in these hot springs to fill this critical knowledge gap. Material and Methods: In this cross-sectional study, radiation levels were measured using the RADDIGI 3000 C, a Geiger-Muller survey meter designed for environmental monitoring. Readings were taken at a height of 1 meter above the water surface, with dose rates recorded hourly. Results: Our findings revealed that Khest hot spring 3 exhibited the highest gamma radiation dose rate, with values ranging from 2.31 to 4.2 µSv/h (mean: 3.2 µSv/h, SD: 0.17). In contrast, Momadi hot spring had the lowest recorded levels, ranging from 0.06 to 0.13 µSv/h (mean: 0.095 µSv/h, SD: 0.005). The results demonstrate that Khest hot spring 3 presents a significantly higher gamma radiation risk compared to all other hot springs examined in this study. Conclusion: This gamma dose rate is comparable to levels recorded in Ramsar, northern Iran, a region globally recognized for its elevated natural background radiation. To mitigate potential health risks for swimmers and local populations, regulatory measures and protective policies should be implemented by regional authorities.
