Optimization of radioprotective dose of Juglans nigra leaf extract using response surface methodology

J. nigra leaf contains mixture of various pharmacologically active compounds and it is assumed that they may have the potential radioprotective effect. The purpose of this work was to predict radioprotective doses by correlating changes in organ distribution of radiopharmaceuticals with extract dose levels and rat body weight using response surface methodology (RSM) based on a second-order polynomial equation. The extract was administered daily by oral gavage to rats at dose of 6.9, 10.3, or 13.7 mg kg-1 body weight (bw) day-1 for 10 days. On the eleventh day, 0.1 ml of the one radiopharmaceutical (Na99mTcO4, 99mTc-dimercaptosuccinic acid and 99mTc-tin colloid) was injected into the tail vein. The statistical parameters: the coefficient of determination (0.81-0.95), the coefficient of variation (3.05-11.1), the adequate precision (8.82-19.12) and the mean relative percentage deviation (± 2.3-8.2) were indicated the precision and reliability of RSM. Using RSM, the predicted daily dose of leaf extract ranging from 11.19 to 11.99 mg kg-1 bw may be considered as an optimal daily radiopotective dose for protection of organs from radiation in cases of planned radiation exposures.

The empirical formula for calculating the incident air Kerma in intraoral radiographic imaging

OBJECTIVES

The aim of this paper is to determine the empirical formula for calculating the incident air kerma (Ki), used as a patient dose descriptor in the intraoral radiographic imaging.

METHODS

The data for the formula were collected during the regular annual inspection of intraoral dental X-ray units in 2018, 2019 and early 2020. The measurement data of 50 X-ray units were processed to develop the formula. Exposure factors for imaging molars of the upper jaw of an average patient in a clinical setting were used in the measurement. The formula validity was statistically evaluated using coefficient of correlation, standard error of the fitted function and the mean relative percentage deviation.

RESULTS

The measurement values of the radiation doses and calculated values obtained by using the final formula showed good agreement - the mean relative percentage deviation values less than ±15%.

CONCLUSIONS

Although there are differences in X-ray units, voltages, manufacturers and device architectures (single-phase and high-frequency), the measurement data comply well with computed ones in all cases.

Modelling and optimisation of treatment parameters in high-dose-rate mono brachytherapy for localised prostate carcinoma using a multilayer artificial neural network and a genetic algorithm: Pilot study

BACKGROUND

High-dose-rate mono brachytherapy (HDR-MB) is employed in the treatment of prostate carcinoma (CaP). As an ideal plan of CaP brachytherapy cannot be created, it is necessary to identify a reliable tool to optimise the parameters of HDR-MB. This paper applies a multilayer artificial neural network (MANN) and a genetic algorithm (GA) to optimise brachytherapy parameters based on an individual dose-volumetric analysis.

METHODS

Patients with localised CaP of various risks were treated with HDR-MB. Consecutive levels of the biochemical control parameter (prostate specific antigen (PSA) nadir) have been collected after completion of HDR-MB in the range 2-9 years. The Kaplan-Meier regression analysis of biochemical-free survival (BFS) was applied. The clinical risk of recurrent CaP (RCaP), the therapy dose (TD), TD coverage index (CI_100%) and PSA nadir were modelled using the MANN and GA.

RESULTS

In the low-risk group, BFS was achieved in 100% of treated patients, while in the group of patients with high risk, BFS was achieved in 95.8% of treated patients. The MANN-GA model optimises a TD of 47.3 Gy and CI_100% of 1.14 as well as a TD of 50.4 Gy and CI_100% of 1.6 for the low-risk group and high-risk group, respectively, of localised CaP. The optimised PSA nadir was 0.047 and 0.25 ng cm^-3 for low-risk group and high-risk group, respectively.

CONCLUSIONS

The developed MANN-GA model presents a method for optimising the treatment parameters in radiation therapy, which could be a valuable tool in planning of the HDR-MB.

Aspergillus fumigatus branching complexity in vitro: 2D images and dynamic modeling

BACKGROUND

Aspergillus fumigatus causes serious infections in humans, and its virulence correlates with hyphal growth, branching and formation of the filamentous mycelium. The filamentous mycelium is a complex structure inconvenient for quantity analysis. In this study, we monitored the branching of A. fumigatus filamentous mycelium in vitro at different points in time in order to assess the complexity degree and develop a dynamic model for the branching complexity.

METHOD

We used fractal analysis of microscopic images (FAMI) to measure the fractal dimensions (D) of the branching complexity within 24 h of incubation.

RESULTS

By photographing the filamentous mycelium dynamically and processing the images, the D variation curve of A. fumigatus complexity degree was obtained. We acquired the D variation curve which contained initial exponential period and stationary period of A. fumigatus branching. Further, the obtained data of D was modeled via the logistic model (LM) to develop a dynamic model of A. fumigatus branching for the prediction of the specific growth rate of branching value (0.23 h^-1).

CONCLUSIONS

Developed FAMI and LM models present a simple and non-destructive method of predicting the evolution of branching complexity of A. fumigatus. These models are useful as laboratory measurements for the prediction of hyphal and mycelium development, especially relevant to the pathogenesis study of aspergillosis, as well as pathogenesis of other diseases caused by moulds.

Kinetic modeling and optimization of maceration and ultrasound-extraction of resinoid from the aerial parts of white lady's bedstraw (Galium mollugo L.)

In this paper, extraction of resinoid from the aerial parts of white lady's bedstraw (Galium mollugo L.) using an aqueous ethanol solution (50% by volume) was studied at different temperatures in the absence and the presence of ultrasound. This study indicated that ultrasound-assisted extraction was effective for extracting the resinoid and gave better resinoid yields at lower extraction temperature and in much shorter time than the maceration. A phenomenological model was developed for modeling the kinetics of the extraction process. The model successfully describes the two-step extraction consisting of washing followed by diffusion of extractable substances and shows that ultrasound influences only the first step. The extraction process was optimized using response surface methodology (RMS) and artificial neural network (ANN) models. For the former modeling, the second-order polynomial equation was applied, while the second one was performed by an ANN-GA combination. The high coefficient of determination and the low MRPD between the ANN prediction and the corresponding experimental data proved that modeling the extraction process in the absence and the presence of ultrasound using ANN was more accurate than RSM modeling. The optimum extraction temperature was determined to be 80 and 40 °C, respectively for the maceration and the ultrasound-assisted extraction, ensuring the highest resinoid yield of 22.0 g/100g in 4h and 25.1g/100g in 30 min, which agreed with the yields obtained experimentally for the same time (21.7 and 25.3g/100g, respectively).