Photosensitivity and Radiosensitivity of Indocyanine Green on Human Cell Lines MCF7 and DFW

Abstract: Background & Aims: In this study with the aim of benefiting from non-laser sources in photodynamic therapy, photo and radio sensitivity of indocyanine green as a sensitizer in photodynamic and radiation therapies were investigated. Methods: Based on the broad absorption peak of indocyanine green and using non-coherent light, the experiments were performed on human cells derived from breast cancer and melanoma. To investigate chemical, optical and radiational cytotoxicity and also photosensitivity and radiosensitivity of indocyanine green, different dozes of this material were examined. After 24 h of incubation of cells with indocyanine green, independent treatment groups were exposed to 730±20 nm light with power densities of 30, 60, and 108 J/cm2 and 100 kVp x-ray (2 and 4 Gy). The effect of therapy on cells was determined by MTT test. Results: Indocyanine green showed no significant cytotoxicity. It had a good efficiency for photodynamic therapy using non-coherent sources in the wavelength of 730±20 nm, and the efficiency of treatment was dependent on the dosage of light. No significant relation between indocyanine green and radiation was observed. Conclusion: According to the findings, indocyanine green can be used as a photosensitizer in the range of 730±20 nm. Since there was no significant difference between groups which received both radiation and drug and those which received only radiation, indocyanine green cannot be considered as a radiosensitizer. Keywords: Photodynamic therapy, Breast Neoplasms, Melanoma, Indocyanine Green, Radio sensitivity Non-coheren

Noise Pollution and Traffic Noise Index on Mashhad Main Streets during the Busiest Hours of Summer

Introduction: Among the environmental pollutions, noise is very important for its physiological  and psychological effects on human. Traffic noise is one of the most important pollutants and the  hospitals  are  one  of  the  critical  places  regarding  this  type  of  noise.  For  these  reasons,  in  the  summer  of  1382,  the  traffic  noise  of  Mashhad  main  streets  around  the  hospitals  was  assessed  during the busiest hours.  Materials and Methods: The noise indexes such as L Aeq , L Afmax , L 10 , L 50  and L 90  were measured  by a Sound-Level-Meter, model Investigator 2260. The traffic load was also determined. On the  basis  of  these  results,  Noise  Pollution  Level  (NPL)  and  Traffic  Noise  Index  (TNI)  were  calculated. The assessment was done during three different periods of the days in twelve stations.   Results: Based on the obtained results, the maximum L Aeq  was recorded on Bahar Street during  the morning hours and on Koohsangi Street during the noon and night periods. Throughout the  three  periods  the  maximum  NPL  and  TNI  were  estimated  on  Bahar  and  Nakhrisi  Streets,  respectively.  The  correlation  between  all  of  the  indexes  was  analyzed  and  a  logarithmic  correlation was observed between L Aeq  and the traffic load.   Discussion  and  Conclusion:  On  the  basis  of  the  noise  standard  in  free  field  in  Iran,  noise  pollution is a serious problem in Mashhad

Vis-NIR hyperspectral imaging and multivariate analysis for prediction of the moisture content and hardness of Pistachio kernels roasted in different conditions

Introduction: Pistachio nut is one of the most delicious and nutritious nuts in the world and it is being used as a salted and roasted product or as an ingredient in snacks, ice cream, desserts, etc. (Maghsudi, 2010; Kashaninejad et al. 2006). Roasting is one of the most important food processes which provides useful attributes to the product. One of the objectives of nut roasting is to alter and significantly enhance the flavor, texture, color and appearance of the product (Ozdemir, 2001). In recent years, spectral imaging techniques (i.e. hyperspectral and multispectral imaging) have emerged as powerful tools for safequality inspection of various agricultural commodities (Gowen et al., 2007). The objectives of this study were to apply reflectance hyperspectral imaging for non-destructive determination of moisture content and hardness of pistachio kernels roasted in different conditions. Materials and methods: Dried O’hadi pistachio nuts were supplied from a local market in Mashhad. Pistachio nuts were soaked in 5L of 20% salt solution for 20min (Goktas Seyhan, 2003). For roasting process, three temperatures (90, 120 and 150°C), three times (20, 35 and 50 min) and three air velocities (0.5, 1.5 and 2.5 m s-1) were applied. The moisture content of pistachio kernels was measured in triplicate using oven drying (3 gr samples at 105 °C for 12 hours). Uniaxial compression test by a 35mm diameter plastic cylinder, was made on the pistachio kernels, which were mounted on a platform. Samples were compressed at a depth of 2mm and speed of 30 mm min-1. A hyperspectral imaging system in the Vis-NIR range (400-1000 nm) was employed. The spectral pre-processing techniques: first derivative and second derivative, median filter, Savitzkye-Golay, wavelet, multiplicative scatter correction (MSC) and standard normal variate transformation (SNV) were used. To make models at PLSR and ANN methods, ParLeS software and Matlab R2009a were used, respectively. The coefficient of determination (R2), the root mean square error of prediction (RMSEP) and the ratio of the standard deviation of the response variable to RMSEP (known as relative performance determinant (RPD)) were calculated. Results and discussion: Interpretation of hyperspectral data: The results showed that the spectra of the shell, the whole kernel and the internal part of the kernel have different patterns. The internal part of thekernel had 2 peaks at 630 nm and 690 nm, while the shell and the whole kernel had 1 peak at 670 nm and 720 nm, respectively and the peak of the whole kernel was sharper than that of the shell. The highest and lowest intensities were for the internal part of the kernel and the whole kernel, respectively. The spectral slope of the internal part is higher than that of the shell and the whole kernel at 500-700 nm. The effect of different pre-processing techniques and analysis on prediction of pistachio kernels properties: In the absence of pre-processing techniques, low correlation coefficients were observed for prediction of moisture content and hardness. However, with the use of pre-processing techniques, in some models, correlation coefficient and RPD increased and the RMSEP decreased. The results revealed that ANN models would predict moisture content and textural characteristics of roasted pistachio kernels better than PLSR models. Moisture content: ANN models can predict moisture content of roasted pistachio kernels better than PLSR models. In total, PLSR models showed low RPD and R2. For all samples, RPD was lower than 1.5, indicating that the developed models do not give an accurate prediction for moisture content. The best results with ANN method were achieved using a combination of SNV, wavelet and D1 for predicting moisture content with R2 =0.907 and RMSEP=0.179. Hardness: The results indicated that ANN models can predict the hardness better than PLSR models. The best results with PLSR models were achieved using a combination of SNV, wavelet and D1 with R2= 0.643, RMSEP=10.78, RDP= 1.48 and 2 PLSR factors. However, due to high RMSEP and low R2 and RPD, it can be mentioned that prediction of hardness values with ANN model was not sufficiently desirable. However it was better than the PLSR models. The best results with ANN models were achieved using a combination of SNV, wavelet and D2 with R2=0.876 and RMSEP=5.216. Conclusions: The results of this study showed that employing pre-processing methods causesa decrease in prediction error and improves the quality of the models. ANN models could predict moisture content and hardness of roasted pistachio kernels better than the PLSR models

Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. The objective of this study was to develop calibration models for prediction of moisture content and textural characteristics (fracture force, hardness, apparent modulus of elasticity and compressive energy) of pistachio kernels roasted in different conditions (temperatures 90, 120 and 150 °C; times 20, 35 and 50 min and air velocities 0.5, 1.5 and 2.5 m/s) using Vis/NIR hyperspectral imaging and multivariate analysis. The effects of different pre-processing methods and spectral treatments such as normalization [multiplicative scatter correction (MSC), standard normal variate transformation (SNV)], smoothing (median filter, Savitzky–Golay and Wavelet) and differentiation (first derivative, D1 and second derivative, D2) on the obtained data were investigated. The prediction models were developed by partial least square regression (PLSR) and artificial neural network (ANN). The results indicated that ANN models have higher potential to predict moisture content and textural characteristics of roasted pistachio kernels comparing to PLSR models. High correlation was observed between reflectance data and fracture force (R2 = 0.957 and RMSEP = 3.386) using MSC, Savitzky–Golay and D1, compressive energy (R2 = 0.907 and RMSEP = 15.757) using the combination of MSC, Wavelet and D1, moisture content (R2 = 0.907 and RMSEP = 0.179) and apparent modulus of elasticity (R2 = 0.921 and RMSEP = 2.366) employing combination of SNV, Wavelet and D1, respectively. Moreover, Vis–NIR data correlated well with hardness (R2 = 0.876 and RMSEP = 5.216) using SNV, Wavelet and D2. These results showed the capability of Vis/NIR hyperspectral imaging and the central role of multivariate analysis in developing accurate models for prediction of moisture content and textural properties of roasted pistachio kernels