Journal of Sciences, Islamic Republic of Iran
Volume:35 Issue: 2, Spring 2024

Nowadays the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become an endemic disease throughout the world on the other hand intensive worldwide vaccination programs decreased the severity of the affection but early virus detection and disease diagnosis are still important healthcare management of infectious disease control. Therefore, in this research, we introduce an electrochemical genosensor based on a DNA probe that can hybridize directly to the viral genome or its transcripts and therefore does not need cDNA synthesis following RNA extraction from patient samples, a necessary and challenging step in routine RNA virus detection methods like Real-time PCR. Altogether, in this research, an electrochemical biosensor based on a virus-specific probe with thiol modification was designed and immobilization of the probe was carried out through self-assembly by thiol binding on reduced graphene oxide (RGO) and gold nanoparticles composite-modified pencil graphite electrode (PGE). The hybridizations of probe and target sequences were analyzed by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) methods. The linear range was found to be within 10-12 - 10-6 M and the limit of detection (LOD) was at 3× 10 -13 M. The time of 20 minutes was chosen as the optimal hybridization time. The results showed that the fabricated biosensor can be recovered and reused up to 6 times. This means significant time, and expense savings when compared with other conventional detection methods for this virus. Therefore, this biosensor is suggested for clinical applications especially when time and sensitivity are the most limited elements.

Prostate cancer stands as the second most prevalent cancer among men globally and represents a significant cause of mortality in Iran. Notably, nanotechnology has emerged as a valuable tool in the realm of medical research, offering advancements in both cancer diagnosis and treatment. Prior research has shown that nanoparticles, when entering biological environments like plasma or serum, are surrounded by a layer of proteins referred to as the protein corona. The protein coronas' composition differs across various disorders, affecting the kind and amount of proteins that attach to the nanoparticle surface. This study aimed to assess the toxicity of protein coronas loaded onto various nanoparticles, including gold, graphene, and superparamagnetic iron oxide nanoparticles (SPIONs), in prostate cancer and normal cell lines. Plasma samples from cancer patients and healthy individuals were procured, and nanoparticles (gold, SPIONs, graphene oxide) were synthesized, with their charge and size verified using zeta method. Subsequently, the MTT assay was used to study the toxicity of combinations of nanoparticles (gold, SPIONs, graphene oxide) and their associated protein coronas on the LNCaP prostate cancer cell line and healthy HFF fibroblast cells. Gold nanoparticles exhibited higher toxicity towards cancer cells compared to the other two nanoparticles. Conversely, SPIONs and graphene oxide did not manifest significant toxicity on healthy cells. The increased toxicity of graphene oxide-associated protein coronas highlights the complex relationship between nanoparticle composition and protein corona properties, offering important insights for targeted cancer therapy techniquesthe quantisation of aromatic amines simultaneously in fairly complex matrix of dyes effluents and biological samples (human serum) by simple GC-FID with adequate sensitivity.

A GC-FID procedure was developed for the separation and analysis of six isomers of xylidines (di-methylanilines), aniline and 1,4-Phenylenediamine after derivatization via ethyl chloroformate (ECF). GC separation was from column DB-5 (30m x 0.32mm) with the 0.25 µm layer thickness, 90 ˚C column temperature for 3 min, followed via heating rate 10 to 200 ˚C followed by hold of temperature for 7 min. The 1.5 ml /min was nitrogen flow with divided ratio 10:1. Linear calibration range of each of the compound was obtained with 1-20 ng/ml with coefficient of determination (r2) 0.9969-0.9970. Limits of detections (LOD) calculated as indication to 3:1 noise ratio was within 0.10-0.99 ng/ml. Derivatization, separation and quantitation were replicate in terms of retention time and peak height/peak area with the relative standard deviations within 2.1%. Method was employed for analysis of effluents of dyes manufacturing company and blood samples of workers employed in dyes manufacturing sector. All the six isomers of xylidines and aniline were detected in effluents and human serum samples at the concentration levels within 49-200 µg/ml and 1.7-9.8 ng/ml respectively. Results of analysis were further confirmed by standard addition technique and percent recoveries were calculated within 96-99 and 95-97 along with % RSD within 3.2 and 2.9 from the effluents and the human serum respectively. Central composite design (CCD) was employed to optimise the parameters. The work examines the quantisation of aromatic amines simultaneously in fairly complex matrix of dyes effluents and biological samples (human serum) by simple GC-FID with adequate sensitivity.

When discussing non-Gaussian spatially correlated variables, generalized linear mixed models have enough flexibility for modeling various data types. However, the maximum likelihood methods are plagued with substantial calculations for large data sets, resulting in long waiting times for estimating the model parameters. To alleviate this drawback, composite likelihood functions obtained from the product of the likelihoods of subsets of observations are used. The current paper uses the pairwise likelihood method to study the parameter estimations of spatial generalized linear mixed models. Then, we use the weighted pairwise and penalized likelihood functions to estimate the parameters of the mentioned models. The accuracy of estimates based on these likelihood functions is evaluated and compared with full likelihood function-based estimation using simulation studies. Based on our results, the penalized likelihood function improved parameter estimation. Prediction using penalized likelihood functions is applied. Ultimately, pairwise and penalized pairwise likelihood methods are applied to analyze count real data sets.

This study develops a machine learning model to predict the classification of divorce cases in Iranian Judiciary Courts based on socioeconomic factors. Using data collected between 2011 and 2018 and various machine learning algorithms, the study evaluates the performance of predictive models through a rigorous 10-fold cross-validation process. Results highlight the Random Forest and Neural Network classifiers as the most accurate. Key socioeconomic factors influencing divorce cases, such as unemployment rate and urbanization rate, are identified. The findings provide actionable insights for policymakers to develop data-driven strategies for social policy and resource allocation.

Information regarding rainfall can be obtained from global data, namely the global climate model that can be accessed through the statistical downscaling approach. Linear spline quantile regression with principal component is a statistical method that can be employed in statistical downscaling to address multicollinearity and outliers in data by using nonparametric estimators. This method is applied to rainfall data in Pangkep Regency from January 2008 to December 2022 as the response variable and global climate model data as the predictor variable. The aim of this research is to obtain the best regression model used for predicting rainfall data. The results obtained indicate that statistical downscaling with two principal components at the 0.50 quantile with respective knot points of -10.20 and -0.30 is the best model with the lowest generalized cross-validation value. The forecasted rainfall data using this model shows a high level of accuracy with a correlation of 89%.