Journal of Sciences, Islamic Republic of Iran
Volume:35 Issue: 1, Winter 2024

Research has shown that the gut microbiome affects memory processes. Different antibiotic treatments lead to changes in the intestine microbiota and the dysbiosis caused by it is associated with changes in brain behavior. This study examined the effect of azithromycin on the intestine microbiome, oxidative stress, and memory in male Wistar rats. Two groups of adult male rats were established (n=16). In both groups, the first collection of the feces samples was done on the first day to identify the gut microbiome, then the animals were gavaged with normal saline or azithromycin (15 mg) in a volume of 1 ml daily for seven days. At the end of treatment, The second phase of collecting feces samples was completed. Then locomotion activity, novel object recognition test, Passive avoidance test, hippocampal neurons count and oxidative stress measurement in blood serum were performed. Azithromycin treatment induced a significant decrease in the number of aerobic and anaerobic colonies and led to the elimination of Enterococcus faecalis and Lactobacillus acidophilus species in the experimental group intestine. Azithromycin significantly decreased memory function, number of hippocampal healthy neurons, total antioxidant capacity, superoxide dismutase, and catalase enzymes and significantly increased the amount of malondialdehyde in blood serum compared to the control group. In this research, azithromycin by disrupting the intestinal microbiome, reduces diversity and suppresses some bacteria, raises levels of oxidative agents in blood serum, and by reducing the number of hippocampus-healthy neurons decreases cognitive functions.

In this study, a bimetallic Co-Zn metal-organic framework (Co-Zn-MOF) was synthesized through solvothermal conditions by using Co2+ and Zn2+ salts, DABCO and H2BDC as the rigid ligands (DABCO= 1,4-diazabicyclo (2.2.2) octane, H2BDC=Benzene-1,4-dicarboxylic acid). This bimetallic MOF was used as a catalyst for the Knoevenagel condensation reaction and exhibited enhanced catalytic activity compared to its parents' single metal MOFs derived from DABCO and H2BDC precursors (Co-MOF and Zn-MOF) at mild reaction conditions, which could be attributed to the synergistic effect between the metal ions.

Cancer is one of the most common diseases that affects many people around the world, and one of the challenges of the scientific community in dealing with cancer is to deliver drugs to cancerous tumors. Various reports show that boron nitride nanoparticles can be effective as drug nanocarriers and drug delivery in the target cell. In the present work, capsulation of anticancer drug of mercaptopurine (MCP) upon the boron nitride (6,6) nanotube, using DFT: B3LYP/6-31G* and the natural bond orbital analysis in the gas phase was investigated for the first time. Additionally, NCI analysis is used in this study to examine interactions between MCP and boron nitride nanotubes. To ascertain the impact of MCP adsorption into the nanotube, HOMO-LUMO orbitals, DOS (Density of States) plots, and molecular electrostatic potential maps (MEPs) were used. Furthermore, the effect of the abovementioned interactions between the drug and boron nitride nanotube on the electronic characteristics, and natural charges were estimated. Based on the gained results, the thermodynamic parameters of MCP nanotube and the results of NCI analysis were in close agreement with each other and it was also shown that the MCP adsorption process on the nanotube is a physical adsorption type, and the absorption process is associated with the release of heat, and it was in a favorable state in terms of thermodynamics. Furthermore, the results of IR spectra of drug, nanotube, and drug-nanotube mixture were investigated. Therefore, using boron nitride nanotube as a carrier for MCP drugs has been confirmed theoretically.

In this paper, focused on the Late Cretaceous Serow ophiolite related gabbros from the Torshab area, NW Iran, to enhance our understanding on the tectonic setting of ophiolite formation in terms of pressure-temperature and fluid conditions. The applied methods encompassed field geological observations, petrographic and mineralogical analyses, and whole-rock chemistry assessments. The findings revealed that the calc-alkaline gabbros predominantly consist of hornblende gabbro, olivine gabbro, and minerals such as amphibole, ortho-/clinopyroxene, olivine, and plagioclase. According to geochemical signatures such as the depletion of high field strength elements (Hf, Zr, Nb, and Ta) and the enrichment of large ionic lithophile elements (Ba and K) the Serow-Torshab gabbro is considered in relation to an arc setting indicating their origin from a mantle wedge, potentially enriched by subducting crust-derived melts/fluids. The mineral chemical study on mafic phases also suggests a supra-subduction zone (SSZ, fore-arc) environment for the Serow ophiolite, offering valuable insights into the region's geodynamic evolution.

Clustering, a fundamental multivariate statistical method, serves as a valuable tool for extracting meaningful insights from complex datasets. Analyzing high-dimensional data, however, presents challenges, notably the curse of dimensionality. While various methods have been developed to address the dimensionality reduction, most overlooked the role of dependent variables. In contrast, supervised clustering leverages the inherent information in response variables, offering substantial benefits in data dimension reduction and accelerating clustering computations. This paper evaluates the efficacy of supervised clustering in the analysis of Persian handwritten images. Focusing on the multi-class nature of Persian handwritten data, the identification of important variables for each digit not only reduces data dimensionality but also this reduction in dimensionality does not compromise the accuracy of predicting new observations at any stage of the algorithm. Additionally, the approach demonstrates relatively high accuracy in predicting the response variable. This study contributes a novel perspective toward clustering methods, highlighting the integration of supervised techniques for improved performance in high-dimensional data analysis.

The objective of this study is to investigate the factors influencing asthma attacks in children under six years old using machine learning (ML) methods. There are many statistical methods for data classification that can be used to classify medical data. But using the data itself as well as a set of different methods in machine learning can provide vast and more comparable results. Hence, this study applied ML approaches to predict asthma and second anoxic tonic seizures due to asthma (ATSA) based on variables such as first ATSA, age, region of residence, parent smoking status, and parents' asthma history. The results revealed that children's age and place of residence significantly affected the duration of asthma attacks, with children living in certain areas of Tehran experiencing shorter intervals between attacks due to high air pollution. Machine learning techniques proved useful in predicting ATSA based on age, gender, living region, parents' smoking status, and asthma history, with the AdaBoost method highlighting the importance of the child's age and living area in predicting ATSA.