https://jsciences.ut.ac.ir/ Journal of Sciences, Islamic Republic of Iran en daily 1 Wed, 01 Jan 2025 00:00:00 +0330 Wed, 01 Jan 2025 00:00:00 +0330 https://jsciences.ut.ac.ir/article_105956.html This study presents a comprehensive genetic analysis of 17 Y-chromosomal short tandem repeat (Y-STR) loci in two Iranian sub-populations from the Fars (n=109) and Isfahan (n=180) provinces. The loci investigated included DYS19, DYS385a/b, DYS389I/II, DYS390, DYS391, DYS392, DYS393, DYS437, DYS438, DYS439, DYS448, DYS456, DYS458, DYS635 (Y-GATA-C4), and Y-GATA-H4. Results demonstrated that the DYS385a/b locus exhibited the greatest allelic diversity in both populations, with 11 distinct alleles detected and mean allele counts of 6.29 and 5.88 in the Fars and Isfahan groups, respectively. Conversely, the Fars cohort showed the lowest allelic variation (three alleles) at DYS439 and Y-GATA-H4 loci, while the Isfahan population exhibited minimal variation (four alleles) at DYS19 and DYS439. Haplotype analyses revealed intra-population sharing rates of 2.75% in Fars and 10.0% in Isfahan, with an overall 8.3% haplotype overlap observed across the combined dataset of 289 individuals. Both populations exhibited high haplotype diversity values approaching 0.99, indicating substantial genetic variability. The haplotype discrimination capacity varied among populations, with value of 0.9725 for Fars, 0.8519 for Isfahan, and 0.9170 for the entire sample set. Population differentiation was assessed using pairwise FST and RST metrics, which confirmed significant genetic divergence between Fars and Isfahan groups (FST = 0.00743, p < 0.001; RST = 0.0106, p < 0.01). These findings underscore the genetic distinctness of the two sub-populations. The study highlights the necessity for further research incorporating Y-chromosomal single-nucleotide polymorphisms (Y-SNPs), larger sample sizes, and additional ancestral information to enhance the understanding of genetic structure and demographic history within Iranian populations. https://jsciences.ut.ac.ir/article_105957.html The risk of mother-to-child transmission (MTCT) of human immunodeficiency virus (HIV) infection is approximately 30%. However, antiretroviral drugs can reduce MTCT to less than 2%. This study was designed to determine the effect of antiretroviral therapy among HIV-infected women and its maternal and neonatal outcomes in Iran. The study is a retrospective analysis of mother-infant data from Shiraz, Southern Iran, between 2006 and 2012. HIV-infected pregnant women were divided into two groups of intervention (receiving treatment or chemoprophylaxis) and control (not receiving any treatment). Maternal and neonatal information were extracted and recorded. The data were entered into SPSS software and were analyzed. The mother-to-child transmission was 2.9% in the intervention group compared to 15.8% in the control group (OR=0.01, 95% CI: 0.002-0.125, p<0.0001). The infant HIV infection rate was significantly higher in male infants (OR=2.76, CI 95% 2.213-3.327), NVD delivery (OR=3.78, CI 95% 3.140-4.409), and breastfeeding (OR=26, CI 95% 7.87-85.90). Treatment intervention significantly reduces the HIV transmission from infected mothers to their infants. However, the rate of vertical transmission in Iran remains higher than those reported in developed countries despite treatment interventions, and additional preventive measures appear necessary. https://jsciences.ut.ac.ir/article_105958.html Satisfying the normality assumption is fundamental to many statistical inferences, as its violation can significantly affect the validity and reliability of conclusions drawn from the data. In this paper, we introduce a novel method for normalizing data that applies to both parametric and non-parametric cases. This method is grounded in a refined version of the empirical distribution function (EDF), which enhances its flexibility and accuracy compared to traditional normalization techniques. By leveraging this new EDF formulation, our approach effectively addresses common issues associated with existing methods, such as sensitivity to outliers and the inability to handle skewed distributions efficiently. A key advantage of our technique is its reversibility, which enables normalized data to be effortlessly transformed back into their original form, thereby preserving the integrity of the raw data for further analysis or interpretation. To demonstrate the efficacy of our method, we evaluate its performance using multiple real-world examples, including datasets related to the COVID-19 pandemic. These datasets, characterized by their complexity and variability, provide a rigorous test of the proposed normalization approach. The results confirm that our method successfully normalizes the data while maintaining their underlying structure and relationships, thus improving the robustness of subsequent statistical analyses. This innovation not only expands the toolkit available for data preprocessing but also enhances the applicability of standard statistical techniques to a broader range of real-life datasets. https://jsciences.ut.ac.ir/article_105959.html With the growing utilization of advanced machine-learning techniques, such as random forests, understanding the significance of spatial factors within these models is increasingly imperative. This study proposes a novel approach to develop spatially explicit classification random forest models by integrating spatially lagged variables, mirroring various spatial panel data econometric specifications. We assess the comparative performance of these models against traditional spatial and non-spatial regression methods to predict suicide mortality rates across 31 provinces in Iran, utilizing data from 2011 to 2021. Results reveal that the spatial random forest model, incorporating spatial lag parameters, achieves a remarkable accuracy of 89.19% in predicting suicide mortality levels, surpassing traditional spatial econometric models (46.51%) and non-spatial random forest models (27.03%). While highlighting the effectiveness of spatial random forest models with spatial lag parameters, this study also recognizes the continued relevance of traditional spatial econometric models in predicting suicide mortality rates. These findings offer valuable insights into the interplay between spatial considerations and predictive modeling, providing essential guidance for researchers in selecting appropriate models for spatial data analysis. https://jsciences.ut.ac.ir/article_105960.html Colorectal cancer presents a significant therapeutic challenge, largely due to robust chemoresistance mechanisms, including the upregulation of antioxidant pathways. While cold atmospheric plasma is a promising anti-cancer modality, its efficacy can be limited by these cellular defenses. This study introduces a kilohertz AC-driven argon plasma jet with independently tunable voltage (1–20 kV) and frequency (18–28 kHz) as a novel platform for overcoming this resistance. We demonstrate that precision tuning of these electrical parameters allows for the controlled delivery of extracellular hydrogen peroxide (H₂O₂), a key long-lived reactive species. In the chemoresistant HT29 colorectal cancer cell line, we achieved a modulation of H₂O₂ concentrations in the culture medium, ranging from 291 to 371 µM. This H₂O₂ dosage showed a linear correlation with dose-dependent cytotoxicity (R² = 0.995, p < 0.001). Optimized parameters (10.5 kV, 28 kHz) overwhelmed the cells' redox defenses, reducing viability to 9.2% ± 3.6% after a 3-minute treatment. This approach successfully bypasses the Nrf2/Srx antioxidant pathway, which is known to confer resistance to helium plasma jets. Our findings establish that precisely controlling H₂O₂ delivery via a tunable argon plasma jet is a potent strategy for circumventing intrinsic chemoresistance in colorectal cancer, positioning this technology as a promising modality for precision oncology. https://jsciences.ut.ac.ir/article_105961.html Anisotropic distributions and deviations from velocity equilibrium play a crucial role in plasma physics and nuclear fusion processes. The emergence of high-energy tails in non-equilibrium distributions increases the population of energetic particles, thereby enhancing the probability of quantum tunneling and, consequently, fusion reaction rates. In this work, we investigate how the velocity-space anisotropy and deviations from the equilibrium affect the optimization of the fusion yield. Specifically, we analyze non-Maxwellian distribution models, including kappa and anisotropic kappa distributions, to evaluate their impact on fusion reactivity. Our results show that anisotropic distributions outperform isotropic ones at lower temperatures, whereas isotropic distributions dominate at higher temperatures. These findings provide new insights for the design of fusion devices and contribute to improving the efficiency of fusion processes. https://jsciences.ut.ac.ir/article_106066.html https://jsciences.ut.ac.ir/article_107399.html Objective: The DFT description and comparison of the loading of Letrozole (LTZ) on boron nitride (AlNNT) and aluminum nitride (BNNT) nanotubes were studied herein. The results of the thermochemical parameters indicate that LTZ with AlNNT nanotube would be a more spontaneous and exothermic process in comparison with that of BNNT. The study included electron delocalization effects, stereoelectronic interaction, steric repulsion effects, electronic properties, and reactivity of LTZ on the AlNNT nanotube using DFT B3LYP/6-31G* level of theory. Methods: The UV-Vis absorption analysis and the IR spectrum were used to ascertain the changes that take place on adsorption of LTZ onto AlNNT. The molecular orbital distribution was further assessed to monitor electronic structure changes, adsorption energies (Ead), and electrical conductivity during the adsorption process on both substrates. Results: The NBO analysis of the LTZ-AlNNT complex shows that the highest resonance energy is provided due to the instability of electrons of LP(1)N73→ BD*(2)N75-C85 (51.96 kcal.mol-1), which in turn signifies that there is electron transfer from the LTZ drug in the LTZ-AlNNT complex. This in the UV-Vis spectrum corroborated by the drug adsorption-related wavelength change from 226.7 nm to 254.3 nm on the AlNNT demonstrates bathochromic shift.Conclusion: We hope that this study can aid in modeling and designing a suitable adsorbent for drug delivery based on its findings. Thus, electronic, thermochemical, and structural properties of LTZ drug complexes with the nanotubes AlNNT and BNNT were elucidated with a combination of DFT calculations and graphical analysis of the non-covalent interactions index (NCI) analysis. https://jsciences.ut.ac.ir/article_107400.html ABSTRACTThe objective of developing analytical methods for ethanol analysis is to find a simple and quick method. This study compared GC-MS against a conventional method for determining ethanolic content in alcoholic beverages. GC-MS parameters such as oven temperature rate and total flow rate of the mobile phase were set to 10 °C and 104 mL/min, respectively. The study can detect ethanol in alcoholic beverages in under 2 minutes. This method has a determination coefficient of 0.994 within the 0.1-5% standard curve and produces good selectivity, accuracy, and precision (6-7, 100.8%, and 3.01-3.25%, respectively). The conventional method was used for 24 hours, with standard curves ranging from 0-0.05%. However, this study reported that GC-MS perform better than the conventional approach for ethanol analysis in alcoholic beverages. However, the conventional method is time-consuming, non-specific to ethanol, less accurate, and less precise. Therefore, for ethanol analysis in alcoholic beverages, GC-MS can be used as confirmatory testing, while a conventional method can be used as a preliminary investigation https://jsciences.ut.ac.ir/article_107911.html By reacting 1,2-diamines with aromatic, aliphatic, and heteroaromatic nitroalkenes while using ammonium iodide (NH4I) as a green catalyst, a simple, effective, and environmentally friendly synthetic pathway was created for the selective synthesis of 2-substituted benzimidazole derivatives in good to excellent yields (78-94%). A plausible reaction mechanism was proposed, and the scope and limitations were further examined. https://jsciences.ut.ac.ir/article_107912.html While the associations between pollution, human health, and behavior have been widely studied, the relationship between air pollution and violent crime remains underexplored. Using an interdisciplinary approach within green criminology, this study examines statistical associations between air pollution, mental health, and violent behavior in Iran, aiming to contribute to evidence-informed environmental security discussions. Time-series analysis, regression models, and panel data techniques were used to explore relationships between key pollutants (PM10, PM2.5, CO, NO₂, SO₂, and O₃) and violent crime rates in Tehran and the provinces of Mashhad, Isfahan, Fars, Alborz, and Rasht from 2016 to 2021. Given the observational nature of the data, all results represent correlations rather than causal effects.In Tehran, ozone (O₃) and nitrogen dioxide (NO₂) show statistically significant positive associations with violent crime rates, while carbon monoxide (CO) displays a weak negative association. By contrast, PM2.5 shows no statistically significant relationship with violent crime. Although province-level descriptive patterns appear heterogeneous, the overall correlation between PM2.5 and crime is very small (r = 0.068), and the panel regression coefficient is statistically insignificant, indicating that PM2.5 is not a meaningful correlational predictor in this context.The study identifies pollutant–crime co-movements rather than causal effects, with only O₃ and NO₂ exhibiting consistent statistical associations with violent crime. These findings highlight the importance of considering environmental, economic, and social conditions when examining pollution crime relationships. The results contribute to a more nuanced understanding of environmental correlates relevant to urban planning, environmental management, and public security in Asian metropolitan regions. https://jsciences.ut.ac.ir/article_107913.html The study investigates the relationships among orebody geometry, stripping intensity, and adequate areal leasing in three open-cast manganese mines (A, B, and C) in the Eastern Ghats Mobile Belt of India. The deposits are hosted in high-grade metamorphic Khondalite–charnockite assemblages and exhibit variable structural continuity, dip, and lens-shaped formation. An integrated geological–spatial assessment framework is applied, combining orebody characterization from approved mining plans with GIS-based land-use analysis and normalized efficiency metrics (stripping ratio, waste area per unit of extractable ore, ratio of undisturbed/inactive land). The results reveal that stripping intensity and surface occupation are notably near the boundaries of structural complexity and orebody discontinuity. High stripping ratios and waste intensity in Mines A and B (which exhibit irregular, pinching–swelling geometries with increased dip angles) progressively saturate their lease areas. Mine B is a spatially constrained system in which high stripping burden intersects with limited lease extent. Conversely, Mine C shows relatively high geological continuity and large untouched territory, leading to lower stripping intensity and greater operational flexibility. Rather than relying solely on regulatory limits, this analysis shows that lease adequacy is a function of coupled orebody geometry and surface land availability. Normalized spatial efficiency metrics provide a functional foundation for assessing extractability risk in structurally complex manganese deposits. Land-use assessment to create continuity in the geological picture is a key part of lease planning. This facilitates long-term material recovery and minimizes the risk of premature sterilization, particularly in high-grade metamorphic terrains. https://jsciences.ut.ac.ir/article_107956.html The electrolysis of heavy water may produce deuterium (D), the naturally occurring isotope of hydrogen. Nature is rich in deuterium; the typical human body has more than 1 gram of this element. Human clinical trials examining pharmacokinetics and metabolism have made extensive use of deuterium, a stable and non-radioactive cousin of hydrogen. Within a pharmacological molecule, deuterium takes on the same size and shape as hydrogen, which is a crucial characteristic. Hence, chemical compounds exhibiting isotopic, structural, and kinetic consequences are the outcome of deuterium's selective replacement of hydrogen. In turn, these activities provide deuterated molecules with unique medicinal characteristics. The potential for better medication effectiveness, safety, and tolerability may be realized by the enhancement of bond strength in particular modified molecules. This, in turn, can have a favorable effect on the ADME aspects of some pharmaceuticals. From a drug-development standpoint, this is one of deuterium's most compelling properties. The structural change known as deuteration may improve the pharmacokinetic and/or toxicological properties of medications, making them more effective and safer than their non-deuterated counterparts.