University of TehranJournal of Sciences, Islamic Republic of Iran1016-110436120250101Genetic Analysis of Y-STRs in Two Iranian Sub-Populations51910595610.22059/jsciences.2025.386444.1007900ENMostafaGhaderi-Zefrehei1 Department of Animal Science, Faculty of Agricultural Sciences, Yasouj University, Yasouj, Islamic Republic of IranFarjadRafeie2 Department of Agricultural Biotechnology, Faculty of Agricultural Sciences, University of Guilan, Rasht, Islamic Republic of IranArashAlipour Tabrizi3 Molecular Genetics Department, Mashhad Forensic Medical Center, Mashhad, Islamic Republic of IranZohrehBaratieh4 Forensic Medical Research Center, National Forensic Medical Organization, Tehran, Islamic Republic of IranSeyyed KamalFotouhi4 Forensic Medical Research Center, National Forensic Medical Organization, Tehran, Islamic Republic of IranJacquelineSmith5 The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK0000-0002-2813-7872MustafaMuhaghegh Dolatabady1 Department of Animal Science, Faculty of Agricultural Sciences, Yasouj University, Yasouj, Islamic Republic of IranJournal Article20241203This 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_105956_12516b307b5d18589683ffc1a94eb78f.pdfUniversity of TehranJournal of Sciences, Islamic Republic of Iran1016-110436120250101Antiretroviral Therapy Among HIV-Infected Pregnant Women on Their Offspring212710595710.22059/jsciences.2025.364675.1007825ENMaryamMontazeri1 Razi Clinical Researches Development, Mazandaran University of Medical Sciences, Sari, Islamic Republic of IranMohammad AliDavar Panah2 Department of Infectious and Tropical Diseases, Faculty of Medicine, Shiraz University of Medical Sciences, Shiraz, Islamic Republic of IranMohammadMontazeri3 Sarem Fertility and Infertility Research Center (SAFIR), Sarem Women's Hospital, Tehran, Islamic Republic of IranLotfollahDavoodi4 Department of Infectious Diseases, Antimicrobial Resistance Research Center, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Islamic Republic of IranJournal Article20230902The 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_105957_8527b4a8d9e90dc0ce7d381e00a94ad7.pdfUniversity of TehranJournal of Sciences, Islamic Republic of Iran1016-110436120250101A New Approach for Normalizing Continuous Data, Applicable in Parametric and Nonparametric Continuous Studies293810595810.22059/jsciences.2025.384823.1007893ENMohammad MehdiSaber1 Department of Statistics, Higher Education Center of Eghlid, Eghlid, Islamic Republic of Iran.MehrdadTaghipour2 Department of Statistics, Faculty of Sciences, University of Qom, Qom, Islamic Republic of Iran.MohsenSalehi2 Department of Statistics, Faculty of Sciences, University of Qom, Qom, Islamic Republic of Iran.YousofHaitham3 Department of Statistics, Mathematics and Insurance, Faculty of Commerce, Benha University, EgyptJournal Article20241105Satisfying 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_105958_8a9248c80b1a09d531fce5aacd082149.pdfUniversity of TehranJournal of Sciences, Islamic Republic of Iran1016-110436120250101Hybrid Prediction Models for Suicide Mortality Levels in Iranian Provinces: Spatial Econometrics vs. Random Forests394910595910.22059/jsciences.2025.397759.1007936ENMohadesehFarzammehr1 Judiciary Research Institute, Tehran, Islamic Republic of IranGeoffrey JMcLachlan2 Department of Mathematics, University of Queensland, Brisbane, AustraliaJournal Article20250701With 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_105959_946777c125cd78dc9a816ee82fbd1987.pdfUniversity of TehranJournal of Sciences, Islamic Republic of Iran1016-110436120250101Precision Tuning of a kHz-Driven Argon Plasma Jet Enables Dose-Controlled H₂O₂ Delivery to Overcome Chemoresistance in Colorectal Cancer515910596010.22059/jsciences.2025.401773.1007952ENAfshinEftekharinasabDepartment of Physics and Institute for Plasma Research, Kharazmi University, Tehran, Islamic Republic of IranHassanMehdianDepartment of Physics and Institute for Plasma Research, Kharazmi University, Tehran, Islamic Republic of IranAliHasanbeigiDepartment of Physics and Institute for Plasma Research, Kharazmi University, Tehran, Islamic Republic of IranJournal Article20250903Colorectal 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, <em>p</em> < 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_105960_a671d4b5ac1cfbebb39a0062d1df0ffb.pdfUniversity of TehranJournal of Sciences, Islamic Republic of Iran1016-110436120250101Fusion Reactivity of Plasma with Anisotropic Lorentzian Distribution616710596110.22059/jsciences.2025.398234.1007939ENFahimehKhoshdon1 Department of Physics, Faculty of Science, Arak University, Arak, Islamic Republic of IranMehranShahmansouri2 Department of Atomic and Molecular Physics, Faculty of Physics, Alzahra University, Tehran, Islamic Republic of IranJournal Article20250709Anisotropic 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_105961_d348e2fecbbd99cdd67931448d97221c.pdfUniversity of TehranJournal of Sciences, Islamic Republic of Iran1016-110436120250101Binder 202510606610.22059/jsciences.2025.106066ENJournal Article20260216