Background: Infant Respiratory Distress Syndrome (IRDS) is a major cause of neonatal morbidity and mortality, often linked to genetic mutations affecting pulmonary surfactant metabolism. Mutations in genes such as SFTPA1, SFTPA2, SFTPB, SFTPC, SFTPD, ABCA3, and NKX2-1 have been implicated in surfactant dysfunction and IRDS pathogenesis. However, the functional consequences of many nonsynonymous single nucleotide polymorphisms (nsSNPs) remain poorly characterized. Objective: This study aims to identify and prioritize potentially deleterious nsSNPs in surfactant-associated genes using an integrated computational approach, providing insights into their structural and functional impacts for future experimental validation. Methods: We performed a comprehensive in silico analysis of rare missense variants (MAF < 1%) obtained from public databases (dbSNP, ClinVar, UniProt). Functional impact was predicted using SIFT, PolyPhen-2, Panther, and CADD. Protein stability changes were assessed using I-Mutant2.0 and MUpro. Evolutionary conservation was evaluated via ConSurf, and structural modeling was carried out with AlphaFold, SWISS-MODEL, PyMOL, and GROMACS to analyze RMSD and hydrogen bonding patterns. Results: Mutation frequency analysis revealed ABCA3 as the most variant-rich gene (n = 36,391), while SFTPD had the lowest mutation load (n = 4,646). Consensus prediction identified several high-risk nsSNPs, including R276W (SFTPB), V48M (SFTPC), G86E (SFTPA2), and P131T (SFTPD), consistently classified as damaging across tools. Protein stability analysis confirmed significant destabilization for R276W (SFTPB) and V48M (SFTPC). Structural modeling showed increased RMSD and reduced hydrogen bonds in variants such as G123V (SFTPA1) and G100S (SFTPC), indicating potential structural disruption. Highly conserved residues (ConSurf score ≥ 7), especially those involving glycine or proline substitutions, were more likely to be functionally critical. Conclusion: This comprehensive in silico analysis identifies several high-confidence deleterious nsSNPs that may contribute to surfactant dysfunction and IRDS pathogenesis. These findings offer valuable insights for future functional studies and may aid in the development of targeted genetic screening strategies for IRDS risk assessment.

This study explores the impact of various teaching methods on the development of English language skills in non-native speakers. With the growing importance of English as a global communication tool, effective teaching strategies are essential for enhancing learners’ proficiency in speaking, listening, reading, and writing. The research compares traditional and modern teaching methods, evaluating their effectiveness in improving students' language skills and their overall learning experiences. Additionally, the study examines the challenges faced by educators and learners in implementing these methods and how they influence language acquisition. The findings aim to provide insights into the most effective teaching practices, offering recommendations for optimizing English language instruction to better serve non-native speakers. This research contributes to the ongoing efforts to improve language education and aims to support the development of practical, adaptable teaching methods that meet the needs of diverse learners in various educational settings.

Nowadays, using advanced structural materials such as preplaced aggregate concrete (PAC) and fiber-reinforced preplaced aggregate concrete (FR-PAC) are widely investigated due to their benefits in designing infrastructures. Therefore, finding the mechanical characteristics of PAC and FR-PAC can be help structural engineers. This study explores the material characteristics, performance, and potential challenges associated with using PAC and FR-PAC, aiming to provide insights into their practical implementation and long-term benefits in construction. In addition, a superior estimation tool based on multi-target stacked machine-learning (ML) model was introduced to reduce the cost of experimental tests and increase the accuracy and speed of finding the best mixture for PAC and FR-PAC. Experimental tests were conducted to prepare unseen dataset to validate the general ability of the ML models. Results show that the proposed multi-target stacked ML models can estimate the compressive and tensile strengths of PAC specimens with an accuracy of 97.4% and 94.7%, respectively; however, for compressive, flexural, and tensile strengths FR-PAC specimens, the accuracy of 97.7%, 98.0% and 98.3%, were determined, respectively. The proposed predictive model was turned into a graphical user interface (GUI) with ability on predicting the mechanical properties of PAC and FR-PAC in different curing day, and updating the database in future.