Islamic Azad University, Arak

In this investigation, we successfully synthesized fused pyridonaphthyridines, a newly identified class of fused heterocyclic compounds, through an efficient multicomponent reaction. The methodology involved utilizing 3-(2-hydroxyphenyl)-3-iminopropanamide, 2-(1,3-diimino-1,3-dihydro-2H-inden-2-ylidene) malononitrile, alkyl bromides, and activated acetylenic derivatives within an aqueous medium at ambient temperature. Notably, the process was facilitated by a reusable catalyst composed of Ag/Fe3O4@GO. The newly synthesized compounds were subsequently evaluated for their antiproliferative potential against MCF-7 breast cancer and HCT-15 colon cancer cell lines using the Sulfo-rhodamine B (SRB) assay, along with their antioxidant capabilities. Among the compounds tested, derivatives 5c and 5 g demonstrated notably superior antiproliferative activity relative to their counterparts. To further explore their interaction with biological macromolecules, these two compounds underwent UV-Vis spectral analysis to determine their binding affinities to DNA and bovine serum albumin (BSA). The results revealed affinity constants of K5c-DNA = 7.32 × 10³ M−1, K5g-DNA = 2.09 × 104 M−1, K5c-BSA = 5.32 × 104 M−1, and K5g-BSA = 8.24 × 104 M−1, respectively. The outcomes of this study could provide novel insights into the relationship between the chemical structure of these pyridonaphthyridine analogs and their capacity to inhibit cancer cell proliferation.

ABSTRACT In this study, the simulation of groundwater fluctuations in the Kermanshah Plain is carried out via three advanced machine learning algorithms, i.e., GMDH, weighted extreme learning machine (W‐ELM) and gene expression programming (GEP). The objective of this research is to evaluate and compare the accuracy of different machine learning models in simulating and predicting such fluctuations under different conditions. The data used in this study include the monthly groundwater level, precipitation, evaporation and temperature data from 2002 to 2020. To increase the accuracy of the models, the data are processed via the min–max normalisation approach. The results obtained from the evaluation of the models using different efficiency indices, such as the root mean square error (RMSE), R 2 and NSE, indicate that the W‐ELM model, which has the best performance in both the training and testing phases, is more exact than the GMDH and GEP models. In particular, the W‐ELM achieves R 2 = 0.999, NSE = 0.979, PBIAS = 0.012 and RMSE = 0.093 in the testing phase, which indicates its high accuracy and efficiency. The results of the sensitivity analysis indicate that all the models are highly sensitive to the groundwater level lag variable GWL₋₁.This research shows that machine learning models, especially the W‐ELM model, can be utilised as effective tools for predicting groundwater level variation and the optimal management of water resources in different regions.

Background: Root canal treatment is one of the most critical dental treatments that help to maintain teeth.However, the growth of microorganisms leads to treatment failure. Considering the widespread use of mineral trioxideaggregate (MTA) in endodontic treatment, in vitro comparison of antibacterial, antifungal and anti-adherence activities of new types of MTA materials is indispensable. Purpose: This study aimed to investigate and compare the antibacterial, antifungal, and anti-adhesion propertiesof three types of MTA, including A.G.M MTA (Andishe Gostar Masud, Tehran, Iran), Ortho MTA(BioMTA, South Korea), and Ultradent™ MTA Flow (Ultradent MTA Flow, USA). Materials and Method: In this laboratory study, the antibacterial activity of three MTA substances against the strainof Enterococcus faecalis (E. faecalis) was carried out by the modified direct contact test (MDCT)method in 1, 3, 7, and 14 days. Antifungal activity of MTA against Candida albicans (C. albicans)was performed using a tube-dilution test in 1, 24, and 72 hours. The antibiofilm property ofthree MTA substances against E. faecalis strain was determined using the crystal violetstaining method and measurement by ELISA. Results: Ortho MTA, A.G.M MTA, and MTA Flow showed intense antibacterial activity. The difference in theantibacterial effect of the three types of MTA was not statistically significant in all periods(p Value> 0.05). In the investigation of antifungal properties after 72 hours, all threetypes of MTA had antifungal properties (p< 0.050). Comparing the anti-adhesion propertiesof three MTA substances on E. faecalis, it is not significant (p< 0.05). Conclusion: A.G.M MTA showed the highest antibacterial activity, and Ultradent MTA Flow and Ortho MTA showed stronger antifungal activity than A.G.M MTA. The highest anti-bacterial property was related to A.G.M MTA.

This study examines the stability properties and robustness of nonlinear general type 2 TSK fuzzy control systems (NGT2 TSKs). Due to their higher design flexibility and nonlinear functions applied in their rule consequences, NGT2 TSK fuzzy controllers (NGT2 TSK-Cs) outperform other types of fuzzy controllers in the presence of complex nonlinearities and high degrees of uncertainty. With the help of describing function and parameter plane approaches, the limit cycles and asymptotically stable regions are determined to analyze the stability. Control systems with high unacceptable vibration levels, such as limit cycles, could become unstable as a result. Therefore, particle swarm optimization (PSO) is used to identify the minimum limit cycle region to enhance stability. To assess the robustness of closed-loop systems during uncertainty, minimum gain and phase margins are calculated. Finally, simulation examples are presented to verify the validity of the suggested methodology.

Abstract Background: There are some studies that sodium valproate is associated with some major and minor malformations and its placental transfer influences oxidative stress processes. Resveratrol is a drug with few side effects and high antioxidant and cell protection properties,. Objectives: This study aimed to evaluate the protective effects of Resveratrol as a natural compound with antioxidant properties on oxidative stress and teratogenic effect induced by transfer of transplacental sodium valperoate in mice Methods: The female mice (25-30 gm, 3 weeks years old) were divided into 5 groups (6 mice per group). Of control, Sodium Valperoate (400 mg/kg/day by gavage from day 8 to 18 of pregnancy.) and Resveratrol (225,350,600 mg/kg/day by IP+SodiumValperoate 400mg/kg/day). All groups were received as a single injection. Administration for 10 consecutive days of pregnancy period were done. After baby mice were born, all neonatal mice were killed with beheading. Then, the liver,heart, brain, and kidney tissues were excited. Then samples were used for assessment of oxidative stress markers including lipid peroxidation (LPO), glutathione (GSH), and protein carbonyl (PrC) content .and inflammatory markers as IL6,NfKB,Sirt1 by realtime PCR and their tissues for histopathology studies Results: This study showed that SodiumValperoate caused a significant decrease in GSH concentration. Also were observed a significant increase in lipid peroxidation (LPO) and protein carbonyl contents. However, Resveratrol treatment significantly inhibited oxidative stress markers, decreased IL6, NfKB, and Sirt1 in heart, kidney and liver and brain of neonatal mice significantly which affected by sodium valproate.(P&lt;0.05).Also histopathology studies show protective effect of Resveratrol in theses tissues Conclusions: Our findings showed that resveratrol has a protective effect against oxidative stress and inflammatory process induced by placental transfer of sodium valproate in mice

The rapid advancement of Industry 4.0 has paved the way for decentralized and collaborative manufacturing systems. In such systems, ensuring the smooth operation of machines and efficient task management is c ritical. This paper presents a blockchain-based framework for collaborative manufacturing that integrates machine management, task assignment, fault detection, and reward distribution. Using Ethereum smart contracts, the framework guarantees transparency, security, and traceability of machine activities and tasks. The system comprises three key layers: the Data Synchronization Layer, the Fault Management Layer, and the Security and Compliance Layer, which together facilitate seamless operation and data flow a cross the manufacturing ecosystem. We evaluate the performance of the proposed framework through comprehensive metrics, including gas consumption, transaction execution time, and transaction costs. The results show the framework's efficiency i n terms of transaction execution, with minimal gas consumption per operation. However, for large-scale deployments, optimization techniques such as off-chain data storage and layer-2 solutions are recommended to mitigate gas costs and improve scalability. This paper provides a robust solution for secure and efficient machine and task management in collaborative manufacturing, addressing both operational and economic challenges.

The mechanism of epitope mimicry could have implications for the safety of vaccine development. OmpA is one of the most promising antigens of Acinetobacter baumannii. This property convinced us to investigate OmpA’s potential to trigger autoimmune responses. To this end, BLAST searches were performed using the OmpA sequence and its overlapping peptides to find identical peptides in the human proteome. These peptides were analyzed for their epitopic and HLA binding properties. The population coverage was then calculated for the peptides. OmpA showed high identity among numerous strains of A. baumannii and had no similar counterparts in the human proteome. Three OmpA peptides (TKNYDSKI, LSLARANS, and GQEAAAPA) shared identity and similarity with human proteins. Amongst, LSLARANS, which was found in Isthmin-1, the one with the highest potential to induce autoimmune responses was identified. LSLARANS was found in a B-cell positive assay of OmpA and acted as an HLA binder (such as HLA-A*03:01). Approximately 18% of the world’s population were determined to be more susceptible to probable A. baumannii-post-infection autoimmune disease, which could be rooted in OmpA similarity. Mutation sensitivity analyses indicated that the TKNYDSKI peptide is sensitive to engineering and modification. Given these circumstances, these peptides should be avoided in vaccine design efforts to reduce the risk of autoimmune responses.Graphical

Glioblastoma (GBM), the most aggressive type of primary brain tumor in adults, is characterized by a poor prognosis and considerable resistance to current therapies. Traditional treatment approaches-surgery, radiation, and chemotherapy-offer few clinical benefits and are often linked to increased recurrence rates and significant adverse effects. This underscores the urgent need for novel therapeutic strategies. Targeting the transforming growth factor-beta (TGF-β) signaling pathway is a viable strategy. TGF-β acts as a tumor suppressor during the first stages of cancer but promotes tumor progression, angiogenesis, immune evasion, and the creation of an immunosuppressive tumor microenvironment in GBM. The deregulation of TGF-β signaling has been shown to interact with essential carcinogenic pathways, including the Wnt/β-catenin, PI3K/AKT, and Notch pathways, therefore aggravating GBM pathogenesis. TGF-β plays a pivotal role in controlling glioma stem cells and influencing the permeability of the blood-brain barrier, making it an attractive target for therapeutic intervention. Novel treatment approaches, including small-molecule inhibitors and monoclonal antibodies, as well as the use of phytochemicals such as flavonoids, quercetin, and other phytochemicals targeting specific elements of the TGF-β signaling pathway, have shown encouraging results in preclinical investigations and early-stage clinical trials. These novel strategies may enhance clinical outcomes and mitigate treatment limitations in GBM therapy by leveraging this system's combined tumor-suppressive and immune-regulatory properties.

A three-dimensional (3D) scaffold that enables optimal cell–matrix interactions is essential for developing physiologically relevant neural tissue models. In this study, wet electrospinning was optimized to fabricate nanofibrous PCL/gelatin scaffolds with a well-controlled 3D architecture, using centrifugal force to tune scaffold morphology, porosity, and mechanical properties. The effects of centrifugal force intensity (5000 vs. 10,000 rpm) and application time (5 vs. 10 min) were systematically investigated. Scaffolds fabricated at 5000 rpm exhibited poor structural integrity and were excluded from further analysis. Among scaffolds produced at 10,000 rpm, blends of PCL/gelatin at 70:30 and 60:40 demonstrated excellent porosity (98.1 ± 1.9% and 97.3 ± 1.1%, respectively) and favorable fiber architecture. The 70:30–10 min scaffold achieved the highest tensile strength (57.03 ± 1.50 kPa) and modulus (53.00 ± 2.00 kPa), aligning with the physiological range of neural tissues. MTT assays confirmed robust biocompatibility, with C6 glial cell viability increasing by + 4.30% on the 70:30–10 min scaffold and + 5.88% on the 60:40–10 min scaffold over 14 days. Although the 70:30–5 min scaffold showed the highest proliferation (+ 12.16%), the 10-min variant was selected for detailed morphological evaluation due to its superior mechanical performance and structural uniformity. DAPI and H&E staining further validated the enhanced cell aggregation, ECM deposition, and neural-like morphology within the 70:30–10 min scaffold. These results collectively highlight the critical role of scaffold composition and processing parameters in engineering 3D neural tissue scaffolds, with the optimized 70:30–10 min scaffold emerging as a promising candidate for advanced neural tissue engineering applications.

ObjectiveThe growing trend of induced demand for medicine negatively impacts health system performance and imposes significant financial burdens on both individuals and society. Therefore, the present study aims to analyze the management of induced demand for medicine in Iranian hospitals.ResultsThis applied qualitative research, using grounded theory, achieved data saturation after interviewing 20 experts. Key factors creating induced demand were identified as social, structural-organizational, economic, and stakeholder factors. Effective management strategies include planning, organizing, monitoring, and securing financial resources. These findings underscore the importance of payment system reform and enhanced physician education.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13104-025-07194-3.