University College of Nabi Akram

Abstract This article describes a study of One Nation/One Project, a national post-pandemic arts-and-public-health initiative in the US. This values-based, convergent mixed-methods study found significant associations between arts participation, social cohesion, and well-being and offers a theory of change that illustrates these associations, as well as how arts participation increased social cohesion and enhanced well-being. At a moment when the severities of social divisions and loneliness are increasing in the US, this theory of change may enhance socia cohesion and well-being by encouraging cross-sector collaboration between the arts, public health, and municipal sectors and supporting investment in the arts.

Introduction:Breast cancer is one of the most common types of cancer among women.This complication can negatively affect the physical health, psychological health, and marital relationship of women with their husbands, leading to serious problems.The present study aimed to determine the effectiveness of compassion-focused couples therapy (CFCT) on marital conflicts and emotional intimacy in couples with a spouse suffering from breast cancer.Methods: This quasi-experimental study was conducted using a pretest/posttest design and follow-up, with a control group.The statistical population consisted of all women with breast cancer in Tabriz and their spouses who had been referred to Al-Zahra Hospital, Tabriz, Iran, during five months from May to the end of September 2024.In total, 16 couples were selected using a convenience sampling method and randomly divided into two intervention and control groups.The intervention group received eight 1.5-hour CFCT sessions (two months, one session per week), according to the treatment plan of Kazemi et al.On the other hand, the control group did not receive any treatment.Both groups responded to the 54-item revised Marital Conflict Questionnaire of Bustanipour and Sanaei Zakir (2016) and the 36-item Intimacy in Relationships Questionnaire of Schaefer and Olson (1981) before, after, and at a 4-month follow-up.Findings were analyzed through repeated measures analysis of variance using SPSS software (version 24).Results: At the pretest, there was no significant difference between the groups in terms of marital conflicts and emotional intimacy (P>0.05).According to the results, the experimental group showed a significant decrease in marital conflicts (mean difference = -8.25)and a significant increase in emotional intimacy (mean difference = +8.50)(P < 0.001) after implementing CFCT.At the 4-month follow-up phase, the effect of CFCT on marital conflicts and emotional intimacy of couples was permanent (P<0.001). Conclusion:Based on the findings of this study, it can be concluded that CFCT can reduce marital conflicts and increase emotional intimacy among couples with a spouse suffering from breast cancer.These results support the integration of CFCT into psychooncology care programs to enhance relational resilience and overall family well-being during the cancer treatment process.

Hepatocellular carcinoma (HCC) is a major worldwide health burden due to poor outcomes. Identifying dysregulated molecular circuits in HCC is critical for developing precise treatments. A systems-level approach using multi-omics data is required to reveal the intricate non-linear interactions underlying liver carcinogenesis. Both tumor and control tissues contained differentially expressed genes (DEGs). Hub genes with the strongest connection were identified as potential drivers. Protein-protein interaction (PPI) mapping verified hub connectivity. Perturbed functions were evaluated using Gene Ontology and KEGG pathway enrichment analysis. Cytoscape clustering separated the interactome into modules. Motif discovery indicated a shift in cis-regulatory logic. Expression analysis, survival analysis, and drug screening were performed on the hub genes.Network hub gene analysis identified 11 hub genes, including DLGAP5, KIF23, KIF11, CCNB1, CDK1, BRCA1, CCNA2, SHCBP1, KIAA0101, FAM83D, and SPC25. Gene set enrichment analysis (GSEA) revealed dysregulation in cell cycle progression, DNA damage response, and metabolic pathways, and an association of these genes with reduced overall survival in HCC patients. Also, drug screening identified potential therapeutic agents targeting these hub genes.The findings increase mechanistic understanding with potential clinical applications. Future validation studies that include multi-omic data may strengthen current hypotheses and enable targeted therapy design against crucial in HCC.

Abstract Dyslipidemia is characterized by abnormal lipid levels in the bloodstream and can be influenced by liver diseases and smoking. Nicotine exposure and liver damage are linked to lipid metabolism impairments. This study investigated the effects of nicotine on dyslipidemia in a cholestasis rat model using bile duct ligation (BDL) to establish liver fibrosis. Wistar rats received intraperitoneal nicotine at doses of 10 mg/kg (high) and 1 mg/kg (low) for three weeks. Serum levels of triglycerides (TG), cholesterol (Chol), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) were quantified via a standard colorimetric kit (Pars Azmoon). Liver fibrosis confirmed through histological investigation. Additionally, the mRNA and protein expression of fatty acid synthase (FAS) were assessed using quantitative RT-PCR and immunofluorescence. Results showed that nicotine administration in healthy rats significantly increased TG, Chol, and LDL levels while decreasing HDL. In BDL rats, nicotine further reduced HDL and increased TG levels without affecting Chol and LDL. Histological analysis confirmed hepatic fibrosis, and both nicotine exposure and liver fibrosis elevated FAS expression in liver tissues. These findings indicate that BDL-induced liver fibrosis causes dyslipidemia in rats, and nicotine exposure exacerbates serum lipid profile alterations, potentially through increased FAS expression. Therefore, it is recommended that individuals with liver disease avoid nicotine to manage dyslipidemia.

Humanity has long sought precise and scientific answers to its endless questions about the universe.One of the greatest challenges in this pursuit is the discovery of extraterrestrial life and Earth like planets.The Kepler mission, launched in 2009, marked a major milestone in this direction.Its primary objective was to discover exoplanets and assess their habitability.With the vast amount of data transmitted by the Kepler telescope, scientists faced a new challenge: analyzing and extracting meaningful insights from this data.Initially, classical computational algorithms such as regression, k-nearest neighbor, decision trees, random forests, and various types ofneural networks proved effective for this task.However, as the volume of data grew, the limitations of these methods became evident.Questions arose regarding the ability of these algorithms to detect complex patterns and the computational time required for extensive calculations, prompting researchers to explore alternative solutions.Quantum computing, leveraging the principles of quantum mechanics such as superposition and entanglement, exponentially enhances the ability to perform complex computations.Unlike classical bits, qubits allow quantum computers to process multiple calculations simultaneously, making them particularly valuable for analyzing large and intricate datasets.Among the quantum algorithms currently in use, notable examples include quantum support vector machines, quantum neural networks, quantum k-nearest neighbor, and variational algorithms.Currently, both classical and quantum algorithms have distinct advantages and limitations.Classical algorithms are widely applied to everyday computational problems and are relatively easy to implement.In contrast, quantum algorithms hold the potential to solve complex problems that are either infeasible or extremely time-consuming for classical computers.The ongoing competition between these two paradigms has motivated us to compare their efficiency, aiming to assess their current capabilities and future potential.

This research offers an in-depth examination of predicting the closing prices of the metal ores industry index on the Tehran Stock Exchange (TSE) using a Gated Recurrent Unit (GRU) model. The GRU, a type of recurrent neural network, shows great promise for tasks involving time series forecasting. The historical daily price data from October 2017 to October 2022, was used in the study after carefully preprocessing it for further analysis. The study begins with a univariate analysis to reveal distribution characteristics and the relationships between essential variables. A customized GRU model that is trained on 70% of the time series data, with its performance assessed through metrics such as Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and the R-squared (R2) score is used for prediction. The results indicate that the GRU model provides accurate predictions for the metal ores industry index, outperforming traditional forecasting techniques. The model's recurrent nature enables it to capture both short-term and long-term temporal dependencies within the data. This research highlights the significant potential of GRU networks in the realm of financial forecasting. Future improvements will focus on hyperparameter optimization and further integrating additional input variables to enhance predictive accuracy.

Autophagy is a significant catabolic procedure that increases in stressful conditions. This mechanism is mostly triggered after damage to the organelles, the presence of unnatural proteins, and nutrient recycling in reaction to these stresses. One of the key points in this article is that cleaning and preserving damaged organelles and accumulated molecules through autophagy in normal cells helps prevent cancer. Since dysfunction of autophagy is associated with various diseases, including cancer, it has a dual function in tumor suppression and expansion. It has newly become clear that the regulation of autophagy can be used for the treatment of breast cancer, which has a promising effect of increasing the efficiency of anticancer treatment in a tissue- and cell-type-specific manner by affecting the fundamental molecular mechanisms. Regulation of autophagy and its function in tumorigenesis is a vital part of modern anticancer techniques. This study discusses the current advances related to the mechanisms that describe essential modulators of autophagy involved in the metastasis of cancers and the development of new breast cancer treatments.

Abstract One of the three causes of death in the world is liver cancer, which is common in today's society. One of the new methods in the treatment of liver cancer is the identification of new and less invasive biomarkers, among which microRNA can be mentioned. Therefore, in this study, the bioinformatic role of mir-101 on the target genes PTEN and Mcl1 in liver cancer was evaluated. Materials and methods: In this theoretical study, Target Scan, miR Base, DIANA Tool, miRanda, miRTarBase, miR Walk, miR path target genes and signaling pathway of miR-101 were investigated using bioinformatics databases. Then, GEO data was analyzed with R software to analyze miR-101. Finally, the biomarker role of miR-101 was analyzed with ROC diagram. Results: Investigations conducted using different databases as well as statistical methods using R software and graph prism provided a comprehensive analysis of miR-101-3p/5p and based on the analysis of the ROC diagram, the specificity of miR-101 was reported to be higher than 0.5. Also, investigations and target gene selection from different bioinformatics databases showed that PTEN genes with their tumor suppressive role and Mcl1 with their anti-apoptotic role in the TGF- β signaling pathway had dual behavior against each of the studied target genes. Finally, it inhibited metastasis in the signaling pathway of liver cancer, thus reducing the expression of microRNA in patients with liver cancer. Conclusion: According to the obtained results, miR-101 is a targeted therapy for the diagnosis, prognosis and treatment of HCC.

As a novel group of non-coding RNAs, long non-coding RNA (lncRNAs) can substantially regulate various biological processes. Downregulated tumor-suppressive lncRNAs and upregulated oncogenic lncRNAs (onco-lncRNAs) have been implicated in gastric cancer (GC) development. These dysregulations have been associated with decreased chemosensitivity, inhibited apoptosis, and increased tumor migration in GC. Besides, growing evidence indicates that lncRNAs can be a valuable diagnostic and prognostic biomarker, and their expression levels are substantially associated with the clinicopathological features of affected patients. The current study aims to review the recent findings of the tumor-suppressive lncRNAs and onco-lncRNAs in GC development and highlight their therapeutic, diagnostic, and prognostic values in treating GC cells. Besides, it intends to highlight the future direction of lncRNAs in treating GC.

Diverse chemicals and some physical phenomena recently introduced in nanotechnology have enabled scientists to develop useful devices in the field of food sciences. Concerning such developments, detecting foodborne pathogenic bacteria is now an important issue. These kinds of bacteria species have demonstrated severe health effects after consuming foods and high mortality related to acute cases. The most leading path of intoxication and infection has been through food matrices. Hence, quick recognition of foodborne bacteria agents at low concentrations has been required in current diagnostics. Lateral flow assays (LFAs) are one of the urgent and prevalently applied quick recognition methods that have been settled for recognizing diverse types of analytes. Thus, the present review has stressed on latest developments in LFAs-based platforms to detect various foodborne pathogenic bacteria such as Salmonella, Listeria, Escherichia coli, Brucella, Shigella, Staphylococcus aureus, Clostridium botulinum, and Vibrio cholera. Proper prominence has been given on exactly how the labels, detection elements, or procedures have affected recent developments in the evaluation of diverse bacteria using LFAs. Additionally, the modifications in assays specificity and sensitivity consistent with applied food processing techniques have been discussed. Finally, a conclusion has been drawn for highlighting the main challenges confronted through this method and offered a view and insight of thoughts for its further development in the future.