Over the last year, RMRC members acquired great achievements in the field of kidney disorders and regenerative potential of multinucleated cells. Articles have been published in reputable journals such as Scientific Reports, NPj Systems Biology and Applications, BMC immunology, and BMC nephrology. Besides, a patent on the use of Dianthus powder as an anesthetic substance for Zebrafish was accepted by Iran Patent Office. A brief description of 2021 published studies as well as the accepted patent are presented in the following:

• Comprehensive analysis of IgA nephropathy expression profiles: identification of potential biomarkers and therapeutic agents

Immunoglobulin A (IgAN) nephropathy is a kidney disease that is detected by deposition of IgA antibodies in the kidneys. Although many studies have examined the underlying mechanisms of this complex disorder, suitable biomarkers for prediction of disease status are not well-defined. In this study, by merging two different expression datasets and constructing a multilayer regulatory network, not only deeper insights into the pathogenesis of immunoglobulin A nephropathy but also several key molecules were introduced as non-invasive biomarkers in this disease.

• Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm

Diabetic nephropathy (DN) is the main complication of diabetes and the leading cause of end-stage renal disease. The main molecular mechanisms of DN disease are not yet fully understood. The aim of this study was to analyze the DN microarray data set using the weighted gene expression co-expression algorithm (WGCNA) algorithm to better understand the DN pathogenesis and explore key genes in disease progression. After normalization and data set analysis, 2475 genes with altered expression were identified and clustered in six different expression modules by WGCNA algorithm. Then, genes with altered expression of each module were subjected to functional enrichment analysis and protein interaction network construction. Metabolic processes, cell cycle control and apoptosis were among the enriched terms. Next, 23 major genes were identified among the genes in the modules, and five of them, including FN1, SLC2A2, FABP1, EHHADH, and PIPOX, were confirmed in another DN dataset. The major identified genes not only give us a better understanding of the developmental processes of DN disease, but also serve as more specific targets for future research into the treatment of this disease.

• Optimization of Mouse-on-Mouse Immunohistochemistry by Utilizing Fluorescent-dye Conjugated Secondary Anti-Mouse Antibody. Applied immunohistochemistry & molecular morphology

The application of mouse monoclonal antibody for immunostaining the mouse tissues results in a high rate of background noise because of the interaction of the secondary antibody with endogenous immunoglobulins and other immune components. The most advised blocking strategy for the mouse-on-mouse immunostaining is the use of anti-mouse Fab fragments. Nevertheless, the commercial kits containing Fab fragment are costly and unavailable in many research laboratories. In this study, we provide evidence showing the potential of the fluorescent-dye conjugated secondary anti-mouse antibody for reducing the background noise in the mouse-on-mouse immunohistochemistry. Furthermore, our findings demonstrate the inadequacy of goat serum/protein-blocking solution alone as an immunohistochemistry blocking system for reducing the background noise.

• Inefficiency of SIR models in forecasting COVID-19 epidemic: a case study of Isfahan

The multifaceted destructions caused by COVID‑19 have been compared to that of World War II. What makes the situation even more complicated is the ambiguity about the duration and ultimate spread of the pandemic. It is especially critical for the governments, healthcare systems, and economic sectors to have an estimate of the future of this disaster. By using different mathematical approaches, including the classical susceptible‑infected‑recovered (SIR) model and its derivatives, many investigators have tried to predict the outbreak of COVID‑19. In this study, we simulated the epidemic in Isfahan province of Iran for the period from Feb 14th to April 11th and also forecasted the remaining course with three scenarios that differed in terms of the stringency level of social distancing. Despite the prediction of disease course in short‑term intervals, the constructed SIR model was unable to forecast the actual spread and pattern of epidemic in the long term. Remarkably, most of the published SIR models developed to predict COVID‑19 for other communities, suffered from the same inconformity. The SIR models are based on assumptions that seem not to be true in the case of the COVID‑19 epidemic. Hence, more sophisticated modeling strategies and detailed knowledge of the biomedical and epidemiological aspects of the disease are needed to forecast the pandemic.

• Non-invasive metabolic biomarkers for early diagnosis of diabetic nephropathy: Meta-analysis of profiling metabolomics studies

Diabetic nephropathy (DN) is one of the worst complications of diabetes. Despite a growing number of DN metabolite profiling studies, most studies are suffering from inconsistency in their findings. The main goal of this meta-analysis was to reach to a consensus panel of significantly dysregulated metabolites as potential biomarkers in DN. To identify the significant dysregulated metabolites, meta-analysis was performed by "vote-counting rank" and "robust rank aggregation" strategies. Bioinformatics analyses were performed to identify the most affected genes and pathways. Among 44 selected studies consisting of 98 metabolite profiles, 17 metabolites (9 up-regulated and 8 down-regulated metabolites), were identified as significant ones by both the meta-analysis strategies (p-value<0.05 and OR>2 or <0.5) and selected as DN metabolite meta-signature. Furthermore, enrichment analyses confirmed the involvement of various effective biological pathways in DN pathogenesis, such as urea cycle, TCA cycle, glycolysis, and amino acid metabolisms. Finally, by performing a meta-analysis over existing time-course studies in DN, the results indicated that lactic acid, hippuric acid, allantoin (in urine), and glutamine (in blood), are the topmost non-invasive early diagnostic biomarkers. The identified metabolites are potentially involved in diabetic nephropathy pathogenesis and could be considered as biomarkers or drug targets in the disease.