Effect of induced diabetes on morphometric indexes of the cerebellar cortex and gene expression in C57BL mice.

Objectives: Diabetes is a metabolic disorder that affects the development of the central nervous system and plays an important role in learning and memory. Diabetes increases the reactive oxygen species (ROS) level in cells and changes the expression of several genes, including SYP, BDNF, PAX7, and SYNCAM1, through the FOXO transcription factor. This study was done to assess the effect of diabetes on morphometric indexes of the cerebellar cortex and gene expression in mice. Materials and Methods: Diabetes was induced in twelve adult, male C57BL mice using an injection of streptozotocin. After two months, the mice were dissected, and the cerebellum was stored for further analysis. For the morphometric analysis, tissue sections were stained with cresyl violet and examined with a light microscope. For gene expression analysis, the RNA was extracted, and cDNA was synthesized. The mRNA levels of SYP, BDNF, PAX7, and SYNCAM1 genes were measured by the real-time PCR method. Results: The thickness of the molecular layer and Purkinje layer, and the number of Purkinje and granular cells in the diabetic group were significantly reduced compared to controls P<0.0 1). The area, perimeter, and diameter of Purkinje cells in the diabetic group were significantly reduced compared to controls P<0.0 1). The expression of PAX7, SYP, and BDNF genes of the diabetic group was significantly reduced. However, SYNCAM1 expression in the cerebellum of the diabetic group was significantly increased compared to controls (P<0.05). Conclusion: Induced diabetes in mice can decrease the expression of memory-related genes in the cerebellum. Also, these genes affect the morphology and thickness of the cerebellum.

Integrated co-expression analysis of regulatory elements (miRNA, lncRNA, and TFs) in bovine monocytes induced by Str. uberis.

Non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), together with transcription factors, are critical pre-, co-, and post-transcriptional regulators. In addition to their criteria as ideal biomarkers, they have great potential in disease prognosis, diagnosis, and treatment of complex diseases. Investigation of regulatory mechanisms in the context of bovine mastitis, as most common and economic disease in the dairy industry, to identify elements influencing the expression of candidate genes as key regulators of the mammary immune response is not yet fully understood. Transcriptome profiles (50 RNA-Seq and 50 miRNA-Seq samples) of bovine monocytes induced by Str. uberis were used for co-expression module detection and preservation analysis using the weighted gene co-expression network analysis (WGCNA) approach. Assigned mi-, lnc-, and m-modules used to construct the integrated regulatory networks and miRNA-lncRNA-mRNA regulatory sub-networks. Remarkably, we have identified 18 miRNAs, five lncRNAs, and seven TFs as key regulators of str. uberis-induced mastitis. Most of the genes introduced here, mainly involved in immune response, inflammation, and apoptosis, were new to mastitis. These findings may help to further elucidate the underlying mechanisms of bovine mastitis, and the discovered genes may serve as signatures for early diagnosis and treatment of the disease.

An Analysis for Variable Physical Properties Involved in the Nano-Biofilm Transportation of Sutterby Fluid across Shrinking/Stretching Surface.

In this article, we explore how activation energy and varied transit parameters influence the two-dimensional stagnation point motion of nano-biofilm of Sutterby fluids incorporating gyrotactic microbes across a porous straining/shrinking sheet. Prior investigations implied that fluid viscosity as well as thermal conductance are temperature based. This research proposes that fluid viscosity, heat capacity and nanofluid attributes are all modified by solute concentration. According to some empirical research, the viscosity as well as heat conductivity of nanoparticles are highly based on the concentration of nanoparticles instead of only the temperature. The shooting approach with the RK-4 technique is applied to acquire analytical results. We contrast our outcomes with those in the existing research and examine their consistency and reliability. The graphic performance of relevant factors on heat, velocity, density and motile concentration domains are depicted and discussed. The skin friction factor, Nusselt number, Sherwood number and the motile density are determined. As the concentration-dependent properties are updated, the speed, temperature, concentration and motile density profiles are enhanced, but for all concentration-varying factors, other physical quantities deteriorate.

Genome-wide post-transcriptional regulation of bovine mammary gland response to Streptococcus uberis.

MicroRNAs (miRNAs) as post-transcriptionally regulators of gene expression have been shown to be critical regulators to fine-tuning immune responses, besides their criteria for being an ideal biomarker. The regulatory role of miRNAs in responses to most mastitis-causing pathogens is not well understood. Gram-positive Streptococcus uberis (Str. uberis), the leading pathogen in dairy herds, cause both clinical and subclinical infections. In this study, a system biology approach was used to better understand the main post-transcriptional regulatory functions and elements of bovine mammary gland response to Str. uberis infection. Publicly available miRNA-Seq data containing 50 milk samples of the ten dairy cows (five controls and five infected) were retrieved for this current research. Functional enrichment analysis of predicted targets revealed that highly confident responsive miRNAs (4 up- and 19 downregulated) mainly regulate genes involved in the regulation of transcription, apoptotic process, regulation of cell adhesion, and pro-inflammatory signaling pathways. Time series analysis showed that six gene clusters significantly differed in comparisons between Str. uberis-induced samples with controls. Additionally, other bioinformatic analysis, including upstream network analysis, showed essential genes, including TP53 and TGFB1 and some small molecules, including glucose, curcumin, and LPS, commonly regulate most of the downregulated miRNAs. Upregulated miRNAs are commonly controlled by the most important genes, including IL1B, NEAT1, DICER1 enzyme and small molecules including estradiol, tamoxifen, estrogen, LPS, and epigallocatechin. Our study used results of next-generation sequencing to reveal key miRNAs as the main regulator of gene expression responses to a Gram-positive bacterial infection. Furthermore, by gene regulatory network (GRN) analysis, we can introduce the common upregulator transcription factor of these miRNAs. Such milk-based miRNA signature(s) would facilitate risk stratification for large-scale prevention programs and provide an opportunity for early diagnosis and therapeutic intervention.

Systems Biology-Derived Genetic Signatures of Mastitis in Dairy Cattle: A New Avenue for Drug Repurposing.

Mastitis, a disease with high incidence worldwide, is the most prevalent and costly disease in the dairy industry. Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the leading agents causing acute severe infection with clinical signs. E. Coli, environmental mastitis pathogens, are the primary etiological agents of bovine mastitis in well-managed dairy farms. Response to E. Coli infection has a complex pattern affected by genetic and environmental parameters. On the other hand, the efficacy of antibiotics and/or anti-inflammatory treatment in E. coli mastitis is still a topic of scientific debate, and studies on the treatment of clinical cases show conflicting results. Unraveling the bio-signature of mastitis in dairy cattle can open new avenues for drug repurposing. In the current research, a novel, semi-supervised heterogeneous label propagation algorithm named Heter-LP, which applies both local and global network features for data integration, was used to potentially identify novel therapeutic avenues for the treatment of E. coli mastitis. Online data repositories relevant to known diseases, drugs, and gene targets, along with other specialized biological information for E. coli mastitis, including critical genes with robust bio-signatures, drugs, and related disorders, were used as input data for analysis with the Heter-LP algorithm. Our research identified novel drugs such as Glibenclamide, Ipratropium, Salbutamol, and Carbidopa as possible therapeutics that could be used against E. coli mastitis. Predicted relationships can be used by pharmaceutical scientists or veterinarians to find commercially efficacious medicines or a combination of two or more active compounds to treat this infectious disease.

The Role of Students with Practical Nursing Diplomas in the Process of Undergraduate Nursing Education: A Qualitative Study.

BACKGROUND: Awareness of the experiences of students and educators in the educational process plays an important role in facilitating and improving this process. This study aimed to explain and investigate the experiences of nursing educators and students about the presence of students with practical nursing diplomas in the process of undergraduate education. MATERIALS AND METHODS: In this qualitative study conducted from 2016 to 2018, which adopted a content analysis approach, 24 nursing students and educators of Lorestan University of Medical Sciences were selected using the purposive sampling method and were interviewed in depth. The data were manually recorded and analyzed using the Graneheim and Lundman method. RESULTS: The three main themes of "from contrast to alignment with professional competence," "peaceful coexistence in the educational process," and "reflection of the perceived reality of the clinical environment," along with six categories and 30 subcategories were extracted from the interviews. CONCLUSIONS: It seems that monitoring and managing undergraduate courses more thoroughly, as well as raising the awareness of educators about the positive and negative dimensions of the presence of nursing students with practical nursing diplomas in undergraduate nursing courses can make it possible to optimize and improve students' learning process.

Prediction of key regulators and downstream targets of E. coli induced mastitis.

Mastitis, an inflammatory response of mammary glands to invading bacteria, is one of the most economically costly diseases affecting dairy animals. Escherichia coli can be introduced as a major etiological agent of bovine mastitis in well-managed dairy farms. It is of great significance to understand the regulatory mechanisms by which the disease can be controlled. High-throughput technologies combined with novel computational systems biology tools have provided new opportunities for a better understanding of the molecular mechanisms that underlie disease. In the current study, the results of microarray meta-analysis research were used to perform a network analysis to potentially identify molecular mechanisms that regulate gene expression profile in response to E. coli mastitis. In our result, transcription factors, TP53, SP1, ligands, INS, IFNG, EGF, and protein kinases, MAPK1, MAPK14, AKT1, were identified as the key upstream regulators whereas protein kinases, MAPK3, MAPK8, MAPK14, ligands, VEGFA, IL10, an extracellular protein, MMP2, and a mitochondrial membrane protein, BCL2, were identified as the key downstream targets of differentially expressed genes. The results of this research revealed important genes that have the key functions in immune response, inflammation, or mastitis which can provide the basis for strategies to improve the diagnosis and treatment of mastitis in dairy cows.

Integration of machine learning and meta-analysis identifies the transcriptomic bio-signature of mastitis disease in cattle.

Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the main agents that cause severe mastitis disease with clinical signs in dairy cattle. Rapid detection of this disease is so important in order to prevent transmission to other cows and helps to reduce inappropriate use of antibiotics. With the rapid progress in high-throughput technologies, and accumulation of various kinds of '-omics' data in public repositories, there is an opportunity to retrieve, integrate, and reanalyze these resources to improve the diagnosis and treatment of different diseases and to provide mechanistic insights into host resistance in an efficient way. Meta-analysis is a relatively inexpensive option with good potential to increase the statistical power and generalizability of single-study analysis. In the current meta-analysis research, six microarray-based studies that investigate the transcriptome profile of mammary gland tissue after induced mastitis by E. coli infection were used. This meta-analysis not only reinforced the findings in individual studies, but also several novel terms including responses to hypoxia, response to drug, anti-apoptosis and positive regulation of transcription from RNA polymerase II promoter enriched by up-regulated genes. Finally, in order to identify the small sets of genes that are sufficiently informative in E. coli mastitis, the differentially expressed gene introduced by meta-analysis were prioritized by using ten different attribute weighting algorithms. Twelve meta-genes were detected by the majority of attribute weighting algorithms (with weight above 0.7) as most informative genes including CXCL8 (IL8), NFKBIZ, HP, ZC3H12A, PDE4B, CASP4, CXCL2, CCL20, GRO1(CXCL1), CFB, S100A9, and S100A8. Interestingly, the results have been demonstrated that all of these genes are the key genes in the immune response, inflammation or mastitis. The Decision tree models efficiently discovered the best combination of the meta-genes as bio-signature and confirmed that some of the top-ranked genes -ZC3H12A, CXCL2, GRO, CFB- as biomarkers for E. coli mastitis (with the accuracy 83% in average). This research properly indicated that by combination of two novel data mining tools, meta-analysis and machine learning, increased power to detect most informative genes that can help to improve the diagnosis and treatment strategies for E. coli associated with mastitis in cattle.