Influencing factors of professional socialization of clinical nurses

Background and aims: Professional socialization is a process in which nurses compose values, attitudes and acquired knowledge from educational situation with organizational condition and adjust with them. This process is affected by individual organizational and situational factors. So, the aim of this study was to investigate regarding influencing factors to professional socialization of clinical nurses. Methods: This was a cross sectional analytical study. 300 and 200 nurses working in Tehran and Shahrekord teaching hospitals were selected by convenience sampling, respectively on 2013. Data were collected by a valid and reliable questionnaire made by researcher in 2 parts; demographic data and professional socialization and its domain. Data were analyzed using SPSS and descriptive statistics, independent t- test and Chi square test. Results: Results showed there was a significant relationship among professional socialization influencing factors and sex and occupational location (P=0.01). Also, There was a significant relationship among domains of professional socialization and sex, professional commitment (P=0.013) and belongingness (P=0.44). There was significance between occupational location and professional autonomy (P=0.016) and competency (P=0.051) domains of professional socialization. Whereas there was no significant relationship between other influencing factors such as marital status (P=0.707), age (P=0.650), training location (P=0.551), role model (P=0.453) and etc and professional socialization. Conclusion: It seems feminine framework of nursing profession is still an influencing factor of professional socialization. Moreover, working in larger cities provides more opportunity for nursing practice and clinical experiences

TranSQL: A Transformer-based Model for Classifying SQL Queries

Domain-Specific Languages (DSL) are becoming popular in various fields as they enable domain experts to focus on domain-specific concepts rather than software-specific ones. Many domain experts usually reuse their previously-written scripts for writing new ones; however, to make this process straightforward, there is a need for techniques that can enable domain experts to find existing relevant scripts easily. One fundamental component of such a technique is a model for identifying similar DSL scripts. Nevertheless, the inherent nature of DSLs and lack of data makes building such a model challenging. Hence, in this work, we propose TRANSQL, a transformer-based model for classifying DSL scripts based on their similarities, considering their few-shot context. We build TRANSQL using BERT and GPT-3, two performant language models. Our experiments focus on SQL as one of the most commonly-used DSLs. The experiment results reveal that the BERT-based TRANSQL cannot perform well for DSLs since they need extensive data for the fine-tuning phase. However, the GPT-based TRANSQL gives markedly better and more promising results.acceptedVersio

A Review on Brain Evolution and Development

Development of human brain is the essential process in the prenatal period of human growth. The total surface of human brain area is 1820 cm2, and the average cortical thickness is 2.7 mm. We reviewed and referred to several articles in this field. Comparative studies of the primate's brain show that there are general architectural basis governing the brain growth and evolutionary development. In this study, it is discussed about the human brain development with highlighting on the main mechanisms in the embryonic stage and early postnatal life as well as the general architectural values in brain evolution from primates to now. It is suggested that neurodevelopment involves some genetic bases in the neural stem cells proliferation, cortical neurons migration, cerebral cortex folding, synaptogenesis, gliogenesis, and myelination of neural fibers

Comparison of liquid-based and conventional cytology in diagnosis of breast mass

Background: Among all diagnostic techniques for breast lesions, fine-needle aspiration (FNA) is the simplest, most reliable and cheapest one. Aim: To compare liquid-based and conventional cytology in diagnosis of breast mass. Materials and Methods: About 101 patients with breast mass were enrolled. The aspirated materials were divided into two parts. One was poured into a liquid medium and the other part was directly spread on clean glass slides. Conventional and liquid-based preparations were compared using several criteria including adequacy (presence of the epithelial cluster or myoepithelial cells), overall cellularity, presence of single epithelial cells, presence of myoepithelial cells, epithelial architecture, nuclear detail, nuclear atypia and inflammatory/proteinaceous background and final diagnosis. Results: Among 101 cases, 85 (84.1%) were malignant and 16 (15.9%) were benign. Conventional and liquid-based cytology were similar according to adequacy (P = 0.65), cellularity (P = 0.13), epithelial architecture (P = 0.15), presence of myoepithelial cells (P = 0.61), nuclear detail (P = 0.57) and nuclear atypia (P = 0.44), while there were a significant difference between the two methods according to the presence of single epithelial cells (P < 0.001) and background (P < 0.001). Conclusion: Liquid-based cytology of breast specimens is an accurate diagnostic tool with high diagnostic yield in benign and malignant lesions

A comprehensive meta-analysis of transcriptome data to identify signature genes associated with pancreatic ductal adenocarcinoma.

PurposePancreatic ductal adenocarcinoma (PDAC) has a five-year survival rate of less than 5%. Absence of symptoms at primary tumor stages, as well as high aggressiveness of the tumor can lead to high mortality in cancer patients. Most patients are recognized at the advanced or metastatic stage without surgical symptom, because of the lack of reliable early diagnostic biomarkers. The objective of this work was to identify potential cancer biomarkers by integrating transcriptome data.MethodsSeveral transcriptomic datasets comprising of 11 microarrays were retrieved from the GEO database. After pre-processing, a meta-analysis was applied to identify differentially expressed genes (DEGs) between tumor and nontumor samples for datasets. Next, co-expression analysis, functional enrichment and survival analyses were used to determine the functional properties of DEGs and identify potential prognostic biomarkers. In addition, some regulatory factors involved in PDAC including transcription factors (TFs), protein kinases (PKs), and miRNAs were identified.ResultsAfter applying meta-analysis, 1074 DEGs including 539 down- and 535 up-regulated genes were identified. Pathway enrichment analyzes using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that DEGs were significantly enriched in the HIF-1 signaling pathway and focal adhesion. The results also showed that some of the DEGs were assigned to TFs that belonged to 23 conserved families. Sixty-four PKs were identified among the DEGs that showed the CAMK family was the most abundant group. Moreover, investigation of corresponding upstream regions of DEGs identified 11 conserved sequence motifs. Furthermore, weighted gene co-expression network analysis (WGCNA) identified 8 modules, more of them were significantly enriched in Ras signaling, p53 signaling, MAPK signaling pathways. In addition, several hubs in modules were identified, including EMP1, EVL, ELP5, DEF8, MTERF4, GLUP1, CAPN1, IGF1R, HSD17B14, TOM1L2 and RAB11FIP3. According to survival analysis, it was identified that the expression levels of two genes, EMP1 and RAB11FIP3 are related to prognosis.ConclusionWe identified several genes critical for PDAC based on meta-analysis and system biology approach. These genes may serve as potential targets for the treatment and prognosis of PDAC

Fifty-three significant biological processes were identified in eight modules with P-value<0.05.

Fifty-three significant biological processes were identified in eight modules with P-value<0.05.</p

Top first hub for each module.

PurposePancreatic ductal adenocarcinoma (PDAC) has a five-year survival rate of less than 5%. Absence of symptoms at primary tumor stages, as well as high aggressiveness of the tumor can lead to high mortality in cancer patients. Most patients are recognized at the advanced or metastatic stage without surgical symptom, because of the lack of reliable early diagnostic biomarkers. The objective of this work was to identify potential cancer biomarkers by integrating transcriptome data.MethodsSeveral transcriptomic datasets comprising of 11 microarrays were retrieved from the GEO database. After pre-processing, a meta-analysis was applied to identify differentially expressed genes (DEGs) between tumor and nontumor samples for datasets. Next, co-expression analysis, functional enrichment and survival analyses were used to determine the functional properties of DEGs and identify potential prognostic biomarkers. In addition, some regulatory factors involved in PDAC including transcription factors (TFs), protein kinases (PKs), and miRNAs were identified.ResultsAfter applying meta-analysis, 1074 DEGs including 539 down- and 535 up-regulated genes were identified. Pathway enrichment analyzes using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that DEGs were significantly enriched in the HIF-1 signaling pathway and focal adhesion. The results also showed that some of the DEGs were assigned to TFs that belonged to 23 conserved families. Sixty-four PKs were identified among the DEGs that showed the CAMK family was the most abundant group. Moreover, investigation of corresponding upstream regions of DEGs identified 11 conserved sequence motifs. Furthermore, weighted gene co-expression network analysis (WGCNA) identified 8 modules, more of them were significantly enriched in Ras signaling, p53 signaling, MAPK signaling pathways. In addition, several hubs in modules were identified, including EMP1, EVL, ELP5, DEF8, MTERF4, GLUP1, CAPN1, IGF1R, HSD17B14, TOM1L2 and RAB11FIP3. According to survival analysis, it was identified that the expression levels of two genes, EMP1 and RAB11FIP3 are related to prognosis.ConclusionWe identified several genes critical for PDAC based on meta-analysis and system biology approach. These genes may serve as potential targets for the treatment and prognosis of PDAC.</div

S8 Table -

PurposePancreatic ductal adenocarcinoma (PDAC) has a five-year survival rate of less than 5%. Absence of symptoms at primary tumor stages, as well as high aggressiveness of the tumor can lead to high mortality in cancer patients. Most patients are recognized at the advanced or metastatic stage without surgical symptom, because of the lack of reliable early diagnostic biomarkers. The objective of this work was to identify potential cancer biomarkers by integrating transcriptome data.MethodsSeveral transcriptomic datasets comprising of 11 microarrays were retrieved from the GEO database. After pre-processing, a meta-analysis was applied to identify differentially expressed genes (DEGs) between tumor and nontumor samples for datasets. Next, co-expression analysis, functional enrichment and survival analyses were used to determine the functional properties of DEGs and identify potential prognostic biomarkers. In addition, some regulatory factors involved in PDAC including transcription factors (TFs), protein kinases (PKs), and miRNAs were identified.ResultsAfter applying meta-analysis, 1074 DEGs including 539 down- and 535 up-regulated genes were identified. Pathway enrichment analyzes using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that DEGs were significantly enriched in the HIF-1 signaling pathway and focal adhesion. The results also showed that some of the DEGs were assigned to TFs that belonged to 23 conserved families. Sixty-four PKs were identified among the DEGs that showed the CAMK family was the most abundant group. Moreover, investigation of corresponding upstream regions of DEGs identified 11 conserved sequence motifs. Furthermore, weighted gene co-expression network analysis (WGCNA) identified 8 modules, more of them were significantly enriched in Ras signaling, p53 signaling, MAPK signaling pathways. In addition, several hubs in modules were identified, including EMP1, EVL, ELP5, DEF8, MTERF4, GLUP1, CAPN1, IGF1R, HSD17B14, TOM1L2 and RAB11FIP3. According to survival analysis, it was identified that the expression levels of two genes, EMP1 and RAB11FIP3 are related to prognosis.ConclusionWe identified several genes critical for PDAC based on meta-analysis and system biology approach. These genes may serve as potential targets for the treatment and prognosis of PDAC.</div

Workflow including meta-analysis and bioinformatics pipeline.

Gene expression datasets of PDAC and pancreatic cancer were obtained from the GEO. The datasets were normalized and processed to identify differentially expressed genes (DEGs) between normal and tumor tissues. The significantly enriched pathways and Gene Ontology were identified through enrichment analyses. Conserved motifs and consensus cis-regulatory elements (CREs) of DEGs were detected. The WGCNA was used to cluster genes with the highest connection and identification of co-expression modules.</p