Application, knowledge and training needs regarding comprehensive geriatric assessment among geriatric practitioners in healthcare institutions: a cross-sectional study.

BACKGROUND: This study aimed to investigate the actual application, knowledge, and training needs of comprehensive geriatric assessment (CGA) among geriatric practitioners in China. METHODS: A total of 225 geriatric practitioners attending the geriatric medicine or geriatric nursing training were recruited for this cross-sectional study. The questionnaire included demographics, healthcare institution characteristics, the actual application, knowledge, training needs, and barriers to CGA and geriatric syndromes (GS). RESULTS: Physicians and nurses were 57.3% and 42.7%, respectively. 71.1% were female, with a median age was 35 years. Almost two-thirds (140/225) of geriatric practitioners reported exposure to CGA in their clinical practice. The top five CGA evaluation items currently used were malnutrition risk (49.8%), fall risk (49.8%), activity of daily living (48.0%), pain (44.4%), and cognitive function (42.7%). Median knowledge scores for the management procedures of GS ranged from 2 to 6. Physicians identified medical insurance payment issues (29.5%) and a lack of systematic specialist knowledge and technology (21.7%) as the two biggest barriers to practicing geriatrics. Nurses cited a lack of systematic specialist knowledge and technology (52.1%) as the primary barrier. In addition, physicians and nurses exhibited significant differences in their knowledge of CGA-specific evaluation items and management procedures for GS (all P < 0.05). However, there were no significant differences in their training needs, except for polypharmacy. CONCLUSIONS: The rate of CGA application at the individual level, as well as the overall knowledge among geriatric practitioners, was not adequate. Geriatric education and continuous training should be tailored to address the specific roles of physicians and nurses, as well as the practical knowledge reserves, barriers, and training needs they face.

Identification of Acute Pancreatitis-Related Genes and Pathways by Integrated Bioinformatics Analysis.

BACKGROUND AND AIMS: The present study aimed to identify the differential expressed genes that are related to acute pancreatitis. METHODS: Microarray datasets GSE109227 and GSE3644 were downloaded from the public database and analyzed to screen the genes. Afterward, integrated analysis of these genes were performed, including gene ontology and pathway enrichment analysis, protein-protein interaction network construction, expression level evaluation in human organs, relevant miRNAs and TFs prediction, and prognosis values of hub genes in pancreatic carcinoma. RESULTS: A total number of 93 differential expressed genes were screened from the datasets, and EGFR, CDH1, ACTB, CD44, and VCL were identified as hub DEGs. Functional enrichment analysis demonstrated that these genes were mostly enriched in biological processes such as cell adhesion, platelet aggregation, glycoprotein binding, and also involved in multiple pathways included adherent junction, proteoglycans in cancer, bacterial invasion of epithelial cells, focal adhesion, Rap1 signaling pathway, regulation of actin cytoskeleton, and pathways in cancers. The five hub genes were all expressed in human pancreas organs with various levels. Hub gene-related network investigation predicted core miRNAs including hsa-mir-16-5p and main TFs like SOX9 with close interactions with these hub genes. Survival analysis also indicated that the high expression of EGFR, CDH1, ACTB, CD44, and VCL were significantly associated with poor prognosis in pancreatic carcinoma. CONCLUSIONS: The study suggested that hub genes EGFR, CDH1, ACTB, CD44, and VCL may play vital role in the pathogenesis of acute pancreatitis and may serve as potential biomarkers to facilitate future acute pancreatitis diagnosis and treatment.

Identification of novel genes associated with a poor prognosis in pancreatic ductal adenocarcinoma via a bioinformatics analysis.

Pancreatic ductal adenocarcinoma (PDAC) is a class of the commonest malignant carcinomas. The present study aimed to elucidate the potential biomarker and prognostic targets in PDAC. The array data of GSE41368, GSE43795, GSE55643, and GSE41369 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and differentially expressed microRNAs (DEmiRNAs) in PDAC were obtained by using GEO2R, and overlapped DEGs were acquired with Venn Diagrams. Functional enrichment analysis of overlapped DEGs and DEmiRNAs was conducted with Metascape and FunRich, respectively. The protein-protein interaction (PPI) network of overlapped DEGs was constructed by STRING and visualized with Cytoscape. Overall survival (OS) of DEmiRNAs and hub genes were investigated by Kaplan-Meier (KM) plotter (KM plotter). Transcriptional data and correlation analyses among hub genes were verified through GEPIA and Human Protein Atlas (HPA). Additionally, miRNA targets were searched using miRTarBase, then miRNA-DEG regulatory network was visualized with Cytoscape. A total of 32 DEmiRNAs and 150 overlapped DEGs were identified, and Metascape showed that DEGs were significantly enriched in cellular chemical homeostasis and pathways in cancer, while DEmiRNAs were mainly enriched in signal transduction and Glypican pathway. Moreover, seven hub genes with a high degree, namely, V-myc avian myelocytomatosis viral oncogene homolog (MYC), solute carrier family 2 member 1 (SLC2A1), PKM, plasminogen activator, urokinase (PLAU), peroxisome proliferator activated receptor γ (PPARG), MET proto-oncogene, receptor tyrosine kinase (MET), and integrin subunit α 3 (ITGA3), were identified and found to be up-regulated between PDAC and normal tissues. miR-135b, miR-221, miR-21, miR-27a, miR-199b-5p, miR-143, miR-196a, miR-655, miR-455-3p, miR-744 and hub genes predicted poor OS of PDAC. An integrative bioinformatics analysis identified several hub genes that may serve as potential biomarkers or targets for early diagnosis and precision target treatment of PDAC.