Intelligent Algorithm-Based Ultrasound Image for Evaluating the Effect of Comprehensive Nursing Scheme on Patients with Diabetic Kidney Disease.

This study was aimed at exploring the effect of ultrasound image evaluation of comprehensive nursing scheme based on artificial intelligence algorithms on patients with diabetic kidney disease (DKD). 44 patients diagnosed with DKD were randomly divided into two groups: group A (no nursing intervention) and group B (comprehensive nursing). In the same period, 32 healthy volunteers were selected as the control group. Ultrasonographic images based on the K non-local-means (KNL-Means) filtering algorithm were used to perform imaging examinations in healthy people and DKD patients before and after care. The results suggested that compared with those of the SAE reconstruction algorithm and KAVD reconstruction algorithm, the PSNR value of artificial bee colony algorithm reconstruction of image was higher and the MSE value was lower. The resistant index (RI) of DKD patients in group B after nursing was 0.63 ± 0.06, apparently distinct from the RI of the healthy people (controls) in the same group (0.58 ± 0.06) and the RI of DKD patients in group A (0.68 ± 0.07) (P < 0.05). The incidence rate of complications in DKD patients in group B was apparently inferior to that in group A. After comprehensive nursing intervention (CNI), the scores of all dimensions of quality of life (QoL) in DKD patients in group B were obviously superior versus those in DKD patients in group A. It suggests that implementation of nursing intervention for DKD patients can effectively help patients improve and control the level of renal function, while ultrasound images based on intelligent algorithm can dynamically detect the changes in the level of renal function in patients, which has the value of clinical promotion.

Identifying the mRNAs associated with Bladder cancer recurrence.

OBJECTIVE: To identify the mRNAs associated with bladder cancer (BC) recurrence. METHODS: The transcription profile of GSE31684 including 39 recurrent BC tumor samples and 54 non-recurrent BC tumor samples as well as transcription profile of GSE13507 including 36 recurrent BC tumor samples and 67 non-recurrent BC tumor samples were downlaoded from the Gene Expression Omnibus. Then, the differentially expressed genes (DEGs) were identified using linear models for microarray data (limma) and the intersections of DEGs from the two datasets were further screened. The weighed gene co-expression network analysis (WGCNA) was used to screen the modules related to BC recurrence. Protein-protein interaction (PPI) network analysis was used to analyze the genes interaction. Their functions were predicted by Gene Ontology and KEGG pathway enrichment. Moreover, The Comparative Toxicogenomics Database 2017 update (CTD) was used to search the BC related pathway. The univariate cox regression analysis was used to identify DEGs associated to the recurrence. Kaplan-Meier plots were used to illustrate recurrence free survival time (RFS). RESULTS: A total of 692 intersections DEGs were screened. WGCNA showed that 7 modules (2279 genes) were stable in both the datasets. A total of 169 intersection DEGs were mapped to the 7 modules. There existed 149 interaction relationships among 81 proteins (18 down-regulated and 63 up-regulated DEGs) in the PPI network. Two KEGG pathways including Focal adhesion and ECM-receptor interaction were enriched which were also found in the CTD. The univariate cox regression analysis showed that 3 DEGs (COL4A1, COL1A2 and COL5A1) were significant related to the prognosis. Multivariate cox regression analysis revealed that pathologic_N (N0-N1 vs N2-N3, p= 0.033) were independent prognostic factors for overall survival in patients with BC. CONCLUSION: COL4A1, COL1A2 and COL5A1 could be associated with BC recurrence.