Objective: To discover molecular markers associated with lung adenocarcinoma diagnosis/prognosis and drug resistance through screening of differentially expressed genes based on published chip data in gene expression databases using bioinformatics methods. Methods: Comprehensive analysis was performed in available mRNA microarray datasets including lung adenocarcinoma tissues dataset GSE32863 and lung adenocarcinoma taxane-platin resistance dataset GSE77209 from the gene expression omnibus(GEO) database. Gene ontology enrichment analysis, gene pathway enrichment analysis and protein interaction network analysis were performed based on significantly correlated genes. The expression level of genes was validated in the cancer genome atlas(TCGA) dataset. Survival differences were assessed by the log-rank test in TCGA lung adenocarcinoma dataset. Based on the publications genomics of drug sensitivity in cancer(GDSC) database in CellMiner cross database(CellMiner CDB), Pearson correlation analysis was used to analyze the correlation between differentially expressed genes and the half-maximal inhibitory concentration(IC50) of anticancer drugs. Results : There were a total of 77 genes which had a different expression in resistance lung adenocarcinoma cells and lung adenocarcinoma cancer tissues. The functional enrichment analysis showed that these co-different expression genes were mainly enriched in microtubule, extracellular exosome, cell cycle and signaling by nuclear receptors. Protein-protein interactions(PPI) network screened 6 most connected genes as molecular complex(MCODE). Among the MCODE, overexpressed ubiquitin conjugating enzyme E2 T(UBE2T), kinesin family member 20A(KIF20A), PCNA clamp associated factor(KIAA0101), pituitary tumor-transforming gene 1(PTTG1) and NIMA related kinase 2(NEK2) were associated with poor outcomes. Survival analysis results showed that these five genes were upregulated in lung adenocarcinoma tissues and drug-resistant cells and were significantly associated with poor prognosis in lung adenocarcinoma patients. Drug sensitivity analysis results suggested that high expression of PTTG1 and UBE2T was significantly associated with sensitivity to multiple anticancer drugs, including paclitaxel and docetaxel. RT-PCR validation showed that PTTG1 andUBE2T were highly expressed in docetaxel-resistant cells A549-TXR and H358-TXR. Conclusion PTTG1 andUBE2T holds the potential to be chemoresistance markers in lung adenocarcinoma.

Objective:To explore the effect of variable priority cognitive-motor dual-task training in aged cognitive frailty patients with type 2 diabetes mellitus.Methods:From September 2022 to September 2023, a total of 108 aged cognitive frailty patients with type 2 diabetes mellitus were selected in the Department of Endocrinology of North China University of Science and Technology Affiliated Hospital as research subjects.The subjects were divided into three groups in the order of admission, with 36 people in each group.Three groups of patients were intervened for 8 weeks using variable priority cognitive-motor dual-task training (VP group), exercise combined cognitive intervention with sequential task (ST group), and endocrinology routine treatment and nursing (control group), respectively.The scores of mini-mental state examination(MMSE), frailty phenotype(FP) and Berg balance scale(BBS) were compared among the three groups before and after the intervention.Repeated measurement variance analysis was used to compare scale results by SPSS 22.0.Results:(1)The MMSE score of patients before and after intervention had significant interaction effect between time and groups ( F=69.929, P<0.05).The MMSE scores of the VP group and ST group after 8 weeks of intervention were higher than those before intervention and after 4 weeks of intervention, and the MMSE scores of the two groups after 4 weeks of intervention were also higher than those before intervention (all P<0.05).After 4 weeks and 8 weeks of intervention, the MMSE score of the VP group was higher than that of the ST group and the control group, and the MMSE score of the ST group was higher than that of the control group after 8 weeks of intervention(all P<0.05).(2)The FP score of patients before and after intervention had a significant interaction effect between time and groups ( F=46.425, P<0.05).The FP score of the VP group at 8 weeks of intervention (2.64±0.59) was lower than that after 4 weeks of intervention (3.28±0.51) and before intervention (3.56±0.61), and the FP score after 4 weeks of intervention was lower than before intervention (all P<0.05).The FP score of the ST group after 8 weeks of intervention (3.44±0.56) was lower than before the intervention (3.59±0.56) ( P<0.05); After 4 weeks of intervention, the FP score of the VP group was lower than that of the control group (3.36±0.54) ( P<0.05).After 8 weeks of intervention, the FP score of the VP group was lower than that of the ST group and the control group (3.39±0.55) (both P<0.05).(3)The FP score of patients before and after intervention had a significant interaction effect between time and groups ( F=135.791, P<0.05).The BBS scores of the VP group and ST group after 8 weeks of intervention were higher than those before and after 4 weeks of intervention, and the BBS scores after 4 weeks of intervention were also higher than those before intervention (all P<0.05).After 4 weeks of intervention, the BBS score of the VP group was higher than that of the control group ( P<0.05).After 8 weeks of intervention, the BBS score of the VP group was higher than that of the ST group and the control group, and the BBS score of the ST group was also higher than that of the control group (all P<0.05). Conclusion:The variable priority cognitive-motor dual-task training can improve the cognitive function, physical frailty and balance function in aged cognitive frailty patients with type 2 diabetes mellitus, and the intervention effect is better than that of exercise combined cognitive intervention with sequential task and endocrinology routine treatment and nursing.

The incidence of in-hospital death in acute myocardial infarction (AMI) is high, which seriously threatens the life and health of patients. At present, many countries and regions have established a variety of objective assessment models for predicting the in-hospital mortality of patients with AMI, providing important decision-making support for patients with different risk levels when formulating treatment plans. With the rise of artificial intelligence, many new modeling methods also show certain advantages over the traditional models. This article systematically introduces the commonly used and newly constructed risk prediction models for in-hospital mortality of AMI, in order to provide help for medical staff to assist decision-making in clinical practice, and provide reference for the establishment of a safe and more effective risk prediction model in the future.