Objective:To explore the personnel arrangement in the outpatient pharmacy by calculating working hour to provide ref-erence for the rational staffing in hospital. Methods:The daily work content and working hour of 18 pharmacists in the outpatient phar-macy of a large general hospital from January to March in 2013 were following-up observed and recorded using the working hour meas-urement. The data were input the EXcellsoftware to establish the database, and the workload in various positions was collected and sorted. The obtained relative parameters were used to calculate the needed worker number on the basis of manpower planning model. Results:The research confirmed the mean operation time for 9 work programs in the outpatient pharmacy, and the time for drug dispen-sing and distributing was detailed. The needed number of pharmacists was 13. 29 according to the calculation, plus the officer-in-charge and sanitation workers, the total number was 15. 29(approx. 16). Conclusion:The working hour measurement can scientifically de-termine the time for each job, and the workload should be used as the foundation for configuring personnel qualification and the number in outpatient pharmacy.

Objective To explore the prognostic value and immune infiltration landscape of anoikis-related long noncoding RNAs (arlncRNAs) in lung adenocarcinoma. Methods RNA-seq and clinical data of lung adenocarcinoma were downloaded from the TCGA database, and anoikis-related genes were obtained from the GeneCards and Harmonizome databases. Coexpression, differential, and WGCNA analyses were performed to screen differentially expressed arlncRNAs closely related to the occurrence of lung adenocarcinoma. A prognostic risk model was then constructed based on the arlncRNAs, and its predictive efficacy was further validated. Finally, consensus clustering was used to identify the molecular subtypes associated with anoikis in lung adenocarcinoma. Results Seven prognostic arlncRNAs were identified, and the prognostic risk models established based on them had AUC values of ROC curves greater than 0.7. Survival and immune infiltration analyses revealed that low-risk patients had high overall survival and immune infiltration, implying that they experienced good immune treatment effects. Drug sensitivity analysis showed that the high-risk patients were more sensitive to commonly used chemotherapeutic agents than the low-risk patients. According to the expression of model genes, subtypes C1 and C2 were identified through consensus clustering, and C1 showed a good prognosis. Conclusion The prognostic risk model based on the seven arlncRNAs can effectively predict the prognosis of lung adenocarcinoma patients. The results of immune-related and drug sensitivity analyses provide a reference for the precise individualized treatment of patients with lung adenocarcinoma.