OBJECTIVE:To provide reference for the generalization of automatic unit-dose tablet sorting and counting machine (ATM) in hospital pharmacy. METHODS: The preparative work and the working procedure of the ATM were introduced, and the advantages, the causes of medication errors and the problems related to the application of the ATM were analyzed. RESULTS: ATM helped to enhance the dispensing efficiency, improve the sanitation of drugs, ensure drug safety to great extent and improve the drug control. However, sometimes it might bring about medication errors. 236 (0.48%) of the total 49 059 medical orders analyzed in our study were found to be of medication errors. CONCLUSIONS: ATM has great application value. The wide use of ATM is consistent with the tendency of pharmacy automation, yet its use remains to be further improved.

OBJECTIVE: To improve the quality, efficiency and management of hospital pharmacy dispensing work. METHODS: Following the principle of logistics, the drug dispensing path was planned as a whole to reduce the ineffective drug handling, and the dispensing process was designed and machines were reasonable utilized. RESULT & CONCLUSION: Improving pharmacy dispensing work in line with the principle of logistics is conducive to the improving of both quality and efficiency of dispensing work, strengthening of management and lowering of labor intensity.

Associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) is an innovation in liver surgery in recent years.It provides opportunities of surgery for those with inadequate remnant liver volume by rapidly remnant liver hypertrophy.Comparing with conventional two stage hepatectomy (TSH),such as portal vein embolization,transcatheter arterial chemoembolization and hepatic artery ligation,ALPPS has advantages of fastened future liver remnant hypertrophy,shortened waiting time for the second stage of surgery and higher resection rate.But as for long-term curative effect,no strong evidence shows ALPPS is better than TSH.

Objective:To investigate the protective role of extracellular signal-regulated kinase (ERK) signaling pathway in the process that vasonatrin peptide (VNP) reduces hepatic ischemia-reperfusion injury in rats.Methods:Twenty SD rats, weighting 200-250 g, were randomly divided into four groups and each group has five rats. The four groups were sham operation group (S group), ischemia-reperfusion group (I/R group), VNP group (V group) and PD98059+ VNP group (P+ V group). In the rat model of hepatic warm ischemia and reperfusion, the hepatic artery and portal vein of the left lobe and middle lobe of the liver were clamped with arterial clamp for 45 min followed by reperfusion for 120 min. In the V group, VNP (50 μg/kg) was injected 10 minutes before ischemia. In the P+ V group, PD98059 (2 mg/kg) was injected 20 min before VNP injection followed by VNP administration and I/R treatment. The serum levels of alanine amino transaminase (ALT), aspartate amino transferase (AST), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β) and the superoxide dismutase (SOD) in liver tissue homogenate and malondialdehyde (MDA) were measured. The histopathology of liver tissue was observed. The contents of p-ERK1/2 were detected by Western blot.Results:Compared with S group, in I/R group and P+ V group the serum levels of ALT [(489.65±11.22), (333.05±24.77) vs. (33.78±4.88) U/L], AST [(651.43±14.99), (503.18±21.48) vs. (154.84±12.32) U/L], TNF-α [(12.83±1.09), (9.64±0.57) vs. (2.11±0.11) ng/L], IL-1β [(7.19±0.62), (5.12±0.22) vs. (1.10±0.49) ng/L], MDA [(8.00±0.88), (5.60±1.01) vs. (2.76±1.29) μmol/mg] increased, while SOD [(54.89±10.60), (68.85±8.33) vs. (126.10±15.63) nmol/mg]decreased (all P<0.05). The histopathology of liver tissue revealed that liver structure damaged more seriously in I/R group and P+ V group. Western blot analysis showed that p-ERK1/2 decreased significantly in I/R group and P+ V group. Compared with I/R group, ALT, AST, MDA, TNF-α and IL-1β decreased significantly and SOD increased significantly in V group (all P<0.05). The histopathology of liver tissue revealed that liver structure was damaged slightly, and p-ERK1/2 increased significantly in V group compared with I/R group ( P<0.05). Conclusion:VNP can significantly reduce hepatic ischemia-reperfusion injury through activation of p-ERK1/2 signaling pathway and inhibition of hepatocyte inflammatory response.

Objective:To explore the performance of the attention-multiple instance learning (MIL) framework, an attention fusion network-based MIL, in the automated diagnosis of chronic gastritis with multiple indicators.Methods:A total of 1 015 biopsy cases of gastritis diagnosed in Fudan University Cancer Hospital, Shanghai, China and 115 biopsy cases of gastritis diagnosed in Shanghai Pudong Hospital, Shanghai, China were collected from January 1st to December 31st in 2018. All pathological sections were digitally converted into whole slide imaging (WSI). The WSI label was based on the corresponding pathological report, including "activity" "atrophy" and "intestinal metaplasia". The WSI were divided into a training set, a single test set, a mixed test set and an independent test set. The accuracy of automated diagnosis for the Attention-MIL model was validated in three test sets.Results:The area under receive-operator curve (AUC) values of Attention-MIL model in single test sets of 240 WSI were: activity 0.98, atrophy 0.89, and intestinal metaplasia 0.98; the average accuracy of the three indicators was 94.2%. The AUC values in mixed test sets of 117 WSI were: activity 0.95, atrophy 0.86, and intestinal metaplasia 0.94; the average accuracy of the three indicators was 88.3%. The AUC values in independent test sets of 115 WSI were: activity 0.93, atrophy 0.84, and intestinal metaplasia 0.90; the average accuracy of the three indicators was 85.5%.Conclusions:To assist in pathological diagnosis of chronic gastritis, the diagnostic accuracy of Attention-MIL model is very close to that of pathologists. Thus, it is suitable for practical application of artificial intelligence technology.

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological contexts.To fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico pre-diction of TSSs solely based on genomic sequences.Most of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome scale.Here,we present DeeReCT-TSS,a deep learning-based method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing data.We show that by effectively incorporating these two sources of information,DeeReCT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types.Furthermore,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific TSSs.Finally,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin states.The source code for DeeReCT-TSS is available at https://github.-com/JoshuaChou2018/DeeReCT-TSS_release and https://ngdc.cncb.ac.cn/biocode/tools/BT007316.