Objective To assess the predictive value of proprotein convertase subtilisin/kexin type-9(PCSK9)combined with lactic acid(Lac)and Sequential Organ Failure Assessment(SOFA)score in determining the 28-day mortality risk of patients with sepsis.Methods A total of 203 patients with sepsis were followed up for 28 days.Patients were divided into the death group(n=56)and the survival group(n=147)according to the clinical outcome.The general data and clinical indicators were compared between the two groups.The prediction model of 28 day mortality risk of sepsis patients was established by multivariate Logistic regression analysis,and the nomogram of the prediction model visualization was drawn.Results Alanine aminotransferase,serum creatinine,blood urea nitrogen,Lac,PCSK9 and SOFA score were higher in the death group than those in the survival group(P＜0.05),while the hemoglobin level was lower than that in the survival group(P＜0.05).The multivariable Logistic regression analysis identified that elevated levels of Lac(OR=1.197,95％CI:1.019-1.435),PCSK9(OR=1.002,95％CI:1.001-1.003)and SOFA score(OR=1.858,95％CI:1.528-2.340)were independent risk factors for 28-day mortality in patients with sepsis(P＜0.05).Nomogram was developed using these three indicators,and the linear predictor cutoff value was-1.06.Patients were divided into the low-risk group(n=126)and the high-risk group(n=77).The 28-day mortality rates were 4.0％for the low-risk group and 66.2％for the high-risk group.Internal validation showed that the model with these three indicators had good discrimination(C-index=0.924,95％CI:0.881～0.960,P＜0.05),calibration(x2=11.543,P＞0.05)and clinical utility.Conclusion The combination of serum PCSK9,Lac and SOFA scores at admission effectively predicts 28-day mortality risk in sepsis patients.

Objective To assess the predictive value of proprotein convertase subtilisin/kexin type-9(PCSK9)combined with lactic acid(Lac)and Sequential Organ Failure Assessment(SOFA)score in determining the 28-day mortality risk of patients with sepsis.Methods A total of 203 patients with sepsis were followed up for 28 days.Patients were divided into the death group(n=56)and the survival group(n=147)according to the clinical outcome.The general data and clinical indicators were compared between the two groups.The prediction model of 28 day mortality risk of sepsis patients was established by multivariate Logistic regression analysis,and the nomogram of the prediction model visualization was drawn.Results Alanine aminotransferase,serum creatinine,blood urea nitrogen,Lac,PCSK9 and SOFA score were higher in the death group than those in the survival group(P＜0.05),while the hemoglobin level was lower than that in the survival group(P＜0.05).The multivariable Logistic regression analysis identified that elevated levels of Lac(OR=1.197,95％CI:1.019-1.435),PCSK9(OR=1.002,95％CI:1.001-1.003)and SOFA score(OR=1.858,95％CI:1.528-2.340)were independent risk factors for 28-day mortality in patients with sepsis(P＜0.05).Nomogram was developed using these three indicators,and the linear predictor cutoff value was-1.06.Patients were divided into the low-risk group(n=126)and the high-risk group(n=77).The 28-day mortality rates were 4.0％for the low-risk group and 66.2％for the high-risk group.Internal validation showed that the model with these three indicators had good discrimination(C-index=0.924,95％CI:0.881～0.960,P＜0.05),calibration(x2=11.543,P＞0.05)and clinical utility.Conclusion The combination of serum PCSK9,Lac and SOFA scores at admission effectively predicts 28-day mortality risk in sepsis patients.

Objective To screen for ferroptosis-related differentially expressed genes(DEGs)of epidermal growth factor receptor(EGFR)-tyrosine kinase inhibitors(TKIs)resistance in non-small cell lung cancer(NSCLC).Methods The gene sequencing dataset GSE117846 of NSCLC EGFR-TKIs resistant cells was se-lected from the Gene Expression Omnibus data base(GEO)and screened for DEGs with P＜0.05 and | log2 FC |1.Ferroptosis-related genes were collected using the FerrDb database and jvenn was used to intersected the DEGs screened from GSE117846 dataset with the ferroptosis-related genes obtained from FerrDb database.GO function and KEGG pathway enrichment analysis of intersection genes were performed,and protein-pro-tein interaction(PPI)network was drawn.The score of intersection genes was calculated by using Cytohubba plug-in in Cytoscape software,and the top 10 genes were used for Hub genes screening.ULCAN and GEPIA2 databases were used to analyze the expression of Hub genes in NSCLC and its effect on the survival prognosis of patients.Real-time fluorescence quantitative PCR(qPCR)was used to detect the relative expression levels of Hub gene mRNA in NSCLC patients'cancer tissues,adjacent tissues and in vitro cells to verify the results of bioinformatics analysis.Results A total of 60 ferroptosis-related DEGs of EGFR-TKIs resistance in NSCLC were screened out,including 30 up-regulated genes and 30 down-regulated genes.The 60 genes were mainly enriched in P53 signaling pathway,ferroptosis pathway and FoxO signaling pathway.There were 57 nodes and 99 edges in the PPI network,with an average clustering coefficient of 0.377 and PPI enrichment P＜0.01.The Hub gene screened out by Cytohubba plug-in was tumor protein P63(TP63).ULCAN and GE-PIA2 database analysis showed that the expression of TP63 in lung adenocarcinoma tissue was significantly lower than that in normal tissue,while the expression of TP63 in lung squamous cell carcinoma tissue was sig-nificantly higher than that in normal tissue,and the differences were statistically significant(P＜0.05).In pa-tients with lung adenocarcinoma,there was no significant difference in the survival prognosis between TP63 high and low expression groups(P＞0.05),while in patients with lung squamous cell carcinoma,the survival prognosis of TP63 low expression group was better,and the difference was statistically significant(P＜0.05).QPCR showed that TP63 mRNA highly expressed in lung squamous cell carcinoma tissue and lowly expressed in lung adenocarcinoma tissue compared with adjacent tissues(P＜0.05).The expression of TP63 mRNA was down-regulated in gefitinib-resistant PC9/GR cells(P＜0.05),which was consistent with the re-sults of bioinformatics analysis.Conclusion TP63 may be an important gene linking NSCLC EGFR-TKIs re-sistance to ferroptosis.

Objective:To explore the learning curve of central pancreatectomy (CP) and provide an excellent reference for surgeons to get the point of this operation.Methods:Clinical data of 73 patients who underwent CP in the same operation team from January 2006 to January 2018 were collected and retrospectively analyzed by the moving average method (MAM) and the cumulative sum method (CUSUM). Data was analyzed by statistical package for social science (SPSS) software.Results:According to the MAM and CUSUM curves, the learning process of CP could be divided into two stages. At the first stage ( n=1-11), the median operation time was 340 minutes and the median intraoperative hemorrhage was 400 ml. In the second stage ( n=12-73), the median operation time was 213 minutes and the median intraoperative hemorrhage was 100 ml. The difference was statistically significant ( P<0.001). There were no significant differences between the two stages of patients in terms of other aspects ( P>0.05). Conclusions:CP can be mastered after 11 cases of exercises. In the first 11 operations, surgeons should get familiar with the operation process, respond actively to emergencies and accumulate experience to gain this surgical technique fast.

Objective:To explore the learning curve of central pancreatectomy (CP) and provide an excellent reference for surgeons to get the point of this operation.Methods:Clinical data of 73 patients who underwent CP in the same operation team from January 2006 to January 2018 were collected and retrospectively analyzed by the moving average method (MAM) and the cumulative sum method (CUSUM). Data was analyzed by statistical package for social science (SPSS) software.Results:According to the MAM and CUSUM curves, the learning process of CP could be divided into two stages. At the first stage ( n=1-11), the median operation time was 340 minutes and the median intraoperative hemorrhage was 400 ml. In the second stage ( n=12-73), the median operation time was 213 minutes and the median intraoperative hemorrhage was 100 ml. The difference was statistically significant ( P<0.001). There were no significant differences between the two stages of patients in terms of other aspects ( P>0.05). Conclusions:CP can be mastered after 11 cases of exercises. In the first 11 operations, surgeons should get familiar with the operation process, respond actively to emergencies and accumulate experience to gain this surgical technique fast.

OBJECTIVE： To provide reference for strengthening clinical application of key monitoring drugs and promoting rational drug use in clinic. METHODS： Based on evidence-based medicine， taking key monitoring drugs Shuxuetong injection as example， clinical evidence of domestic and foreign clinical studies were collected. The included literatures were graded according to the quality of GRADE evidence and recommended strength system. Evidence-based medicine evidence for the indications of Shuxuetong injection were evaluated， and criterion for clinical use of Shuxuetong injection was formulated in Huaihua First People’s Hospital （our hospital）. RESULTS： The main content of criterion for clinical application of Shuxuetong injection formulated by our hospital was that there was A-level evidence support for acute ischemic cerebral infarction， but it was weakly recommended and only used for adjuvant therapy； there was B-level evidence support for anticoagulation （for preventing DVT）， diabetic peripheral nerve lesion， but it was weakly recommended； there was only C-level or D-level evidence support for other indications， it was strongly recommendation against use. CONCLUSIONS： Clinical pharmacists formulate the criterion for clinical application of Shuxuetong injection by evidence quality evaluation method， provide reference for clinical application management of key monitoring drug and play an important effect on rational drug use in clinic.