Objective To determine whether anticoagulation markers can improve mortality prediction in patients of surgical intensive care unit (ICU).Methods A case-control study was adopted,252 patients from Tongji hospital's surgical ICU and 30 healthy control individuals were investigated.The protein C,antithrombin,thrombomodulin,and other coagulation/ inflammatory markers were detected.The Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) score were obtained.Markers level comparison among survivors,non-survivors and controls were conducted with single factor variance analysis,Kruskal-Wallis test or Mann-Whitney U test.Results Between survivors and non-survivors after 28-day hospitalization,there were significant difference on protein C levels[(70.2 ±22.7)％ vs (48.6 ±29.8)％,t=2.84,P＜0.01],APACHE Ⅱ scores[(21.0±8.2) vs (29.5 ±10.9),t =-2.51,P＜0.05] and prothrombin times[(12.9-± 3.5) s vs (18.8 ± 10.2) s,t =-2.13,P ＜ 0.05].Combining protein C levels with APACHE Ⅱ score could obtain a higher mortality prediction efficiency in patients of surgical ICU than any single marker (AUC =0.806).That protein C concentration less than 47.5％ [OR =6.40,95％confidence interval(CI) 2.526-16.216,P ＜0.001] and APACHE Ⅱ score (OR =1.123,95％ CI 1.012 -1.250,P ＜ 0.05) were the independent risk factors for surgical ICU death.Conclusion Decrease of protein C levels predict increase of mortality risk in patients of surgical ICU,combining protein C with APACHE Ⅱ score can improve the prognostic accuracy for patients of surgical ICU.(Chin J Lab Med,2013,36:339-342)

Objective To investigate the change of soluble P-selectin (sPsel)and von Willebrand factor (vWF)levels after ad-mission in traumatic patients and their relation with the coagulation indexes levels,coagulation disorders and prognosis.Methods 82 cases of severe trauma in ICU of Affiliated Tongji Hospital were prospectively selected and detected plasma sPsel,VWF anti-gen,protein C,activity of coagulation factor Ⅶ and routine coagulation indexes on admission and on every day within the first week after admission.The 30 d fatality rate was recorded.Results The sPsel and vWF levels on admission in the patients with coagula-tion disorders were lower than those in the patients without coagulation disorders (P <0.05)and significantly correlated with the coagulation indexes (protein C and coagulation factor Ⅶ)levels (P <0.05).The vWF level within 3 d after admission in the death patients was significantly lower than that in the survival patients,but which on 7 d after admission in the death patients was signifi-cantly higher than that in the survival patients (P <0.05 );no significant difference in sPsel level within 1 week after admission were found between the survival patients and the death patients.Conclusion Among severe traumatic patients in ICU,the low lev-els of sPsel and VWF on admission are associated with the coagulation disorders,the significant rise of vWF level on 7 d after ad-mission is associated with the increase of the 30 d fatality rate.

Objective To investigate the risk factors and the influence of intravenous thrombolysis of acute cerebral infarction with cerebral microbleeds(CMBs)by SWI.Methods 1 64 patients with acute cerebral infarction were enrolled in this study.All pa-tients were scanned with routine MRI and SWI.According to the presence of CMBs on SWI,the patients were classified into two groups:CMBs group(73 cases)and non-CMBs group(91 cases).Past history was recorded and risk factors of CMBs were explored. 76 cases patients(including 35 cases of CMBs group and 41 cases of non-CMBs group)were treated by intravenous thrombolysis and rescanned with routine MRI and SWI to compare the changes in the number of CMBs and hemorrhage transformation 24 hours after thrombolysis.Results The difference age,hypertension,lacunar infarction and leukoaraiosis between the two groups were significant (P <0.05).The difference of CMBs and hemorrhagic transformation between the two groups treated by thrombolysis were not sig-nificant(P >0.05).Conclusion Acute cerebral infarction with CMBs are influenced by age,hypertension,lacunar infarction and leu-koaraiosis.Thrombolysis in acute cerebral infarction with CMBs can not augment the incidence of hemorrhagic transformation.

Objective To explore the application status and development trend of machine learning in the field of pharmacovigilance worldwide,and to provide reference for the research on the application of machine learning in the field of pharmacovigilance.Methods Relevant literature was searched in the Web of Science with the key words of"machine learning"and"pharmacovigilance"from the inception to March 1,2023.R language and other software were used to quantitatively analyze the literature data in this field.The clustering,co-occurrence and emergence visual analysis were carried out on the characteristics of annual published papers,institutions,countries,keywords and other aspects.Results A total of 904 literature were included.The number of literature published showed a fluctuating upward trend since 1994.There was cross-regional,cross-regional and cross-agency cooperation among the cooperative network institutions.The top 5 countries in the number of publications were the United States,China,Japan,South Korea and India,China and the United States had relatively close cooperation in this field.Signal detection,social media and electronic health records were high-frequency keywords in this field.Clustering and association rule analysis showed that this field focused on three aspects signal recognition,unstructured text mining and analysis,and processing and analysis of electronic medical information.At present,machine learning has made significant progress in signal recognition,social media information mining,and unstructured text processing of electronic medical information,which broaden the data sources of pharmacovigilance,improve the real-time monitoring ability of adverse drug reactions,bringing innovation impetus to the field of pharmacovigilance.Conclusion The rapid development of big data and artificial intelligence technologies has led to an increasing integration of machine learning into the field of pharmacovigilance,which promotes technical exchanges and cooperation and cross-disciplinary integration.It is necessary to optimize each machine learning algorithm to improve its accuracy and stability in pharmacovigilance,strengthen the protection measures of data privacy and security to ensure the safety of patient information.Integrating expertise in the fields of science,medicine,and data statistics with a view to promoting technological progress in the field of pharmacovigilance.

OBJECTIVE To mine the risk sig nals o f iodine contrast media from spontaneous reporting system. METHODS Reporting odds ratio ，proportional reporting ratio ，Medicines and Healthcare Products Regulatory Agency and Bayesian confidence propagation neural network were used to mine risk signals of 5 iodine contrast media （iopamidol，iohexol，iopromide，ioversol， iodixanol）. RESULTS 1 164（2 446 case times ）adverse drug reaction of iodine contrast media were included ，a total of 14 risk signals involving systems/organs such as respiratory system （3，2，4，3，2 for the above 5 iodine contrast media ）and immune system and 32 specific adverse drug reaction signals including anaphylactic shock ，rash and flushing （11，7，7，3，4 for the above 5 iodine contrast media ）were found in 5 iodine contrast media. CONCLUSIONS The risk signals of 5 iodine contrast media verify that there is a certain correlation between these drugs and above adverse drug reactions. It is suggested that before using iodine contrast media in clinic ，it is necessary to pay attention to whether the patient has a history of tumor and combined medication ，evaluate the patient’s renal function ，and give preventive measures such as hydration in advance. When using iodine contrast media ，it is necessary to pay attention to the temperature ，dose and injection rate. And medical staff need to follow up the patient ’s situation in time after using iodine contrast media to avoiding the impact of delayed adverse reactions.