Objective To explore the relationship between serum soluble human matrix lysine 2 (sST2) with chronic heart failure(CHF) and its clinical value for diagnosis and prognosis of CHF .Methods 60 cases of CHF and 60 cases of non-CHF were selected as the CHF group and control group respectively ,and the CHF group was divided into sST2 low level group and sST2 high level group according to the diagnostic threshold .The ELISA method was used to detect the serum sST 2 level of each group .The CHF group were followed up for 6 months .Then the influence of sST2 on CHF prognosis survival rate was observed .Results There was no statistical difference in age ,gender ,body mass index ,basic disease history ,basic medication situation and blood lipid indexes between the CHF group and control group(P＞0 .05);serum brain natriuretic peptide(BNP) level in the CHF group was obviously higher than that in the control group(P＜0 .01);serum sST2 levels in the CHF group and control group were (55 .08 ± 3 .98)ng/mL and (10 .46 ± 0 .72)ng/mL ,the difference was statistically significant (P＜0 .01) .Serum sST2 was positively correlated with BNP(r=0 .4606 ,P＜0 .01) ,moreover 95% CI was 0 .3066-0 .5911 .When the critical value was 0 .5303 ,the area under curve ,95% CI ,sensitivity ,specificity and likelihood ratio of sST 2 combined BNP detection were 0 .9362 ,0 .8853 -0 .9877 , 85 .00% (73 .43% -92 .90% ) ,98 .33% (91 .06% -99 .96% ) and 50 .00 respectively .The survival curve had statistical difference between the sST2 low level group and sST2 high level group(P=0 .0149) .Conclusion Serum sST2 can be used as a new biomarker for the diagnosis and prognosis of CHF ,and its combined with BNP may have better diagnostic value.

BackgroundMental illness during pregnancy has become a major public health problem in China over the recent years， and depression is the most common psychological symptom during pregnancy. Current research efforts are directed towards the therapy on prenatal depression， whereas the construction of prediction model for prenatal depression risk has been little studied. ObjectiveTo construct a simple model for predicting the risk of prenatal depression， thus providing a valuable reference for the prevention of maternal depression during pregnancy. MethodsA total of 803 pregnant women attending three hospitals in Nanchong city were consecutively recruited from May 2021 to February 2022. A self-administered questionnaire was developed for the assessment of social demographic variables， obstetrical and general medical indexes and psychological status of all participants， and Self-rating Depression Scale （SDS） was utilized to screen for the presence of maternal depression. Subjects were randomly assigned into modelling group （n=635） and validation group （n=168） at the ratio of 8∶2 under simple random sampling with replacement. The candidate risk factors of maternal depression during pregnancy were screened using binary Logistic regression analysis， and the predictive model was constructed. Then the performance of the predictive model was validated using receiver operating characteristics （ROC） curve. Results① Lack of companionship （β=-0.692， OR=0.501， 95% CI： 0.289~0.868）， low mood during the last menstrual period （β=-1.510， OR=0.221， 95% CI： 0.074~0.656）， emotional stress during the last menstrual period （β=-1.082， OR=0.339， 95% CI： 0.135~0.853）， unsatisfactory relationship between mother-in-law and daughter-in-law （β=-1.228， OR=0.293， 95% CI： 0.141~0.609）， and indifferent generally relationship between mother-in-law and daughter-in-law （β=-0.831， OR=0.436， 95% CI： 0.260~0.730） were risk factors for prenatal depression in pregnant women （P<0.05 or 0.01）. ② Model for predicting the prenatal depression risk yielded an area under curve （AUC） of 0.698 （95% CI： 0.646~0.749）， the maximum Youden index was 0.357 in modelling group with the sensitivity and specificity was 0.606 and 0.751， and an AUC of 0.672 （95% CI： 0.576~0.767） and maximum Youden index of 0.263 in validation group with the sensitivity and specificity of 0.556 and 0.707. ConclusionThe simple model constructed in this study has good discriminant validity in predicting of the risk of prenatal depression. ［Funded by Nanchong Social Science Research Project of the 14th Five-Year Plan （number， NC21B165）］