Objective The abnormal ankle-brachial index ( ABI) is associated with the incidence of cardiocerebral vascular diseases, but little is known about its relationship with cerebral microbleeds (CMB).This study aimed to investigate the correlation be-tween ABI≤0.9 and different distribution patterns of CMB . Methods We enrolled 187 patients with acute lacunar infarction , inclu-ding 115 non-CMB cases and 72 CMB cases (20 strictly lobar, 24 strictly deep, and 28 lobar and deep).We analyzed the differences between the two groups and the association of abnormal ABI with the occurrence and distribution of CMB by logistic regression analysis . Results ABI≤0.9 was found in 57 (30.5%) of the patients, with a significantly higher incidence rate in the CMB group than in the non-CMB group (43.1%vs 22.6%, P=0.003).The level of ABI was negatively correlated with the number of CMBs (r=-0.211, P=0.006).Multivariate logistic regression analysis after adjusted for confounders indicated that ABI ≤0.9 was significantly associated with the presence of CMB (OR=2.363;95%CI:1.181-4.729), deep CMB (OR=3.434;95%CI:1.283-9.187), and lobar and deep CMB ( OR=2.837;95%CI:1.098-7.333) in patients with ischemic cerebrovascular disease . Conclusion Decreased ABI is a risk factor of CMB, particularly deep CMB, in patients with ischemic stroke.

Objective To investigate the risk factors for reduced renal function in patients with ischemic stroke.Methods The medical records of patients with ischemic stroke were analyzed retrospectively.They were divided into normal renal function group and reduced renalfunction group.Reduced renal function was defined as estimated glomerular filtration rate (eGFR) ＜60 ml/(min·1.73 m2).Multivariate logistic regression analysis was used to identify the risk factors for reduced renal function in patients with ischemic stroke.Results A total of 805 patients with ischemic stroke were enrolled in the study.8.8％ of patients had a reduced renal function.There was no significant differences in the proportion of patients with mild and moderate neurological deficit between the reduced renal function group and the normal renal function group (all P ＞ 0.05),however,the proportion of patients with severe neurological deficit was significantly higher than that in the normal renal function group (8.4％vs.2.6％,x2 =5.573,P =0.017).The proportion of small artery occlusion in the reduced renal function group was sigaificantly higher than that in the normal renal function group (66.2％ vs.46.5％,x2 =9.962,P =0.002),and the proportion of large artery atherosclerosis was significantly lower than that in the normal renal function group (19.7％ vs.43.5％,x2 =15.045,P =0.000).Multivariate logistic regression analysis indicated that old age (odds ratio [ OR] 3.301,95％ confidence interval [ CI],1.575 to 6.918; P=0.002) was the most important independent risk factor for reduced renal function,then was female (OR,2.291,95％ CI 1.355to 3.872; P=0.002) and hyperlipidemia (OR,2.527,95％ CI 1.095 to 5.831; P=0.030).Conclusions Reduced renal function in patients with ischemic stroke is strongly associated with old age,female,and hyperlipidemia.

Objective To establish a method for the simultaneous determination of Forsythoside A, Phillyrin, (R,S)-Epigoitrin, Chlorgenic Acid and Isochlorogenic Acid A by HPLC, and test 16 batches of samples from 14 manufacturers. Methods The test was performed on Kinetex EVO C18 column (150 mm × 4.6 mm, 5 μm) with the column temperature at 35 ℃ . The gradient elution was adopted with the mobile phase of acetonitrile and 0.3% phosphoric acid aqucous at a flow rate of 1.0 ml/min. The detection wavelength of (R,S)-Epigoitrin and Phillyrin were set as 236 nm, the detection wavelength of Forsythoside A, Cholorogenic Acid and Isochlorogenic Acid A were set as 327 nm. Results The good linear relationships were displayed within the linear range of 0.050 45-2.018 00 μg for Forsythoside A (r=0.999 9), 0.018 21-0.728 40 μg for Phillyrin (r=0.999 9), 0.010 16-0.406 40 μg for (R,S)-Epigoitrin (r=0.999 9), 0.006 60-0.263 90 μg for Cholorogenic Acid (r=0.999 9) and 0.0040 44~0.161 76 μg for Isochlorogenic Acid A ( r=0.999 5). The RSDs of reproducibility and stability tests were lower than 2%; recoveries were 97.01%, 98.28%, 99.35% and 96.21%, RSD were 3.19%, 1.19%, 0.81%, 2.88% and 2.96%. The content ranges of Forsythoside A, Phillyrin, (R,S)-Epigoitrin, Chlorgenic Acid and Isochlorogenic Acid A from 16 batches of samples from 14 manufacturers were 0.057 43-1.508 71 mg/g, 0.017 72-0.350 15 mg/g, 0.005 68-0.177 13 mg/g, 0.007 53-0.226 33 mg/g and 0.00308-0.11908 mg/g. Conclusions The established method is simple and accurate, and has a good repeatability. It can be used for the quality analysis of Forsythoside A, Phillyrin, (R,S)-Epigoitrin, Chlorgenic Acid and Isochlorogenic Acid A. The content of the tested chemical components from 16 batches of samples from 14 manufacturers have significant differences which indicate that a reinforcement of the quality control is needed.

Objective To study the incidence of lactase deficiency and the risk factors affecting intestinal lactase secretion in newborns with lactase deficiency.Method From February to December 2016,newborns admitted to the neonatal ward of the Affiliated Hospital of Hangzhou Normal University were enrolled in this prospective study.Urine samples were taken within one to two hours after feeding for galactose qualitative tests,and the related clinical data were recorded.The newborns were assigned into lactase deficient group and non-lactase deficient group according to the test results.Then the risk factors of lactase deficiency were analyzed comparing the clinical data between the two groups.Result A total of 1 022 newborns were hospitalized during the research period,of whom 213 were enrolled in this study according to the inclusion criteria.154 cases had positive results in the urine galactose qualitative tests,yielding the incidence of lactase deficiency of 72.3 ％.42 cases had lactose intolerance symptoms,and the incidence of lactose intolerance was 27.3 ％ (42/154).Age and positive family history in lactase deficient group were higher than non-lactase deficient group (10.3 ±6.4 d vs.8.1 ±5.8 d and 23.4％ vs.10.2％),while the gestational age of lactase deficient group was lower than non-lactase deficient group (37.8 ±2.9 weeks vs.39.0 ± 1.7 weeks),and the differences between the two groups were statistically significant (P ＜ 0.05).No significant differences existed in gender,birth weight,antibiotics use and feeding volumes between the two groups (P ＞ 0.05).Multivariate Logistic regression analysis showed that age (OR =1.065,95％CI 1.007 ～ 1.127) and positive family history (OR =2.912,95％ CI 1.053 ～ 8.056) were the risk factors of lactase deficiency.Gestational age (OR =0.747,95％ CI 0.617 ～ 0.904) was the protective factor of lactase deficiency in newborns.Conclusion The incidence of lactase deficiency in newborns is high,but not all the newborns manifest lactose intolerance symptoms.Age and positive family history were the risk factors while gestational age was the protective factor for lactase deficiency in newborns.

OBJECTIVE: To analyze the risk factors of in-hospital death in patients with sepsis in the intensive care unit (ICU) based on machine learning, and to construct a predictive model, and to explore the predictive value of the predictive model. METHODS: The clinical data of patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to April 2021 were retrospectively analyzed,including demographic information, vital signs, complications, laboratory examination indicators, diagnosis, treatment, etc. Patients were divided into death group and survival group according to whether in-hospital death occurred. The cases in the dataset (70%) were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. Based on seven machine learning models including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), a prediction model for in-hospital mortality of sepsis patients was constructed. The receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the seven models from the aspects of identification, calibration and clinical application, respectively. In addition, the predictive model based on machine learning was compared with the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) models. RESULTS: A total of 741 patients with sepsis were included, of which 390 were discharged after improvement, 351 died in hospital, and the in-hospital mortality was 47.4%. There were significant differences in gender, age, APACHE II score, SOFA score, Glasgow coma score (GCS), heart rate, oxygen index (PaO₂/FiO₂), mechanical ventilation ratio, mechanical ventilation time, proportion of norepinephrine (NE) used, maximum NE, lactic acid (Lac), activated partial thromboplastin time (APTT), albumin (ALB), serum creatinine (SCr), blood urea nitrogen (BUN), blood uric acid (BUA), pH value, base excess (BE), and K⁺ between the death group and the survival group. ROC curve analysis showed that the area under the curve (AUC) of RF, XGBoost, LR, ANN, DT, SVM, KNN models, SOFA score, and APACHE II score for predicting in-hospital mortality of sepsis patients were 0.871, 0.846, 0.751, 0.747, 0.677, 0.657, 0.555, 0.749 and 0.760, respectively. Among all the models, the RF model had the highest precision (0.750), accuracy (0.785), recall (0.773), and F1 score (0.761), and best discrimination. The calibration curve showed that the RF model performed best among the seven machine learning models. DCA curve showed that the RF model exhibited greater net benefit as well as threshold probability compared to other models, indicating that the RF model was the best model with good clinical utility. CONCLUSIONS The machine learning model can be used as a reliable tool for predicting in-hospital mortality in sepsis patients. RF models has the best predictive performance, which is helpful for clinicians to identify high-risk patients and implement early intervention to reduce mortality.
Humans ; Hospital Mortality ; Retrospective Studies ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Intensive Care Units