Objective: To analyze the information of patients with acute myocardial infarction (AMI) in a single center during last 6 years, and to distinguish the clinical differences of patients between TakoSTubo cardiomyopathy (TTC) and ST-segment elevation myocardial infarction (STEMI). Methods: A total of 1042 consecutive patients with primarily diagnosed acute anterior ST-segment elevation (STEMI) admitted in our hospital from 2008-01 to 2014-04 were retrospectively enrolled. The relevant patients were studied in 2 groups:TTC group, the patients with coronary angiography (CAG) and the contrast study of left ventricle corrected TTC diagnosis, n=10, and STEMI group, the patients received CAG within 6 hours of on set with conifrmed left anterior descending singlevessel disease at the same period of time as TTC patients,n=32. The basic clinical characteristics, levels of blood lipids, MI related biomarkers, the incidence rate of pathological Q wave, QTc interval and negative T wave in 12-lead ECG were compared between 2 groups. Results: The percentage of corrected TTC diagnosis in patients with primarily diagnosed STEMI was 1.06%. The female gender in TTC group and STEMI group was 100% vs 9%,P<0.01, TTC group had more patients with stress history before on set than that in STEMI group (70% vs 22%,P=0.02), lower levels of MI related biomarkers as CK (486 ± 249) U/L vs (716 ± 132) U/L, CK-MB (13.5 ± 17.1) mg/L vs (47.5 ± 21.9) mg/L, cTnI (22.8 ± 16.3) ng/mL vs (56.4 ± 24.0) ng/mL, allP<0.01. The age of morbidity, the ratios of hypertension, diabetes mellitus and blood lipids were similar between 2 groups. The frequency of abnormal Q-wave in ECG was similar between 2 groups, while the QTc interval was different in TTC group and STEMI group (630 ± 117) ms vs (540 ± 62) ms,P=0.001, the negative T waves in ECG leads II, III, aVF, aVR and V6 were as (100.00% vs 3.13%), (60.00% vs 6.25%), (90.00% vs 3.13%), (100.00% vs 21.88%), (100.00% vs 46.88%), allP<0.05. Conclusion: TTC patients with the main presentation as ST-segment elevation are usually having emotional or physical stress before on set, with obviously prolonged QTc interval and more frequency of negative T waves in ECG.

Objective To study the prognosis and pre-procedural independent risk factors for pa-tients with no-reflow (NR) phenomenon during percutaneous coronary intervention (PCI). Methods Pa-tients with or without NR phenomenon during PCI procedures from January 2000 to January 2005 were studied retrospectively. The clinical data preoperative and the incidence of major adverse cardiovascular events (MACE) between the two groups were compared. Univariate analysis and multivariate Logistic analysis were used to select the risk factors for NR phenomenon. Retrospectively was reviewed for (35.8 ± 15.3)months. Results The NR group had more significant incidence of MACE. Multivariate Logistic analysis showed that the predictive factors for NR were (1) Smoke index ≥ 300(OR = 2.81,95%CI: 1.61-4.38 ,P =0.007). (2) Fasting blood glucose level before PCI ≥ 11.1 mmol/L (OR = 3.39,95%CI: 1.51-4.89,P = 0.000 ). (3) Absence of angina pectoris attack within one month before PCI (OR = 2.39,95%CI: 1.22-3.78,P = 0.009). Conclusions The prognosis is poor for the PCI patients with NR phenomenon. Those patients whose fasting blood glucose level before PCI ≥ 11.1 mm01/L, smoke index ≥ 300 and absence of angina pec-toffs attack within one month before PCI have higher incidence of NR phenomenon.

Objective:To investigate the accuracy, effectiveness and feasibility of MassARRAY genotyping assay in the diagnoses of neonatal genetic metabolic diseases.Methods:This is a retrospective study. From December 2016 to January 2020, newborns were screened by tandem mass spectrometry at the Zhejiang Newborn Screening Center, among which the data of 7 922 suspected positive cases of genetic metabolic diseases were collected. These patients were then tested for the common variants of 27 genetic metabolic diseases by MassARRAY genotyping assay, along with further testing using Sanger or next-generation sequencing used to verify and/or further search for potential variants.Results:A total of 1 408 cases were tested with MassARRAY. Among these, 307 cases were confirmed with certain genetic metabolic diseases. The detection rate of hyperphenylalaninemia was the highest, followed by primary carnitine deficiency, short acyl-coA dehydrogenase deficiency and methylmalonic acidemia. With these cases, the consistency of Sanger sequencing and MassARRAY was 100% (307/307). Another 287 cases were identified as carriers by MassARRAY with a 49.1% (141/287) consistency in reference to Sanger sequencing, mainly involving SLC22A5 and MCCC1 genes. Meanwhile, 50.8% (146/287) of these cases were found to have another variant mainly involving PAH, PTS and ACADS genes. The remaining 814 cases have no variants; 158 cases out of these patients have continuously abnormal amino acids, acyl carnitines, urine organic acid and/or other biochemical indices, and were tested by next-generation sequencing, among which 38% (60/158) were detected with two variants. In this study, a total of 513 patients with genetic metabolic disease were diagnosed, and the detection rate of MassARRAY was 59.8% (307/513). Conclusions:MassARRAY genotyping assay can be used as an early molecular screening method for neonatal genetic metabolic diseases. The detection rate is particularly high in diseases with a high concentration of hotspot variants, such as hyperphenylalaninemia and primary carnitine deficiency. The future application value of MassARRAY should be further improved by continuously optimizing its ability to identify new disease genes and potential variable sites.

Objective To observe the expression of GLIS3 in triple negative breast cancer(TNBC)and analyze its relationship with the prognosis of TNBC patients.Methods Bioinformatic analysis was applied to analyze the expression level of GLIS3 in Gene Expression Omnibus(GEO).Kaplan-Meier survival curve was plotted to evaluate the impact of GLIS3 expression on the survival rate of patients based on DNA chip data.A total of 125 patients pathologically diagnosed as TNBC in the Fourth Hospital of Hebei Medical University from January to December 2014 were enrolled by cluster random sampling.Among them,53 patients had complete tissue specimens,medical records and follow-up data.Immunohistochemical assay was used to detect the expression of GLIS3 in TNBC and adjacent tissues to tumors,while the relationships between GLIS3 protein expression in breast cancer tissues and clinicopathological parameters such as age,menstrual status,tumor size,clinical stage,histological grade,pathological type,axillary lymph node metastasis,vascular tumor thrombus,and expression of TP53 and Ki-67 were analyzed.Kaplan-Meier survival curve was plotted to analyze the effect of GLIS3 on the overall and disease-free survival of TNBC patients.Cox regression model was established to identify the risk factors impacting the prognosis of the patients.Results Analysis of GEO data showed that the expression of GLIS3 in TNBC was significantly higher than that in paracancer tissues(P＜0.05).The expression of GLIS3 was notably higher in the TNBC tissue than the adjacent tissue to tumor(P＜0.05).A marked augmentation of GLIS3 expression was observed in both the advanced and larger-sized tumors(P＜0.05).Univariate analysis of Cox regression model revealed that lymph node metastasis,TNM stage and GLIS3 expression were all related to disease-free survival of TNBC patients(P＜0.05).Univariate and multivariate analyses displayed that TNM stage was related to the overall survival of TNBC patients(P＜0.05).The patients with high expression of GLIS3 had significant shorter disease-free survival time than those with low expression(P＜0.05),but had no statistical difference in overall survival(P＞0.05).Conclusion GLIS3 protein is highly expressed in TNBC tissues,and tumor size and TNM stage are correlated with its high expression.The high expression of GLIS3 suggests that the patients have poor prognosis and low disease-free survival rate.

BACKGROUND: Limited data are available on the comparison of clinical outcomes of complete vs. incomplete percutaneous coronary intervention (PCI) for patients with chronic total occlusion (CTO) and multi-vessel disease (MVD). The study aimed to compare their clinical outcomes. METHODS: A total of 558 patients with CTO and MVD were divided into the optimal medical treatment (OMT) group ( n = 86), incomplete PCI group ( n = 327), and complete PCI group ( n = 145). Propensity score matching (PSM) was performed between the complete and incomplete PCI groups as sensitivity analysis. The primary outcome was defined as the occurrence of major adverse cardiovascular events (MACEs), and unstable angina was defined as the secondary outcome. RESULTS: At a median follow-up of 21 months, there were statistical differences among the OMT, incomplete PCI, and complete PCI groups in the rates of MACEs (43.0% [37/86] vs. 30.6% [100/327] vs. 20.0% [29/145], respectively, P = 0.016) and unstable angina (24.4% [21/86] vs. 19.3% [63/327] vs. 10.3% [15/145], respectively, P = 0.010). Complete PCI was associated with lower MACE compared with OMT (adjusted hazard ratio [HR] = 2.00; 95% confidence interval [CI] = 1.23-3.27; P = 0.005) or incomplete PCI (adjusted HR = 1.58; 95% CI = 1.04-2.39; P = 0.031). Sensitivity analysis of PSM showed similar results to the above on the rates of MACEs between complete PCI and incomplete PCI groups (20.5% [25/122] vs. 32.6% [62/190], respectively; adjusted HR = 0.55; 95% CI = 0.32-0.96; P = 0.035) and unstable angina (10.7% [13/122] vs. 20.5% [39/190], respectively; adjusted HR = 0.48; 95% CI = 0.24-0.99; P = 0.046). CONCLUSIONS For treatment of CTO and MVD, complete PCI reduced the long-term risk of MACEs and unstable angina, as compared with incomplete PCI and OMT. Complete PCI in both CTO and non-CTO lesions can potentially improve the prognosis of patients with CTO and MVD.
Humans ; Treatment Outcome ; Percutaneous Coronary Intervention/methods* ; Coronary Occlusion/surgery* ; Prognosis ; Angina, Unstable/surgery* ; Chronic Disease ; Risk Factors