1-Year Outcomes With Intracoronary Abciximab in Diabetic Patients Undergoing Primary Percutaneous Coronary Intervention

Retrospective analysis of the preparation and application of immunotherapy in cancer treatment (Review)

Monoclonal antibody technology plays a vital role in biomedical and immunotherapy, which greatly promotes the study of the structure and function of genes and proteins. To date, monoclonal antibodies have gone through four stages: murine monoclonal antibody, chimeric monoclonal antibody, humanised monoclonal antibody and fully human monoclonal antibody; thousands of monoclonal antibodies have been used in the fields of biology and medicine, playing a special role in the pathogenesis, diagnosis and treatment of disease. In this review, we compare the advantages and disadvantages of hybridoma technology, phage display technology, ribosome display technology, transgenic mouse technology, single B cell monoclonal antibody generation technologies, and forecast the promising applications of these technologies in clinical medicine, disease diagnosis and tumour treatment.

NT-proBNP level before primary PCI and risk of poor myocardial reperfusion: Insight from the On-TIME II trial

BACKGROUND

N-terminal fragment of the brain natriuretic peptide prohormone (NT-proBNP), a marker for neurohumoral activation, has been associated with adverse outcome in patients with myocardial infarction. NT-proBNP levels may reflect extensive ischemia and microvascular damage, therefore we investigated the potential association between baseline NT-proBNP level and ST-resolution (STR), a marker of myocardial reperfusion, after primary percutaneous coronary intervention (pPCI).

METHODS

we performed a post-hoc analysis of the On-TIME II trial (which randomized ST-elevation myocardial infarction (STEMI) patients to pre-hospital tirofiban administration vs placebo). Patients with measured NT-proBNP before angiography were included. Multivariate logistic-regression analyses was performed to investigate the association between baseline NTproBNP level and STR one hour after pPCI.

RESULTS

Out of 984 STEMI patients, 918 (93.3%) had NT-proBNP values at baseline. Patients with STR <70% had higher NT-proBNP values compared to patients with complete STR (>70%) [Mean ±SD 375.2 ±1021.7 vs 1007.4 ±2842.3, Median (IQR) 111.7 (58.4-280.0) vs 168.0 (62.3-601.3), P <.001]. At multivariate logistic regression analysis, independent predictors associated with higher risk of poor myocardial reperfusion (STR <70%) were: NT-proBNP (OR 1.17, 95%CI 1.04-1.31, P = .009), diabetes mellitus (OR 1.87, 95%CI 1.14-3.07, P = .013), anterior infarct location (OR 2.74, 95% CI 2.00-3.77, P <.001), time to intervention (OR 1.06, 95%CI 1.01-1.11, P = .021), randomisation to placebo (OR 1.45, 95%CI 1.05-1.99, P = .022).

CONCLUSIONS

In STEMI patients, higher baseline NT-proBNP level was independently associate with higher risk of poor myocardial reperfusion, supporting the potential use of NT-proBNP as an early marker for risk stratification of myocardial reperfusion after pPCI in STEMI patients.

Big Health Data and Cardiovascular Diseases: A Challenge for Research, an Opportunity for Clinical Care

Cardiovascular disease (CVD) accounts for the majority of death and hospitalization, health care expenditures and loss of productivity in developed country. CVD research, thus, plays a key role for improving patients' outcomes as well as for the sustainability of health systems. The increasing costs and complexity of modern medicine along with the fragmentation in healthcare organizations interfere with improving quality care and represent a missed opportunity for research. The advancement in diagnosis, therapy and prognostic evaluation of patients with CVD, indeed, is frustrated by limited data access to selected small patient populations, not standardized nor computable definition of disease and lack of approved relevant patient-centered outcomes. These critical issues results in a deep mismatch between randomized controlled trials and real-world setting, heterogeneity in treatment response and wide inter-individual variation in prognosis. Big data approach combines millions of people's electronic health records (EHR) from different resources and provides a new methodology expanding data collection in three direction: high volume, wide variety and extreme acquisition speed. Large population studies based on EHR holds much promise due to low costs, diminished study participant burden, and reduced selection bias, thus offering an alternative to traditional ascertainment through biomedical screening and tracing processes. By merging and harmonizing large data sets, the researchers aspire to build algorithms that allow targeted and personalized CVD treatments. In current paper, we provide a critical review of big health data for cardiovascular research, focusing on the opportunities of this largely free data analytics and the challenges in its realization.