Origin Identification and Quantitative Analysis of Honeys by Nuclear Magnetic Resonance and Chemometric Techniques

The combination of H-1 NMR spectroscopy and multivariate statistical analysis has become a promising method for the discrimination of food origins. In this paper, this method has been successfully employed to analyze 70 Chinese honey samples from eight botanic origins, three geographical origins, and five production dates. Thirty-three components in honey samples were detected and identified from their H-1 NMR spectra, and 20 of them were accurately quantified by comparing their integral area with that of internal standards with relaxation time correction. Nontargeted principal component analysis (PCA) has been applied to distinguish the honeys from different botanical and geographical origins. The variations of components in the honeys, including saccharides and all kind of amino and organic carboxylic acids, confirmed their clustering according to their origins in PCA scores plots. Orthogonal partial least squares discriminant analysis (OPLS-DA) based on the NMR data for the different pairwise honey samples allows to identify the compositional variations contributed to geographical discrimination and storage time. Hence, NMR spectroscopy coupled with chemometric techniques offers an efficient tool for quality control of honey, and it could further serve to the classification, qualitative and quantitative control of other foods

Focus on vulnerable populations and promoting equity in health service utilization ––an analysis of visitor characteristics and service utilization of the Chinese community health service

Background Community health service in China is designed to provide a convenient and affordable primary health service for the city residents, and to promote health equity. Based on data from a large national study of 35 cities across China, we examined the characteristics of the patients and the utilization of community health institutions (CHIs), and assessed the role of community health service in promoting equity in health service utilization for community residents. Methods Multistage sampling method was applied to select 35 cities in China. Four CHIs were randomly chosen in every district of the 35 cities. A total of 88,482 visitors to the selected CHIs were investigated by using intercept survey method at the exit of the CHIs in 2008, 2009, 2010, and 2011. Descriptive analyses were used to analyze the main characteristics (gender, age, and income) of the CHI visitors, and the results were compared with that from the National Health Services Survey (NHSS, including CHIs and higher levels of hospitals). We also analyzed the service utilization and the satisfactions of the CHI visitors. Results The proportions of the children (2.4%) and the elderly (about 22.7%) were lower in our survey than those in NHSS (9.8% and 38.8% respectively). The proportion of the low-income group (26.4%) was apparently higher than that in NHSS (12.5%). The children group had the lowest satisfaction with the CHIs than other age groups. The satisfaction of the low-income visitors was slightly higher than that of the higher-income visitors. The utilization rate of public health services was low in CHIs. Conclusions The CHIs in China appears to fulfill the public health target of uptake by vulnerable populations, and may play an important role in promoting equity in health service utilization. However, services for children and the elderly should be strengthened

Cumulative subgroup analysis to reduce waste in clinical research for individualised medicine

Background: Although subgroup analyses in clinical trials may provide evidence for individualised medicine, their conduct and interpretation remain controversial. Methods: Subgroup effect can be defined as the difference in treatment effect across patient subgroups. Cumulative subgroup analysis refers to a series of repeated pooling of subgroup effects after adding data from each of related trials chronologically, to investigate the accumulating evidence for subgroup effects. We illustrated the clinical relevance of cumulative subgroup analysis in two case studies using data from published individual patient data (IPD) meta-analyses. Computer simulations were also conducted to examine the statistical properties of cumulative subgroup analysis. Results: In case study 1, an IPD meta-analysis of 10 randomised trials (RCTs) on beta blockers for heart failure reported significant interaction of treatment effects with baseline rhythm. Cumulative subgroup analysis could have detected the subgroup effect 15 years earlier, with five fewer trials and 71% less patients, than the IPD meta-analysis which first reported it. Case study 2 involved an IPD meta-analysis of 11 RCTs on treatments for pulmonary arterial hypertension that reported significant subgroup effect by aetiology. Cumulative subgroup analysis could have detected the subgroup effect 6 years earlier, with three fewer trials and 40% less patients than the IPD meta-analysis. Computer simulations have indicated that cumulative subgroup analysis increases the statistical power and is not associated with inflated false positives. Conclusions: To reduce waste of research data, subgroup analyses in clinical trials should be more widely conducted and adequately reported so that cumulative subgroup analyses could be timely performed to inform clinical practice and further research

The effect of vascular risk factor burden on the severity of COVID-19 illness, a retrospective cohort study

BACKGROUND: Patients with cardiovascular comorbidities are at high risk of poor outcome from COVID-19. However, how the burden (number) of vascular risk factors influences the risk of severe COVID-19 disease remains unresolved. Our aim was to investigate the association of severe COVID-19 illness with vascular risk factor burden. METHODS: We included 164 (61.8 ± 13.6 years) patients with COVID-19 in this retrospective study. We compared the difference in clinical characteristics, laboratory findings and chest computed tomography (CT) findings between patients with severe and non-severe COVID-19 illness. We evaluated the association between the number of vascular risk factors and the development of severe COVID-19 disease, using a Cox regression model. RESULTS: Sixteen (9.8%) patients had no vascular risk factors; 38 (23.2%) had 1; 58 (35.4%) had 2; 34 (20.7%) had 3; and 18 (10.9%) had ≥4 risk factors. Twenty-nine patients (17.7%) experienced severe COVID-19 disease with a median (14 [7-27] days) duration between onset to developing severe COVID-19 disease, an event rate of 4.47 per 1000-patient days (95%CI 3.10-6.43). Kaplan-Meier curves showed a gradual increase in the risk of severe COVID-19 illness (log-rank P < 0.001) stratified by the number of vascular risk factors. After adjustment for age, sex, and comorbidities as potential confounders, vascular risk factor burden remained associated with an increasing risk of severe COVID-19 illness. CONCLUSIONS: Patients with increasing vascular risk factor burden have an increasing risk of severe COVID-19 disease, and this population might benefit from specific COVID-19 prevention (e.g., self-isolation) and early hospital treatment measures