Raw and Cooked Vegetable Consumption and Risk of Cardiovascular Disease:a Study of 400,000 Adults in UK Biobank

OBJECTIVES: Higher levels of vegetable consumption have been associated with a lower risk of cardiovascular disease (CVD), but the independent effect of raw and cooked vegetable consumption remains unclear. METHODS: From the UK Biobank cohort, 399,586 participants without prior CVD were included in the analysis. Raw and cooked vegetable intakes were measured with a validated dietary questionnaire at baseline. Multivariable Cox regression was used to estimate the associations between vegetable intake and CVD incidence and mortality, adjusted for socioeconomic status, health status, and lifestyle factors. The potential effect of residual confounding was assessed by calculating the percentage reduction in the likelihood ratio (LR) statistics after adjustment for the confounders. RESULTS: The mean age was 56 years and 55% were women. Mean intakes of raw and cooked vegetables were 2.3 and 2.8 tablespoons/day, respectively. During 12 years of follow-up, 18,052 major CVD events and 4,406 CVD deaths occurred. Raw vegetable intake was inversely associated with both CVD incidence (adjusted hazard ratio (HR) [95% CI] for the highest vs. lowest intake: 0.89 [0.83–0.95]) and CVD mortality (0.85 [0.74–0.97]), while cooked vegetable intake was not (1.00 [0.91–1.09] and 0.96 [0.80–1.13], respectively). Adjustment for potential confounders reduced the LR statistics for the associations of raw vegetables with CVD incidence and mortality by 82 and 87%, respectively. CONCLUSIONS: Higher intakes of raw, but not cooked, vegetables were associated with lower CVD risk. Residual confounding is likely to account for much, if not all, of the observed associations. This study suggests the need to reappraise the evidence on the burden of CVD disease attributable to low vegetable intake in the high-income populations

Hospitalization Costs of COVID-19 Cases and Their Associated Factors in Guangdong, China: A Cross-Sectional Study

Background: The ongoing COVID-19 pandemic has brought significant challenges to health system and consumed a lot of health resources. However, evidence on the hospitalization costs and their associated factors in COVID-19 cases is scarce.Objectives: To describe the total and components of hospitalization costs of COVID-19 cases, and investigate the associated factors of costs.Methods: We included 876 confirmed COVID-19 cases admitted to 33 designated hospitals from January 15th to April 27th, 2020 in Guangdong, China, and collected their demographic and clinical information. A multiple linear regression model was performed to estimate the associations of hospitalization costs with potential associated factors.Results: The median of total hospitalization costs of COVID-19 cases was $2,869.4 (IQR:$3,916.8). We found higher total costs in male (% difference: 29.7, 95% CI: 15.5, 45.6) than in female cases, in older cases than in younger ones, in severe cases (% difference: 344.8, 95% CI: 222.5, 513.6) than in mild ones, in cases with clinical aggravation than those without, in cases with clinical symptoms (% difference: 47.7, 95% CI: 26.2, 72.9) than those without, and in cases with comorbidities (% difference: 21.1%, 21.1, 95% CI: 4.4, 40.6) than those without. We also found lower non-pharmacologic therapy costs in cases treated with traditional Chinese medicine (TCM) therapy (% difference: −47.4, 95% CI: −64.5 to −22.0) than cases without.Conclusion: The hospitalization costs of COVID-19 cases in Guangdong were comparable to the national level. Factors associated with higher hospitalization costs included sex, older age, clinical severity and aggravation, clinical symptoms and comorbidities at admission. TCM therapy was found to be associated with lower costs for some non-pharmacologic therapies

Predictive Modeling of Suitable Habitats for Cinnamomum Camphora (L.) Presl Using Maxent Model under Climate Change in China

Rapid changes in global climate exert tremendous pressure on forest ecosystems. Cinnamomum camphora (L.) Presl is a multi-functional tree species, and its distribution and growth are also affected by climate warming. In order to realize its economic value and ecological function, it is necessary to explore the impact of climate change on its suitable habitats under different scenarios. In this experiment, 181 geographical distribution data were collected, and the MaxEnt algorithm was used to predict the distribution of suitable habitats. To complete the simulation, we selected two greenhouse gas release scenarios, RCP4.5 and RCP8.5, and also three future time periods, 2025s, 2055s, and 2085s. The importance of environmental variables for modeling was evaluated by jackknife test. Our study found that accumulated temperature played a key role in the distribution of camphor trees. With the change of climate, the area of suitable range will increase and continue to move to the northwest of China. These findings could provide guidance for the plantation establishment and resource protection of camphor in China

Effects of Kinesio tape on lower limb muscle strength, hop test, and vertical jump performances: a meta-analysis

Abstract Background To date, published systematic reviews concerning the effects of Kinesio Taping (KT) on muscle strength have not analysed facilitatory and inhibitory applications separately. As a result, their results could be substantially affected by clinical heterogeneity. This meta-analysis was conducted to determine the effectiveness of using a facilitatory application of KT for lower limb muscle strength and functional performance (distance in a single-leg hop and vertical jump height) in individuals without disabilities and in those with musculoskeletal conditions (muscle fatigue, chronic musculoskeletal diseases, and post-operative orthopaedic conditions). Methods Searches were conducted on six major electronic databases. Randomised controlled trials that used facilitatory KT were included. Standardised mean differences (SMDs) were calculated and random-effects models were used for analysis. Results Thirty-seven randomised controlled trials were included. KT was superior to controls for improving lower limb muscle strength in individuals with muscle fatigue (short-term effect, pooled SMD = 0.53, 95% CI = 0.09 to 0.96; long-term effect, pooled SMD = 0.61, 95% CI = 0.12 to 1.11) and in individuals with chronic musculoskeletal diseases (pooled SMD = 1.24, 95% CI = 0.33 to 2.16) with large effect sizes. The use of KT in populations without disabilities was not supported. There is insufficient evidence for the effect of KT on functional performance in individuals with musculoskeletal conditions. Conclusions Contrary to prior research, the existing evidence shows that KT can improve lower limb muscle strength in individuals with muscle fatigue and chronic musculoskeletal diseases. The effect sizes produced in this meta-analysis show that KT may be superior to some existing treatments for these conditions. In addition, this study suggests that practitioners may wish to avoid the use of KT in individuals without disabilities. Trial registration PROSPERO registration number CRD42017075490, registered on 21 November 2017

Association of non-selective β blockers with the development of renal dysfunction in liver cirrhosis: a systematic review and meta-analysis

AbstractBackground & Aims Non-selective β blockers (NSBBs) may negatively influence renal function through decreasing heart rate and cardiac output. This study aimed to systematically investigate their association.Methods PubMed, EMBASE, and Cochrane library databases were searched to identify all relevant studies evaluating the association of NSBBs with renal dysfunction in cirrhotic patients. Unadjusted and adjusted data were separately extracted. Odds ratios (ORs) and hazard ratios (HRs) were pooled. Subgroup meta-analyses were performed according to the proportions of ascites and Child-Pugh class B/C and the mean model for end-stage liver disease (MELD) score. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation framework.Results Fourteen studies were finally included. Based on unadjusted data, NSBBs significantly increased the risk of developing renal dysfunction (OR = 1.49; p = 0.03), and this association remained significant in subgroup analyses of studies where the proportions of ascites was >70% and Child-Pugh class B/C was 100%. Based on adjusted data with propensity score matching (adjusted OR = 0.61; p = 0.08) and multivariable regression modelling (adjusted HR = 0.86; p = 0.713), NSBBs did not increase the risk of developing renal dysfunction, and this association remained not significant in subgroup analyses of studies where the proportions of ascites was >70% and <70%, the proportion of Child-Pugh class B/C was <100%, and the mean MELD score was <15. The quality of evidence was very low for all meta-analyses.Conclusions NSBBs may not be associated with the development of renal dysfunction in liver cirrhosis. However, more evidence is required to clarify their association in specific populations

Deep Partial Rank Aggregation for Personalized Attributes

In this paper, we study the problem of how to aggregate pairwise personalized attributes (PA) annotations (e.g., Shoes A is more comfortable than B) from different annotators on the crowdsourcing platforms, which is an emerging topic gaining increasing attention in recent years. Given the crowdsourced annotations, the majority of the traditional literature assumes that all the pairs in the collected dataset are distinguishable. However, this assumption is incompatible with how humans perceive attributes since indistinguishable pairs are ubiquitous for the annotators due to the limitation of human perception. To attack this problem, we propose a novel deep prediction model that could simultaneously detect the indistinguishable pairs and aggregate ranking results for distinguishable pairs. First of all, we represent the pairwise annotations as a multi-graph. Based on such data structure, we propose an end-to-end partial ranking model which consists of a deep backbone architecture and a probabilistic model that captures the generative process of the partial rank annotations. Specifically, to recognize the indistinguishable pairs, the probabilistic model we proposed is equipped with an adaptive perception threshold, where indistinguishable pairs could be automatically detected when the absolute value of the score difference is below the learned threshold. In our empirical studies, we perform a series of experiments on three real-world datasets: LFW-10, Shoes, and Sun. The corresponding results consistently show the superiority of our proposed model