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Background: The recent outbreak of coronavirus disease 2019 (COVID-19) has been rapidly spreading on a global scale and poses a great threat to human health. Acute respiratory distress syndrome, characterized by a rapid onset of generalized inflammation, is the leading cause of mortality in patients with COVID-19. We thus aimed to explore the effect of risk factors on the severity of the disease, focusing on immune-inflammatory parameters, which represent the immune status of patients. Methods: A comprehensive systematic search for relevant studies published up to April 2020 was performed by using the PubMed, Web of Science, EMBASE, and China National Knowledge Internet (CNKI) databases. After extracting all available data of immune-inflammatory indicators, we statistically analyzed the risk factors of severe and non-severe COVID-19 patients with a meta-analysis. Results: A total of 4,911 patients from 29 studies were included in the final meta-analysis. The results demonstrated that severe patients tend to present with increased white blood cell (WBC) and neutrophil counts, neutrophil-lymphocyte ratio (NLR), procalcitonin (PCT), C-reaction protein (CRP), erythrocyte sedimentation rate (ESR), and Interleukin-6 (IL-6) and a decreased number of total lymphocyte and lymphocyte subtypes, such as CD4+ T lymphocyte and CD8+ T lymphocyte, compared to the non-severe patients. In addition, the WBC count>10 × 10 9 /L, lymphocyte count<1 × 10 9 /L, PCT>0.5 ng/mL, and CRP>10 mg/L were risk factors for disease progression in patients with COVID-19 (WBC count>10 × 10 9 /L: OR = 2.92, 95% CI: 1.96-4.35; lymphocyte count<1 × 10 9 /L: OR = 4.97, 95% CI: 3.53-6.99; PCT>0.5 ng/mL: OR = 6.33, 95% CI: 3.97-10.10; CRP>10 mg/L: OR = 3.51, 95% CI: 2.38-5.16). Furthermore, we found that NLR, as a novel marker of systemic inflammatory response, can also help predict clinical severity in patients with COVID-19 (OR = 2.50, 95% CI: 2.04-3.06). Conclusions: Immune-inflammatory parameters, such as WBC, lymphocyte, PCT, CRP, and NLR, could imply the progression of COVID-19. NLR has taken both the levels Feng et al. Immune-Inflammatory Parameters in COVID-19 of neutrophil and lymphocyte into account, indicating a more complete, accurate, and reliable inspection efficiency; surveillance of NLR may help clinicians identify high-risk COVID-19 patients at an early stage.

A number of prospective cohort studies have investigated the associations between consumption of sugar-sweetened beverages (SSB) and the risk of hypertension, CHD and stroke, but revealed mixed results. In the present study, we aimed to perform a dose -response metaanalysis of these prospective studies to clarify these associations. A systematic literature search was conducted using the PubMed and Embase databases up to 5 May 2014. Random-or fixed-effects models were used to calculate the pooled relative risks (RR) with 95 % CI for the highest compared with the lowest category of SSB consumption, and to conduct a dose -response analysis. A total of six prospective studies (240 726 participants and 80 411 incident cases of hypertension) from four publications on hypertension were identified. A total of four prospective studies (194 664 participants and 7396 incident cases of CHD) from four publications on CHD were identified. A total of four prospective studies (259 176 participants and 10 011 incident cases of stroke) from four publications on stroke were identified. The summary RR for incident hypertension was 1·08 (95 % CI 1·04, 1·12) for every additional one serving/d increase in SSB consumption. The summary RR for incident CHD was 1·17 (95 % CI 1·10, 1·24) for every serving/d increase in SSB consumption. There was no significant association between SSB consumption and total stroke (summary RR 1·06, 95 % CI 0·97, 1·15) for every serving/d increase in SSB consumption. The present meta-analysis suggested that a higher consumption of SSB was associated with a higher risk of hypertension and CHD, but not with a higher risk of stroke.

BackgroundSeveral prospective studies have been conducted to examine the relationship between fruit juice intake and risk of incident type 2 diabetes, but results have been mixed. In the present study, we aimed to estimate the association between fruit juice intake and risk of type 2 diabetes.MethodsPubMed and Embase databases were searched up to December 2013. All prospective cohort studies of fruit juice intake with risk of type 2 diabetes were included. The pooled relative risks (RRs) with 95% confidence intervals (CIs) for highest vs. lowest category of fruit juice intake were estimated using a random-effects model.ResultsA total of four studies (191,686 participants, including 12,375 with type 2 diabetes) investigated the association between sugar-sweetened fruit juice and risk of incident type 2 diabetes, and four studies (137,663 participants and 4,906 cases) investigated the association between 100% fruit juice and risk of incident type 2 diabetes. A higher intake of sugar-sweetened fruit juice was significantly associated with risk of type 2 diabetes (RR = 1.28, 95%CI = 1.04–1.59, p = 0.02), while intake of 100% fruit juice was not associated with risk of developing type 2 diabetes (RR = 1.03, 95% CI = 0.91–1.18, p = 0.62).ConclusionsOur findings support dietary recommendations to limit sugar-sweetened beverages, such as fruit juice with added sugar, to prevent the development of type 2 diabetes.

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A higher consumption of nuts was associated with reduced risk of CAD and hypertension but not stroke or T2D. Large randomized controlled trials are warranted to confirm the observed associations.

In this paper, we compared the concept of agricultural drought and its relationship with other types of droughts and reviewed the progress of research on agricultural drought monitoring indices on the basis of station data and remote sensing. Applicability and limitations of different drought monitoring indices were also compared. Meanwhile, development history and the latest progress in agricultural drought monitoring were evaluated through statistics and document comparison, suggesting a transformation in agricultural drought monitoring from traditional single meteorological monitoring indices to meteorology and remote sensing-integrated monitoring indices. Finally, an analysis of current challenges in agricultural drought monitoring revealed future research prospects for agricultural drought monitoring, such as investigating the mechanism underlying agricultural drought, identifying factors that influence agricultural drought, developing multi-spatiotemporal scales models for agricultural drought monitoring, coupling qualitative and quantitative agricultural drought evaluation models, and improving the application levels of remote sensing data in agricultural drought monitoring.