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Increasing arbovirus infections have been a global burden in recent decades. FEN1-IN-4 research buy Many countries have experienced the periodic emergence of arbovirus diseases. However, information on the prevalence of arboviruses is largely unknown or infrequently updated because of the lack of surveillance studies, especially in Africa.

A surveillance study was conducted in Gabon, Central Africa, on arboviruses, which are a major public health concern in Africa, including West Nile virus (WNV), dengue virus (DENV), Zika virus (ZIKV), yellow fever virus (YFV), chikungunya virus (CHIKV), and Rift Valley fever virus (RVFV). Serological and molecular assays were performed to investigate past infection history and the current status of infection, using serum samples collected from healthy individuals and febrile patients, respectively.

The overall seroprevalence during 2014-2017 was estimated to be 25.3% for WNV, 20.4% for DENV, 40.3% for ZIKV, 60.7% for YFV, 61.2% for CHIKV, and 14.3% for RVFV. No significant differences were ence of WNV and DENV in Gabon as well as the latest seroprevalence state of the major arboviruses, which indicated the different potential risks of virus infections and virus-specific circulation patterns. This information will be helpful for public health organizations and will enable a rapid response towards these arbovirus infections, thereby preventing future spread in the country.

Natural model systems are indispensable for exploring adaptations in response to environmental pressures. Sinocyclocheilus of China, the most diverse cavefish clade in the world (75 species), provide unique opportunities to understand recurrent evolution of stereotypic traits (such as eye loss and sensory expansion) in the context of a deep and diverse phylogenetic group. However, they remain poorly understood in terms of their morphological evolution. Therefore, we explore key patterns of morphological evolution, habitat utilization and geographic distribution in these fishes.

We constructed phylogenies and categorized 49 species based on eye-related condition (Blind, Micro-eyed, and Normal-eyed), habitat types (Troglobitic-cave-restricted; Troglophilic-cave-associated; Surface-outside caves) and existence of horns. Geometric-morphometric analyses show Normal-eyed morphs with fusiform shapes segregating from Blind/Micro-eyed deeper bodied morphs along the first principal-component axis; second axis accouremarkable patterns of evolution, but also interesting exceptions to these patterns signifying the diversification of Sinocyclocheilus as an invaluable model system to explore evolutionary novelty.

An increasing number ofclinical trials require biomarker-driven patient stratification, especially for revolutionary immune checkpoint blockade therapy. Due to the complicated interaction between a tumor and its microenvironment, single biomarkers, such as PDL1 protein level, tumor mutational burden (TMB), single gene mutation and expression, are far from satisfactory for response prediction or patient stratification. Recently, combinatorial biomarkers were reported to be more precise and powerful for predicting therapy response and identifying potential target populations with superior survival. However, there is a lack of dedicated tools for such combinatorial biomarker analysis.

Here, we present dualmarker, an R package designed to facilitate the data exploration for dual biomarker combinations. Given two biomarkers, dualmarker comprehensively visualizes their association with drug response and patient survival through 14 types of plots, such as boxplots, scatterplots, ROCs, and Kaplan-Meier plots. Usilysis flow into user-friendly functions and can be used for data exploration and hypothesis generation. Its code is freely available at GitHub at https//github.com/maxiaopeng/dualmarker under MIT license.

The dualmarker package is an open-source tool for the visualization and identification of combinatorial dual biomarkers. It streamlines the dual marker analysis flow into user-friendly functions and can be used for data exploration and hypothesis generation. Its code is freely available at GitHub at https//github.com/maxiaopeng/dualmarker under MIT license.

In the future, co-circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses A/B is likely. From a clinical point of view, differentiation of the two disease entities is crucial for patient management. We therefore aim to detect clinical differences between Coronavirus Disease 2019 (COVID-19) and seasonal influenza patients at time of hospital admission.

In this single-center observational study, we included all consecutive patients hospitalized for COVID-19 or influenza between November 2019 and May 2020. Data were extracted from a nationwide surveillance program and from electronic health records. COVID-19 and influenza patients were compared in terms of baseline characteristics, clinical presentation and outcome. We used recursive partitioning to generate a classification tree to discriminate COVID-19 from influenza patients.

We included 96 COVID-19 and 96 influenza patients. Median age was 68 vs. 70 years (p = 0.90), 72% vs. 56% (p = 0.024) were males, and mediating COVID-19 from influenza patients based on clinical presentation is challenging. Time from symptom onset to hospital admission is considerably longer in COVID-19 than in influenza patients and showed the strongest discriminatory power in our classification tree. Although they had fewer comorbidities, in-hospital mortality was higher for COVID-19 patients.

A large number of studies have explored the association between frailty and mortality among COVID-19 patients, with inconsistent results. The aim of this meta-analysis was to synthesize the evidence on this issue.

Three databases, PubMed, Embase, and Cochrane Library, from inception to 20th January 2021 were searched for relevant literature. The Newcastle-Ottawa Scale (NOS) was used to assess quality bias, and STATA was employed to pool the effect size by a random effects model. Additionally, potential publication bias and sensitivity analyses were performed.

Fifteen studies were included, with a total of 23,944 COVID-19 patients, for quantitative analysis. Overall, the pooled prevalence of frailty was 51% (95% CI 44-59%). Patients with frailty who were infected with COVID-19 had an increased risk of mortality compared to those without frailty, and the pooled hazard ratio (HR) and odds ratio (OR) were 1.99 (95% CI 1.66-2.38) and 2.48 (95% CI 1.78-3.46), respectively. In addition, subgroup analysis based on population showed that the pooled ORs for hospitalized patients in eight studies and nursing home residents in two studies were 2.