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Head and neck squamous cell carcinomas (HNSCCs) belong to a group of diverse tumors, which can be induced by infection with human papillomavirus (HPV) or tobacco and alcohol consumption. The viral etiology of HNSCC relates to better clinical outcomes reflecting a different immune system response. Here, we retrospectively analyzed 97 tissue samples from oral and oropharyngeal carcinomas associated and non-associated with HPV infection using multispectral fluorescent immunohistochemistry. To evaluate the immune cell infiltration in tumor and stroma compartments, we designed four panels of four to five antibodies. We detected more T lymphocytes in the stroma, compared to the tumor parenchyma. In HPV positive (HPV+) in comparison to HPV negative (HPV−) tumors, higher counts of CD3+CD4+, CD3+CD8+, PD1+CD4+, PD1+CD8+ T cells, and ICOS− Treg cells were detected while more ICOS+ Treg cells and CTLA4+CD4+ T cells were observed in HPV− than in HPV+ tumors. The results of the univariate and multivariate analyses confirmed the predominant impact of HPV status on prognosis. More importantly, the number of CD8+PD-1+ T cells was identified as an independent factor, influencing the overall and/or disease-specific survival of patients with oral cavity or oropharyngeal carcinomas. [ABSTRACT FROM AUTHOR]

Increasing attention is given to carbon sequestration in forest soil with regard to climate change and the mitigation of its impacts; therefore, it is very important to know which parameters and variables could influence carbon sequestration and throw light on their relationships. The aim of this study is to assess the role of abiotic variables, chemical elements and stand parameters in soil carbon sequestration, and clarify which of these could affect soil organic carbon (SOC) content in the surface mineral horizon in Norway spruce and European beech stands in the Czech Republic. We analyzed 81 monitoring plots within pure and mixed stands of spruce and beech with different degrees of forest naturalness. In each monitoring plot, SOC content, chemical elements (content of bound forms of oxides: tFe, tAl, tCa, tMg, tK, tMn, tP, tN) and related variables (BS, pH, C/N) were measured. The effect of these variables, including abiotic variables (elevation, temperature, precipitation, duration of growing season and soil group) on SOC content was tested, and differences between represented stands (natural vs. unnatural, pure vs. mixed, spruce vs. beech) were analyzed. The results showed that elevation has a positive relationship to SOC content. Of the studied chemical elements and related variables, only tN content was significantly related to SOC content. A positive relationship was also demonstrated between forest naturalness and SOC content. The highest SOC and tN contents were observed in pure natural Norway spruce stands, which likely play a very important role in SOC sequestration. In the context of the current issue of unnatural Norway spruce stands in the Czech Republic, a higher SOC content was found in mixed natural European beech stands than in either pure or mixed unnatural Norway spruce stands. Therefore, replacing the unnatural Norway spruce stands in the study area with mixed natural European beech stands could represent a viable alternative to current forest management in terms of soil carbon sequestration, especially in the context of global climate change and spruce dieback. [ABSTRACT FROM AUTHOR]

Ordered categorical data occur in many applied fields, such as geochemistry, econometrics, sociology and demography or even transportation research, for example, in the form of results from various questionnaires. There are different possibilities for modelling proportions of individual categories. Generalised linear models (GLMs) are traditionally used for this purpose, but also methods of compositional data analysis (CoDa) can be considered. Here, both approaches are compared in depth. Particularly, different assumptions of the models on variability are highlighted. Advantages and disadvantages of individual models are pointed out. While the CoDa model may be inappropriate when the variability of the compositional coordinates depends on the regressors, for example, due to different total counts on which the coordinates are based, the GLM may underestimate the uncertainty of the predictions considerably in case of large-scale data. [ABSTRACT FROM AUTHOR]

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Seroprevalence studies represent a very important tool to find out what fraction of population has already met with the new type of coronavirus (e.g. SARS-CoV-2). Without these data, it is almost impossible for the state authorities to manage the epidemic and adopt rational measures. This article brings the results of a medium-sized seroprevalence study which was carried out in the spring of 2020 in South Bohemia. In the Strakonice and Písek regions, the ELISA method was used to test the prevalence of IgA and IgG antibodies in 2011 subjects, volunteers from general public and selected professions working in areas with a higher exposure to the infection. The study showed that already in May 2020, 2.9% of inhabitants of the Strakonice region and 1.9% of inhabitants of the Písek region had antibodies against the coronavirus. These numbers imply that for each PCR positive person, there were at least fifty others who had probably already undergone the infection. The article points out three types of problems that might occur in such a study. First, the study must be planned correctly, and possible outcomes must be pre-assessed. Second, an appropriate test must be selected with known parameters. This enables us to correctly estimate the share of false positive and false negative results. Third, the data must be evaluated in a reasonable way and correct inference must be performed. We offer a set of recommendations how to manage these issues and how to solve problems that inevitably arise in such a large-scale testing.