In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data.

• MeSH
• adaptace psychologická MeSH
• COVID-19 * MeSH
• lidé MeSH
• pandemie MeSH
• průzkumy a dotazníky MeSH
• zdravé chování MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• dataset MeSH

At the time of the COVID-19 pandemic, providing access to data (properly optimised regarding personal data protection) plays a crucial role in providing the general public and media with up-to-date information. Open datasets also represent one of the means for evaluation of the pandemic on a global level. The primary aim of this paper is to describe the methodological and technical framework for publishing datasets describing characteristics related to the COVID-19 epidemic in the Czech Republic (epidemiology, hospital-based care, vaccination), including the use of these datasets in practice. Practical aspects and experience with data sharing are discussed. As a reaction to the epidemic situation, a new portal COVID-19: Current Situation in the Czech Republic (https://onemocneni-aktualne.mzcr.cz/covid-19) was developed and launched in March 2020 to provide a fully-fledged and trustworthy source of information for the public and media. The portal also contains a section for the publication of (i) public open datasets available for download in CSV and JSON formats and (ii) authorised-access-only section where the authorised persons can (through an online generated token) safely visualise or download regional datasets with aggregated data at the level of the individual municipalities and regions. The data are also provided to the local open data catalogue (covering only open data on healthcare, provided by the Ministry of Health) and to the National Catalogue of Open Data (covering all open data sets, provided by various authorities/publishers, and harversting all data from local catalogues). The datasets have been published in various authentication regimes and widely used by general public, scientists, public authorities and decision-makers. The total number of API calls since its launch in March 2020 to 15 December 2020 exceeded 13 million. The datasets have been adopted as an official and guaranteed source for outputs of third parties, including public authorities, non-governmental organisations, scientists and online news portals. Datasets currently published as open data meet the 3-star open data requirements, which makes them machine-readable and facilitates their further usage without restrictions. This is essential for making the data more easily understandable and usable for data consumers. In conjunction with the strategy of the MH in the field of data opening, additional datasets meeting the already implemented standards will be also released, both on COVID-19 related and unrelated topics.

• MeSH
• COVID-19 * epidemiologie   MeSH
• lidé MeSH
• pandemie prevence a kontrola   MeSH
• SARS-CoV-2 MeSH
• šíření informací MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Česká republika epidemiologie   MeSH

We present a novel approach to estimate the time-varying ascertainment rate in almost real-time, based on the surveillance of positively tested infectious and hospital admission data. We also address the age dependence of the estimate. The ascertainment rate estimation is based on the Bayes theorem. It can be easily calculated and used (i) as part of a mechanistic model of the disease spread or (ii) to estimate the unreported infections or changes in their proportion in almost real-time as one of the early-warning signals in case of undetected outbreak emergence. The paper also contains a case study of the COVID-19 epidemic in the Czech Republic. The case study demonstrates the usage of the ascertainment rate estimate in retrospective analysis, epidemic monitoring, explanations of differences between waves, usage in the national Anti-epidemic system, and monitoring of the effectiveness of non-pharmaceutical interventions on Czech nationwide surveillance datasets. The Czech data reveal that the probability of hospitalization due to SARS-CoV-2 infection for the senior population was 12 times higher than for the non-senior population in the monitored period from the beginning of March 2020 to the end of May 2021. In a mechanistic model of COVID-19 spread in the Czech Republic, the ascertainment rate enables us to explain the links between all basic compartments, including new cases, hospitalizations, and deaths.

• MeSH
• Bayesova věta MeSH
• COVID-19 * epidemiologie   MeSH
• hospitalizace MeSH
• infekční nemoci * MeSH
• lidé MeSH
• retrospektivní studie MeSH
• SARS-CoV-2 MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Česká republika epidemiologie   MeSH

Over the past decade, the use of deep learning has been widely increasing in the medical image diagnosis field. Deep learning-based methods' (DLMs) performance strongly relies on training data. Therefore, researchers often focus on collecting as much data as possible from different medical facilities or developing approaches to avoid the impact of inter-category imbalance (ICI), which means a difference in data quantity among categories. However, due to the ICI within each medical facility, medical data are often isolated and acquired in different settings among medical facilities, known as the issue of intra-source imbalance (ISI) characteristic. This imbalance also impacts the performance of DLMs but receives negligible attention. In this study, we study the impact of the ISI on DLMs by comparison of the version of a deep learning model that was trained separately by an intra-source imbalanced chest X-ray (CXR) dataset and an intra-source balanced CXR dataset for COVID-19 diagnosis. The finding is that using the intra-source imbalanced dataset causes a serious training bias, although the dataset has a good inter-category balance. In contrast, the deep learning model performed a reliable diagnosis when trained on the intra-source balanced dataset. Therefore, our study reports clear evidence that the intra-source balance is vital for training data to minimize the risk of poor performance of DLMs.

• MeSH
• COVID-19 * diagnostické zobrazování   MeSH
• deep learning * MeSH
• hrudník MeSH
• lidé MeSH
• rentgenové záření MeSH
• testování na COVID-19 MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

INTRODUCTION: Acute appendicitis (AA) is the most common abdominal emergency. This article aims to document the impact of the ongoing COVID-19 pandemic on timely diagnosis of AA, duration of symptoms before examination in a medical institution, levels of laboratory inflammatory markers, and the length of hospital stay. Collected data were compared with current world literature. METHOD: Two datasets were created, comprising patients with the histological diagnosis of AA determined from March 1 to June 30, 2019 (before of the onset of the COVID-19 pandemic) and in the same period of the spring pandemic of COVID-19 in 2020. The following information was obtained from patient medical records: Demographic data, information on symptom duration before AA diagnosis, information on laboratory inflammatory marker levels, the used surgical method, antibiotic treatment, histopathological findings, and the length of hospital stay. These data were processed using descriptive statistics methods and the two created datasets were compared with the use of statistical methods (an unpaired t-test and Welchs t-test). RESULTS: Thirty seven patients (26 men and 11 women) with the median age of 41 years were operated on for acute appendicitis at the Department of Surgery, Military University Hospital in Prague from March 1 to June 30, 2019. Thirty four patients (19 men and 15 women) with the median age of 42 years were operated on in the same period of 2020. No significant differences were found between these two compared datasets in terms of symptom duration, laboratory inflammatory marker levels or the length of hospital stay. The distributions of histopathological findings and used antibiotic treatments were also similar. CONCLUSION: In our study, we were unable to demonstrate any statistically significant differences between the datasets of patients operated on before and after the onset of the COVID-19 pandemic.

• Klíčová slova
• acute appendicitis − COVID-19, appendectomy − conservative treatment,
• MeSH
• akutní nemoc MeSH
• apendektomie škodlivé účinky   MeSH
• apendicitida * epidemiologie   chirurgie   MeSH
• COVID-19 * MeSH
• délka pobytu MeSH
• dospělí MeSH
• lidé MeSH
• pandemie MeSH
• retrospektivní studie MeSH
• SARS-CoV-2 MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Feature selection is one of the most important challenges in machine learning and data science. This process is usually performed in the data preprocessing phase, where the data is transformed to a proper format for further operations by machine learning algorithm. Many real-world datasets are highly dimensional with many irrelevant, even redundant features. These kinds of features do not improve classification accuracy and can even shrink down performance of a classifier. The goal of feature selection is to find optimal (or sub-optimal) subset of features that contain relevant information about the dataset from which machine learning algorithms can derive useful conclusions. In this manuscript, a novel version of firefly algorithm (FA) is proposed and adapted for feature selection challenge. Proposed method significantly improves performance of the basic FA, and also outperforms other state-of-the-art metaheuristics for both, benchmark bound-constrained and practical feature selection tasks. Method was first validated on standard unconstrained benchmarks and later it was applied for feature selection by using 21 standard University of California, Irvine (UCL) datasets. Moreover, presented approach was also tested for relatively novel COVID-19 dataset for predicting patients health, and one microcontroller microarray dataset. Results obtained in all practical simulations attest robustness and efficiency of proposed algorithm in terms of convergence, solutions' quality and classification accuracy. More precisely, the proposed approach obtained the best classification accuracy on 13 out of 21 total datasets, significantly outperforming other competitor methods.

• Klíčová slova
• COVID-19 dataset, Feature selection, Firefly algorithm, Genetic operators, Quasi-reflection-based learning, Swarm intelligence,
• Publikační typ
• časopisecké články MeSH

During SARS-CoV-2 infection, the virus transforms the infected host cell into factories that produce new viral particles. As infection progresses, the infected cells undergo numerous changes in various pathways. One of these changes is the occurrence of a cytokine storm, which leads to severe symptoms. In this study, we examined the transcriptomic changes caused by COVID-19 by analyzing RNA-seq data obtained from COVID-19-positive patients as well as COVID-19-negative donors. RNA-seq data were collected for the purpose of identification of potential biomarkers associated with a different course of the disease. We analyzed the first datasets, consisting of 96 samples to validate our methods. The objective of this publication is to report the pilot results. To explore potential biomarkers related to disease severity, we conducted a differential expression analysis of human transcriptome, focusing on COVID-19 positivity and symptom severity. Given the large number of potential biomarkers we identified, we further performed pathway enrichment analysis with terms from Kyoto Encyclopedia of Genes and Genomics (KEGG) to obtain a more profound understanding of altered pathways. Our results indicate that pathways related to immune processes, response to infection, and multiple signaling pathways were affected. These findings align with several previous studies that also reported the influence of SARS-CoV-2 infection on these pathways.

• Klíčová slova
• COVID-19, Differentially expressed genes, Enriched pathways, Gene enrichment analysis, RNA-seq, SARS-CoV-2, Transcriptomics,
• MeSH
• biologické markery MeSH
• COVID-19 * genetika   MeSH
• genomika MeSH
• lidé MeSH
• SARS-CoV-2 genetika   MeSH
• stanovení celkové genové exprese MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• biologické markery MeSH

OBJECTIVES: We investigated the validity of claims of the healthy vaccinee effect (HVE) in COVID-vaccine studies by analyzing associations between all-cause mortality (ACM) and COVID-19 vaccination status. METHODS: Approximately 2.2 million individual records from two Czech health insurance companies were retrospectively analyzed. Each age group was stratified according to the vaccination status (unvaccinated vs. individuals less than 4 weeks vs. more than 4 weeks from Doses 1, 2, 3, and 4 or more doses of vaccine). ACMs in these groups were computed and compared. RESULTS: Consistently over datasets and age categories, ACM was substantially lower in the vaccinated than unvaccinated groups regardless of the presence or absence of a wave of COVID-19 deaths. Moreover, the ACMs in groups more than 4 weeks from Doses 1, 2, or 3 were consistently several times higher than in those less than 4 weeks from the respective dose. HVE appears to be the only plausible explanation for this, which is further corroborated by a created mathematical model. CONCLUSIONS: In view of the presence of HVE, the baseline difference in the frailty of vaccinated and unvaccinated populations in periods without COVID-19 must be taken into account when estimating COVID-19 vaccine effectiveness from observational data.

• Klíčová slova
• All-cause mortality, COVID-19, Healthy vaccinee bias, Healthy vaccinee effect, Observation studies, Vaccination,
• MeSH
• aklarubicin MeSH
• COVID-19 * prevence a kontrola   MeSH
• lidé MeSH
• retrospektivní studie MeSH
• vakcinace MeSH
• vakcíny proti COVID-19 MeSH
• zdraví MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• aklarubicin MeSH
• vakcíny proti COVID-19 MeSH

During the COVID-19 pandemic, the European biobanking infrastructure is in a unique position to preserve valuable biological material complemented with detailed data for future research purposes. Biobanks can be either integrated into healthcare, where preservation of the biological material is a fork in clinical routine diagnostics and medical treatment processes or they can also host prospective cohorts or material related to clinical trials. The paper discussed objectives of BBMRI-ERIC, the European research infrastructure established to facilitate access to quality-defined biological materials and data for research purposes, with respect to the COVID-19 crisis: (a) to collect information on available European as well as non-European COVID-19-relevant biobanking resources in BBMRI-ERIC Directory and to facilitate access to these via BBMRI-ERIC Negotiator platform; (b) to help harmonizing guidelines on how data and biological material is to be collected to maximize utility for future research, including large-scale data processing in artificial intelligence, by participating in activities such as COVID-19 Host Genetics Initiative; (c) to minimize risks for all involved parties dealing with (potentially) infectious material by developing recommendations and guidelines; (d) to provide a European-wide platform of exchange in relation to ethical, legal, and societal issues (ELSI) specific to the collection of biological material and data during the COVID-19 pandemic.

• MeSH
• antivirové látky terapeutické užití   MeSH
• banky biologického materiálu zásobování a distribuce   MeSH
• Betacoronavirus účinky léků   genetika   patogenita   MeSH
• biomedicínský výzkum organizace a řízení   MeSH
• COVID-19 MeSH
• datové soubory jako téma MeSH
• klinické zkoušky jako téma MeSH
• koronavirové infekce diagnóza   farmakoterapie   epidemiologie   genetika   MeSH
• lidé MeSH
• mezinárodní spolupráce zákonodárství a právo   MeSH
• pandemie * MeSH
• SARS-CoV-2 MeSH
• šíření informací etika   metody   MeSH
• směrnice pro lékařskou praxi jako téma MeSH
• umělá inteligence MeSH
• veřejné zdravotnictví ekonomika   MeSH
• virová pneumonie diagnóza   farmakoterapie   epidemiologie   genetika   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Evropa epidemiologie   MeSH
• Názvy látek
• antivirové látky MeSH

The current population worldwide extensively uses social media to share thoughts, societal issues, and personal concerns. Social media can be viewed as an intelligent platform that can be augmented with a capability to analyze and predict various issues such as business needs, environmental needs, election trends (polls), governmental needs, etc. This has motivated us to initiate a comprehensive search of the COVID-19 pandemic-related views and opinions amongst the population on Twitter. The basic training data have been collected from Twitter posts. On this basis, we have developed research involving ensemble deep learning techniques to reach a better prediction of the future evolutions of views in Twitter when compared to previous works that do the same. First, feature extraction is performed through an N-gram stacked autoencoder supervised learning algorithm. The extracted features are then involved in a classification and prediction involving an ensemble fusion scheme of selected machine learning techniques such as decision tree (DT), support vector machine (SVM), random forest (RF), and K-nearest neighbour (KNN). all individual results are combined/fused for a better prediction by using both mean and mode techniques. Our proposed scheme of an N-gram stacked encoder integrated in an ensemble machine learning scheme outperforms all the other existing competing techniques such unigram autoencoder, bigram autoencoder, etc. Our experimental results have been obtained from a comprehensive evaluation involving a dataset extracted from open-source data available from Twitter that were filtered by using the keywords "covid", "covid19", "coronavirus", "covid-19", "sarscov2", and "covid_19".

• Klíčová slova
• COVID-19, N-gram feature extraction, data prediction, ensemble machine learning, twitter data,
• MeSH
• COVID-19 * MeSH
• lidé MeSH
• pandemie MeSH
• SARS-CoV-2 MeSH
• sociální média * MeSH
• sociální sítě MeSH
• strojové učení MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH