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Abstract Background Tumor-Adipose-Feature (TAF) as well as SARIFA (Stroma AReactive Invasion Front Areas) are two histologic features/biomarkers linking tumor-associated adipocytes to poor outcomes in colorectal cancer (CRC) patients. Whereas TAF was identified by deep learning (DL) algorithms, SARIFA was established as a human-observed histopathologic biomarker. Methods To study the overlap between TAF and SARIFA, we performed a systematic pathological review of TAF based on all published image tiles. Additionally, we analyzed the presence/absence of TAF in SARIFA-negative CRC cases to elucidate the biologic and prognostic role of a direct tumor-adipocyte contact. TCGA-CRC gene expression data is investigated to assess the association of FABP4 (fatty-acid binding protein 4) and CD36 (fatty-acid translocase) with both TAF and CRC prognosis. Results By investigating the TAF/SARIFA overlap, we show that many TAF patches correspond to the recently described SARIFA-phenomenon. Even though there is a pronounced morphological and biological overlap, there are differences in the concepts. The presence of TAF in SARIFA-negative CRCs is not associated with poor outcomes in this cohort, potentially highlighting the importance of a direct tumor-adipocyte interaction. Upregulation of FABP4 and CD36 gene expression seem both linked to a poor prognosis in CRC. Conclusions By proving the substantial overlap between human-observed SARIFA and DL-based TAF as morphologic biomarkers, we demonstrate that linking DL-based image features to independently developed histopathologic biomarkers is a promising tool in the identification of clinically and biologically meaningful biomarkers. Adipocyte-tumor-cell interactions seem to be crucial in CRC, which should be considered as biomarkers for further investigations.

This article presents the possibilities of substituting expensive and scarce wood materials in the construction of piano cases, especially the front panels of upright pianos. Three-layer blockboard, multi-layer plywood, medium-density fibreboard (MDF), and Purenit were selected for the study. These materials were long-time climatised at 20 °C and 50 % relative air humidity. Their frequencies, damping coefficient and relative amplitude were measured. The density r, sound velocity v, dynamic modulus of elasticity E´, sound impedance Zn, and Acoustic Conversion Efficiency (ACE) were calculated. With the materials used to make the front panels of an upright piano, a subjective assessment of the instrument’s acoustic response was made. The presence of front panels of any type was found to have a negative effect on the sound except at low frequencies. With panels fitted, the best acoustic properties were achieved by blockboard, followed by plywood, MDF and Purenit panels in that order; this was affirmed by the subjective assessment. The best acoustic performance was achieved by blockboard and plywood. Taking both price and performance into consideration, MDF presented the best compromise. Purenit was ruled out due to its high damping properties.

Abstract Studies have shown that colorectal cancer prognosis can be predicted by deep learning-based analysis of histological tissue sections of the primary tumor. So far, this has been achieved using a binary prediction. Survival curves might contain more detailed information and thus enable a more fine-grained risk prediction. Therefore, we established survival curve-based CRC survival predictors and benchmarked them against standard binary survival predictors, comparing their performance extensively on the clinical high and low risk subsets of one internal and three external cohorts. Survival curve-based risk prediction achieved a very similar risk stratification to binary risk prediction for this task. Exchanging other components of the pipeline, namely input tissue and feature extractor, had largely identical effects on model performance independently of the type of risk prediction. An ensemble of all survival curve-based models exhibited a more robust performance, as did a similar ensemble based on binary risk prediction. Patients could be further stratified within clinical risk groups. However, performance still varied across cohorts, indicating limited generalization of all investigated image analysis pipelines, whereas models using clinical data performed robustly on all cohorts.

Abstract In this work, we present DrugSolver CavitomiX, a novel computational pipeline for drug repurposing and identifying ligands and inhibitors of target enzymes. The pipeline is based on cavity point clouds representing physico-chemical properties of the cavity induced solely by the protein. To test the pipeline’s ability to identify inhibitors, we chose enzymes essential for SARS-CoV-2 replication as a test system. The active-site cavities of the viral enzymes main protease (Mpro) and papain-like protease (Plpro), as well as of the human transmembrane serine protease 2 (TMPRSS2), were selected as target cavities. Using active-site point-cloud comparisons, it was possible to identify two compounds—flufenamic acid and fusidic acid—which show strong inhibition of viral replication. The complexes from which fusidic acid and flufenamic acid were derived would not have been identified using classical sequence- and structure-based methods as they show very little structural (TM-score: 0.1 and 0.09, respectively) and very low sequence (~ 5%) identity to Mpro and TMPRSS2, respectively. Furthermore, a cavity-based off-target screening was performed using acetylcholinesterase (AChE) as an example. Using cavity comparisons, the human carboxylesterase was successfully identified, which is a described off-target for AChE inhibitors.

Abstract Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre‐analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross‐organizational documentation, traceability, and non‐repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine‐actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

Abstract Access to large volumes of so-called whole-slide images—high-resolution scans of complete pathological slides—has become a cornerstone of the development of novel artificial intelligence methods in pathology for diagnostic use, education/training of pathologists, and research. Nevertheless, a methodology based on risk analysis for evaluating the privacy risks associated with sharing such imaging data and applying the principle “as open as possible and as closed as necessary” is still lacking. In this article, we develop a model for privacy risk analysis for whole-slide images which focuses primarily on identity disclosure attacks, as these are the most important from a regulatory perspective. We introduce a taxonomy of whole-slide images with respect to privacy risks and mathematical model for risk assessment and design . Based on this risk assessment model and the taxonomy, we conduct a series of experiments to demonstrate the risks using real-world imaging data. Finally, we develop guidelines for risk assessment and recommendations for low-risk sharing of whole-slide image data.

Abstract Background Presence of lymph node metastasis (LNM) influences prognosis and clinical decision-making in colorectal cancer. However, detection of LNM is variable and depends on a number of external factors. Deep learning has shown success in computational pathology, but has struggled to boost performance when combined with known predictors. Methods Machine-learned features are created by clustering deep learning embeddings of small patches of tumor in colorectal cancer via k-means, and then selecting the top clusters that add predictive value to a logistic regression model when combined with known baseline clinicopathological variables. We then analyze performance of logistic regression models trained with and without these machine-learned features in combination with the baseline variables. Results The machine-learned extracted features provide independent signal for the presence of LNM (AUROC: 0.638, 95% CI: [0.590, 0.683]). Furthermore, the machine-learned features add predictive value to the set of 6 clinicopathologic variables in an external validation set (likelihood ratio test, p

Kristian Brat,1– 3 Michal Svoboda,4 Jaromir Zatloukal,5,6 Marek Plutinsky,1,2 Eva Volakova,5,6 Patrice Popelkova,7,8 Barbora Novotna,9 Tomas Dvorak,10 Vladimir Koblizek11,12 1Department of Respiratory Diseases, University Hospital Brno, Brno, Czech Republic; 2Faculty of Medicine, Masaryk University, Brno, Czech Republic; 3International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic; 4Institute of Biostatistics and Analyses, Ltd., Brno, Czech Republic; 5Pulmonary Department, University Hospital Olomouc, Olomouc, Czech Republic; 6Faculty of Medicine, Palacky University, Olomouc, Czech Republic; 7Pulmonary Department, University Hospital Ostrava, Ostrava, Czech Republic; 8Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic; 9Pulmonary Department, Bulovka Hospital, Prague, Czech Republic; 10Pulmonary Department, Mlada Boleslav Hospital, Mlada Boleslav, Czech Republic; 11Pulmonary Department, University Hospital Hradec Kralove, Hradec Kralove, Czech Republic; 12Faculty of Medicine in Hradec Kralove, Charles University, Hradec Kralove, Czech RepublicCorrespondence: Kristian Brat, Department of Respiratory Diseases, University Hospital Brno and Faculty of Medicine, Masaryk University, Jihlavska street 20, Brno, 62500, Czech Republic, Tel +420 532 232 556, Email brat.kristian@fnbrno.czIntroduction: Recently, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) has published an update on the Global Strategy for Prevention, Diagnosis and Management of COPD, introducing a new classification of chronic obstructive pulmonary disease (COPD). Our aim was to assess the prognostic value of the new GOLD classification system in comparison with the previous GOLD classification systems (GOLD stages I–IV and GOLD groups A-D) and the BODE index.Methods: We used the data of 784 patients with COPD from the Czech Multicenter Research Database of COPD. Patient survival was analyzed with the use of Kaplan–Meier estimate and Cox model of proportional risks. ROC analysis and area under curve (AUC) were used for comparison of GOLD classifications and BODE index. The analyses were performed with the use of software R (version 4.2.0).Results: We analyzed data of 782 patients with complete data on GOLD classifications. The study population comprised 72.9% of men, 89.1% current or former smokers, with a mean age of 66.6 years, a mean BMI of 27.4 and a mean FEV1 44.9% of predicted. Probability of 5-year survival differed by GOLD classification. Application of the 2023 GOLD classification showed increased risk of death in group B (HR 1.82, 95% CI 1.14– 2.92; p = 0.013) and in group E (HR 2.48, 95% CI 1.54– 3.99; p˂0.001). The ROC analysis showed that the overall prognostic value of the 2023 GOLD classification was similarly weak to previous A-D GOLD classification schemes (AUCs 0.557– 0.576) and was lower compared to the GOLD 1– 4 system (AUC 0.614) and even lower when compared to the BODE index (AUC 0.715).Conclusion: We concluded that the new GOLD classification system has poor prognostic properties and that specific prediction tools (eg, the BODE index) should be used for mortality risk assessment.Keywords: GOLD classification, COPD, mortality, prognosis

Abstract The current COVID19 pandemic is caused by a positive-sense single-stranded RNA virus, which presents high mutational rates. The development of effective therapeutics and mitigation strategies using vaccination or therapeutic antibodies faces serious challenges because of the regular emergence of immune escape variants of the virus. An efficient approach would involve the use of an agent to non-specifically limit or block viruses contacting the mucosae and therefore entering the body. Here, we investigated the ability of a micronized purified clinoptilolite-tuff to bind and neutralize different viruses from the Coronaviridae family. Using plaque assay, RT-qPCR and immunostaining, the adsorption and inactivation of the seasonal human coronavirus HCoV-229E and of 2 SARS-CoV-2 variants were demonstrated. The resulting data suggest that purified clinoptilolite-tuff could be used as an ingredient in new medical devices and/or pharmaceuticals to prevent or mitigate SARS-CoV-2 dissemination.

Background: Alcohol-associated liver disease (ALD) is a worldwide health problem, of which the effective treatment is still lacking. Both detrimental and protective roles of adipose tissue have been implicated in ALD. Although alcohol increases adipose tissue lipolysis to promote alcohol-induced liver injury, alcohol also activates brown adipose tissue (BAT) thermogenesis as an adaptive response in protecting against alcohol-induced liver injury. Moreover, aging and obesity are also risk factors for ALD. In the present study, we investigated the effects of autophagy receptor protein SQSTM1/p62 on adipose tissue and obesity in alcohol-induced liver injury in both young and aged mice. Methods: Young and aged whole-body SQSTM1/p62 knockout (KO) and their age-matched wild-type (WT) mice were subjected to chronic plus binge (Gao-binge) alcohol feeding. Blood, adipose and liver tissues were collected for biochemical and histologic analysis. Results: Aged but not young SQSTM1/p62 KO mice had significantly increased body weight and fat mass compared with the matched WT mice. Gao-binge alcohol feeding induced white adipose atrophy and decreased levels of SQSTM1/p62 levels in adipose tissue in aged WT mice. SQSTM1/p62 KO aged mice were resistant to Gao-binge alcohol-induced white adipose atrophy. Alcohol feeding increased the expression of thermogenic genes in WT mouse BAT, which was significantly blunted in SQSTM1/p62 KO aged mice. Alcohol-fed aged SQSTM1/p62 KO mice showed significantly higher levels of serum alanine aminotransferase, hepatic triglyceride, and inflammation compared with young and aged WT mice fed with alcohol. Alcohol-fed SQSTM1/p62 KO mice also increased secretion of proinflammatory and angiogenic adipokines that may promote alcohol-induced liver injury. Conclusions: Loss of SQSTM1/p62 in aged mice leads to obesity and impairs alcohol-induced BAT adaptation, resulting in exacerbated alcohol-induced liver injury in mice.

Abstract Treatment of COVID-19 with a soluble version of ACE2 that binds to SARS-CoV-2 virions before they enter host cells is a promising approach, however it needs to be optimized and adapted to emerging viral variants. The computational workflow presented here consists of molecular dynamics simulations for spike RBD-hACE2 binding affinity assessments of multiple spike RBD/hACE2 variants and a novel convolutional neural network architecture working on pairs of voxelized force-fields for efficient search-space reduction. We identified hACE2-Fc K31W and multi-mutation variants as high-affinity candidates, which we validated in vitro with virus neutralization assays. We evaluated binding affinities of these ACE2 variants with the RBDs of Omicron BA.3, Omicron BA.4/BA.5, and Omicron BA.2.75 in silico. In addition, candidates produced in Nicotiana benthamiana, an expression organism for potential large-scale production, showed a 4.6-fold reduction in half-maximal inhibitory concentration (IC50) compared with the same variant produced in CHO cells and an almost six-fold IC50 reduction compared with wild-type hACE2-Fc.

Abstract The coronavirus disease (COVID-19) pandemic caused unprecedented research activity all around the world but publications from Central-Eastern European countries remain scarce. Therefore, our aim was to characterise the features of the pandemic in the intensive care units (ICUs) among members of the SepsEast (Central-Eastern European Sepsis Forum) initiative. We conducted a retrospective, international, multicentre study between March 2020 and February 2021. All adult patients admitted to the ICU with pneumonia caused by COVID-19 were enrolled. Data on baseline and treatment characteristics, organ support and mortality were collected. Eleven centres from six countries provided data from 2139 patients. Patient characteristics were: median 68, [IQR 60–75] years of age; males: 67%; body mass index: 30.1 [27.0–34.7]; and 88% comorbidities. Overall mortality was 55%, which increased from 2020 to 2021 (p = 0.004). The major causes of death were respiratory (37%), cardiovascular (26%) and sepsis with multiorgan failure (21%). 1061 patients received invasive mechanical ventilation (mortality: 66%) without extracorporeal membrane oxygenation (n = 54). The rest of the patients received non-invasive ventilation (n = 129), high flow nasal oxygen (n = 317), conventional oxygen therapy (n = 122), as the highest level of ventilatory support, with mortality of 50%, 39% and 22%, respectively. This is the largest COVID-19 dataset from Central-Eastern European ICUs to date. The high mortality observed especially in those receiving invasive mechanical ventilation renders the need of establishing national–international ICU registries and audits in the region that could provide high quality, transparent data, not only during the pandemic, but also on a regular basis.

Immunoglobulin M (IgM) is the largest antibody isotype with unique features like extensive glycosylation and oligomerization. Major hurdles in characterizing its properties are difficulties in the production of well-defined multimers. Here we report the expression of two SARS-CoV-2 neutralizing monoclonal antibodies in glycoengineered plants. Isotype switch from IgG1 to IgM resulted in the production of IgMs, composed of 21 human protein subunits correctly assembled into pentamers. All four recombinant monoclonal antibodies carried a highly reproducible human-type N-glycosylation profile, with a single dominant N-glycan species at each glycosite. Both pentameric IgMs exhibited increased antigen binding and virus neutralization potency, up to 390-fold, compared to the parental IgG1. Collectively, the results may impact on the future design of vaccines, diagnostics and antibody-based therapies and emphasize the versatile use of plants for the expression of highly complex human proteins with targeted posttranslational modifications.

This research article aims to explore how employees are coping with working from home and provides practical suggestions for employers and managers adapting to the “new normal”. The main contribution of this research lies in the comprehensive integration of various aspects of managing a home office into four strategies, surpassing the findings of prior studies that focused on specific areas. This was achieved through a qualitative descriptive approach. Through semi-structured interviews with employees in the Czech Republic during the COVID-19 pandemic, four distinct employee types (“Working from Home is Not for Me”, “Yin and Yang”, “Holiday with Emergency”, and “Chameleon”) were identified based on their adaptation strategies. These strategies encompass aspects like time management, self- and family management, setting boundaries, task management, stress reduction, and adherence to rules. The article concludes by offering suggestions for more effective collaboration and employee management, taking into consideration their approach to remote work.

SARS-CoV-2 infects human cells via binding of the viral spike glycoprotein to its main cellular receptor, angiotensin-converting enzyme 2 (ACE2). The spike protein-ACE2 receptor interaction is therefore a major target for the development of therapeutic or prophylactic drugs to combat coronavirus infections. Various engineered soluble ACE2 variants (decoys) have been designed and shown to exhibit virus neutralization capacity in cell-based assays and in vivo models. Human ACE2 is heavily glycosylated and some of its glycans impair binding to the SARS-CoV-2 spike protein. Therefore, glycan-engineered recombinant soluble ACE2 variants might display enhanced virus-neutralization potencies. Here, we transiently co-expressed the extracellular domain of ACE2 fused to human Fc (ACE2-Fc) with a bacterial endoglycosidase in Nicotiana benthamiana to produce ACE2-Fc decorated with N-glycans consisting of single GlcNAc residues. The endoglycosidase was targeted to the Golgi apparatus with the intention to avoid any interference of glycan removal with concomitant ACE2-Fc protein folding and quality control in the endoplasmic reticulum. The in vivo deglycosylated ACE2-Fc carrying single GlcNAc residues displayed increased affinity to the receptor-binding domain (RBD) of SARS-CoV-2 as well as improved virus neutralization activity and thus is a promising drug candidate to block coronavirus infection.

Abstract Members of the Viola genus play important roles in traditional Asian herbal medicine. This study investigates the ability of Viola odorata L. extracts to inhibit Na+,K+-ATPase, an essential animal enzyme responsible for membrane potential maintenance. The root extract of V. odorata strongly inhibited Na+,K+-ATPase, while leaf and seeds extracts were basically inactive. A UHPLC-QTOF-MS/MS metabolomic approach was used to identify the chemical principle of the root extract’s activity, resulting in the detection of 35,292 features. Candidate active compounds were selected by correlating feature area with inhibitory activity in 14 isolated fractions. This yielded a set of 15 candidate compounds, of which 14 were preliminarily identified as procyanidins. Commercially available procyanidins (B1, B2, B3 and C1) were therefore purchased and their ability to inhibit Na+,K+-ATPase was investigated. Dimeric procyanidins B1, B2 and B3 were found to be inactive, but the trimeric procyanidin C1 strongly inhibited Na+,K+-ATPase with an IC50 of 4.5 µM. This newly discovered inhibitor was docked into crystal structures mimicking the Na3E1∼P·ADP and K2E2·Pi states to identify potential interaction sites within Na+,K+-ATPase. Possible binding mechanisms and the principle responsible for the observed root extract activity are discussed.

Thermal actions considered on abutments of integral bridges by the European standard (Eurocode 1) might be in variety of certain situations underestimated. Based on this assumption, thermal monitoring system has been designed and installed into structure of bridge No. 27-117. Temperature profiles in five certain spots are being measured to provide sufficient basis for evaluation of real thermal actions on abutments of integral bridges. For purpose of measurement verification, elementary heat transfer models for finite element method solver were created. These models are being loaded by simplified temperature profiles reached in real time in-situ. Evaluated models provide verification data to compare with measured temperature profiles in structure and might provide information about temperature profiles probably reached during extreme weather situations.

Abstract Background Since December 2019, SARS-CoV-2 virus has infected millions of people worldwide. In patients with COVID-19 pneumonia in need of oxygen therapy or mechanical ventilation, dexamethasone 6 mg per day is currently recommended. However, the dose of 6 mg of dexamethasone is currently being reappraised and may miss important therapeutic potential or may prevent potential deleterious effects of higher doses of corticosteroids. Methods REMED is a prospective, open-label, randomised controlled trial testing the superiority of dexamethasone 20 mg (dexamethasone 20 mg on days 1–5, followed by dexamethasone 10 mg on days 6–10) vs 6 mg administered once daily intravenously for 10 days in adult patients with moderate or severe ARDS due to confirmed COVID-19. Three hundred participants will be enrolled and followed up for 360 days after randomization. Patients will be randomised in a 1:1 ratio into one of the two treatment arms. The following stratification factors will be applied: age, Charlson Comorbidity Index, CRP levels and trial centre. The primary endpoint is the number of ventilator-free days (VFDs) at 28 days after randomisation. The secondary endpoints are mortality from any cause at 60 days after randomisation; dynamics of the inflammatory marker, change in WHO Clinical Progression Scale at day 14; and adverse events related to corticosteroids and independence at 90 days after randomisation assessed by the Barthel Index. The long-term outcomes of this study are to assess long-term consequences on mortality and quality of life at 180 and 360 days. The study will be conducted in the intensive care units (ICUs) of ten university hospitals in the Czech Republic. Discussion We aim to compare two different doses of dexamethasone in patients with moderate to severe ARDS undergoing mechanical ventilation regarding efficacy and safety. Trial registration EudraCT No. 2020-005887-70. ClinicalTrials.gov NCT04663555. Registered on December 11, 2020

Acetaminophen (APAP) overdose can induce liver injury and is the most frequent cause of acute liver failure in the United States. We investigated the role of p62/SQSTM1 (referred to as p62) in APAP-induced liver injury (AILI) in mice. We found that the hepatic protein levels of p62 dramatically increased at 24 h after APAP treatment, which was inversely correlated with the hepatic levels of APAP-adducts. APAP also activated mTOR at 24 h, which is associated with increased cell proliferation. In contrast, p62 knockout (KO) mice showed increased hepatic levels of APAP-adducts detected by a specific antibody using Western blot analysis but decreased mTOR activation and cell proliferation with aggravated liver injury at 24 h after APAP treatment. Surprisingly, p62 KO mice recovered from AILI whereas the wild-type mice still sustained liver injury at 48 h. We found increased number of infiltrated macrophages in p62 KO mice that were accompanied with decreased hepatic von Willebrand factor (VWF) and platelet aggregation, which are associated with increased cell proliferation and improved liver injury at 48 h after APAP treatment. Our data indicate that p62 inhibits the late injury phase of AILI by increasing autophagic selective removal of APAP-adducts and mitochondria but impairs the recovery phase of AILI likely by enhancing hepatic blood coagulation.