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Introduction: Histone deacetylase inhibitors (HDACi) have shown promising preclinical activity in gastric cancer cells; unfortunately, however, these could not be confirmed in clinical trials. This highlights the need for the identification of underlying reasons, which may also provide the basis for possible combination therapies. Here, we delineated the effects of HDACi on components of EGFR signalling in gastric cancer cells. Methods: We investigated entinostat effects on EGFR and amphiregulin (AREG) expression in various cell line- and primary patient tumor-based in vitro, ex vivo and in vivo models, on the mRNA and protein level. Based on these results, a combined entinostat plus EGFR inhibitor erlotinib treatment in vitro and in vivo was studied. Results: Proteomics analyses in gastric cancer cells treated with entinostat revealed a marked upregulation of EGFR in the majority of cell lines and an even more robust induction of the EGFR ligand AREG. This was confirmed in a panel of different cell lines in vitro, in tumor tissue-slice cultures ex vivo and in cell line- or patient-derived tumor xenografts in mice. Since previous studies in other tumor entities showed a downregulation of EGFR by HDACi, our findings thus indicate essential differences in the adaptive response of gastric carcinoma cells. Moreover, our results provided the basis for combined entinostat + EGFR inhibitor (erlotinib) treatment, and indeed we demonstrate synergistic effects in combination therapy studies. Conclusion: Our findings establish the profound upregulation of the EGFR/AREG axis by entinostat as starting point for a rational combination therapy in gastric carcinoma.

Abstract A growing body of evidence has revealed the negative effects of COVID‐19‐induced school closures on learning, with the largest losses often observed among the most vulnerable children. Learning disparities widened due to six potential reasons related to marginalized children, who (a) were already behind even before the pandemic, (b) were more likely to experience longer school closures, (c) had limited access to and support on remote learning, (d) were less likely to receive supervision and learning support at home, (e) face greater obstacles to return to school, and (f) return to school systems that are less able to provide for their needs. To ensure all children—especially the most vulnerable—can catch up on missed learning, five urgent actions are needed for a RAPID learning recovery and acceleration: (a) Reach every child and keep them in school, (b) Assess learning levels regularly, (c) Prioritize teaching the fundamentals, (d) Increase the efficiency of instruction, and (e) Develop psychosocial health and wellbeing. Implementation of RAPID actions must be strengthened and supported by enabling systems to address the widening disparities in learning.

Dynamic positron emission tomography and kinetic modeling play a critical role in tracer development research using small animals. Kinetic modeling from dynamic PET imaging requires accurate knowledge of an input function, ideally determined through arterial blood sampling. Arterial cannulation in mice, however, requires complex, time-consuming and terminal surgery, meaning that longitudinal studies are impossible. The aim of the current work was to develop and evaluate a non-invasive, deep-learning-based prediction model (DLIF) that directly takes the PET data as input to predict a usable input function. We first trained and evaluated the DLIF model on 68 [18F]Fluorodeoxyglucose mouse scans with image-derived targets using cross validation. Subsequently, we evaluated the performance of a trained DLIF model on an external dataset consisting of 8 mouse scans where the input function was measured by continuous arterial blood sampling. The results showed that the predicted DLIF and image-derived targets were similar, and the net influx rate constants following from Patlak modeling using DLIF as input function were strongly correlated to the corresponding values obtained using the image-derived input function. There were somewhat larger discrepancies when evaluating the model on the external dataset, which could be attributed to systematic differences in the experimental setup between the two datasets. In conclusion, our non-invasive DLIF prediction method may be a viable alternative to arterial blood sampling in small animal [18F]FDG imaging. With further validation, DLIF could overcome the need for arterial cannulation and allow fully quantitative and longitudinal experiments in PET imaging studies of mice.

Histone deacetylase inhibitors (HDACi) show high antineoplastic potential in preclinical studies in various solid tumors, including gastric carcinoma; however, their use in clinical studies has not yet yielded convincing efficacies. Thus, further studies on cellular/molecular effects of HDACi are needed, for improving clinical efficacy and identifying suitable combination partners. Here, we investigated the role of oxidative stress in gastric cancer cells upon treatment with HDACi. A particular focus was laid on the role of the Nrf2 pathway, which can mediate resistance to cell-inhibitory effects of reactive oxidative species (ROS). Using fluorescence-based ROS sensors, oxidative stress was measured in human gastric cancer cell lines. Activation of the Nrf2 pathway was monitored in luciferase reporter assays as well as by mRNA and proteomic expression analyses of Nrf2 regulators and Nrf2-induced genes. Furthermore, the effects of ROS scavenger N-acetyl-L-cysteine (NAC) and Nrf2-knockdown on HDACi-dependent antiproliferative effects were investigated in colorimetric formazan-based and clonogenic survival assays. HDACi treatment led to increased oxidative stress levels and consequently, treatment with NAC reduced cytotoxicity of HDACi. In addition, vorinostat treatment stimulated expression of a luciferase reporter under the control of an antioxidative response element, indicating activation of the Nrf2 system. This Nrf2 activation was only partially reversible by treatment with NAC, suggesting ROS independent pathways to contribute to HDACi-promoted Nrf2 activation. In line with its cytoprotective role, Nrf2 knockdown led to a sensitization against HDACi. Accordingly, the expression of antioxidant and detoxifying Nrf2 target genes was upregulated upon HDACi treatment. In conclusion, oxidative stress induction upon HDAC inhibition contributes to the antitumor effects of HDAC inhibitors, and activation of Nrf2 represents a potentially important adaptive response of gastric cancer cells in this context.

Abstract Background Parameters from maximal expiratory flow-volume curves (MEFVC) have been linked to CT-based parameters of COPD. However, the association between MEFVC shape and phenotypes like emphysema, small airways disease (SAD) and bronchial wall thickening (BWT) has not been investigated. Research question We analyzed if the shape of MEFVC can be linked to CT-determined emphysema, SAD and BWT in a large cohort of COPDGene participants. Study design and methods In the COPDGene cohort, we used principal component analysis (PCA) to extract patterns from MEFVC shape and performed multiple linear regression to assess the association of these patterns with CT parameters over the COPD spectrum, in mild and moderate-severe COPD. Results Over the entire spectrum, in mild and moderate-severe COPD, principal components of MEFVC were important predictors for the continuous CT parameters. Their contribution to the prediction of emphysema diminished when classical pulmonary function test parameters were added. For SAD, the components remained very strong predictors. The adjusted R2 was higher in moderate-severe COPD, while in mild COPD, the adjusted R2 for all CT outcomes was low; 0.28 for emphysema, 0.21 for SAD and 0.19 for BWT. Interpretation The shape of the maximal expiratory flow-volume curve as analyzed with PCA is not an appropriate screening tool for early disease phenotypes identified by CT scan. However, it contributes to assessing emphysema and SAD in moderate-severe COPD.

Today, nanotechnologies (NTs) are well established in both private households and commercial markets. NTs are fully accepted in several sectors, such as medicine and pharmacy, and in industries, such as chemistry, electricity, food production, military, and other commercial branches, due to their unique properties. With regard to the growing demands for environmental resources caused by the still-growing global population, the application of NTs is an extremely important new branch in the environmental sector, delivering several advantages. Our review provides a comprehensive overview of the current developments in environmental remediation, wastewater treatment, drinking water treatment, and agriculture. More specifically, in the section on environmental remediation, we review the application of NTs towards enhanced reductive dechlorination, removal of heavy metals and remediation of oil spills. In the section on wastewater treatment, we highlight developments in the adsorption of heavy metals and persistent substances, advanced photocatalytic degradation of common wastewater pollutants, and improvements in membrane filtration processes. In the section on drinking water treatment, we discuss applications for the enhanced disinfection of pathogens, removal of heavy metals, point-of-use treatments, and the removal of organic matter. In the final section, on agriculture, we provide an overview of precision farming and the current state of the art concerning nanofertilisers, nanopesticides, nanoherbicides, and nano(bio)sensors.

The continuous development of production processes is currently observed in the fourth industrial revolution, where the key place is the digital transformation of production is known as Industry 4.0. The main technologies in the context of Industry 4.0 consist Cyber-Physical Systems (CPS) and Internet of Things (IoT), which create the capabili-ties needed for smart factories. Implementation of CPS solutions result in new possibilities creation – mainly in areas such as remote diagnosis, remote services, remote control, condition monitoring, etc. In this paper, authors indicated the im-portance of Cyber-Physical Systems in the process of the Industry 4.0 and the Smart Manufacturing development. Firstly, the basic information about Cyber-Physical Production Systems were outlined. Then, the alternative definitions and different authors view of the problem were discussed. Secondly, the conceptual model of Cybernetic Physical Production System was presented. Moreover, the case study of proposed solution implementation in the real manufacturing process was presented. The key stage of the verification concerned the obtained data analysis and results discussion.

Modulation of commensal gut microbiota is increasingly recognized as a promising strategy to reduce mortality in patients with malignant diseases, but monitoring for dysbiosis is generally not routine clinical practice due to equipment, expertise and funding required for sequencing analysis. A low-threshold alternative is microbial diversity profiling by single-cell flow cytometry (FCM), which we compared to 16S rRNA sequencing in human fecal samples and employed to characterize longitudinal changes in the microbiome composition of patients with aggressive B-cell non-Hodgkin lymphoma undergoing chemoimmunotherapy. Diversity measures obtained from both methods were correlated and captured identical trends in microbial community structures, finding no difference in patients’ pretreatment alpha or beta diversity compared to healthy controls and a significant and progressive loss of alpha diversity during chemoimmunotherapy. Our results highlight the potential of FCM-based microbiome profiling as a reliable and accessible diagnostic tool that can provide novel insights into cancer therapy-associated dysbiosis dynamics.

The analysis of maritime traffic patterns for safety and security purposes is increasing in importance and, hence, Vessel Traffic Service operators need efficient and contextualized tools for the detection of abnormal maritime behavior. Current models lack interpretability and contextualization of their predictions and are generally not quantitatively evaluated on a large annotated dataset comprising all expected traffic in a Region of Interest. We propose a model for the detection of abnormal maritime behaviors that provides the closest behaviors as context to the predictions. The normalcy model relies on two-step clustering, which is first computed based on the positions of the vessels and then refined based on their kinematics. We design for each step a similarity measure, which combined are able to distinguish boats cruising shipping lanes in different directions, but also vessels with more freedom, such as pilot boats. Our proposed abnormality detection model achieved, on a large annotated dataset extracted from AIS logs that we publish, an ROC-AUC of 0.79, which is on a par with State-of-the-Art deep neural networks, while being more computationally efficient and more interpretable, thanks to the contextualization offered by our two-step clustering.

Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently to gain insight into, among others, DNNs’ generalization ability. However, it is by no means obvious how to estimate the mutual information (MI) between each hidden layer and the input/desired output to construct the IP. For instance, hidden layers with many neurons require MI estimators with robustness toward the high dimensionality associated with such layers. MI estimators should also be able to handle convolutional layers while at the same time being computationally tractable to scale to large networks. Existing IP methods have not been able to study truly deep convolutional neural networks (CNNs). We propose an IP analysis using the new matrix-based Rényi’s entropy coupled with tensor kernels, leveraging the power of kernel methods to represent properties of the probability distribution independently of the dimensionality of the data. Our results shed new light on previous studies concerning small-scale DNNs using a completely new approach. We provide a comprehensive IP analysis of large-scale CNNs, investigating the different training phases and providing new insights into the training dynamics of large-scale neural networks.

Abstract Fatigue is the most prevalent symptom of cancer and its treatments. Changes in the intestinal microbiome have been identified in chronic fatigue syndrome and other neuropsychiatric disorders, and cancer patients. However, the association between intestinal microbiome and fatigue in patients with advanced cancers has not been evaluated. Understanding the connection between intestinal microbiome and fatigue will provide interventional and therapeutic opportunities to manipulate the microbiome to improve fatigue and other patients’ reported outcomes. In this project, we aimed to identify associations between microbiome composition and fatigue in advanced cancer patients. In this cross-sectional observational study at a tertiary cancer care center, we included 88 patients with advanced, metastatic, unresectable cancers who were in a washout period from chemotherapy. We measured fatigue using the MD Anderson Symptom Inventory—Immunotherapy fatigue score, and used 16srRNA to analyze intestinal microbiome. Using correlation analysis we found that Eubacterium hallii was negatively associated with fatigue severity scores (r = − 0.30, p = 0.005), whereas Cosenzaea was positively associated with fatigue scores (r = 0.33, p = 0.0002). We identified microbial species that exhibit distinct composition between high-fatigued and low-fatigued cancer patients. Further studies are warranted to investigate whether modulating the microbiome reduces cancer patients’ fatigue severity and improves their quality of life.