The role of epithelial-mesenchymal transition in pulmonary fibrosis: lessons from idiopathic pulmonary fibrosis and COVID-19.

Despite the tremendous advancements in the knowledge of the pathophysiology and clinical aspects of SARS-CoV-2 infection, still many issues remain unanswered, especially in the long-term effects. Mounting evidence suggests that pulmonary fibrosis (PF) is one of the most severe complications associated with COVID-19. Therefore, understanding the molecular mechanisms behind its development is helpful to develop successful therapeutic strategies.

The Neurofunctional Correlates of Morphosyntactic and Thematic Impairments in Aphasia: A Systematic Review and Meta-analysis.

Lesion-symptom studies in persons with aphasia showed that left temporoparietal damage, but surprisingly not prefrontal damage, correlates with impaired ability to process thematic roles in the comprehension of semantically reversible sentences (The child is hugged by the mother). This result has led to challenge the time-honored view that left prefrontal regions are critical for sentence comprehension. However, most studies focused on thematic role assignment and failed to consider morphosyntactic processes that are also critical for sentence processing.

Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality.

Myoelectric prostheses have recently shown significant promise for restoring hand function in individuals with upper limb loss or deficiencies, driven by advances in machine learning and increasingly accessible bioelectrical signal acquisition devices. Here, we first introduce and validate a novel experimental paradigm using a virtual reality headset equipped with hand-tracking capabilities to facilitate the recordings of synchronized EMG signals and hand pose estimation. Using both the phasic and tonic EMG components of data acquired through the proposed paradigm, we compare hand gesture classification pipelines based on standard signal processing features, convolutional neural networks, and covariance matrices with Riemannian geometry computed from raw or xDAWN-filtered EMG signals.

Mechanisms of mesothelial cell response to viral infections: HDAC1-3 inhibition blocks poly(I:C)-induced type I interferon response and modulates the mesenchymal/inflammatory phenotype.

Infectious peritonitis is a leading cause of peritoneal functional impairment and a primary factor for therapy discontinuation in peritoneal dialysis (PD) patients. Although bacterial infections are a common cause of peritonitis episodes, emerging evidence suggests a role for viral pathogens. Toll-like receptors (TLRs) specifically recognize conserved pathogen-associated molecular patterns (PAMPs) from bacteria, viruses, and fungi, thereby orchestrating the ensuing inflammatory/immune responses.

HDAC1/2 control mesothelium/ovarian cancer adhesive interactions impacting on Talin-1-α5β1-integrin-mediated actin cytoskeleton and extracellular matrix protein remodeling.

Background: Peritoneal metastasis, which accounts for 85% of all epithelial ovarian carcinoma (EOC) metastases, is a multistep process that requires the establishment of adhesive interactions between cancer cells and the peritoneal membrane. Interrelations between EOC and the mesothelial stroma are critical to facilitate the metastatic process. No data is available so far on the impact of histone acetylation/deacetylation, a potentially relevant mechanism governing EOC metastasis, on mesothelial cells (MCs)-mediated adhesion.

HDAC1-3 inhibition increases SARS-CoV-2 replication and productive infection in lung mesothelial and epithelial cells.

Background: Despite the significant progress achieved in understanding the pathology and clinical management of SARS-CoV-2 infection, still pathogenic and clinical issues need to be clarified. Treatment with modulators of epigenetic targets, i.e.

BET Protein Inhibitor JQ1 Ameliorates Experimental Peritoneal Damage by Inhibition of Inflammation and Oxidative Stress.

Peritoneal dialysis (PD) is a current replacement therapy for end-stage kidney diseases (ESKDs). However, long-term exposure to PD fluids may lead to damage of the peritoneal membrane (PM) through mechanisms involving the activation of the inflammatory response and mesothelial-to-mesenchymal transition (MMT), leading to filtration failure. Peritoneal damage depends on a complex interaction among external stimuli, intrinsic properties of the PM, and subsequent activities of the local innate-adaptive immune system.

Novel pyridine-containing histone deacetylase inhibitors strongly arrest proliferation, induce apoptosis and modulate miRNAs in cancer cells.

After over 30 years of research, the development of HDAC inhibitors led to five FDA/Chinese FDA-approved drugs and many others under clinical or preclinical investigation to treat cancer and non-cancer diseases. Herein, based on our recent development of pyridine-based isomers as HDAC inhibitors, we report a series of novel 5-acylamino-2-pyridylacrylic- and -picolinic hydroxamates and 2'-aminoanilides 5-8 as anticancer agents. The hydroxamate 5d proved to be quite HDAC3/6-selective exhibiting IC values of 80 and 11 nM, respectively, whereas the congener 5e behaved as inhibitor of HDAC1-3, -6, -8, and -10 (class I/IIb-selective inhibitor) at nanomolar level.

Improving Infinium MethylationEPIC data processing: re-annotation of enhancers and long noncoding RNA genes and benchmarking of normalization methods.

Illumina Infinium DNA Methylation (5mC) arrays are a popular technology for low-cost, high-throughput, genome-scale measurement of 5mC distribution, especially in cancer and other complex diseases. After the success of its HumanMethylation450 array (450k), Illumina released the MethylationEPIC array (850k) featuring increased coverage of enhancers. Despite the widespread use of 850k, analysis of the corresponding data remains suboptimal: it still relies mostly on Illumina's default annotation, which underestimates enhancerss and long noncoding RNAs.

The role of diversity and ensemble learning in credit card fraud detection.

The number of daily credit card transactions is inexorably growing: the e-commerce market expansion and the recent constraints for the Covid-19 pandemic have significantly increased the use of electronic payments. The ability to precisely detect fraudulent transactions is increasingly important, and machine learning models are now a key component of the detection process. Standard machine learning techniques are widely employed, but inadequate for the evolving nature of customers behavior entailing continuous changes in the underlying data distribution.

Exposure to b-LED Light While Exerting Antimicrobial Activity on Gram-Negative and -Positive Bacteria Promotes Transient EMT-like Changes and Growth Arrest in Keratinocytes.

While blue LED (b-LED) light is increasingly being studied for its cytotoxic activity towards bacteria in therapy of skin-related infections, its effects on eukaryotic cells plasticity are less well characterized. Moreover, since different protocols are often used, comparing the effect of b-LED towards both microorganisms and epithelial surfaces may be difficult. The aim of this study was to analyze, in the same experimental setting, both the bactericidal activity and the effects on human keratinocytes.

Factor-Based Framework for Multivariate and Multi-step-ahead Forecasting of Large Scale Time Series.

State-of-the-art multivariate forecasting methods are restricted to low dimensional tasks, linear dependencies and short horizons. The technological advances (notably the Big data revolution) are instead shifting the focus to problems characterized by a large number of variables, non-linear dependencies and long forecasting horizons. In the last few years, the majority of the best performing techniques for multivariate forecasting have been based on deep-learning models.

The CLAIRE COVID-19 initiative: approach, experiences and recommendations.

A volunteer effort by Artificial Intelligence (AI) researchers has shown it can deliver significant research outcomes rapidly to help tackle COVID-19. Within two months, CLAIRE's self-organising volunteers delivered the World's first comprehensive curated repository of COVID-19-related datasets useful for drug-repurposing, drafted review papers on the role CT/X-ray scan analysis and robotics could play, and progressed research in other areas. Given the pace required and nature of voluntary efforts, the teams faced a number of challenges.

Hyperscanning EEG and Classification Based on Riemannian Geometry for Festive and Violent Mental State Discrimination.

Interactions between two brains constitute the essence of social communication. Daily movements are commonly executed during social interactions and are determined by different mental states that may express different positive or negative behavioral intent. In this context, the effective recognition of festive or violent intent before the action execution remains crucial for survival.

The Use of Antimicrobials in Italian Heavy Pig Fattening Farms.

Data on antimicrobial use (AMU) in heavy pig production (>150 kg) are limited. The aim of this study was to investigate the AMU in this production. Data from 2015 were collected for 143 fattening farms.

New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx.

The advent of Next-Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data. The Genomic Data Commons (GDC) Data Portal is a platform that contains different genomic studies including the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients.

Comprehensive identification of long noncoding RNAs in colorectal cancer.

Colorectal cancer (CRC) is one of the most common cancers in humans and a leading cause of cancer-related deaths worldwide. As in the case of other cancers, CRC heterogeneity leads to a wide range of clinical outcomes and complicates therapy. Over the years, multiple factors have emerged as markers of CRC heterogeneity, improving tumor classification and selection of therapeutic strategies.

In-Silico Integration Approach to Identify a Key miRNA Regulating a Gene Network in Aggressive Prostate Cancer.

Like other cancer diseases, prostate cancer (PC) is caused by the accumulation of genetic alterations in the cells that drives malignant growth. These alterations are revealed by gene profiling and copy number alteration (CNA) analysis. Moreover, recent evidence suggests that also microRNAs have an important role in PC development.

Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis.

Background: Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies.

Combination of Gene Expression Signature and Model for End-Stage Liver Disease Score Predicts Survival of Patients With Severe Alcoholic Hepatitis.

Background & Aims: Patients with severe alcoholic hepatitis (AH) have a high risk of death within 90 days. Corticosteroids, which can cause severe adverse events, are the only treatment that increases short-term survival. It is a challenge to predict outcomes of patients with severe AH.

Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy.

Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers' habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds), and verification latency (only a small set of transactions are timely checked by investigators). However, the vast majority of learning algorithms that have been proposed for fraud detection rely on assumptions that hardly hold in a real-world fraud-detection system (FDS).