Hyaluronic acid levels predict risk of hepatic encephalopathy and liver-related death in HIV/viral hepatitis coinfected patients

Background: Whereas it is well established that various soluble biomarkers can predict level of liver fibrosis, their ability to predict liver-related clinical outcomes is less clearly established, in particular among HIV/viral hepatitis co-infected persons. We investigated plasma hyaluronic acid’s (HA) ability to predict risk of liver-related events (LRE; hepatic coma or liver-related death) in the EuroSIDA study. Methods: Patients included were positive for anti-HCV and/or HBsAg with at least one available plasma sample. The earliest collected plasma sample was tested for HA (normal range 0–75 ng/mL) and levels were associated with risk of LRE. Change in HA per year of follow-up was estimated after measuring HA levels in latest sample before the LRE for those experiencing this outcome (cases) and in a random selection of one sixth of the remaining patients (controls). Results: During a median of 8.2 years of follow-up, 84/1252 (6.7%) patients developed a LRE. Baseline median (IQR) HA in those without and with a LRE was 31.8 (17.2–62.6) and 221.6 ng/mL (74.9–611.3), respectively (p<0.0001). After adjustment, HA levels predicted risk of contracting a LRE; incidence rate ratios for HA levels 75–250 or ≥250 vs. <75 ng/mL were 5.22 (95% CI 2.86–9.26, p<0.0007) and 28.22 (95% CI 14.95–46.00, p<0.0001), respectively. Median HA levels increased substantially prior to developing a LRE (107.6 ng/mL, IQR 0.8 to 251.1), but remained stable for controls (1.0 ng/mL, IQR –5.1 to 8.2), (p<0.0001 comparing cases and controls), and greater increases predicted risk of a LRE in adjusted models (p<0.001). Conclusions: An elevated level of plasma HA, particularly if the level further increases over time, substantially increases the risk of contracting LRE over the next five years. HA is an inexpensive, standardized and non-invasive supplement to other methods aimed at identifying HIV/viral hepatitis co-infected patients at risk of hepatic complications

The L2L System for Second Language Learning Using Visualised Zoom Calls Among Students

An important part of second language learning is conversation which is best practised with speakers whose native language is the language being learned. We facilitate this by pairing students from different countries learning each others' native language. Mixed groups of students have Zoom calls, half in one language and half in the other, in order to practice and improve their conversation skills. We use Zoom video recordings with audio transcripts enabled which generates recognised speech from which we extract timestamped utterances and calculate and visualise conversation metrics on a dashboard. A timeline highlights each utterance, colour coded per student, with links to the video in a playback window. L2L was deployed for a semester and recorded almost 250 hours of zoom meetings. The conversation metrics visualised on the dashboard are a beneficial asset for both students and lecturers.Comment: 16th European Conference on Technology-Enhanced Learning (EC-TEL), Bozen-Bolzano, Italy (online), September 202

Antibacterial properties of sophorolipid-modified gold surfaces against Gram positive and Gram negative pathogens.

International audienceSophorolipids are bioderived glycolipids displaying interesting antimicrobial properties. We show that they can be used to develop biocidal monolayers against Listeria ivanovii, a Gram-positive bacterium. The present work points out the dependence between the surface density and the antibacterial activity of grafted sophorolipids. It also emphasizes the broad spectrum of activity of these coatings, demonstrating their potential against both Gram-positive strains (Enteroccocus faecalis, Staphylococcus epidermidis, Streptococcus pyogenes) and Gram-negative strains (Escherichia coli, Pseudomonas aeruginosa and Salmonella typhymurium). After exposure to sophorolipids grafted onto gold, all these bacterial strains show a significant reduction in viability resulting from membrane damage as evidenced by fluorescent labelling and SEM-FEG analysis

Facilitating reflection in teletandem through automatically generated conversation metrics and playback video

This pilot study focuses on a tool called L2L that allows second language (L2) learners to visualise and analyse their Zoom interactions with native speakers. L2L uses the Zoom transcript to automatically generate conversation metrics and its playback feature with timestamps allows students to replay any chosen portion of the conversation for post- session reflection and self-review. This exploratory study investigates a seven-week teletandem project, where undergraduate students from an Irish university learning French (B2) interacted with their peers from a French university learning English (B2+) via Zoom. The data collected from a survey (N=43) and semi-structured interviews (N=35) show that the quantitative conversation metrics and qualitative review of the synchronous content helped raise students’ confidence levels while engaging with native speakers. Furthermore, it allowed them to set tangible goals to improve their participation, and be more aware of what, why, and how they are learning

Hyaluronic acid levels predict risk of hepatic encephalopathy and liver-related death in HIV/viral hepatitis coinfected patients

Background: Whereas it is well established that various soluble biomarkers can predict level of liver fibrosis, their ability to predict liver-related clinical outcomes is less clearly established, in particular among HIV/viral hepatitis co-infected persons. We investigated plasma hyaluronic acid's (HA) ability to predict risk of liver-related events (LRE; hepatic coma or liver-related death) in the EuroSIDA study. Methods: Patients included were positive for anti-HCV and/or HBsAg with at least one available plasma sample. The earliest collected plasma sample was tested for HA (normal range 0-75 ng/mL) and levels were associated with risk of LRE. Change in HA per year of follow-up was estimated after measuring HA levels in latest sample before the LRE for those experiencing this outcome (cases) and in a random selection of one sixth of the remaining patients (controls). Results: During a median of 8.2 years of follow-up, 84/1252 (6.7%) patients developed a LRE. Baseline median (IQR) HA in those without and with a LRE was 31.8 (17.2-62.6) and 221.6 ng/mL (74.9-611.3), respectively (p,0.0001). After adjustment, HA levels predicted risk of contracting a LRE; incidence rate ratios for HA levels 75-250 or $250 vs. ,75 ng/mL were 5.22 (95% CI 2.86-9.26, p,0.0007) and 28.22 (95% CI 14.95-46.00, p,0.0001), respectively. Median HA levels increased substantially prior to developing a LRE (107.6 ng/mL, IQR 0.8 to 251.1), but remained stable for controls (1.0 ng/mL, IQR -5.1 to 8.2), (p,0.0001 comparing cases and controls), and greater increases predicted risk of a LRE in adjusted models (p,0.001). Conclusions: An elevated level of plasma HA, particularly if the level further increases over time, substantially increases the risk of contracting LRE over the next five years. HA is an inexpensive, standardized and non-invasive supplement to other methods aimed at identifying HIV/viral hepatitis co-infected patients at risk of hepatic complications

Hyaluronic Acid Levels Predict Risk of Hepatic Encephalopathy and Liver-Related Death in HIV/Viral Hepatitis Coinfected Patients

Whereas it is well established that various soluble biomarkers can predict level of liver fibrosis, their ability to predict liver-related clinical outcomes is less clearly established, in particular among HIV/viral hepatitis co-infected persons. We investigated plasma hyaluronic acid's (HA) ability to predict risk of liver-related events (LRE; hepatic coma or liver-related death) in the EuroSIDA study

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches