Work Hard, Play Hard: Collecting Acceptability Annotations through a 3D Game

Corpus-based studies on acceptability judgements have always stimulated the interest of researchers, both in theoretical and computational fields. Some approaches focused on spontaneous judgements collected through different types of tasks, others on data annotated through crowd-sourcing platforms, still others relied on expert annotated data available from the literature. The release of CoLA corpus, a large-scale corpus of sentences extracted from linguistic handbooks as examples of acceptable/non acceptable phenomena in English, has revived interest in the reliability of judgements of linguistic experts vs. non-experts. Several issues are still open. In this work, we contribute to this debate by presenting a 3D video game that was used to collect acceptability judgments on Italian sentences. We analyse the resulting annotations in terms of agreement among players and by comparing them with experts{'} acceptability judgments. We also discuss different game settings to assess their impact on participants{'} motivation and engagement. The final dataset containing 1,062 sentences, which were selected based on majority voting, is released for future research and comparisons

IPO-V2: A prospective, multicenter, randomized, comparative clinical investigation of the effects of sulodexide in preventing cardiovascular accidents in the first year after acute myocardial infarction

AbstractObjectives. This study was conducted to assess the efficacy of sulodexide, a glycosaminoglycan compound with antithrombotic properties, in preventing death and thromboembotic events after acute myocardial infarction.Background. Antithrombotic therapy has been found to play an important role in the prevention of cardiovascular events and death after acute myocardial infarction. Glycosaminoglycan-containing compounds, including sulodexide, show profibrinolytic and antithrombotic properties that render them suitable for use in patients after infarction.Methods. A total of 3,986 patients who had recovered from acute myocardial infarction were randomized to receive either the standard therapy routinely administered at each study center, excluding antiplatelet and anticoagulant drugs (control group, 1,970 patients), or the standard therapy plus sulodexide (treated group, 2,016 patients). Between 7 and 10 days after the episode of acute myocardial infarction, sulodexide was administered as a single daily 600-lipoprotein-lipase-releasing unit (LRU) intramuscular injection for the 1st month, followed by oral capsules of 500 LRU twice daily. Patients were evaluated for ≥12 months.Results. At the end of the study, 140 (7.1%) were recorded in the control group and 97 (4.8%) in the sulodexide group (32% risk reduction, p = 0.0022, chi-square test). A total of 90 patients (4.6%) in the control group had a further infarction, compared with 66 (33%) in the sulodexide group (28% risk reduction, p = 0.035). Furthermore, a reduction in left ventricular thrombus formation (evaluated by echocardiography) was observed in the sulodeside group (n = 12; 0.6%), compared with values in the control group (n = 25; 1.3%) (53% risk reduction, p = 0.027). Sulodexide was well tolerated and devoid of significant adverse events. All significant results were confirmed by “actual treatment” analyses.Conclusions. The study provides evidence that long-term therapy with sulodexide started early after an episode of acute myocardial infarction is associated with reductions in total mortality, rate of reinfarction and mural thrombus formation

Paclitaxel-Loaded Nanosponges Inhibit Growth and Angiogenesis in Melanoma Cell Models

This study investigated the effects of free paclitaxel (PTX) and PTX-loaded in pyromellitic nanosponges (PTX-PNS) in reducing in vitro and in vivo melanoma cell growth and invasivity, and in inhibiting angiogenesis. To test the response of cells to the two PTX formulations, the cell viability was evaluated by MTT assay in seven continuous cell lines, in primary melanoma cells, both in 2D and 3D cultures, and in human umbilical vein endothelial cells (HUVECs) after exposure to different concentrations of PTX or PTX-PNS. Cell motility was assessed by a scratch assay or Boyden chamber assay, evaluating cell migration in presence or absence of diverse concentrations of PTX or PTX-PNS. The effect of PTX and PTX-PNS on angiogenesis was evaluated as endothelial tube formation assay, a test able to estimate the formation of three-dimensional vessels in vitro. To assess the anticancer effect of PTX and PTX-PNS in in vivo experiments, the two drug formulations were tested in a melanoma mouse model obtained by B16-BL6 cell implantation in C57/BL6 mice. Results obtained were as follows: 1) MTT analysis revealed that cell proliferation was more affected by PTX-PNS than by PTX in all tested cell lines, in both 2D and 3D cultures; 2) the analysis of the cell migration showed that PTX-PNS acted at very lower concentrations than PTX; 3) tube formation assay showed that PTX-PNS were more effective in inhibiting tube formation than free PTX; and 4) in vivo experiments demonstrated that tumor weights, volumes, and growth were significantly reduced by PTX-PNS treatment with respect to PTX; the angiogenesis and the cell proliferation, detected in the tumor samples with CD31 and Ki-67 antibodies, respectively, indicated that, in the PTX-PNS-treated tumors, the tube formation was inhibited, and a low amount of proliferating cells was present. Taken together, our data demonstrated that our new PTX nanoformulation can respond to some important issues related to PTX treatment, lowering the anti-tumor effective doses and increasing the effectiveness in inhibiting melanoma growth in vivo

FEVER OF UNKNOWN ORIGIN AND MULTIDRUG-RESISTANT ORGANISMS COLONIZATION IN AML PATIENTS.

Abstract. Background: Colonization by multidrug-resistant organisms (MDRO) is a frequent complication in hematologic departments, which puts patients at risk of life-threatening bacterial sepsis. Fever of unknown origin (FUO) is a condition related to the delivery of chemotherapy in hematologic malignancies, in which the use of antibiotics is debated. The incidence, risk factors, and influence on the outcome of these conditions in patients with acute myeloid leukemia (AML) are not clearly defined. Methods: We retrospectively analyzed 132 consecutive admissions of non-promyelocytic AML patients at the Hematology Unit of the University Tor Vergata in Rome between June 2019 and February 2022. MDRO swab-based screening was performed in all patients on the day of admission and once weekly after that. FUO was defined as fever with no evidence of infection. Results: Of 132 consecutive hospitalizations (69 AML patients), MDRO colonization was observed in 35 cases (26%) and resulted independently related to a previous MDRO colonization (p=0.001) and length of hospitalization (p=0.03). The colonization persistence rate in subsequent admissions was 64%. MDRO-related bloodstream infection was observed in 8 patients (23%) and correlated with grade III/IV mucositis (p=0.008) and length of hospitalization (p=0.02). FUO occurred in 68 cases (51%) and correlated with an absolute neutrophilic count <500μ/L at admission (0.04). Conclusion: In our experience, MDRO colonization is a frequent and difficult-to-eradicate condition that can arise at all stages of treatment. Prompt discharge of patients as soon as clinical conditions allow could limit the spread of MDRO. In addition, the appropriate use of antibiotics, especially in the case of FUO, and the contraction of hospitalization length, when feasible, are measures to tackle the further spread of MDRO

Thinning Can Reduce Losses in Carbon Use Efficiency and Carbon Stocks in Managed Forests Under Warmer Climate

Forest carbon use efficiency (CUE, the ratio of net to gross primary productivity) represents the fraction of photosynthesis that is not used for plant respiration. Although important, it is often neglected in climate change impact analyses. Here we assess the potential impact of thinning on projected carbon cycle dynamics and implications for forest CUE and its components (i.e., gross and net primary productivity and plant respiration), as well as on forest biomass production. Using a detailed process-based forest ecosystem model forced by climate outputs of five Earth System Models under four representative climate scenarios, we investigate the sensitivity of the projected future changes in the autotrophic carbon budget of three representative European forests. We focus on changes in CUE and carbon stocks as a result of warming, rising atmospheric CO2 concentration, and forest thinning. Results show that autotrophic carbon sequestration decreases with forest development, and the decrease is faster with warming and in unthinned forests. This suggests that the combined impacts of climate change and changing CO2 concentrations lead the forests to grow faster, mature earlier, and also die younger. In addition, we show that under future climate conditions, forest thinning could mitigate the decrease in CUE, increase carbon allocation into more recalcitrant woody pools, and reduce physiological-climate-induced mortality risks. Altogether, our results show that thinning can improve the efficacy of forest-based mitigation strategies and should be carefully considered within a portfolio of mitigation options

A Structured and Unstructured grid Relocatable ocean platform for Forecasting (SURF)

AbstractWe present a numerical platform named Structured and Unstructured grid Relocatable ocean platform for Forecasting (SURF). The platform is developed for short-time forecasts and is designed to be embedded in any region of the large-scale Mediterranean Forecasting System (MFS) via downscaling. We employ CTD data collected during a campaign around the Elba island to calibrate and validate SURF. The model requires an initial spin up period of a few days in order to adapt the initial interpolated fields and the subsequent solutions to the higher-resolution nested grids adopted by SURF. Through a comparison with the CTD data, we quantify the improvement obtained by SURF model compared to the coarse-resolution MFS model

Effects of Heavy Metals and Arbuscular Mycorrhiza on the Leaf Proteome of a Selected Poplar Clone: A Time Course Analysis

Arbuscular mycorrhizal (AM) fungi establish a mutualistic symbiosis with the roots of most plant species. While receiving photosynthates, they improve the mineral nutrition of the plant and can also increase its tolerance towards some pollutants, like heavy metals. Although the fungal symbionts exclusively colonize the plant roots, some plant responses can be systemic. Therefore, in this work a clone of Populus alba L., previously selected for its tolerance to copper and zinc, was used to investigate the effects of the symbiosis with the AM fungus Glomus intraradices on the leaf protein expression. Poplar leaf samples were collected from plants maintained in a glasshouse on polluted (copper and zinc contaminated) or unpolluted soil, after four, six and sixteen months of growth. For each harvest, about 450 proteins were reproducibly separated on 2DE maps. At the first harvest the most relevant effect on protein modulation was exerted by the AM fungi, at the second one by the metals, and at the last one by both treatments. This work demonstrates how importantly the time of sampling affects the proteome responses in perennial plants. In addition, it underlines the ability of a proteomic approach, targeted on protein identification, to depict changes in a specific pattern of protein expression, while being still far from elucidating the biological function of each protein

An explainable model of host genetic interactions linked to COVID-19 severity

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as "Respiratory or thoracic disease", supporting their link with COVID-19 severity outcome.A multifaceted computational strategy identifies 16 genetic variants contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing dataset of a cohort of Italian patients

The Role of Attitudes Toward Medication and Treatment Adherence in the Clinical Response to LAIs: Findings From the STAR Network Depot Study

Background: Long-acting injectable (LAI) antipsychotics are efficacious in managing psychotic symptoms in people affected by severe mental disorders, such as schizophrenia and bipolar disorder. The present study aimed to investigate whether attitude toward treatment and treatment adherence represent predictors of symptoms changes over time. Methods: The STAR Network \u201cDepot Study\u201d was a naturalistic, multicenter, observational, prospective study that enrolled people initiating a LAI without restrictions on diagnosis, clinical severity or setting. Participants from 32 Italian centers were assessed at three time points: baseline, 6-month, and 12-month follow-up. Psychopathological symptoms, attitude toward medication and treatment adherence were measured using the Brief Psychiatric Rating Scale (BPRS), the Drug Attitude Inventory (DAI-10) and the Kemp's 7-point scale, respectively. Linear mixed-effects models were used to evaluate whether attitude toward medication and treatment adherence independently predicted symptoms changes over time. Analyses were conducted on the overall sample and then stratified according to the baseline severity (BPRS &lt; 41 or BPRS 65 41). Results: We included 461 participants of which 276 were males. The majority of participants had received a primary diagnosis of a schizophrenia spectrum disorder (71.80%) and initiated a treatment with a second-generation LAI (69.63%). BPRS, DAI-10, and Kemp's scale scores improved over time. Six linear regressions\u2014conducted considering the outcome and predictors at baseline, 6-month, and 12-month follow-up independently\u2014showed that both DAI-10 and Kemp's scale negatively associated with BPRS scores at the three considered time points. Linear mixed-effects models conducted on the overall sample did not show any significant association between attitude toward medication or treatment adherence and changes in psychiatric symptoms over time. However, after stratification according to baseline severity, we found that both DAI-10 and Kemp's scale negatively predicted changes in BPRS scores at 12-month follow-up regardless of baseline severity. The association at 6-month follow-up was confirmed only in the group with moderate or severe symptoms at baseline. Conclusion: Our findings corroborate the importance of improving the quality of relationship between clinicians and patients. Shared decision making and thorough discussions about benefits and side effects may improve the outcome in patients with severe mental disorders

Prescription appropriateness of anti-diabetes drugs in elderly patients hospitalized in a clinical setting: evidence from the REPOSI Register

Diabetes is an increasing global health burden with the highest prevalence (24.0%) observed in elderly people. Older diabetic adults have a greater risk of hospitalization and several geriatric syndromes than older nondiabetic adults. For these conditions, special care is required in prescribing therapies including anti- diabetes drugs. Aim of this study was to evaluate the appropriateness and the adherence to safety recommendations in the prescriptions of glucose-lowering drugs in hospitalized elderly patients with diabetes. Data for this cross-sectional study were obtained from the REgistro POliterapie-Società Italiana Medicina Interna (REPOSI) that collected clinical information on patients aged ≥ 65 years acutely admitted to Italian internal medicine and geriatric non-intensive care units (ICU) from 2010 up to 2019. Prescription appropriateness was assessed according to the 2019 AGS Beers Criteria and anti-diabetes drug data sheets.Among 5349 patients, 1624 (30.3%) had diagnosis of type 2 diabetes. At admission, 37.7% of diabetic patients received treatment with metformin, 37.3% insulin therapy, 16.4% sulfonylureas, and 11.4% glinides. Surprisingly, only 3.1% of diabetic patients were treated with new classes of anti- diabetes drugs. According to prescription criteria, at admission 15.4% of patients treated with metformin and 2.6% with sulfonylureas received inappropriately these treatments. At discharge, the inappropriateness of metformin therapy decreased (10.2%, P &lt; 0.0001). According to Beers criteria, the inappropriate prescriptions of sulfonylureas raised to 29% both at admission and at discharge. This study shows a poor adherence to current guidelines on diabetes management in hospitalized elderly people with a high prevalence of inappropriate use of sulfonylureas according to the Beers criteria