Effects of a feed supplement, containing undenatured type II collagen (UC II®) and Boswellia Serrata, in the management of mild/moderate mobility disorders in dogs: A randomized, double-blind, placebo controlled, cross-over study.

This study was designed as a randomized, placebo-controlled, double-blinded, cross-over trial performed to investigate the effects of a dietary supplement containing undenatured type II collagen (UCII®) and Boswellia Serrata on mobility, pain and joint metabolism in mild moderate osteoarthritis (OA) in dogs. A total of 60 dogs with mobility problems were evaluated and enrolled in the study. Seventeen of these dogs with mild/moderate OA were randomized to receive the product A (UCII® + Boswellia Serrata supplement-UCII®-BW) or product B (Placebo -PL), 1 chew per day for 8 weeks by oral route, and repeated in a crossover design after 4 weeks of washout period.

Transition to Subcutaneous Infliximab vs Vedolizumab in Inflammatory Bowel Disease: A Prospective Multicenter Study.

Background And Aims: Transition from intravenous (IV) to subcutaneous (SC) administration is an option in inflammatory bowel disease (IBD) with Infliximab (IFX) or Vedolizumab (VDZ). The aim was to compare the adherence, the persistence in therapy, and quality of life (QoL) at baseline, at 6, at 12 months of SC IFX versus SC VDZ.

Methods: This was a prospective, observational, multicenter study on patients with IBD in treatment with IV IFX or VDZ who switched to SC.

The flashbulb-like nature of memory for the first COVID-19 case and the impact of the emergency. A cross-national survey.

Flashbulb memories (FBMs) refer to vivid and long-lasting autobiographical memories for the circumstances in which people learned of a shocking and consequential public event. A cross-national study across eleven countries aimed to investigate FBM formation following the first COVID-19 case news in each country and test the effect of pandemic-related variables on FBM. Participants had detailed memories of the date and others present when they heard the news, and had partially detailed memories of the place, activity, and news source.

Beyond multilayer perceptrons: Investigating complex topologies in neural networks.

This study delves into the crucial aspect of network topology in artificial neural networks (NNs) and its impact on model performance. Addressing the need to comprehend how network structures influence learning capabilities, the research contrasts traditional multilayer perceptrons (MLPs) with models built on various complex topologies using novel network generation techniques. Drawing insights from synthetic datasets, the study reveals the remarkable accuracy of complex NNs, particularly in high-difficulty scenarios, outperforming MLPs.

Causal influence of brainstem response to transcutaneous vagus nerve stimulation on cardiovagal outflow.

Background: The autonomic response to transcutaneous auricular vagus nerve stimulation (taVNS) has been linked to the engagement of brainstem circuitry modulating autonomic outflow. However, the physiological mechanisms supporting such efferent vagal responses are not well understood, particularly in humans.

Hypothesis: We present a paradigm for estimating directional brain-heart interactions in response to taVNS.

Heritability of human "directed" functional connectome.

Introduction: The functional connectivity patterns in the brain are highly heritable; however, it is unclear how genetic factors influence the directionality of such "information flows." Studying the "directionality" of the brain functional connectivity and assessing how heritability modulates it can improve our understanding of the human connectome.

Methods: Here, we investigated the heritability of "directed" functional connections using a state-space formulation of Granger causality (GC), in conjunction with blind deconvolution methods accounting for local variability in the hemodynamic response function.

Perceived climate change risk and global green activism among young people.

In recent years, the increasing number of natural disasters has raised concerns about the sustainability of our planet's future. As young people comprise the generation that will suffer from the negative effects of climate change, they have become involved in a new climate activism that is also gaining interest in the public debate thanks to the Fridays for Future (FFF) movement. This paper analyses the results of a survey of 1,138 young people in a southern Italian region to explore their perceptions of the extent of environmental problems and their participation in protests of green movements such as the FFF.

Can machine learning of post-procedural cone-beam CT images in acute ischemic stroke improve the detection of 24-h hemorrhagic transformation? A preliminary study.

Purpose: Hemorrhagic transformation (HT) is an independent predictor of unfavorable outcome in acute ischemic stroke (AIS) patients undergoing endovascular thrombectomy (EVT). Its early identification could help tailor AIS management. We hypothesize that machine learning (ML) applied to cone-beam computed tomography (CB-CT), immediately after EVT, improves performance in 24-h HT prediction.

Spatiotemporal learning of dynamic positron emission tomography data improves diagnostic accuracy in breast cancer.

Positron emission tomography (PET) can reveal metabolic activity in a voxelwise manner. PET analysis is commonly performed in a static manner by analyzing the standardized uptake value (SUV) obtained from the plateau region of PET acquisitions. A dynamic PET acquisition can provide a map of the spatiotemporal concentration of the tracer in vivo, hence conveying information about radiotracer delivery to tissue, its interaction with the target and washout.

Physically constrained neural networks for inferring physiological system models.

Systems biology and systems neurophysiology in particular have recently emerged as powerful tools for a number of key applications in the biomedical sciences. Nevertheless, such models are often based on complex combinations of multiscale (and possibly multiphysics) strategies that require ad hoc computational strategies and pose extremely high computational demands. Recent developments in the field of deep neural networks have demonstrated the possibility of formulating nonlinear, universal approximators to estimate solutions to highly nonlinear and complex problems with significant speed and accuracy advantages in comparison with traditional models.

A text data mining approach to the study of emotions triggered by new advertising formats during the COVID-19 pandemic.

Under the influence of the health emergency triggered by the COVID-19 pandemic, many brands changed their communication strategy and included more or less explicit references to the principles of solidarity and fraternity in their TV commercials to boost the confidence and hope of Italian families during the lockdown. The traditional attitudes of the advertising format, which focused on product characteristics, were relegated to the background in order to reinforce the "brand image" through words, signs, hashtags and music that spread empathetic messages to all those who needed to regain hope and trust in a time of extreme emotional fragility. The objective of this paper is to identify the emotions and brand awareness during the lockdown using text mining techniques by measuring customer sentiment expressed on the Twitter social network.

Effectiveness and safety of dupilumab in patients with chronic rhinosinusitis with nasal polyps and associated comorbidities: a multicentric prospective study in real life.

Background: Biologics are currently one of the main treatment options for a number of diseases. The IgG4 monoclonal antibody dupilumab targets the Interleukin-4 receptor alpha chain, thus preventing the biological effects of the cytokines IL-4 and IL-13, that are essential for the Th2 response. Several controlled trials showed that dupilumab is effective and safe in patients with atopic dermatitis (AD), severe asthma and chronic rhinosinusitis with nasal polyps (CRSwNP), thus resulting in approval by regulatory agencies.

In Vivo and In Vitro Assessment and Proteomic Analysis of the Effectiveness of Physical Treatments in Reducing Allergenicity of Hazelnut Proteins.

Hazelnut is a widespread nut species, especially present in Europe, that can be consumed raw or roasted thanks to its pleasant taste and nutritional properties. In addition to renowned beneficial properties hazelnuts contain several proteins capable of inducing food allergy in sensitized individuals, including Cor a 2 (a profilin), Cor a 8 (a lipid transfer protein), Cor a 9 (an 11S seed storage globulin, legumin-like), and Cor a 11 (a 7S seed storage globulin, vicilin-like). In the present paper we investigated the effectiveness of autoclave-based treatments in decreasing the allergic potential of hazelnut as assessed by submitting the treated material to an in vivo skin prick test and an in vitro immunoblot analysis, with sera of allergic individuals exposed to the treated food material.

Patient-clinician brain concordance underlies causal dynamics in nonverbal communication and negative affective expressivity.

Patient-clinician concordance in behavior and brain activity has been proposed as a potential key mediator of mutual empathy and clinical rapport in the therapeutic encounter. However, the specific elements of patient-clinician communication that may support brain-to-brain concordance and therapeutic alliance are unknown. Here, we investigated how pain-related, directional facial communication between patients and clinicians is associated with brain-to-brain concordance.

Classification of real-world pathological phonocardiograms through multi-instance learning.

Heart auscultation is an inexpensive and fundamental technique to effectively to diagnose cardiovascular disease. However, due to relatively high human error rates even when auscultation is performed by an experienced physician, and due to the not universal availability of qualified personnel e.g.

A novel multi-branch architecture for state of the art robust detection of pathological phonocardiograms.

Heart auscultation is an inexpensive and fundamental technique to effectively diagnose cardiovascular disease. However, due to relatively high human error rates even when auscultation is performed by an experienced physician, and due to the not universal availability of qualified personnel, e.g.

Echo state network models for nonlinear Granger causality.

While Granger causality (GC) has been often employed in network neuroscience, most GC applications are based on linear multivariate autoregressive (MVAR) models. However, real-life systems like biological networks exhibit notable nonlinear behaviour, hence undermining the validity of MVAR-based GC (MVAR-GC). Most nonlinear GC estimators only cater for additive nonlinearities or, alternatively, are based on recurrent neural networks or long short-term memory networks, which present considerable training difficulties and tailoring needs.

Effectiveness of benralizumab in severe eosinophilic asthma: Distinct sub-phenotypes of response identified by cluster analysis.

Background: Benralizumab is effective in severe eosinophilic asthma (SEA), but suboptimal responses are observed in some patients. Although several factors have been associated with benralizumab response, no cluster analysis has yet been undertaken to identify different responsiveness sub-phenotypes.

Objective: To identify SEA sub-phenotypes with differential responsiveness to benralizumab.

Serological determinants of COVID-19.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection spreaded rapidly worldwide, as far as it has become a global pandemic. Therefore, the introduction of serological tests for determination of IgM and IgG antibodies has become the main diagnostic tool, useful for tracking the spread of the virus and for consequently allowing its containment. In our study we compared point of care test (POCT) lateral flow immunoassay (FIA) vs automated chemiluminescent immunoassay (CLIA), in order to assess their specificity and sensibility for COVID-19 antibodies detection.

Thalamic neuroinflammation as a reproducible and discriminating signature for chronic low back pain.

Using positron emission tomography, we recently demonstrated elevated brain levels of the 18 kDa translocator protein (TSPO), a glial activation marker, in chronic low back pain (cLBP) patients, compared to healthy controls (HCs). Here, we first sought to replicate the original findings in an independent cohort (15 cLBP, 37.8 ± 12.

Frequency dependent functional brain reorganization in anesthesia is specific to drug concentration.

The differential effects of general anesthesia on brain activity in terms of drug selection, concentration and combination remain to be elucidated. Using fMRI, it has been shown that increasing doses of sevoflurane is associated with progressive breakdown in brain functional connectivity, while EEG studies have shown that higher activity in the delta band is associated with unconsciousness. Despite these promising results, the band- specific neural substrates of brain changes which occur during sevoflurane anesthesia have not yet been investigated.

Unsupervised stratification in neuroimaging through deep latent embeddings.

There is growing evidence that the use of stringent and dichotomic diagnostic categories in many medical disciplines (particularly 'brain sciences' as neurology and psychiatry) is an oversimplification. Although clear diagnostic boundaries remain useful for patients, families, and their access to dedicated NHS and health care services, the traditional dichotomic categories are not helpful to describe the complexity and large heterogeneity of symptoms across many and overlapping clinical phenotypes. With the advent of 'big' multimodal neuroimaging databases, data-driven stratification of the wide spectrum of healthy human physiology or disease based on neuroimages is theoretically become possible.

Deep computational pathology in breast cancer.

Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-world datasets for cross-domain and cross-discipline prediction and classification tasks. DL architectures excel in computer vision tasks, and in particular image processing and interpretation. This has prompted a wave of disruptingly innovative applications in medical imaging, where DL strategies have the potential to vastly outperform human experts.

Radiomics in breast cancer classification and prediction.

Breast Cancer (BC) is the common form of cancer in women. Its diagnosis and screening are usually performed through different imaging modalities such as mammography, magnetic resonance imaging and ultrasound. However, mammography and ultrasound-imaging techniques have limited sensitivity and specificity both in identifying lesions and in differentiating malign from benign lesions, especially in presence of dense breast parenchyma.