Studying the Changes in Physical Functioning and Oxidative Stress-Related Molecules in People Living with HIV after Switching from Triple to Dual Therapy.

Background: Physical activity could increase the production of oxidative stress biomarkers, affecting the metabolism and excretion of antiretroviral drugs and, consequently, the clinical outcome. Nowadays, people living with HIV (PLWH) are mostly switching from triple to dual therapy, but no data are available in terms of physical functioning and oxidative stress. The aim of this study was to evaluate if some antioxidant biomarkers and physical functioning tests could be different according to triple or dual antiretroviral therapy.

Reduction of the microbial load in meat maturation rooms with and without alkaline electrolyzed water fumigation.

Dry aging is a process during which meat is stored within maturation chambers at low temperatures and low relative humidity, resulting in improved tenderness and flavor development. The cuts are exposed to the atmosphere by hanging them or setting them on racks in the maturation chamber without any protective packaging. Animals and humans are usually the major sources of bacterial food contamination in the meat industry, but other routes might be involved.

Multidimensional frailty and its association with quality of life and disability: A cross-sectional study in people with multiple sclerosis.

Background: People with multiple sclerosis (pwMS) have a high risk of frailty. We aim to evaluate frailty using the Tilburg frailty indicator (TFI), a multidimensional self-reported questionnaire, and to explore its relationship with autonomy, quality of life (QoL), and disability.

Methods: All the patients with MS enrolled completed TFI (frail when TFI score ≥ 5 points), the Groningen Activities Restriction Scale to evaluate autonomy, and the Multiple Sclerosis Impact Scale-29 to evaluate QoL.

"Stay at Home" during the COVID-19 Pandemic: Effects on Physical Activity and Sedentary Behavior in an Italian Academic Community.

This study investigates changes in physical activity (PA) and sedentary behavior in an Italian university community during Phase 1 of SARS-CoV-2 lockdown (“stay at home” government decree, from March 8th to May 4th, 2020) compared to their habits prior to the COVID-19 pandemic. We also examine differences according to gender, university position, BMI categories, and sport participation. A total of 2596 people (median age 24, IQR 11 years; 70.

Effectiveness of alkaline electrolyzed water in reducing bacterial load on surfaces intended to come into contact with food.

Alkaline electrolyzed water (REW) is known for its cleaning action. The aim of this work was to assess REW effectiveness in reducing microbial load on surfaces intended for contact with food. Stainlesssteel surfaces were experimentally contaminated, bacterial inactivation was tested before and after treatment with REW.

A machine learning-enhanced biosensor for mercury detection based on an hydrophobin chimera.

Marine waters are becoming contaminated by diverse pollutants at a fast rate, and detection of these water pollutants has become a major concern in recent years. Among these, mercury is considered the most toxic element for human health. At present, despite the commonly used methods for its detection are accurate, they often require sophisticated equipments, have relatively high costs, are demanding and time-consuming.

An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors.

Vulnerable road users (VRUs) are exposed to the highest risk in the road traffic environment. Analyzing contributing factors that affect injury severity facilitates injury severity prediction and further application in developing countermeasures to guarantee VRUs safety. Recently, machine learning approaches have been introduced, in which analyses tend to be one-sided and may ignore important information.

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic.

The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months.

Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease.

Potential Celiac Patients (PCD) bear the Celiac Disease (CD) genetic predisposition, a significant production of antihuman transglutaminase antibodies, but no morphological changes in the small bowel mucosa. A minority of patients (17%) showed clinical symptoms and need a gluten free diet at time of diagnosis, while the majority progress over several years (up to a decade) without any clinical problem neither a progression of the small intestine mucosal damage even when they continued to assume gluten in their diet. Recently we developed a traditional multivariate approach to predict the natural history, on the base of the information at enrolment (time 0) by a discriminant analysis model.

A deep learning approach for facility patient attendance prediction based on medical booking data.

Nowadays, data-driven methodologies based on the clinical history of patients represent a promising research field in which personalized and intelligent healthcare systems can be opportunely designed and developed. In this perspective, Machine Learning (ML) algorithms can be efficiently adopted to deploy smart services to enhance the overall quality of healthcare systems. In this work, starting from an in-depth analysis of a data set composed of millions of medical booking records collected from the public healthcare organization in the region of Campania, Italy, we have developed a predictive model to extract useful knowledge on patients, medical staff, and related healthcare structures.

A network-based method with privacy-preserving for identifying influential providers in large healthcare service systems.

In data science, networks provide a useful abstraction of the structure of many complex systems, ranging from social systems and computer networks to biological networks and physical systems. Healthcare service systems are one of the main social systems that can also be understood using network-based approaches, for example, to identify and evaluate influential providers. In this paper, we propose a network-based method with privacy-preserving for identifying influential providers in large healthcare service systems.

Nurse Practitioner-Driven Optimization of Presurgical Testing.

Nurse practitioners play a pivotal role as members of the perioperative team. This article outlines the evolution of presurgical testing from the vantage of generic testing to one that is tailored to the patient's clinical presentation and the type of surgery to be performed, whether it is high, intermediate, or low risk. Emphasis is placed on indicated testing, screening tools to identify patients at risk for perioperative complications, the optimization of patients before undergoing major cancer surgery, and enhanced recovery after surgery.

[Ambulatory dynamic Holter monitoring: a practical guide to appropriate use and reporting in clinical cardiology].

Ambulatory electrocardiographic monitoring is still a useful tool in the hands of the clinical cardiologist, for both the diagnosis of many pathologies and the management of long-term therapy. The evolution of the instrumentation, which has become sophisticated but at the same time more manageable and easy to use, has made it possible to evaluate many parameters and data. The aim of this review is to provide a general overview of this old, but not obsolete tool, to describe the main indications for its use in the light of new scientific evidence and to offer a practical guide for its appropriate use and reporting.

3D data denoising via Nonlocal Means filter by using parallel GPU strategies.

Nonlocal Means (NLM) algorithm is widely considered as a state-of-the-art denoising filter in many research fields. Its high computational complexity leads researchers to the development of parallel programming approaches and the use of massively parallel architectures such as the GPUs. In the recent years, the GPU devices had led to achieving reasonable running times by filtering, slice-by-slice, and 3D datasets with a 2D NLM algorithm.

Effects of increasing CREB-dependent transcription on the storage and recall processes in a hippocampal CA1 microcircuit.

The involvement of the hippocampus in learning processes and major brain diseases makes it an ideal candidate to investigate possible ways to devise effective therapies for memory-related pathologies like Alzheimer's Disease (AD). It has been previously reported that augmenting CREB activity increases the synaptic Long-Term Potentiation (LTP) magnitude in CA1 pyramidal neurons and their intrinsic excitability in healthy rodents. It has also been suggested that hippocampal CREB signaling is likely to be down-regulated during AD, possibly degrading memory functions.

An atypical assessment of Ebstein's anomaly in an 86-year-old man.

We present the echocardiographic analysis of an 86-year-old man affected by Ebstein's anomaly. In the natural history of this congenital disease only 5% of patients survive beyond the fifth decade. The patient presented severe right atrial dilatation and right heart failure, and he was referred to our institution for supraventricular tachycardia.

Association between right ventricular two-dimensional strain and exercise capacity in patients with either idiopathic or ischemic dilated cardiomyopathy.

Objectives: To detect right ventricular myocardial function in dilated cardiomyopathy (DCM) using two-dimensional strain echocardiography (2DSE) and to evaluate the relationship between right ventricular dysfunction and response to cardiopulmonary exercise test (CPET).

Methods: Seventy-five DCM patients (44 idiopathic and 31 ischemic) without clinical signs of right ventricular failure underwent standard echo, 2DSE analysis of right ventricle and bicycle CPET.

Results: The two groups were comparable for clinical and standard two-dimensional echocardiographic and Doppler variables, except for right ventricular diameters that were mildly increased in patients with idiopathic DCM.

Range of right heart measurements in top-level athletes: the training impact.

Objectives: To explore the full range of right heart dimensions and the impact of long-term intensive training in athletes.

Background: Although echocardiography has been widely used to distinguish the athlete's heart from pathologic left ventricular (LV) hypertrophy, only few reports have described right ventricular (RV) and right atrial (RA) adaptations to extensive physical exercise.

Methods: 650 top-level athletes [395 endurance- (ATE) and 255 strength-trained (ATS); 410 males (63.

Right ventricular ejection fraction and left ventricular dyssynchrony by 3D echo correlate with functional impairment in patients with dilated cardiomyopathy.

Background: The aim of the study was to detect if right ventricular (RV) ejection fraction assessed by real-time 3-dimensional echocardiography (RT3DE) could predict patients with dilated cardiomyopathy (DCM) with greater functional impairment in response to cardiopulmonary exercise.

Methods And Results: Seventy chronic heart failure patients with DCM (55.5 ± 9.

Left ventricular myocardial velocities and deformation indexes in top-level athletes.

Background: The aim of this study was to define the range of left ventricular (LV) velocities and deformation indexes in highly trained athletes, analyzing potential differences induced by different long-term training protocols.

Methods: Standard echocardiography, pulsed-wave tissue Doppler echocardiography, and two-dimensional strain echocardiography of the interventricular septum and lateral wall were performed in 370 endurance athletes and 280 power athletes. Using pulsed-wave tissue Doppler, the following parameters of myocardial function were assessed: systolic peak velocities (S(m)), early (E(m)) and late (A(m)) diastolic velocities, and the E(m)/A(m) ratio.

Global longitudinal speckle-tracking strain is predictive of left ventricular remodeling after coronary angioplasty in patients with recent non-ST elevation myocardial infarction.

Aims: To test whether two-dimensional longitudinal strain (2DSE) performed after revascularization by percutaneous coronary intervention (PCI) could predict left ventricular (LV) remodeling in patients with recent non-ST elevation myocardial infarction (NSTEMI).

Methods: In 70 patients (62.7 ± 8.

Right ventricular myocardial involvement in either physiological or pathological left ventricular hypertrophy: an ultrasound speckle-tracking two-dimensional strain analysis.

Aims: To analyse right ventricular (RV) myocardial deformation in patients with left ventricular (LV) hypertrophy secondary to either hypertrophic cardiomyopathy (HCM) or athlete's competitive endurance training.

Methods And Results: Standard Doppler echo, exercise stress echo, and 2D speckle-tracking strain echocardiography (2DSE) of RV longitudinal deformation in RV septal and lateral walls were performed in 50 top-level endurance athletes and in 35 patients with HCM, all men, having evidence of LV hypertrophy. Right ventricular global longitudinal strain (GLS) was calculated by averaging local strains along the entire right ventricle.