Event-Driven User-Centric Middleware for Energy Efficient Buildings and Public Spaces

In this work, the design of an event-driven user-centric middleware for monitoring and managing energy consumption in public buildings and spaces is presented. The main purpose is to increase the energy efficiency, reducing consumption, in buildings and public spaces. To achieve this, the proposed service-oriented middleware has been designed to be event based, also exploiting the user behaviours patterns of the people who live and work into the building. Furthermore, it allows an easy integration of heterogeneous technologies in order to enable a hardware independent interoperability between them. Moreover, a Heating Ventilation and Air Conditioning (HVAC) control strategy has been developed and the whole infrastructure has been deployed in a real-world case study consisting of a historical building. Finally the results will be presented and discusse

Effects of 5-Week of FIFA 11+ Warm-Up Program on Explosive Strength, Speed, and Perception of Physical Exertion in Elite Female Futsal Athletes

Futsal is a sport that originates from soccer and is increasingly practiced all over the world. Since training and warm-up protocols should be sport-specific in order to reduce injuries and maximize performance, this study aimed to evaluate the effects of 5 weeks of the FIFA 11+ warm-up program on explosive strength, speed, and perception of physical exertion in elite female futsal athletes. Twenty-nine elite female futsal athletes participating in the Italian national championships were divided into two groups: the experimental group (EG) underwent 5 weeks of the FIFA 11+ warmup program, and the control group (CG) underwent 5 weeks of a dynamic warm-up. We evaluated any effect on explosive strength (by Squat Jump test), speed (by Agility T-test), and perception of physical exertion (by Borg CR-10 scale). All measurements were carried out by a technician of the Italian Football Federation before (T0), at the middle (T1), and at the end (T2) of the protocol. The EG showed significant improvements on performances between T0 vs. T1 and T0 vs. T2 both in the Squat Jump test (p = 0.0057 and p = 0.0030, respectively) and in the Agility T-test (p = 0.0075 and p = 0.0122). No significant differences were found in the Squat Jump test performances in the CG, while significant improvements were detected in the Agility T-test performances (p = 0.0004 and p = 0.0053, T0 vs. T1 and T0 vs. T2, respectively). As for the Borg CR-10 scale, we found a significant difference between T0 and T2 in the EG (p = 0.017) and no differences in the CG. This study showed that 5 weeks of the FIFA 11+ warm-up program improves the jumping performance of female futsal athletes without adversely affecting speed. These findings can be useful for coaches and athletic trainers in order to consider FIFA 11+ warm-up program also in female futsal athletes

MicroRNAs as Regulators of Neo-Angiogenesis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a highly vascularized neoplasm. In the tumor niche, abundant angiogenesis is fundamental in providing nutrients for tumor growth and represents the first escape route for metastatic cells. Active angiogenesis, together with metastasis, are responsible for the reduction of recurrence-free survival of HCC. MicroRNAs (miRNAs) are small non-coding RNAs that have recently drawn attention in molecular targeted therapy or as diagnostic and prognostic biomarkers. MiRNA expression in HCC has been widely studied in the last decade. Some miRNAs have been found to be up- or down-regulated, besides association with apoptosis, metastasis progression and drug resistance have been found. This review article aims to summarize the angiogenetic process in tumor diseases and to update on what has been found in the vast world of HCC-related-miRNAs and, eventually, to report the latest finding on several miRNAs involved in HCC angiogenesis. We searched the state of the arts for the 12 miRNAs found to be involved with angiogenesis in HCC (miR-29b, miR-126-3p, miR-144-3p, miR-146a, miR-195, miR-199a-3p, miR-210-3p, miR-338- 3p, mir-491, mir-497, mir-638, mir-1301) and reported their main molecular targets and their overall effect in the sprouting of new vessels

A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial

BACKGROUND Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313). METHODS We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0-9.6; High→Int, HR: 2.3, 95% CI: 1.5-4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential

A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial

SIMPLE SUMMARY: The interest in using Machine-Learning (ML) techniques in clinical research is growing. We applied ML to build up a novel prognostic model from patients affected with Mantle Cell Lymphoma (MCL) enrolled in a phase III open-labeled, randomized clinical trial from the Fondazione Italiana Linfomi (FIL)—MCL0208. This is the first application of ML in a prospective clinical trial on MCL lymphoma. We applied a novel ML pipeline to a large cohort of patients for which several clinical variables have been collected at baseline, and assessed their prognostic value based on overall survival. We validated it on two independent data series provided by European MCL Network. Due to its flexibility, we believe that ML would be of tremendous help in the development of a novel MCL prognostic score aimed at re-defining risk stratification. ABSTRACT: Background: Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313). Methods: We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0–9.6; High→Int, HR: 2.3, 95% CI: 1.5–4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential

Lopinavir/Ritonavir and Darunavir/Cobicistat in Hospitalized COVID-19 Patients: Findings From the Multicenter Italian CORIST Study

Background: Protease inhibitors have been considered as possible therapeutic agents for COVID-19 patients. Objectives: To describe the association between lopinavir/ritonavir (LPV/r) or darunavir/cobicistat (DRV/c) use and in-hospital mortality in COVID-19 patients. Study Design: Multicenter observational study of COVID-19 patients admitted in 33 Italian hospitals. Medications, preexisting conditions, clinical measures, and outcomes were extracted from medical records. Patients were retrospectively divided in three groups, according to use of LPV/r, DRV/c or none of them. Primary outcome in a time-to event analysis was death. We used Cox proportional-hazards models with inverse probability of treatment weighting by multinomial propensity scores. Results: Out of 3,451 patients, 33.3% LPV/r and 13.9% received DRV/c. Patients receiving LPV/r or DRV/c were more likely younger, men, had higher C-reactive protein levels while less likely had hypertension, cardiovascular, pulmonary or kidney disease. After adjustment for propensity scores, LPV/r use was not associated with mortality (HR = 0.94, 95% CI 0.78 to 1.13), whereas treatment with DRV/c was associated with a higher death risk (HR = 1.89, 1.53 to 2.34, E-value = 2.43). This increased risk was more marked in women, in elderly, in patients with higher severity of COVID-19 and in patients receiving other COVID-19 drugs. Conclusions: In a large cohort of Italian patients hospitalized for COVID-19 in a real-life setting, the use of LPV/r treatment did not change death rate, while DRV/c was associated with increased mortality. Within the limits of an observational study, these data do not support the use of LPV/r or DRV/c in COVID-19 patients

3D bioactive composite scaffolds for bone tissue engineering

Bone is the second most commonly transplanted tissue worldwide, with over four million operations using bone grafts or bone substitute materials annually to treat bone defects. However, significant limitations affect current treatment options and clinical demand for bone grafts continues to rise due to conditions such as trauma, cancer, infection and arthritis. Developing bioactive three-dimensional (3D) scaffolds to support bone regeneration has therefore become a key area of focus within bone tissue engineering (BTE). A variety of materials and manufacturing methods including 3D printing have been used to create novel alternatives to traditional bone grafts. However, individual groups of materials including polymers, ceramics and hydrogels have been unable to fully replicate the properties of bone when used alone. Favourable material properties can be combined and bioactivity improved when groups of materials are used together in composite 3D scaffolds. This review will therefore consider the ideal properties of bioactive composite 3D scaffolds and examine recent use of polymers, hydrogels, metals, ceramics and bio-glasses in BTE. Scaffold fabrication methodology, mechanical performance, biocompatibility, bioactivity, and potential clinical translations will be discussed

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 < 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