Thermal and Acoustic Properties of Aerogels: Preliminary Investigation of the Influence of Granule Size

8th International Conference on Sustainability in Energy and Buildings, SEB 2016 The influence of granules size in silica aerogels is experimentally investigated in terms of thermal and acoustic performance characteristics. The transmission loss (TL) is measured at normal incidence in a traditional impedance tube, whereas the thermal conductivity (?) is evaluated using a Hot Plate apparatus, setting up an appropriate methodology, due to the nature of the sample. The results reveal that the small granules (granules size in the 0.01-1.2 mm range), which have the highest density, have the best performance both in terms of thermal and acoustic properties. Depending on the granules size, ? varies in 19-22 mW/mK range at 10°C, whereas a TL equal to 13 dB at about 6400 Hz for 20 mm thickness is obtained for small granules. © 2017 The Authors

Analysis of Nano Silica Aerogel Based Glazing Effect on the Solar Heat Gain and Cooling Load in a School under Different Climatic Conditions

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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

Italian real life experience with ibrutinib: Results of a large observational study on 77 relapsed/refractory mantle cell lymphoma

Although sometimes presenting as an indolent lymphoma, mantle cell lymphoma (MCL) is an aggressive disease, hardly curable with standard chemo-immunotherapy. Current approaches have greatly improved patients' outcomes, nevertheless the disease is still characterized by high relapse rates. Before approval by EMA, Italian patients with relapsed/refractory MCL were granted ibrutinib early access through a Named Patient Program (NPP). An observational, retrospective, multicenter study was conducted. Seventyseven heavily pretreated patients were enrolled. At the end of therapy there were 14 complete responses and 14 partial responses, leading to an overall response rate of 36.4%. At 40 months overall survival was 37.8% and progression free survival was 30%; disease free survival was 78.6% at 4 years: 11/14 patients are in continuous complete response with a median of 36 months of follow up. Hematological toxicities were manageable, and main extra-hematological toxicities were diarrhea (9.4%) and lung infections (9.0%). Overall, 4 (5.2%) atrial fibrillations and 3 (3.9%) hemorrhagic syndromes occurred. In conclusions, thrombocytopenia, diarrhea and lung infections are the relevant adverse events to be clinically focused on; regarding effectiveness, ibrutinib is confirmed to be a valid option for refractory/relapsed MCL also in a clinical setting mimicking the real world

Serological response and breakthrough infection after COVID-19 vaccination in patients with cirrhosis and post-liver transplant

BACKGROUND: Vaccine hesitancy and lack of access remain major issues in disseminating COVID-19 vaccination to liver patients globally. Factors predicting poor response to vaccination and risk of breakthrough infection are important data to target booster vaccine programs. The primary aim of the current study was to measure humoral responses to 2 doses of COVID-19 vaccine. Secondary aims included the determination of factors predicting breakthrough infection. METHODS: COVID-19 vaccination and Biomarkers in cirrhosis And post-Liver Transplantation is a prospective, multicenter, observational case-control study. Participants were recruited at 4-10 weeks following first and second vaccine doses in cirrhosis [n = 325; 94% messenger RNA (mRNA) and 6% viral vaccine], autoimmune liver disease (AILD) (n = 120; 77% mRNA and 23% viral vaccine), post-liver transplant (LT) (n = 146; 96% mRNA and 3% viral vaccine), and healthy controls (n = 51; 72% mRNA, 24% viral and 4% heterologous combination). Serological end points were measured, and data regarding breakthrough SARS-CoV-2 infection were collected. RESULTS: After adjusting by age, sex, and time of sample collection, anti-Spike IgG levels were the lowest in post-LT patients compared to cirrhosis (p < 0.0001), AILD (p < 0.0001), and control (p = 0.002). Factors predicting reduced responses included older age, Child-Turcotte-Pugh B/C, and elevated IL-6 in cirrhosis; non-mRNA vaccine in AILD; and coronary artery disease, use of mycophenolate and dysregulated B-call activating factor, and lymphotoxin-α levels in LT. Incident infection occurred in 6.6%, 10.6%, 7.4%, and 15.6% of cirrhosis, AILD, post-LT, and control, respectively. The only independent factor predicting infection in cirrhosis was low albumin level. CONCLUSIONS: LT patients present the lowest response to the SARS-CoV-2 vaccine. In cirrhosis, the reduced response is associated with older age, stage of liver disease and systemic inflammation, and breakthrough infection with low albumin level