Engineering thermoplastics for additive manufacturing: a critical perspective with experimental evidence to support functional applications

Among additive manufacturing techniques, the filament-based technique involves what is referred to as fused deposition modeling (FDM). FDM materials are currently limited to a selected number of polymers. The present study focused on investigating the potential of using high-end engineering polymers in FDM. In addition, a critical review of the materials available on the market compared with those studied here was completed

Exploring Social Sustainability and Economic Practices: Multi-Journal Compendium

In consideration of the three pillars of sustainability, alongside the environment, social and economic dimensions interplay valuable insight into how society is molded and what key components should be considered. In terms of social sustainability, there are processes and framework objectives that promote wellbeing integral to the balance of people, planet, and profit. Economic practices consider the system of production, resource allocation, and distribution of goods and services with respect to demand and supply between economic agents. As a result, an economic system is a variant of the social system in which it exists. At present, the forefront of social sustainability research partially encompasses the impact economic practices have on people and society—with notable emphasis centered on the urban environment. Specific interdisciplinary analyses within the scope of sustainability, social development, competitiveness, and motivational management as well as decision making within the urban landscape are considered. This book contains nine thoroughly refereed contributions that interconnect detailed research into the two pillars reviewed

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

In recent years, the field of Deep Learning has seen many disruptive and impactful advancements. Given the increasing complexity of deep neural networks, the need for efficient hardware accelerators has become more and more pressing to design heterogeneous HPC platforms. The design of Deep Learning accelerators requires a multidisciplinary approach, combining expertise from several areas, spanning from computer architecture to approximate computing, computational models, and machine learning algorithms. Several methodologies and tools have been proposed to design accelerators for Deep Learning, including hardware-software co-design approaches, high-level synthesis methods, specific customized compilers, and methodologies for design space exploration, modeling, and simulation. These methodologies aim to maximize the exploitable parallelism and minimize data movement to achieve high performance and energy efficiency. This survey provides a holistic review of the most influential design methodologies and EDA tools proposed in recent years to implement Deep Learning accelerators, offering the reader a wide perspective in this rapidly evolving field. In particular, this work complements the previous survey proposed by the same authors in [203], which focuses on Deep Learning hardware accelerators for heterogeneous HPC platforms

Safety of extended interval dosing immune checkpoint inhibitors:a multicenter cohort study

BACKGROUND: Real-life spectrum and survival implications of immune-related adverse events (irAEs) in patients treated with extended interval dosing (ED) immune checkpoint inhibitors (ICIs) are unknown. METHODS: Characteristics of 812 consecutive solid cancer patients who received at least 1 cycle of ED monotherapy (pembrolizumab 400 mg Q6W or nivolumab 480 mg Q4W) after switching from canonical interval dosing (CD; pembrolizumab 200 mg Q3W or nivolumab 240 mg Q2W) or treated upfront with ED were retrieved. The primary objective was to compare irAEs patterns within the same population (before and after switch to ED). irAEs spectrum in patients treated upfront with ED and association between irAEs and overall survival were also described. RESULTS: A total of 550 (68%) patients started ICIs with CD and switched to ED. During CD, 225 (41%) patients developed any grade and 17 (3%) G3 or G4 irAEs; after switching to ED, any grade and G3 or G4 irAEs were experienced by 155 (36%) and 20 (5%) patients. Switching to ED was associated with a lower probability of any grade irAEs (adjusted odds ratio [aOR] = 0.83, 95% confidence interval [CI] = 0.64 to 0.99; P = .047), whereas no difference for G3 or G4 events was noted (aOR = 1.55, 95% CI = 0.81 to 2.94; P = .18). Among patients who started upfront with ED (n = 232, 32%), 107 (41%) developed any grade and 14 (5%) G3 or G4 irAEs during ED. Patients with irAEs during ED had improved overall survival (adjusted hazard ratio [aHR] = 0.53, 95% CI = 0.34 to 0.82; P = .004 after switching; aHR = 0.57, 95% CI = 0.35 to 0.93; P = .025 upfront). CONCLUSIONS: Switching ICI treatment from CD and ED did not increase the incidence of irAEs and represents a safe option also outside clinical trials.</p

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research

Introduction: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). Methods and objectives: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. Conclusion: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project

Determinants of worse liver‐related outcome according to HDV infection among HBsAg positive persons living with HIV: Data from the ICONA cohort

Objectives: We aimed to study hepatitis D virus (HDV) prevalence and risk of progression to severe liver-related events (SLRE) in HBsAg positive people living with HIV (PLWH) in Italy; role of HDV-RNA copy levels, HCV coinfection and nadir CD4 counts were also investigated.Methods: People living with HIV (PLWH) from Italian Foundation cohort Naive antiretrovirals (ICONA) with available HBsAg and HDV Ab were enrolled. HBsAg, HDV Ab, HDV-RNA and HDV genotypes were tested. Primary end-point: time from first HDV screening to Severe Liver Related Events (SLRE: decompensated cirrhosis, liver transplantation, HCC). Fine-grey regression models were used to evaluate the association of HDV Ab, HDV-RNA, HDV/HCV coinfection, CD4 nadir and outcome. Secondary end-points: time to SLRE or death; HDV Ab and HDV-RNA prevalence.Results: A total of 152/809 (18.8%) HBsAg positive PLWH showed HDV Ab reactivity; 63/93 (67.7%) were HDV-RNA positive. Being male, persons who inject drugs (PWID), HCV Ab positive, with FIB-4 &gt; 3.25 were independent factors of HDV Ab positivity. In a median follow-up of 5 years, 37 PLWH (4.1% at 5-year) developed SLRE and 97 (12.0%) reached the SLRE or death end-point. HDV-RNA positive (independently from HDV-RNA copy level) PLWH had a 4.6-fold (95%CI 2.0-10.5) higher risk of SLRE than HDV negatives. PLWH positive for both HCV Ab and HDV Ab showed the highest independent risk of SLRE (ASHR: 11.9, 95%CI: 4.6-30.9 vs. HCV neg/HDV neg). Nadir CD4 &lt; 200/mL was associated with SLRE (ASHR: 3.9, 95% 1.0-14.5).Conclusions: One-fifth of the HBsAg positive PLWH harbour HDV infection, and are at high risk of progression to advanced liver disease. HCV contributes to worse outcomes. This population needs urgently effective treatments

Carriers of ADAMTS13 Rare Variants Are at High Risk of Life-Threatening COVID-19

Thrombosis of small and large vessels is reported as a key player in COVID-19 severity. However, host genetic determinants of this susceptibility are still unclear. Congenital Thrombotic Thrombocytopenic Purpura is a severe autosomal recessive disorder characterized by uncleaved ultra-large vWF and thrombotic microangiopathy, frequently triggered by infections. Carriers are reported to be asymptomatic. Exome analysis of about 3000 SARS-CoV-2 infected subjects of different severities, belonging to the GEN-COVID cohort, revealed the specific role of vWF cleaving enzyme ADAMTS13 (A disintegrin-like and metalloprotease with thrombospondin type 1 motif, 13). We report here that ultra-rare variants in a heterozygous state lead to a rare form of COVID-19 characterized by hyper-inflammation signs, which segregates in families as an autosomal dominant disorder conditioned by SARS-CoV-2 infection, sex, and age. This has clinical relevance due to the availability of drugs such as Caplacizumab, which inhibits vWF-platelet interaction, and Crizanlizumab, which, by inhibiting P-selectin binding to its ligands, prevents leukocyte recruitment and platelet aggregation at the site of vascular damage

A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death

: The clinical manifestations of SARS-CoV-2 infection vary widely among patients, from asymptomatic to life-threatening. Host genetics is one of the factors that contributes to this variability as previously reported by the COVID-19 Host Genetics Initiative (HGI), which identified sixteen loci associated with COVID-19 severity. Herein, we investigated the genetic determinants of COVID-19 mortality, by performing a case-only genome-wide survival analysis, 60 days after infection, of 3904 COVID-19 patients from the GEN-COVID and other European series (EGAS00001005304 study of the COVID-19 HGI). Using imputed genotype data, we carried out a survival analysis using the Cox model adjusted for age, age2, sex, series, time of infection, and the first ten principal components. We observed a genome-wide significant (P-value < 5.0 × 10-8) association of the rs117011822 variant, on chromosome 11, of rs7208524 on chromosome 17, approaching the genome-wide threshold (P-value = 5.19 × 10-8). A total of 113 variants were associated with survival at P-value < 1.0 × 10-5 and most of them regulated the expression of genes involved in immune response (e.g., CD300 and KLR genes), or in lung repair and function (e.g., FGF19 and CDH13). Overall, our results suggest that germline variants may modulate COVID-19 risk of death, possibly through the regulation of gene expression in immune response and lung function pathways

Pathogen-sugar interactions revealed by universal saturation transfer analysis

Many pathogens exploit host cell-surface glycans. However, precise analyses of glycan ligands binding with heavily modified pathogen proteins can be confounded by overlapping sugar signals and/or compounded with known experimental constraints. Universal saturation transfer analysis (uSTA) builds on existing nuclear magnetic resonance spectroscopy to provide an automated workflow for quantitating protein-ligand interactions. uSTA reveals that early-pandemic, B-origin-lineage severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike trimer binds sialoside sugars in an “end-on” manner. uSTA-guided modeling and a high-resolution cryo–electron microscopy structure implicate the spike N-terminal domain (NTD) and confirm end-on binding. This finding rationalizes the effect of NTD mutations that abolish sugar binding in SARS-CoV-2 variants of concern. Together with genetic variance analyses in early pandemic patient cohorts, this binding implicates a sialylated polylactosamine motif found on tetraantennary N-linked glycoproteins deep in the human lung as potentially relevant to virulence and/or zoonosis