Amyotrophic lateral sclerosis phenotypes significantly differ in terms of magnetic susceptibility properties of the precentral cortex

The aim of our study was to investigate whether the magnetic susceptibility varies according to the amyotrophic lateral sclerosis (ALS) phenotypes based on the predominance of upper motor neuron (UMN)/lower motor neuron (LMN) impairment. We retrospectively collected imaging and clinical data of 47 ALS patients (12 with UMN predominance (UMN-ALS), 16 with LMN predominance (LMN-ALS), and 19 with no clinically defined predominance (Np-ALS)). We further enrolled 23 healthy controls (HC) and 15 ALS mimics (ALS-Mim). These participants underwent brain 3-T magnetic resonance imaging (3-T MRI) with T1-weighted and gradient-echo multi-echo sequences. Automatic segmentation and quantitative susceptibility mapping (QSM) were performed. The skewness of the susceptibility values in the precentral cortex (SuscSKEW) was automatically computed, compared among the groups, and correlated to the clinical variables. The Kruskal-Wallis test showed significant differences in terms of SuscSKEW among groups (χ2(3) = 24.2, p < 0.001), and pairwise tests showed that SuscSKEW was higher in UMN-ALS compared to those in LMN-ALS (p < 0.001), HC (p < 0.001), Np-ALS (p = 0.012), and ALS-Mim (p < 0.001). SuscSKEW was highly correlated with the Penn UMN score (Spearman's rho 0.612, p < 0.001). This study demonstrates that the clinical ALS phenotypes based on UMN/LMN sign predominance significantly differ in terms of magnetic susceptibility properties of the precentral cortex. Combined MRI-histopathology investigations are strongly encouraged to confirm whether this evidence is due to iron overload in UMN-ALS, unlike in LMN-ALS. • Magnetic susceptibility in the precentral cortex reflects the prevalence of UMN/LMN impairment in the clinical ALS phenotypes. • The degree of UMN/LMN impairment might be well described by the automatically derived measure of SuscSKEW in the precentral cortex. • Increased SuscSKEW in the precentral cortex is more relevant in UMN-ALS patients compared to those in Np-ALS and LMN-ALS patients

Molecular Imaging Diagnosis of Renal Cancer Using 99mTc-Sestamibi SPECT/CT and Girentuximab PET-CT-Current Evidence and Future Development of Novel Techniques

: Novel molecular imaging opportunities to preoperatively diagnose renal cell carcinoma is under development and will add more value in limiting the postoperative renal function loss and morbidity. We aimed to comprehensively review the research on single photon emission computed tomography/computed tomography (SPECT/CT) and positron emission tomography computed tomography (PET-CT) molecular imaging and to enhance the urologists' and radiologists' knowledge of the current research pattern. We identified an increase in prospective and also retrospective studies that researched to distinguish between benign and malignant lesions and between different clear cell renal cell carcinoma subtypes, with small numbers of patients studied, nonetheless with excellent results on specificity, sensitivity and accuracy, especially for 99mTc-sestamibi SPECT/CT that delivers quick results compared to a long acquisition time for girentuximab PET-CT, which instead gives better image quality. Nuclear medicine has helped clinicians in evaluating primary and secondary lesions, and has lately returned with new and exciting insights with novel radiotracers to reinforce its diagnostic potential in renal carcinoma. To further limit the renal function loss and post-surgery morbidity, future research is mandatory to validate the results and to clinically implement the diagnostic techniques in the context of precision medicine

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

Nuclear localization and new isoforms detection give new insights on Hsp10 functions in normal and cigarette smoke-stressed lung cells

Heat-shock protein (Hsp)10 is the co-chaperone for Hsp60 inside mitochondria, but it also resides outside the organelle. Variations in its levels and intracellular dis- tribution have been documented in pathological conditions, e.g. cancer and chronic obstructive pulmonary disease (COPD). Cigarette smoke (CS) is a potent stressor for the respiratory system, but its effects on the expression, function, and cellular locali- zation of mitochondrial chaperonins are still largely unknown. We studied in vivo (airways biopsies) the localization of Hsp10 and Hsp60 in patients (smokers and non-smokers) affected by mild-moderate COPD, and charac- terized the effects of non-lethal doses of CS extract (CSE) on the expression of these molecules in two human cell lines: lung fibroblasts (HFL-1) and bronchial epithelial cells (16HBE). We applied various in vitro methods: IHC, subcellular fractionation analyses (SFA), western blotting (WB), ICC, transmission electron microscopy (TEM) immunogold, chromati protein extracts (CPE), as well as 2D-gel based proteomics analyses. Bioinformatics was used to gather structural in silico data. IHC showed that Hsp10 occurred in nuclei of epithelial and lamina propria cells of bronchial mucosa from non-smokers and smokers. ICC, SFA, and WB showed that 16HBE and HFL-1 cells featured nuclear Hsp10, before and after CSE exposure; TEM immunogold further confirmed this observation. Proteomics data showed that CSE stimulation did not increase the levels of Hsp10 but did elicit qualitative changes as indicated by molecular weight and isoelectric point shifts. Bioinformatics analyses indicated that Hsp10 can localize in extramitochondrial sites, such as the nucleus, even if Hsp10 lacks known DNA-binding motifs or nuclear import signals. Hsp10 nuclear levels increased after CSE stimulation in HFL-1, indicating cytosol to nucleus migration, and although Hsp10 did not bind DNA, it bound a DNA-associated protein as suggested by CPE/gel retardation experiments. Data reported here indicate that in human cells of the respiratory mucosa there are at least three different intracellular locales for Hsp10: mitochondrial, nuclear, and cyto- solic. Further experiments are en route for the definition of the mechanisms underlying the transfer of Hsp10 to the nucleus and other cellular/extracellular compartments. This work was supported by grants from University of Palermo (FFR 2012) to GLR

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

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

NURE: An ERC project to study nuclear reactions for neutrinoless double beta decay

Neutrinoless double beta decay (0νββ) is considered the best potential resource to access the absolute neutrino mass scale. Moreover, if observed, it will signal that neutrinos are their own anti-particles (Majorana particles). Presently, this physics case is one of the most important research “beyond Standard Model” and might guide the way towards a Grand Unified Theory of fundamental interactions. Since the 0νββ decay process involves nuclei, its analysis necessarily implies nuclear structure issues. In the NURE project, supported by a Starting Grant of the European Research Council (ERC), nuclear reactions of double charge-exchange (DCE) are used as a tool to extract information on the 0νββ Nuclear Matrix Elements. In DCE reactions and ββ decay indeed the initial and final nuclear states are the same and the transition operators have similar structure. Thus the measurement of the DCE absolute cross-sections can give crucial information on ββ matrix elements. In a wider view, the NUMEN international collaboration plans a major upgrade of the INFN-LNS facilities in the next years in order to increase the experimental production of nuclei of at least two orders of magnitude, thus making feasible a systematic study of all the cases of interest as candidates for 0νββ

An explainable model of host genetic interactions linked to COVID-19 severity

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as "Respiratory or thoracic disease", supporting their link with COVID-19 severity outcome.A multifaceted computational strategy identifies 16 genetic variants contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing dataset of a cohort of Italian patients

Dupilumab in the treatment of severe uncontrolled chronic rhinosinusitis with nasal polyps (CRSwNP): A multicentric observational Phase IV real-life study (DUPIREAL)

Background Chronic rhinosinusitis with nasal polyps (CRSwNP) is associated with significant morbidity and reduced health-related quality of life. Findings from clinical trials have demonstrated the effectiveness of dupilumab in CRSwNP, although real-world evidence is still limited. Methods This Phase IV real-life, observational, multicenter study assessed the effectiveness and safety of dupilumab in patients with severe uncontrolled CRSwNP (n = 648) over the first year of treatment. We collected data at baseline and after 1, 3, 6, 9, and 12 months of follow-up. We focused on nasal polyps score (NPS), symptoms, and olfactory function. We stratified outcomes by comorbidities, previous surgery, and adherence to intranasal corticosteroids, and examined the success rates based on current guidelines, as well as potential predictors of response at each timepoint. Results We observed a significant decrease in NPS from a median value of 6 (IQR 5–6) at baseline to 1.0 (IQR 0.0–2.0) at 12 months (p &lt; .001), and a significant decrease in Sino-Nasal Outcomes Test-22 (SNOT-22) from a median score of 58 (IQR 49–70) at baseline to 11 (IQR 6–21; p &lt; .001) at 12 months. Sniffin' Sticks scores showed a significant increase over 12 months (p &lt; .001) compared to baseline. The results were unaffected by concomitant diseases, number of previous surgeries, and adherence to topical steroids, except for minor differences in rapidity of action. An excellent-moderate response was observed in 96.9% of patients at 12 months based on EPOS 2020 criteria. Conclusions Our findings from this large-scale real-life study support the effectiveness of dupilumab as an add-on therapy in patients with severe uncontrolled CRSwNP in reducing polyp size and improving the quality of life, severity of symptoms, nasal congestion, and smell

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London