Explore the vast range of titles available.

The study shows the feasibility of predicting firms' expenditures in innovation, as reported in the Community Innovation Survey, applying a supervised machine-learning approach on a sample of Italian firms. Using an integrated dataset of administrative records and balance sheet data, designed to include all informative variables related to innovation but also easily accessible for most of the cohort, random forest algorithm is implemented to obtain a classification model aimed to identify firms that are potential innovation performers. The performance of the classifier, estimated in terms of AUC, is 0.794. Although innovation investments do not always result in patenting, the model is able to identify 71.92% of firms with patents. More encouraging results emerge from the analysis of the inner working of the model: predictors identified as most important-such as firm size, sector belonging and investment in intangible assets-confirm previous findings of literature, but in a completely different framework. The outcomes of this study are considered relevant for both economic analysts, because it demonstrates the potential of data-driven models for understanding the nature of innovation behaviour, and practitioners, such as policymakers or venture capitalists, who can benefit by evidence-based tools in the decision-making process.

Patients with type 2 diabetes mellitus (T2DM) have more than twice the risk of developing heart failure (HF) compared to patients without diabetes. The present study is aimed to build an artificial intelligence (AI) prognostic model that takes in account a large and heterogeneous set of clinical factors and investigates the risk of developing HF in diabetic patients. We carried out an electronic health records- (EHR-) based retrospective cohort study that included patients with cardiological clinical evaluation and no previous diagnosis of HF. Information consists of features extracted from clinical and administrative data obtained as part of routine medical care. The primary endpoint was diagnosis of HF (during out-of-hospital clinical examination or hospitalization). We developed two prognostic models using (1) elastic net regularization for Cox proportional hazard model (COX) and (2) a deep neural network survival method (PHNN), in which a neural network was used to represent a non-linear hazard function and explainability strategies are applied to estimate the influence of predictors on the risk function. Over a median follow-up of 65 months, 17.3% of the 10,614 patients developed HF. The PHNN model outperformed COX both in terms of discrimination (c-index 0.768 vs 0.734) and calibration (2-year integrated calibration index 0.008 vs 0.018). The AI approach led to the identification of 20 predictors of different domains (age, body mass index, echocardiographic and electrocardiographic features, laboratory measurements, comorbidities, therapies) whose relationship with the predicted risk correspond to known trends in the clinical practice. Our results suggest that prognostic models for HF in diabetic patients may improve using EHRs in combination with AI techniques for survival analysis, which provide high flexibility and better performance with respect to standard approaches.

Background Machine learning (ML) methods to build prediction models starting from electrocardiogram (ECG) signals are an emerging research field. The aim of the present study is to investigate the performances of two ML approaches based on ECGs for the prediction of new-onset atrial fibrillation (AF), in terms of discrimination, calibration and sample size dependence. Methods We trained two models to predict new-onset AF: a convolutional neural network (CNN), that takes as input the raw ECG signals, and an eXtreme Gradient Boosting model (XGB), that uses the signal’s extracted features. A penalized logistic regression model (LR) was used as a benchmark. Discrimination was evaluated with the area under the ROC curve, while calibration with the integrated calibration index. We investigated the dependence of models’ performances on the sample size and on class imbalance corrections introduced with random under-sampling. Results CNN's discrimination was the most affected by the sample size, outperforming XGB and LR only around n  = 10.000 observations. Calibration showed only a small dependence on the sample size for all the models considered. Balancing the training set with random undersampling did not improve discrimination in any of the models. Instead, the main effect of imbalance corrections was to worsen the models’ calibration (for CNN, integrated calibration index from 0.014 [0.01, 0.018] to 0.17 [0.16, 0.19]). The sample size emerged as a fundamental point for developing the CNN model, especially in terms of discrimination (AUC = 0.75 [0.73, 0.77] when n  = 10.000, AUC = 0.80 [0.79, 0.81] when n  = 150.000). The effect of the sample size on the other two models was weaker. Imbalance corrections led to poorly calibrated models, for all the approaches considered, reducing the clinical utility of the models. Conclusions Our results suggest that the choice of approach in the analysis of ECG should be based on the amount of data available, preferring more standard models for small datasets. Moreover, imbalance correction methods should be avoided when developing clinical prediction models, where calibration is crucial.

The genomic variation of the Italian peninsula populations is currently under characterised: the only Italian whole-genome reference is represented by the Tuscans from the 1000 Genome Project. To address this issue, we sequenced a total of 947 Italian samples from three different geographical areas. First, we defined a new Italian Genome Reference Panel (IGRP1.0) for imputation, which improved imputation accuracy, especially for rare variants, and we tested it by GWAS analysis on red blood traits. Furthermore, we extended the catalogue of genetic variation investigating the level of population structure, the pattern of natural selection, the distribution of deleterious variants and occurrence of human knockouts (HKOs). Overall the results demonstrate a high level of genomic differentiation between cohorts, different signatures of natural selection and a distinctive distribution of deleterious variants and HKOs, confirming the necessity of distinct genome references for the Italian population.

Idiopathic pulmonary fibrosis (IPF) is a rare and severe disease with a median survival of ~3 years. Nintedanib (NTD) has been shown to be useful in controlling interstitial lung disease (ILD) in IPF. Here we describe the experience of NTD use in IPF in a real-life setting. Objective. Our objective was to examine the safety profile and efficacy of nintedanib even in subjects treated with anticoagulants. Clinical data of patients with IPF treated with NTD at our center were retrospectively evaluated at baseline and at 6 and 12 months after the introduction of NTD. The following parameters were recorded: IPF clinical features, NTD tolerability, and pulmonary function tests (PFT) (i.e., Forced Vital Capacity (FVC) and carbon monoxide diffusing capacity (DLCO)). In total, 56 IPF patients (34% female and 66% male, mean onset age: 71 ± 11 years, mean age at baseline: 74 ± 9 years) treated with NTD were identified. At enrollment, HRCT showed an UIP pattern in 45 (80%) and a NSIP in 11 (20%) patients. For FVC and FEV1 we found no significant change between baseline and 6 months, but for DLCO we observed a decrease (p = 0.012). We identified a significant variation between baseline and 12 months for FEV1 (p = 0.039) and for DLCO (p = 0.018). No significant variation was observed for FVC. In the cohort, 18 (32%) individuals suspended NTD and 10 (18%) reduced the dosage. Among individuals that suspended the dosage, 14 (78%) had gastrointestinal (GI) collateral effects (i.e., diarrhea being the most common complaint (67%), followed by nausea/vomiting (17%) and weight loss (6%). Bleeding episodes have also not been reported in patients taking anticoagulant therapy. (61%). One patient died within the first 6 months and two subjects died within the first 12 months. In a real-life clinical scenario, NTD may stabilize the FVC values in IPF patients. However, GI side effects are frequent and NTD dose adjustment may be necessary to retain the drug in IPF patients. This study confirms the safety of NTD, even in patients treated with anticoagulant drugs.

Dyskerin is a nucleolar protein involved in the small nucleolar RNA (snoRNA)-guided pseudouridylation of specific uridines on ribosomal RNA (rRNA), and in the stabilization of the telomerase RNA component (hTR). Loss of function mutations in DKC1 causes X-linked dyskeratosis congenita, which is characterized by a failure of proliferating tissues and increased susceptibility to cancer. However, several tumors show dyskerin overexpression. We observed that patients with primary breast cancers with high dyskerin levels are more frequently characterized by shorter survival rates and positive lymph node status than those with tumors with a lower dyskerin expression. To functionally characterize the effects of high dyskerin expression, we generated stably overexpressing DKC1 models finding that increased dyskerin levels conferred a more aggressive cellular phenotype in untransformed immortalized MCF10A cells. Contextually, DKC1 overexpression led to an upregulation of some snoRNAs, including SNORA67 and a significantly increased U1445 modification on 18S rRNA, the known target of SNORA67. Lastly, we found that dyskerin overexpression strongly enhanced the synthetic activity of ribosomes increasing translational efficiency in MCF10A. Altogether, our results indicate that dyskerin may sustain the neoplastic phenotype from an early stage in breast cancer endowing ribosomes with an augmented translation efficiency.

Genome-wide association studies (GWAS) of quantitative electrocardiographic (ECG) traits in large consortia have identified more than 130 loci associated with QT interval, QRS duration, PR interval, and heart rate (RR interval). In the current study, we meta-analyzed genome-wide association results from 30,000 mostly Dutch samples on four ECG traits: PR interval, QRS duration, QT interval, and RR interval. SNP genotype data was imputed using the Genome of the Netherlands reference panel encompassing 19 million SNPs, including millions of rare SNPs (minor allele frequency < 5%). In addition to many known loci, we identified seven novel locus-trait associations: KCND3, NR3C1, and PLN for PR interval, KCNE1, SGIP1, and NFKB1 for QT interval, and ATP2A2 for QRS duration, of which six were successfully replicated. At these seven loci, we performed conditional analyses and annotated significant SNPs (in exons and regulatory regions), demonstrating involvement of cardiac-related pathways and regulation of nearby genes.

Background. Systemic sclerosis (SSc) is an incurable connective tissue disease characterized by decreased peripheral blood perfusion due to microvascular damage and skin thickening/hardening. The microcirculation deficit is typically secondary to structural vessel damage, which can be assessed morphologically and functionally in a variety of ways, exploiting different technologies. Objective. This paper focuses on reviewing new studies regarding the correlation between microvascular damage, endothelial dysfunction, and internal organ involvement, particularly pulmonary changes in SSc. Methods. We critically reviewed the most recent literature on the correlation between blood perfusion and organ involvement. Results. Many papers have demonstrated the link between structural microcirculatory damage and pulmonary involvement; however, studies that have investigated correlations between microvascular functional impairment and internal organ damage are scarce. Overall, the literature supports the correlation between organ involvement and functional microcirculatory impairment in SSc patients. Conclusions. Morphological and functional techniques appear to be emerging biomarkers in SSc, but obviously need further investigation.

Background: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), including granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic granulomatosis with polyangiitis (EGPA), represent a spectrum of systemic disorders characterized by necrotizing inflammation of small- to medium-sized vessels. Nailfold videocapillaroscopy (NVC) is a validated, non-invasive technique routinely employed in the assessment of microvascular involvement in systemic sclerosis and in the differential diagnosis of Raynaud’s phenomenon; its application in the context of AAV, particularly EGPA, has not been investigated yet. The present study aims to assess the presence and the possible pattern of microcirculatory abnormalities detected by NVC in EGPA patients, and to explore potential correlations between capillaroscopic findings and disease activity status. Methods: A total of 29 patients with EGPA (19 women and 10 men), aged between 51 and 73 years, and 29 age- and sex-matched healthy controls were retrospectively enrolled between October 2023 and April 2025, after providing informed consent and meeting the inclusion and exclusion criteria. NVC was conducted in both groups to assess various morphological parameters, and mean capillary density was also calculated. Results: This study observed the presence of capillaroscopic alterations in the EGPA group, including decreased capillary density (38%), neoangiogenesis (72%), rolling (100%), pericapillary stippling (66%), and inverted capillary apex (52%). Overall, when comparing healthy controls with EGPA patients, microcirculatory abnormalities were significantly more prevalent in the latter. Specifically, scores for neoangiogenesis, capillary rolling, pericapillary stippling, and inverted capillary apex showed p-values < 0.001. Conclusions: Our study demonstrates a higher prevalence of four nailfold videocapillaroscopic abnormalities in patients with EGPA compared to healthy controls. However, the identification of these capillaroscopic alterations as specific to EGPA requires further confirmation. Ongoing studies aim to explore the potential role of NVC as a diagnostic marker and to investigate its correlation with the clinical manifestations of EGPA.

Background: Sarcoidosis is a systemic inflammatory disease characterized by an altered inflammatory response. Objective: The aim of this study was to evaluate whether immune system alterations detected by lymphocyte typing in peripheral blood correlate with the severity of sarcoidosis, calculated according to two separate severity scores proposed by Wasfi in 2006 and Hamzeh in 2010. Materials and Methods: Eighty-one patients were recruited, and clinical data and laboratory tests at the time of diagnosis were obtained in order to assess the severity index score and investigate any statistically significant correlation with the cytofluorimetry data. Results: Our data demonstrated that none of the two scores show an association with the level of total lymphocytes or lymphocyte subclasses. Limitations: First of all, the sample taken into consideration is small. The assessment was performed only at disease onset and not during the disease. Furthermore, the severity scores do not take into account disease activity (measured by PET/CT or gallium scintigraphy). Conclusions: Lymphocyte subpopulation values at the time of diagnosis do not appear to correlate with disease severity at onset.