A historical perspective of biomedical explainable AI research

The black-box nature of most artificial intelligence (AI) models encourages the development of explainability methods to engender trust into the AI decision-making process. Such methods can be broadly categorized into two main types: post hoc explanations and inherently interpretable algorithms. We aimed at analyzing the possible associations between COVID-19 and the push of explainable AI (XAI) to the forefront of biomedical research. We automatically extracted from the PubMed database biomedical XAI studies related to concepts of causality or explainability and manually labeled 1,603 papers with respect to XAI categories. To compare the trends pre- and post-COVID-19, we fit a change point detection model and evaluated significant changes in publication rates. We show that the advent of COVID-19 in the beginning of 2020 could be the driving factor behind an increased focus concerning XAI, playing a crucial role in accelerating an already evolving trend. Finally, we present a discussion with future societal use and impact of XAI technologies and potential future directions for those who pursue fostering clinical trust with interpretable machine learning models.</p

Clinical features and comorbidity pattern of HCV infected migrants compared to native patients in care in Italy: A real-life evaluation of the PITER cohort

Background: Direct-acting antivirals are highly effective for the treatment of hepatitis C virus (HCV) infection, regardless race/ethnicity. We aimed to evaluate demographic, virological and clinical data of HCV-infected migrants vs. natives consecutively enrolled in the PITER cohort. Methods: Migrants were defined by country of birth and nationality that was different from Italy. Mann-Whitney U test, Chi-squared test and multiple logistic regression were used. Results: Of 10,669 enrolled patients, 301 (2.8%) were migrants: median age 47 vs. 62 years, (p &lt; 0.001), females 56.5% vs. 45.3%, (p &lt; 0.001), HBsAg positivity 3.8% vs. 1.4%, (p &lt; 0.05). Genotype 1b was prevalent in both groups, whereas genotype 4 was more prevalent in migrants (p &lt; 0.05). Liver disease severity and sustained virologic response (SVR) were similar. A higher prevalence of comorbidities was reported for natives compared to migrants (p &lt; 0.05). Liver disease progression cofactors (HBsAg, HIV coinfection, alcohol abuse, potential metabolic syndrome) were present in 39.1% and 47.1% (p &gt; 0.05) of migrants and natives who eradicated HCV, respectively. Conclusion: Compared to natives, HCV-infected migrants in care have different demographics, HCV genotypes, viral coinfections and comorbidities and similar disease severity, SVR and cofactors for disease progression after HCV eradication. A periodic clinical assessment after HCV eradication in Italians and migrants with cofactors for disease progression is warranted

International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist

Device and method for generating random bit sequences

Device for generating random bit sequences comprising: at least one Silicon Photomultiplier sensor configured to generate a sequence of endogenous random current pulses as a result of an impact ionisation driven self-amplification of thermally generated charge carriers to which the at least one SiPM sensor can be subject; a data processing unit configured to receive said sequence of endogenous random current pulses and to determine a random bit sequence to be proved to an end-user

Qualification and performance of a neutron detector system with enhanced gamma discrimination, based on Silicon Photomultiplier arrays coupled to an EJ-299-34 scintillator

Silicon Photomultipliers (SiPM) are a new class of photon sensors featuring high detection efficiency, single photon sensitivity and extended dynamic range; they represent a valuable solution for compact, lightweight hand-held instruments, possibly battery operated. This paper reports the studies performed with an array of 16 SiPM coupled to a novel plastic scintillator sensitive to fast neutrons with gamma discrimination, a proof-of-concept towards the development of instruments for border control against the illicit trafficking of radioactive material. The array of SiPM provides 16 individual outputs but, in this application, a single signal proportional to the total amount of light seen by the sensor is required. Two different Front-End EIectronics (FEE) circuits to sum the analogue signals have been considered and compared. The comparison has been based on the impact of the FEE on the neutron-gamma discrimination capability, based on on Pulse Shape Discrimination (PSD) algorithms. The optimal configuration has been shown to provide a Figure of Merit (FoM) of 2.56\ub10.0I in the energy window of 1 - I.5~mathrm Mmathrm emathrm V, confirming the state-of-the-art performance of this solution

Measuring Nonclassicality of Mesoscopic Twin-Beam States with Silicon Photomultipliers

The study of nonclassical properties of quantum states is a relevant topic for fundamental Quantum Optics and Quantum Information applications. In the mesoscopic domain, promising results have been obtained using photon-number-resolving detectors. Here we show recent results achieved with the class of Silicon Photomultipliers: by a proper analysis of the output signal, the nonclassicality of twin-beam states can be detected and exploited