Steatosis, steatohepatitis and cancer immunotherapy: An intricate story

Immune checkpoint inhibitors represent one of the most significant recent advances in clinical oncology, since they dramatically improved the prognosis of deadly cancers such as melanomas and lung cancer. Treatment with these drugs may be complicated by the occurrence of clinically-relevant adverse drug reactions, most of which are immune-mediated, such as pneumonitis, colitis, endocrinopathies, nephritis, Stevens Johnson syndrome and toxic epidermal necrolysis. Drug-induced steatosis and steatohepatitis are not included among the typical forms of cancer immunotherapy-induced liver toxicity, which, instead, usually occurs as a panlobular hepatitis with prominent lymphocytic infiltrates. Nonetheless, non-alcoholic fatty liver disease is a risk factor for immunotherapy-induced hepatitis, and steatosis and steatohepatitis are frequently observed in this condition. In the present review we discuss how these pathology findings could be explained in the context of current models suggesting immune-mediated pathogenesis for steatohepatitis. We also review evidence suggesting that in patients with hepatocellular carcinoma, the presence of steatosis or steatohepatitis could predict a poor therapeutic response to these agents. How these findings could fit with immune-mediated mechanisms of these liver diseases will also be discussed

Audio-based anomaly detection on edge devices via self-supervision and spectral analysis

In real-world applications, audio surveillance is often performed by large models that can detect many types of anomalies. However, typical approaches are based on centralized solutions characterized by significant issues related to privacy and data transport costs. In addition, the large size of these models prevented a shift to contexts with limited resources, such as edge devices computing. In this work we propose conv-SPAD, a method for convolutional SPectral audio-based Anomaly Detection that takes advantage of common tools for spectral analysis and a simple autoencoder to learn the underlying condition of normality of real scenarios. Using audio data collected from real scenarios and artificially corrupted with anomalous sound events, we test the ability of the proposed model to learn normal conditions and detect anomalous events. It shows performances in line with larger models, often outperforming them. Moreover, the model’s small size makes it usable in contexts with limited resources, such as edge devices hardware

A deep learning approach for detecting security attacks on blockchain

In these last years, Blockchain technologies have been widely used in several application fields to improve data privacy and trustworthiness and security of systems. Although the blockchain is a powerful tool, it is not immune to cyber attacks: for instance, recently (January 2019) a successful 51% attack on Ethereum Classic has revealed security vulnerabilities of its platform. Under a statistical perspective, attacks can be seen as an anomalous observation, with a strong deviation from the regular behavior. Machine Learning is a science whose goal is to learn insights, patterns and outliers within large data repositories; hence, it can be exploit for blockchain attack detection. In this work, we define an anomaly detection system based on a encoder-decoder deep learning model, that is trained exploiting aggregate information extracted by monitoring blockchain activities. Experiments on complete historical logs of Ethereum Classic network prove the capability of the our model to effectively detect the publicly reported attacks. To the best of our knowledge, our approach is the first one that provides a comprehensive and feasible solution to monitor the security of blockchain transactions

Challenges and (Un)Certainties for DNAm Age Estimation in Future

Age estimation is a paramount issue in criminal, anthropological, and forensic research. Because of this, several areas of research have focused on the establishment of new approaches for age prediction, including bimolecular and anthropological methods. In recent years, DNA methylation (DNAm) has arisen as one of the hottest topics in the field. Many studies have developed age- prediction models (APMs) based on evaluation of DNAm levels of many genes in different tissue types and using different methodological approaches. However, several challenges and confounder factors should be considered before using methylation levels for age estimation in forensic contexts. To provide in-depth knowledge about DNAm age estimation (DNAm age) and to understand why it is not yet a current tool in forensic laboratories, this review encompasses the literature for the most relevant scientific works published from 2015 to 2021 to address the challenges and future directions in the field. More than 60 papers were considered focusing essentially on studies that developed models for age prediction in several sample typesinfo:eu-repo/semantics/publishedVersio

A Blood–bone–tooth model for age prediction in forensic contexts

The development of age prediction models (APMs) focusing on DNA methylation (DNAm) levels has revolutionized the forensic age estimation field. Meanwhile, the predictive ability of multi-tissue models with similar high accuracy needs to be explored. This study aimed to build multi-tissue APMs combining blood, bones and tooth samples, herein named blood–bone–tooth-APM (BBT-APM), using two different methodologies. A total of 185 and 168 bisulfite-converted DNA samples previously addressed by Sanger sequencing and SNaPshot methodologies, respectively, were considered for this study. The relationship between DNAm and age was assessed using simple and multiple linear regression models. Through the Sanger sequencing methodology, we built a BBT-APM with seven CpGs in genes ELOVL2, EDARADD, PDE4C, FHL2 and C1orf132, allowing us to obtain a Mean Absolute Deviation (MAD) between chronological and predicted ages of 6.06 years, explaining 87.8% of the variation in age. Using the SNaPshot assay, we developed a BBT-APM with three CpGs at ELOVL2, KLF14 and C1orf132 genes with a MAD of 6.49 years, explaining 84.7% of the variation in age. Our results showed the usefulness of DNAm age in forensic contexts and brought new insights into the development of multi-tissue APMs applied to blood, bone and teethinfo:eu-repo/semantics/publishedVersio

A semi-automatic methodology for tire’s wear evaluation

In this work, the authors aim at developing a reliable and fast methodology to evaluate the wear evolution in tire starting from a complete optical 3D scanning. Starting from a data cloud, a semi-automatic methodology was implemented in MATLAB to extract mean tread radial profiles in correspondence of the desired angular position of the tire. These profiles could be numerically evaluated to establish the presence of irregular wear and the characteristic parameter of the groove depth. The reliability and the robustness of this methodology was firstly tested by applying it to several synthetic case studies modeled in CATIA V5®, where ovalization and presence of defects were also simulated. The groove depth was determined with an error lower than 1% for the ideal model, while the introduction of ovalization and defects leaded to an error of 2.6% in the worst condition. In a second time, the methodology has been successfully applied to experimental measurements carried out in two different wear life of the tire, allowing the tracking of the wear phenomena through the evaluation of the progressive lowering of tread radial profiles

Neuro-Symbolic techniques for Predictive Maintenance

Predictive maintenance plays a key role in the core business of the industry due to its potential in reducing unexpected machine downtime and related cost. To avoid such issues, it is crucial to devise artificial intelligence models that can effectively predict failures. Predictive maintenance current approaches have several limitations that can be overcome by exploiting hybrid approaches such as Neuro-Symbolic techniques. Neuro-symbolic models combine neural methods with symbolic ones leading to improvements in efficiency, robustness, and explainability. In this work, we propose to exploit hybrid approaches by investigating their advantage over classic predictive maintenance approaches