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Anti-double-stranded DNA antibodies (anti-dsDNA) are considered a specific marker for systemic lupus erythematosus (SLE). Though the Farr technique was once the reference method for their detection, it has been almost entirely replaced by more recently developed assays. However, there is still no solid evidence of the commutability of these methods in terms of diagnostic accuracy and their correlation with the Crithidia luciliae immunofluorescence test (CLIFT). Anti-dsDNA antibody levels were measured in 80 subjects: 24 patients with SLE, 36 disease controls drawn from different autoimmune rheumatic diseases (14 systemic sclerosis, 10 Sjögren's syndrome, nine autoimmune myositis, three mixed connective tissue disease), 10 inflammatory arthritis and 10 apparently healthy blood donors by eight different methods: fluorescence enzyme immunoassay, microdot array, chemiluminescent immunoassay (two assays), multiplex flow immunoassay, particle multi-analyte technology immunoassay and two CLIFT. At the recommended manufacturer cut-off, the sensitivity varied from 67% to 92%, while the specificity ranged from 84% to 98%. Positive agreement among CLIFT and the other assays was higher than negative agreement. Mean agreement among methods assessed by the Cohen's kappa was 0.715, ranging from moderate (0.588) to almost perfect (0.888). Evaluation of the concordance among quantitative values by regression analysis showed a poor correlation index (mean r2, 0.66). The present study shows that current technologies for anti-dsDNA antibody detection are not fully comparable. In particular, their different correlation with CLIFT influences their positioning in the diagnostic algorithm for SLE (either in association or sequentially). Considering the high intermethod variability, harmonization and commutability of anti-dsDNA antibody testing remains an unachieved goal.

Recent years have witnessed the rise of pathomics as a mean to describe histopathological images with quantitative biomarkers for predictive and prognostic ends, combining digital pathology, omic science and artificial intelligence. This novel research branch is the counterpart of radiomics which pursues the same aims extracting knowledge from radiological images. In this paper, we present the design of a pathomic deep learning-based system to predict the treatment outcome in non-small cell lung cancer patients. We describe the system design and optimization under the condition of limited data and limited training, with corresponding tests. The experimental results show the feasibility of the proposed scalable architecture providing also a comparison between different transfer learning strategies.

Lung cancer is by far the leading cause of cancer death among both men and women. Radiation therapy is one of the main approaches to lung cancer treatment, and its planning is crucial for the therapy outcome. However, the current practice that uniformly delivers the dose does not take into account the patient-specific tumour features that may affect treatment success. Since radiation therapy is by its very nature a sequential procedure, Deep Reinforcement Learning (DRL) is a well-suited methodology to overcome this limitation. In this respect, in this work we present a DRL controller optimizing the daily dose fraction delivered to the patient on the basis of CT scans collected over time during the therapy, offering a personalized treatment not only for volume adaptation, as currently intended, but also for daily fractionation. Furthermore, this contribution introduces a virtual radiotherapy environment based on a set of ordinary differential equations modelling the tissue radiosensitivity by combining both the effect of the radiotherapy treatment and cell growth. Their parameters are estimated from CT scans routinely collected using the Particle Swarm Optimization algorithm. This permits the DRL to learn the optimal behaviour through an iterative trial and error process with the environment. We performed several experiments considering three rewards functions modelling treatment strategies with different tissue aggressiveness and two exploration strategies for the exploration-exploitation dilemma. The results show that our DRL approach can adapt to radiation therapy treatment, optimizing its behaviour according to the different reward functions and outperforming the current clinical practice.

The COVID-19 disease caused by the coronavirus SARS-nCoV2 is currently a global public health threat and Italy is one of the countries mostly suffering from this epidemic. It is therefore important to analyze epidemic data, considering also that the government deployed laws limiting the societal activities. We model COVID-19 dynamics with a SIQR (susceptible - infectious - quarantined - recovered) model, where we take into account the temporal variability of its parameters. Particle Swarm Optimization is used to find out the best parameters in the case of Italy and of Italian regions where the epidemic has the greatest impact. The basic reproductive number is estimated by a novel approach that averages out different PSO fits computed considering different temporal time-windows and reducing possible noise in the data. The results on data collected from February 24 to April 24 show that our approach is able to fit the data with low errors and that the basic reproductive number is characterized by a descending trend in time from 3.5 to a value below 1.