COVID-19 therapy optimization by AI-driven biomechanical simulations.

The COVID-19 disease causes pneumonia in many patients that in the most serious cases evolves into the Acute Distress Respiratory Syndrome (ARDS), requiring assisted ventilation and intensive care. In this context, identification of patients at high risk of developing ARDS is a key point for early clinical management, better clinical outcome and optimization in using the limited resources available in the intensive care units. We propose an AI-based prognostic system that makes predictions of oxygen exchange with arterial blood by using as input lung Computed Tomography (CT), the air flux in lungs obtained from biomechanical simulations and Arterial Blood Gas (ABG) analysis. We developed and investigated the feasibility of this system on a small clinical database of proven COVID-19 cases where the initial CT and various ABG reports were available for each patient. We studied the time evolution of the ABG parameters and found correlation with the morphological information extracted from CT scans and disease outcome. Promising results of a preliminary version of the prognostic algorithm are presented. The ability to predict the evolution of patients' respiratory efficiency would be of crucial importance for disease management.

Preliminary results in using Deep Learning to emulate BLOB, a nuclear interaction model.

PURPOSE: A reliable model to simulate nuclear interactions is fundamental for Ion-therapy. We already showed how BLOB ("Boltzmann-Langevin One Body"), a model developed to simulate heavy ion interactions up to few hundreds of MeV/u, could simulate also 12C reactions in the same energy domain. However, its computation time is too long for any medical application. For this reason we present the possibility of emulating it with a Deep Learning algorithm. METHODS: The BLOB final state is a Probability Density Function (PDF) of finding a nucleon in a position of the phase space. We discretised this PDF and trained a Variational Auto-Encoder (VAE) to reproduce such a discrete PDF. As a proof of concept, we developed and trained a VAE to emulate BLOB in simulating the interactions of 12C with 12C at 62 MeV/u. To have more control on the generation, we forced the VAE latent space to be organised with respect to the impact parameter (b) training a classifier of b jointly with the VAE. RESULTS: The distributions obtained from the VAE are similar to the input ones and the computation time needed to use the VAE as a generator is negligible. CONCLUSIONS: We show that it is possible to use a Deep Learning approach to emulate a model developed to simulate nuclear reactions in the energy range of interest for Ion-therapy. We foresee the implementation of the generation part in C++ and to interface it with the most used Monte Carlo toolkit: Geant4.

MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer.

PURPOSE: To develop and validate an Artificial Intelligence (AI) model based on texture analysis of high-resolution T2 weighted MR images able 1) to predict pathologic Complete Response (CR) and 2) to identify non-responders (NR) among patients with locally-advanced rectal cancer (LARC) after receiving neoadjuvant chemoradiotherapy (CRT). METHOD: Fifty-five consecutive patients with LARC were retrospectively enrolled in this study. Patients underwent 3 T Magnetic Resonance Imaging (MRI) acquiring T2-weighted images before, during and after CRT. All patients underwent complete surgical resection and histopathology was the gold standard. Textural features were automatically extracted using an open-source software. A sub-set of statistically significant textural features was selected and two AI models were built by training a Random Forest (RF) classifier on 28 patients (training cohort). Model performances were estimated on 27 patients (validation cohort) using a ROC curve and a decision curve analysis. RESULTS: Sixteen of 55 patients achieved CR. The AI model for CR classification showed good discrimination power with mean area under the receiver operating curve (AUC) of 0.86 (95% CI: 0.70, 0.94) in the validation cohort. The discriminatory power for the NR classification showed a mean AUC of 0.83 (95% CI: 0.71,0.92). Decision curve analysis confirmed higher net patient benefit when using AI models compared to standard-of-care. CONCLUSIONS: AI models based on textural features of MR images of patients with LARC may help to identify patients who will show CR at the end of treatment and those who will not respond to therapy (NR) at an early stage of the treatment.

Expression in mammalian cells, purification and characterization of recombinant human pancreatic ribonuclease.

A synthetic cDNA coding for human pancreatic RNase, equipped with a secretion signal sequence, was cloned and stably expressed in Chinese hamster ovary cells. The recombinant RNase, secreted into the culture medium, was purified and characterized. It was found to be indistinguishable, by structural and catalytic parameters, from the enzyme isolated from human pancreas. Furthermore, the glycosylated forms were separated from the non-glycosylated form. Up until now, human RNases have been isolated only in small amounts from autopic specimens. This has hindered the exploitation of a human RNase for the construction of immunotolerated immunotoxins. On the other hand, the availability of an effective system for the expression of a human RNase may render feasible the transfer, by protein engineering, of the interesting pharmacological actions of non-human RNase [1993 Trends Cell Biol. 3, 106-109] to an immunotolerated, human RNase.

Repair of isopeptide bonds by protein carboxyl O-methyltransferase: seminal ribonuclease as a model system.

Previous work has shown that in the peptide segment 62-76 of naturally deamidated alpha subunit of bovine seminal ribonuclease (BS-RNase) the alpha-carboxyl group of iso-Asp67 is selectively methylated by S-adenosylmethionine:protein carboxyl O-methyltransferase [Di Donato, A., Galletti, P., & D'Alessio, G. (1986) Biochemistry 25, 8361-8368]. In the present study this reaction has been characterized, by using the tryptic segment 62-76 of the protein chain (peptide alpha 16). The peptide is stoichiometrically methyl esterified with a Km of 6.17 microM and a Vmax of 19.56 nmol min-1 mg-1, and the product of demethylation has been identified as the cyclic succinimidyl derivative of iso-Asp67-Gly68. The cleavage of the succinimidyl ring yields two isomeric peptides containing an aspartyl residue (peptide alpha 17) and an isoaspartyl residue (peptide alpha 16). On the basis of these results conditions were defined in which repeated cycles of methylation-demethylation led to an effective conversion of peptide alpha 16 into peptide alpha 17, a process that can be interpreted as the repair of an altered isopeptide bond. When the methyl esterification reaction was studied on the native dimeric isoenzymes of seminal RNase and on catalytically active monomeric derivatives, including a stabilized alpha-type subunit, the results of these experiments showed that none of the protein forms were substrates for the methyltransferase. Only the unfolded alpha-type subunit was methylated to a stoichiometric extent. These results indicate that the repair of altered isopeptide bonds is chemically feasible in peptides but is hindered in the case of seminal RNase by its three-dimensional structure.

Distribution of radiolabelled azelaic cid in eye membranes and fluids of rabbits.

A direct cytotoxic effect of azelaic acid on melanocytes of human melanoma has been demonstrated. In view of a possible future therapeutic employment of this drug in the treatment of primary ocular melanoma, we investigated the route of choice of azelaic acid administration in rabbits. Our results evidenced a suden and direct blood absorption of topically (by retrobulbar injection) administered azelaic acid. This is in agreement with the high water solubility of azelaic acid sodium salt. These preliminary reports indicate that the elective route of azelaic administration in primitive eye melanoma is intravenously by continuous infusion.