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The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19), resulting in acute respiratory disease, is a worldwide emergency. Because recently it has been found that SARS-CoV is dependent on host transcription factors (TF) to express the viral genes, efforts are required to understand the molecular interplay between virus and host response. By bioinformatic analysis, we investigated human TF that can bind the SARS-CoV-2 sequence and can be involved in viral transcription. In particular, we analysed the key role of TF involved in interferon (IFN) response. We found that several TF could be induced by the IFN antiviral response, specifically some induced by IFN-stimulated gene factor 3 (ISGF3) and by unphosphorylated ISGF3, which were found to promote the transcription of several viral open reading frame. Moreover, we found 22 TF binding sites present only in the sequence of virus infecting humans but not bat coronavirus RaTG13. The 22 TF are involved in IFN, retinoic acid signalling and regulation of transcription by RNA polymerase II, thus facilitating its own replication cycle. This mechanism, by competition, may steal the human TF involved in these processes, explaining SARS-CoV-2's disruption of IFN-I signalling in host cells and the mechanism of the SARS retinoic acid depletion syndrome leading to the cytokine storm. We identified three TF binding sites present exclusively in the Brazilian SARS-CoV-2 P.1 variant that may explain the higher severity of the respiratory syndrome. These data shed light on SARS-CoV-2 dependence from the host transcription machinery associated with IFN response and strengthen our knowledge of the virus's transcription and replicative activity, thus paving the way for new targets for drug design and therapeutic approaches.

Ad oggi l'origine dei fibroblasti, le principali cellule responsabili dello sviluppo della fibrosi renale, è ancora incerta. In un recente lavoro pubblicato da Asada e colleghi, gli Autori dimostrano che la maggior parte dei fibroblasti secernenti eritropoietina nel rene normale hanno un'origine extra renale e provengono da cellule della Cresta Neurale. Durante lo sviluppo della fibrosi renale, queste cellule trans-differenziano in miofibroblasti, perdendo la loro capacità intrinseca di produrre eritropoietina. Questi interessanti dati potrebbero rappresentare il punto di partenza per lo sviluppo di nuovi farmaci capaci da un lato di ridurre la fibrosi renale e dall'altro di preservare la produzione di eritropoietina.

Liquid biopsy is a valuable emerging alternative to tissue biopsy with great potential in the noninvasive early diagnostics of cancer. Liquid biopsy based on single cell analysis can be a powerful approach to identify circulating tumor cells (CTCs) in the bloodstream and could provide new opportunities to be implemented in routine screening programs. Since CTCs are very rare, the accurate classification based on high-throughput and highly informative microscopy methods should minimize the false negative rates. Here, we show that holographic flow cytometry is a valuable instrument to obtain quantitative phase-contrast maps as input data for artificial intelligence (AI)-based classifiers. We tackle the problem of discriminating between A2780 ovarian cancer cells and THP1 monocyte cells based on the phase-contrast images obtained in flow cytometry mode. We compare conventional machine learning analysis and deep learning architectures in the non-ideal case of having a dataset with unbalanced populations for the AI training step. The results show the capacity of AI-aided holographic flow cytometry to discriminate between the two cell lines and highlight the important role played by the phase-contrast signature of the cells to guarantee accurate classification.

We show how reflectarray antenna synthesis can take profit from parallel computing on Graphics Processing Units (GPUs). The presented approach uses an implementation of Particle Swarm Optimization in CUDA language and accelerates the computation of the field radiated by the reflectarray using a GPU- mplemented Non-Uniform FFT routine. Numerical results show how computing time can be kept convenient for the application at hand.

We compare the computational performance of the Fast Marching Method, the Fast Sweeping Method and the Fast Iterative Method to determine a numerical solution to the eikonal equation. We point out how the Fast Iterative Method outperforms the other two thanks to its parallel processing capabilities.