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Biology subjects

Ferraro, A.

Publications and source records attributed to Ferraro, A..

2 recordsLinked to original sources

Nirmatrelvir treatment blunts the development of antiviral adaptive immune responses in SARS-CoV-2 infected mice

Alongside vaccines, antiviral drugs are becoming an integral part of our response to the SARS-CoV-2 pandemic. Nirmatrelvir - an orally available inhibitor of the 3-chymotrypsin-like cysteine protease - has been shown to reduce the risk of progression to severe COVID-19. However, the impact of nirmatrelvir treatment on the development of SARS-CoV-2-specific adaptive immune responses is unknown. Here, by using a mouse model of SARS-CoV-2 infection, we show that nirmatrelvir administration early after infection blunts the development of SARS-CoV-2-specific antibody and T cell responses. Accordingly, upon secondary challenge, nirmatrelvir-treated mice recruited significantly fewer memory T and B cells to the infected lungs and to mediastinal lymph nodes, respectively. Together, the data highlight a potential negative impact of nirmatrelvir treatment with important implications for clinical management and might help explain the virological and/or symptomatic relapse after treatment completion reported in some individuals.

immunology↗

Nanoscale prognosis of colorectal cancer metastasis from AFM image processing of histological sections

Early ascertainment of metastatic tumour phases is crucial to improve cancer survival, formulate an accurate prognostic report of disease advancement and, most important, quantify the metastatic progression and malignancy state of primary cancer cells with a universal numerical indexing system. This work proposes an early improvement of cancer detection with 97 nm spatial resolution by indexing the metastatic cancer phases from the analysis of atomic force microscopy images of human colorectal cancer histological sections. The procedure applies variograms of residuals of Gaussian filtering and theta statistics of colorectal cancer tissue image settings. The methodology elucidates the early metastatic progression at the nanoscale level by setting metastatic indexes and critical thresholds from relatively large histological sections and categorising the malignancy state of a few suspicious cells not identified with optical image analysis. In addition, we sought to detect early tiny morphological differentiations indicating potential cell transition from epithelial cell phenotypes of low to high metastatic potential. The metastatic differentiation, also identified by higher moments of variograms, sets different hierarchical levels for the metastatic progression dynamic, potentially impacting therapeutic cancer protocols.

cancer biology↗