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Calabrese, A.

Publications and source records attributed to Calabrese, A..

3 recordsLinked to original sources

Differential serum binding patterns predicting healthy subjects and cancer patients

A viral exposure signature (VES) has been previously described predicting the development of Hepatocellular carcinoma (HCC) in at-risk patients. This has been achieved by a serological profiling of the viral infection history using a synthetic human virome including >100k epitopes (VirScan). In the present study we applied the same VirScan strategy to identify a differential serum binding pattern (DSBP) for classifying patients of different cancer types from healthy individuals. In particular, the healthy group included both age-matched (ADULTS) as well as elderly (ELDERS) individuals, the latter counting also nonagenarians and centenarians. The class comparison performed with serological data show DSBPs supporting class predictions, as confirmed by the receiver operating characteristic (ROC) curve analysis. Antibody responses supporting the class predictions are specific to peptides from persistent herpesviruses, acute-infecting viruses and, consistently in all comparisons, human respiratory syncytial virus (HRSV). Strikingly, the DSB of the ELDERS vs. CANCER comparison is characterized by higher titers in the healthy subjects; on the contrary, the DSB of the ADULTS vs. CANCER comparison is characterized by lower titers in the healthy subjects. Overall, the results show a differential serological binding pattern predicting healthy individuals (ADULTS or ELDERS) from patients with different types of cancer. Such results provide the first evidence suggesting a close link between anti-microbial immunity and cancer development. They may be of the highest relevance in terms of predictive, diagnostic and/or prognostic impact in oncology.

immunology↗

GENis, an open-source multi-tier forensic DNA information system

GENis is an open source multi-tier information system developed to run a forensic DNA database at local, regional and national levels.1 It was conceived as a highly customizable system, enforcing several security policies including: data encryption, double factor identification, structure of users roles and permissions, system-wide secure log auditing, non-repudiation protocols and a blockchain-based option to reinforce genetic profiles integrity. GENis is able to perform genetic profile queries of autosomal STRs and its design follows ENFSI2 and ISFG3 standards and recommendations. In this work, we present a summary of GENis architecture, the implemented matching rule definitions, and the Bayesian framework used to provide statistical significance of cold hits, that includes a new strategy to identify common contributors in mixture pairs.

bioinformatics↗

A Statistical Model of Shared Variability in the Songbird Auditory System

Vocal communication evokes robust responses in primary auditory cortex (A1) of songbirds, and single neurons from superficial and deep regions of A1 have been shown to respond selectively to songs over complex, synthetic sounds. However, little is known about how this song selectivity arises and manifests itself on the level of networks of neurons in songbird A1. Here, we examined the network-level coding of song and synthetic sounds in A1 by simultaneously recording the responses of multiple neurons in unanesthetized zebra finches. We developed a latent factor model of the joint simultaneous activity of these neural populations, and found that the shared variability in the activity has a surprisingly simple structure; it is dominated by an unobserved latent source with one degree-of-freedom. This simple model captures the structure of the correlated activity in these populations in both spontaneous and stimulus-driven conditions, and given both song and synthetic stimuli. The inferred latent variability is strongly suppressed under stimulation, consistent with similar observations in a range of mammalian cortical regions.

neuroscience↗