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Eleonora Sabetta

Researcher at Vita-Salute San Raffaele University

Publications -  8
Citations -  183

Eleonora Sabetta is an academic researcher from Vita-Salute San Raffaele University. The author has contributed to research in topics: Vaccination & Population. The author has an hindex of 3, co-authored 6 publications receiving 51 citations.

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Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests.

TL;DR: ML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients in developing countries, or in countries facing an increase in contagions.
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The Gender Impact Assessment among Healthcare Workers in the SARS-CoV-2 Vaccination—An Analysis of Serological Response and Side Effects

TL;DR: In this article, a large cohort of healthcare workers participating in the Italian vaccination campaign against SARS-CoV-2 has been studied to establish the impact of sex and gender on vaccination coverage using the Gender Impact Assessment approach.
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Long-term antibody persistence and exceptional vaccination response on previously SARS-CoV-2 infected subjects.

TL;DR: In this article, the authors verify whether previously SARS-CoV-2 infected subjects, a considerable portion of the population, should receive the same vaccination treatment of seronegative individuals.

Routine blood analysis greatly reduces the false-negative rate of RT-PCR testing for Covid-19.

TL;DR: This study retrospectively analyzed 24 patients showing multiple and inconsistent RT-PCR, test during their first hospitalization period, and compared the genetic tests results with their AST and LDH levels and showed that when considering the hematological parameters, the RT- PCR false-negative rates were reduced by almost 4-fold.
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Role of time-normalized laboratory findings in predicting COVID-19 outcome.

TL;DR: This longitudinal study is the first study describing the laboratory characteristics of Italian COVID-19 patients on a normalized time-scale and shows that some parameters can be considered as early prognostic indicators whereas others exhibit statistically significant differences only at a later stage of the disease.