Publications by authors named "S Di Maria"

Efforts to understand radical stability have led to considerable progress in radical chemistry. In this article, we investigated a novel approach to enhancing the radical stability of carbon-centered radicals through space electron delocalization within [2,2]-paracyclophanes. Alkoxyamines possessing a paracyclophane scaffold exploit face-to-face π-π-interactions between the aromatic rings to effectively lower bond dissociation energy (BDE) for NO-C bond homolysis.

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Background/objectives: As people with human immunodeficiency virus experience longer life expectancy, other causes of morbidity and mortality are being increasingly identified. The incidence of non-alcoholic fatty liver disease has recently been on the rise in Indonesia. People with human immunodeficiency virus on antiretroviral therapy are also at an increased risk of having non-alcoholic fatty liver disease.

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Introduction: Pre-alerts from paramedics to trauma centers are important for ensuring the highest quality of trauma care. Despite this, there is a paucity of data to support best practices in trauma pre-alert notifications. Within the trauma system of Ontario, Canada, the provincial critical care transport organization, Ornge, provides pre-alerts to major trauma centers, but standardization is currently lacking.

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Background: Conventional mammography remains the primary imaging modality for state-of-the-art breast imaging practice and its benefit (both on diagnostic and screening) was largely reported. In mammography, the typical Mean Glandular Dose (MGD) from X-ray radiation to the breast spans, on average, from 1 to 10 mGy, depending on breast thicknesses, percentage of fibroglandular tissue, and on the examination purpose.

Methods: The aim of this narrative review is to describe the extent of radiation risk in X-ray breast imaging and discuss the main steps and parameters (e.

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Background: Healthcare-associated infections are infections that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome in healthcare, can be entirely prevented, and pose a burden in terms of financial and human costs. With the development of new AI and ML algorithms, hospitals could develop new and automated surveillance and prevention models for HAIs, leading to improved patient safety.

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