Publications by authors named "P Borrelli"
Background: We aim to investigate the proportion of patients (pts) with long-term cognitive outcomes (CO) of PACC and identify associated features.
Methods: We assessed participants through a neuropsychological assessment. The chi-square test was used for comparisons according with time of NPA (within or beyond 6 months since COVID19) and with previously hospitalization status (hospitalized patients, PH; not hospitalized patients, nPH).
Article Synopsis
- In utero exposure to maternal conditions like obesity and Gestational Diabetes Mellitus (GDM) can negatively impact newborns through changes in DNA methylation patterns.
- The study examined 101 Caucasian mother-infant pairs, finding that lower DNA methylation levels in newborns from GDM mothers were linked to lower birth weight and size.
- Additionally, higher DNA methylation levels on the fetal side of the placenta were observed in obese mothers, correlating with increased maternal cholesterol levels, further influencing the metabolic health of the children.
Article Synopsis
- This study investigates the early diagnosis of preeclampsia (PE) by analyzing biochemical changes and immune system activity in pregnant women.
- Researchers compared 30 women divided into three groups: healthy pregnant women, those with early preeclampsia, and those with late preeclampsia, focusing on various immune markers and lipid profiles.
- Results showed notable increases in immune markers IL-6 and IL-8 in early preeclampsia cases, indicating heightened immune activity, while a decrease in PCSK9 gene expression was observed in early preeclampsia, suggesting potential implications for placental hormone synthesis.
Numerous hydrological applications, such as soil erosion estimation, water resource management, and rain driven damage assessment, demand accurate and reliable rainfall erosivity data. However, the scarcity of gauge rainfall records and the inherent uncertainty in satellite and reanalysis-based rainfall datasets limit rainfall erosivity assessment globally. Here, we present a new global rainfall erosivity dataset (0.
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- - This research presents a new method using deep learning and active learning to create accurate global maps of land susceptibility to wind erosion, employing models like RNN and GRU alongside various interpretation techniques.
- - Thirteen environmental factors were analyzed, and through optimization, eight significant factors (such as wind speed and soil moisture) were selected to predict wind erosion risk.
- - The findings show that the GRU-AL model was the most effective, categorizing global land into different levels of susceptibility, with significant insights into how factors like soil carbon and precipitation interact with erosion risk predictions.