() is a coagulase-negative staphylococci. This group of bacteria is typically part of the skin flora but can become pathogenic in susceptible hosts, such as preterm infants with prolonged stays in neonatal intensive care units. Two preterm newborns with late-onset sepsis caused by , one of whom also developed meningitis.
View Article and Find Full Text PDFSerum metabolome analysis is essential for identifying disease biomarkers and predicting patient outcomes in precision medicine. Thus, this study aims to compare Ultra-High Performance Liquid Chromatography-High-Resolution Mass Spectrometry (UHPLC-HRMS) with Fourier Transform Infrared (FTIR) spectroscopy in acquiring the serum metabolome of critically ill patients, associated with invasive mechanical ventilation (IMV), and predicting death. Three groups of 8 patients were considered.
View Article and Find Full Text PDFIdentifying high-risk patients, particularly in intensive care units (ICUs), enhances treatment and reduces severe outcomes. Since the pandemic, numerous studies have examined COVID-19 patient profiles and factors linked to increased mortality. Despite six pandemic waves, to the best of our knowledge, there is no extensive comparative analysis of patients' characteristics across these waves in Portugal.
View Article and Find Full Text PDFDelirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium.
View Article and Find Full Text PDFRobust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than disparities between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g.
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