Publications by authors named "Adolfo Ruiz-Sanmartin"

In COVID-19, hyperinflammatory and dysregulated immune responses contribute to severity. Patients with pre-existing autoimmune conditions can therefore be at increased risk of severe COVID-19 and/or associated sequelae, yet SARS-CoV-2 infection in this group has been little studied. Here, we performed single-cell analysis of peripheral blood mononuclear cells from patients with three major autoimmune diseases (rheumatoid arthritis, psoriasis, or multiple sclerosis) during SARS-CoV-2 infection.

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Early diagnosis and appropriate treatments are crucial to reducing mortality risk in septic patients. Low SOFA scores and current biomarkers may not adequately discern patients that could develop severe organ dysfunction or have an elevated mortality risk. The aim of this prospective observational study was to evaluate the predictive value of the biomarkers mid-regional pro-adrenomedullin (MR-proADM), procalcitonin (PCT), C-reactive protein (CRP), and lactate for 28-day mortality in patients with sepsis, and patients with a SOFA score ≤6.

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Article Synopsis
  • - The study aims to find new protein biomarkers for early diagnosis of sepsis and to understand their connection to organ dysfunction and mortality using advanced omics techniques.
  • - Researchers analyzed plasma proteins from 141 adult sepsis patients using mass spectrometry, identifying 177 proteins and establishing that nine are linked to organ dysfunction and twenty-two are associated with mortality.
  • - Results show these biomarkers have high accuracy and reliability, suggesting that specific protein patterns can indicate serious outcomes in sepsis patients.
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Background: COVID-19 manifests with a wide spectrum of clinical phenotypes, ranging from asymptomatic and mild to severe and critical. Severe and critical COVID-19 patients are characterized by marked changes in the myeloid compartment, especially monocytes. However, little is known about the epigenetic alterations that occur in these cells during hyperinflammatory responses in severe COVID-19 patients.

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Sepsis is a heterogeneous disease with variable clinical course and several clinical phenotypes. As it is associated with an increased risk of death, patients with this condition are candidates for receipt of a very well-structured and protocolized treatment. All patients should receive the fundamental pillars of sepsis management, which are infection control, initial resuscitation, and multiorgan support.

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Article Synopsis
  • Serological tests are crucial for managing COVID-19, focusing on diagnostics, surveillance, and immunity studies.
  • A new plasmonic biosensor assay identifies SARS-CoV-2 antibodies in under 15 minutes using a custom-designed sensor, without needing signal amplification.
  • Clinical validation shows the biosensor offers high diagnostic sensitivity (99%) and specificity (100%), making it a reliable tool for rapid COVID-19 serology in various settings.
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Microbial challenges, such as widespread bacterial infection in sepsis, induce endotoxin tolerance, a state of hyporesponsiveness to subsequent infections. The participation of DNA methylation in this process is poorly known. In this study, we perform integrated analysis of DNA methylation and transcriptional changes following exposure to gram-negative bacterial lipopolysaccharide, together with analysis of monocytes from septic patients.

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Background: Sepsis, a life-threatening organ dysfunction caused by a dysregulated systemic immune response to infection, associates with reduced responsiveness to subsequent infections. How such tolerance is acquired is not well understood but is known to involve epigenetic and transcriptional dysregulation.

Methods: Bead arrays were used to compare global DNA methylation changes in patients with sepsis, non-infectious systemic inflammatory response syndrome, and healthy controls.

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Background: Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the daunting task of extracting usable knowledge from these data using algorithmic methods. In the medical context this may for instance realized through the design of medical decision support systems for diagnosis, prognosis and patient management.

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