Shock : molecular, cellular, and systemic pathobiological aspects and therapeutic approaches : the official journal the Shock Society, the European Shock Society, the Brazilian Shock Society, the International Federation of Shock Societies
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Hypoxia inducible factor 1 alpha (HIF-1α) is linked to the metabolic and immune alterations in septic patients. Stabilization of HIF-1α by hypoxia or inflammation promotes the expression of several genes related to glycolytic metabolism, angiogenesis, coagulation, cell proliferation, and apoptosis. Here, we analyzed public available blood transcriptome datasets from septic patients and evaluated by PCR array the expression of HIF-1α and other hypoxia responsive genes in peripheral blood mononuclear cells from patients with sepsis secondary to community acquired infections. ⋯ EGLN1, EGLN2, and HIF1AN, inhibitors of HIF-1α activation were downregulated in patients, regardless of the outcome, while HIF-1α and other target genes, such as PDK1 and HMOX1, expression were higher in non-survivors than in survivors, mainly at D7. Non-survivor patients also presented a higher SOFA score and lower PaO2/FiO2 ratio. Our results indicate a differential modulation of hypoxia pathway in leukocytes between septic patients who survived and those who did not survive with an increased intensity at D7, which is possibly influenced by disease severity and may affect the immune response in sepsis.
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Advancing age is an independent predictor of mortality in septic patients. Recent animal studies were unable to reflect this clinical pathophysiological process, largely hampering the development of new efficacious therapies. Triggering receptor expressed on myeloid cells-2 (TREM-2) is a novel immune regulator with multiple activities. However, very little is known about the regulatory role of TREM-2 in sepsis upon aging. ⋯ TREM-2 prolonged survival of aged mice from sepsis by finely modulating the IL-23/IL-17A immune pathway. These results provide previously unidentified mechanistic insight into immune regulation by TREM-2 and new therapeutic targets in sepsis upon aging.
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Multicenter Study
Predicting the Need for Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.
Previous models on prediction of shock mostly focused on septic shock and often required laboratory results in their models. The purpose of this study was to use deep learning approaches to predict vasopressor requirement for critically ill patients within 24 h of intensive care unit (ICU) admission using only vital signs. ⋯ We used Bi-LSTM to develop a model to predict the need for vasopressor for critically ill patients for the first 24 h of ICU admission. With attention mechanism, respiratory rate, mean arterial pressure, and heart rate were identified as key sequential determinants of vasopressor requirements.