Increased TNF-α production in response to IL-6 in patients with systemic inflammation without infection.

Acute systemic inflammation can lead to life-threatening organ dysfunction. In patients with sepsis, systemic inflammation is triggered in response to infection, but in other patients, a systemic inflammatory response syndrome (SIRS) is triggered by non-infectious events. IL-6 is a major mediator of inflammation, including systemic inflammatory responses. In homeostatic conditions, when IL-6 engages its membrane-bound receptor on myeloid cells, it promotes pro-inflammatory cytokine production, phagocytosis, and cell migration. However, under non-physiologic conditions, such as SIRS and sepsis, leucocyte dysfunction could modify the response of these cells to IL-6. So, our aim was to evaluate the response to IL-6 of monocytes from patients diagnosed with SIRS or sepsis. We observed that monocytes from patients with SIRS, but not from patients with sepsis, produced significantly more TNF-α than monocytes from healthy volunteers, after stimulation with IL-6. Monocytes from SIRS patients had a significantly increased baseline phosphorylation of the p65 subunit of NF-κB, with no differences in STAT3 phosphorylation or SOCS3 levels, compared with monocytes from septic patients, and this increased phosphorylation was maintained during the IL-6 activation. We found no significant differences in the expression levels of the membrane-bound IL-6 receptor, or the serum levels of IL-6, soluble IL-6 receptor, or soluble gp130, between patients with SIRS and patients with sepsis. Our results suggest that, during systemic inflammation in the absence of infection, IL-6 promotes TNF-α production by activating NF-κB, and not the canonical STAT3 pathway.

Accurate diagnosis of sepsis using a neural network: Pilot study using routine clinical variables.

BACKGROUND AND OBJECTIVES: Sepsis is a severe infection that increases mortality risk and is one if the main causes of death in intensive care units. Accurate detection is key to successful interventions, but diagnosis of sepsis is complicated because the initial signs and symptoms are not specific. Biomarkers that have been proposed have low specificity and sensitivity, are expensive, and not available in every hospital. In this study, we propose the use of artificial intelligence in the form of a neural network to diagnose sepsis using only common laboratory tests and vital signs that are routine and widely available. METHODS: A retrospective, cross sectional cohort of 113 patients from an intensive care unit, each with 48 routinely evaluated vital signs and biochemical parameters was used to train, validate and test a neural network with 48 inputs, 10 neurons in a single hidden layer and one output. The sensitivity and specificity of the neural network as a point sampled diagnostic test was calculated. RESULTS: All but one case were correctly diagnosed by the neural network, with 91% sensitivity and 100% specificity in the validation data set, and 100% sensitivity and specificity in the test data set. CONCLUSIONS: The designed neural network system can identify patients with sepsis, with minimal resources using standard laboratory tests widely available in most health care facilities. This should reduce the burden on the medical staff of a difficult diagnosis and should improve outcomes for patients with sepsis.