Sialoglycan recognition is a common connection linking acidosis, zinc, and HMGB1 in sepsis.

Blood pH is tightly maintained between 7.35 and 7.45, and acidosis (pH <7.3) indicates poor prognosis in sepsis, wherein lactic acid from anoxic tissues overwhelms the buffering capacity of blood. Poor sepsis prognosis is also associated with low zinc levels and the release of High mobility group box 1 (HMGB1) from activated and/or necrotic cells. HMGB1 added to whole blood at physiological pH did not bind leukocyte receptors, but lowering pH with lactic acid to mimic sepsis conditions allowed binding, implying the presence of natural inhibitor(s) preventing binding at normal pH. Testing micromolar concentrations of divalent cations showed that zinc supported the robust binding of sialylated glycoproteins with HMGB1. Further characterizing HMGB1 as a sialic acid-binding lectin, we found that optimal binding takes place at normal blood pH and is markedly reduced when pH is adjusted with lactic acid to levels found in sepsis. Glycan array studies confirmed the binding of HMGB1 to sialylated glycan sequences typically found on plasma glycoproteins, with binding again being dependent on zinc and normal blood pH. Thus, HMGB1-mediated hyperactivation of innate immunity in sepsis requires acidosis, and micromolar zinc concentrations are protective. We suggest that the potent inflammatory effects of HMGB1 are kept in check via sequestration by plasma sialoglycoproteins at physiological pH and triggered when pH and zinc levels fall in late stages of sepsis. Current clinical trials independently studying zinc supplementation, HMGB1 inhibition, or pH normalization may be more successful if these approaches are combined and perhaps supplemented by infusions of heavily sialylated molecules.

Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validation.

BACKGROUND: Point-of-care arterial blood gas (ABG) is a blood measurement test and a useful diagnostic tool that assists with treatment and therefore improves clinical outcomes. However, numerically reported test results make rapid interpretation difficult or open to interpretation. The arterial blood gas algorithm (ABG-a) is a new digital diagnostics solution that can provide clinicians with real-time interpretation of preliminary data on safety features, oxygenation, acid-base disturbances and renal profile. The main aim of this study was to clinically validate the algorithm against senior experienced clinicians, for acid-base interpretation, in a clinical context. METHODS: We conducted a prospective international multicentre observational cross-sectional study. 346 sample sets and 64 inpatients eligible for ABG met strict sampling criteria. Agreement was evaluated using Cohen's kappa index, diagnostic accuracy was evaluated with sensitivity, specificity, efficiency or global accuracy and positive predictive values (PPV) and negative predictive values (NPV) for the prevalence in the study population. RESULTS: The concordance rates between the interpretations of the clinicians and the ABG-a for acid-base disorders were an observed global agreement of 84,3% with a Cohen's kappa coefficient 0.81; 95% CI 0.77 to 0.86; p < 0.001. For detecting accuracy normal acid-base status the algorithm has a sensitivity of 90.0% (95% CI 79.9 to 95.3), a specificity 97.2% (95% CI 94.5 to 98.6) and a global accuracy of 95.9% (95% CI 93.3 to 97.6). For the four simple acid-base disorders, respiratory alkalosis: sensitivity of 91.2 (77.0 to 97.0), a specificity 100.0 (98.8 to 100.0) and global accuracy of 99.1 (97.5 to 99.7); respiratory acidosis: sensitivity of 61.1 (38.6 to 79.7), a specificity of 100.0 (98.8 to 100.0) and global accuracy of 98.0 (95.9 to 99.0); metabolic acidosis: sensitivity of 75.8 (59.0 to 87.2), a specificity of 99.7 (98.2 to 99.9) and a global accuracy of 97.4 (95.1 to 98.6); metabolic alkalosis sensitivity of 72.2 (56.0 to 84.2), a specificity of 95.5 (92.5 to 97.3) and a global accuracy of 93.0 (88.8 to 95.3); the four complex acid-base disorders, respiratory and metabolic alkalosis, respiratory and metabolic acidosis, respiratory alkalosis and metabolic acidosis, respiratory acidosis and metabolic alkalosis, the sensitivity, specificity and global accuracy was also high. For normal acid-base status the algorithm has PPV 87.1 (95% CI 76.6 to 93.3) %, and NPV 97.9 (95% CI 95.4 to 99.0) for a prevalence of 17.4 (95% CI 13.8 to 21.8). For the four-simple acid-base disorders and the four complex acid-base disorders the PPV and NPV were also statistically significant. CONCLUSIONS: The ABG-a showed very high agreement and diagnostic accuracy with experienced senior clinicians in the acid-base disorders in a clinical context. The method also provides refinement and deep complex analysis at the point-of-care that a clinician could have at the bedside on a day-to-day basis. The ABG-a method could also have the potential to reduce human errors by checking for imminent life-threatening situations, analysing the internal consistency of the results, the oxygenation and renal status of the patient.

New onset adrenal insufficiency in a patient with COVID-19.

SARS-CoV-2 is the cause of COVID-19. Since the outbreak and rapid spread of COVID-19, it has been apparent that the disease is having multi-organ system involvement. Still its effect in the endocrine system is not fully clear and data on cortisol dynamics in patients with COVID-19 are not yet available. SARS-CoV-2 can knock down the host's cortisol stress response. Here we present a case of a 51-year-old man vomiting for 10 days after having confirmed COVID-19 infection. He had hypotension and significant hyponatraemia. Work-up was done including adrenocorticotropic hormone stimulation test. He was diagnosed as suffering from adrenal insufficiency and started on steroids with subsequent improvement in both blood pressure and sodium level. COVID-19 can cause adrenal insufficiency. Clinicians must be vigilant about the possibility of an underlying relative cortisol deficiency in patients with COVID-19.