Abstract:-Since the first report addressing quantification of cerebral blood flow (CBF), concomitant assessment of cerebral oxygen consumption was also carried out. Over the years, however, some investigators have emphatically and mistakenly addressed cerebral ischemia in comatose patients, on the basis of CBF measurements alone. In contrast, we have repeatedly reported that ischemia in these patients must be precisely evaluated based on CBF-metabolism coupling or uncoupling, rather than CBF alone. Based on these previ… Show more
“…The classic work of Obrist and colleagues 32 shows how patients with low flow pattern achieve near-normal values of arteriovenous oxygen difference (DavO 2 ), however, patients with hyperemic pattern show DavO 2 below the limits of normality. The article by Obrist et al 32 and more recently that of Cruz et al 33 , have shown that the worst result is given by the decoupling between cerebral blood flow (CBF) and cerebral metabolic rate of O 2 (flow -metabolism), with unfavourable results associated with hyperemic patterns.…”
Section: Cerebral Hemodynamics By Transcranial Doppler and Protein S1mentioning
Introduction: Sepsis-associated encephalopathy (SAE) is a diffuse brain dysfunction secondary to a systemic response to infection. The diagnosis is currently by exclusion. Objectives: to describe cerebral hemodynamic patterns, cerebral hemodynamic reserve (CHR) and the protein biomarker S100β in patients with SAE. Methods: a prospective, longitudinal and descriptive study was carried out in the intensive care unit of the Centro de Investigaciones Médicos Quirúrgicas, from January 2014 to March 2016 in 20 patients with SAE in which the cerebral hemodynamic pattern and CHR were determined by transcranial Doppler (TCD) sonography and the protein biomarker S100β. The study variables are related. Results: cerebral hemodynamic patterns most frequently found were: low flow and hyperemic, 35% respectively and cerebrovascular reserve capacity was variable (50% normal vs 50% decreased). The protein S100β was found to be elevated in 80% of the sample. The existence of hyperemic pattern, decreased cerebrovascular reserve capacity and high S100β protein was associated to mortality. Conclusions: in patients with SAE there is not a typical cerebral hemodynamic pattern nor CHR. The protein S100 β can be used as a marker of brain damage in SAE. The existence of the triad: hyperemic pattern, diminished cerebrovascular reserve capacity and high S100β protein is indicative of poor prognosis.
“…The classic work of Obrist and colleagues 32 shows how patients with low flow pattern achieve near-normal values of arteriovenous oxygen difference (DavO 2 ), however, patients with hyperemic pattern show DavO 2 below the limits of normality. The article by Obrist et al 32 and more recently that of Cruz et al 33 , have shown that the worst result is given by the decoupling between cerebral blood flow (CBF) and cerebral metabolic rate of O 2 (flow -metabolism), with unfavourable results associated with hyperemic patterns.…”
Section: Cerebral Hemodynamics By Transcranial Doppler and Protein S1mentioning
Introduction: Sepsis-associated encephalopathy (SAE) is a diffuse brain dysfunction secondary to a systemic response to infection. The diagnosis is currently by exclusion. Objectives: to describe cerebral hemodynamic patterns, cerebral hemodynamic reserve (CHR) and the protein biomarker S100β in patients with SAE. Methods: a prospective, longitudinal and descriptive study was carried out in the intensive care unit of the Centro de Investigaciones Médicos Quirúrgicas, from January 2014 to March 2016 in 20 patients with SAE in which the cerebral hemodynamic pattern and CHR were determined by transcranial Doppler (TCD) sonography and the protein biomarker S100β. The study variables are related. Results: cerebral hemodynamic patterns most frequently found were: low flow and hyperemic, 35% respectively and cerebrovascular reserve capacity was variable (50% normal vs 50% decreased). The protein S100β was found to be elevated in 80% of the sample. The existence of hyperemic pattern, decreased cerebrovascular reserve capacity and high S100β protein was associated to mortality. Conclusions: in patients with SAE there is not a typical cerebral hemodynamic pattern nor CHR. The protein S100 β can be used as a marker of brain damage in SAE. The existence of the triad: hyperemic pattern, diminished cerebrovascular reserve capacity and high S100β protein is indicative of poor prognosis.
A mathematical model of the whole-body metabolism is developed to predict fuel homeostasis during exercise by using hormonal control over cellular metabolic processes. The whole body model is composed of seven tissue compartments: brain, heart, liver, GI (gastrointestinal) tract, skeletal muscle, adipose tissue, and "other tissues". Each tissue compartment is described by dynamic mass balances and major cellular metabolic reactions. The glucagon-insulin controller is incorporated into the whole body model to predict hormonal changes during exercise. Moderate [150 W power output at 60% of peak oxygen consumption (VO(2max))] exercise for 60 min was implemented by increasing ATP utilization rates in heart and skeletal muscle. Arterial epinephrine level was given as an input function, which directly affects heart and skeletal muscle metabolism and indirectly other tissues via glucagon-insulin controller. Model simulations were validated with experimental data from human exercise studies. The exercise induced changes in hormonal signals modulated metabolic flux rates of different tissues in a coordinated way to achieve glucose homeostasis, demonstrating the efficacy of hormonal control over cellular metabolic processes. From experimental measurements of whole body glucose balance and arterial substrate concentrations, this model could predict the dynamic changes of hepatic glycogenolysis and gluconeogenesis, which are not easy to measure experimentally, suggesting the higher contribution of glycogenolysis ( approximately 75%). In addition, it could provide dynamic information on the relative contribution of carbohydrates and lipids for fuel oxidation in skeletal muscle. Model simulations indicate that external fuel supplies from other tissue/organ systems to skeletal muscle become important for prolonged exercise emphasizing the significance of interaction among tissues. In conclusion, this model can be used as a valuable complement to experimental studies due to its ability to predict what is difficult to measure directly, and usefulness to provide information about dynamic behaviors.
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