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•Hypoxia impairs inhibitory control and sustained attention during the Go/No-Go task.•A modified Davis model for normoxia-hypoxia differences estimates CMRO2 changes.•Regional CMRO2 reductions reveal significant heterogeneity across brain networks.•Attention and executive frontoparietal networks exhibited the largest CMRO2 reductions.•Adaptive prioritization of brain networks explains cognitive impairments in hypoxia. Acute exposure to severe hypoxia impairs cognitive performance, yet the integrated brain mechanisms underlying this temporary decline remain unclear. This study examined regional variations in cerebral oxygen metabolism during acute hypoxia and their relationship to cognitive impairment. Eleven young, healthy participants (26.5 ± 4.5 years old) performed the Go/No-Go task during two sessions, each of which includes three minutes of hypoxia (FiO2 = 7.7 %). Cerebral blood flow (CBF) was assessed using pCASL MRI in one session, while blood-oxygen-level-dependent (BOLD) signals were acquired in another. Fractional changes in CBF (δCBF) and BOLD (δBOLD) were combined using a modified Davis model, adjusted for physiological differences between normoxia and acute and severe hypoxia, to calculate the fractional change in cerebral metabolic rate of oxygen (δCMRO2). Group-level z-normalized δCMRO2 maps revealed significant regional heterogeneity, with most pronounced reductions in areas associated with the dorsal and ventral attention networks and executive frontoparietal networks. These regions exhibited δCMRO2 reductions exceeding the hemispheric average (-9.6 ± 7.9 %) and were associated with increased commission errors during the Go/No-Go task, reflecting impaired inhibitory control and sustained attention. This study highlights the brain's adaptive prioritization of certain networks under oxygen deprivation, providing insights into the physiological mechanisms underlying hypoxia-induced cognitive impairments. These findings enhance our understanding of how acute hypoxia affects brain function, emphasizing the importance of network-specific adaptations in maintaining cognitive performance during oxygen deprivation.

The application of artificial intelligence (AI) has provided new capabilities to develop advanced medical monitoring sensors for detection of clinical conditions of low circulating blood volume such as hemorrhage. The purpose of this study was to compare for the first time the discriminative ability of two machine learning (ML) algorithms based on real-time feature analysis of arterial waveforms obtained from a non-invasive continuous blood pressure system (Finometer®) signal to predict the onset of decompensated shock: the compensatory reserve index (CRI) and the compensatory reserve metric (CRM). One hundred ninety-one healthy volunteers underwent progressive simulated hemorrhage using lower body negative pressure (LBNP). The least squares means and standard deviations for each measure were assessed by LBNP level and stratified by tolerance status (high vs. low tolerance to central hypovolemia). Generalized Linear Mixed Models were used to perform repeated measures logistic regression analysis by regressing the onset of decompensated shock on CRI and CRM. Sensitivity and specificity were assessed by calculation of receiver-operating characteristic (ROC) area under the curve (AUC) for CRI and CRM. Values for CRI and CRM were not distinguishable across levels of LBNP independent of LBNP tolerance classification, with CRM ROC AUC (0.9268) being statistically similar (p = 0.134) to CRI ROC AUC (0.9164). Both CRI and CRM ML algorithms displayed discriminative ability to predict decompensated shock to include individual subjects with varying levels of tolerance to central hypovolemia. Arterial waveform feature analysis provides a highly sensitive and specific monitoring approach for the detection of ongoing hemorrhage, particularly for those patients at greatest risk for early onset of decompensated shock and requirement for implementation of life-saving interventions.

Abstract BACKGROUND: Obesity rates continue to rise along with the number of obese patients undergoing elective spinal fusion. OBJECTIVE: To evaluate the impact of obesity on resource utilization and early complications in patients undergoing surgery for degenerative spine disease. METHODS: A single-institution retrospective analysis was conducted on patients with degenerative spine disease requiring instrumentation between 2008 and 2012. The 801 identified patients were grouped based on a body mass index (BMI) of <30 (nonobese, n = 478), ≥30 and <40 (obese, n = 283), and alternatively BMIs of ≥40 (morbidly obese, n = 40). Baseline characteristics, surgical outcomes and requirements, complications, and cost were compared. Logistic and linear regression analyses were used to determine the strength of association between obesity and outcomes for categorical and continuous data, respectively. RESULTS: Significant differences were found in comorbidities between cohorts. Multivariate analysis revealed significant associations between obesity and longer anesthesia times (30 minutes, P = .008), and surgical times (24 minutes, P = .02). Additionally, there was a 2.8 times higher rate of wound complications in obese patients (4.2% vs 1.5, P = .03), and 2.5 times higher rate of major medical complications (7.8% vs 3.1, P = .01). Morbid obesity resulted in a 10 times higher rate of wound complications (P < .001). Morbid obesity resulted in a $9078 (P = .005) increase in overall cost of care. CONCLUSION: Increased BMI is associated with longer operative times, increased complication rates, and increased cost independent of comorbidities. These effects are more pronounced with morbidly obese patients, further supporting a role for preoperative weight loss.

Occult hemorrhages after trauma can be present insidiously, and if not detected early enough can result in patient death. This study evaluated a hemorrhage model on 18 human subjects, comparing the performance of traditional vital signs to multiple off-the-shelf non-invasive biomarkers. A validated lower body negative pressure (LBNP) model was used to induce progression towards hypovolemic cardiovascular instability. Traditional vital signs included mean arterial pressure (MAP), electrocardiography (ECG), plethysmography (Pleth), and the test systems utilized electrical impedance via commercial electrical impedance tomography (EIT) and multifrequency electrical impedance spectroscopy (EIS) devices. Absolute and relative metrics were used to evaluate the performance in addition to machine learning-based modeling. Relative EIT-based metrics measured on the thorax outperformed vital sign metrics (MAP, ECG, and Pleth) achieving an area-under-the-curve (AUC) of 0.99 (CI 0.95–1.00, 100% sensitivity, 87.5% specificity) at the smallest LBNP change (0–15 mmHg). The best vital sign metric (MAP) at this LBNP change yielded an AUC of 0.6 (CI 0.38–0.79, 100% sensitivity, 25% specificity). Out-of-sample predictive performance from machine learning models were strong, especially when combining signals from multiple technologies simultaneously. EIT, alone or in machine learning-based combination, appears promising as a technology for early detection of progression toward hemodynamic instability.

An independent association exists between sleep apnea and diabetes. Animal models suggest exposure to intermittent hypoxia, a consequence of sleep apnea, results in altered glucose metabolism and fasting hyperglycemia. However, it is unknown if acute exposure to intermittent hypoxia increases glucose concentrations in nondiabetic humans. We hypothesized plasma glucose would be increased from baseline following 3 h of intermittent hypoxia in healthy humans independent of any effect on insulin sensitivity. Eight (7M/1F, 21–34 years) healthy subjects completed two study visits randomized to 3 h of intermittent hypoxia or continuous normoxia, followed by an oral glucose tolerance test. Intermittent hypoxia consisted of 25 hypoxic events per hour where oxygen saturation (SpO2) was significantly reduced (Normoxia: 97 ± 1%, Hypoxia: 90 ± 2%, P < 0.01). Venous plasma glucose concentrations were measured on both visits before and after the 3 h protocol. No changes in plasma glucose were observed from baseline after 3 h of continuous normoxia (5.1 ± 0.2 vs. 5.1 ± 0.1 mmol/L, P > 0.05). In contrast, circulating glucose concentrations were increased after 3 h of intermittent hypoxia when compared to baseline (5.0 ± 0.2 vs. 5.3 ± 0.2 mmol/L, P = 0.01). There were no detectable changes in insulin sensitivity following intermittent hypoxia when compared to continuous normoxia, as assessed by the oral glucose tolerance test (P > 0.05). Circulating glucose is increased after 3 h of intermittent hypoxia in healthy humans, independent of any lasting changes in insulin sensitivity. These novel findings could explain, in part, the high prevalence of diabetes in patients with sleep apnea and warrant future studies to identify underlying mechanisms. Circulating glucose is increased after 3 h of intermittent hypoxia in healthy humans, independent of any lasting changes in insulin sensitivity. These novel findings could explain, in part, the high prevalence of diabetes in patients with sleep apnea and warrant future studies to identify underlying mechanisms.

Women with uterine fibroids (UF), benign tumors of the myometrium, have a higher prevalence of hypertension than women without UF. The cause for this relationship is unclear. Muscle sympathetic nerve activity (MSNA) is a regulator of arterial blood pressure, and it is possible that variations in MSNA predispose women with UF to develop hypertension. The purpose of this study was to assess baseline blood pressure and MSNA and the relationships between MSNA and systemic hemodynamics in women with and without UF. We measured blood pressure (brachial intra‐arterial line), MSNA (microneurography), and systemic hemodynamics (total peripheral resistance and cardiac output) at rest in 14 healthy, normotensive, premenopausal women with UF (42 ± 2 years old) and 9 healthy, normotensive, premenopausal women without UF (41 ± 2 years old). Baseline blood pressure and MSNA did not differ between groups (p > 0.05 for both). In women with UF, there was a positive correlation between MSNA and total peripheral resistance (r = 0.75, p = 0.02), as well as a negative correlation between MSNA and cardiac output (r = −0.73, p = 0.03). In contrast, these relationships were not seen in women without UF (p > 0.05 for both relationships). These data suggest that autonomic interactions with systemic hemodynamics, and thus blood pressure regulation, are different in healthy women with UF compared to healthy women without UF. Muscle sympathetic nerve activity is positively correlated with total peripheral resistance in women with uterine fibroids but not in women without uterine fibroids. Autonomic interactions with systemic hemodynamics, and thus blood pressure regulation, are different in women with uterine fibroids (who are otherwise healthy) compared to healthy women without uterine fibroids.

Introduction It remains unclear if naturally occurring respiratory muscle (RM) work influences leg diffusive O2 transport during exercise in heart failure patients with reduced ejection fraction (HFrEF). In this retrospective study, we hypothesized that RM unloading during submaximal exercise will lead to increases in locomotor muscle O2 diffusion capacity (DMO2) contributing to the greater leg VO2. Methods Ten HFrEF patients and 10 healthy control matched participants performed two submaximal exercise bouts (i.e., with and without RM unloading). During exercise, leg blood flow was measured via constant infusion thermodilution. Intrathoracic pressure was measured via esophageal balloon. Radial arterial and femoral venous blood gases were measured and used to calculate leg arterial and venous content (CaO2 and CvO2, respectively), VO2, O2 delivery, and DMO2. Results From CTL to RM unloading, leg VO2, O2 delivery, and DMO2 were not different in healthy participants during submaximal exercise (all, p > .15). In HFrEF, leg VO2 (CTL: 0.7 ± 0.3 vs. RM unloading: 1.0 ± 0.4 L/min, p < .01), leg O2 delivery (CTL: 0.9 ± 0.4 vs. RM unloading: 1.4 ± 0.5 L/min, p < .01), and leg DMO2 (CTL: 31.5 ± 11.4 vs. RM unloading: 49.7 ± 18.6 ml min−1 mmHg−1) increased from CTL to RM unloading during submaximal exercise (all, p < .01), whereas CaO2‐CvO2 was not different (p = .51). The degree of RM unloading (i.e., % decrease in esophageal pressure‐time integral during inspiration) was related to the % increase in leg DMO2 with RM unloading (r = −.76, p = .01). Conclusion Our data suggest RM unloading leads to increased leg VO2 due to greater convective and diffusive O2 transport during submaximal exercise in HFrEF patients. Respiratory muscle work negatively influences convective O2 transport in heart failure patients. The present study investigated the impact of respiratory muscle work on leg O2 diffusive capacity during submaximal exercise. We found the degree of respiratory muscle unloading in heart failure patients is related to the improvement in leg O2 diffusive capacity during submaximal exercise.

Serotonin syndrome is gaining attention in perioperative and chronic pain settings due to the growing prevalence of multimodal therapies that increase serotonin levels and thereby heighten patient risk. A patient's genetic make-up may further increase the risk of serotonin syndrome. A case of serotonin syndrome on emergence after general anesthesia is presented. A subsequent cytochrome P4502D6 genetic test result suggested a potential alteration in metabolism. For this patient, who was taking combination antidepressant medications and receiving common perioperative medicines, additive pharmacodynamic effects converged with a pharmacogenetic predisposition, resulting in serotonin syndrome.