Daily Anesthesiology Research Analysis

THIK1 tandem pore domain (K2P) potassium channels regulate microglial surveillance of the central nervous system and responsiveness to inflammatory insults. With microglia recognized as critical to the pathogenesis of neurodegenerative diseases, THIK1 channels are putative therapeutic targets to control microglia dysfunction. While THIK channels can principally be distinguished from other K2Ps by their distinctive inhibitory response to volatile anesthetics (VAs), molecular details governing THIK channel gating remain largely unexplored. Here, we report a 3.2 Å cryo-electron microscopy structure of the THIK1 channel in a closed conformation. A central pore gate located directly below the THIK1 selectivity filter is formed by inward-facing TM4 helix tyrosine residues that occlude the ion conduction pathway. VA inhibition of THIK requires closure of this central pore gate. Using a combination of anesthetic photolabeling, electrophysiology, and molecular dynamics simulation, we identify a functionally critical THIK1 VA binding site positioned between the central gate and a structured section of the THIK1 TM2/TM3 loop. Our results demonstrate the molecular architecture of the THIK1 channel and elucidate critical structural features involved in regulation of THIK1 channel gating and anesthetic inhibition.

BACKGROUND: The conscious state is maintained through intact communication between brain regions. However, studies on global and regional connectivity changes in unconscious state have been inconsistent. These inconsistencies could arise from unclear definition of unconsciousness, spatial and temporal limitations of neuroimaging modalities, and estimating only single connectivity measure. This study investigated global and regional changes in amplitude and phase-based functional connectivity in propofol-induced unconsciousness, which is widely recognized as unconsciousness. METHODS: Amplitude and phase-based functional connectivity was calculated using amplitude envelope correlation, weighted phase lag index, and magnitude squared coherence from intracranial electroencephalography data of 73 patients. Global changes in connectivity, complexity, and network efficiency were estimated. Regional connectivity changes between Brodmann areas, between seven cortical lobes, and between resting state networks were assessed across all frequency bands. Additionally, machine learning analysis was employed to identify specific regions in classifying conscious and unconscious states. RESULTS: In the unconscious state, global connectivity increased across all frequency bands, while global complexity and efficiency decreased, accompanied by increased delta and decreased high gamma power spectral density. Regional connectivity increased between entire cortical regions across all frequency bands. Machine learning analysis revealed that posterior connectivity was the most influential in classifying consciousness. Amplitude-based connectivity predominantly increased in the delta and theta bands, while phase-based connectivity predominantly increased from the beta to high gamma bands. CONCLUSIONS: Propofol anesthesia suppresses cortical activity and induces oscillatory changes characterized by increased delta power and decreased high gamma power. These changes are accompanied by increased functional connectivity and reduced network complexity and efficiency. These changes limit the brain's ability to generate a diverse repertoire of activity, ultimately leading to unconsciousness. Posterior connectivity, which showed high accuracy in predicting conscious states, would be crucial for sustaining consciousness.

BACKGROUND: Perioperative venous thromboembolism (VTE), including pulmonary embolism and deep vein thrombosis, contributes significantly to morbidity, mortality, and healthcare costs of care. A reliable risk assessment model is essential for identifying patients at risk for perioperative VTE. This study aimed to develop and validate a model to predict VTE aligned with the Agency for Healthcare Research and Quality's Patient Safety Indicator 12, which tracks VTE occurrences from hospital admission through discharge. This approach may improve early identification and targeted prevention. METHODS: We retrospectively analyzed hospital registry data from surgical patients at two tertiary care hospitals in the United States: Montefiore Medical Center in the Bronx, New York, and Beth Israel Deaconess Medical Center in Boston, Massachusetts. Data from Montefiore Medical Center between 2016 and 2021 were used for prediction model creation, while data from 2021 to 2023 served for internal temporal validation. We classified perioperative VTE if patients carried a new International Classification of Diseases code for deep vein thrombosis or pulmonary embolism, and a VTE-related imaging order was documented. Stepwise backward logistic regression and bootstrap resampling were employed for model development. Model performance was evaluated using the receiver operating characteristic curves and Brier score. RESULTS: Among 319,134 surgical patients included in the study, 2,647 (0.8%) were diagnosed with perioperative VTE after hospital admission. The model exhibited robust discriminatory performance across all cohorts, with areas under the receiver operating characteristic curve (AUC) of 0.87 (95% CI, 0.86 to 0.89) in the development cohort, 0.84 (95% CI, 0.81 to 0.87) in the internal temporal validation cohort, and 0.76 (95% CI, 0.75 to 0.77) in the external validation cohort. By contrast, the Caprini score and Rogers risk assessment model exhibited significantly lower predictive accuracies of 0.66 and 0.51, respectively. Additionally, the prediction score exhibited strong performance in predicting VTE both in patients before surgery (AUC, 0.91; 95% CI, 0.89 to 0.93) and in those after surgery (AUC, 0.84; 95% CI, 0.82 to 0.86). CONCLUSIONS: We developed a clinically intuitive risk assessment model that predicts perioperative VTE across diverse surgical populations, based on the Agency for Healthcare Research and Quality's definition. This model demonstrates superior performance compared to existing instruments, offering the potential for improved VTE prevention during hospitalization.