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Sleep interacts reciprocally with immune system activity, but its specific relationship with microglia—the resident immune cells in the brain—remains poorly understood. Here, we show in mice that microglia can regulate sleep through a mechanism involving G i -coupled GPCRs, intracellular Ca 2+ signaling and suppression of norepinephrine transmission. Chemogenetic activation of microglia G i signaling strongly promoted sleep, whereas pharmacological blockade of G i -coupled P2Y12 receptors decreased sleep. Two-photon imaging in the cortex showed that P2Y12–G i activation elevated microglia intracellular Ca 2+ , and blockade of this Ca 2+ elevation largely abolished the G i -induced sleep increase. Microglia Ca 2+ level also increased at natural wake-to-sleep transitions, caused partly by reduced norepinephrine levels. Furthermore, imaging of norepinephrine with its biosensor in the cortex showed that microglia P2Y12–G i activation significantly reduced norepinephrine levels, partly by increasing the adenosine concentration. These findings indicate that microglia can regulate sleep through reciprocal interactions with norepinephrine transmission. Immune activity can influence sleep, but the role of microglia has remained unclear. Ma, Li and colleagues show that microglia can promote sleep through P2Y12–G i -coupled GPCR signaling, intracellular calcium increase and suppression of norepinephrine transmission.

The arousal state of the brain covaries with the motor state of the animal. How these state changes are coordinated remains unclear. We discovered that sleep–wake brain states and motor behaviors are coregulated by shared neurons in the substantia nigra pars reticulata (SNr). Analysis of mouse home-cage behavior identified four states with different levels of brain arousal and motor activity: locomotion, nonlocomotor movement, quiet wakefulness, and sleep; transitions occurred not randomly but primarily between neighboring states. The glutamic acid decarboxylase 2 but not the parvalbumin subset of SNr γ-aminobutyric acid (GABA)–releasing (GABAergic) neurons was preferentially active in states of low motor activity and arousal. Their activation or inactivation biased the direction of natural behavioral transitions and promoted or suppressed sleep, respectively. These GABAergic neurons integrate wide-ranging inputs and innervate multiple arousal-promoting and motor-control circuits through extensive collateral projections.

Acute respiratory distress syndrome (ARDS) is a condition that is often considered to have broad and subjective diagnostic criteria and is associated with significant mortality and morbidity. Early and accurate prediction of ARDS and related conditions such as hypoxemia and sepsis could allow timely administration of therapies, leading to improved patient outcomes. The aim of this study is to perform an exploration of how multilabel classification in the clinical setting can take advantage of the underlying dependencies between ARDS and related conditions to improve early prediction of ARDS in patients. The electronic health record data set included 40,703 patient encounters from 7 hospitals from April 20, 2018, to March 17, 2021. A recurrent neural network (RNN) was trained using data from 5 hospitals, and external validation was conducted on data from 2 hospitals. In addition to ARDS, 12 target labels for related conditions such as sepsis, hypoxemia, and COVID-19 were used to train the model to classify a total of 13 outputs. As a comparator, XGBoost models were developed for each of the 13 target labels. Model performance was assessed using the area under the receiver operating characteristic curve. Heat maps to visualize attention scores were generated to provide interpretability to the neural networks. Finally, cluster analysis was performed to identify potential phenotypic subgroups of patients with ARDS. The single RNN model trained to classify 13 outputs outperformed the individual XGBoost models for ARDS prediction, achieving an area under the receiver operating characteristic curve of 0.842 on the external test sets. Models trained on an increasing number of tasks resulted in improved performance. Earlier prediction of ARDS nearly doubled the rate of in-hospital survival. Cluster analysis revealed distinct ARDS subgroups, some of which had similar mortality rates but different clinical presentations. The RNN model presented in this paper can be used as an early warning system to stratify patients who are at risk of developing one of the multiple risk outcomes, hence providing practitioners with the means to take early action.

Background: A high number of patients who are hospitalized with COVID-19 develop acute respiratory distress syndrome (ARDS). Objective: In response to the need for clinical decision support tools to help manage the next pandemic during the early stages (ie, when limited labeled data are present), we developed machine learning algorithms that use semisupervised learning (SSL) techniques to predict ARDS development in general and COVID-19 populations based on limited labeled data. Methods: SSL techniques were applied to 29,127 encounters with patients who were admitted to 7 US hospitals from May 1, 2019, to May 1, 2021. A recurrent neural network that used a time series of electronic health record data was applied to data that were collected when a patient’s peripheral oxygen saturation level fell below the normal range (<97%) to predict the subsequent development of ARDS during the remaining duration of patients’ hospital stay. Model performance was assessed with the area under the receiver operating characteristic curve and area under the precision recall curve of an external hold-out test set. Results: For the whole data set, the median time between the first peripheral oxygen saturation measurement of <97% and subsequent respiratory failure was 21 hours. The area under the receiver operating characteristic curve for predicting subsequent ARDS development was 0.73 when the model was trained on a labeled data set of 6930 patients, 0.78 when the model was trained on the labeled data set that had been augmented with the unlabeled data set of 16,173 patients by using SSL techniques, and 0.84 when the model was trained on the entire training set of 23,103 labeled patients. Conclusions: In the context of using time-series inpatient data and a careful model training design, unlabeled data can be used to improve the performance of machine learning models when labeled data for predicting ARDS development are scarce or expensive.

Despite limitations on the use of cycle threshold (CT) values for individual patient care, population distributions of CT values may be useful indicators of local outbreaks. Conduct an exploratory analysis of potential correlations between the population distribution of cycle threshold (CT) values and COVID-19 disease dynamics, operationalized as percent positivity, transmission rate and COVID-19 hospitalizations. 148,410 specimens collected between September 15th, 2020 and January 11th, 2021 from the greater El Paso area were processed in the Dascena COVID-19 Laboratory. Daily median CT value, daily transmission rate R(t), daily count of COVID-19 hospitalizations, daily change in percent positivity, and rolling averages of these features were plotted over time. Two-way scatterplots and linear regression evaluated possible associations between daily median CT and outbreak measures. Cross-correlation plots determined whether a time delay existed between changes in the daily median CT value and measure of community disease dynamics. Daily median CT was negatively correlated with the daily R(t), the daily COVID-19 hospitalization count (with a 33 day time delay), and the daily change in percent positivity among testing samples (p<.001 for all correlations). Despite visual trends suggesting time delays in the plots for median CT and outbreak measures, a statistically significant delay was only detected between changes in median CT and COVID-19 hospitalization count (p<.001). This study adds to the literature by analyzing samples collected from an entire geographical area, and contextualizing the results with other research investigating population CT values.

Astrocytes are active partners in neural information processing. However, the roles of astrocytes in regulating behavior remain unclear. Because astrocytes have persistent circadian clock gene expression and ATP release in vitro, I hypothesized that they regulate daily rhythms in neurons and behavior. Here, I demonstrated that daily rhythms in astrocytes within the mammalian master circadian pacemaker, the suprachiasmatic nucleus (SCN), determine the period of wheel-running activity. Ablating the essential clock gene Bmal1 specifically in SCN astrocytes lengthened the circadian period of clock gene expression in the SCN and in locomotor behavior. Similarly, excision of the short-period CK1? tau mutation specifically from SCN astrocytes also resulted in lengthened rhythms in the SCN and behavior. These results indicate that astrocytes within the SCN communicate to neurons to determine circadian rhythms in physiology and in wheel-running activity. As a first step to understanding how the two cell types interact, I attempted to delineate the circadian phase relationship of clock gene expression between neurons and astrocytes. With limited success, I will discuss both preliminary findings and challenges I faced. Lastly, I will present SCN single-cell transcriptomics data as a first step to understand properties of SCN astrocytes and diversity of SCN cell types. Clock genes enriched in SCN astrocytes identified by single-cell transcriptomics here can serve as a launching point to investigate how SCN astrocytes communicate to SCN neurons.

Sleep interacts reciprocally with immune system activity, but its specific relationship with microglia – the resident immune cells in the brain – remains poorly understood. Here we show that microglia can regulate sleep through a mechanism involving Gi-coupled GPCRs, intracellular Ca2+ signaling, and suppression of norepinephrine transmission. Chemogenetic activation of microglia Gi signaling strongly promoted sleep, whereas pharmacological blockade of Gi-coupled P2Y12 receptors decreased sleep. Two-photon imaging showed that P2Y12/Gi activation elevated microglia intracellular Ca2+, and blockade of this Ca2+ elevation largely abolished the Gi-induced sleep increase. Microglia Ca2+ level also increased at natural wake-to-sleep transitions, caused partly by reduced norepinephrine. Furthermore, imaging of norepinephrine activity with its biosensor showed that microglia P2Y12/Gi activation significantly reduced norepinephrine, partly by increasing the adenosine concentration. Thus, microglia can regulate sleep through reciprocal interactions with norepinephrine transmission.

Circadian clock dysfunction is a common symptom of aging and neurodegenerative diseases, though its impact on brain health is poorly understood. Astrocyte activation occurs in response to diverse insults, and plays a critical role in brain health and disease. We report that the core clock protein BMAL1 regulates astrogliosis in a synergistic manner via a cell-autonomous mechanism, and via a lesser non-cell-autonomous signal from neurons. Astrocyte-specific Bmal1 deletion induces astrocyte activation in vitro and in vivo, mediated in part by suppression of glutathione-s-transferase signaling. Functionally, loss of Bmal1 in astrocytes promotes neuronal death in vitro. Our results demonstrate that the core clock protein BMAL1 regulates astrocyte activation and function in vivo, elucidating a novel mechanism by which the circadian clock could influence many aspects of brain function and neurologic disease.