Propofol disrupts alpha dynamics in functionally distinct thalamocortical networks during loss of consciousness.

During propofol-induced general anesthesia, alpha rhythms measured using electroencephalography undergo a striking shift from posterior to anterior, termed anteriorization, where the ubiquitous waking alpha is lost and a frontal alpha emerges. The functional significance of alpha anteriorization and the precise brain regions contributing to the phenomenon are a mystery. While posterior alpha is thought to be generated by thalamocortical circuits connecting nuclei of the sensory thalamus with their cortical partners, the thalamic origins of the propofol-induced alpha remain poorly understood. Here, we used human intracranial recordings to identify regions in sensory cortices where propofol attenuates a coherent alpha network, distinct from those in the frontal cortex where it amplifies coherent alpha and beta activities. We then performed diffusion tractography between these identified regions and individual thalamic nuclei to show that the opposing dynamics of anteriorization occur within two distinct thalamocortical networks. We found that propofol disrupted a posterior alpha network structurally connected with nuclei in the sensory and sensory associational regions of the thalamus. At the same time, propofol induced a coherent alpha oscillation within prefrontal cortical areas that were connected with thalamic nuclei involved in cognition, such as the mediodorsal nucleus. The cortical and thalamic anatomy involved, as well as their known functional roles, suggests multiple means by which propofol dismantles sensory and cognitive processes to achieve loss of consciousness.

Long wavelength light reduces the negative consequences of dim light at night.

Many patients with autism spectrum disorders (ASD) show disturbances in their sleep/wake cycles, and they may be particularly vulnerable to the impact of circadian disruptors. We have previously shown that a 2-weeks exposure to dim light at night (DLaN) disrupts diurnal rhythms, increases repetitive behaviors and reduces social interactions in contactin-associated protein-like 2 knock out (Cntnap2 KO) mice. The deleterious effects of DLaN may be mediated by intrinsically photosensitive retinal ganglion cells (ipRGCs) expressing the photopigment melanopsin, which is maximally sensitive to blue light (480 nm). In this study, the usage of a light-emitting diode array enabled us to shift the spectral properties of the DLaN while keeping the intensity of the illumination at 10 lx. First, we confirmed that the short-wavelength enriched lighting produced strong acute suppression of locomotor activity (masking), robust light-induced phase shifts, and cFos expression in the suprachiasmatic nucleus in wild-type (WT) mice, while the long-wavelength enriched lighting evoked much weaker responses. Opn4DTA mice, lacking the melanopsin expressing ipRGCs, were resistant to DLaN effects. Importantly, shifting the DLaN stimulus to longer wavelengths mitigated the negative impact on the activity rhythms and 'autistic' behaviors (i.e. reciprocal social interactions, repetitive grooming) in the Cntnap2 KO as well as in WT mice. The short-, but not the long-wavelength enriched, DLaN triggered cFos expression in in the basolateral amygdala (BLA) as well as in the peri-habenula region raising that possibility that these cell populations may mediate the effects. Broadly, our findings are consistent with the recommendation that spectral properties of light at night should be considered to optimize health in neurotypical as well as vulnerable populations.

Antigen presenting cell response to polysaccharide A is characterized by the generation of anti-inflammatory macrophages.

Polysaccharide A (PSA) is the immunodominant capsular carbohydrate from the gram negative commensal microbe Bacteroides fragilis that has shown remarkable potency in ameliorating many rodent models of inflammatory disease by eliciting downstream suppressive CD4+ T cells. PSA is composed of a zwitterionic repeating unit that allows it to be processed by antigen presenting cells (APCs) and presented by MHCII in a glycosylation-dependent manner. While previous work has uncovered much about the interactions between MHCII and PSA, as well as the downstream T cell response, little is known about how PSA affects the phenotype of MHCII+ APCs, including macrophages. Here, we utilized an unbiased systems approach consisting of RNAseq transcriptomics, high-throughput flow cytometry, Luminex analysis and targeted validation experiments to characterize the impact of PSA-mediated stimulation of splenic MHCII+ cells. The data revealed that PSA potently elicited the upregulation of an alternatively activated M2 macrophage transcriptomic and cell surface signature. Cell-type-specific validation experiments further demonstrated that PSA-exposed bone marrow-derived macrophages (BMDMs) induced cell surface and intracellular markers associated with M2 macrophages compared with conventional peptide ovalbumin (ova)-exposed BMDMs. In contrast to macrophages, we also found that CD11c+ dendritic cells (DCs) upregulated the pro-T cell activation costimulatory molecule CD86 following PSA stimulation. Consistent with the divergent BMDM and DC changes, PSA-exposed DCs elicited an antigen-experienced T cell phenotype in co-cultures, whereas macrophages did not. These findings collectively demonstrate that the PSA-induced immune response is characterized by both T cell stimulation via presentation by DCs, and a previously unrecognized anti-inflammatory polarization of macrophages.

Personalized Connectome Mapping to Guide Targeted Therapy and Promote Recovery of Consciousness in the Intensive Care Unit.

There are currently no therapies proven to promote early recovery of consciousness in patients with severe brain injuries in the intensive care unit (ICU). For patients whose families face time-sensitive, life-or-death decisions, treatments that promote recovery of consciousness are needed to reduce the likelihood of premature withdrawal of life-sustaining therapy, facilitate autonomous self-expression, and increase access to rehabilitative care. Here, we present the Connectome-based Clinical Trial Platform (CCTP), a new paradigm for developing and testing targeted therapies that promote early recovery of consciousness in the ICU. We report the protocol for STIMPACT (Stimulant Therapy Targeted to Individualized Connectivity Maps to Promote ReACTivation of Consciousness), a CCTP-based trial in which intravenous methylphenidate will be used for targeted stimulation of dopaminergic circuits within the subcortical ascending arousal network (ClinicalTrials.gov NCT03814356). The scientific premise of the CCTP and the STIMPACT trial is that personalized brain network mapping in the ICU can identify patients whose connectomes are amenable to neuromodulation. Phase 1 of the STIMPACT trial is an open-label, safety and dose-finding study in 22 patients with disorders of consciousness caused by acute severe traumatic brain injury. Patients in Phase 1 will receive escalating daily doses (0.5-2.0 mg/kg) of intravenous methylphenidate over a 4-day period and will undergo resting-state functional magnetic resonance imaging and electroencephalography to evaluate the drug's pharmacodynamic properties. The primary outcome measure for Phase 1 relates to safety: the number of drug-related adverse events at each dose. Secondary outcome measures pertain to pharmacokinetics and pharmacodynamics: (1) time to maximal serum concentration; (2) serum half-life; (3) effect of the highest tolerated dose on resting-state functional MRI biomarkers of connectivity; and (4) effect of each dose on EEG biomarkers of cerebral cortical function. Predetermined safety and pharmacodynamic criteria must be fulfilled in Phase 1 to proceed to Phase 2A. Pharmacokinetic data from Phase 1 will also inform the study design of Phase 2A, where we will test the hypothesis that personalized connectome maps predict therapeutic responses to intravenous methylphenidate. Likewise, findings from Phase 2A will inform the design of Phase 2B, where we plan to enroll patients based on their personalized connectome maps. By selecting patients for clinical trials based on a principled, mechanistic assessment of their neuroanatomic potential for a therapeutic response, the CCTP paradigm and the STIMPACT trial have the potential to transform the therapeutic landscape in the ICU and improve outcomes for patients with severe brain injuries.

OSPREY 3.0: Open-source protein redesign for you, with powerful new features.

We present osprey 3.0, a new and greatly improved release of the osprey protein design software. Osprey 3.0 features a convenient new Python interface, which greatly improves its ease of use. It is over two orders of magnitude faster than previous versions of osprey when running the same algorithms on the same hardware. Moreover, osprey 3.0 includes several new algorithms, which introduce substantial speedups as well as improved biophysical modeling. It also includes GPU support, which provides an additional speedup of over an order of magnitude. Like previous versions of osprey, osprey 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational entropy. Finally, we show here empirically that osprey 3.0 accurately predicts the effect of mutations on protein-protein binding. Osprey 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open-source software. © 2018 Wiley Periodicals, Inc.

Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability.

OBJECTIVE: To develop a personalizable algorithm to discriminate between sedation levels in ICU patients based on heart rate variability. DESIGN: Multicenter, pilot study. SETTING: Several ICUs at Massachusetts General Hospital, Boston, MA. PATIENTS: We gathered 21,912 hours of routine electrocardiogram recordings from a heterogenous group of 70 adult ICU patients. All patients included in the study were mechanically ventilated and were receiving sedatives. MEASUREMENTS AND MAIN RESULTS: As "ground truth" for developing our method, we used Richmond Agitation Sedation Scale scores grouped into four levels denoted "comatose" (-5), "deeply sedated" (-4 to -3), "lightly sedated" (-2 to 0), and "agitated" (+1 to +4). We trained a support vector machine learning algorithm to calculate the probability of each sedation level from heart rate variability measures derived from the electrocardiogram. To estimate algorithm performance, we calculated leave-one-subject out cross-validated accuracy. The patient-independent version of the proposed system discriminated between the four sedation levels with an overall accuracy of 59%. Upon personalizing the system supplementing the training data with patient-specific calibration data, consisting of an individual's labeled heart rate variability epochs from the preceding 24 hours, accuracy improved to 67%. The personalized system discriminated between light- and deep-sedation states with an average accuracy of 75%. CONCLUSIONS: With further refinement, the methodology reported herein could lead to a fully automated system for depth of sedation monitoring. By enabling monitoring to be continuous, such technology may help clinical staff to monitor sedation levels more effectively and to reduce complications related to over- and under sedation.

Automatic Classification of Sedation Levels in ICU Patients Using Heart Rate Variability.

OBJECTIVE: To explore the potential value of heart rate variability features for automated monitoring of sedation levels in mechanically ventilated ICU patients. DESIGN: Multicenter, pilot study. SETTING: Several ICUs at Massachusetts General Hospital, Boston, MA. PATIENTS: Electrocardiogram recordings from 40 mechanically ventilated adult patients receiving sedatives in an ICU setting were used to develop and test the proposed automated system. MEASUREMENTS AND MAIN RESULTS: Richmond Agitation-Sedation Scale scores were acquired prospectively to assess patient sedation levels and were used as ground truth. Richmond Agitation-Sedation Scale scores were grouped into four levels, denoted "unarousable" (Richmond Agitation- Sedation Scale = -5, -4), "sedated" (-3, -2, -1), "awake" (0), "agitated" (+1, +2, +3, +4). A multiclass support vector machine algorithm was used for classification. Classifier training and performance evaluations were carried out using leave-oneout cross validation. An overall accuracy of 69% was achieved for discriminating between the four levels of sedation. The proposed system was able to reliably discriminate (accuracy = 79%) between sedated (Richmond Agitation-Sedation Scale < 0) and nonsedated states (Richmond Agitation-Sedation Scale > 0). CONCLUSIONS: With further refinement, the methodology reported herein could lead to a fully automated system for depth of sedation monitoring. By enabling monitoring to be continuous, such technology may help clinical staff to monitor sedation levels more effectively and to reduce complications related to over- and undersedation.

Intestinal epithelial vitamin D receptor deletion leads to defective autophagy in colitis.

OBJECTIVE: Vitamin D and the vitamin D receptor (VDR) appear to be important immunological regulators of inflammatory bowel diseases (IBD). Defective autophagy has also been implicated in IBD, where interestingly, polymorphisms of genes such as ATG16L1 have been associated with increased risk. Although vitamin D, the microbiome and autophagy are all involved in pathogenesis of IBD, it remains unclear whether these processes are related or function independently. DESIGN: We investigated the effects and mechanisms of intestinal epithelial VDR in healthy and inflamed states using cell culture models, a conditional VDR knockout mouse model (VDR(ΔIEC)), colitis models and human samples. RESULTS: Absence of intestinal epithelial VDR affects microbial assemblage and increases susceptibility to dextran sulfate sodium-induced colitis. Intestinal epithelial VDR downregulates expressions of ATG16L1 and lysozyme, and impairs antimicrobial function of Paneth cells. Gain and loss-of-function assays showed that VDR levels regulate ATG16L1 and lysozyme at the transcriptional and translational levels. Moreover, low levels of intestinal epithelial VDR correlated with reduced ATG16L1 and representation by intestinal Bacteroides in patients with IBD. Administration of the butyrate (a fermentation product of gut microbes) increases intestinal VDR expression and suppresses inflammation in a colitis model. CONCLUSIONS: Our study demonstrates fundamental relationship between VDR, autophagy and gut microbial assemblage that is essential for maintaining intestinal homeostasis, but also in contributing to the pathophysiology of IBD. These insights can be leveraged to define therapeutic targets for restoring VDR expression and function.

Antagonistic interaction between Wnt and Notch activity modulates the regenerative capacity of a zebrafish fibrotic liver model.

UNLABELLED: In chronic liver failure patients with sustained fibrosis, excessive accumulation of extracellular matrix proteins substantially dampens the regenerative capacity of the hepatocytes, resulting in poor prognosis and high mortality. Currently, the mechanisms and the strategies of inducing endogenous cellular sources such as hepatic progenitor cells (HPCs) to regenerate hepatocytes in various contexts of fibrogenic stimuli remain elusive. Here we aim to understand the molecular and cellular mechanisms that mediate the effects of sustained fibrosis on hepatocyte regeneration using the zebrafish as a model. In the ethanol-induced fibrotic zebrafish model, we identified a subset of HPCs, responsive to Notch signaling, that retains its capacity to regenerate as hepatocytes. Discrete levels of Notch signaling modulate distinct cellular outcomes of these Notch-responsive HPCs in hepatocyte regeneration. Lower levels of Notch signaling promote amplification and subsequent differentiation of these cells into hepatocytes, while high levels of Notch signaling suppress these processes. To identify small molecules facilitating hepatocyte regeneration in the fibrotic liver, we performed chemical screens and identified a number of Wnt agonists and Notch antagonists. Further analyses demonstrated that these Wnt agonists are capable of attenuating Notch signaling by inducing Numb, a membrane-associated protein that inhibits Notch signaling. This suggests that the antagonistic interplay between Wnt and Notch signaling crucially affects hepatocyte regeneration in the fibrotic liver. CONCLUSION: Our findings not only elucidate how signaling pathways and cell-cell communications direct the cellular response of HPCs to fibrogenic stimuli, but also identify novel potential therapeutic strategies for chronic liver disease.

Percolation Model of Sensory Transmission and Loss of Consciousness Under General Anesthesia.

Neurons communicate with each other dynamically; how such communications lead to consciousness remains unclear. Here, we present a theoretical model to understand the dynamic nature of sensory activity and information integration in a hierarchical network, in which edges are stochastically defined by a single parameter p representing the percolation probability of information transmission. We validate the model by comparing the transmitted and original signal distributions, and we show that a basic version of this model can reproduce key spectral features clinically observed in electroencephalographic recordings of transitions from conscious to unconscious brain activities during general anesthesia. As p decreases, a steep divergence of the transmitted signal from the original was observed, along with a loss of signal synchrony and a sharp increase in information entropy in a critical manner; this resembles the precipitous loss of consciousness during anesthesia. The model offers mechanistic insights into the emergence of information integration from a stochastic process, laying the foundation for understanding the origin of cognition.

Clinical impact of tumor-infiltrating inflammatory cells in primary small cell esophageal carcinoma.

Primary small cell esophageal carcinoma is a rare and aggressive type of gastrointestinal cancer with poor prognosis. In the present study, the impact of tumour infiltrating inflammatory cells on clinico-pathological characteristics and the patients' prognosis were analysed. A total of 36 small cell esophageal carcinomas, 19 adjacent normal tissues and 16 esophageal squamous cell carcinoma samples were collected. Qualified pathologists examined eosinophils, neutrophils, lymphocytes and macrophages on histochemical slides. The infiltration of eosinophils and macrophages in small cell esophageal carcinoma was significantly increased as compared with tumor adjacent normal tissues, and was significantly less in esophageal squamous cell carcinoma. Macrophage count was significantly associated with (p = 0.015) lymph node-stage in small cell esophageal carcinoma. When we grouped patients into two groups by counts of infiltrated inflammatory cells, Kaplan-Meier analysis revealed that high macrophage infiltration group (p = 0.004) and high eosinophil infiltration group (p = 0.027) had significantly enhanced survival. In addition, multivariate analysis unveiled that eosinophil count (p = 0.002) and chemotherapy (Yes vs. No, p = 0.001) were independent prognostic indicators. Taken together, infiltration of macrophages and eosinophils into the solid tumor appear to be important in the progression of small cell esophageal carcinoma and patients' prognosis.

Comparative Analysis of Endoscope Obscuration With Utilization of an Endonasal Access Guide for Endonasal Skull Base Surgery.

BACKGROUND AND OBJECTIVES: In endoscopic endonasal approaches (EEAs) for skull base pathologies, endoscope view obscuration remains a persistent, time-consuming, and distracting issue for surgeons and may result in increased operative time. The endonasal access guide (EAG) has been demonstrated as a possible adjunct to minimize these events. However, to date, there have been no comparative studies performed and the potential time savings by using EAGs have yet to be quantified. This cohort study aimed to determine the operative efficiency benefits of the EAG in EEA operations. METHODS: Analysis of EEA operative videos from an EAG cohort (n = 20) and a control cohort (n = 20) was performed, assessing 12-minute segments in the first, middle, and last third of each operation. The first segment in each cohort was selected before EAG placement, serving as an internal control. Every endoscope lens soiling instance was counted (measured as cleaning actions per minute), timed (obscuration time %), and identified as a withdrawal, irrigation, or other cleaning action. Perioperative variables including skull base repair and postoperative cerebrospinal fluid leakage were assessed. RESULTS: Within the EAG cohort, obscuration time was reduced in the middle and last third compared with the first third (3.73% [CI: 2.39-5.07] vs 12.97% [CI: 10.24-15.70], P < .001; 4.19% [CI: 2.83-5.55] vs 12.97% [CI: 10.24-15.70], P < .001) and cleaning actions were also significantly reduced by EAG (0.69/min [CI: 0.39-0.99] vs 1.67/min [CI: 1.34-2.00], P = .001; 0.66/min [CI: 0.35-0.97] vs 1.67/min [CI: 1.34-2.00], P < .001). Between the control and EAG cohorts, there was no significant difference between obscuration time and cleaning actions in the first third (9.33% vs 12.97%, P = .086; 1.34/min vs 1.67/min, P = .151) or in the middle third (6.24% vs 3.73%, P = .140; 0.80/min vs 0.69/min, P = .335), but there was a significant difference in the last third (9.25% [CI: 6.95-11.55] vs 4.19% [CI: 2.83-5.55], P < .001; 0.95/min [CI: 0.73-1.17] vs 0.66/min [CI: 0.35-0.97], P = .018). CONCLUSION: EAG significantly reduces lens obscurations and cleaning events, particularly during the intradural portion of operations. This technology may offer a greater time-saving impact with patients undergoing long EEA operations.

Characterizing brain dynamics during ketamine-induced dissociation and subsequent interactions with propofol using human intracranial neurophysiology.

Ketamine produces antidepressant effects in patients with treatment-resistant depression, but its usefulness is limited by its psychotropic side effects. Ketamine is thought to act via NMDA receptors and HCN1 channels to produce brain oscillations that are related to these effects. Using human intracranial recordings, we found that ketamine produces gamma oscillations in prefrontal cortex and hippocampus, structures previously implicated in ketamine's antidepressant effects, and a 3 Hz oscillation in posteromedial cortex, previously proposed as a mechanism for its dissociative effects. We analyzed oscillatory changes after subsequent propofol administration, whose GABAergic activity antagonizes ketamine's NMDA-mediated disinhibition, alongside a shared HCN1 inhibitory effect, to identify dynamics attributable to NMDA-mediated disinhibition versus HCN1 inhibition. Our results suggest that ketamine engages different neural circuits in distinct frequency-dependent patterns of activity to produce its antidepressant and dissociative sensory effects. These insights may help guide the development of brain dynamic biomarkers and novel therapeutics for depression.

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS.

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user's desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.

Electrophysiological correlates of thalamocortical function in acute severe traumatic brain injury.

Tools assaying the neural networks that modulate consciousness may facilitate tracking of recovery after acute severe brain injury. The ABCD framework classifies resting-state EEG into categories reflecting levels of thalamocortical network function that correlate with outcome in post-cardiac arrest coma. In this longitudinal cohort study, we applied the ABCD framework to 20 patients with acute severe traumatic brain injury requiring intensive care (12 of whom were also studied at ≥6-months post-injury) and 16 healthy controls. We tested four hypotheses: 1) EEG ABCD classifications are spatially heterogeneous and temporally variable; 2) ABCD classifications improve longitudinally, commensurate with the degree of behavioral recovery; 3) ABCD classifications correlate with behavioral level of consciousness; and 4) the Coma Recovery Scale-Revised arousal facilitation protocol yields improved ABCD classifications. Channel-level EEG power spectra were classified based on spectral peaks within pre-defined frequency bands: 'A' = no peaks above delta (<4 Hz) range (complete thalamocortical disruption); 'B' = theta (4-8 Hz) peak (severe thalamocortical disruption); 'C' = theta and beta (13-24 Hz) peaks (moderate thalamocortical disruption); or 'D' = alpha (8-13 Hz) and beta peaks (normal thalamocortical function). Acutely, 95% of patients demonstrated 'D' signals in at least one channel but exhibited within-session temporal variability and spatial heterogeneity in the proportion of different channel-level ABCD classifications. By contrast, healthy participants and patients at follow-up consistently demonstrated signals corresponding to intact thalamocortical network function. Patients demonstrated longitudinal improvement in ABCD classifications (p < .05) and ABCD classification distinguished patients with and without command-following in the subacute-to-chronic phase of recovery (p < .01). In patients studied acutely, ABCD classifications improved after the Coma Recovery Scale-Revised arousal facilitation protocol (p < .05) but did not correspond with behavioral level of consciousness. These findings support the use of the ABCD framework to characterize channel-level EEG dynamics and track fluctuations in functional thalamocortical network integrity in spatial detail.

"Not a perfect situation, but..." A single-practice survey of patient experience of phone consultations during COVID-19 Alert Level 4 in New Zealand.

AIM: To explore patients' experiences of virtual consultations during the COVID-19 Alert Level 4 lockdown in New Zealand. METHOD: A single-practice retrospective phone survey exploring patients' satisfaction with the phone consultation process during Alert Level 4 lockdown. RESULTS: Of 259 eligible patients, 108 (42%) participated in the survey. Overall satisfaction with phone consultations was high, with a median score 9 out of 10 (95% CI 9-9). Participants were highly likely to recommend phone consultations to others, with a median score of 9 (95% CI 7-9). This was consistent across age groups, ethnicities and socioeconomic groupings. Men were less satisfied with phone consultations than women, with a 2 point (95% CI -3--1) lower median score than women, but they were not less likely to recommend phone consultations. Most participants found phone consultations to be convenient and time-saving and considered not seeing the doctor to be acceptable in the context of the lockdown. Few participants experienced technical difficulties over the phone. Issues of communication and appropriateness of consultations to the medium of the phone were raised. CONCLUSION: This single-centre study demonstrates the acceptability of phone consults for most patients presenting to general practice during a pandemic. These findings need further exploration in broader general practice settings and non-pandemic contexts.

ICU delirium burden predicts functional neurologic outcomes.

BACKGROUND: We investigated the effect of delirium burden in mechanically ventilated patients, beginning in the ICU and continuing throughout hospitalization, on functional neurologic outcomes up to 2.5 years following critical illness. METHODS: Prospective cohort study of enrolling 178 consecutive mechanically ventilated adult medical and surgical ICU patients between October 2013 and May 2016. Altogether, patients were assessed daily for delirium 2941days using the Confusion Assessment Method for the ICU (CAM-ICU). Hospitalization delirium burden (DB) was quantified as number of hospital days with delirium divided by total days at risk. Survival status up to 2.5 years and neurologic outcomes using the Glasgow Outcome Scale were recorded at discharge 3, 6, and 12 months post-discharge. RESULTS: Of 178 patients, 19 (10.7%) were excluded from outcome analyses due to persistent coma. Among the remaining 159, 123 (77.4%) experienced delirium. DB was independently associated with >4-fold increased mortality at 2.5 years following ICU admission (adjusted hazard ratio [aHR], 4.77; 95% CI, 2.10-10.83; P < .001), and worse neurologic outcome at discharge (adjusted odds ratio [aOR], 0.02; 0.01-0.09; P < .001), 3 (aOR, 0.11; 0.04-0.31; P < .001), 6 (aOR, 0.10; 0.04-0.29; P < .001), and 12 months (aOR, 0.19; 0.07-0.52; P = .001). DB in the ICU alone was not associated with mortality (HR, 1.79; 0.93-3.44; P = .082) and predicted neurologic outcome less strongly than entire hospital stay DB. Similarly, the number of delirium days in the ICU and for whole hospitalization were not associated with mortality (HR, 1.00; 0.93-1.08; P = .917 and HR, 0.98; 0.94-1.03, P = .535) nor with neurological outcomes, except for the association between ICU delirium days and neurological outcome at discharge (OR, 0.90; 0.81-0.99, P = .038). CONCLUSIONS: Delirium burden throughout hospitalization independently predicts long term neurologic outcomes and death up to 2.5 years after critical illness, and is more predictive than delirium burden in the ICU alone and number of delirium days.

Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia.

In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without directly monitoring the brain. Drug- and patient-specific electroencephalographic (EEG) signatures of anesthesia-induced unconsciousness have been identified previously. We applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. We used cross-validation to select and train the best performing models using 33,159 2s segments of EEG data recorded from 7 healthy volunteers who received increasing infusions of propofol while responding to stimuli to directly assess unconsciousness. Cross-validated models of unconsciousness performed very well when tested on 13,929 2s EEG segments from 3 left-out volunteers collected under the same conditions (median volunteer AUCs 0.99-0.99). Models showed strong generalization when tested on a cohort of 27 surgical patients receiving solely propofol collected in a separate clinical dataset under different circumstances and using different hardware (median patient AUCs 0.95-0.98), with model predictions corresponding with actions taken by the anesthesiologist during the cases. Performance was also strong for 17 patients receiving sevoflurane (alone or in addition to propofol) (median AUCs 0.88-0.92). These results indicate that EEG spectral features can predict unconsciousness, even when tested on a different anesthetic that acts with a similar neural mechanism. With high performance predictions of unconsciousness, we can accurately monitor anesthetic state, and this approach may be used to engineer infusion pumps to intelligibly respond to patients' neural activity.

Correcting an instance of synthetic lethality with a pro-survival sequence.

A human cDNA encoding the LIM domain containing 194 amino acid cysteine and glycine rich protein 3 (CSRP3) was identified as a BAX suppressor in yeast and a pro-survival sequence that abrogated copper mediated regulated cell death (RCD). Yeast lacks a CSRP3 orthologue but it has four LIM sequences, namely RGA1, RGA2, LRG1 and PXL1. These are known regulators of stress responses yet their roles in RCD remain unknown. Given that LIMs interact with other LIMs, we ruled out the possibility that overexpressed yeast LIMs alone could prevent RCD and that CSRP3 functions by acting as a dominant regulator of yeast LIMs. Of interest was the discovery that even though yeast cells lacking the LIM encoding PXL1 had no overt growth defect, it was nevertheless supersensitive to the effects of sublethal levels of copper. Heterologous expression of human CSPR3 as well as the pro-survival 14-3-3 sequence corrected this copper supersensitivity. These results show that the pxl1∆-copper synthetic lethality is likely due to the induction of RCD. This differs from the prevailing model in which synthetic lethality occurs because of specific defects generated by the combined loss of two overlapping but non-essential functions.