Recovery From Acute Respiratory Distress Syndrome Is Associated With Increasing Alpha Power in the Frontal Electroencephalogram During Propofol Sedation: A Case Report.

The effects of critical illness on electroencephalographic (EEG) signatures of sedatives have not been described, limiting the use of EEG-guided sedation in the intensive care unit (ICU). We report the case of a 36-year-old man recovering from acute respiratory distress syndrome (ARDS). Severe ARDS was characterized by slow-delta (0.1-4 Hz) and theta (4-8 Hz) oscillations but lacked the alpha (8-14 Hz) power expected during propofol sedation in a patient of this age. The alpha power emerged as ARDS resolved. This case raises the question of whether inflammatory states can alter EEG signatures during sedation.

Modulatory dynamics mark the transition between anesthetic states of unconsciousness.

Unconsciousness maintained by GABAergic anesthetics, such as propofol and sevoflurane, is characterized by slow-delta oscillations (0.3 to 4 Hz) and alpha oscillations (8 to 14 Hz) that are readily visible in the electroencephalogram. At higher doses, these slow-delta-alpha (SDA) oscillations transition into burst suppression. This is a marker of a state of profound brain inactivation during which isoelectric (flatline) periods alternate with periods of the SDA patterns present at lower doses. While the SDA and burst suppression patterns have been analyzed separately, the transition from one to the other has not. Using state-space methods, we characterize the dynamic evolution of brain activity from SDA to burst suppression and back during unconsciousness maintained with propofol or sevoflurane in volunteer subjects and surgical patients. We uncover two dynamical processes that continuously modulate the SDA oscillations: alpha-wave amplitude and slow-wave frequency modulation. We present an alpha modulation index and a slow modulation index which characterize how these processes track the transition from SDA oscillations to burst suppression and back to SDA oscillations as a function of increasing and decreasing anesthetic doses, respectively. Our biophysical model reveals that these dynamics track the combined evolution of the neurophysiological and metabolic effects of a GABAergic anesthetic on brain circuits. Our characterization of the modulatory dynamics mediated by GABAergic anesthetics offers insights into the mechanisms of these agents and strategies for monitoring and precisely controlling the level of unconsciousness in patients under general anesthesia.

Continuous action deep reinforcement learning for propofol dosing during general anesthesia.

PURPOSE: Anesthesiologists simultaneously manage several aspects of patient care during general anesthesia. Automating administration of hypnotic agents could enable more precise control of a patient's level of unconsciousness and enable anesthesiologists to focus on the most critical aspects of patient care. Reinforcement learning (RL) algorithms can be used to fit a mapping from patient state to a medication regimen. These algorithms can learn complex control policies that, when paired with modern techniques for promoting model interpretability, offer a promising approach for developing a clinically viable system for automated anesthestic drug delivery. METHODS: We expand on our prior work applying deep RL to automated anesthetic dosing by now using a continuous-action model based on the actor-critic RL paradigm. The proposed RL agent is composed of a policy network that maps observed anesthetic states to a continuous probability density over propofol-infusion rates and a value network that estimates the favorability of observed states. We train and test three versions of the RL agent using varied reward functions. The agent is trained using simulated pharmacokinetic/pharmacodynamic models with randomized parameters to ensure robustness to patient variability. The model is tested on simulations and retrospectively on nine general anesthesia cases collected in the operating room. We utilize Shapley additive explanations to gain an understanding of the factors with the greatest influence over the agent's decision-making. RESULTS: The deep RL agent significantly outperformed a proportional-integral-derivative controller (median episode median absolute performance error 1.9% ± 1.8 and 3.1% ± 1.1). The model that was rewarded for minimizing total doses performed the best across simulated patient demographics (median episode median performance error 1.1% ± 0.5). When run on real-world clinical datasets, the agent recommended doses that were consistent with those administered by the anesthesiologist. CONCLUSIONS: The proposed approach marks the first fully continuous deep RL algorithm for automating anesthestic drug dosing. The reward function used by the RL training algorithm can be flexibly designed for desirable practices (e.g. use less anesthetic) and bolstered performances. Through careful analysis of the learned policies, techniques for interpreting dosing decisions, and testing on clinical data, we confirm that the agent's anesthetic dosing is consistent with our understanding of best-practices in anesthesia care.

Analysis of the functional sequences in the promoter region of the human adhesion molecule close homolog of L1.

BACKGROUND: Close Homolog of L1 (CHL1) is a member of the L1 family of cell adhesion molecules. CHL1 gene is located on human chromosome 3 and has been linked to several pathologies, including 3p deletion syndrome, schizophrenia, and tumor growth and metastasis. OBJECTIVE: The goal of the present study was to determine which region of the CHL1 promoter is most competent in driving CHL1 gene expression. Methods: Five candidate DNA fragments in the promoter regions were selected by screening across six species for evolutionary conserved sequences. The activity of these five promoter regions was quantitatively evaluated using a GFP reporter gene in transfection experiments, performed in C6 glioma cells. RESULTS: Of the five promoter regions tested, three drove reporter GFP expression, with the conserved region 6 (CR6, Gene ID AC066595.5, 25851-26850) being the most active for transcription. CONCLUSION: The identification of the CR6 activity provides a better understanding of the regulatory mechanisms underlying CHL1 expression. It may help future discovery of therapeutic strategies that involve influencing critical promoter regions to drive transcriptional regulation of the mammalian CHL1 gene.HIGHLIGHTSConserved regions of CHL1 promoter sequences were identified by in-silico analysis.Five conserved regions were tested for gene regulatory activity using a reporter assay.Conserved regions CR5, CR6 and CR7 show gene regulatory function in a reporter assay.Co-transfection of CR5 and CR6 yielded the highest reporter activity.The core region of CR6 (CR6core) was identified as a cis-acting element.In-tandem promoter CR5core-CR6core was the best in a reporter assay.

MyelTracer: A Semi-Automated Software for Myelin g-Ratio Quantification.

In the central and peripheral nervous systems, the myelin sheath promotes neuronal signal transduction. The thickness of the myelin sheath changes during development and in disease conditions like multiple sclerosis. Such changes are routinely detected using electron microscopy through g-ratio quantification. While g-ratio is one of the most critical measurements in myelin studies, a major drawback is that g-ratio quantification is extremely laborious and time-consuming. Here, we report the development and validation of MyelTracer, an installable, stand-alone software for semi-automated g-ratio quantification based on the Open Computer Vision Library (OpenCV). Compared with manual g-ratio quantification, using MyelTracer produces consistent results across multiple tissues and animal ages, as well as in remyelination after optic nerve crush, and reduces total quantification time by 40-60%. With g-ratio measurements via MyelTracer, a known hypomyelination phenotype can be detected in a Williams syndrome mouse model. MyelTracer is easy to use and freely available for Windows and Mac OS X (https://github.com/HarrisonAllen/MyelTracer).

The L1 adhesion molecule normalizes neuritogenesis in Rett syndrome-derived neural precursor cells.

Therapeutic intervention is an important need in ameliorating the severe consequences of Rett Syndrome (RTT), a neurological disorder caused by mutations in the X-linked gene methyl-CpG-binding protein-2 (MeCP2). Following previously observed morphological defects in induced pluripotent stem cell (iPSC)-derived neurons obtained from female RTT patients, we hypothesized that transfection with the L1 cell adhesion molecule (L1) could contribute to normalizing a pathological male cell system bearing a nonsense mutation of MeCP2. We found a decreased expression of L1 in RTT iPSCs-derived neural precursor cells (RTT NPCs) and decreased neuritogenesis. Expression of wild-type MeCP2 in RTTNPCs revealed a positive correlation between the levels of MeCP2 and L1, and normalization of cell survival. Expression of L1 in RTTNPCs enhanced neuritogenesis and soma size. Knock-down of MeCP2 in wild type NPCs reduced neuritogenesis. L1 expression is regulated by the MeCP2 promoter. These results suggest that a deficiency in L1 may partially account for RTT phenotypes.

Differences between lipopolysaccharide and double-stranded RNA in innate immune responses of BV2 microglial cells.

Microglial cells are thought to be major inflammatory cells in the central nervous system; however, sufficient information about the effects of double-stranded RNA (dsRNA) in microglial cells is lacking. The present study compared the innate immune responses of the murine microglial cell line BV2 to dsRNA and lipopolysaccharide (LPS). It showed that the effect of dsRNA was similar to that of LPS treatment. The dsRNA induced several pro-inflammatory factors such as TNF-alpha, IL-6, IL-1beta, and IL-1Ra. Furthermore, the expression level of COX-2 was increased after treatment with dsRNA. However, the induction level of IL-1beta by dsRNA was less than those of the other cytokines that were measured. These results suggest that, although both dsRNA and LPS trigger pro-inflammatory responses, the intracellular signaling pathway and inflammation pattern of dsRNA and LPS may be different. Therefore, dsRNA produced during viral infection could precipitate neurological abnormalities through chronic inflammation.