Preferential Inhibition of Frontal-to-Parietal Feedback Connectivity Is a Neurophysiologic Correlate of General Anesthesia in Surgical Patients

BACKGROUND: The precise mechanism and optimal measure of anesthetic-induced unconsciousness has yet to be elucidated. Preferential inhibition of feedback connectivity from frontal to parietal brain networks is one potential neurophysiologic correlate, but has only been demonstrated in animals or under limited conditions in healthy volunteers. METHODS AND FINDINGS: We recruited eighteen patients presenting for surgery under general anesthesia; electroencephalography of the frontal and parietal regions was acquired during (i) baseline consciousness, (ii) anesthetic induction with propofol or sevoflurane, (iii) general anesthesia, (iv) recovery of consciousness, and (v) post-recovery states. We used two measures of effective connectivity, evolutional map approach and symbolic transfer entropy, to analyze causal interactions of the frontal and parietal regions. The dominant feedback connectivity of the baseline conscious state was inhibited after anesthetic induction and during general anesthesia, resulting in reduced asymmetry of feedback and feedforward connections in the frontoparietal network. Dominant feedback connectivity returned when patients recovered from anesthesia. Both analytic techniques and both classes of anesthetics demonstrated similar results in this heterogeneous population of surgical patients. CONCLUSIONS: The disruption of dominant feedback connectivity in the frontoparietal network is a common neurophysiologic correlate of general anesthesia across two anesthetic classes and two analytic measures. This study represents a key translational step from the underlying cognitive neuroscience of consciousness to more sophisticated monitoring of anesthetic effects in human surgical patients

A search for exoplanets around north circumpolar stars. VII. Detection of planetary companion orbiting the largest host star HD 18438

We have been conducting a exoplanet search survey using Bohyunsan Observatory Echelle Spectrograph (BOES) for the last 18 years. We present the detection of exoplanet candidate in orbit around HD 18438 from high-precision radial velocity (RV) mesurements. The target was already reported in 2018 (Bang et al. 2018). They conclude that the RV variations with a period of 719 days are likely to be caused by the pulsations because the Lomb-Scargle periodogram of HIPPARCOS photometric and Ha EW variations for HD 18438 show peaks with periods close to that of RV variations and there were no correlations between bisectors and RV measurements. However, the data were not sufficient to reach a firm conclusion. We obtained more RV data for four years. The longer time baseline yields a more accurate determination with a revised period of 803 +/- 5 days and the planetary origin of RV variations with a minimum planetary companion mass of 21 +/- 1 MJup. Our current estimate of the stellar parameters for HD 18438 makes it currently the largest star with a planetary companion.Comment: 6 pages, 4 figures, Accept to the Journal of the Korean Astronomical Societ

Time-resolved pathogenic gene expression analysis of the plant pathogen Xanthomonas oryzae pv. oryzae

Virulence of wild-type and mutant Xoo strains on rice. (DOCX 16 kb

Depression and suicide risk prediction models using blood-derived multi-omics data

More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R2 values of 0.961 and 0.943 for Hamilton Rating Scale for Depression???17 and Scale for Suicide Ideation, respectively. Multi-omics data were used to construct psychiatric status prediction models for improved mental health treatment