Kaempferol 3-O-Rutinoside, a Flavone Derived from Tetrastigma hemsleyanum Diels et Gilg, Reduces Body Temperature through Accelerating the Elimination of IL-6 and TNF-α in a Mouse Fever Model.

Fever is a serious condition that can lead to various consequences ranging from prolonged illness to death. Tetrastigma hemsleyanum Diels et Gilg (T. hemsleyanum) has been used for centuries to treat fever, but the specific chemicals responsible for its antipyretic effects are not well understood. This study aimed to isolate and identify the chemicals with antipyretic bioactivity in T. hemsleyanum extracts and to provide an explanation for the use of T. hemsleyanum as a Chinese herbal medicine for fever treatment. Our results demonstrate that kaempferol 3-rutinoside (K3OR) could be successfully isolated and purified from the roots of T. hemsleyanum. Furthermore, K3OR exhibited a significant reduction in rectal temperature in a mouse model of fever. Notably, a 4 µM concentration of K3OR showed more effective antipyretic effects than ibuprofen and acetaminophen. To explore the underlying mechanism, we conducted an RNA sequencing analysis, which revealed that PXN may act as a key regulator in the fever process induced by lipopolysaccharide (LPS). In the mouse model of fever, K3OR significantly promoted the secretion of IL-6 and TNF-α during the early stage in the LPS-treated group. However, during the middle to late stages, K3OR facilitated the elimination of IL-6 and TNF-α in the LPS-treated group. Overall, our study successfully identified the chemicals responsible for the antipyretic bioactivity in T. hemsleyanum extracts, and it answered the question as to why T. hemsleyanum is used as a traditional Chinese herbal medicine for treating fever. These findings contribute to a better understanding of the therapeutic potential of T. hemsleyanum in managing fever, and they provide a basis for further research and development in this field.

Oroxin A from Oroxylum indicum improves disordered lipid metabolism by inhibiting SREBPs in oleic acid-induced HepG2 cells and high-fat diet-fed non-insulin-resistant rats.

Background: Lipid metabolism disorders have become a major global public health issue. Due to the complexity of these diseases, additional research and drugs are needed. Oroxin A, the major component of Oroxylum indicum (L.) Kurz (Bignoniaceae), can improve the lipid profiles of diabetic and insulin-resistant (IR) rats. Because insulin resistance is strongly correlated with lipid metabolism, improving insulin resistance may also constitute an effective strategy for improving lipid metabolism. Thus, additional research on the efficacy and mechanism of oroxin An under non-IR conditions is needed. Methods: In this study, we established lipid metabolism disorder model rats by high-fat diet feeding and fatty HepG2 cell lines by treatment with oleic acid and evaluated the therapeutic effect and mechanism of oroxin A in vitro and in vivo through biochemical indicator analysis, pathological staining, immunoblotting, and immunofluorescence staining. Results: Oroxin A improved disordered lipid metabolism under non-IR conditions, improved the plasma and hepatic lipid profiles, and enhanced the lipid-lowering action of atorvastatin. Additionally, oroxin A reduced the total triglyceride (TG) levels by inhibiting sterol regulatory element-binding protein 1 (SREBP1) expression and reducing the expression of acetyl coenzyme A carboxylase (ACC) and fatty acid synthase (FASN) in vivo and in vitro. Oroxin A also reduced the total cholesterol (TC) levels by inhibiting SREBP2 expression and reducing HMGCR expression in vivo and in vitro. In addition, oroxin A bound to low-density lipoprotein receptor (LDLR) and increased AMPK phosphorylation. Conclusions: Our results suggested that oroxin A may modulate the nuclear transcriptional activity of SREBPs by binding to LDLR proteins and increasing AMPK phosphorylation. Oroxin A may thus reduce lipid synthesis and could be used for the treatment and prevention of lipid metabolism disorders.

Investigating Emotion EEG Patterns for Depression Detection with Attentive Simple Graph Convolutional Network.

Depression severely limits daily functioning, diminishes quality of life and possibly leads to self-harm and suicide. Noninvasive electroencephalography (EEG) has been shown effective as biomarkers for objective depression diagnose and treatment response prediction, and dry EEG electrodes further extend its availability for clinical use. Even though many efforts have been made to identify depression biomarkers, searching reliable EEG biomarkers for depression detection remains challenging. This work presents a systematic investigation of capabilities of emotion EEG patterns for depression detection using a dry EEG electrode system. We design an emotion elicitation paradigm with happy, neutral and sad emotions and collect EEG signals during film watching from 33 depressed patients and 40 healthy controls. The mean activation levels at frontal and temporal sites in the alpha, beta and gamma bands of the depressed group are different to those of the healthy group, indicating the impacts of depressive symptoms on the emotion experiences. To leverage the topology information among EEG channels for emotion recognition and depression detection, an Attentive Simple Graph Convolutional network is built. The deep depression-health classifier achieves a sensitivity of 81.93% and a specificity of 91.69% on the happy emotions, suggesting the promising use of the emotion neural patterns for distinguishing the depressed patients from the healthy controls.

Objective Depression Detection Using EEG and Eye Movement Signals Induced by Oil Paintings.

Depression is a mental disorder characterized by persistent sadness and loss of interest, which has become one of the leading causes of disability worldwide. There are currently no objective diagnostic standards for depression in clinical practice. Previous studies have shown that depression causes both brain abnormalities and behavioral disorders. In this study, both electroencephalography (EEG) and eye movement signals were used to objectively detect depression. By presenting 40 carefully selected oil paintings-20 positive and 20 negative-as stimuli, we were able to successfully evoke emotions in 48 depressed patients (DPs) and 40 healthy controls (HCs) from three centers. We then used Transformer, a deep learning model, to conduct emotion recognition and depression detection. The experimental results demonstrate that: a) Transformer achieves the best accuracies of 89.21% and 92.19% in emotion recognition and depression detection, respectively; b) The HC group has higher accuracies than the DP group in emotion recognition for both subject-dependent and subject-independent experiments; c) The neural pattern differences do exist between DPs and HCs, and we find the consistent asymmetry of the neural patterns in DPs; d) For depression detection, using single oil painting achieves the best accuracies, and using negative oil paintings has higher accuracies than using positive oil paintings. These findings suggest that EEG and eye movement signals induced by oil paintings can be used to objectively identify depression.

Tagging Continuous Labels for EEG-based Emotion Classification.

EEG-based emotion classification has long been a critical task in the field of affective brain-computer interface (aBCI). The majority of leading researches construct supervised learning models based on labeled datasets. Several datasets have been released, including different kinds of emotions while utilizing various forms of stimulus materials. However, they adopt discrete labeling methods, in which the EEG data collected during the same stimulus material are given a same label. These methods neglect the fact that emotion changes continuously, and mislabeled data possibly exist. The imprecision of discrete labels may hinder the progress of emotion classification in concerned works. Therefore, we develop an efficient system in this paper to support continuous labeling by giving each sample a unique label, and construct a continuously labeled EEG emotion dataset. Using our dataset with continuous labels, we demonstrate the superiority of continuous labeling in emotion classification through experiments on several classification models. We further utilize the continuous labels to identify the EEG features under induced and non-induced emotions in both our dataset and a public dataset. Our experimental results reveal the learnability and generality of the relation between the EEG features and their continuous labels.

Deep Depression Detection with Resting-State and Cognitive-Task EEG.

Depression is a common mental disorder that negatively affects physical health and personal, social and occupational functioning. Currently, accurate and objective diagnosis of depression remains challenging, and electroencephalography (EEG) provides promising clinical practice or home use due to considerable performance and low cost. This work investigates the capabilities of deep neural networks with EEG-based neural patterns from both resting states and cognitive tasks for depression detection. We collect EEG signals from 33 depressed patients and 40 healthy controls using wearable dry electrodes and build Attentive Simple Graph Convolutional network and Transformer neural network for objective depression detection. Four experiment stages, including two resting states and two cognitive tasks, are designed to characterize the alteration of relevant neural patterns in the depressed patients, in terms of decreased energy and impaired performance in sustained attention and response inhibition. The Transformer model achieves an AUC of 0.94 on the Continuous Performance Test-Identical Pairs version (sensitivity: 0.87, specificity: 0.91) and the Stroop Color Word Test (sensitivity: 0.93, specificity: 0.88), and an AUC of 0.89 on the two resting states (sensitivity: 0.85 and 0.87, specificity: 0.88 and 0.90, respectively), indicating the potential of EEG-based neural patterns in identifying depression. These findings provide new insights into the research of depression mechanisms and EEG-based depression biomarkers.

The International Cardiac Arrest Research Consortium Electroencephalography Database.

OBJECTIVES: To develop the International Cardiac Arrest Research (I-CARE), a harmonized multicenter clinical and electroencephalography database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest. DESIGN: Multicenter cohort, partly prospective and partly retrospective. SETTING: Seven academic or teaching hospitals from the United States and Europe. PATIENTS: Individuals 16 years old or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous electroencephalography monitoring were included. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: Clinical and electroencephalography data were harmonized and stored in a common Waveform Database-compatible format. Automated spike frequency, background continuity, and artifact detection on electroencephalography were calculated with 10-second resolution and summarized hourly. Neurologic outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical data and 56,676 hours (3.9 terabytes) of continuous electroencephalography data for 1,020 patients. Most patients died ( n = 603, 59%), 48 (5%) had severe neurologic disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean electroencephalography recording duration depending on the neurologic outcome (range, 53-102 hr for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least 1 hour was seen in 258 patients (25%) (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least 1 hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively. CONCLUSIONS: The I-CARE consortium electroencephalography database provides a comprehensive real-world clinical and electroencephalography dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal electroencephalography patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.

Identifying sex differences in EEG-based emotion recognition using graph convolutional network with attention mechanism.

OBJECTIVE: Sex differences in emotions have been widely perceived via self-reports, peripheral physiological signals and brain imaging techniques. However, how sex differences are reflected in the EEG neural patterns of emotions remains unresolved. In this paper, we detect sex differences in emotional EEG patterns, investigate the consistency of such differences in various emotion datasets across cultures, and study how sex as a factor affects the performance of EEG-based emotion recognition models. APPROACH: We thoroughly assess sex differences in emotional EEG patterns on five public datasets, including SEED, SEED-IV, SEED-V, DEAP and DREAMER, systematically examine the sex-specific EEG patterns for happy, sad, fearful, disgusted and neutral emotions, and implement deep learning models for sex-specific emotion recognition. MAIN RESULTS: (1) Sex differences exist in various emotion types and both Western and Eastern cultures; (2) The emotion patterns of females are more stable than those of males, and the patterns of happiness from females are in sharp contrast with the patterns of sadness, fear and disgust, while the energy levels are more balanced for males; (3) The key features for emotion recognition are mainly located at the frontal and temporal sites for females and distributed more evenly over the whole brain for males, and (4) The same-sex emotion recognition models outperform the corresponding cross-sex models. SIGNIFICANCE: These findings extend efforts to characterize sex differences in emotional brain activation, provide new physiological evidence for sex-specific emotion processing, and reinforce the message that sex differences should be carefully considered in affective research and precision medicine.

Effects of biochar application and nutrient fluctuation on the growth, and cadmium and nutrient uptake of Trifolium repens with different planting densities in Cd-contaminated soils.

Biochar has been used to remediate contaminated-soil with heavy metals, however, less is known on how biochar interacts with planting density and nutrient fluctuation to affect the remediation. A pot experiment was conducted in the greenhouse to investigate the effects of biochar application (without vs. with 1% biochar, g/g substrate), nutrient fluctuation (constant vs. pulsed) and planting density (1-, 3- and 6-individuals per pot) on the growth, and cadmium (Cd) and nutrient uptake of Trifolium repens population. Our results found that the growth of T. repens population increased significantly with increasing planting density, and the increment decreased with increasing planting density. Both the Cd and nutrient uptake were higher at higher planting density (e.g., 3- and 6-individuals) than at lower planting density (e.g., 1-individual). Biochar application increased the biomass and shoot Cd uptake, but decreased the ratio of root to shoot and root Cd uptake of T. repens population, the effects of which were significantly influenced by planting density. Although nutrient fluctuation had no effect on the growth of T. repens population, but its interaction with planting density had significant effects on Cd uptake in tissues. Overall, the effects of biochar application and nutrient fluctuation on the growth and Cd uptake were both influenced by planting density in the present study. Our findings highlight that biochar application and constant nutrient supply at an appropriate planting density, such as planting density of 3-individuals per pot in the present study, could promote the growth, and Cd and nutrient uptake of T. repens population.

Development of a Strategy for L-Lactic Acid Production by Rhizopus oryzae Using Zizania latifolia Waste and Cane Molasses as Carbon Sources.

As a biodegradable and renewable material, polylactic acid is considered a major environmentally friendly alternative to petrochemical plastics. Microbial fermentation is the traditional method for lactic acid production, but it is still too expensive to compete with the petrochemical industry. Agro-industrial wastes are generated from the food and agricultural industries and agricultural practices. The utilization of agro-industrial wastes is an important way to reduce costs, save energy and achieve sustainable development. The present study aimed to develop a method for the valorization of Zizania latifolia waste and cane molasses as carbon sources for L-lactic acid fermentation using Rhizopus oryzae LA-UN-1. The results showed that xylose derived from the acid hydrolysis of Z. latifolia waste was beneficial for cell growth, while glucose from the acid hydrolysis of Z. latifolia waste and mixed sugars (glucose and fructose) from the acid hydrolysis of cane molasses were suitable for the accumulation of lactic acid. Thus, a three-stage carbon source utilization strategy was developed, which markedly improved lactic acid production and productivity, respectively reaching 129.47 g/L and 1.51 g/L·h after 86 h of fermentation. This work demonstrates that inexpensive Z. latifolia waste and cane molasses can be suitable carbon sources for lactic acid production, offering an efficient utilization strategy for agro-industrial wastes.

The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database.

Objective: To develop a harmonized multicenter clinical and electroencephalography (EEG) database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest. Design: Multicenter cohort, partly prospective and partly retrospective. Setting: Seven academic or teaching hospitals from the U.S. and Europe. Patients: Individuals aged 16 or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous EEG monitoring were included. Interventions: not applicable. Measurements and Main Results: Clinical and EEG data were harmonized and stored in a common Waveform Database (WFDB)-compatible format. Automated spike frequency, background continuity, and artifact detection on EEG were calculated with 10 second resolution and summarized hourly. Neurological outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical and 56,676 hours (3.9 TB) of continuous EEG data for 1,020 patients. Most patients died (N=603, 59%), 48 (5%) had severe neurological disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean EEG recording duration depending on the neurological outcome (range 53-102h for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least one hour was seen in 258 (25%) patients (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least one hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively. Conclusions: The International Cardiac Arrest Research (I-CARE) consortium database provides a comprehensive real-world clinical and EEG dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal EEG patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.

Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest.

BACKGROUND AND OBJECTIVES: Epileptiform activity and burst suppression are neurophysiology signatures reflective of severe brain injury after cardiac arrest. We aimed to delineate the evolution of coma neurophysiology feature ensembles associated with recovery from coma after cardiac arrest. METHODS: Adults in acute coma after cardiac arrest were included in a retrospective database involving 7 hospitals. The combination of 3 quantitative EEG features (burst suppression ratio [BSup], spike frequency [SpF], and Shannon entropy [En]) was used to define 5 distinct neurophysiology states: epileptiform high entropy (EHE: SpF ≥4 per minute and En ≥5); epileptiform low entropy (ELE: SpF ≥4 per minute and <5 En); nonepileptiform high entropy (NEHE: SpF <4 per minute and ≥5 En); nonepileptiform low entropy (NELE: SpF <4 per minute and <5 En), and burst suppression (BSup ≥50% and SpF <4 per minute). State transitions were measured at consecutive 6-hour blocks between 6 and 84 hours after return of spontaneous circulation. Good neurologic outcome was defined as best cerebral performance category 1-2 at 3-6 months. RESULTS: One thousand thirty-eight individuals were included (50,224 hours of EEG), and 373 (36%) had good outcome. Individuals with EHE state had a 29% rate of good outcome, while those with ELE had 11%. Transitions out of an EHE or BSup state to an NEHE state were associated with good outcome (45% and 20%, respectively). No individuals with ELE state lasting >15 hours had good recovery. DISCUSSION: Transition to high entropy states is associated with an increased likelihood of good outcome despite preceding epileptiform or burst suppression states. High entropy may reflect mechanisms of resilience to hypoxic-ischemic brain injury.

Isolation and screening of a chitin deacetylase producing Bacillus cereus and its potential for chitosan preparation.

Chitosan is a biopolymer material extracted from marine biomass waste such as shrimp and crab shells, which has good biocompatibility and degradability with great potential for application in the field of wastewater treatment and soil remediation. The higher the degree of deacetylation (DD), the better the adsorption performance of chitosan. Chitin deacetylase (CDA) can specifically catalyze the deacetylate of chitin in a green reaction that is environmentally friendly. However, the scarcity of high yielding chitin deacetylase strains has been regarded as the technical bottleneck of chitosan green production. Here, we screened a natural chitin degrading bacterium from coastal mud and identified it as Bacillus cereus ZWT-08 by re-screening the chitin deacetylase activity and degree of deacetylation values. By optimizing the medium conditions and enzyme production process, ZWT-08 was cultured in fermentation medium with 1% (m/V) glucose and yeast extract at pH 6.0, 37°C, and a stirring speed of 180 r/min. After fermenting in 5 L fermenter for 48 h, the deacetylation activity of the supernatant reached 613.25 U/mL. Electron microscopic examination of the chitin substrate in the fermentation medium revealed a marshmallow-like fluffy texture on its structural surface. Meanwhile, 89.29% of the acetyl groups in this chitin substrate were removed by enzymatic digestion of chitin deacetylase produced by ZWT-08, resulting in the preparation of chitosan a degree of deacetylation higher than 90%. As an effective strain for chitosan production, Bacillus cereus ZWT-08 plays a positive role in the bioconversion of chitin and the upgrading of the chitosan industry.

Efficient Co-production of Docosahexaenoic Acid Oil and Carotenoids in Aurantiochytrium sp. Using a Light Intensity Gradient Strategy.

Aurantiochytrium is a promising source of docosahexaenoic acid (DHA) and carotenoids, but their synthesis is influenced by environmental stress factors. In this study, the effect of different light intensities on the fermentation of DHA oil and carotenoids using Aurantiochytrium sp. TZ209 was investigated. The results showed that dark culture and low light intensity conditions did not affect the normal growth of cells, but were not conducive to the accumulation of carotenoids. High light intensity promoted the synthesis of DHA and carotenoids, but caused cell damage, resulting in a decrease of oil yield. To solve this issue, a light intensity gradient strategy was developed, which markedly improved the DHA and carotenoid content without reducing the oil yield. This strategy produced 30.16 g/L of microalgal oil with 15.11 g/L DHA, 221 µg/g astaxanthin, and 386 µg/g ß-carotene. This work demonstrates that strain TZ209 is a promising DHA producer and provides an efficient strategy for the co-production of DHA oil together with carotenoids.

Simultaneous improvement of the thermostability and activity of lactic dehydrogenase from Lactobacillus rossiae through rational design.

d-Phenyllactic acid, is a versatile organic acid with wide application prospects in the food, pharmaceutical and material industries. Wild-type lactate dehydrogenase LrLDH from Lactobacillus rossiae exhibits a high catalytic performance in the production of d-phenyllactic acid from phenylpyruvic acid or sodium phenylpyruvate, but its industrial application is hampered by poor thermostability. Here, computer aided rational design was applied to improve the thermostability of LrLDH. By using HotSpot Wizard 3.0, five hotspot residues (N218, L237, T247, D249 and S301) were identified, after which site-saturation mutagenesis and combined mutagenesis were performed. The double mutant D249A/T247I was screen out as the best variant, with optimum temperature, t 1/2, and T 10 50 that were 12 °C, 17.96 min and 19 °C higher than that of wild-type LrLDH, respectively. At the same time, the k cat/K m of D249A/T247I was 1.47 s-1 mM-1, which was 3.4 times higher than that of the wild-type enzyme. Thus rational design was successfully applied to simultaneously improve the thermostability and catalytic activity of LrLDH to a significant extent. The results of molecular dynamics simulations and molecular structure analysis could explain the mechanisms for the improved performance of the double mutant. This study shows that computer-aided rational design can greatly improve the thermostability of d-lactate dehydrogenase, offering a reference for the modification of other enzymes.

SLC25A25-AS1 over-expression could be predicted the dismal prognosis and was related to the immune microenvironment in prostate cancer.

It has been established that long-chain coding RNA (lncRNA) SLC25A25-AS1 is associated with cancer progression. However, the roles and mechanisms of SLC25A25-AS1 in prostate cancer (PC) have not been reported in the literature. The present study explored the relationship between SLC25A25-AS1 expression and PC progression via comprehensive analysis. The pan-cancer expression of SLC25A25-AS1 was identified using data from The Cancer Genome Atlas (TCGA) database and tissue specimens from our hospital. The expression levels of SLC25A25-AS1 in various subgroups based on the clinical features were identified. The prognostic value of SLC25A25-AS1 and SLC25A25-AS1 co-expressed lncRNAs in PC patients was assessed by survival analysis and ROC analysis, and prognosis-related risk models of SLC25A25-AS1 were constructed. The relationship between SLC25A25-AS1 and the PC immune microenvironment was investigated using correlation analysis. SLC25A25-AS1 expression in PC was significantly increased and correlated with the T stage, clinical stage, Gleason score (GS), and dismal prognosis. SLC25A25-AS1 overexpression exhibited good performance in evaluating the prognosis of PC patients. The area under the curves (AUCs) of the 1-, 3-, and 5-year overall survival (OS) for SLC25A25-AS1 was 1, 0.876, and 0.749. Moreover, the AUCs for the 1-, 3-, and 5-year progress free interval (PFI) for SLC25A25-AS1 were 0.731, 0.701, and 0.718. SLC25A25-AS1 overexpression correlated with the infiltration of CD8 T cells, interstitial dendritic cells (IDC), macrophages and other cells. AC020558.2, ZNF32-AS2, AP4B1-AS1, AL355488.1, AC109460.3, SNHG1, C3orf35, LMNTD2-AS1, and AL365330.1 were significantly associated with SLC25A25-AS1 expression, and short OS and PFI in PC patients. The risk models of the SLC25A25-AS1-related lncRNAs were associated with a dismal prognosis in PC. Overall, SLC25A25-AS1 expression was increased in PC and related to the prognosis and PC immune microenvironment. The risk model of SLC25A25-AS1 have huge prospect for application as prognostic tools in PC.

Automated EEG-based prediction of delayed cerebral ischemia after subarachnoid hemorrhage.

OBJECTIVE: Delayed cerebral ischemia (DCI) is a leading complication of aneurysmal subarachnoid hemorrhage (SAH) and electroencephalography (EEG) is increasingly used to evaluate DCI risk. Our goal is to develop an automated DCI prediction algorithm integrating multiple EEG features over time. METHODS: We assess 113 moderate to severe grade SAH patients to develop a machine learning model that predicts DCI risk using multiple EEG features. RESULTS: Multiple EEG features discriminate between DCI and non-DCI patients when aligned either to SAH time or to DCI onset. DCI and non-DCI patients have significant differences in alpha-delta ratio (0.08 vs 0.05, p < 0.05) and percent alpha variability (0.06 vs 0.04, p < 0.05), Shannon entropy (p < 0.05) and epileptiform discharge burden (205 vs 91 discharges per hour, p < 0.05) based on whole brain and vascular territory averaging. Our model improves predictions by emphasizing the most informative features at a given time with an area under the receiver-operator curve of 0.73, by day 5 after SAH and good calibration between 48-72 hours (calibration error 0.13). CONCLUSIONS: Our proposed model obtains good performance in DCI prediction. SIGNIFICANCE: We leverage machine learning to enable rapid, automated, multi-featured EEG assessment and has the potential to increase the utility of EEG for DCI prediction.

Multimodal Emotion Recognition in Response to Oil Paintings.

Most previous affective studies use facial expression pictures, music or movie clips as emotional stimuli, which are either too simplified without contexts or too dynamic for emotion annotations. In this work, we evaluate the effectiveness of oil paintings as stimuli. We develop an emotion stimuli dataset with 114 oil paintings selected from subject ratings to evoke three emotional states (i.e., negative, neutral and positive), and acquire both EEG and eye tracking data from 20 subjects while watching the oil paintings. Furthermore, we propose a novel affective model for multimodal emotion recognition by 1) extracting informative features of EEG signals from both the time domain and the frequency domain, 2) exploring topological information embedded in EEG channels with graph neural networks (GNNs), and 3) combining EEG and eye tracking data with a deep autoencoder neural network. From the exper-iments, our model obtains an averaged classification accuracy of 94.72 % ± 1.47 %, which demonstrates the feasibility of using oil paintings as emotion elicitation material.

Measuring expertise in identifying interictal epileptiform discharges.

OBJECTIVE: Interictal epileptiform discharges on EEG are integral to diagnosing epilepsy. However, EEGs are interpreted by readers with and without specialty training, and there is no accepted method to assess skill in interpretation. We aimed to develop a test to quantify IED recognition skills. METHODS: A total of 13,262 candidate IEDs were selected from EEGs and scored by eight fellowship-trained reviewers to establish a gold standard. An online test was developed to assess how well readers with different training levels could distinguish candidate waveforms. Sensitivity, false positive rate and calibration were calculated for each reader. A simple mathematical model was developed to estimate each reader's skill and threshold in identifying an IED, and to develop receiver operating characteristics curves for each reader. We investigated the number of IEDs needed to measure skill level with acceptable precision. RESULTS: Twenty-nine raters completed the test; nine experts, seven experienced non-experts and thirteen novices. Median calibration errors for experts, experienced non-experts and novices were -0.056, 0.012, 0.046; median sensitivities were 0.800, 0.811, 0.715; and median false positive rates were 0.177, 0.272, 0.396, respectively. The number of test questions needed to measure those scores was 549. Our analysis identified that novices had a higher noise level (uncertainty) compared to experienced non-experts and experts. Using calculated noise and threshold levels, receiver operating curves were created, showing increasing median area under the curve from novices (0.735), to experienced non-experts (0.852) and experts (0.891). SIGNIFICANCE: Expert and non-expert readers can be distinguished based on ability to identify IEDs. This type of assessment could also be used to identify and correct differences in thresholds in identifying IEDs.

High incidence of epileptiform activity in adults undergoing extracorporeal membrane oxygenation.

OBJECTIVE: The prevalence of seizures and other types of epileptiform brain activity in patients undergoing extracorporeal membrane oxygenation (ECMO) is unknown. We aimed to estimate the prevalence of seizures and ictal-interictal continuum patterns in patients undergoing electroencephalography (EEG) during ECMO. METHODS: Retrospective review of a prospective ECMO registry from 2011-2018 in a university-affiliated academic hospital. Adult subjects who had decreased level of consciousness and underwent EEG monitoring for seizure screening were included. EEG classification followed the American Clinical Neurophysiology Society criteria. Poor neurological outcome was defined as a Cerebral Performance Category of 3-5 at hospital discharge. RESULTS: Three hundred and ninety-five subjects had ECMO, and one hundred and thirteen (28.6%) had EEG monitoring. Ninety-two (23.3%) subjects had EEG performed during ECMO and were included in the study (average EEG duration 54 h). Veno-arterial ECMO was the most common cannulation strategy (83%) and 26 (28%) subjects had extracorporeal cardiopulmonary resuscitation. Fifty-eight subjects (63%) had epileptiform activity or ictal-interictal continuum patterns on EEG, including three (3%) subjects with nonconvulsive status epilepticus, 33 (36%) generalized periodic discharges, and 4 (5%) lateralized periodic discharges. Comparison between subjects with or without epileptiform activity showed comparable in-hospital mortality (57% vs. 47%, p = 0.38) and poor neurological outcome (and 56% and 36%, p = 0.23). Twenty-seven subjects (33%) had acute neuroimaging abnormalities (stroke N = 21). CONCLUSIONS: Seizures and ictal-interictal continuum patterns are commonly observed in patients managed with ECMO. Further studies are needed to evaluate whether epileptiform activity is an actionable target for interventions. SIGNIFICANCE: Epileptiform and ictal-interictal continuum abnormalities are frequently observed in patients supported with ECMO undergoing EEG monitoring.