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947; 95% CI 0.931-0.962). A second panel consisting of C102, butyric acid, and pyruvic acid distinguished PCR+/not-hospitalized from PCR+/hospitalized and PCR+/intubated (AUC = 0.975; 95% CI 0.968-0.983). Only lysoPC a C280 differentiated PCR+/hospitalized from PCR+/intubated patients (AUC = 0.770; 95% CI 0.736-0.803). If additional studies with targeted metabolomics confirm the diagnostic value of these plasma biomarkers, such panels could eventually be of clinical use in medical practice.Cognitive control processes encompass many distinct components, including response inhibition (stopping a prepotent response), proactive control (using prior information to enact control), reactive control (last-minute changing of a prepotent response), and conflict monitoring (choosing between two competing responses). While frontal midline theta activity is theorized to be a general marker of the need for cognitive control, a stringent test of this hypothesis would require a quantitative, within-subject comparison of the neural activation patterns indexing many different cognitive control strategies, an experiment lacking in the current literature. We recorded EEG from 176 participants as they performed tasks that tested inhibitory control (Go/Nogo Task), proactive and reactive control (AX-Continuous Performance Task), and resolving response conflict (Global/Local Task-modified Flanker Task). As activity in the theta (4-8 Hz) frequency band is thought to be a common signature of cognitive control, we assessork will need to focus on the differential role of theta in differing cognitive control strategies.Finding effective and objective biomarkers to inform the diagnosis of schizophrenia is of great importance yet remains challenging. Relatively little work has been conducted on multi-biological data for the diagnosis of schizophrenia. In this cross-sectional study, we extracted multiple features from three types of biological data, including gut microbiota data, blood data, and electroencephalogram data. Then, an integrated framework of machine learning consisting of five classifiers, three feature selection algorithms, and four cross validation methods was used to discriminate patients with schizophrenia from healthy controls. see more Our results show that the support vector machine classifier without feature selection using the input features of multi-biological data achieved the best performance, with an accuracy of 91.7% and an AUC of 96.5% (p  less then  0.05). These results indicate that multi-biological data showed better discriminative capacity for patients with schizophrenia than single biological data. The top 5% discriminative features selected from the optimal model include the gut microbiota features (Lactobacillus, Haemophilus, and Prevotella), the blood features (superoxide dismutase level, monocyte-lymphocyte ratio, and neutrophil count), and the electroencephalogram features (nodal local efficiency, nodal efficiency, and nodal shortest path length in the temporal and frontal-parietal brain areas). The proposed integrated framework may be helpful for understanding the pathophysiology of schizophrenia and developing biomarkers for schizophrenia using multi-biological data.Acute myeloid leukemia (AML) is the most prevalent form of acute leukemia. Patients with AML often have poor clinical prognoses. Hypoxia can activate a series of immunosuppressive processes in tumors, resulting in diseases and poor clinical prognoses. However, how to evaluate the severity of hypoxia in tumor immune microenvironment remains unknown. In this study, we downloaded the profiles of RNA sequence and clinicopathological data of pediatric AML patients from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, as well as those of AML patients from Gene Expression Omnibus (GEO). In order to explore the immune microenvironment in AML, we established a risk signature to predict clinical prognosis. Our data showed that patients with high hypoxia risk score had shorter overall survival, indicating that higher hypoxia risk scores was significantly linked to immunosuppressive microenvironment in AML. Further analysis showed that the hypoxia could be used to serve as an independent prognostic indicator for AML patients. Moreover, we found gene sets enriched in high-risk AML group participated in the carcinogenesis. In summary, the established hypoxia-related risk model could act as an independent predictor for the clinical prognosis of AML, and also reflect the response intensity of the immune microenvironment in AML.To estimate the frequency of headache in patients with confirmed COVID-19 and characterize the phenotype of headache attributed to COVID-19, comparing patients depending on the need of hospitalization and sex, an observational study was done. We systematically screened all eligible patients from a reference population of 261,431 between March 8 (first case) and April 11, 2020. A physician administered a survey assessing demographic and clinical data and the phenotype of the headache. During the study period, 2194 patients out of the population at risk were diagnosed with COVID-19. Headache was described by 514/2194 patients (23.4%, 95% CI 21.7-25.3%), including 383/1614 (23.7%) outpatients and 131/580 (22.6%) inpatients. The headache phenotype was studied in detail in 458 patients (mean age, 51 years; 72% female; prior history of headache, 49%). Headache was the most frequent first symptom of COVID-19. Median headache onset was within 24 h, median duration was 7 days and persisted after 1 month in 13% of patients. Pain was bilateral (80%), predominantly frontal (71%), with pressing quality (75%), of severe intensity. Systemic symptoms were present in 98% of patients. Headache frequency and phenotype was similar in patients with and without need for hospitalization and when comparing male and female patients, being more intense in females.Trial registration This study was supported by the Institute of Health Carlos III (ISCIII), code 07.04.467804.74011 and Regional Health Administration, Gerencia Regional de Salud, Castilla y Leon (GRS 2289/A/2020).