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Additional file 4: Fig. S1. The microbial reads percentage of EBV samples and EV71 samples. A. The microbial reads percentage of EBV samples in methods Total DNA , Total NA ,Total RNA and WTA. B. The microbial reads percentage of EV71 samples in methods Total NA ,Total RNA and WTA. Fig. S2. NR, RPMratio and Coverage percent of target pathogens. Subfigure A/B/C shows NR/ RPMratio/coverage percentage of E. coli respectively comparing four sample pretreatment/nuclear acid extraction methods including Microbial DNA, Total DNA, Total NA and Total RNA. The red dots represent concentration of 106cfu/ml and the blue dots represent concentration of 5x104 cfu/ml. Subfigure D/E/F shows NR/ RPMratio/coverage percentage of SP comparing four sample pretreatment/nuclear acid extraction methods respectively. The red dots represent concentration of 106cfu/ml and the blue dots represent concentration of 5x104 cfu/ml. Subfigure G/H/I shows NR/RPMratio/coverage percentage of EBV respectively comparing four sample pretreatment/nuclear acid extraction methods including Total DNA, Total NA, Total RNA and WTA. The red dots represent concentration of 5x105 copies/ml and the blue dots represent concentration of 5x103 copies/ml. Subfigure J/K/L shows NR/RPMratio/coverage percentage of EV71 respectively comparing four sample pretreatment/nuclear acid extraction methods respectively. The red dots represent concentration of 5x105 copies/ml and the blue dots represent concentration of 5x103 copies/ml. Fig. S3. Genomic coverage of pathogens in CSF from encephalitis patients. A. Genomic coverage of Echovirus 30 in clinical CSF 0006. B. Genomic coverage of coxsackievirus B5 in clinical CSF 0010. Fig. S4. Genomic coverage of Streptococcus pneumoniae, Streptococcus thermophilus and Mogibacterium diversum in sample Microbial DNA-ILL -1. A. Genomic coverage of Streptococcus pneumoniae. B. Genomic coverage of Streptococcus thermophilus. C. Genomic coverage of Mogibacterium diversum.

Additional file 3: Table S3. Metagenomic sequencing results of each sample.

Additional file 1: Table S1. Sample ID and Pathogens contained.

Background Major depressive disorder (MDD) is highly heterogeneous in pathogenesis and manifestations. Further classification may help characterize its heterogeneity. We previously have shown differential metabolomic profiles of traditional Chinese medicine (TCM) diagnostic subtypes of MDD. We further determined brain connectomic associations with TCM subtypes of MDD. Methods In this naturalistic study, 44 medication-free patients with a recurrent depressive episode were classified into liver qi stagnation (LQS, n = 26) and Heart and Spleen Deficiency (HSD, n = 18) subtypes according to TCM diagnosis. Healthy subjects (n = 28) were included as controls. Whole-brain white matter connectivity was analyzed on diffusion tensor imaging. Results The LQS subtype showed significant differences in multiple network metrics of the angular gyrus, middle occipital gyrus, calcarine sulcus, and Heschl’s gyrus compared to the other two groups. The HSD subtype had markedly greater regional connectivity of the insula, parahippocampal gyrus, and posterior cingulate gyrus than the other two groups, and microstructural abnormalities of the frontal medial orbital gyrus and middle temporal pole. The insular betweenness centrality was strongly inversely correlated with the severity of depression and dichotomized the two subtypes at the optimal cutoff value with acceptable sensitivity and specificity. Conclusions The LQS subtype is mainly characterized by aberrant connectivity of the audiovisual perception-related temporal-occipital network, whereas the HSD subtype is more closely associated with hyperconnectivity and microstructural abnormalities of the limbic-paralimbic network. Insular connectivity may serve a biomarker for TCM-based classification of depression. Trial registration Registered at http://www.clinicaltrials.gov (NCT02346682) on January 27, 2015

Major depressive disorder (MDD) is a severe and devastating condition. However, the anatomical basis behind the affective symptoms, cognitive symptoms, and somatic-vegetative symptoms of MDD is still unknown. To explore the mechanism behind the depressive symptoms in MDD, we used diffusion tensor imaging (DTI)-based structural brain connectivity analysis to investigate the network difference between MDD patients and healthy controls (CN), and to explore the association between network metrics and patients' clinical symptoms. Twenty-six patients with MDD and 25 CN were included. A baseline 24-item Hamilton rating scale for depression (HAMD-24) score ≥ 21 and seven factors (anxiety/somatization, weight loss, cognitive disturbance, diurnal variation, retardation, sleep disturbance, hopelessness) scores were assessed. When compared with healthy subjects, significantly higher characteristic path length and clustering coefficient as well as significantly lower network efficiencies were observed in patients with MDD. Furthermore, MDD patients demonstrated significantly lower nodal degree and nodal efficiency in multiple brain regions including superior frontal gyrus (SFG), supplementary motor area (SMA), calcarine fissure, middle temporal gyrus (MTG), and inferior temporal gyrus (ITG). We also found that the characteristic path length of MDD patients was associated with weight loss. Moreover, significantly lower global efficiency of MDD patients was correlated with higher total HAMD score, anxiety somatization, and cognitive disturbance. The nodal degree in SFG was also found to be negatively associated with disease duration. In conclusion, our results demonstrated that MDD patients had impaired structural network features compared to CN, and disruption of optimal network architecture might be the mechanism behind the depressive symptoms and emotion disturbance in MDD patients.

Background: As a high-prevalence health condition, hypertension is clinically costly, difficult to manage, and often leads to severe and life-threatening diseases such as cardiovascular disease (CVD) and stroke. Objective: The aim of this study was to develop and validate prospectively a risk prediction model of incident essential hypertension within the following year. Methods: Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. Retrospective (N=823,627, calendar year 2013) and prospective (N=680,810, calendar year 2014) cohorts were formed. A machine learning algorithm, XGBoost, was adopted in the process of feature selection and model building. It generated an ensemble of classification trees and assigned a final predictive risk score to each individual. Results: The 1-year incident hypertension risk model attained areas under the curve (AUCs) of 0.917 and 0.870 in the retrospective and prospective cohorts, respectively. Risk scores were calculated and stratified into five risk categories, with 4526 out of 381,544 patients (1.19%) in the lowest risk category (score 0-0.05) and 21,050 out of 41,329 patients (50.93%) in the highest risk category (score 0.4-1) receiving a diagnosis of incident hypertension in the following 1 year. Type 2 diabetes, lipid disorders, CVDs, mental illness, clinical utilization indicators, and socioeconomic determinants were recognized as driving or associated features of incident essential hypertension. The very high risk population mainly comprised elderly (age>50 years) individuals with multiple chronic conditions, especially those receiving medications for mental disorders. Disparities were also found in social determinants, including some community-level factors associated with higher risk and others that were protective against hypertension. Conclusions: With statewide EHR datasets, our study prospectively validated an accurate 1-year risk prediction model for incident essential hypertension. Our real-time predictive analytic model has been deployed in the state of Maine, providing implications in interventions for hypertension and related diseases and hopefully enhancing hypertension care. [J Med Internet Res 2018;20(1):e22]