Epilepsy is one of the most prevalent chronic neurological disorders, afflicting about 3.5–6.5 per 1000 children and 10.8 per 1000 elderly people. With intensive effort made during the last two decades, numerous genes and mutations have been published to be associated with the disease. An organized resource integrating and annotating the ever-increasing genetic data will be imperative to acquire a global view of the cutting-edge in epilepsy research. Herein, we developed EpilepsyGene (http://61.152.91.49/EpilepsyGene). It contains cumulative to date 499 genes and 3931 variants associated with 331 clinical phenotypes collected from 818 publications. Furthermore, in-depth data mining was performed to gain insights into the understanding of the data, including functional annotation, gene prioritization, functional analysis of prioritized genes and overlap analysis focusing on the comorbidity. An intuitive web interface to search and browse the diversified genetic data was also developed to facilitate access to the data of interest. In general, EpilepsyGene is designed to be a central genetic database to provide the research community substantial convenience to uncover the genetic basis of epilepsy.

The human microbiome plays an important role in autoimmune diseases. However, there is limited knowledge regarding the microbiota in individuals with primary Sjögren's syndrome (pSS). Here, we perform 16S ribosomal RNA gene sequencing of fecal, oral, and vaginal samples from a cohort of 133 individuals with pSS, 56 with non-pSS, and 40 healthy control (HC) individuals. Dysbiosis in the gut, oral, and vaginal microbiome is evident in patients with pSS, and oral samples demonstrate the greatest extent of microbial variation. Multiple key indicator bacteria and clinical characteristics are identified across different body sites, implying that microbial dysbiosis has important roles in the pathogenesis of pSS. Furthermore, we observe pSS-like dysbiosis in individuals with pre-clinical pSS or non-pSS-related disease, revealing that microbial shifts could appear prior to pSS. After hydroxychloroquine (HCQ) treatment, microbial dysbiosis in individuals with pSS is partially resolved, although the microbiota composition remain disordered. These results contribute to the overall understanding of the relationship between the microbiome and pSS.

In reduced representation bisulfite sequencing (RRBS), genomic DNA is digested with the restriction enzyme and then subjected to next‐generation sequencing, which enables detection and quantification of DNA methylation at whole‐genome scale with low cost. However, the data processing, interpretation, and analysis of the huge amounts of data generated pose a bioinformatics challenge. We developed RRBS‐Analyser, a comprehensive genome‐scale DNA methylation analysis server based on RRBS data. RRBS‐Analyser can assess sequencing quality, generate detailed statistical information, align the bisulfite‐treated short reads to reference genome, identify and annotate the methylcytosines (5mCs) and associate them with different genomic features in CG, CHG, and CHH content. RRBS‐Analyser supports detection, annotation, and visualization of differentially methylated regions (DMRs) for multiple samples from nine reference organisms. Moreover, RRBS‐Analyser provides researchers with detailed annotation of DMR‐containing genes, which will greatly aid subsequent studies. The input of RRBS‐Analyser can be raw FASTQ reads, generic SAM format, or self‐defined format containing individual 5mC sites. RRBS‐Analyser can be widely used by researchers wanting to unravel the complexities of DNA methylome in the epigenetic community. RRBS‐Analyser is freely available at http://122.228.158.106/RRBSAnalyser/.

Background: Early and accurate identification of septic patients at high risk for ICU mortality can help clinicians make optimal clinical decisions and improve the patients’ outcomes. This study aimed to develop and validate (internally and externally) a mortality prediction score for sepsis following admission in the ICU. Methods: We extracted data retrospectively regarding adult septic patients from one teaching hospital in Wenzhou, China and a large multi-center critical care database from the USA. Demographic data, vital signs, laboratory values, comorbidities, and clinical outcomes were collected. The primary outcome was ICU mortality. Through multivariable logistic regression, a mortality prediction score for sepsis was developed and validated. Results: Four thousand two hundred and thirty six patients in the development cohort and 8359 patients in three validation cohorts. The Prediction of Sepsis Mortality in ICU (POSMI) score included age ≥ 50 years, temperature < 37 °C, Respiratory rate > 35 breaths/min, MAP ≤ 50 mmHg, SpO2 < 90%, albumin ≤ 2 g/dL, bilirubin ≥ 0.8 mg/dL, lactate ≥ 4.2 mmol/L, BUN ≥ 21 mg/dL, mechanical ventilation, hepatic failure and metastatic cancer. In addition, the area under the receiver operating characteristic curve (AUC) for the development cohort was 0.831 (95% CI, 0.813–0.850) while the AUCs ranged from 0.798 to 0.829 in the three validation cohorts. Moreover, the POSMI score had a higher AUC than both the SOFA and APACHE IV scores. Notably, the Hosmer–Lemeshow (H–L) goodness-of-fit test results and calibration curves suggested good calibration in the development and validation cohorts. Additionally, the POSMI score still exhibited excellent discrimination and calibration following sensitivity analysis. With regard to clinical usefulness, the decision curve analysis (DCA) of POSMI showed a higher net benefit than SOFA and APACHE IV in the development cohort. Conclusion: POSMI was validated to be an effective tool for predicting mortality in ICU patients with sepsis.