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Background Infectious meningitis/encephalitis (IM) is a severe neurological disease that can be caused by bacterial, viral, and fungal pathogens. IM suffers high morbidity, mortality, and sequelae in childhood. Metagenomic next-generation sequencing (mNGS) can potentially improve IM outcomes by sequencing both pathogen and host responses and increasing the diagnosis accuracy. Methods Here we developed an optimized mNGS pipeline named comprehensive mNGS (c-mNGS) to monitor DNA/RNA pathogens and host responses simultaneously and applied it to 142 cerebrospinal fluid samples. According to retrospective diagnosis, these samples were classified into three categories: confirmed infectious meningitis/encephalitis (CIM), suspected infectious meningitis/encephalitis (SIM), and noninfectious controls (CTRL). Results Our pipeline outperformed conventional methods and identified RNA viruses such as Echovirus E30 and etiologic pathogens such as HHV-7, which would not be clinically identified via conventional methods. Based on the results of the c-mNGS pipeline, we successfully detected antibiotic resistance genes related to common antibiotics for treating Escherichia coli, Acinetobacter baumannii, and Group B Streptococcus. Further, we identified differentially expressed genes in hosts of bacterial meningitis (BM) and viral meningitis/encephalitis (VM). We used these genes to build a machine-learning model to pinpoint sample contaminations. Similarly, we also built a model to predict poor prognosis in BM. Conclusions This study developed an mNGS-based pipeline for IM which measures both DNA/RNA pathogens and host gene expression in a single assay. The pipeline allows detecting more viruses, predicting antibiotic resistance, pinpointing contaminations, and evaluating prognosis. Given the comparable cost to conventional mNGS, our pipeline can become a routine test for IM.

The rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. An early warning system (EWS) to identify patients at high risk of subsequent intrahospital death can be an effective tool for ensuring patient safety and quality of care and reducing avoidable harm and costs. The aim of this study was to prospectively validate a real-time EWS designed to predict patients at high risk of inpatient mortality during their hospital episodes. Data were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprising 54,246 inpatient admissions from January 1, 2015, to September 30, 2017, of which 2.30% (1248/54,246) resulted in intrahospital deaths. Multiple machine learning methods (linear and nonlinear) were explored and compared. The tree-based random forest method was selected to develop the predictive application for the intrahospital mortality assessment. After constructing the model, we prospectively validated the algorithms as a real-time inpatient EWS for mortality. The EWS algorithm scored patients' daily and long-term risk of inpatient mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, the EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 69% (68/99) of whom died during the episodes. It accurately predicted the possibility of death for the top 13.3% (34/255) of the patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs, and laboratory test results were recognized as impactful predictors in the final EWS. In this study, we prospectively demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for patients' better health outcomes in target medical facilities.

Background The purpose of this study was to evaluate different pretreatment, extraction, amplification, and library generation methods for metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) and to develop an efficient procedure for the simultaneous detection of DNA and RNA pathogens. Methods We generated thirteen mock CSF samples with four representative pathogens of encephalitis. Each sample was subjected to ten different methods by varying sample pretreatment/nucleic acid extraction (microbial DNA, total DNA, total NA, total RNA, Whole Transcriptome Amplification (WTA)) and library generation (Illumina or NEB). Negative extraction controls (NECs) were used for each method variation. Results We found that the quality of mNGS sequencing reads was higher from the NEB kit for library generation. Microbial DNA and total RNA increased microbial deposition by depleting the host DNA. Methods total NA and total RNA can detect gram-positive, gram-negative, RNA and DNA pathogens. We applied mNGS, including total NA and NEB library generation, to CSF samples from five patients diagnosed with infectious encephalitis and correctly determined all pathogens identified in clinical etiological tests. Conclusions Our findings suggested that total nucleic acid extraction combined with NEB library generation is the most effective mNGS procedure in CSF pathogen detection. The optimization of positive criteria and databases can improve the specificity and sensitivity of mNGS diagnosis. Trial registration: Chinese Clinical Trial Registry, ChiCTR1800015425 (29/03/2018), https://www.chictr.org.cn/edit.aspx?pid=26292&htm=4 .

Among patients who are discharged from the Emergency Department (ED), about 3% return within 30 days. Revisits can be related to the nature of the disease, medical errors, and/or inadequate diagnoses and treatment during their initial ED visit. Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization. A decision tree based model with discriminant Electronic Medical Record (EMR) features was developed and validated, estimating patient ED 30 day revisit risk. A retrospective cohort of 293,461 ED encounters from HealthInfoNet (HIN), Maine's Health Information Exchange (HIE), between January 1, 2012 and December 31, 2012, was assembled with the associated patients' demographic information and one-year clinical histories before the discharge date as the inputs. To validate, a prospective cohort of 193,886 encounters between January 1, 2013 and June 30, 2013 was constructed. The c-statistics for the retrospective and prospective predictions were 0.710 and 0.704 respectively. Clinical resource utilization, including ED use, was analyzed as a function of the ED risk score. Cluster analysis of high-risk patients identified discrete sub-populations with distinctive demographic, clinical and resource utilization patterns. Our ED 30-day revisit model was prospectively validated on the Maine State HIN secure statewide data system. Future integration of our ED predictive analytics into the ED care work flow may lead to increased opportunities for targeted care intervention to reduce ED resource burden and overall healthcare expense, and improve outcomes.

Background Acute meningitis and encephalitis syndromes (AMES) is a severe neurological infection which causes high case fatality and severe sequelae in children. To determine the etiology of childhood AMES in Shenzhen, a hospital-based study was undertaken. Methods A total of 240 cerebrospinal fluid (CSF) samples from 171 children meeting the case definition were included and screened for 12 common causative organisms. The clinical data and conventional testing results were collected and analyzed. Whole genome sequencing was performed on a Neisseria meningitidis isolate. Results A pathogen was found in 85 (49.7%) cases; Group B Streptococcus (GBS) was detected in 17 cases, Escherichia coli in 15, Streptococcus pneumoniae in 14, enterovirus (EV) in 13, herpes simplex virus (HSV) in 3, N. meningitidis in 1, Haemophilus influenzae in 1, and others in 23. Notably, HSV was found after 43 days of treatment. Twelve GBS and 6 E. coli meningitis were found in neonates aged less than 1 month; 13 pneumococcal meningitis in children aged > 3 months; and 12 EV infections in children aged > 1 year old. The multilocus sequence typing of serogroup B N. meningitidis isolate was ST-3200/CC4821. High resistance rate to tetracycline (75%), penicillin (75%), and trimethoprim/sulfamethoxazole (75%) was found in 4 of S. pneumoniae isolates; clindamycin (100%) and tetracycline (100%) in 9 of GBS; and ampicillin (75%) and trimethoprim/sulfamethoxazole (67%) in 12 of E. coli . Conclusions The prevalence of N. meningitidis and JEV was very low and the cases of childhood AMES were mainly caused by other pathogens. GBS and E. coli were the main causative organisms in neonates, while S. pneumoniae and EV were mainly found in older children. HSV could be persistently found in the CSF samples despite of the treatment. A better prevention strategy for GBS, the introduction of pneumococcal vaccine, and incorporation of PCR methods were recommended.

Identifying patients at risk of a 30-day readmission can help providers design interventions, and provide targeted care to improve clinical effectiveness. This study developed a risk model to predict a 30-day inpatient hospital readmission for patients in Maine, across all payers, all diseases and all demographic groups. Our objective was to develop a model to determine the risk for inpatient hospital readmission within 30 days post discharge. All patients within the Maine Health Information Exchange (HIE) system were included. The model was retrospectively developed on inpatient encounters between January 1, 2012 to December 31, 2012 from 24 randomly chosen hospitals, and then prospectively validated on inpatient encounters from January 1, 2013 to December 31, 2013 using all HIE patients. A risk assessment tool partitioned the entire HIE population into subgroups that corresponded to probability of hospital readmission as determined by a corresponding positive predictive value (PPV). An overall model c-statistic of 0.72 was achieved. The total 30-day readmission rates in low (score of 0-30), intermediate (score of 30-70) and high (score of 70-100) risk groupings were 8.67%, 24.10% and 74.10%, respectively. A time to event analysis revealed the higher risk groups readmitted to a hospital earlier than the lower risk groups. Six high-risk patient subgroup patterns were revealed through unsupervised clustering. Our model was successfully integrated into the statewide HIE to identify patient readmission risk upon admission and daily during hospitalization or for 30 days subsequently, providing daily risk score updates. The risk model was validated as an effective tool for predicting 30-day readmissions for patients across all payer, disease and demographic groups within the Maine HIE. Exposing the key clinical, demographic and utilization profiles driving each patient's risk of readmission score may be useful to providers in developing individualized post discharge care plans.

Background Although the prognosis of chronic myeloid leukemia (CML) has dramatically improved, the pathogenesis of CML remains elusive. Studies have shown that sustained phosphorylation of AKT1 plays a crucial role in the proliferation of CML cells. Evidence indicates that in tongue cancer cells, FAM168A, also known as tongue cancer resistance-associated protein (TCRP1), can directly bind to AKT1 and regulate AKT1/NFκB signaling pathways. This study aimed to investigate the role of FAM168A in regulation of AKT1/NFκB signaling pathway and cell cycle in CML. Methods FAM168A interference was performed, and the expression and phosphorylation of FAM168A downstream proteins were measured in K562 CML cell line. The possible roles of FAM168A in the proliferation of CML cells were investigated using in vitro cell culture, in vivo animal models and clinical specimens. Results We found that the expression of FAM168A significantly increased in the peripheral blood mononuclear cells of CML patients, compared with normal healthy controls. FAM168A interference did not change AKT1 protein expression, but significantly decreased AKT1 phosphorylation, significantly increased IκB-α protein level, and significantly reduced nuclear NFκB protein level. Moreover, there was a significant increase of G2/M phase cells and Cyclin B1 level. Immunoprecipitation results showed that FAM168A interacts with breakpoint cluster region (BCR) -Abelson murine leukemia (ABL1) fusion protein and AKT1, respectively. Animal experiments confirmed that FAM168A interference prolonged the survival and reduced the tumor formation in mice inoculated with K562 cells. The results of clinical specimens showed that FAM168A expression and AKT1 phosphorylation were significantly elevated in CML patients. Conclusion This study demonstrates that FAM168A may act as a linker protein that binds to BCR-ABL1 and AKT1, which further mediates the downstream signaling pathways in CML. Our findings demonstrate that FAM168A may be involved in the regulation of AKT1/NFκB signaling pathway and cell cycle in CML.

Type 2 diabetes mellitus (T2DM), with increased risk of serious long-term complications, currently represents 8.3% of the adult population. We hypothesized that a critical transition state prior to the new onset T2DM can be revealed through the longitudinal electronic medical record (EMR) analysis. We applied the transition-based network entropy methodology which previously identified a dynamic driver network (DDN) underlying the critical T2DM transition at the tissue molecular biological level. To profile pre-disease phenotypical changes that indicated a critical transition state, a cohort of 7,334 patients was assembled from the Maine State Health Information Exchange (HIE). These patients all had their first confirmative diagnosis of T2DM between January 1, 2013 and June 30, 2013. The cohort's EMRs from the 24 months preceding their date of first T2DM diagnosis were extracted. Analysis of these patients' pre-disease clinical history identified a dynamic driver network (DDN) and an associated critical transition state six months prior to their first confirmative T2DM state. This 6-month window before the disease state provides an early warning of the impending T2DM, warranting an opportunity to apply proactive interventions to prevent or delay the new onset of T2DM.

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. The aim of this study was to develop and validate prospectively a risk prediction model of incident essential hypertension within the following year. 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. 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. 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.

Background: Streptococcus pneumoniae is a common respiratory pathogen that poses significant health concerns in children, particularly serotype 19F strains that demonstrate high level of invasiveness in China. To investigate the genetic variations associated with high invasiveness of serotype 19F S. pneumoniae strains isolated from children in Shenzhen. Methods: We compared the genomic profiles of 42 invasive and 162 noninvasive strains from children's respiratory tracts and employed pan-genome-wide association methods to elucidate the origins of genetic variation. Results: Significant gene presence variability was observed between invasive and noninvasive strains, suggesting a genetic basis for their pathogenicity differences. Invasive 19F strains demonstrated enhanced adhesion in co-culture experiments with human epithelial cells, with adhesion abilities correlating with the presence of specific genes. Despite high non-susceptibility to common antibiotics across all strains, no significant differences in antimicrobial susceptibility patterns were found between invasive and noninvasive groups. Conclusion: Although genomic differences within serotype 19F were relatively minor, invasive and noninvasive strains exhibited significant differences in adherence and invasiveness in the host microenvironment. While the underlying regulatory mechanisms remain uncertain, genetic differences play a crucial role in determining the invasiveness of S. pneumoniae serotype 19F strains in children. Keywords: pneumococcus, serotype 19F, invasive strains, pan-GWAS, genetic variation