Integrating DNA/RNA microbe detection and host response for accurate diagnosis, treatment and prognosis of childhood infectious meningitis and encephalitis.

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.

Contributions of ARID5B, IKZF1, PIP4K2A, and GATA3 Gene Polymorphisms to Childhood Acute Lymphoblastic Leukemia in a Chinese Population.

Various studies have shown that single nucleotide polymorphisms in the AT-rich interaction domain 5B (ARID5B), IKAROS family zinc finger 1 (IKZF1), phosphatidylinositol-5-phosphate 4-kinase type 2 alpha (PIP4K2A), and GATA binding protein 3 (GATA3) genes may be associated with the susceptibility and prognosis of childhood acute lymphoblastic leukemia (ALL). The present study aimed to investigate the association of ARID5B rs10821936, IKZF1 rs4132601, PIP4K2A rs7088318, and GATA3 rs3824662 gene polymorphisms with the susceptibility and prognosis of childhood ALL in China. We found that the C allele of rs10821936 (ARID5B) and the A allele of rs3824662 (GATA3) were associated with an increased risk of childhood ALL in the Chinese population. There was no significant difference in frequencies of rs4132601 (IKZF1) and rs7088318 (PIP4K2A) genotypes and alleles between the childhood ALL and control groups. We observed that CC genotype of rs10821936 (ARID5B) was associated with increased rates of high-risk and moderate-risk childhood ALL. The rs10821936 (ARID5B) could serve as a potential biomarker for assessing the risk of childhood ALL in Chinese children.

Ubiquitin-specific peptidase 18 negatively regulates and inhibits lipopolysaccharide-induced sepsis by targeting transforming growth factor-ß-activated kinase 1 activity.

Sepsis is an inflammatory disease with exacerbated inflammation at early stages. Inflammatory cytokines play critical roles in the pathophysiology of sepsis. Ubiquitin-specific peptidase 18 (USP18), a deubiquitinating enzyme, has been shown to modulate transforming growth factor-ß-activated kinase 1 (TAK1) activity. However, the precise role of USP18 in sepsis is not clear. Here, we investigated the potential effect of USP18 on inflammation in sepsis. We generated mice with USP18 or/and TAK1 deficiency in macrophages (USP18MKO mice, TAK1MKO mice and USP18MKO-TAK1MKO mice) and established a lipopolysaccharide (LPS)-induced sepsis model in mice. Bone marrow-derived macrophages were isolated from wild-type (WT), USP18MKO or TAK1MKO mice and treated with LPS or CpG, and the expression of cytokines including IL-6, IL-10, IL-1ß and tumor necrosis factor α (TNF-α) was measured. The activation of NF-κB, ERK and p38 signaling pathways and ubiquitination of TAK1 were detected. We induced sepsis in WT, USP18MKO, TAK1MKO or USP18MKO-TAK1MKO mice and evaluated the survival rate, lung pathology and inflammatory cytokine levels in serum. Macrophages deficient in USP18 produced significantly increased IL-6, IL-1ß and TNF-α post-LPS or -CpG stimulation. Macrophages deficient in USP18 had promoted activation of NF-κB, p38 and ERK, and increased ubiquitination of TAK1. Mice with TAK1 deficiency in macrophages had increased survival rates, decreased immune cell infiltration in lung and decreased pro-inflammatory cytokines in serum. In contrast, mice with USP18 deficiency in macrophages had decreased survival rates, increased cell infiltration in lung and increased pro-inflammatory cytokines in serum. USP18 alleviated LPS-induced sepsis by inhibiting TAK1 activity.

Comparison and development of a metagenomic next-generation sequencing protocol for combined detection of DNA and RNA pathogens in cerebrospinal fluid.

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 .

FAM168A participates in the development of chronic myeloid leukemia via BCR-ABL1/AKT1/NFκB pathway.

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.

The etiology of acute meningitis and encephalitis syndromes in a sentinel pediatric hospital, Shenzhen, China.

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.

A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning.

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.

Assessing Statewide All-Cause Future One-Year Mortality: Prospective Study With Implications for Quality of Life, Resource Utilization, and Medical Futility.

BACKGROUND: For many elderly patients, a disproportionate amount of health care resources and expenditures is spent during the last year of life, despite the discomfort and reduced quality of life associated with many aggressive medical approaches. However, few prognostic tools have focused on predicting all-cause 1-year mortality among elderly patients at a statewide level, an issue that has implications for improving quality of life while distributing scarce resources fairly. OBJECTIVE: Using data from a statewide elderly population (aged ≥65 years), we sought to prospectively validate an algorithm to identify patients at risk for dying in the next year for the purpose of minimizing decision uncertainty, improving quality of life, and reducing futile treatment. METHODS: Analysis was performed using electronic medical records from the Health Information Exchange in the state of Maine, which covered records of nearly 95% of the statewide population. The model was developed from 125,896 patients aged at least 65 years who were discharged from any care facility in the Health Information Exchange network from September 5, 2013, to September 4, 2015. Validation was conducted using 153,199 patients with same inclusion and exclusion criteria from September 5, 2014, to September 4, 2016. Patients were stratified into risk groups. The association between all-cause 1-year mortality and risk factors was screened by chi-squared test and manually reviewed by 2 clinicians. We calculated risk scores for individual patients using a gradient tree-based boost algorithm, which measured the probability of mortality within the next year based on the preceding 1-year clinical profile. RESULTS: The development sample included 125,896 patients (72,572 women, 57.64%; mean 74.2 [SD 7.7] years). The final validation cohort included 153,199 patients (88,177 women, 57.56%; mean 74.3 [SD 7.8] years). The c-statistic for discrimination was 0.96 (95% CI 0.93-0.98) in the development group and 0.91 (95% CI 0.90-0.94) in the validation cohort. The mortality was 0.99% in the low-risk group, 16.75% in the intermediate-risk group, and 72.12% in the high-risk group. A total of 99 independent risk factors (n=99) for mortality were identified (reported as odds ratios; 95% CI). Age was on the top of list (1.41; 1.06-1.48); congestive heart failure (20.90; 15.41-28.08) and different tumor sites were also recognized as driving risk factors, such as cancer of the ovaries (14.42; 2.24-53.04), colon (14.07; 10.08-19.08), and stomach (13.64; 3.26-86.57). Disparities were also found in patients' social determinants like respiratory hazard index (1.24; 0.92-1.40) and unemployment rate (1.18; 0.98-1.24). Among high-risk patients who expired in our dataset, cerebrovascular accident, amputation, and type 1 diabetes were the top 3 diseases in terms of average cost in the last year of life. CONCLUSIONS: Our study prospectively validated an accurate 1-year risk prediction model and stratification for the elderly population (≥65 years) at risk of mortality with statewide electronic medical record datasets. It should be a valuable adjunct for helping patients to make better quality-of-life choices and alerting care givers to target high-risk elderly for appropriate care and discussions, thus cutting back on futile treatment.

Prospective stratification of patients at risk for emergency department revisit: resource utilization and population management strategy implications.

BACKGROUND: Estimating patient risk of future emergency department (ED) revisits can guide the allocation of resources, e.g. local primary care and/or specialty, to better manage ED high utilization patient populations and thereby improve patient life qualities. METHODS: We set to develop and validate a method to estimate patient ED revisit risk in the subsequent 6 months from an ED discharge date. An ensemble decision-tree-based model with Electronic Medical Record (EMR) encounter data from HealthInfoNet (HIN), Maine's Health Information Exchange (HIE), was developed and validated, assessing patient risk for a subsequent 6 month return ED visit based on the ED encounter-associated demographic and EMR clinical history data. A retrospective cohort of 293,461 ED encounters that occurred between January 1, 2012 and December 31, 2012, was assembled with the associated patients' 1-year clinical histories before the ED discharge date, for model training and calibration purposes. To validate, a prospective cohort of 193,886 ED encounters that occurred between January 1, 2013 and June 30, 2013 was constructed. RESULTS: Statistical learning that was utilized to construct the prediction model identified 152 variables that included the following data domains: demographics groups (12), different encounter history (104), care facilities (12), primary and secondary diagnoses (10), primary and secondary procedures (2), chronic disease condition (1), laboratory test results (2), and outpatient prescription medications (9). The c-statistics for the retrospective and prospective cohorts were 0.742 and 0.730 respectively. Total medical expense and ED utilization by risk score 6 months after the discharge were analyzed. Cluster analysis identified discrete subpopulations of high-risk patients with distinctive resource utilization patterns, suggesting the need for diversified care management strategies. CONCLUSIONS: Integration of our method into the HIN secure statewide data system in real time prospectively validated its performance. It promises to provide increased opportunity for high ED utilization identification, and optimized resource and population management.

Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study.

BACKGROUND: The increasing rate of health care expenditures in the United States has placed a significant burden on the nation's economy. Predicting future health care utilization of patients can provide useful information to better understand and manage overall health care deliveries and clinical resource allocation. OBJECTIVE: This study developed an electronic medical record (EMR)-based online risk model predictive of resource utilization for patients in Maine in the next 6 months across all payers, all diseases, and all demographic groups. METHODS: In the HealthInfoNet, Maine's health information exchange (HIE), a retrospective cohort of 1,273,114 patients was constructed with the preceding 12-month EMR. Each patient's next 6-month (between January 1, 2013 and June 30, 2013) health care resource utilization was retrospectively scored ranging from 0 to 100 and a decision tree-based predictive model was developed. Our model was later integrated in the Maine HIE population exploration system to allow a prospective validation analysis of 1,358,153 patients by forecasting their next 6-month risk of resource utilization between July 1, 2013 and December 31, 2013. RESULTS: Prospectively predicted risks, on either an individual level or a population (per 1000 patients) level, were consistent with the next 6-month resource utilization distributions and the clinical patterns at the population level. Results demonstrated the strong correlation between its care resource utilization and our risk scores, supporting the effectiveness of our model. With the online population risk monitoring enterprise dashboards, the effectiveness of the predictive algorithm has been validated by clinicians and caregivers in the State of Maine. CONCLUSIONS: The model and associated online applications were designed for tracking the evolving nature of total population risk, in a longitudinal manner, for health care resource utilization. It will enable more effective care management strategies driving improved patient outcomes.

A clade of Streptococcus pneumoniae clonal complex 320 with increased tolerance to ß-lactam antibiotics in a Chinese metropolitan city.

OBJECTIVES: We characterized the population structure and features of clinical Streptococcus pneumoniae isolates associated with invasive pneumococcal disease (IPD) from 2009 to 2017 in a Chinese metropolitan city using a whole-genome sequencing approach. METHODS: Seventy-nine pneumococcal strains, including 60 serogroup-19 strains from children enduring IPD from a paediatric hospital in Shenzhen, were subjected to whole-genome sequencing. Population structure was characterized through phylogenetic analysis, sequence typing, serotyping, virulence factor, and antimicrobial drug resistance (AMR) gene profiling, combining the publicly available related WGS data. Clinical demography and antibiotic susceptibility profiles were compared among different populations to emphasize the higher-risk populations. Genetic regions associated with AMR gene mobilization were identified through comparative genomics. RESULTS: These IPD strains mainly belonged to clonal complex 320 (CC320) and were composed of serotypes 19A and 19F. In addition to sporadic possible importation-related isolates (ST320), we identified an independent clade, CC320_SZpop (ST271), that predominantly circulated in Shenzhen and possibly expanded its range. Clinical features and antibiotic susceptibility analysis revealed that CC320_SZpop might manifest much higher pathogenicity and tolerance to ß-lactams. Specific virulence factors in Shenzhen isolates of CC320_SZpop were identified. Furthermore, an ca. 40 kb hotspot genomic region enduring frequent recombination was identified, possibly associated with the divergence of S. pneumoniae strains. CONCLUSION: A novel pneumococcal clade, CC320_SZpop, circulating in Shenzhen and other regions in China, possibly under expansion, was found and deserves more study and surveillance. Our study also emphasizes the importance of continuous genomic surveillance of clinical S. pneumoniae isolates, especially IPD isolates.

Molecular characteristics and antimicrobial resistance of invasive pneumococcal isolates from children in the post-13-valent pneumococcal conjugate vaccine era in Shenzhen, China.

OBJECTIVES: This study aimed to evaluate the molecular epidemiology and antimicrobial resistance of invasive pneumococcal isolates from children in Shenzhen, China, in the early stage of the pneumococcal 13-valent conjugated vaccine (PCV-13) era from 2018 to 2020. METHODS: Invasive pneumococcal strains were isolated from hospitalized children with invasive pneumococcal diseases (IPDs) from January 2018 to December 2020. The serotype identification, multilocus sequence typing (MLST), and antibiotic susceptibility tests were performed on all culture-confirmed strains. RESULTS: Sixty-four invasive strains were isolated mainly from blood (70.3%). Prevalent serotypes were 23F (28.1%), 14 (18.8%), 19F (15.6%), 6A/B (14.1%), and 19A (12.5%), with a serotype coverage rate of 96.9% for PCV13. The most common sequence types (STs) were ST876 (17.1%), ST271 (10.9%), and ST320 (7.8%). Half of the strains were grouped in clonal complexes (CCs): CC271 (21.9%), CC876 (20.3%), and CC90 (14.1%). Meningitis isolates showed a higher resistance rate (90.9% and 45.5%) to penicillin and ceftriaxone than the rate (3.8% and 9.4%) of non-meningitis isolates. The resistance rates for penicillin (oral), cefuroxime, and erythromycin were 53.13%, 73.4%, and 96.9%, respectively. The dual ermB and mefA genotype was found in 81.3% of erythromycin-resistant strains. The elevated minimum inhibitory concentration (MIC) of ß-lactam antibiotics and dual-genotype macrolide resistance were related mainly to three major serotype-CC combinations: 19F-CC271, 19A-CC271, and 14-CC876. CONCLUSION: Invasive pneumococcus with elevated MICs of ß-lactams and increased dual ermB and mefA genotype macrolide resistance were alarming. Expanded PCV13 vaccination is expected to reduce the burden of paediatric IPD and to combat antibiotic-resistant pneumococcus in Shenzhen.

Escaping but not the inactive X-linked protein complex coding genes may achieve X-chromosome dosage compensation and underlie X chromosome inactivation-related diseases.

X chromosome dosage compensation (XDC) refers to the process by which X-linked genes acquire expression equivalence between two sexes. Ohno proposed that XDC is achieved by two-fold upregulations of X-linked genes in both sexes and by silencing one X chromosome (X chromosome inactivation, XCI) in females. However, genes subject to two-fold upregulations as well as the underlying mechanism remain unclear. It's reported that gene dosage changes may only affect X-linked dosage-sensitive genes, such as protein complex coding genes (PCGs). Our results showed that in human PCGs are more likely to escape XCI and escaping PCGs (EsP) show two-fold higher expression than inactivated PCGs (InP) or other X-linked genes at RNA and protein levels in both sexes, which suggest that EsP may achieve upregulations and XDC. The higher expressions of EsP possibly result from the upregulations of the single active X chromosome (Xa), rather than escaping expressions from the inactive X chromosome (Xi). EsP genes have relatively high expression levels in humans and lower dN/dS ratios, suggesting that they are likely under stronger selection pressure over evolutionary time. Our study also suggests that SP1 transcription factor is significantly enriched in EsP and may be involved in the up-regulations of EsP on the active X. Finally, human EsP genes in this study are enriched in the toll-like receptor pathway, NF-kB pathway, apoptotic pathway, and abnormal mental, developmental and reproductive phenotypes. These findings suggest misregulations of EsP may be involved in autoimmune, reproductive, and neurological diseases, providing insight for the diagnosis and treatment of these diseases.

Performance of targeted next-generation sequencing in the detection of respiratory pathogens and antimicrobial resistance genes for children.

Introduction. Respiratory tract infection, which is associated with high morbidity and mortality, occurs frequently in children. At present, the main diagnostic method is culture. However, the low pathogen detection rate of the culture approach prevents timely and accurate diagnosis. Fortunately, next-generation sequencing (NGS) can compensate for the deficiency of culture, and its application in clinical diagnostics has become increasingly available.Gap Statement. Targeted NGS (tNGS) is a platform that can select and enrich specific regions before data enter the NGS pipeline. However, the performance of tNGS in the detection of respiratory pathogens and antimicrobial resistance genes (ARGs) in infections in children is unclear.Aim and methodology. In this study, we estimated the performance of tNGS in the detection of respiratory pathogens and ARGs in 47 bronchoalveolar lavage fluid (BALF) specimens from children using conventional culture and antimicrobial susceptibility testing (AST) as the gold standard.Results. RPIP (Respiratory Pathogen ID/AMR enrichment) sequencing generated almost 500 000 reads for each specimen. In the detection of pathogens, RPIP sequencing showed targeted superiority in detecting difficult-to-culture bacteria, including Mycoplasma pneumoniae. Compared with the results of culture, the sensitivity and specificity of RPIP were 84.4 % (confidence interval 70.5-93.5 %) and 97.7 % (95.9 -98.8%), respectively. Moreover, RPIP results showed that a single infection was detected in 10 of the 47 BALF specimens, and multiple infections were detected in 34, with the largest number of bacterial/viral coinfections. Nevertheless, there were also three specimens where no pathogen was detected. Furthermore, we analysed the drug resistance genes of specimens containing Streptococcus pneumoniae, which was detected in 25 out of 47 specimens in the study. A total of 58 ARGs associated with tetracycline, macrolide-lincosamide-streptogramin, beta-lactams, sulfonamide and aminoglycosides were identified by RPIP in 19 of 25 patients. Using the results of AST as a standard, the coincidence rates of erythromycin, tetracycline, penicillin and sulfonamides were 89.5, 79.0, 36.8 and 42.1 %, respectively.Conclusion. These results demonstrated the superiority of RPIP in pathogen detection, particularly for multiple and difficult-to-culture pathogens, as well as in predicting resistance to erythromycin and tetracycline, which has significance for the accurate diagnosis of pathogenic infection and in the guidance of clinical treatment.

PspA Diversity, Serotype Distribution and Antimicrobial Resistance of Invasive Pneumococcal Isolates from Paediatric Patients in Shenzhen, China.

INTRODUCTION: To determine the phenotypes and genotypes of invasive Streptococcus pneumoniae (S. pneumoniae), 108 strains were isolated from paediatric patients with invasive pneumococcal diseases (IPDs) in Shenzhen from 2014 to 2018. METHODS: Serotype profiles were defined by multiplex PCR of the capsule gene. Pneumococcal surface protein A (PspA) classification was performed through pspA gene sequencing. Antimicrobial resistance was examined by broth microdilution. Multilocus sequence typing (MLST) was determined based on next-generation sequencing data. RESULTS: Eighty-one S. pneumoniae of 17 serotypes were finally collected. The coverage of the 13-conjugated polysaccharide vaccine (PCV13) was 88.9%. After the introduction of PCV13, the nonvaccine serotypes were added by serotypes 15b, 16F and 20. Vaccine serotype 3 increased by four serious cases. The pspA family 1 and pspA family 2 are predominant. The multiple drug resistance rate is 91.3%. None of the nonmeningitis isolates were resistant to penicillin, while 98.8% of all the isolates were resistant to erythromycin. DISCUSSION: This work characterizes the molecular epidemiology of invasive S. pneumoniae in Shenzhen. Continued surveillance of serotype distribution and antimicrobial susceptibility is necessary to alert antibiotic-resistant nonvaccine serotypes and highly virulent serotypes.

Disrupted Structural Brain Network Organization Behind Depressive Symptoms in Major Depressive Disorder.

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.

Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm.

OBJECTIVE: Predicting the risk of falls in advance can benefit the quality of care and potentially reduce mortality and morbidity in the older population. The aim of this study was to construct and validate an electronic health record-based fall risk predictive tool to identify elders at a higher risk of falls. METHODS: The one-year fall prediction model was developed using the machine-learning-based algorithm, XGBoost, and tested on an independent validation cohort. The data were collected from electronic health records (EHR) of Maine from 2016 to 2018, comprising 265,225 older patients (≥65 years of age). RESULTS: This model attained a validated C-statistic of 0.807, where 50 % of the identified high-risk true positives were confirmed to fall during the first 94 days of next year. The model also captured in advance 58.01 % and 54.93 % of falls that happened within the first 30 and 30-60 days of next year. The identified high-risk patients of fall showed conditions of severe disease comorbidities, an enrichment of fall-increasing cardiovascular and mental medication prescriptions and increased historical clinical utilization, revealing the complexity of the underlying fall etiology. The XGBoost algorithm captured 157 impactful predictors into the final predictive model, where cognitive disorders, abnormalities of gait and balance, Parkinson's disease, fall history and osteoporosis were identified as the top-5 strongest predictors of the future fall event. CONCLUSIONS: By using the EHR data, this risk assessment tool attained an improved discriminative ability and can be immediately deployed in the health system to provide automatic early warnings to older adults with increased fall risk and identify their personalized risk factors to facilitate customized fall interventions.

Brain connectomic associations with traditional Chinese medicine diagnostic classification of major depressive disorder: a diffusion tensor imaging study.

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.

Estimating One-Year Risk of Incident Chronic Kidney Disease: Retrospective Development and Validation Study Using Electronic Medical Record Data From the State of Maine.

BACKGROUND: Chronic kidney disease (CKD) is a major public health concern in the United States with high prevalence, growing incidence, and serious adverse outcomes. OBJECTIVE: We aimed to develop and validate a model to identify patients at risk of receiving a new diagnosis of CKD (incident CKD) during the next 1 year in a general population. METHODS: The study population consisted of patients who had visited any care facility in the Maine Health Information Exchange network any time between January 1, 2013, and December 31, 2015, and had no history of CKD diagnosis. Two retrospective cohorts of electronic medical records (EMRs) were constructed for model derivation (N=1,310,363) and validation (N=1,430,772). The model was derived using a gradient tree-based boost algorithm to assign a score to each individual that measured the probability of receiving a new diagnosis of CKD from January 1, 2014, to December 31, 2014, based on the preceding 1-year clinical profile. A feature selection process was conducted to reduce the dimension of the data from 14,680 EMR features to 146 as predictors in the final model. Relative risk was calculated by the model to gauge the risk ratio of the individual to population mean of receiving a CKD diagnosis in next 1 year. The model was tested on the validation cohort to predict risk of CKD diagnosis in the period from January 1, 2015, to December 31, 2015, using the preceding 1-year clinical profile. RESULTS: The final model had a c-statistic of 0.871 in the validation cohort. It stratified patients into low-risk (score 0-0.005), intermediate-risk (score 0.005-0.05), and high-risk (score ≥ 0.05) levels. The incidence of CKD in the high-risk patient group was 7.94%, 13.7 times higher than the incidence in the overall cohort (0.58%). Survival analysis showed that patients in the 3 risk categories had significantly different CKD outcomes as a function of time (P<.001), indicating an effective classification of patients by the model. CONCLUSIONS: We developed and validated a model that is able to identify patients at high risk of having CKD in the next 1 year by statistically learning from the EMR-based clinical history in the preceding 1 year. Identification of these patients indicates care opportunities such as monitoring and adopting intervention plans that may benefit the quality of care and outcomes in the long term.