Integrating central nervous system metagenomics and host response for diagnosis of tuberculosis meningitis and its mimics.

The epidemiology of infectious causes of meningitis in sub-Saharan Africa is not well understood, and a common cause of meningitis in this region, Mycobacterium tuberculosis (TB), is notoriously hard to diagnose. Here we show that integrating cerebrospinal fluid (CSF) metagenomic next-generation sequencing (mNGS) with a host gene expression-based machine learning classifier (MLC) enhances diagnostic accuracy for TB meningitis (TBM) and its mimics. 368 HIV-infected Ugandan adults with subacute meningitis were prospectively enrolled. Total RNA and DNA CSF mNGS libraries were sequenced to identify meningitis pathogens. In parallel, a CSF host transcriptomic MLC to distinguish between TBM and other infections was trained and then evaluated in a blinded fashion on an independent dataset. mNGS identifies an array of infectious TBM mimics (and co-infections), including emerging, treatable, and vaccine-preventable pathogens including Wesselsbron virus, Toxoplasma gondii, Streptococcus pneumoniae, Nocardia brasiliensis, measles virus and cytomegalovirus. By leveraging the specificity of mNGS and the sensitivity of an MLC created from CSF host transcriptomes, the combined assay has high sensitivity (88.9%) and specificity (86.7%) for the detection of TBM and its many mimics. Furthermore, we achieve comparable combined assay performance at sequencing depths more amenable to performing diagnostic mNGS in low resource settings.

First and second trimester urinary metabolic profiles and fetal growth restriction: an exploratory nested case-control study within the infant development and environment study.

BACKGROUND: Routine prenatal care fails to identify a large proportion of women at risk of fetal growth restriction (FGR). Metabolomics, the comprehensive analysis of low molecular weight molecules (metabolites) in biological samples, can provide new and earlier biomarkers of prenatal health. Recent research has suggested possible predictive first trimester urine metabolites correlating to fetal growth restriction in the third trimester. Our objective in this current study was to examine urinary metabolic profiles in the first and second trimester of pregnancy in relation to third trimester FGR in a US population from a large, multi-center cohort study of healthy pregnant women. METHODS: We conducted a nested case-control study within The Infant Development and the Environment Study (TIDES), a population-based multi-center pregnancy cohort study. We identified 53 cases of FGR based on the AUDIPOG [Neonatal growth - AUDIPOG [Internet]. [cited 29 Nov 2016]. Available from: http://www.audipog.net/courbes_morpho.php?langue=en ] formula for birthweight percentile considering maternal height, age, and prenatal weight, as well as infant sex, gestational age, and birth rank. Cases were matched to 106 controls based on study site, maternal age (± 2 years), parity, and infant sex. NMR spectroscopy was used to assess concentrations of four urinary metabolites that have been previously associated with FGR (tyrosine, acetate, formate, and trimethylamine) in first and second trimester urine samples. We fit multivariate conditional logistic regression models to estimate the odds of FGR in relation to urinary concentrations of these individual metabolites in the first and second trimesters. Exploratory analyses of custom binned spectroscopy results were run to consider other potentially related metabolites. RESULTS: We found no significant association between the relative concentrations of each of the four metabolites and odds of FGR. Exploratory analyses did not reveal any significant differences in urinary metabolic profiles. Compared with controls, cases delivered earlier (38.6 vs 39.8, p < 0.001), and had lower birthweights (2527 g vs 3471 g, p < 0.001). Maternal BMI was similar between cases and controls. CONCLUSIONS: First and second trimester concentrations of urinary metabolites (acetate, formate, trimethylamine and tyrosine) did not predict FGR. This inconsistency with previous studies highlights the need for more rigorous investigation and data collection in this area before metabolomics can be clinically applied to obstetrics.

Defining the Tipping Point in Surgical Performance for Laparoscopic Donor Nephrectomy Among Transplant Surgery Fellows: A Risk-Adjusted Cumulative Summation Learning Curve Analysis.

The United Network for Organ Sharing recommends that fellowship-trained surgeons participate in 15 laparoscopic donor nephrectomy (LDN) procedures to be considered proficient. The American Society of Transplant Surgeons (ASTS) mandates 12 LDNs during an abdominal transplant surgery fellowship. We performed a retrospective intraoperative case analysis to create a risk-adjusted cumulative summation (RACUSUM) model to assess the learning curve of novice transplant surgery fellows (TSFs). Between January 2000 and December 2014, 30 novice TSFs participated in the organ procurement rotation of our ASTS-approved abdominal transplant surgery fellowship. Measures of surgical performance included intraoperative time, estimated blood loss, and incidence of intraoperative complications. The performance of senior TSFs was used to benchmark novice TSF performance. Scores were tabulated in a learning curve model, adjusting for case complexity and prior TSF case volume. Rates of adverse surgical events were significantly higher for novice TSFs than for senior TSFs. In univariable analysis, multiple renal arteries, high BMI, prior abdominal surgery, male donor, and nephrolithiasis were correlated with higher incidence of adverse surgical events. Based on the RACUSUM model, high intraoperative time is mitigated after 28 procedures, incidence of intraoperative complications tends to diminish after 24 procedures, and improvement in estimated blood loss did not remain consistent. TSFs exhibit a tipping point in LDN performance by 24-28 cases and proficiency by 35-38 cases.