A retrospective description of anesthetic medication dosing in overweight and obese children.

INTRODUCTION: Pediatric obesity is a major health concern in the United States and as many as 34% of those who require general anesthesia are overweight or obese (OW). The lack of data and recommendations for dosing medications in obese children leaves significant gaps in the understanding of correct dosing in the clinical setting. OBJECTIVE: To determine whether OW children were more likely to receive doses of medications outside the recommended range. METHODS: Following IRB approval, patient medical records were queried to identify children 2 through 17 years who underwent noncardiac surgeries and received at least one medication of interest. Children with hepatic disease, renal disease, neurological impairment, sleep-disordered breathing, or missing height or weight measurements were excluded. Children were stratified into weight categories based on age and gender percentiles as per CDC guidelines. Those ≥85th percentile were classified as overweight/obese. Ideal and lean weight (for age, gender) were calculated. Drug doses were stratified as under-dosed (>10% below minimum recommended dose), overdosed (>10% above maximum recommended dose), or within recommended dose (dose ± 10%). Actual doses were compared to recommended doses as per actual, ideal, or lean weight (as recommended for specific drugs) in the overweight/obese groups vs the control weight (CW) group. RESULTS: Ten thousand five hundred and nine doses were reviewed. Overweight/obese children were more likely to receive doses outside the recommended dose range than the CW group. CONCLUSIONS: Overweight/obese children were more likely to receive doses of common anesthetic medications outside the recommended doses potentially adding risk of adverse outcomes in these children.

Integrative urinary peptidomics in renal transplantation identifies biomarkers for acute rejection.

Noninvasive methods to diagnose rejection of renal allografts are unavailable. Mass spectrometry followed by multiple-reaction monitoring provides a unique approach to identify disease-specific urine peptide biomarkers. Here, we performed urine peptidomic analysis of 70 unique samples from 50 renal transplant patients and 20 controls (n = 20), identifying a specific panel of 40 peptides for acute rejection (AR). Peptide sequencing revealed suggestive mechanisms of graft injury with roles for proteolytic degradation of uromodulin (UMOD) and several collagens, including COL1A2 and COL3A1. The 40-peptide panel discriminated AR in training (n = 46) and test (n = 24) sets (area under ROC curve >0.96). Integrative analysis of transcriptional signals from paired renal transplant biopsies, matched with the urine samples, revealed coordinated transcriptional changes for the corresponding genes in addition to dysregulation of extracellular matrix proteins in AR (MMP-7, SERPING1, and TIMP1). Quantitative PCR on an independent set of 34 transplant biopsies with and without AR validated coordinated changes in expression for the corresponding genes in rejection tissue. A six-gene biomarker panel (COL1A2, COL3A1, UMOD, MMP-7, SERPING1, TIMP1) classified AR with high specificity and sensitivity (area under ROC curve = 0.98). These data suggest that changes in collagen remodeling characterize AR and that detection of the corresponding proteolytic degradation products in urine provides a noninvasive diagnostic approach.

FDR made easy in differential feature discovery and correlation analyses.

SUMMARY: Rapid progress in technology, particularly in high-throughput biology, allows the analysis of thousands of genes or proteins simultaneously, where the multiple comparison problems occurs. Global false discovery rate (gFDR) analysis statistically controls this error, computing the ratio of the number of false positives over the total number of rejections. Local FDR (lFDR) method can associate the corrected significance measure with each hypothesis testing for its feature-by-feature interpretation. Given the large feature number and sample size in any genomics or proteomics analysis, FDR computation, albeit critical, is both beyond the regular biologists' specialty and computationally expensive, easily exceeding the capacity of desktop computers. To overcome this digital divide, a web portal has been developed that provides bench-side biologists easy access to the server-side computing capabilities to analyze for FDR, differential expressed genes or proteins, and for the correlation between molecular data and clinical measurements. AVAILABILITY: (http://translationalmedicine.stanford.edu/Mass-Conductor/FDR.html).