Citations

Background. In December 2018, United Network for Organ Sharing approved an allocation scheme based on recipients’ geographic distance from a deceased donor (acuity circles [ACs]). Previous analyses suggested that ACs would reduce waitlist mortality overall, but their impact on pediatric subgroups was not considered. Methods. We applied Scientific Registry of Transplant Recipients data from 2011 to 2016 toward the Liver Simulated Allocation Model to compare outcomes by age and illness severity for the United Network for Organ Sharing–approved AC and the existing donor service area–/region-based allocation schemes. Means from each allocation scheme were compared using matched-pairs t tests. Results. During a 3-year period, AC allocation is projected to decrease waitlist deaths in infants (39 versus 55; P < 0.001), children (32 versus 50; P < 0.001), and teenagers (15 versus 25; P < 0.001). AC allocation would increase the number of transplants in infants (707 versus 560; P < 0.001), children (677 versus 547; P < 0.001), and teenagers (404 versus 248; P < 0.001). AC allocation led to decreased median pediatric end-stage liver disease/model for end-stage liver disease at transplant for infants (29 versus 30; P = 0.01), children (26 versus 29; P < 0.001), and teenagers (26 versus 31; P < 0.001). Additionally, AC allocation would lead to fewer transplants in status 1B in children (97 versus 103; P = 0.006) but not infants or teenagers. With AC allocation, 77% of pediatric donor organs would be allocated to pediatric candidates, compared to only 46% in donor service area–/region-based allocation (P < 0.001). Conclusions. AC allocation will likely address disparities for pediatric liver transplant candidates and recipients by increasing transplants and decreasing waitlist mortality. It is more consistent with federally mandated requirements for organ allocation.

ABSTRACT We evaluated stool specimens known to contain or be free of Campylobacter by traditional culture, using the ProSpecT Campylobacter microplate assay (Alexon-Trend, Ramsey, Minn.). This rapid enzyme immunoassay for the detection of Campylobacter -specific antigens demonstrated 96% sensitivity and 99% specificity and is an acceptable alternative method of Campylobacter detection.

ABSTRACT We studied the recovery of 1,270 fungal isolates from 176,144 Isolator blood cultures (0.72% positive) on bacterial and fungal media, under routine and differing incubation conditions. Except with Histoplasma capsulatum , chocolate agar incubated for only 3 days proved to be an excellent medium for the recovery of fungi from the Isolator system.

Abstract Background Although 20 pancreatic cancer susceptibility loci have been identified through genome-wide association studies in individuals of European ancestry, much of its heritability remains unexplained and the genes responsible largely unknown. Methods To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74–421 samples). Results We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate &lt; .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction. Conclusions By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.

AbstractIntroductionConventional Z‐scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these “adjusted” Z‐scores better represent whether an individual's cognitive performance is abnormal. Extreme negative Z‐scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency.MethodsIn this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance).ResultsCorrected Z‐scores based on nonlinear shape constrained additive models provide improved adjustment for age, sex, and education, as indicated by higher adjusted‐R2.DiscussionNonlinearly corrected Z‐scores with respect to age, sex, and education with age‐varying residual standard deviation allow for improved detection of non‐normative extreme cognitive scores.