A Surgical Collaborative to Empower Medical Student Research.

Mini abstracts: Faculty at the Baylor College of Medicine have developed a flexible research collaborative through which students gain research skills and individualized mentorship. This division has produced 86 trainee first author publications, 64 manuscripts by 34 different medical students with an average Scimago Journal Rank of 1.293 (range: 1.035-1.551) since 2015.

A humanized mouse model for adeno-associated viral gene therapy.

Clinical translation of AAV-mediated gene therapy requires preclinical development across different experimental models, often confounded by variable transduction efficiency. Here, we describe a human liver chimeric transgene-free Il2rg-/-/Rag2-/-/Fah-/-/Aavr-/- (TIRFA) mouse model overcoming this translational roadblock, by combining liver humanization with AAV receptor (AAVR) ablation, rendering murine cells impermissive to AAV transduction. Using human liver chimeric TIRFA mice, we demonstrate increased transduction of clinically used AAV serotypes in primary human hepatocytes compared to humanized mice with wild-type AAVR. Further, we demonstrate AAV transduction in human teratoma-derived primary cells and liver cancer tissue, displaying the versatility of the humanized TIRFA mouse. From a mechanistic perspective, our results support the notion that AAVR functions as both an entry receptor and an intracellular receptor essential for transduction. The TIRFA mouse should allow prediction of AAV gene transfer efficiency and the study of AAV vector biology in a preclinical human setting.

Prediction of Kidney Allograft Discard Before Procurement: The Kidney Discard Risk Index.

OBJECTIVES: There is an 18.9% discard rate among kidney allografts. Here, we aimed to determine predictors of kidney discard and construct an index to identify high-probability discard kidney allografts prior to procurement. MATERIALS AND METHODS: A total of 102 246 potential kidney allograft donors from the Organ Procurement and Transplantation Network database were used in this analysis. The cohort was randomized into 2 groups. The training set included 67% of the cohort and was used to derive a predictive index for discard that comprised 21 factors identified by univariate and multivariate logistic regression analysis. The validation set included 33% and was used to internally validate the kidney discard risk index. RESULTS: In 77.3% of donors, at least 1 kidney was used for transplant, whereas in 22.7% of donors, both kidneys were discarded. The kidney discard risk index was highly predictive of discard with a C statistic of 0.89 (0.88-0.89). The bottom 10th percentile had a discard rate of 0.73%, whereas the top 10th percentile had a discard rate of 83.65%. The 3 most predictive factors for discard were age, creatinine level, and hepatitis C antibody status. CONCLUSIONS: We identified 21 factors predictive of discard prior to donor procurement and used these to develop a kidney discard risk index with a C statistic of 0.89.