Parvovirus B19 in kidney transplant patients

Renal transplant patients were screened for the presence of parvovirus B19, before transplantation and monthly for 4 months after transplantation, by means of a sensitive nested PCR assay. Upon screening plasma from 110 patients, we found that two asymptomatic patients were B19 DNA positive. One of these patients was PCR positive in the plasma sample taken 2 months after transplantation; the plasma contained anti-B19 IgG antibodies before transplant and throughout the follow-up period, with an increase in the IgG level in the second posttransplant sample coinciding with the detection of B19 DNA. IgM antibodies to B19 were not detected in this patient. Because, for this patient, the donor's spleen DNA was also B19 DNA positive, we suspect B19 transmission from the donor and limited B19 replication, inasmuch as this patient already had a primed immune response to B19. The other patient was PCR positive in the pretransplant and in the plasma sample taken 1 month after transplant and contained a strong anti-B19 IgG response in the pretransplant sample and throughout the follow-up period-and anti-B19 IgM antibodies were not detected before or after transplantation. By testing samples taken from this patient at 2 weeks, 2 months, and 3 months before transplantation, we were able to determine that the infection occurred shortly before transplantation. Unexpectedly, this graft failed and was removed 2 days after transplantation despite a negative cross-match. A pathological examination of the kidney indicated acute vascular rejection, suggesting a possible role for B19 in this complication.     

Effects of differential privacy techniques: Considerations for end users

We study the effects of differentially private (DP) noise injection techniques in a survey data setting, using the release of cost of early care and education estimates from the National Survey of Early Care and Education as a motivating example. As an example of how DP noise injection affects statistical estimates, our analysis compares the relative performance of DP techniques in the context of releasing estimates of means, medians, and regression coefficients. The results show that for many statistics, basic DP techniques show good performance provided that the privacy budget does not need to be split over too many estimates. Throughout, we show that small decisions, such as the number of bins in a histogram or the scaling of a variable in a regression equation, can have sometimes dramatic effects on the end results. Because of this, it is important to develop DP techniques with an eye towards the most important aspects of the data for end users.