Managing highly sensitized renal transplant candidates in the era of kidney paired donation and the new kidney allocation system: Is there still a role for desensitization?

Kidney paired donation (KPD) and the new kidney allocation system (KAS) in the United States have led to improved transplantation rates for highly sensitized candidates. We aimed to assess the potential need for other approaches to improve the transplantation rate of highly sensitized candidates such as desensitization. Using the UNOS STAR file, we analyzed transplant rates in a prevalent active waiting-list cohort as of June 1, 2016, followed for 1 year. The overall transplantation rate was 18.9% (11 129/58769). However, only 9.7% (213/2204) of candidates with a calculated panel reactive antibody ≥99.9% received a transplant, and highly sensitized candidates were less likely to receive a living donor transplant. Among candidates with a CPRA ≥ 99.5% (ie. 100%), only 2.5% of transplants were from living donors (13 total, 7 from KPD). Nearly 4 years after KAS (6/30/2018), 1791 actively wait-listed candidates had a CPRA of ≥99.9% and 34.6% (620/1791) of these had ≥5 years of waiting time. Thus, despite KPD and KAS, many sensitized candidates have not been transplanted even with prolonged waiting time. We conclude that candidates with a CPRA ≥ 99.9% and sensitized candidates with an incompatible living donor and prolonged waiting time may benefit from desensitization to improve their ability to receive a transplant.

Deep Learning-Based Histopathologic Assessment of Kidney Tissue.

BACKGROUND: The development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with periodic acid-Schiff (PAS). METHODS: We trained the network using multiclass annotations from 40 whole-slide images of stained kidney transplant biopsies and applied it to four independent data sets. We assessed multiclass segmentation performance by calculating Dice coefficients for ten tissue classes on ten transplant biopsies from the Radboud University Medical Center in Nijmegen, The Netherlands, and on ten transplant biopsies from an external center for validation. We also fully segmented 15 nephrectomy samples and calculated the network's glomerular detection rates and compared network-based measures with visually scored histologic components (Banff classification) in 82 kidney transplant biopsies. RESULTS: The weighted mean Dice coefficients of all classes were 0.80 and 0.84 in ten kidney transplant biopsies from the Radboud center and the external center, respectively. The best segmented class was "glomeruli" in both data sets (Dice coefficients, 0.95 and 0.94, respectively), followed by "tubuli combined" and "interstitium." The network detected 92.7% of all glomeruli in nephrectomy samples, with 10.4% false positives. In whole transplant biopsies, the mean intraclass correlation coefficient for glomerular counting performed by pathologists versus the network was 0.94. We found significant correlations between visually scored histologic components and network-based measures. CONCLUSIONS: This study presents the first convolutional neural network for multiclass segmentation of PAS-stained nephrectomy samples and transplant biopsies. Our network may have utility for quantitative studies involving kidney histopathology across centers and provide opportunities for deep learning applications in routine diagnostics.

Negative ions of p-nitroaniline: photodetachment, collisions, and ab initio calculations.

The structures of parent anion, M(-), and deprotonated molecule, [M-H](-), anions of the highly polar p-nitroaniline (pNA) molecule are studied experimentally and theoretically. Photoelectron spectroscopy (PES) of the parent anion is employed to estimate the adiabatic electron affinity (EAa = 0.75 ± 0.1 eV) and vertical detachment energy (VDE = 1.1 eV). These measured energies are in good agreement with computed values of 0.73 eV for the EAa and the range of 0.85 to 1.0 eV for the VDE at the EOM-CCSD∕Aug-cc-pVTZ level. Collision induced dissociation (CID) of deprotonated pNA, [pNA - H](-), with argon yielded [pNA - H - NO](-) (i.e., rearrangement to give loss of NO) with a threshold energy of 2.36 eV. Calculations of the energy difference between [pNA - H](-) and [pNA - H - NO](-) give 1.64 eV, allowing an estimate of a 0.72 eV activation barrier for the rearrangement reaction. Direct dissociation of [pNA - H](-) yielding NO2(-) occurs at a threshold energy of 3.80 eV, in good agreement with theory (between 3.39 eV and 4.30 eV). As a result of the exceedingly large dipole moment for pNA (6.2 Debye measured in acetone), we predict two dipole-bound states, one at ~110 meV and an excited state at 2 meV. No dipole-bound states are observed in the photodetachment experiments due the pronounced mixing between states with dipole-bound and valence character similar to what has been observed in other nitro systems. For the same reason, dipole-bound states are expected to provide highly efficient "doorway states" for the formation of the pNA(-) valence anion, and these states should be observable as resonances in the reverse process, that is, in the photodetachment spectrum of pNA(-) near the photodetachment threshold.