Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study

Background Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most challenging areas of pathology, requiring considerable expertise, time, and effort. We aimed to analyse the utility of deep learning to preclassify histology of kidney allograft biopsies into three main broad categories (ie, normal, rejection, and other diseases) as a potential biopsy triage system focusing on transplant rejection.Methods We performed a retrospective, multicentre, proof-of-concept study using 5844 digital whole slide images of kidney allograft biopsies from 1948 patients. Kidney allograft biopsy samples were identified by a database search in the Departments of Pathology of the Amsterdam UMC, Amsterdam, Netherlands (1130 patients) and the University Medical Center Utrecht, Utrecht, Netherlands (717 patients). 101 consecutive kidney transplant biopsies were identified in the archive of the Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany. Convolutional neural networks (CNNs) were trained to classify allograft biopsies as normal, rejection, or other diseases. Three times cross-validation (1847 patients) and deployment on an external real-world cohort (101 patients) were used for validation. Area under the receiver operating characteristic curve (AUROC) was used as the main performance metric (the primary endpoint to assess CNN performance).Findings Serial CNNs, first classifying kidney allograft biopsies as normal (AUROC 0.87 [ten times bootstrapped CI 0.85-0.88]) and disease (0.87 [0.86-0.88]), followed by a second CNN classifying biopsies classified as disease into rejection (0.75 [0.73-0.76]) and other diseases (0.75 [0.72-0.77]), showed similar AUROC in cross-validation and deployment on independent real-world data (first CNN normal AUROC 0.83 [0.80-0.85], disease 0.83 [0.73-0.91]; second CNN rejection 0.61 [0.51-0.70], other diseases 0.61 [0.50-4.74]). A single CNN classifying biopsies as normal, rejection, or other diseases showed similar performance in cross-validation (normal AUROC 0.80 [0.73-0.84], rejection 0.76 [0.66-0.80], other diseases 0.50 [0.36-0.57]) and generalised well for normal and rejection classes in the real-world data. Visualisation techniques highlighted rejection-relevant areas of biopsies in the tubulointerstitium.Interpretation This study showed that deep learning-based classification of transplant biopsies could support pathological diagnostics of kidney allograft rejection. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Immunopathology of vascular and renal diseases and of organ and celltransplantationIP

Simulating VLBI observations of supermassive black holes from the ground and from space

Contains fulltext : 225107.pdf (publisher's version ) (Open Access)Radboud University, 28 oktober 2020Promotor : Falcke, H.D.E.iii, 184 p

Birth weight and holeboard performance of first- and lastborn piglets

Decades of selective breeding have resulted in increased litter sizes in domesticated pig breeds, since the commercial industry strives to enhance productivity. However, this expansion will reach an inevitable limit, in which production levels will decline due to accompanying issues and welfare related problems. Large litter sizes, in addition to the presence of stillbirths, prolong parturition durations. Accordingly, the lastborn (LB) piglets may be at greater risk of birth complications compared to firstborn (FB) piglets, resulting in cognitive deficits and a distorted stress-response. Furthermore, more LB- than FB piglets may have low birth weight, which has been correlated to impaired memory functioning. Therefore, in this study, FB- and LB piglets from twelve different litters were selected, weighed at birth and subsequently tested in a cognitive holeboard task, in which animals learn the locations of hidden food rewards. During the first reversal trial, in which piglets are confronted with a reversed reward configuration, saliva cortisol samples were taken. We hypothesized that LB piglets would have lower birth weight, lower reference- and/or working memory scores and longer trial durations than FB piglets. Furthermore, we expected LB piglets to have elevated cortisol levels after experiencing acute stress. The average amount of piglets born alive was14 (13.75±3.62), with a minimum of 10 per litter. The mean farrowing duration was 191.45±88.94 minutes and in five litters, 1-2 stillbirths occurred. Surprisingly, none of the LB-, while one of the FB piglets had low birth weight (<0.96 kg) and the average weight of the FB- and LB piglets was 1.50±0.46 kg and 1.49±0.25 kg respectively, which did not differ significantly (p=0.75, Mann-Whitney U test). Therefore, if any difference in holeboard performance exists, this cannot be due to difference in birth weight. The main results of the holeboard experiment are currently being analysed and will be presented at the conference

Surfactant-free Preparation of Highly Stable Zwitterionic Poly(amido amine) Nanogels with Minimal Cytotoxicity

Narrowly dispersed zwitterionic poly(amido amine) (PAA) nanogels with a diameter of approximately 100 nm were prepared by a high-yielding and surfactant-free, inverse nanoprecipitation of PAA polymers. The resulting, negatively charged, nanogels (PAA-NG1) were functionalized with N,N-dimethylethylenediamine via EDC/NHS coupling chemistry. This resulted in nanogels with a positive surface charge (PAA-NG2). Both types of nanogels were fluorescently labelled via isothiocyanate coupling. PAA-NG1 displays high colloidal stability both in PBS and Fetal Bovine Serum solution. Moreover, both nanogels exhibit a distinct zwitterionic swelling profile in response to pH changes. Cellular uptake of FITC-labelled nanogels with RAW 264.7, PC-3 and COS-7 cells was evaluated by fluorescence microscopy. These studies showed that nanogel surface charge greatly influences nanogel–cell interactions. The PAA polymer and PAA-NG1 showed minimal cell toxicity as was evaluated by MTT assays. The findings reported here demonstrate that PAA nanogels possess interesting properties for future studies in both drug delivery and imaging