Behaviour of non-donor specific antibodies during rapid re-synthesis of donor specific HLA antibodies after antibody incompatible renal transplantation

Background: HLA directed antibodies play an important role in acute and chronic allograft rejection. During viral infection of a patient with HLA antibodies, the HLA antibody levels may rise even though there is no new immunization with antigen. However it is not known whether the converse occurs, and whether changes on non-donor specific antibodies are associated with any outcomes following HLA antibody incompatible renal transplantation. Methods: 55 patients, 31 women and 24 men, who underwent HLAi renal transplant in our center from September 2005 to September 2010 were included in the studies. We analysed the data using two different approaches, based on; i) DSA levels and ii) rejection episode post transplant. HLA antibody levels were measured during the early post transplant period and corresponding CMV, VZV and Anti-HBs IgG antibody levels and blood group IgG, IgM and IgA antibodies were quantified. Results: Despite a significant DSA antibody rise no significant non-donor specific HLA antibody, viral or blood group antibody rise was found. In rejection episode analyses, multiple logistic regression modelling showed that change in the DSA was significantly associated with rejection (p = 0.002), even when adjusted for other antibody levels. No other antibody levels were predictive of rejection. Increase in DSA from pre treatment to a post transplant peak of 1000 was equivalent to an increased chance of rejection with an odds ratio of 1.47 (1.08, 2.00). Conclusion: In spite of increases or decreases in the DSA levels, there were no changes in the viral or the blood group antibodies in these patients. Thus the DSA rise is specific in contrast to the viral, blood group or third party antibodies post transplantation. Increases in the DSA post transplant in comparison to pre-treatment are strongly associated with occurrence of rejection

Subclass analysis of donor HLA-specific IgG in antibody-incompatible renal transplantation reveals a significant association of IgG4 with rejection and graft failure

Donor HLA-specific antibodies (DSAs) can cause rejection and graft loss after renal transplantation, but their levels measured by the current assays are not fully predictive of outcomes. We investigated whether IgG subclasses of DSA were associated with early rejection and graft failure. DSA levels were determined pretreatment, at the day of peak pan-IgG level and at 30 days post-transplantation in eighty HLA antibody-incompatible kidney transplant recipients using a modified microbead assay. Pretreatment IgG4 levels were predictive of acute antibody-mediated rejection (P = 0.003) in the first 30 days post-transplant. Pre-treatment presence of IgG4 DSA (P = 0.008) and day 30 IgG3 DSA (P = 0.03) was associated with poor graft survival. Multivariate regression analysis showed that in addition to pan-IgG levels, total IgG4 levels were an independent risk factor for early rejection when measured pretreatment, and the presence of pretreatment IgG4 DSA was also an independent risk factor for graft failure. Pretreatment IgG4 DSA levels correlated independently with higher risk of early rejection episodes and medium-term death-censored graft survival. Thus, pretreatment IgG4 DSA may be used as a biomarker to predict and risk stratify cases with higher levels of pan-IgG DSA in HLA antibody-incompatible transplantation. Further investigations are needed to confirm our results

Dynamic behaviour of donor specific antibodies in the early period following HLA incompatible kidney transplantation

In HLA-incompatible kidney transplantation, monitoring donor-specific antibodies (DSA) plays a crucial role in providing appropriate treatment and increases kidney survival times. This work aimed to determine if early post-transplant DSA dynamics inform graft outcome over and above other predictive factors. Eighty-eight cases were classified by unsupervised machine learning into five distinct DSA response groups: no response, fast modulation, slow modulation, rise to sustained and sustained. Fast modulation dynamics gave an 80% rate for early acute rejection, whereas the sustained group was associated with the lowest rejection rates (19%). In complete contrast, the five-year graft failure was lowest in the modulation groups (4–7%) and highest in the sustained groups (25–31%). Multivariable analysis showed that a higher pre-treatment DSA level, male gender and absence of early acute rejection were strongly associated with a sustained DSA response. The modulation group had excellent five-year outcomes despite higher rates of early rejection episodes. This work further develops an understanding of post-transplant DSA dynamics and their influence on graft survival following HLA-incompatible kidney transplantation

Landscape Movements of Migratory Birds and Bats Reveal an Expanded Scale of Stopover

Many species of birds and bats undertake seasonal migrations between breeding and over-wintering sites. En-route, migrants alternate periods of flight with time spent at stopover – the time and space where individuals rest and refuel for subsequent flights. We assessed the spatial scale of movements made by migrants during stopover by using an array of automated telemetry receivers with multiple antennae to track the daily location of individuals over a geographic area ∼20×40 km. We tracked the movements of 322 individuals of seven migratory vertebrate species (5 passerines, 1 owl and 1 bat) during spring and fall migratory stopover on and adjacent to a large lake peninsula. Our results show that many individuals leaving their capture site relocate within the same landscape at some point during stopover, moving as much as 30 km distant from their site of initial capture. We show that many apparent nocturnal departures from stopover sites are not a resumption of migration in the strictest sense, but are instead relocations that represent continued stopover at a broader spatial scale

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Impact of color augmentation and tissue type in deep learning for hematoxylin and eosin image super resolution

Single image super-resolution is an important computer vision task with applications including remote sensing, medical imaging, and surveillance. Modern work on super-resolution utilizes deep learning to synthesize high resolution (HR) images from low resolution images (LR). With the increased utilization of digitized whole slide images (WSI) in pathology workflows, digital pathology has emerged as a promising domain for super-resolution. Despite extensive existing research into super-resolution, there remain challenges specific to digital pathology. Here, we investigated image augmentation techniques for hematoxylin and eosin (H&E) WSI super-resolution and model generalizability across diverse tissue types. In addition, we investigated shortcomings with common quality metrics (peak signal-to-noise ratio (PSNR), structure similarity index (SSIM)) by conducting a perceptual quality survey for super-resolved pathology images. High performing deep super-resolution models were used to generate 20X HR images from LR images (5X or 10X equivalent) for 11 different tissues and 30 human evaluators were asked to score the quality of the generated versus the ground truth 20X HR images. The scores given by a human rater and the PSNR or the SSIM were compared to investigate the correlation between model training parameters. We found that models trained on multiple tissues generalized better than those trained on a single tissue type. We also found that PSNR correlated with perceptual quality (R = 0.26) less accurately than did SSIM (R = 0.64), suggesting that the SSIM quality metric is insufficient. The methods proposed in this study can be used to virtually magnify H&E images with better perceptual quality than interpolation methods (i.e., bicubic interpolation) commonly implemented in digital pathology software. The impact of deep SISR methods is more notable when scaling to 4X is needed, such as in the case of super-resolving a low magnification WSI from 10X to 40X

Significant IgG subclass heterogeneity in HLA-specific antibodies : implications for pathogenicity, prognosis, and the rejection response

IgG subclasses differ in their ability to fix complement and bind Fc receptors. This study describes a detailed analysis of the distribution of HLA-specific IgG subclasses in order to define how this varies in sensitised waiting-list patients. We found significant variation in the level, presence and combinations of each HLA-specific IgG subclass between and within individuals and this is influenced by the type of sensitising event. Graft failure in particular provokes higher levels of IgG1 (vs transfusion, p = 0.071 and pregnancy, p = 0.042), IgG2 (vs transfusion, p = 0.001 and pregnancy, p = 0.016), and IgG4 (vs transfusion, p = 0.052). Both graft failure and pregnancy tend to stimulate multiple IgG subclass responses against HLA, whereas transfusion stimulated antibodies are dominated by responses limited to IgG1 (p = 0.033) and have a low incidence of IgG4 (p = 0.046). In marked contrast, IgG4 characterised nearly all HLA DQ-specific antibodies stimulated by graft rejection (p = 0.006). Such widely varying IgG subclass heterogeneity is likely to be due to underlying immunological processes dependent on the route of sensitisation. This diversity, which implies functional variation, may help explain why HLA-specific antibodies are an obstacle to transplantation in some circumstances but not others. The subclass association with rejection has potential as a biomarker for chronic rejectio

Multiscale generative model using regularized skip-connections and perceptual loss for anomaly detection in toxicologic histopathology

Background: Automated anomaly detection is an important tool that has been developed for many real-world applications, including security systems, industrial inspection, and medical diagnostics. Despite extensive use of machine learning for anomaly detection in these varied contexts, it is challenging to generalize and apply these methods to complex tasks such as toxicologic histopathology (TOXPATH) assessment (i.e.,finding abnormalities in organ tissues). In this work, we introduce an anomaly detection method using deep learning that greatly improves model generalizability to TOXPATH data. Methods: We evaluated a one-class classification approach that leverages novel regularization and perceptual techniques within generative adversarial network (GAN) and autoencoder architectures to accurately detect anomalous histopathological findings of varying degrees of complexity. We also utilized multiscale contextual data and conducted a thorough ablation study to demonstrate the efficacy of our method. We trained our models on data from normal whole slide images (WSIs) of rat liver sections and validated on WSIs from three anomalous classes. Anomaly scores are collated into heatmaps to localize anomalies within WSIs and provide human-interpretable results. Results: Our method achieves 0.953 area under the receiver operating characteristic on a real-worldTOXPATH dataset. The model also shows good performance at detecting a wide variety of anomalies demonstrating our method’s ability to generalize to TOXPATH data. Conclusion: Anomalies in both TOXPATH histological and non-histological datasets were accurately identified with our method, which was only trained with normal data

Increased presence of complement fixing IgG isotypes and its association with acute rejection in antibody incompatible transplantation

Donor specific antibodies (DSA) represent a risk factor for early antibody-mediated rejection and decreased allograft survival. However, local studies in antibody incompatible transplant (AiT) cases have shown that not all HLA-specific DSA are detrimental. Recent studies have shown that the analysis of pre-transplant DSA IgG subclass does not appear to predict development of acute rejection (Honger et al, 2011). Here we seek to show that changes in subclass composition to predict early rejection episodes in the acute post-transplant period. Fifty-one previous AiT cases were selected comprising 26 rejectors and 25 non-rejectors, with rejection diagnosed on the basis of clinical symptoms and/or histology. Daily serum samples were taken post-transplant with total level of HLA-specific IgG determined by single antigen bead assay. IgG1,2,3 and 4 HLA specific antibody levels were determined for all pretreatment, pretransplant and post-transplant peak samples. Samples taken prior to starting antibody removal therapy showed no difference between rejector and non-rejector groups in concordance with previously published data. However samples tested post-transplant at the time at which pan-IgG specific DSA peaked (days 8-11 post-transplant) clearly show an increase in the rejector group of donor-specific IgG1 (p = 0.01), and also the proportion of the total IgG response attributable to the complement fixing isotypes IgG1/IgG3 were greatly increased in the rejector group (p = 0.007). In addition the rejector group also showed a significant increase in donor specific IgG4 as the antibody response matured beyond 30 days posttransplant (p = 0.03). These data show a clear link between acute antibody mediated rejection (AMR) and the increased presence of complement fixing IgG isotypes. Increased levels of IgG4 associated with AMR may highlight progressive stimulation of the immune response, or immunoglobulin heavy chain class switching. Analysis of IgG subclass composition may be an important tool in the analysis of AMR in AiT

Direct quantitative measurement of the kinetics of HLA-specific antibody interactions with isolated HLA proteins

HLA specific antibodies vary in their pathogenicity and this is likely to be the net effect of constant chain usage, quantity, specificity, and affinity. Here we have measured the affinity of human monoclonal antibodies for a range of HLA proteins. Purified antibodies and ligands allowed dynamic interactions to be measured directly by surface plasmon resonance. Physiochemical differences between pairs of ligands were quantified using electrostatic mismatch and hydrophobic mismatch scores. All antibodies were characterized by fast on-rates and slow off rates but with a wide range of association rates (kon, 3.63-24.25 × 10(5) per mol per second) and dissociation rates (koff, 0.99-10.93 × 10(-3) per second). Dissociation constants (KD) ranged from 5.9 × 10(-10) M to 3.0 × 10(-8) M. SN320G6 has approximately a twenty-fold greater affinity for HLA A2 compared with SN607D8, but has a similar affinity for HLA-A2 and B57. In contrast, SN607D8 has greater than a twofold greater affinity for HLA-A2 compared with A68. Similarly, WK1D12 has about a threefold greater affinity for HLA-B27 compared with B7. The higher affinity interactions correlate with the specificity of stimulating antigen. This is the first study to directly measure the binding kinetics and affinity constants for human alloantibodies against HLA