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Introduction The gold standard for diagnosis of active lupus nephritis (ALN), a kidney biopsy, is invasive with attendant morbidity and cannot be serially repeated. Urinary ALCAM (uALCAM) has shown high diagnostic accuracy for renal pathology activity in ALN patients. Methods Lateral flow assays (LFA) for assaying uALCAM were engineered using persistent luminescent nanoparticles, read by a smartphone. The stability and reproducibility of the assembled LFA strips and freeze-dried conjugated nanoparticles were verified, as was analyte specificity. Results The LFA tests for both un-normalized uALCAM (AUC=0.93) and urine normalizer (HVEM)-normalized uALCAM (AUC=0.91) exhibited excellent accuracies in distinguishing ALN from healthy controls. The accuracies for distinguishing ALN from all other lupus patients were 0.86 and 0.74, respectively. Conclusion Periodic monitoring of uALCAM using this easy-to-use LFA test by the patient at home could potentially accelerate early detection of renal involvement or disease flares in lupus patients, and hence reduce morbidity and mortality. 

The classification of Antibody Mediated Rejection (AMR) in kidney transplant remains challenging even for experienced nephropathologists; this is partly because histological tissue stain analysis is often characterized by low inter-observer agreement and poor reproducibility. One of the implicated causes for inter-observer disagreement is the variability of tissue stain quality between (and within) pathology labs, coupled with the gradual fading of archival sections. Variations in stain colors and intensities can make tissue evaluation difficult for pathologists, ultimately affecting their ability to describe relevant morphological features. Being able to accurately predict the AMR status based on kidney histology images is crucial for improving patient treatment and care. We propose a novel pipeline to build robust deep neural networks for AMR classification based on StyPath, a histological data augmentation technique that leverages a light weight style-transfer algorithm as a means to reduce sample-specific bias. Each image was generated in 1.84 ± 0.03 s using a single GTX TITAN V gpu and pytorch, making it faster than other popular histological data augmentation techniques. We evaluated our model using a Monte Carlo (MC) estimate of Bayesian performance and generate an epistemic measure of uncertainty to compare both the baseline and StyPath augmented models. We also generated Grad-CAM representations of the results which were assessed by an experienced nephropathologist; we used this qualitative analysis to elucidate on the assumptions being made by each model. Our results imply that our style-transfer augmentation technique improves histological classification performance (reducing error from 14.8% to 11.5%) and generalization ability. 

ObjectiveTo obtain the comprehensive transcriptome profile of human citrulline‐specific B cells from patients with rheumatoid arthritis (RA). MethodsCitrulline‐ and hemagglutinin‐specific B cells were sorted by flow cytometry using peptide–streptavidin conjugates from the peripheral blood ofRApatients and healthy individuals. The transcriptome profile of the sorted cells was obtained byRNA‐sequencing, and expression of key protein molecules was evaluated by aptamer‐basedSOMAscan assay and flow cytometry. The ability of these proteins to effect differentiation of osteoclasts and proliferation and migration of synoviocytes was examined by in vitro functional assays. ResultsCitrulline‐specific B cells, in comparison to citrulline‐negative B cells, from patients withRAdifferentially expressed the interleukin‐15 receptor α (IL‐15Rα) gene as well as genes related to protein citrullination and cyclicAMPsignaling. In analyses of an independent cohort of cyclic citrullinated peptide–seropositiveRApatients, the expression ofIL‐15Rα protein was enriched in citrulline‐specific B cells from the patients’ peripheral blood, and surprisingly, all B cells fromRApatients were capable of producing the epidermal growth factor ligand amphiregulin (AREG). Production ofAREGdirectly led to increased migration and proliferation of fibroblast‐like synoviocytes, and, in combination with anti–citrullinated protein antibodies, led to the increased differentiation of osteoclasts. ConclusionTo the best of our knowledge, this is the first study to document the whole transcriptome profile of autoreactive B cells in any autoimmune disease. These data identify several genes and pathways that may be targeted by repurposing severalUSFood and Drug Administration–approved drugs, and could serve as the foundation for the comparative assessment of B cell profiles in other autoimmune diseases.