Morphometric and visual evaluation of fibrosis in renal biopsies

Dihydromyricetin ameliorates diabetic renal fibrosis via regulating SphK1 to suppress the activation of NF-κB pathway

Dihydromyricetin (DHM) is a flavonoid from vine tea with broad pharmacological benefits, which improve inflammation by blocking the NF-κB pathway. A growing body of research indicates that chronic kidney inflammation is vital to the pathogenesis of diabetic renal fibrosis. Sphingosine kinase-1 (SphK1) is a key regulator of diabetic renal inflammation, which triggers the NF-κB pathway. Hence, we evaluated whether DHM regulates diabetic renal inflammatory fibrosis by acting on SphK1. Here, we demonstrated that DHM effectively suppressed the synthesis of fibrotic and inflammatory adhesion factors like ICAM-1, and VCAM-1 in streptozotocin-treated high-fat diet-induced diabetic mice and HG-induced glomerular mesangial cells (GMCs). Moreover, DHM significantly suppressed NF-κB pathway activation and reduced SphK1 activity and protein expression under diabetic conditions. Mechanistically, the results of molecular docking, molecular dynamics simulation, and cellular thermal shift assay revealed that DHM stably bound to the binding pocket of SphK1, thereby reducing sphingosine-1-phosphate content and SphK1 enzymatic activity, which ultimately inhibited NF-κB DNA binding, transcriptional activity, and nuclear translocation. In conclusion, our data suggested that DHM inhibited SphK1 phosphorylation to prevent NF-κB activation thus ameliorating diabetic renal fibrosis. This supported the clinical use and further drug development of DHM as a potential candidate for treating diabetic renal fibrosis.

Development of a suitable vibration pad for renal MR elastography

The aim of this study was to develop a vibration pad suitable for renal MR elastography (MRE). Chronic kidney disease (CKD) is a progressive condition affecting >800 million people worldwide. Renal fibrosis is a common pathological feature of CKD that causes fibrotic regions to be much stiffer than those in normal renal tissues. Therefore, MRE can be used to diagnose CKD because it can image organ stiffness. In MRE, the shear modulus is obtained from the wavelength of the shear waves. Therefore, it is highly important to propagate shear waves with sufficient vibration strength in the tissue. By using a three-dimensional (3D) printer, we created a "Flexible Pad" suitable for renal MRE. The Flexible Pad was placed under the back of the participant in the supine position and deformed in response to the participant's weight, adhering closely to the body surface. Six healthy volunteers participated in this study. Our Flexible Pad allowed for coherent shear waves (clear waves with little scattering and interference) to be efficiently transmitted to the kidney deep-lying tissues in the abdomen. The shear moduli of the kidney (n = 6) were 8.95 ± 0.84 kPa in the right kidney and 9.70 ± 0.99 kPa in the left kidney. Our results indicate that using our Flexible Pad for renal MRE can provide a more reliable measurement of renal shear modulus.

Deletion of IRE1α in podocytes exacerbates diabetic nephropathy in mice

Protein misfolding in the endoplasmic reticulum (ER) of podocytes contributes to the pathogenesis of glomerular diseases. Protein misfolding activates the unfolded protein response (UPR), a compensatory signaling network. We address the role of the UPR and the UPR transducer, inositol-requiring enzyme 1α (IRE1α), in streptozotocin-induced diabetic nephropathy in mice. Diabetes caused progressive albuminuria in control mice that was exacerbated in podocyte-specific IRE1α knockout (KO) mice. Compared to diabetic controls, diabetic IRE1α KO mice showed reductions in podocyte number and synaptopodin. Glomerular ultrastructure was altered only in diabetic IRE1α KO mice; the major changes included widening of podocyte foot processes and glomerular basement membrane. Activation of the UPR and autophagy was evident in diabetic control, but not diabetic IRE1α KO mice. Analysis of human glomerular gene expression in the JuCKD-Glom database demonstrated induction of genes associated with the ER, UPR and autophagy in diabetic nephropathy. Thus, mice with podocyte-specific deletion of IRE1α demonstrate more severe diabetic nephropathy and attenuation of the glomerular UPR and autophagy, implying a protective effect of IRE1α. These results are consistent with data in human diabetic nephropathy and highlight the potential for therapeutically targeting these pathways.

Predicting clinical endpoints and visual changes with quality-weighted tissue-based renal histological features

Two common obstacles limiting the performance of data-driven algorithms in digital histopathology classification tasks are the lack of expert annotations and the narrow diversity of datasets. Multi-instance learning (MIL) can address the former challenge for the analysis of whole slide images (WSI), but performance is often inferior to full supervision. We show that the inclusion of weak annotations can significantly enhance the effectiveness of MIL while keeping the approach scalable. An analysis framework was developed to process periodic acid-Schiff (PAS) and Sirius Red (SR) slides of renal biopsies. The workflow segments tissues into coarse tissue classes. Handcrafted and deep features were extracted from these tissues and combined using a soft attention model to predict several slide-level labels: delayed graft function (DGF), acute tubular injury (ATI), and Remuzzi grade components. A tissue segmentation quality metric was also developed to reduce the adverse impact of poorly segmented instances. The soft attention model was trained using 5-fold cross-validation on a mixed dataset and tested on the QUOD dataset containing n = 373 PAS and n = 195 SR biopsies. The average ROC-AUC over different prediction tasks was found to be 0.598 ± 0.011 , significantly higher than using only ResNet50 ( 0.545 ± 0.012 ), only handcrafted features ( 0.542 ± 0.011 ), and the baseline ( 0.532 ± 0.012 ) of state-of-the-art performance. In conjunction with soft attention, weighting tissues by segmentation quality has led to further improvement ( A U C = 0.618 ± 0.010 ) . Using an intuitive visualisation scheme, we show that our approach may also be used to support clinical decision making as it allows pinpointing individual tissues relevant to the predictions.

From inflammation to renal fibrosis: A one-way road in autoimmunity?

Renal fibrosis is now recognized as a main determinant of renal pathology to include chronic kidney disease. Deposition of pathological matrix in the walls of glomerular capillaries, the interstitial space, and around arterioles predicts and contributes to the functional demise of the nephron and its surrounding vasculature. The recent identification of the major cell populations of fibroblast precursors in the kidney interstitium such as pericytes and tissue-resident mesenchymal stem cells, or bone-marrow-derived macrophages, and in the glomerulus such as podocytes, parietal epithelial and mesangial cells, has enabled the study of the fibrogenic process thought the lens of involved immunological pathways. Besides, a growing body of evidence is supporting the role of the lymphatic system in modulating the immunological response potentially leading to inflammation and ultimately renal damage. These notions have moved our understanding of renal fibrosis to be recognized as a clinical entity and new main player in autoimmunity, impacting directly the management of patients.

Reproducibility and prognostic ability of chronicity parameters in kidney biopsy - Comprehensive evaluation comparing microscopy and artificial intelligence in digital pathology

INTRODUCTION

Semi-quantitative scoring of various parameters in renal biopsy is accepted as an important tool to assess disease activity and prognostication. There are concerns on the impact of interobserver variability in its prognostic utility, generating a need for computerized quantification.

METHODS

We studied 94 patients with renal biopsies, 45 with native diseases and 49 transplant patients with index biopsies for Polyomavirus nephropathy. Chronicity scores were evaluated using two methods. A standard definition diagram was agreed after international consultation and four renal pathologists scored each parameter in a double-blinded manner. Interstitial fibrosis (IF) score was assessed with five different computerized and AI-based algorithms on trichrome and PAS stains.

RESULTS

There was strong prognostic correlation with renal function and graft outcome at a median follow-up ranging from 24 to 42 months respectively, independent of moderate concordance for pathologists scores. IF scores with two of the computerized algorithms showed significant correlation with estimated glomerular filtration rate (eGFR) at biopsy but not at the end of follow-up. There was poor concordance for AI based platforms.

CONCLUSION

Chronicity scores are robust prognostic tools despite interobserver reproducibility. AI-algorithms have absolute precision but are limited by significant variation when different hardware and software algorithms are used for quantification.

Computational pathology model to assess acute and chronic transformations of the tubulointerstitial compartment in renal allograft biopsies

Managing patients with kidney allografts largely depends on biopsy diagnosis which is based on semiquantitative assessments of rejection features and extent of acute and chronic changes within the renal parenchyma. Current methods lack reproducibility while digital image data-driven computational models enable comprehensive and quantitative assays. In this study we aimed to develop a computational method for automated assessment of histopathology transformations within the tubulointerstitial compartment of the renal cortex. Whole slide images of modified Picrosirius red-stained biopsy slides were used for the training (n = 852) and both internal (n = 172) and external (n = 94) tests datasets. The pipeline utilizes deep learning segmentations of renal tubules, interstitium, and peritubular capillaries from which morphometry features were extracted. Seven indicators were selected for exploring the intrinsic spatial interactions within the tubulointerstitial compartment. A principal component analysis revealed two independent factors which can be interpreted as representing chronic and acute tubulointerstitial injury. A K-means clustering classified biopsies according to potential phenotypes of combined acute and chronic transformations of various degrees. We conclude that multivariate analyses of tubulointerstitial morphometry transformations enable extraction of and quantification of acute and chronic components of injury. The method is developed for renal allograft biopsies; however, the principle can be applied more broadly for kidney pathology assessment.

Morphometric analysis of chronicity on kidney biopsy: a useful prognostic exercise

Chronic changes on kidney biopsy specimens include increasing amounts of arteriosclerosis, glomerulosclerosis, interstitial fibrosis and tubular atrophy, enlarged nephron size, and reduced nephron number. These chronic changes are difficult to accurately assess by visual inspection but are reasonably quantified using morphometry. This review describes the various patient populations that have undergone morphometric analysis of kidney biopsies. The common approaches to morphometric analysis are described. The chronic kidney disease outcomes associated with various chronic changes by morphometry are also summarized. Morphometry enriches the characterization of chronicity on a kidney biopsy and this can supplement the pathologist's diagnosis. Artificial intelligence image processing tools are needed to automate the annotations needed for practical morphometric analysis of kidney biopsy specimens in routine clinical care.

Automated evaluation with deep learning of total interstitial inflammation and peritubular capillaritis on kidney biopsies

BACKGROUND

Interstitial inflammation and peritubular capillaritis are observed in many diseases on native and transplant kidney biopsies. A precise and automated evaluation of these histological criteria could help stratify patients' kidney prognoses and facilitate therapeutic management.

METHODS

We used a convolutional neural network to evaluate those criteria on kidney biopsies. A total of 423 kidney samples from various diseases were included; 83 kidney samples were used for the neural network training, 106 for comparing manual annotations on limited areas to automated predictions, and 234 to compare automated and visual gradings.

RESULTS

The precision, recall and F-score for leukocyte detection were, respectively, 81%, 71% and 76%. Regarding peritubular capillaries detection the precision, recall and F-score were, respectively, 82%, 83% and 82%. There was a strong correlation between the predicted and observed grading of total inflammation, as for the grading of capillaritis (r = 0.89 and r = 0.82, respectively, all P < .0001). The areas under the receiver operating characteristics curves for the prediction of pathologists' Banff total inflammation (ti) and peritubular capillaritis (ptc) scores were respectively all above 0.94 and 0.86. The kappa coefficients between the visual and the neural networks' scores were respectively 0.74, 0.78 and 0.68 for ti ≥1, ti ≥2 and ti ≥3, and 0.62, 0.64 and 0.79 for ptc ≥1, ptc ≥2 and ptc ≥3. In a subgroup of patients with immunoglobulin A nephropathy, the inflammation severity was highly correlated to kidney function at biopsy on univariate and multivariate analyses.

CONCLUSION

We developed a tool using deep learning that scores the total inflammation and capillaritis, demonstrating the potential of artificial intelligence in kidney pathology.

Superiority of systemic bleomycin to intradermal HOCl for the study of interstitial lung disease

Systemic sclerosis (SSc) is an autoimmune disease characterized by vasculopathy, immune dysregulation, and multi-organ fibrosis. Interstitial lung disease (ILD) is a complication of SSc and a leading cause of SSc-death. The administration of hypochlorous acid (HOCl) intradermally in the mouse (HOCl-SSc) purportedly shows several features typical of SSc. We studied the model by injecting BALB/c mice daily intradermally with HOCl for 6-weeks, an exposure reported to induce lung fibrosis. On day 42, the skinfold thickness and the dermal thickness were two and three times larger respectively in the HOCl group compared to controls. HOCl treatment did not result in histological features of pulmonary fibrosis nor significant changes in lung compliance. Automated image analysis of HOCl mice lungs stained with picrosirius red did not show increased collagen deposition. HOCl injections did not increase pulmonary mRNA expression of pro-fibrotic genes nor induced the production of serum advanced oxidation protein products and anti-topoisomerase 1 antibodies. Immune cells in bronchoalveolar lavage fluid (BALF) and whole lung digests were not increased in HOCl-treated animals. Since lung fibrosis is proposed to be triggered by oxidative stress, we injected HOCl to Nrf2-/- mice, a mouse deficient in many antioxidant proteins. Lung compliance, histology, and BALF leukocyte numbers were comparable between Nrf2-/- mice and wild-type controls. We conclude that the HOCl-SSc model does not manifest SSc-lung disease.

Probing Renal Microstructure and Function with Advanced Diffusion MRI: Concepts, Applications, Challenges, and Future Directions

Diffusion measurements in the kidney are affected not only by renal microstructure but also by physiological processes (i.e., glomerular filtration, water reabsorption, and urine formation). Because of the superposition of passive tissue diffusion, blood perfusion, and tubular pre-urine flow, the limitations of the monoexponential apparent diffusion coefficient (ADC) model in assessing pathophysiological changes in renal tissue are becoming apparent and motivate the development of more advanced diffusion-weighted imaging (DWI) variants. These approaches take advantage of the fact that the length scale probed in DWI measurements can be adjusted by experimental parameters, including diffusion-weighting, diffusion gradient directions and diffusion time. This forms the basis by which advanced DWI models can be used to capture not only passive diffusion effects, but also microcirculation, compartmentalization, tissue anisotropy. In this review, we provide a comprehensive overview of the recent advancements in the field of renal DWI. Following a short introduction on renal structure and physiology, we present the key methodological approaches for the acquisition and analysis of renal DWI data, including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), non-Gaussian diffusion, and hybrid IVIM-DTI. We then briefly summarize the applications of these methods in chronic kidney disease and renal allograft dysfunction. Finally, we discuss the challenges and potential avenues for further development of renal DWI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Molecular MR Imaging of Renal Fibrogenesis in Mice

BACKGROUND

In most CKDs, lysyl oxidase oxidation of collagen forms allysine side chains, which then form stable crosslinks. We hypothesized that MRI with the allysine-targeted probe Gd-oxyamine (OA) could be used to measure this process and noninvasively detect renal fibrosis.

METHODS

Two mouse models were used: hereditary nephritis in Col4a3-deficient mice (Alport model) and a glomerulonephritis model, nephrotoxic nephritis (NTN). MRI measured the difference in kidney relaxation rate, ΔR1, after intravenous Gd-OA administration. Renal tissue was collected for biochemical and histological analysis.

RESULTS

ΔR1 was increased in the renal cortex of NTN mice and in both the cortex and the medulla of Alport mice. Ex vivo tissue analyses showed increased collagen and Gd-OA levels in fibrotic renal tissues and a high correlation between tissue collagen and ΔR1.

CONCLUSIONS

Magnetic resonance imaging using Gd-OA is potentially a valuable tool for detecting and staging renal fibrogenesis.

Comparative Analysis of Hypertensive Tubulopathy in Animal Models of Hypertension and Its Relevance to Human Pathology

Assessment of hypertensive tubulopathy for more than fifty animal models of hypertension in experimental pathology employs criteria that do not correspond to lesional descriptors for tubular lesions in clinical pathology. We provide a critical appraisal of experimental hypertension with the same approach used to estimate hypertensive renal tubulopathy in humans. Four models with different pathogenesis of hypertension were analyzed-chronic angiotensin (Ang) II-infused and renin-overexpressing (TTRhRen) mice, spontaneously hypertensive (SHR), and Goldblatt two-kidney one-clip (2K1C) rats. Mouse models, SHR, and the nonclipped kidney in 2K1C rats had no regular signs of hypertensive tubulopathy. Histopathology in animals was mild and limited to variations in the volume density of tubular lumen and epithelium, interstitial space, and interstitial collagen. Affected kidneys in animals demonstrated lesion values that are significantly different compared with healthy controls but correspond to mild damage if compared with hypertensive humans. The most substantial human-like hypertensive tubulopathy was detected in the clipped kidney of 2K1C rats. For the first time, our study demonstrated the regular presence of chronic progressive nephropathy (CPN) in relatively young mice and rats with induced hypertension. Because CPN may confound the assessment of rodent models of hypertension, proliferative markers should be used to verify nonhypertensive tubulopathy.

Visual interstitial fibrosis assessment as continuous variable in protocol renal transplant biopsies

AIMS

In current renal transplant pathology practice, interstitial fibrosis is visually assessed in categories according to the Banff classification. As this has a moderate reproducibility, which is little ameliorated by morphometric analysis, we investigated whether visual renal fibrosis assessment is feasible on a continuous scale, i.e. as a percentage of affected area of the cortex.

METHODS AND RESULTS

Protocol renal biopsies taken at transplantation (n = 125), three (n = 73) and 12 months (n = 88) after transplantation were visually scored in categories (Banff) and percentages for interstitial fibrosis (ci). Interobserver variation (ICC and weighted κ) was assessed, and morphometric analysis on Sirius red-stained sections was performed. Correlations between the different methods and their association with donor age and eGFR 1 and 5 years post-transplant were analysed using Pearson's or Spearman's rho. Interobserver agreement was equivalent for Banff and %ci (κ = 0.713 versus ICC = 0.792), and for Banff IF/TA and %IF/TA (κ = 0.615 versus ICC = 0.743). Both Banff and %ci were associated with Sirius red morphometry in 3 and 12 months. With all three methods, a significant correlation was found between donor age and fibrosis in the implantation biopsy and between fibrosis in the 12 months' biopsy and eGFR at 1 and 5 years (eGFR at 1 year: Sirius red ρ = 0.487, %ci ρ = 0.393, Banff ρ = 0.413, all P < 0.01, eGFR at 5 years: Sirius red ρ = 0.392, %ci ρ = 0.333, Banff ρ = 0.435, all P < 0.01).

CONCLUSION

Interstitial fibrosis assessment on a continuous scale can be used next to scoring in categories according to the Banff classification in protocol renal transplant biopsies.

Adapted clustering method for generic analysis of histological fibrosis staining as an open source tool

Pathological remodeling of the extracellular matrix is a hallmark of cardiovascular disease. Abnormal fibrosis causes cardiac dysfunction by reducing ejection fraction and impairing electrical conductance, leading to arrhythmias. Hence, accurate quantification of fibrosis deposition in histological sections is of extreme importance for preclinical and clinical studies. Current automatic tools do not perform well under variant conditions. Moreover, users do not have the option to evaluate data from staining methods of their choice according to their purpose. To overcome these challenges, we underline a novel machine learning-based tool (FibroSoft) and we show its feasibility in a model of cardiac hypertrophy and heart failure in mice. Our results demonstrate that FibroSoft can identify fibrosis in diseased myocardium and the obtained results are user-independent. In addition, the results acquired using our software strongly correlate to those obtained by Western blot analysis of collagen 1 expression. Additionally, we could show that this method can be used for Masson's Trichrome and Picosirius Red stained histological images. The evaluation of our method also indicates that it can be used for any particular histology segmentation and quantification. In conclusion, our approach provides a powerful example of the feasibility of machine learning strategies to enable automatic analysis of histological images.

MCP-1/IL-12 ratio expressions correlated with adventitial collagen depositions in renal vessels and IL-4/IFN-γ expression correlated with interstitial collagen depositions in the kidneys of dogs with canine leishmaniasis

Collagen deposition is a common event in chronic inflammation, and canine Leishmaniosis (CanL) is generally associated with a long and chronic evolution. Considering that the kidney shows fibrinogenic changes during CanL, and the balance of cytokines/chemokines regulates the profibrinogenic and antifibrinogenic immune responses differently, it can be hypothesized that the balance of cytokines/chemokines can be differentially expressed in the renal tissue in order to determine the expression of collagen depositions in the kidneys. This study aimed to measure collagen deposition and to evaluate cytokine/chemokine expressions in the kidney by means of qRT-PCR in sixteen Leishmania-infected dogs and six uninfected controls. Kidney fragments were stained with hematoxylin & eosin (H&E), Masson's Trichrome, Picrosirius Red, and Gomori's reticulin. Intertubular and adventitial collagen depositions were evaluated by the morphometric approach. Cytokine RNA expressions were measured by means of qRT-PCR to identify molecules involved in chronic collagen depositions in kidneys with CanL. Collagen depositions were related to the presence of clinical signs, and more intense intertubular collagen depositions occurred in infected dogs. Adventitial collagen deposition, as morphometrically measured by the average area of the collagen, was more intense in clinically affected dogs than in subclinically infected dogs. TNF-α/TGF-β, MCP1/IL-12, CCL5/IL-12, IL-4/IFN-γ, and IL-12/TGF-β expressions were associated with clinical manifestations in dogs with CanL. The IL-4/IFN-α ratio was more commonly expressed and upregulated in clinically affected dogs, and downregulated in subclinically infected dogs. Furthermore, MCP-1/IL-12 and CCL5/IL-12 were more commonly expressed in subclinically infected dogs. Strong positive correlations were detected between morphometric values of interstitial collagen depositions and MCP-1/IL-12, IL-12, and IL-4 mRNA expression levels in the renal tissues. Adventitial collagen deposition was correlated with TGF-β, IL-4/IFN-γ, and TNF-α/TGF-β. In conclusion, our results showed the association of MCP-1/IL-12 and CCL5/IL-12 ratios with an absence of clinical signs, as well as an IL-4/IFN-α ratio with adventitial and intertubular collagen depositions in dogs with visceral leishmaniosis.

Measurement of renal cortical fibrosis by CT scan

Rationale and objectives

The accurate, non-invasive, and rapid measurement of renal cortical fibrosis is needed for well-defined benchmarks of permanent injury and for use of anti-fibrotic agents. It is also needed for non-invasive and rapid assessment of the chronicity of human renal diseases.

Materials and methods

We have used a non-human primate model of radiation nephropathy to develop a novel method of size-corrected CT imaging to quantify renal cortical fibrosis.

Results

Our method has an area under the receiver operating curve of 0.96, which is superior to any other non-invasive method of measuring renal fibrosis.

Conclusion

Our method is suitable for immediate translation to human clinical renal diseases.

Determination of Interstitial Collagen Deposition and Related Topography Using the Second Harmonic Generation-Based HistoIndex Platform

Interstitial fibrosis is characterized by the increased deposition of extracellular matrix (ECM) components within the interstitial space of various organs, such as the kidneys, heart, lungs, liver, and skin. The primary component of interstitial fibrosis-related scarring is interstitial collagen. Therefore, the therapeutic application of anti-fibrotic medication hinges on the accurate measurement of interstitial collagen levels within tissue samples. Current histological measurement techniques for interstitial collagen are generally semi-quantitative in nature and only provide a ratio of collagen levels within tissues. However, the Genesis™ 200 imaging system and supplemental image analysis software, FibroIndex™, from HistoIndex™, is a novel, automated platform for imaging and characterizing interstitial collagen deposition and related topographical properties of the collagen structures within an organ, in the absence of any staining. This is achieved by using a property of light known as second harmonic generation (SHG). Using a rigorous optimization protocol, collagen structures in tissue sections can be imaged with a high degree of reproducibility and ensures homogeneity across all samples while minimizing the introduction of any imaging artefacts or photobleaching (decreased tissue fluorescence due to prolonged exposure to the laser). This chapter outlines the protocol that should be undertaken to optimize HistoIndex scanning of tissue sections, and the outputs that can be measured and analyzed using the FibroIndex™ software.

A Water-Soluble AIEgen for Noninvasive Diagnosis of Kidney Fibrosis via SWIR Fluorescence and Photoacoustic Imaging

Early diagnosis of renal fibrosis is crucially significant on account of its worldwide prevalent tendency. Optical imaging in the near-infrared window has been recognized as an appealing technique for the timely detection of renal dysfunction. However, formulating a contrast agent that allows early monitoring of renal fibrosis and concurrently renally clearable in a normal group is still challenging. Herein, a nanosized fluorophore with aggregation-induced emission (AIE) features, namely AIE-4PEG550 NPs, is well-tailored and amenable to longitudinal visualization of the fibrosis progression specifically in the early-stage via short-wave infrared (SWIR, 900-1700 nm) fluorescence and photoacoustic bimodal imaging. The small size (≈26 nm), renally filtrable molecular weight (3.3 kDa), high renal clearance efficiency (93.1 ± 1.7% excretion through the kidneys within 24 h), outstanding imaging performance, and good biocompatibility, together make AIE-4PEG550 NPs remarkably impressive and far superior to clinical diagnostic assays. The finding in this study would provide a blueprint for the next generation of diagnostic agents for the extent of renal fibrosis.

Annexin A1 exerts renoprotective effects in experimental crescentic glomerulonephritis

Non-resolving inflammation plays a critical role during the transition from renal injury towards end-stage renal disease. The glucocorticoid-inducible protein annexin A1 has been shown to function as key regulator in the resolution phase of inflammation, but its role in immune-mediated crescentic glomerulonephritis has not been studied so far.

Methods: Acute crescentic glomerulonephritis was induced in annexin A1-deficient and wildtype mice using a sheep serum against rat glomerular basement membrane constituents. Animals were sacrificed at d5 and d10 after nephritis induction. Renal leukocyte abundance was studied by immunofluorescence and flow cytometry. Alterations in gene expression were determined by RNA-Seq and gene ontology analysis. Renal levels of eicosanoids and related lipid products were measured using lipid mass spectrometry.

Results: Histological analysis revealed an increased number of sclerotic glomeruli and aggravated tubulointerstitial damage in the kidneys of annexin A1-deficient mice compared to the wildtype controls. Flow cytometry analysis confirmed an increased number of CD45⁺ leukocytes and neutrophil granulocytes in the absence of annexin A1. Lipid mass spectrometry showed elevated levels of prostaglandins PGE2 and PGD2 and reduced levels of antiinflammatory epoxydocosapentaenoic acid regioisomers. RNA-Seq with subsequent gene ontology analysis revealed induction of gene products related to leukocyte activation and chemotaxis as well as regulation of cytokine production and secretion.

Conclusion: Intrinsic annexin A1 reduces proinflammatory signals and infiltration of neutrophil granulocytes and thereby protects the kidney during crescentic glomerulonephritis. The annexin A1 signaling cascade may therefore provide novel targets for the treatment of inflammatory kidney disease.

Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies

In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive. The goal of this study was to investigate the potential of convolutional neural networks (CNNs) to quantify inflammation and chronic features in kidney transplant biopsies. A structure segmentation CNN and a lymphocyte detection CNN were applied on 125 whole-slide image pairs of periodic acid-Schiff- and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation within both nonatrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlation with Banff lesion scores of five pathologists (A.A., A.Dend., J.H.B., J.K., and T.N.). Analyses on a small subset showed a moderate correlation toward higher CD3⁺ cell density within scarred regions and higher CD3⁺ cell count inside atrophic tubuli correlated with long-term change of estimated glomerular filtration rate. The presented CNNs are valid tools to yield objective quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and non-scarred kidney parenchyma in a reproducible manner. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate end points for large-scale clinical studies.

Prognostic Implications of a Morphometric Evaluation for Chronic Changes on All Diagnostic Native Kidney Biopsies

BACKGROUND

Semiquantitative visual inspection for glomerulosclerosis, interstitial fibrosis, and arteriosclerosis is often used to assess chronic changes in native kidney biopsies. Morphometric evaluation of these and other chronic changes may improve the prognostic assessment.

METHODS

We studied a historical cohort of patients who underwent a native kidney biopsy between 1993 and 2015 and were followed through 2021 for ESKD and for progressive CKD (defined as experiencing 50% eGFR decline, temporary dialysis, or ESKD). Pathologist scores for the percentages of globally sclerosed glomeruli (GSG), interstitial fibrosis and tubular atrophy (IFTA), and arteriosclerosis (luminal stenosis) were available. We scanned biopsy sections into high-resolution images to trace microstructures. Morphometry measures were percentage of GSG; percentage of glomerulosclerosis (percentage of GSG, ischemic-appearing glomeruli, or segmentally sclerosed glomeruli); percentage of IFTA; IFTA foci density; percentage of artery luminal stenosis; arteriolar hyalinosis counts; and measures of nephron size. Models assessed risk of ESKD or progressive CKD with biopsy measures adjusted for age, hypertension, diabetes, body mass index, eGFR, and proteinuria.

RESULTS

Of 353 patients (followed for a median 7.5 years), 75 developed ESKD and 139 experienced progressive CKD events. Visually estimated scores by pathologists versus morphometry measures for percentages of GSG, IFTA, and luminal stenosis did not substantively differ in predicting outcomes. However, adding percentage of glomerulosclerosis, IFTA foci density, and arteriolar hyalinosis improved outcome prediction. A 10-point score using percentage of glomerulosclerosis, percentage of IFTA, IFTA foci density, and any arteriolar hyalinosis outperformed a 10-point score based on percentages of GSG, IFTA, and luminal stenosis >50% in discriminating risk of ESKD or progressive CKD.

CONCLUSION

Morphometric characterization of glomerulosclerosis, IFTA, and arteriolar hyalinosis on kidney biopsy improves prediction of long-term kidney outcomes.

Automated Computer-Assisted Image Analysis for the Fast Quantification of Kidney Fibrosis

Simple Summary

Chronic kidney disease is a health problem in which the kidneys cannot function normally. Thus, they cannot filter blood effectively and cause waste accumulation in the organism, leading to serious health problems. Researchers use animals as models to replicate the human body’s behavior to understand this disease. In these studies, it is essential to evaluate the percentage of fibrosis (growth of fibrotic tissue similar to a scar in response to damage) to know the degree of kidney damage. Some researchers use programs to make the evaluation of fibrosis easier. However, this analysis is time-consuming because it needs to be made one image at a time and there are hundreds of samples in an animal model study. Here, we explain a method to conduct the same analysis but in a faster automated way with the assistance of a computer and a software package called CellProfiler™. The percentage of fibrosis using CellProfiler™ is similar to that obtained with the most widely used software for this kind of analysis called ImageJ. With the help of this approach, researchers can make more studies faster and easier and find new antifibrogenic therapies to address the common and worldwide health problem caused by chronic kidney disease.

Abstract

Chronic kidney disease (CKD) is a common and worldwide health problem and one of the most important causes of morbidity and mortality. Most primary research on this disease requires evaluating the fibrosis index in animal model kidneys, specifically using Masson’s trichrome stain. Different programs are used to calculate the percentage of fibrosis; however, the analysis is time-consuming since one image must be performed at a time. CellProfiler™ is a program designed to analyze data obtained from biological samples and can process multiple images through pipelines, and the results can be exported to databases. This article explains how CellProfiler™ can be used to automatically analyze kidney histology photomicrographs from samples stained with Masson’s trichrome stain to assess the percentage of fibrosis in an experimental animal model of CKD. A pipeline was created to analyze Masson’s trichrome-stained slides in a model of CDK induced by adenine at doses of 50 mg/kg and 100 mg/kg, in addition to samples with the vehicle (75% glycerin). The results were compared with those obtained by ImageJ, and no significant differences were found between both programs. The CellProfiler™ pipeline made here is a reliable, fast, and easy alternative for kidney fibrosis analysis and quantification in experimental animal models.

Polysaccharide extract of Caesalpinia ferrea (Mart) pods attenuates inflammation and enhances the proliferative phase of rat cutaneous wounds

Pods of Caesalpinia ferrea, popularly used to treat inflammatory processes, were collected to obtain the polysaccharide-rich extract, presenting anti-inflammatory and antinociceptive effects in acute inflammation models. This study aimed to evaluate the anti-inflammatory, antinociceptive and healing activities of the polysaccharide-rich extract from Caesalpinia ferrea pods (PEp-Cf) in the rat model of cutaneous excisional wound. PEp-Cf (0.025-0.1%) or 0.9% NaCl was topically applied in the wounds at dorsal thoracic region (2×/day) during 21 days for measurement of clinical signs (hyperemia, inflammatory exudate, edema, nociception), wound size, histopathological/histomorphometric, oxidative/inflammatory markers and systemic toxicity. PEp-Cf at 0.1% reduced wound area and increased ulcer contraction [days 2 and 10 (21-78%)]. PEp-Cf reduced clinical signs [days 2 and 5 (2.2-2.8×)] and modulated the healing inflammatory phase via stimulation of epithelialization (days 10 and 14), and inhibition of polymorphonuclears [days 2 and 5 (71-74%)], protein leakage [days 2 and 5 (28-41%)], nitrate [days 2 and 5 (2.2-6×)] and malondialdehyde [days 2 and 5 (46-49%)]. PEp-Cf increased the number of blood vessels [days 5 and 7 (3.1-9.6×)], fibroblasts [days 5 and 7 (2.1-6.4×)] and collagen [days 5 to 14 (1.5-1.8×)]. In conclusion, the topical application of PEp-Cf at 0.1% accelerates the healing process of rat cutaneous wounds via modulation of the inflammatory and proliferative phases, being devoid of systemic alterations.

Low-level exposure to lead, cadmium and mercury, and histopathological findings in kidney biopsies

BACKGROUND

Lead (Pb), cadmium (Cd) and mercury (Hg) are all nephrotoxic metals, and a large part of the body burden of Cd and Hg is found in the kidneys. There are, however, few studies on associations between exposure to these toxic metals and renal biopsy findings, and none at low-level exposure.

AIM

To examine the hypothesis that low-level concentration of Pb, Cd or Hg in the kidneys is associated with histopathological changes in the kidneys.

METHODS

We determined concentrations of Pb, Cd and Hg in kidney, blood and urine in 109 healthy kidney donors, aged 24-70 years. The renal biopsies were scored according to the Banff classification regarding tubular atrophy, interstitial fibrosis, glomerulosclerosis, arteriosclerosis, and arteriolohyalinosis. Kidney function was assessed based on glomerular filtration rate (GFR) as well as urinary excretion of albumin, low molecular weight proteins, kidney injury molecule 1 and N-acetylglucose aminidase. Associations between metal concentrations and histopathological changes, were assessed in models also including age, sex and smoking.

RESULTS

The median kidney concentrations of Pb, Cd and Hg were 0.08, 13 and 0.21 μg/g, respectively. There were signs of tubular atrophy in 63%, interstitial fibrosis in 21%, glomerulosclerosis in 71%, arteriosclerosis in 47%, and arteriolohyalinosis in 36% of the donors, but, as could be expected, the histopathological findings were limited, mostly Banff grade 1. In models adjusted for age, sex and smoking, kidney Cd was positively associated with tubular atrophy (p = 0.03) and possibly with arteriolohyalinosis (p = 0.06). Kidney Hg was associated with arteriosclerosis (p = 0.004).

DISCUSSION AND CONCLUSIONS

The results suggest that even low levels of Cd in the kidney can induce a mild degree of tubular atrophy. This is in line with previous findings at high-level Cd exposure. The association between kidney Hg and renal arteriosclerosis was unexpected, and may be a chance finding.

Pilot testing for long-term impact of glycerol-induced acute kidney injury on oxalate homeostasis in rats

Abstract. There is a general lack of research on the long-term effects of acute kidney injury (AKI) on oxalate-degrading bacteria (ODB) and their total oxalate-degrading activity (ODA) in fecal microbiota. In the present pilot study, we separately evaluated the changes in the ODB number and their total ODA in fecal microbiota at 3-time points after glycerol-induced AKI. In addition, we assessed the interactions between AKI-induced renal histopathological changes and ODB, total fecal ODA, and plasma and urine oxalate concentrations in rats. Methods. The male Wistar rats (200-300 g, n = 20) on oxalate-free diet were randomly divided into 2 groups. After 24-h of water deprivation, experimental group 1 (n = 10) received an intramuscular injection of 50% glycerol (10 ml/kg of body weight), and group 2 (n = 10) served as a control. The numbers of ODB (incubated in a highly selective Oxalate Medium and determined using the culture method), total fecal ODA and urinary oxalate (UOx) excretion were measured after injection on days 8, 22 and 70. The method of redoximetric titration with a KMnO4 solution was adopted to evaluate total ODA in fecal microbiota. Renal injury was assessed by histopathology examination, serum creatinine plasma oxalic acid (POx) concentration and daily proteinuria levels after removing the animals from the experiment on day 70. Results. After glycerol injection on days 8 and 22, no differences were found in the numbers of ODB, their total fecal ODA, and UOx excretion level between the experimental and control groups. However, after AKI initiation on day 70, the numbers of ODB, total fecal ODA, and daily UOx excretion were significantly lower in the experimental group as compared with the control group. In addition, in 10 weeks following AKI, the number of ODB had a direct correlation with UOx excretion and an inverse correlation with POx and serum creatinine concentrations and daily proteinuria. Total ODA in fecal microbiota was directly associated with the percentage of renal interstitial fibrosis and the average glomerular volumes in the experimental rats. Conclusions: AKI had long-term negative effects on the quantitative and qualitative characteristics of ODB in fecal microbiota in rats. Moreover, the results of our study confirmed an increasing trend in total fecal ODA according to the aggravation of renal interstitial fibrosis and glomerular volume in rats’ kidneys. Further studies are warranted to gain more insight into the mechanism of oxalate homeostasis impairment in AKI.

MRI-based kidney radiomic analysis during chronic lithium treatment

BACKGROUND

Lithium therapy during bipolar disorder is associated with an increased risk of chronic kidney disease (CKD) that is slowly progressive and undetectable at early stages. We aimed at identifying kidney image texture features as possible imaging biomarkers of decreased measured glomerular filtration rate (mGFR) using radiomics of T2-weighted magnetic resonance imaging (MRI).

METHODS

One hundred and eight patients treated with lithium were evaluated including mGFR and kidney MRI, with T2-weighted sequence single-shot fast spin-echo. Computed radiomic analysis was performed after kidney segmentation. Significant features were selected to build a radiomic signature using multivariable Cox analysis to detect an mGFR <60 ml/min/1.73 m². The texture index was validated using a training and a validation cohort.

RESULTS

Texture analysis index was able to detect an mGFR decrease, with an AUC of 0.85 in the training cohort and 0.71 in the validation cohort. Patients with a texture index below the median were older (59 [42-66] vs. 46 [34-54] years, p = .001), with longer treatment duration (10 [3-22] vs. 6 [2-10] years, p = .02) and a lower mGFR (66 [46-84] vs. 83 [71-94] ml/min/1.73m², p < .001). Texture analysis index was independently and negatively associated with age (β = -.004 ± 0.001, p < .001), serum vasopressin (-0.005 ± 0.002, p = .02) and lithium treatment duration (-0.01 ± 0.003, p = .001), with a significant interaction between lithium treatment duration and mGFR (p = .02).

CONCLUSIONS

A renal texture index was developed among patients treated with lithium associated with a decreased mGFR. This index might be relevant in the diagnosis of lithium-induced renal toxicity.

Computer-assisted evaluation enhances the quantification of interstitial fibrosis in renal implantation biopsies, measures differences between frozen and paraffin sections, and predicts delayed graft function

Background

(Pre-)Implantation biopsies provide important data on the quality of donor kidneys. Interstitial fibrosis, as a known predictor for kidney disease progression, is an essential feature of this evaluation. However, the assessment of frozen sections of implantation biopsies is challenging and can result in the disposal of candidate organs. We sought to apply digital image analysis (DIA) to quantify the differences between frozen and paraffin sections when evaluating interstitial fibrosis, identify factors that influence these variations and test the predictive value of the computerised measures.

Methods

We quantified the differences between frozen and paraffin sections in the same biopsy samples by measuring Sirius red-stained interstitial areas (SRIA) in DIA. We compared them to the original reports, and retrospectively correlated our findings to clinical data, graft function and outcome in 73 patients.

Results

Frozen sections display a broader interstitial area than paraffin sections, in some cases up to one-third more (mean difference + 7.8%, range − 7 to 29%). No donor-related factors (age or gender, cold ischemia time, or non-heart-beating donor) influenced significantly this difference. Compared to the original assessment of frozen vs paraffin sections in optical microscopy, the DIA of interstitial fibrosis shows a higher consistency (ICC 0.69). Our approach further allows to distinguish SRIA in paraffin sections as an independent predictor for delayed graft function (OR = 1.1; p = 0.028).

Conclusions

DIA is superior to and more consistent than routine optic microscopy for interstitial fibrosis evaluation. This method could improve implantation biopsy diagnostics and help to reduce disposal of organs.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40620-022-01315-y.

Are ovine and porcine carotid arteries equivalent animal models for experimental cardiac surgery: A quantitative histological comparison

BACKGROUND

Coronary artery bypass grafting (CABG) is a common cardiac surgery. Manufacturing small-diameter (2-5mm) vascular grafts for CABG is important for patients who lack first-choice autologous arterial, or venous conduits. Ovine and porcine common carotid arteries (CCAs) are used as large animal models for in vivo testing of newly developed tissue-engineered arterial grafts. It is unknown to what extent these models are interchangeable and whether the left and right arteries of the same subjects can be used as experimental controls. Therefore, we compared the microscopic structure of paired left and right ovine and porcine CCAs in the proximodistal direction and compared these animal model samples to samples of human coronary arteries (CAs) and human internal thoracic arteries (ITAs).

METHODS

We compared the histological composition of whole CCAs of sheep (n=22 animals) with whole porcine CCAs (n=21), segments of human CAs (n=21), and human ITAs (n=21). Using unbiased sampling and stereological methods, we quantified the fractions of elastin, total collagen, type I collagen, type III collagen, smooth muscle actin (SMA) and chondroitin sulfate (CS) A, B, and C. We also quantified the densities and distributions of nuclear profiles, nervi vasorum and vasa vasorum as well as the thickness of the intima-media and total wall thickness.

RESULTS

The differences between the paired samples of left and right CCAs in sheep were substantially greater than the differences in laterality in porcine CCAs. The right ovine CCAs had a smaller fraction of elastin (p<0.001), greater fraction of SMA (p<0.01), and greater intima-media thickness (p<0.001) than the paired left side CCAs. In pigs, the right CCAs had a greater fraction of elastin (p<0.05) and a greater density of vasa vasorum in the media (p<0.001) than the left-side CCAs. The fractions of elastin and CS decreased and the fraction of SMA increased in the proximodistal direction in both the ovine (p<0.001) and porcine (p<0.001) CCAs. Ovine CCAs had a muscular phenotype along their entire length, but porcine CCAs were elastic-type arteries in the proximal segments but muscular type arteries in middle and distal segments. The CCAs of both animals differed from the human CAs and ITAs in most parameters, but the ovine CCAs had a comparable fraction of elastin and CS to human ITAs.

CONCLUSIONS

From a histological point of view, ovine and porcine CCAs were not equivalent in most quantitative parameters to human CAs and ITAs. Left and right ovine CCAs did not have the same histological composition, which is limiting for their mutual equivalence as sham-operated controls in experiments. These differences should be taken into account when designing and interpreting experiments using these models in cardiac surgery. The complete morphometric data obtained by quantitative evaluation of arterial segments were provided to facilitate the power analysis necessary for justification of the minimum number of samples when planning further experiments. The middle or distal segments of ovine and porcine CCAs remain the most realistic and the best characterized large animal models for testing artificial arterial CABG conduits.

Assessment of Physiological Rat Kidney Ageing—Implications for the Evaluation of Allograft Quality Prior to Renal Transplantation

Due to organ shortage and rising life expectancy the age of organ donors and recipients is increasing. Reliable biomarkers of organ quality that predict successful long-term transplantation outcomes are poorly defined. The aim of this study was the identification of age-related markers of kidney function that might accurately reflect donor organ quality. Histomorphometric, biochemical and molecular parameters were measured in young (3-month-old) and old (24-month-old) male Sprague Dawley rats. In addition to conventional methods, we used urine metabolomics by NMR spectroscopy and gene expression analysis by quantitative RT-PCR to identify markers of ageing relevant to allograft survival. Beside known markers of kidney ageing like albuminuria, changes in the concentration of urine metabolites such as trimethylamine-N-oxide, trigonelline, 2-oxoglutarate, citrate, hippurate, glutamine, acetoacetate, valine and 1-methyl-histidine were identified in association with ageing. In addition, expression of several genes of the toll-like receptor (TLR) pathway, known for their implication in inflammaging, were upregulated in the kidneys of old rats. This study led to the identification of age-related markers of biological allograft age potentially relevant for allograft survival in the future. Among those, urine metabolites and markers of immunity and inflammation, which are highly relevant to immunosuppression in transplant recipients, are promising and deserve further investigation in humans.

Identification of glomerulosclerosis using IBM Watson and shallow neural networks

Background

Advanced stages of different renal diseases feature glomerular sclerosis at a histological level which is observed by light microscopy on tissue samples obtained by performing a kidney biopsy. Computer-aided diagnosis (CAD) systems leverage the potential of artificial intelligence (AI) in healthcare to support physicians in the diagnostic process.

Methods

We propose a novel CAD system that processes histological images and discriminates between sclerotic and non-sclerotic glomeruli. To this goal, we designed, tested, and compared two artificial neural network (ANN) classifiers. The former implements a shallow ANN classifying hand-crafted features extracted from Regions of Interest (ROIs) by means of image-processing procedures. The latter, instead, employs the IBM Watson Visual Recognition System, which uses a deep artificial neural network making decisions taking the images as input, without the need to design any procedure for describing images with features. The input dataset consisted of 428 sclerotic glomeruli and 2344 non-sclerotic glomeruli derived from images of kidney biopsies scanned by the Aperio ScanScope System.

Results

Both AI approaches allowed to very accurately distinguish (mean MCC 0.95 and mean Accuracy 0.99) between sclerotic and non-sclerotic glomeruli. Although the systems may seem interchangeable, the approach based on feature extraction and classification would allow clinicians to gain information on the most discriminating features. In fact, further procedures could explain the classifier’s decision by analysing which subset of features impacted the most on the final decision.

Conclusions

We developed a customizable support system that can facilitate the work of renal pathologists both in clinical and research settings.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40620-021-01200-0.

Diffusion-Magnetic Resonance Imaging predicts decline of kidney function in chronic kidney disease and in patients with a kidney allograft

Kidney cortical interstitial fibrosis is highly predictive of kidney prognosis and is currently assessed by evaluation of a biopsy. Diffusion-weighted magnetic resonance imaging is a promising non-invasive tool to evaluate kidney fibrosis. We recently adapted diffusion-weighted imaging sequence for discrimination between the kidney cortex and medulla and found that the cortico-medullary difference in apparent diffusion coefficient (ΔADC) correlated with histological interstitial fibrosis. Here, we assessed whether ΔADC as measured with diffusion-weighted magnetic resonance imaging is predictive of kidney function decline and dialysis initiation in chronic kidney disease (CKD) and patients with a kidney allograft in a prospective study encompassing 197 patients. We measured ΔADC in 43 patients with CKD (estimated GFR (eGFR) 55ml/min/1.73m²) and 154 patients with a kidney allograft (eGFR 53ml/min/1.73m²). Patients underwent a kidney biopsy and diffusion-weighted magnetic resonance imaging within one week of biopsy; median follow-up of 2.2 years with measured laboratory parameters. The primary outcome was a rapid decline of kidney function (eGFR decline over 30% or dialysis initiation) during follow up. Significantly, patients with a negative ΔADC had 5.4 times more risk of rapid decline of kidney function or dialysis (95% confidence interval: 2.29-12.58). After correction for kidney function at baseline and proteinuria, low ADC still predicted significant kidney function loss with a hazard ratio of 4.62 (95% confidence interval 1.56-13.67) independent of baseline age, sex, eGFR and proteinuria. Thus, low ΔADC can be a predictor of kidney function decline and dialysis initiation in patients with native kidney disease or kidney allograft, independent of baseline kidney function and proteinuria.

Systematic reporting of medical kidney biopsies

The medical kidney biopsy has an important added value in patient care in nephrology. In order to facilitate communication between the pathologist and the nephrologist and optimize patient care, both the content and form of the medical kidney biopsy report matter. With some exceptions, current guidelines in nephropathology focus on content rather than form and, not surprisingly, medical kidney biopsy reports mostly consist of unformatted and often lengthy free text. In contrast, in oncology, a more systematic reporting called synoptic reporting has become the dominant method. Synoptic formats enable complete, concise and clear reports that comply with agreed upon standards. In this review we discuss the possibilities of systematic reporting in nephropathology (including synoptic reporting). Furthermore, we explore applications of electronic formats with structured data and usage of international terminologies or coding systems. The benefits include the timely collection of high-quality data for benchmarking between centres as well as for epidemiologic and other research studies. Based on these developments, a scenario for future medical kidney biopsy reporting is drafted.

A Higher Foci Density of Interstitial Fibrosis and Tubular Atrophy Predicts Progressive CKD after a Radical Nephrectomy for Tumor

BACKGROUND

Chronic tubulointerstitial injury on kidney biopsy is usually quantified by the percentage of cortex with interstitial fibrosis/tubular atrophy (IF/TA). Whether other patterns of IF/TA or inflammation in the tubulointerstitium have prognostic importance beyond percentage IF/TA is unclear.

METHODS

We obtained, stained, and digitally scanned full cortical thickness wedge sections of renal parenchyma from patients who underwent a radical nephrectomy for a tumor over 2000-2015, and morphometrically analyzed the tubulointerstitium of the cortex for percentage IF/TA, IF/TA density (foci per mm² cortex), percentage subcapsular IF/TA, striped IF/TA, percentage inflammation (both within and outside IF/TA regions), and percentage subcapsular inflammation. Patients were followed with visits every 6-12 months. Progressive CKD was defined as dialysis, kidney transplantation, or 40% decline from the postnephrectomy eGFR. Cox models assessed the risk of CKD or noncancer mortality with morphometric measures of tubulointerstitial injury after adjustment for the percentage IF/TA and clinical characteristics.

RESULTS

Among 936 patients (mean age, 64 years; postnephrectomy baseline eGFR, 48 ml/min per 1.73m²), 117 progressive CKD events and 183 noncancer deaths occurred over a median 6.4 years. Higher IF/TA density predicted both progressive CKD and noncancer mortality after adjustment for percentage IF/TA and predicted progressive CKD after further adjustment for clinical characteristics. Independent of percentage IF/TA, age, and sex, higher IF/TA density correlated with lower eGFR, smaller nonsclerosed glomeruli, more global glomerulosclerosis, and smaller total cortical volume.

CONCLUSIONS

Higher density of IF/TA foci (a more scattered pattern with more and smaller foci) predicts higher risk of progressive CKD after radical nephrectomy compared with the same percentage of IF/TA but with fewer and larger foci.

Elastography ultrasound with machine learning improves the diagnostic performance of traditional ultrasound in predicting kidney fibrosis

BACKGROUND

Noninvasively predicting kidney tubulointerstitial fibrosis is important because it's closely correlated with the development and prognosis of chronic kidney disease (CKD). Most studies of shear wave elastography (SWE) in CKD were limited to non-linear statistical dependencies and didn't fully consider variables' interactions. Therefore, support vector machine (SVM) of machine learning was used to assess the prediction value of SWE and traditional ultrasound techniques in kidney fibrosis.

METHODS

We consecutively recruited 117 CKD patients with kidney biopsy. SWE, B-mode, color Doppler flow imaging ultrasound and hematological exams were performed on the day of kidney biopsy. Kidney tubulointerstitial fibrosis was graded by semi-quantification of Masson staining. The diagnostic performances were accessed by ROC analysis.

RESULTS

Tubulointerstitial fibrosis area was significantly correlated with eGFR among CKD patients (R = 0.450, P < 0.001). AUC of SWE, combined with B-mode and blood flow ultrasound by SVM, was 0.8303 (sensitivity, 77.19%; specificity, 71.67%) for diagnosing tubulointerstitial fibrosis (>10%), higher than either traditional ultrasound, or SWE (AUC, 0.6735 [sensitivity, 67.74%; specificity, 65.45%]; 0.5391 [sensitivity, 55.56%; specificity, 53.33%] respectively. Delong test, p < 0.05); For diagnosing different grades of tubulointerstitial fibrosis, SWE combined with traditional ultrasound by SVM, had AUCs of 0.6429 for mild tubulointerstitial fibrosis (11%-25%), and 0.9431 for moderate to severe tubulointerstitial fibrosis (>50%), higher than other methods (Delong test, p < 0.05).

CONCLUSION

SWE with SVM modeling could improve the diagnostic performance of traditional kidney ultrasound in predicting different kidney tubulointerstitial fibrosis grades among CKD patients.

Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies

Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA estimation using computational techniques can reduce this variability and provide quantitative assessment. Using trichrome-stained whole-slide images (WSIs) processed from human renal biopsies, we developed a deep-learning framework that captured finer pathologic structures at high resolution and overall context at the WSI level to predict IFTA grade. WSIs (n = 67) were obtained from The Ohio State University Wexner Medical Center. Five nephropathologists independently reviewed them and provided fibrosis scores that were converted to IFTA grades: ≤10% (none or minimal), 11% to 25% (mild), 26% to 50% (moderate), and >50% (severe). The model was developed by associating the WSIs with the IFTA grade determined by majority voting (reference estimate). Model performance was evaluated on WSIs (n = 28) obtained from the Kidney Precision Medicine Project. There was good agreement on the IFTA grading between the pathologists and the reference estimate (κ = 0.622 ± 0.071). The accuracy of the deep-learning model was 71.8% ± 5.3% on The Ohio State University Wexner Medical Center and 65.0% ± 4.2% on Kidney Precision Medicine Project data sets. Our approach to analyzing microscopic- and WSI-level changes in renal biopsies attempts to mimic the pathologist and provides a regional and contextual estimation of IFTA. Such methods can assist clinicopathologic diagnosis.

Image Analysis Pipeline for Renal Allograft Evaluation and Fibrosis Quantification

INTRODUCTION

Digital pathology improves the standardization and reproducibility of kidney biopsy specimen assessment. We developed a pipeline allowing the analysis of many images without requiring human preprocessing and illustrate its use with a simple algorithm for quantification of interstitial fibrosis on a large dataset of kidney allograft biopsy specimens.

METHODS

Masson trichrome-stained images from kidney allograft biopsy specimens were used to train and validate a glomeruli detection algorithm using a VGG19 convolutional neural network and an automatic cortical region of interest (ROI) selection algorithm including cortical regions containing all predicted glomeruli. A positive-pixel count algorithm was used to quantify interstitial fibrosis on the ROIs and the association between automatic fibrosis and pathologist evaluation, estimated glomerular filtration rate (GFR) and allograft survival was assessed.

RESULTS

The glomeruli detection (F1 score of 0.87) and ROIs selection (F1 score 0.83 [SD 0.13]) algorithms displayed high accuracy. The correlation between the automatic fibrosis quantification on manually and automatically selected ROIs was high (r = 1.00 [0.99-1.00]). Automatic fibrosis quantification was only moderately correlated with pathologists' assessment and was not significantly associated with eGFR or allograft survival.

CONCLUSION

This pipeline can automatically and accurately detect glomeruli and select cortical ROIs that can easily be used to develop, validate, and apply image analysis algorithms.

A 2020 Banff Antibody-mediatedInjury Working Group examination of international practices for diagnosing antibody-mediated rejection in kidney transplantation - a cohort study

The Banff antibody-mediated rejection (ABMR) classification is vulnerable to misinterpretation, but the reasons are unclear. To better understand this vulnerability, we evaluated how ABMR is diagnosed in practice. To do this, the Banff Antibody-Mediated Injury Workgroup electronically surveyed an international cohort of nephrologists/surgeons (n = 133) and renal pathologists (n = 99). Most providers (97%) responded that they use the Banff ABMR classification at least sometimes, but DSA information is often not readily available. Only 41.1% (55/133) of nephrologists/surgeons and 19.2% (19/99) of pathologists reported that they always have DSA results when the biopsy is available. Additionally, only 19.6% (26/133) of nephrologists/surgeons responded that non-HLA antibody or molecular transcripts are obtained when ABMR histologic features are present but DSA is undetected. Several respondents agreed that histologic features concerning for ABMR in the absence of DSA and/or C4d are not well accounted for in the current classification [31.3% (31/99) pathologists and 37.6% (50/133) nephrologist/surgeons]. The Banff ABMR classification appears widely accepted, but efforts to improve the accessibility of DSA information for the multidisciplinary care team are needed. Further clarity is also needed in Banff ABMR nomenclature to account for the spectrum of ABMR and for histologic features suspicious for ABMR when DSA is absent.

Renal Injuries in Primary Aldosteronism: Quantitative Histopathological Analysis of 19 Patients With Primary Adosteronism

[Figure: see text].

Mesenchymal Stem Cells Attenuate Renal Fibrosis via Exosomes-Mediated Delivery of microRNA Let-7i-5p Antagomir

Background

Renal fibrosis is a chronic and progressive process affecting kidneys in chronic kidney disease (CKD). Mesenchymal stem cells-derived exosomes (MSCs-Exo) have been shown to alleviate renal fibrosis and injury, but the mechanism of MSCs-Exo-induced renal protection remains unknown.

Methods

In this study, MSCs were transfected with let-7i-5p antagomir (anti-let-7i-5p), and then exosomes were isolated from the transfected MSCs to deliver anti-let-7i-5p oligonucleotides to inhibit the level of let-7i-5p in kidney tubular epithelial cells (NRK-52E).

Results

In both NRK-52E cells stimulated by TGF-β1 and the mouse kidneys after unilateral ureteral obstruction (UUO), we demonstrated increased level of let-7i-5p. In addition, MSCs-Exo can deliver anti-let-7i-5p to reduce the level of let-7i-5p in NRK-52E cells and increase the expression of its target gene TSC1. Moreover, exosomal anti-let-7i-5p reduced extracellular matrix (ECM) deposition and attenuated epithelial-mesenchymal transition (EMT) process in transforming growth factor beta 1 (TGF-β1)-stimulated NRK-52E cells and in the kidneys of UUO-treated mice. Meanwhile, mice received exosomal anti-let-7i-5p displayed reduced renal fibrosis and improved kidney function when challenged with UUO. Furthermore, exosomal anti-let-7i-5p promoted the activation the tuberous sclerosis complex subunit 1/mammalian target of rapamycin (TSC1/mTOR) signaling pathway in vivo and in vitro.

Conclusion

In conclusion, exosomal anti-let-7i-5p from MSCs exerts anti-fibrotic effects in TGF-β1-induced fibrogenic responses in NRK52E cells in vitro as well as in UUO-induced renal fibrosis model in vivo. These results provided a novel perspective on improving renal fibrosis by MSCs-Exo.

Development and Validation of a Deep Learning Model to Quantify Interstitial Fibrosis and Tubular Atrophy From Kidney Ultrasonography Images

IMPORTANCE

Interstitial fibrosis and tubular atrophy (IFTA) is a strong indicator of decline in kidney function and is measured using histopathological assessment of kidney biopsy core. At present, a noninvasive test to assess IFTA is not available.

OBJECTIVE

To develop and validate a deep learning (DL) algorithm to quantify IFTA from kidney ultrasonography images.

DESIGN, SETTING, AND PARTICIPANTS

This was a single-center diagnostic study of consecutive patients who underwent native kidney biopsy at John H. Stroger Jr. Hospital of Cook County, Chicago, Illinois, between January 1, 2014, and December 31, 2018. A DL algorithm was trained, validated, and tested to classify IFTA from kidney ultrasonography images. Of 6135 Crimmins-filtered ultrasonography images, 5523 were used for training (5122 images) and validation (401 images), and 612 were used to test the accuracy of the DL system. Kidney segmentation was performed using the UNet architecture, and classification was performed using a convolution neural network-based feature extractor and extreme gradient boosting. IFTA scored by a nephropathologist on trichrome stained kidney biopsy slide was used as the reference standard. IFTA was divided into 4 grades (grade 1, 0%-24%; grade 2, 25%-49%; grade 3, 50%-74%; and grade 4, 75%-100%). Data analysis was performed from December 2019 to May 2020.

MAIN OUTCOMES AND MEASURES

Prediction of IFTA grade was measured using the metrics precision, recall, accuracy, and F1 score.

RESULTS

This study included 352 patients (mean [SD] age 47.43 [14.37] years), of whom 193 (54.82%) were women. There were 159 patients with IFTA grade 1 (2701 ultrasonography images), 74 patients with IFTA grade 2 (1239 ultrasonography images), 41 patients with IFTA grade 3 (701 ultrasonography images), and 78 patients with IFTA grade 4 (1494 ultrasonography images). Kidney ultrasonography images were segmented with 91% accuracy. In the independent test set, the point estimates for performance matrices showed precision of 0.8927 (95% CI, 0.8682-0.9172), recall of 0.8037 (95% CI, 0.7722-0.8352), accuracy of 0.8675 (95% CI, 0.8406-0.8944), and an F1 score of 0.8389 (95% CI, 0.8098-0.8680) at the image level. Corresponding estimates at the patient level were precision of 0.9003 (95% CI, 0.8644-0.9362), recall of 0.8421 (95% CI, 0.7984-0.8858), accuracy of 0.8955 (95% CI, 0.8589-0.9321), and an F1 score of 0.8639 (95% CI, 0.8228-0.9049). Accuracy at the patient level was highest for IFTA grade 1 and IFTA grade 4. The accuracy (approximately 90%) remained high irrespective of the timing of ultrasonography studies and the biopsy diagnosis. The predictive performance of the DL system did not show significant improvement when combined with baseline clinical characteristics.

CONCLUSIONS AND RELEVANCE

These findings suggest that a DL algorithm can accurately and independently predict IFTA from kidney ultrasonography images.

Renal SPECT/CT with 99mTc-dimercaptosuccinic acid is a non-invasive predictive marker for the development of interstitial fibrosis in a rat model of renal insufficiency

BACKGROUND

Chronic kidney disease (CKD) increases cardiovascular risk and mortality. Renal fibrosis plays a major role in the progression of CKD but, to date, histology remains the gold standard to assess fibrosis. Non-invasive techniques are needed to assess renal parenchymal impairment and to perform the longitudinal evaluation of renal structure. Thus we evaluated renal isotopic imaging by single-photon emission computed tomography/computed tomography (SPECT/CT) with technetium-99m (99mTc)-dimercaptosuccinic acid (DMSA) to monitor renal impairment during renal insufficiency in rats.

METHODS

Renal insufficiency was induced by an adenine-rich diet (ARD) at 0.25 and 0.5% for 28 days. Renal dysfunction was evaluated by assaying biochemical markers and renal histology. Renal parenchymal impairment was assessed by SPECT/CT isotopic imaging with 99mTc-DMSA on Days 0, 7, 14, 21, 28, 35 and 49.

RESULTS

Compared with controls, ARD rats developed renal dysfunction characterized by increased serum creatinine and blood urea nitrogen, fibrosis and tubulointerstitial damage in the kidneys, with a dose-dependent effect of the adenine concentration. 99mTc-DMSA SPECT-CT imaging showed a significant decrease in renal uptake over time in 0.25 and 0.5% ARD rats compared with control rats (P = 0.011 and P = 0.0004, respectively). 99mTc-DMSA uptake on Day 28 was significantly inversely correlated with Sirius red staining evaluated on Day 49 (r = 0.89, P < 0.0001, R2 = 0.67).

CONCLUSIONS

99mTc-DMSA renal scintigraphy allows a longitudinal follow-up of risk of renal fibrosis in rats. We found that the reduction of renal parenchyma in ARD rats is inversely proportional to newly formed fibrous tissue in the kidney. Our results suggest that 99mTc-DMSA renal scintigraphy may be a useful non-invasive prognostic marker of the development of renal fibrosis in animals and should be tested in humans.

Machine learning in medicine: Medical droids, tricorders, and a computer named Hal 9000

The usage of artificial intelligence to evaluate histological images was recently explored in many different areas of pathology. Studies focusing on nephropathology demonstrated that algorithms could be trained to identify various structures of the kidney, like glomeruli and interstitium, as well as performing a classification task just as good as highly experienced pathologists. It is conceivable that further development of digitalized pathology in combination with all opportunities that artificial intelligence and machine learning have to offer, will rapidly change the work of the clinical pathologist in a substantial way.

Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis

BACKGROUND

Interstitial fibrosis, tubular atrophy (IFTA), and glomerulosclerosis are indicators of irrecoverable kidney injury. Modern machine learning (ML) tools have enabled robust, automated identification of image structures that can be comparable with analysis by human experts. ML algorithms were developed and tested for the ability to replicate the detection and quantification of IFTA and glomerulosclerosis that renal pathologists perform.

METHODS

A renal pathologist annotated renal biopsy specimens from 116 whole-slide images (WSIs) for IFTA and glomerulosclerosis. A total of 79 WSIs were used for training different configurations of a convolutional neural network (CNN), and 17 and 20 WSIs were used as internal and external testing cases, respectively. The best model was compared against the input of four renal pathologists on 20 new testing slides. Further, for 87 testing biopsy specimens, IFTA and glomerulosclerosis measurements made by pathologists and the CNN were correlated to patient outcome using classic statistical tools.

RESULTS

The best average performance across all image classes came from a DeepLab version 2 network trained at 40× magnification. IFTA and glomerulosclerosis percentages derived from this CNN achieved high levels of agreement with four renal pathologists. The pathologist- and CNN-based analyses of IFTA and glomerulosclerosis showed statistically significant and equivalent correlation with all patient-outcome variables.

CONCLUSIONS

ML algorithms can be trained to replicate the IFTA and glomerulosclerosis assessment performed by renal pathologists. This suggests computational methods may be able to provide a standardized approach to evaluate the extent of chronic kidney injury in situations in which renal-pathologist time is restricted or unavailable.

Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples

Whole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be increased interest in and implementation of image analysis (IA) techniques. IA includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, the number of citations related to these topics has increased in recent years. Renal pathology is one anatomical pathology subspecialty that has utilised WSIs and IA algorithms; it can be argued that renal transplant pathology could be particularly suited for whole slide imaging and IA, as renal transplant pathology is frequently classified by use of the semiquantitative Banff classification of renal allograft pathology. Hypothesis-driven/targeted algorithms have been used in the past for the assessment of a variety of features in the kidney (e.g. interstitial fibrosis, tubular atrophy, inflammation); in recent years, the amount of research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histological segmentation, and for other applications. Deep learning is the form of machine learning that is most often used for such AI approaches to the 'big data' of pathology WSIs, and deep learning methods such as artificial neural networks (ANNs)/convolutional neural networks (CNNs) are utilised. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other IA algorithms applied to WSIs are discussed, and examples from renal pathology are covered, with an emphasis on renal transplant pathology.

Kidney Allograft Fibrosis: Diagnostic and Therapeutic Strategies

Interstitial fibrosis with tubule atrophy (IF/TA) is the response to virtually any sustained kidney injury and correlates inversely with kidney function and allograft survival. IF/TA is driven by various pathways that include hypoxia, renin-angiotensin-aldosterone system, transforming growth factor (TGF)-β signaling, cellular rejection, inflammation and others. In this review we will focus on key pathways in the progress of renal fibrosis, diagnosis and therapy of allograft fibrosis. This review discusses the role and origin of myofibroblasts as matrix producing cells and therapeutic targets in renal fibrosis with a particular focus on renal allografts. We summarize current trends to use multi-omic approaches to identify new biomarkers for IF/TA detection and to predict allograft survival. Furthermore, we review current imaging strategies that might help to identify and follow-up IF/TA complementary or as alternative to invasive biopsies. We further discuss current clinical trials and therapeutic strategies to treat kidney fibrosis.Supplemental Visual Abstract; http://links.lww.com/TP/C141.

Metformin Attenuates Renal Fibrosis in a Mouse Model of Adenine-Induced Renal Injury Through Inhibiting TGF-β1 Signaling Pathways

It is well-known that all progressive chronic kidney disease (CKD) is pathologically characterized by tubulointerstitial fibrosis process. Multiple studies have shown the critical role of inflammation and fibrosis in the development of CKD. Hence strategies that target inflammatory and fibrotic signaling pathways may provide promising opportunities to protect against renal fibrosis. Metformin has been used as the first-line glucose-lowering agent to treat patients with type 2 diabetes mellitus (T2DM) for over 50 years. Accumulating evidence suggests the potential for additional therapeutic applications of metformin, including mitigation of renal fibrosis. In this study, the anti-fibrotic effects of metformin independent of its glucose-lowering mechanism were examined in an adenine -induced mouse model of CKD. Expressions of inflammatory markers MCP-1, F4/80 and ICAM, fibrotic markers type IV collagen and fibronectin, and the cytokine TGF-β1 were increased in adenine-induced CKD when compared to control groups and significantly attenuated by metformin treatment. Moreover, treatment with metformin inhibited the phosphorylation of Smad3, ERK1/2, and P38 and was associated with activation of the AMP-activated protein kinase (AMPK) in the kidneys of adenine-treated mice. These results indicate that metformin attenuates adenine-induced renal fibrosis through inhibition of TGF-β1 signaling pathways and activation of AMPK, independent of its glucose-lowering action.