Interpretable machine learning-based individual analysis of acute kidney injury in immune checkpoint inhibitor therapy.

BACKGROUND: Acute kidney injury (AKI) is a critical complication of immune checkpoint inhibitor therapy. Since the etiology of AKI in patients undergoing cancer therapy varies, clarifying underlying causes in individual cases is critical for optimal cancer treatment. Although it is essential to individually analyze immune checkpoint inhibitor-treated patients for underlying pathologies for each AKI episode, these analyses have not been realized. Herein, we aimed to individually clarify the underlying causes of AKI in immune checkpoint inhibitor-treated patients using a new clustering approach with Shapley Additive exPlanations (SHAP). METHODS: We developed a gradient-boosting decision tree-based machine learning model continuously predicting AKI within 7 days, using the medical records of 616 immune checkpoint inhibitor-treated patients. The temporal changes in individual predictive reasoning in AKI prediction models represented the key features contributing to each AKI prediction and clustered AKI patients based on the features with high predictive contribution quantified in time series by SHAP. We searched for common clinical backgrounds of AKI patients in each cluster, compared with annotation by three nephrologists. RESULTS: One hundred and twelve patients (18.2%) had at least one AKI episode. They were clustered per the key feature, and their SHAP value patterns, and the nephrologists assessed the clusters' clinical relevance. Receiver operating characteristic analysis revealed that the area under the curve was 0.880. Patients with AKI were categorized into four clusters with significant prognostic differences (p = 0.010). The leading causes of AKI for each cluster, such as hypovolemia, drug-related, and cancer cachexia, were all clinically interpretable, which conventional approaches cannot obtain. CONCLUSION: Our results suggest that the clustering method of individual predictive reasoning in machine learning models can be applied to infer clinically critical factors for developing each episode of AKI among patients with multiple AKI risk factors, such as immune checkpoint inhibitor-treated patients.

Proteinuria frequency and subsequent renal dysfunction in bevacizumab-treated patients: a single center, retrospective, observational study.

BACKGROUND: Proteinuria is a common adverse event observed during treatment with antivascular endothelial growth factor (VEGF) antibodies. Proteinuria is a risk factor for renal dysfunction and cardiovascular complications in patients with chronic kidney disease. However, the association between anti-VEGF antibody-induced proteinuria and renal dysfunction or cardiovascular complications remains unclear. METHODS: This retrospective, observational study included patients with cancer that were treated with bevacizumab (BV) at Kyoto University Hospital (Kyoto, Japan) between January 2006 and March 2018. Adverse event rates were compared between patients who developed qualitative ≥ 2 + proteinuria and those who developed < 1 + proteinuria. Adverse events were defined as renal dysfunction (i.e., ≥ 57% decrease in the eGFR, compared to the rate at the initial treatment) and hospitalization due to BV-associated cardiovascular complications and other adverse events. RESULTS: In total, 734 patients were included in this analysis. Renal dysfunction was more common in patients with ≥ 2 + proteinuria than in those with < 1 + proteinuria (13/199, 6.5% vs. 12/535, 2.3%). Seven of these 13 patients with ≥ 2 + proteinuria had transient reversible renal dysfunction. Only four (2.0%) patients had BV-associated renal dysfunction. Of the 734 patients, six patients, 16 patients, and 13 patients were hospitalized because of the adverse events of cardiovascular complications, thromboembolisms, and cerebrovascular complications, respectively. No relationship was observed between these adverse events and proteinuria. CONCLUSION: BV treatment-induced proteinuria was not associated with renal dysfunction or other adverse events. Continuing BV with caution is a possible treatment option, even after proteinuria develops, in patients with cancer and a limited prognosis.

Individual health-disease phase diagrams for disease prevention based on machine learning.

Early disease detection and prevention methods based on effective interventions are gaining attention worldwide. Progress in precision medicine has revealed that substantial heterogeneity exists in health data at the individual level and that complex health factors are involved in chronic disease development. Machine-learning techniques have enabled precise personal-level disease prediction by capturing individual differences in multivariate data. However, it is challenging to identify what aspects should be improved for disease prevention based on future disease-onset prediction because of the complex relationships among multiple biomarkers. Here, we present a health-disease phase diagram (HDPD) that represents an individual's health state by visualizing the future-onset boundary values of multiple biomarkers that fluctuate early in the disease progression process. In HDPDs, future-onset predictions are represented by perturbing multiple biomarker values while accounting for dependencies among variables. We constructed HDPDs for 11 diseases using longitudinal health checkup cohort data of 3,238 individuals, comprising 3,215 measurement items and genetic data. The improvement of biomarker values to the non-onset region in HDPD remarkably prevented future disease onset in 7 out of 11 diseases. HDPDs can represent individual physiological states in the onset process and be used as intervention goals for disease prevention.

Use of proton pump inhibitors and macrolide antibiotics and risk of acute kidney injury: a self-controlled case series study.

BACKGROUND: Proton pump inhibitors (PPIs) are widely used for the treatment of gastrointestinal disorders such as peptic ulcer disease and dyspepsia. However, several studies have suggested that PPI use increases the risk of acute kidney injury (AKI). PPIs are often concomitantly used with antibiotics, such as macrolides and penicillins for Helicobacter pylori eradication. Although macrolide antibiotics are considered to have relatively low nephrotoxicity, they are well known to increase the risk of AKI due to drug-drug interactions. In this study, we aimed to investigate the association between PPI use and the development of AKI. We also evaluated the effect of concomitant use of PPIs and macrolide antibiotics on the risk of AKI. METHODS: This self-controlled case series study was conducted using electronic medical records at Kyoto University Hospital. We identified patients who were prescribed at least one PPI and macrolide antibiotic between January 2014 and December 2019 and underwent blood examinations at least once a year. An adjusted incident rate ratio (aIRR) of AKI with PPI use or concomitant use macrolide antibiotics with PPIs was estimated using a conditional Poisson regression model controlled for the estimated glomerular filtration rate at the beginning of observation and use of potentially nephrotoxic antibiotics. RESULTS: Of the 3,685 individuals who received PPIs and macrolide antibiotics, 766 patients with episodes of stage 1 or higher AKI were identified. Any stage of AKI was associated with PPI use (aIRR, 1.80 (95% confidence interval (CI) 1.60 to 2.04)). Stage 2 or higher AKI was observed in 279 cases, with an estimated aIRR of 2.01 (95% CI 1.57 to 2.58, for PPI use). For the period of concomitant use of macrolide antibiotics with PPIs compared with the period of PPIs alone, an aIRR of stage 1 or higher AKI was estimated as 0.82 (95% CI 0.60 to 1.13). CONCLUSIONS: Our findings added epidemiological information for the association between PPI use and an increased risk of stage 1 or higher AKI. However, we did not detect an association between the concomitant use of macrolide antibiotics and an increased risk of AKI in PPI users.

Robust prognostic prediction model developed with integrated biological markers for acute myocardial infarction.

Commonly used prediction methods for acute myocardial infarction (AMI) were created before contemporary percutaneous coronary intervention was recognized as the primary therapy. Although several studies have used machine learning techniques for prognostic prediction of patients with AMI, its clinical application has not been achieved. Here, we developed an online application tool using a machine learning model to predict in-hospital mortality in patients with AMI. A total of 2,553 cases of ST-elevation AMI were assigned to 80% training subset for cross validation and 20% test subset for model performance evaluation. We implemented random forest classifier for the binary classification of in-hospital mortality. The selected best feature set consisted of ten clinical and biological markers including max creatine phosphokinase, hemoglobin, heart rate, creatinine, systolic blood pressure, blood sugar, age, Killip class, white blood cells, and c-reactive protein. Our model achieved high performance: the area under the curve of the receiver operating characteristic curve for the test subset, 0.95: sensitivity, 0.89: specificity, 0.91: precision, 0.43: accuracy, 0.91 respectively, which outperformed common scoring methods. The freely available application tool for prognostic prediction can contribute to risk triage and decision-making in patient-centered modern clinical practice for AMI.

Lineage tracing analysis defines erythropoietin-producing cells as a distinct subpopulation of resident fibroblasts with unique behaviors.

Erythropoietin (Epo) is produced by a subpopulation of resident fibroblasts in the healthy kidney. We have previously demonstrated that, during kidney fibrosis, kidney fibroblasts including Epo-producing cells transdifferentiate into myofibroblasts and lose their Epo-producing ability. However, it remains unclear whether Epo-producing cells survive and transform into myofibroblasts during fibrosis because previous studies did not specifically label Epo-producing cells in pathophysiological conditions. Here, we generated EpoCreERT2/+ mice, a novel mouse strain that enables labeling of Epo-producing cells at desired time points and examined the behaviors of Epo-producing cells under pathophysiological conditions. Lineage-labeled cells that were producing Epo when labeled were found to be a small subpopulation of fibroblasts located in the interstitium of the kidney, and their number increased during phlebotomy-induced anemia. Around half of lineage-labeled cells expressed Epo mRNA, and this percentage was maintained even 16 weeks after recombination, supporting the idea that a distinct subpopulation of cells with Epo-producing ability makes Epo repeatedly. During fibrosis caused by ureteral obstruction, EpoCreERT2/+-labeled cells were found to transdifferentiate into myofibroblasts with concomitant loss of Epo-producing ability, and their numbers and the proportion among resident fibroblasts increased during fibrosis, indicating their high proliferative capacity. Finally, we confirmed that EpoCreERT2/+-labeled cells that lost their Epo-producing ability during fibrosis regained their ability after kidney repair due to relief of the ureteral obstruction. Thus, our analyses have revealed previously unappreciated characteristic behaviors of Epo-producing cells, which had not been clearly distinguished from those of resident fibroblasts.

Evaluation of Kidney Histological Images Using Unsupervised Deep Learning.

INTRODUCTION: Evaluating histopathology via machine learning has gained research and clinical interest, and the performance of supervised learning tasks has been described in various areas of medicine. Unsupervised learning of histological images has the advantage of reproducibility for labeling; however, the relationship between unsupervised evaluation and clinical information remains unclear in nephrology. METHODS: We propose an unsupervised approach combining convolutional neural networks (CNNs) and a visualization algorithm to cluster the histological images and calculate the score for patients. We applied the approach to the entire images or patched images of the glomerulus of kidney biopsy samples stained with hematoxylin and eosin obtained from 68 patients with immunoglobulin A nephropathy. We assessed the relationship between the obtained scores and clinical variables of urinary occult blood, urinary protein, serum creatinine (SCr), systolic blood pressure, and age. RESULTS: The glomeruli of the patients were classified into 12 distinct classes and 10 patches. The output of the fine-tuned CNN, which we defined as the histological scores, had significant relationships with assessed clinical variables. In addition, the clustering and visualization results suggested that the defined clusters captured important findings when evaluating renal histopathology. For the score of the patch-based cluster containing crescentic glomeruli, SCr (coefficient = 0.09, P = 0.019) had a significant relationship. CONCLUSION: The proposed approach could successfully extract features that were related to the clinical variables from the kidney biopsy images along with the visualization for interpretability. The approach could aid in the quantified evaluation of renal histopathology.

Health improvement framework for actionable treatment planning using a surrogate Bayesian model.

Clinical decision-making regarding treatments based on personal characteristics leads to effective health improvements. Machine learning (ML) has been the primary concern of diagnosis support according to comprehensive patient information. A prominent issue is the development of objective treatment processes in clinical situations. This study proposes a framework to plan treatment processes in a data-driven manner. A key point of the framework is the evaluation of the actionability for personal health improvements by using a surrogate Bayesian model in addition to a high-performance nonlinear ML model. We first evaluate the framework from the viewpoint of its methodology using a synthetic dataset. Subsequently, the framework is applied to an actual health checkup dataset comprising data from 3132 participants, to lower systolic blood pressure and risk of chronic kidney disease at the individual level. We confirm that the computed treatment processes are actionable and consistent with clinical knowledge for improving these values. We also show that the improvement processes presented by the framework can be clinically informative. These results demonstrate that our framework can contribute toward decision-making in the medical field, providing clinicians with deeper insights.

Prediction and visualization of acute kidney injury in intensive care unit using one-dimensional convolutional neural networks based on routinely collected data.

BACKGROUND: Acute kidney injury (AKI) occurs frequently in in-hospital patients, especially in the intensive care unit (ICU), due to various etiologies including septic shock. It is clinically important to identify high-risk patients at an early stage and perform the appropriate intervention. METHODS: We proposed a system to predict AKI using one-dimensional convolutional neural networks (1D-CNN) with the real-time calculation of the probability of developing AKI, along with the visualization of the rationale behind prediction using score-weighted class activation mapping and guided backpropagation. The system was applied to predicting developing AKI based on the KDIGO guideline in time windows of 24 to 48 h using data of 0 to 24 h after admission to ICU. RESULTS: The comparison result of multiple algorithms modeling time series data indicated that the proposed 1D-CNN model achieved higher performance compared to the other models, with the mean area under the receiver operating characteristic curve of 0.742 ± 0.010 for predicting stage 1, and 0.844 ± 0.029 for stage 2 AKI using the input of the vital signs, the demographic information, and serum creatinine values. The visualization results suggested the reasonable interpretation that time points with higher respiratory rate, lower blood pressure, as well as lower SpO2, had higher attention in terms of predicting AKI, and thus important for prediction. CONCLUSIONS: We presumed the proposed system's potential usefulness as it could be applied and transferred to almost any ICU setting that stored the time series data corresponding to vital signs.

Metagenomic profiling of gut microbiome in early chronic kidney disease.

BACKGROUND: The relationship between chronic kidney disease (CKD) and the gut microbiome, which interact through chronic inflammation, uraemic toxin production and immune response regulation, has gained interest in the development of CKD therapies. However, reports using shotgun metagenomic analysis of the gut microbiome are scarce, especially for early CKD. Here we characterized gut microbiome differences between non-CKD participants and ones with early CKD using metagenomic sequencing. METHODS: In total, 74 non-CKD participants and 37 participants with early CKD were included based on propensity score matching, controlling for various factors including dietary intake. Stool samples were collected from participants and subjected to shotgun sequencing. Bacterial and pathway abundances were profiled at the species level with MetaPhlAn2 and HUMAnN2, respectively, and overall microbiome differences were determined using Bray-Curtis dissimilarities. Diabetic and non-diabetic populations were analysed separately. RESULTS: For diabetic and non-diabetic participants, the mean estimated glomerular filtration rates of the CKD group were 53.71 [standard deviation (SD) 3.87] and 53.72 (SD 4.44), whereas those of the non-CKD group were 72.63 (SD 7.72) and 76.10 (SD 9.84), respectively. Alpha and beta diversities were not significantly different between groups. Based on taxonomic analysis, butyrate-producing species Roseburia inulinivorans, Ruminococcus torques and Ruminococcus lactaris were more abundant in the non-CKD group, whereas Bacteroides caccae and Bacteroides coprocora were more abundant in the non-diabetic CKD group. CONCLUSIONS: Although gut microbiome changes in individuals with early CKD were subtle, the results suggest that changes related to producing short-chain fatty acids can already be observed in early CKD.

Association between the size of healthcare facilities and the intensity of hypertension therapy: a cross-sectional comparison of prescription data from insurance claims data.

Hypertension is a heterogeneous disease for which role sharing in treatment between specialized facilities and small clinics is needed for efficient healthcare provision. However, the Japanese healthcare system has a "free access" attribute; therefore, nobody can control treatment resource allocation. We aimed to describe the current situation of role sharing by comparing antihypertensive therapies among different types of medical facilities. We analyzed 1% sampled Japanese medical insurance claims data related to outpatient care as of October 2014. We divided the target patients into four groups according to the size of the facilities that issued the insurance claim for them. Among these groups, we compared the number of antihypertensive drugs and proportion of difficult-to-treat hypertensive cases and performed a stratified analysis. The proportion of patients with hypertension and diabetes mellitus receiving renin-angiotensin-aldosterone system inhibitors (RAASis) as the first-choice drug was also compared. We identified 3465, 1797, 2323, and 34,734 claims issued from large, medium-sized, small hospitals, and clinics, respectively. The mean number of hypertensive drugs was 1.96, 1.87, 1.81, and 1.69, respectively, and the proportion of difficult-to-treat hypertensive cases was 18.9, 17.0, 14.3, and 12.0%, respectively, with both showing significant differences. Stratified analysis showed similar results. The proportion of patients with hypertension and diabetes mellitus receiving RAASis as the first-choice drug was higher in large hospitals than in clinics. In conclusion, facility size is positively associated with the number of antihypertensive drugs and proportions of difficult-to-treat hypertensive cases. This finding describes the current role sharing situation of hypertension therapy in the Japanese healthcare system with a "free-access" attribute.

Spatiotemporal ATP Dynamics during AKI Predict Renal Prognosis.

BACKGROUND: Depletion of ATP in renal tubular cells plays the central role in the pathogenesis of kidney diseases. Nevertheless, inability to visualize spatiotemporal in vivo ATP distribution and dynamics has hindered further analysis. METHODS: A novel mouse line systemically expressing an ATP biosensor (an ATP synthase subunit and two fluorophores) revealed spatiotemporal ATP dynamics at single-cell resolution during warm and cold ischemic reperfusion (IR) with two-photon microscopy. This experimental system enabled quantification of fibrosis 2 weeks after IR and assessment of the relationship between the ATP recovery in acute phase and fibrosis in chronic phase. RESULTS: Upon ischemia induction, the ATP levels of proximal tubule (PT) cells decreased to the nadir within a few minutes, whereas those of distal tubule (DT) cells decreased gradually up to 1 hour. Upon reperfusion, the recovery rate of ATP in PTs was slower with longer ischemia. In stark contrast, ATP in DTs was quickly rebounded irrespective of ischemia duration. Morphologic changes of mitochondria in the acute phase support the observation of different ATP dynamics in the two segments. Furthermore, slow and incomplete ATP recovery of PTs in the acute phase inversely correlated with fibrosis in the chronic phase. Ischemia under conditions of hypothermia resulted in more rapid and complete ATP recovery with less fibrosis, providing a proof of concept for use of hypothermia to protect kidney tissues. CONCLUSIONS: Visualizing spatiotemporal ATP dynamics during IR injury revealed higher sensitivity of PT cells to ischemia compared with DT cells in terms of energy metabolism. The ATP dynamics of PTs in AKI might provide prognostic information.

Classification of glomerular pathological findings using deep learning and nephrologist-AI collective intelligence approach.

BACKGROUND: Automated classification of glomerular pathological findings is potentially beneficial in establishing an efficient and objective diagnosis in renal pathology. While previous studies have verified the artificial intelligence (AI) models for the classification of global sclerosis and glomerular cell proliferation, there are several other glomerular pathological findings required for diagnosis, and the comprehensive models for the classification of these major findings have not yet been reported. Whether the cooperation between these AI models and clinicians improves diagnostic performance also remains unknown. Here, we developed AI models to classify glomerular images for major findings required for pathological diagnosis and investigated whether those models could improve the diagnostic performance of nephrologists. METHODS: We used a dataset of 283 kidney biopsy cases comprising 15,888 glomerular images that were annotated by a total of 25 nephrologists. AI models to classify seven pathological findings: global sclerosis, segmental sclerosis, endocapillary proliferation, mesangial matrix accumulation, mesangial cell proliferation, crescent, and basement membrane structural changes, were constructed using deep learning by fine-tuning of InceptionV3 convolutional neural network. Subsequently, we compared the agreement to truth labels between majority decision among nephrologists with or without the AI model as a voter. RESULTS: Our model for global sclerosis showed high performance (area under the curve: periodic acid-Schiff, 0.986; periodic acid methenamine silver, 0.983); the models for the other findings also showed performance close to those of nephrologists. By adding the AI model output to majority decision among nephrologists, out of the 14 constructed models, the results of the majority decision showed improvement in sensitivity for 10 models (four of them were statistically significant) and specificity for eight models (five significant). CONCLUSION: Our study showed a proof-of-concept for the classification of multiple glomerular findings in a comprehensive method of deep learning and suggested its potential effectiveness in improving diagnostic accuracy of clinicians.

The relationship between cigarette smoking and the tongue microbiome in an East Asian population.

Background: The oral microbiome, which consists of various habitats, has been shown to be influenced by smoking. However, differences in the tongue microbiomes of current and former smokers, as well as their resultant functional consequences, have rarely been investigated in East Asian populations. Methods: We used 16S rRNA amplicon sequencing of tongue-coating samples obtained from East Asian subjects who were current, former, or never smokers to identify differences in their tongue microbiomes and related metagenomic functions. Two sets of participants from 2016 to 2017 (n = 657 and n = 187, respectively) were analyzed separately. Results: We found significant differences between the overall microbiome compositions of current versus never smokers (p = 0.0015), but not between former versus never smokers (p = 0.43) based on the weighted UniFrac distance. Twenty-nine of 43 investigated genera showed significantly different expression levels in current versus never smokers. Neisseria and Capnocytophaga were less abundant, and Streptococcus and Megasphaera were more abundant in current smokers. Moreover, the abundances of metagenomic pathways, including those related to nitrate reduction and the tricarboxylic acid cycle, were significantly different between current and never smokers. Conclusions: The tongue microbiomes and related metagenomic pathways of current smokers differ from those of never smokers among East Asians.

Metagenomic analysis of bacterial species in tongue microbiome of current and never smokers.

Cigarette smoking affects the oral microbiome, which is related to various systemic diseases. While studies that investigated the relationship between smoking and the oral microbiome by 16S rRNA amplicon sequencing have been performed, investigations involving metagenomic sequences are rare. We investigated the bacterial species composition in the tongue microbiome, as well as single-nucleotide variant (SNV) profiles and gene content of these species, in never and current smokers by utilizing metagenomic sequences. Among 234 never smokers and 52 current smokers, beta diversity, as assessed by weighted UniFrac measure, differed between never and current smokers (pseudo-F = 8.44, R2 = 0.028, p = 0.001). Among the 26 species that had sufficient coverage, the SNV profiles of Actinomyces graevenitzii, Megasphaera micronuciformis, Rothia mucilaginosa, Veillonella dispar, and one Veillonella sp. were significantly different between never and current smokers. Analysis of gene and pathway content revealed that genes related to the lipopolysaccharide biosynthesis pathway in Veillonella dispar were present more frequently in current smokers. We found that species-level tongue microbiome differed between never and current smokers, and 5 species from never and current smokers likely harbor different strains, as suggested by the difference in SNV frequency.

MGeND: an integrated database for Japanese clinical and genomic information.

To promote the implementation of genomic medicine, we developed an integrated database, the Medical Genomics Japan Variant Database (MGeND). In its first release, MGeND provides data regarding genomic variations in Japanese individuals, collected by research groups in five disease fields. These variations consist of curated SNV/INDEL variants and susceptibility variants for diseases established by genome-wide association study analysis. Furthermore, we recorded the frequencies of HLA alleles in infectious disease populations.

Myofibroblasts acquire retinoic acid-producing ability during fibroblast-to-myofibroblast transition following kidney injury.

Tubular injury and interstitial fibrosis are the hallmarks of chronic kidney disease. While recent studies have verified that proximal tubular injury triggers interstitial fibrosis, the impact of fibrosis on tubular injury and regeneration remains poorly understood. We generated a novel mouse model expressing diphtheria toxin receptor on renal fibroblasts to allow for the selective disruption of renal fibroblast function. Administration of diphtheria toxin induced upregulation of the tubular injury marker Ngal and caused tubular proliferation in healthy kidneys, whereas administration of diphtheria toxin attenuated tubular regeneration in fibrotic kidneys. Microarray analysis revealed down-regulation of the retinol biosynthesis pathway in diphtheria toxin-treated kidneys. Healthy proximal tubules expressed retinaldehyde dehydrogenase 2 (RALDH2), a rate-limiting enzyme in retinoic acid biosynthesis. After injury, proximal tubules lost RALDH2 expression, whereas renal fibroblasts acquired strong expression of RALDH2 during the transition to myofibroblasts in several models of kidney injury. The retinoic acid receptor (RAR) RARγ was expressed in proximal tubules both with and without injury, and αB-crystallin, the product of an RAR target gene, was strongly expressed in proximal tubules after injury. Furthermore, BMS493, an inverse agonist of RARs, significantly attenuated tubular proliferation in vitro. In human biopsy tissue from patients with IgA nephropathy, detection of RALDH2 in the interstitium correlated with older age and lower kidney function. These results suggest a role of retinoic acid signaling and cross-talk between fibroblasts and tubular epithelial cells during tubular injury and regeneration, and may suggest a beneficial effect of fibrosis in the early response to injury.

Membranous nephropathy associated with pregnancy: an anti-phospholipase A2 receptor antibody-positive case report.

Pregnancy and membranous nephropathy (MN) can occur concurrently with nephrotic syndrome. However, the pathophysiology of MN associated with pregnancy remains unclear, including the involvement of anti-M-type phospholipase A2 receptor (PLA2R) antibody, the major antigen of idiopathic MN (iMN). A treatment for the condition is also not established. We present the case of a 43-year-old pregnant female with incidental proteinuria and hypoalbuminemia. We made a diagnosis of nephrotic syndrome at 11 week gestation. Renal biopsy revealed iMN using predominant granular staining of IgG4 along the glomerular basement membrane. No secondary cause was identified. Oral glucocorticoid therapy was started from 17 week gestation and induced complete remission at 28 week gestation. A healthy infant was born at 38 week gestation. Glucocorticoid therapy was stopped postpartum without MN relapse. Anti-PLA2R antibody was later found to be positive using serum reserved from before treatment. In conclusion, we presented the case of a pregnant woman with iMN and anti-PLA2R antibodies, whose nephrotic syndrome was successfully controlled with oral glucocorticoids to reach complete remission, even after tapering off the medication. Pregnancy per se might be associated with iMN onset.

Renal-limited vasculitis with elevated levels of multiple antibodies.

Renal-limited vasculitis (RLV) is a type of anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis that presents with crescentic glomerulonephritis with no other organ involvement. Although several studies reported patients with crescentic glomerulonephritis who were dual positive for proteinase 3 (PR3)-ANCA and myeloperoxidase (MPO)-ANCA or ANCA and anti-glomerular basement membrane (GBM) antibody, patients positive for all three antibodies, i.e., triple-positive patients, were rarely reported. We herein report the case of a male with pauci-immune type crescentic glomerulonephritis positive for MPO-ANCA, PR3-ANCA, and anti-GBM antibody. Renal biopsy led to the definitive diagnosis of RLV with pauci-immune-type crescentic glomerulonephritis. Fluorescence immunostaining showed no linear deposition of IgG on GBM, indicating no involvement of anti-GBM associated diseases. Intensive therapy, including prednisolone, plasma exchange, and intravenous cyclophosphamide, was effective. We report the case of triple-positive patient with crescentic glomerulonephritis, who was successfully treated with glucocorticoid, plasma exchange, and cyclophosphamide, suggesting that treatment for RLV in the patient with serological triple antibodies positivity in the absence of linear IgG deposition could benefit from the combination therapy regimen for plasma exchange and primary induction of remission against microscopic polyangiitis.

Relationships Between Quantitative Pulse-Echo Ultrasound Parameters from the Superficial Zone of the Human Articular Cartilage and Changes in Surface Roughness, Collagen Content or Collagen Orientation Caused by Early Degeneration.

We aimed to quantitatively investigate the relationship between amplitude-based pulse-echo ultrasound parameters and early degeneration of the knee articular cartilage. Twenty samples from six human femoral condyles judged as grade 0 or 1 according to International Cartilage Repair Society grading were assessed using a 15-MHz pulsed-ultrasound 3-D scanning system ex vivo. Surface roughness (Rq), average collagen content (A1) and collagen orientation (A12) in the superficial zone of the cartilage were measured via laser microscopy and Fourier transform infrared imaging spectroscopy. Multiple regression analysis with a linear mixed-effects model (LMM) revealed that a time-domain reflection coefficient at the cartilage surface (Rc) had a significant coefficient of determination with Rq and A12 (RLMMm2=0.79); however, Rc did not correlate with A1. Concerning the collagen characteristic in the superficial zone, Rc was found to be a sensitive indicator reflecting collagen disorganization, not collagen content, for the early degeneration samples.