Rationale and design of the Japanese Biomarkers in Nephrotic Syndrome (J-MARINE) study.

INTRODUCTION: Disease subtyping and monitoring are essential for the management of nephrotic syndrome (NS). Although various biomarkers for NS have been reported, their clinical efficacy has not been comprehensively validated in adult Japanese patients. METHODS: The Japanese Biomarkers in Nephrotic Syndrome (J-MARINE) study is a nationwide, multicenter, and prospective cohort study in Japan, enrolling adult (≥18 years) patients with minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), membranoproliferative glomerulonephritis (MPGN), C3 glomerulopathy (C3G), and lupus nephritis (LN). Baseline clinical information and plasma and urine samples will be collected at the time of immunosuppressive therapy initiation or biopsy. Follow-up data and plasma and urine samples will be collected longitudinally based on the designated protocols. Candidate biomarkers will be measured: CD80, cytotoxic T-lymphocyte antigen 4, and soluble urokinase plasminogen activator receptor for MCD and FSGS; anti-phospholipase A2 receptor and thrombospondin type-1 domain-containing protein 7A antibodies for MN; fragment Ba, C3a, factor I, and properdin for MPGN/C3G; and CD11b, CD16b, and CD163 for LN. Outcomes include complete and partial remission, relapse of proteinuria, a 30% reduction in estimated glomerular filtration rate (eGFR), eGFR decline, and initiation of renal replacement therapy. The diagnostic accuracy and predictive ability for clinical outcomes will be assessed for each biomarker. RESULTS: From April 2019 to April 2023, 365 patients were enrolled: 145, 21, 138, 10, and 51 cases of MCD, FSGS, MN, MPGN/C3G, and LN, respectively. CONCLUSION: This study will provide valuable insights into biomarkers for NS and serve as a biorepository for future studies.

A case of non-traumatic rectus sheath hematoma in a post-kidney transplant patient undergoing catheter embolization during anticoagulation treatment for atrial fibrillation.

A 65-year-old man, a post living donor kidney transplant patient, was admitted to the intensive care unit (ICU) with a severe bacterial infection. He also tested positive for coronavirus disease and had a cough. On admission, heparin was administered for atrial fibrillation. On the third day of hospitalization, his general condition had recovered, and he was discharged from the ICU to the general ward. On the fourth day of hospitalization, he experienced abdominal pain, and a hard mass was palpated in the left lower abdomen. On the fifth day of hospitalization, contrast-enhanced computed tomography showed an extensive rectus sheath hematoma (RSH) extending from the left lower abdominal wall to the left side of the bladder, with extravasation from a small branch of the left inferior epigastric artery. Heparin was discontinued, and transcatheter arterial embolization was performed to control the bleeding. RSH is a rare disease, and cases of extensive hematoma in post-kidney transplant patients occur even less frequently. Patients taking anticoagulants and those with chronic kidney disease are at high risk for RSH, so physicians should be cognizant of this disease when these patients develop abdominal pain.

CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models.

Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder (http://cohortfinder.com), an open-source tool aimed at mitigating BEs via data-driven cohort partitioning. We demonstrate CohortFinder improves ML model performance in downstream digital pathology and medical image processing tasks. CohortFinder is freely available for download at cohortfinder.com.

Clinical Relevance of Computationally Derived Tubular Features: Spatial Relationships and the Development of Tubulointerstitial Scarring in MCD/FSGS.

Background: Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. We investigate if computationally quantified tubular features can enhance prognostication and reveal spatial relationships with interstitial fibrosis. Methods: Deep-learning and image-processing-based segmentations were employed in N=254/266 PAS-WSIs from the NEPTUNE/CureGN datasets (135/153 focal segmental glomerulosclerosis and 119/113 minimal change disease) for: cortex, tubular lumen (TL), epithelium (TE), nuclei (TN), and basement membrane (TBM). N=104 pathomic features were extracted from these segmented tubular substructures and summarized at the patient level using summary statistics. The tubular features were quantified across the biopsy and in manually segmented regions of mature interstitial fibrosis and tubular atrophy (IFTA), pre-IFTA and non-IFTA in the NEPTUNE dataset. Minimum Redundancy Maximum Relevance was used in the NEPTUNE dataset to select features most associated with disease progression and proteinuria remission. Ridge-penalized Cox models evaluated their predictive discrimination compared to clinical/demographic data and visual-assessment. Models were evaluated in the CureGN dataset. Results: N=9 features were predictive of disease progression and/or proteinuria remission. Models with tubular features had high prognostic accuracy in both NEPTUNE and CureGN datasets and increased prognostic accuracy for both outcomes (5.6%-7.7% and 1.6%-4.6% increase for disease progression and proteinuria remission, respectively) compared to conventional parameters alone in the NEPTUNE dataset. TBM thickness/area and TE simplification progressively increased from non- to pre- and mature IFTA. Conclusions: Previously under-recognized, quantifiable, and clinically relevant tubular features in the kidney parenchyma can enhance understanding of mechanisms of disease progression and risk stratification.