Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies

Long-term, automated stool monitoring using a novel smart toilet: A feasibility study

BACKGROUND

Patients' report of bowel movement consistency is unreliable. We demonstrate the feasibility of long-term automated stool image data collection using a novel Smart Toilet and evaluate a deterministic computer-vision analytic approach to assess stool form according to the Bristol Stool Form Scale (BSFS).

METHODS

Our smart toilet integrates a conventional toilet bowl with an engineered portal to image feces in a predetermined region of the plumbing post-flush. The smart toilet was installed in a workplace bathroom and used by six healthy volunteers. Images were annotated by three experts. A computer vision method based on deep learning segmentation and mathematically defined hand-crafted features was developed to quantify morphological attributes of stool from images.

KEY RESULTS

474 bowel movements images were recorded in total from six subjects over a mean period of 10 months. 3% of images were rated abnormal with stool consistency BSFS 2 and 4% were BSFS 6. Our image analysis algorithm leverages interpretable morphological features and achieves classification of abnormal stool form with 94% accuracy, 81% sensitivity and 95% specificity.

CONCLUSIONS

Our study supports the feasibility and accuracy of long-term, non-invasive automated stool form monitoring with the novel smart toilet system which can eliminate the patient burden of tracking bowel forms.

Clinical Relevance of Computational Pathology Analysis of Interplay Between Kidney Microvasculature and Interstitial Microenvironment

BACKGROUND

Interstitial fibrosis and tubular atrophy (IFTA), and density and shape of peritubular capillaries (PTCs), are independently prognostic of disease progression. This study aimed to identify novel digital biomarkers of disease progression and assess the clinical relevance of the interplay between a variety of PTC characteristics and their microenvironment in glomerular diseases.

METHODS

A total of 344 NEPTUNE/CureGN participants were included: 112 minimal change disease, 134 focal segmental glomerulosclerosis, 61 membranous nephropathy, and 37 IgA nephropathy. A PAS-stained whole slide image per patient was manually segmented for cortex, pre- and mature IFTA. Interstitial fractional space (IFS) was computationally quantified. A deep-learning model was applied to segment PTCs. Spatial and shape PTC pathomic features (230) were extracted from the cortex, IFTA, and non-IFTA sub-regions. Participants were divided into training and testing datasets (1:1). Univariate models incorporating IFTA subregions, and IFS-PTC density were constructed. LASSO regression models were used to identify the top PTC features associated with disease progression stratified by IFTA and non-IFTA sub-regions. Machine learning models were built using the top PTC features in IFTA and non-IFTA sub-regions to prognosticate disease progression.

RESULTS

PTC density in pre+mature IFTA and IFS, shape features in pre+mature IFTA, and spatial architecture features in the non-IFTA regions associated with disease progression. The machine learning generated risk scores showed a significant association with disease progression on the independent testing set.

CONCLUSION

We uncovered previously underrecognized digital biomarkers of disease progression and the clinical relevance of the complex interplay between the status of the PTCs and the interstitial microenvironment.

The Protective Role of Intermedin in Contrast-Induced Acute Kidney Injury: Enhancing Peritubular Capillary Endothelial Cell Adhesion and Integrity Through the cAMP/Rac1 Pathway

Contrast-induced acute kidney injury (CIAKI) is a common complication with limited treatments. Intermedin (IMD), a peptide belonging to the calcitonin gene-related peptide family, promotes vasodilation and endothelial stability, but its role in mitigating CIAKI remains unexplored. This study investigates the protective effects of IMD in CIAKI, focusing on its mechanisms, particularly the cAMP/Rac1 signaling pathway. Human umbilical vein endothelial cells (HUVECs) were treated with iohexol to simulate kidney injury in vitro. The protective effects of IMD were assessed using CCK8 assay, flow cytometry, ELISA, and Western blotting. A CIAKI rat model was utilized to evaluate renal peritubular capillary endothelial cell injury and renal function through histopathology, immunohistochemistry, immunofluorescence, Western blotting, and transmission electron microscopy. In vitro, IMD significantly enhanced HUVEC viability and mitigated iohexol-induced toxicity by preserving intercellular adhesion junctions and activating the cAMP/Rac1 pathway, with Rac1 inhibition attenuating these protective effects. In vivo, CIAKI caused severe damage to peritubular capillary endothelial cell junctions, impairing renal function. IMD treatment markedly improved renal function, an effect negated by Rac1 inhibition. IMD protects against renal injury in CIAKI by activating the cAMP/Rac1 pathway, preserving peritubular capillary endothelial integrity and alleviating acute renal injury from contrast media. These findings suggest that IMD has therapeutic potential in CIAKI and highlight the cAMP/Rac1 pathway as a promising target for preventing contrast-induced acute kidney injury in at-risk patients, ultimately improving clinical outcomes.

Fenestrated Endothelial Cells across Organs: Insights into Kidney Function and Disease

In the human body, the vascular system plays an indispensable role in maintaining homeostasis by supplying oxygen and nutrients to cells and organs and facilitating the removal of metabolic waste and toxins. Blood vessels-the key constituents of the vascular system-are composed of a layer of endothelial cells on their luminal surface. In most organs, tightly packed endothelial cells serve as a barrier separating blood and lymph from surrounding tissues. Intriguingly, endothelial cells in some tissues and organs (e.g., choroid plexus, liver sinusoids, small intestines, and kidney glomerulus) form transcellular pores called fenestrations that facilitate molecular and ionic transport across the vasculature and mediate immune responses through leukocyte transmigration. However, the development and unique functions of endothelial cell fenestrations across organs are yet to be fully uncovered. This review article provides an overview of fenestrated endothelial cells in multiple organs. We describe their development and organ-specific roles, with expanded discussions on their contributions to glomerular health and disease. We extend these discussions to highlight the dynamic changes in endothelial cell fenestrations in diabetic nephropathy, focal segmental glomerulosclerosis, Alport syndrome, and preeclampsia, and how these unique cellular features could be targeted for therapeutic development. Finally, we discuss emerging technologies for in vitro modeling of biological systems, and their relevance for advancing the current understanding of endothelial cell fenestrations in health and disease.

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.

Deep learning applications for kidney histology analysis

PURPOSE OF REVIEW

Nephropathology is increasingly incorporating computational methods to enhance research and diagnostic accuracy. The widespread adoption of digital pathology, coupled with advancements in deep learning, will likely transform our pathology practices. Here, we discuss basic concepts of deep learning, recent applications in nephropathology, current challenges in implementation and future perspectives.

RECENT FINDINGS

Deep learning models have been developed and tested in various areas of nephropathology, for example, predicting kidney disease progression or diagnosing diseases based on imaging and clinical data. Despite their promising potential, challenges remain that hinder a wider adoption, for example, the lack of prospective evidence and testing in real-world scenarios.

SUMMARY

Deep learning offers great opportunities to improve quantitative and qualitative kidney histology analysis for research and clinical nephropathology diagnostics. Although exciting approaches already exist, the potential of deep learning in nephropathology is only at its beginning and we can expect much more to come.

tRigon: an R package and Shiny App for integrative (path-)omics data analysis

BACKGROUND

Pathomics facilitates automated, reproducible and precise histopathology analysis and morphological phenotyping. Similar to molecular omics, pathomics datasets are high-dimensional, but also face large outlier variability and inherent data missingness, making quick and comprehensible data analysis challenging. To facilitate pathomics data analysis and interpretation as well as support a broad implementation we developed tRigon (Toolbox foR InteGrative (path-)Omics data aNalysis), a Shiny application for fast, comprehensive and reproducible pathomics analysis.

RESULTS

tRigon is available via the CRAN repository ( https://cran.r-project.org/web/packages/tRigon ) with its source code available on GitLab ( https://git-ce.rwth-aachen.de/labooratory-ai/trigon ). The tRigon package can be installed locally and its application can be executed from the R console via the command 'tRigon::run_tRigon()'. Alternatively, the application is hosted online and can be accessed at https://labooratory.shinyapps.io/tRigon . We show fast computation of small, medium and large datasets in a low- and high-performance hardware setting, indicating broad applicability of tRigon.

CONCLUSIONS

tRigon allows researchers without coding abilities to perform exploratory feature analyses of pathomics and non-pathomics datasets on their own using a variety of hardware.

[Advances in computational quantitative nephropathology]

BACKGROUND

Semiquantitative histological scoring systems are frequently used in nephropathology. In computational nephropathology, the focus is on generating quantitative data from histology (so-called pathomics). Several recent studies have collected such data using next-generation morphometry (NGM) based on segmentations by artificial neural networks and investigated their usability for various clinical or diagnostic purposes.

AIM

To present an overview of the current state of studies regarding renal pathomics and to identify current challenges and potential solutions.

MATERIALS AND METHODS

Due to the literature restriction (maximum of 30 references), studies were selected based on a database search that processed as much data as possible, used innovative methodologies, and/or were ideally multicentric in design.

RESULTS AND DISCUSSION

Pathomics studies in the kidney have impressively demonstrated that morphometric data are useful clinically (for example, for prognosis assessment) and translationally. Further development of NGM requires overcoming some challenges, including better standardization and generation of prospective evidence.