Kidney360最新文献

Background: The Network for Pancreatic Organ donors with Diabetes-Kidney (nPOD-K) project was initiated to assess the feasibility of using kidneys from organ donors to enhance understanding of diabetic kidney disease (DKD) progression.

Methods: Traditional and digital pathology approaches were employed to characterize the nPOD-K cohort. Periodic acid-Schiff- and Hematoxylin and Eosin-stained sections were used to manually examine and score each nPOD-K case. Brightfield and fluorescently labelled whole slide images of nPOD-K sections were used to train, validate, and test deep learning compartment segmentation and machine learning image analysis tools within Visiopharm software. These digital pathology tools were subsequently employed to evaluate kidney cell-specific markers and pathological indicators.

Results: Digital quantitation of mesangial expansion, tubular atrophy, kidney injury molecule (KIM)-1 expression, cellular infiltration, and fibrosis index aligned with histological DKD classification, as defined by pathologists' review. Histological quantification confirmed loss of podocyte, endothelial, and tubular markers, correlating with DKD progression. Altered expression patterns of prominin-1, protein-tyrosine phosphatase receptor type O, and coronin 2B were validated, in agreement with reported literature.

Conclusions: The nPOD-K cohort provides a unique open resource opportunity to not only validate putative drug targets but also better understand DKD pathophysiology. A broad range of pathogenesis can be visualized in each case, providing a simulated timeline of DKD progression. We conclude that organ donor-derived tissues serve as high-quality samples, provide a comprehensive view of tissue pathology, and address the need for human kidney tissues available for research.

Traditional medical artificial intelligence models that are approved for clinical use restrict themselves to single-modal data ( e.g ., images only), limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal transformer models in health care can effectively process and interpret diverse data forms, such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks, like United States Medical Licensing Examination question banks, and continue to improve with scale. However, the adoption of these advanced artificial intelligence models is not without challenges. While multimodal deep learning models like transformers offer promising advancements in health care, their integration requires careful consideration of the accompanying ethical and environmental challenges.

Our understanding of the pathogenesis of uremic pruritus (also known as CKD-associated pruritus [CKD-aP]) remains elusive. Although multiple discrete changes in the immunochemical milieu of the skin of patients with CKD-aP have been described, a coherent theory of mechanism is absent. This article proposes a theoretical model of mechanism. It concentrates on the initiation phase of CKD-aP and its three parts: ( 1 ) genesis, triggered by first precipitants; ( 2 ) cascade of cytokine release that follows and the cross-talking of multiple skin cells with each other and afferent nerve fibers; and ( 3 ) enhancement. The limitation of the model will be described and ideas for future research proposed. Implications for management shall be examined.