Fibroblast heterogeneity and tertiary lymphoid tissues in the kidney

Fibroblasts reside in various organs and support tissue structure and homeostasis under physiological conditions. Phenotypic alterations of fibroblasts underlie the development of diverse pathological conditions, including organ fibrosis. Recent advances in single-cell biology have revealed that fibroblasts comprise heterogeneous subpopulations with distinct phenotypes, which exert both beneficial and detrimental effects on the host organs in a context-dependent manner. In the kidney, phenotypic alterations of resident fibroblasts provoke common pathological conditions of chronic kidney disease (CKD), such as renal anemia and peritubular capillary loss. Additionally, in aged injured kidneys, fibroblasts provide functional and structural supports for tertiary lymphoid tissues (TLTs), which serve as the ectopic site of acquired immune reactions in various clinical contexts. TLTs are closely associated with aging and CKD progression, and the developmental stages of TLTs reflect the severity of renal injury. In this review, we describe the current understanding of fibroblast heterogeneity both under physiological and pathological conditions, with special emphasis on fibroblast contribution to TLT formation in the kidney. Dissecting the heterogeneous characteristics of fibroblasts will provide a promising therapeutic option for fibroblast-related pathological conditions, including TLT formation

Prevalence, recognition and management of chronic kidney disease in Japan: population-based estimate using a healthcare database with routine health checkup data

[Background] We aimed to update information on the prevalence of chronic kidney disease (CKD) in Japan. We also explored whether CKD was properly recognized and managed. [Methods] We used data from annual health checkups in 2017, compiling records for 5 million persons. These included laboratory results and were linked to healthcare utilization records via personal identifiers. CKD was defined as an estimated glomerular filtration rate 95% sought medical services and 64.6% received laboratory tests within 180 days of the checkup. However, the diagnostic code suggestive of CKD was recorded in only 23.2% of patients and prescriptions for DM and HT were found in 31.2% (1590/5096) and 36.7% (8081/22 019) of comorbid persons, respectively. [Conclusions] The prevalence of CKD in Japan has increased over the past decade. However, recognition of CKD is likely suboptimal and there is room to improve the management of comorbid DM and HT

Calcium channel blocker in patients with chronic kidney disease

[Background] Chronic kidney disease (CKD) is involved in a progressive deterioration in renal function over the years and is now a global public health problem. Currently, reducing the number of patients progressing to end-stage renal failure is urgently necessary. Hypertension and CKD interact with each other, and good control of blood pressure (BP) can improve CKD patients’ prognosis. With the current global trend for more strict BP control, the importance of BP management and the need for medication to achieve this strict goal are increasing. Calcium channel blockers (CCBs), which target voltage-dependent calcium channels, are frequently used in combination with renin–angiotensin–aldosterone system inhibitors for CKD patients because of their strong BP-lowering properties and relatively few adverse side effects. Calcium channels have several subtypes, including L, N, T, P/Q, and R, and three types of CCBs, L-type CCBs, L-/T-type CCBs, and L-/N-type CCBs, that are available. Nowadays, the new functions and effects of the CCBs are being elucidated. [Conclusion] We should use different types of CCBs properly depending on their pharmacological effects, such as the strength of antihypertensive effects and the organ protection effects, taking into account the pathophysiology of the patients. In this article, the role and the use of CCBs in CKD patients are reviewed

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

Heterogeneity of Fibroblasts in Healthy and Diseased Kidneys

Chronic kidney disease (CKD) is a worldwide health problem affecting 9.1% of the world’s population. The treatments to prevent the progression of CKD remain limited, however. Resident fibroblasts in the kidneys play crucial roles in the pathological conditions commonly recognized in CKD, such as renal fibrosis, renal anemia, and peritubular capillary loss. Fibroblasts in the kidney provide structural backbone by producing extracellular matrix proteins and produce erythropoietin for normal hematopoiesis under physiological conditions. In the diseased condition, however, fibroblasts differentiate into myofibroblasts that produce excessive extracellular matrix proteins at the cost of the inherent erythropoietin-producing abilities, resulting in renal fibrosis and renal anemia. Pericytes, which are mesenchymal cells that enwrap peritubular capillaries and highly overlap with resident fibroblasts, detach from peritubular capillary walls in response to kidney injury, resulting in peritubular capillary loss and tissue hypoxia. Several reports have demonstrated the beneficial roles of fibroblasts in the regeneration of renal tubules Renal fibroblasts also have the potential to differentiate into a proinflammatory state, producing various cytokines and chemokines and prolonging inflammation by forming tertiary lymphoid tissues, functional lymphoid aggregates, in some pathological conditions. In this article, we describe the heterogenous functions of renal fibroblasts under healthy and diseased conditions

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

Inflammatory Signals Induce AT2 Cell-Derived Damage-Associated Transient Progenitors that Mediate Alveolar Regeneration.

Tissue regeneration is a multi-step process mediated by diverse cellular hierarchies and states that are also implicated in tissue dysfunction and pathogenesis. Here we leveraged single-cell RNA sequencing in combination with in vivo lineage tracing and organoid models to finely map the trajectories of alveolar-lineage cells during injury repair and lung regeneration. We identified a distinct AT2-lineage population, damage-associated transient progenitors (DATPs), that arises during alveolar regeneration. We found that interstitial macrophage-derived IL-1β primes a subset of AT2 cells expressing Il1r1 for conversion into DATPs via a HIF1α-mediated glycolysis pathway, which is required for mature AT1 cell differentiation. Importantly, chronic inflammation mediated by IL-1β prevents AT1 differentiation, leading to aberrant accumulation of DATPs and impaired alveolar regeneration. Together, this stepwise mapping to cell fate transitions shows how an inflammatory niche controls alveolar regeneration by controlling stem cell fate and behavior.We would like to thank Emma Rawlins (University of Cambridge, UK) for valuable scientific discussions and sharing the mouse lines; We would like to thank Randall Johnson (University of Cambridge, UK) for sharing Hif1aflox/flox mouse line; Nisha Narayan and Brian Huntly for sharing materials and discussion on glycolysis experiments; Irina Pshenichnaya (Histology), Maike Paramor (NGS library), Peter Humphreys (Imaging), Andy Riddell (Flow cytometry), Simon McCallum (Flow cytometry, Cambridge NIHR BRC Cell Phenotyping Hub), Katarzyna Kania (single cell sequencing at Cancer Research UK), and Cambridge Stem Cell Institute core facilities for technical assistance; Papworth Hospital Research Tissue Bank for providing tissue samples from IPF and lung adenocarcinoma (T02233); Kelly Evans for sharing histology samples of human lung tissue samples; Seungmin Han and Woochang Hwang for discussion on the scRNA-seq analysis; Life Science Editors for editorial assistance; All Lee Lab members for helpful discussion. This work was supported by Wellcome and the Royal Society (107633/Z/15/Z) and European Research Council Starting Grant (679411). J.C. was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1A6A3A03005399)

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

効果的な健康改善プランを提案するAIを開発 --個別化医療における健康介入への活用に期待--. 京都大学プレスリリース. 2021-05-28.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

A New Deep State-Space Analysis Framework for Patient Latent State Estimation and Classification from EHR Time Series Data

Many diseases, including cancer and chronic conditions, require extended treatment periods and long-term strategies. Machine learning and AI research focusing on electronic health records (EHRs) have emerged to address this need. Effective treatment strategies involve more than capturing sequential changes in patient test values. It requires an explainable and clinically interpretable model by capturing the patient's internal state over time. In this study, we propose the "deep state-space analysis framework," using time-series unsupervised learning of EHRs with a deep state-space model. This framework enables learning, visualizing, and clustering of temporal changes in patient latent states related to disease progression. We evaluated our framework using time-series laboratory data from 12,695 cancer patients. By estimating latent states, we successfully discover latent states related to prognosis. By visualization and cluster analysis, the temporal transition of patient status and test items during state transitions characteristic of each anticancer drug were identified. Our framework surpasses existing methods in capturing interpretable latent space. It can be expected to enhance our comprehension of disease progression from EHRs, aiding treatment adjustments and prognostic determinations.Comment: 21 pages, 6 figure

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