Glutamyl-prolyl-tRNA synthetase (EPRS1) drives tubulointerstitial nephritis-induced fibrosis by enhancing T cell proliferation and activity

Toxin- and drug-induced tubulointerstitial nephritis (TIN), characterized by interstitial infiltration of immune cells, frequently necessitates dialysis for patients due to irreversible fibrosis. However, agents modulating interstitial immune cells are lacking. Here, we addressed whether the housekeeping enzyme glutamyl-prolyl-transfer RNA synthetase 1 (EPRS1), responsible for attaching glutamic acid and proline to transfer RNA, modulates immune cell activity during TIN and whether its pharmacological inhibition abrogates fibrotic transformation. The immunological feature following TIN induction by means of an adenine-mixed diet was infiltration of EPRS1high T cells, particularly proliferating T and γδ T cells. The proliferation capacity of both CD4+ and CD8+ T cells, along with interleukin-17 production of γδ T cells, was higher in the kidneys of TIN-induced Eprs1+/+ mice than in the kidneys of TIN-induced Eprs1+/- mice. This discrepancy contributed to the fibrotic amelioration observed in kidneys of Eprs1+/- mice. TIN-induced fibrosis was also reduced in Rag1-/- mice adoptively transferred with Eprs1+/- T cells compared to the Rag1-/- mice transferred with Eprs1+/+ T cells. The use of an EPRS1-targeting small molecule inhibitor (bersiporocin) under clinical trials to evaluate its therapeutic potential against idiopathic pulmonary fibrosis alleviated immunofibrotic aggravation in TIN. EPRS1 expression was also observed in human kidney tissues and blood-derived T cells, and high expression was associated with worse patient outcomes. Thus, EPRS1 may emerge as a therapeutic target in toxin- and drug-induced TIN, modulating the proliferation and activity of infiltrated T cells.

Intradialytic hypotension and worse outcomes in patients with acute kidney injury requiring intermittent hemodialysis

Background

Intradialytic hypotension (IDH) is a critical complication related to worse outcomes in patients undergoing maintenance hemodialysis. Herein, we addressed the impact of IDH on mortality and other outcomes in patients with severe acute kidney injury (AKI) requiring intermittent hemodialysis.

Methods

We retrospectively reviewed 1,009 patients who underwent intermittent hemodialysis due to severe AKI. IDH was defined as either dialysis discontinuation due to hemodynamic instability or a decrease in systolic blood pressure (BP) of ≥30 mmHg, with or without a nadir systolic BP of <90 mmHg during the first session. The primary outcome was all-cause mortality, and transfer to the intensive care unit (ICU) due to unstable status was additionally analyzed. Hazard ratios (HRs) of outcomes were calculated using a Cox regression model after adjusting for multiple variables. Risk factors for IDH were evaluated using a logistic regression model.

Results

IDH occurred in 449 patients (44.5%) during the first hemodialysis session. Patients with IDH had a higher mortality rate than those without IDH (40% vs. 23%; HR, 1.30; 95% confidence interval [CI], 1.02-1.65). The rate of ICU transfer was higher in patients experiencing IDH than in those without IDH (17% vs. 11%; HR, 1.43; 95% CI, 1.02-2.02). Factors such as old age, high BP and pulse rate, active malignancy, cirrhosis, and hypoalbuminemia were associated with an increased risk of IDH episodes.

Conclusion

The occurrence of IDH is associated with worse outcomes in patients with AKI requiring intermittent hemodialysis. Therefore, careful monitoring and early intervention of IDH may be necessary in this patient subset.

Mortality associated with the neutrophil-lymphocyte ratio in septic acute kidney injury requiring continuous renal replacement therapy

Background

Sepsis is an important cause of acute kidney injury in intensive care unit patients, accounting for 15% to 20% of renal replacement therapy prescriptions. The neutrophil-lymphocyte ratio (NLR), a maker of systemic inflammation and immune response, was previously associated with the mortality rate in multiple conditions. Herein, we aimed to examine how the NLR relates to the mortality rate in septic acute kidney injury patients requiring continuous renal replacement therapy (CRRT).

Methods

The NLRs of 6 and 18 were used for dividing NLRs into three groups and, thus, were set higher than those in previous studies accounting for steroid use in sepsis. Cox proportional hazard models were used to calculate hazard ratios of mortality outcomes before and after matching their propensity scores.

Results

A total of 798 septic acute kidney injury patients requiring CRRT were classified into three NLR groups (low, <6 [n = 277]; medium, ≥6 and <18 [n = 115], and high, ≥18 [n = 406], respectively). The in-hospital mortality rates per group were 83.4%, 74.8%, and 70.4%, respectively (p < 0.001). Per the univariable Cox survival analysis after propensity score matching, a high NLR was related to approximately 24% reduced mortality. The survival benefit of the high NLR group compared with the other two groups remained consistent across all subgroups, showing any p for interactions of >0.05.

Conclusion

A high NLR is associated with better clinical outcomes, such as low mortality, in septic acute kidney injury patients undergoing CRRT.

Automatic segmentation of atrial fibrillation and flutter in single-lead electrocardiograms by self-supervised learning and Transformer architecture

OBJECTIVES

Automatic detection of atrial fibrillation and flutter (AF/AFL) is a significant concern in preventing stroke and mitigating hemodynamic instability. Herein, we developed a Transformer-based deep learning model for AF/AFL segmentation in single-lead electrocardiograms (ECGs) by self-supervised learning with masked signal modeling (MSM).

MATERIALS AND METHODS

We retrieved data from 11 open-source databases on PhysioNet; 7 of these databases included labeled ECGs, while the other 4 were without labels. Each database contained ECG recordings with durations of ≥30 s. A total of 24 intradialytic ECGs with paroxysmal AF/AFL during 4 h of hemodialysis sessions at Seoul National University Hospital were used for external validation. The model was pretrained by predicting masked areas of ECG signals and fine-tuned by predicting AF/AFL areas. Cross-database validation was used for evaluation, and the intersection over union (IOU) was used as a main performance metric in external database validation.

RESULTS

In the 7 labeled databases, the areas marked as AF/AFL constituted 41.1% of the total ECG signals, ranging from 0.19% to 51.31%. In the evaluation per ECG segment, the model achieved IOU values of 0.9254 and 0.9477 for AF/AFL segmentation and other segmentation tasks, respectively. When applied to intradialytic ECGs with paroxysmal AF/AFL, the IOUs for the segmentation of AF/AFL and non-AF/AFL were 0.9896 and 0.9650, respectively. Model performance by different training procedure indicated that pretraining with MSM and the application of an appropriate masking ratio both contributed to the model performance. It also showed higher IOUs of AF/AFL labels than in previous studies when training and test databases were matched.

CONCLUSION

The present model with self-supervised learning by MSM performs robustly in segmenting AF/AFL.

Real-time dual prediction of intradialytic hypotension and hypertension using an explainable deep learning model

Both intradialytic hypotension (IDH) and hypertension (IDHTN) are associated with poor outcomes in hemodialysis patients, but a model predicting dual outcomes in real-time has never been developed. Herein, we developed an explainable deep learning model with a sequence-to-sequence-based attention network to predict both of these events simultaneously. We retrieved 302,774 hemodialysis sessions from the electronic health records of 11,110 patients, and these sessions were split into training (70%), validation (10%), and test (20%) datasets through patient randomization. The outcomes were defined when nadir systolic blood pressure (BP) < 90 mmHg (termed IDH-1), a decrease in systolic BP ≥ 20 mmHg and/or a decrease in mean arterial pressure ≥ 10 mmHg (termed IDH-2), or an increase in systolic BP ≥ 10 mmHg (i.e., IDHTN) occurred within 1 h. We developed a temporal fusion transformer (TFT)-based model and compared its performance in the test dataset, including receiver operating characteristic curve (AUROC) and area under the precision-recall curves (AUPRC), with those of other machine learning models, such as recurrent neural network, light gradient boosting machine, random forest, and logistic regression. Among all models, the TFT-based model achieved the highest AUROCs of 0.953 (0.952-0.954), 0.892 (0.891-0.893), and 0.889 (0.888-0.890) in predicting IDH-1, IDH-2, and IDHTN, respectively. The AUPRCs in the TFT-based model for these outcomes were higher than the other models. The factors that contributed the most to the prediction were age and previous session, which were time-invariant variables, as well as systolic BP and elapsed time, which were time-varying variables. The present TFT-based model predicts both IDH and IDHTN in real time and offers explainable variable importance.

Discovery of Novel Digital Biomarkers for Type 2 Diabetic Nephropathy Classification via Integration of Urinary Proteomics and Pathology

Background

The heterogeneous phenotype of diabetic nephropathy (DN) from type 2 diabetes complicates appropriate treatment approaches and outcome prediction. Kidney histology helps diagnose DN and predict its outcomes, and an artificial intelligence (AI)-based approach will maximize clinical utility of histopathological evaluation. Herein, we addressed whether AI-based integration of urine proteomics and image features improves DN classification and its outcome prediction, altogether augmenting and advancing pathology practice.

Methods

We studied whole slide images (WSIs) of periodic acid-Schiff-stained kidney biopsies from 56 DN patients with associated urinary proteomics data. We identified urinary proteins differentially expressed in patients who developed end-stage kidney disease (ESKD) within two years of biopsy. Extending our previously published human-AI-loop pipeline, six renal sub-compartments were computationally segmented from each WSI. Hand-engineered image features for glomeruli and tubules, and urinary protein measurements, were used as inputs to deep-learning frameworks to predict ESKD outcome. Differential expression was correlated with digital image features using the Spearman rank sum coefficient.

Results

A total of 45 urinary proteins were differentially detected in progressors, which was most predictive of ESKD (AUC=0.95), while tubular and glomerular features were less predictive (AUC=0.71 and AUC=0.63, respectively). Accordingly, a correlation map between canonical cell-type proteins, such as epidermal growth factor and secreted phosphoprotein 1, and AI-based image features was obtained, which supports previous pathobiological results.

Conclusions

Computational method-based integration of urinary and image biomarkers may improve the pathophysiological understanding of DN progression as well as carry clinical implications in histopathological evaluation.

Risk of ventricular tachycardia and its outcomes in patients undergoing continuous renal replacement therapy due to acute kidney injury

Background

Despite efforts to treat critically ill patients who require continuous renal replacement therapy (CRRT) due to acute kidney injury (AKI), their mortality risk remains high. This condition may be attributable to complications of CRRT, such as arrhythmias. Here, we addressed the occurrence of ventricular tachycardia (VT) during CRRT and its relationship with patient outcomes.

Methods

This study retrospectively enrolled 2,397 patients who started CRRT due to AKI from 2010 to 2020 at Seoul National University Hospital in Korea. The occurrence of VT was evaluated from the initiation of CRRT until weaning from CRRT. The odds ratios (ORs) of mortality outcomes were measured using logistic regression models after adjustment for multiple variables.

Results

VT occurred in 150 patients (6.3%) after starting CRRT. Among them, 95 cases were defined as sustained VT (i.e., lasting ≥30 seconds), and the other 55 cases were defined as non-sustained VT (i.e., lasting <30 seconds). The occurrence of sustained VT was associated with a higher mortality rate than a nonoccurrence (OR, 2.04 and 95% confidence interval [CI], 1.23-3.39 for the 30-day mortality; OR, 4.06 and 95% CI, 2.04-8.08 for the 90-day mortality). The mortality risk did not differ between patients with non-sustained VT and nonoccurrence. A history of myocardial infarction, vasopressor use, and certain trends of blood laboratory findings (such as acidosis and hyperkalemia) were associated with the subsequent risk of sustained VT.

Conclusion

Sustained VT occurrence after starting CRRT is associated with increased patient mortality. The monitoring of electrolytes and acid-base status during CRRT is essential because of its relationship with the risk of VT.

Spatially Aware Transformer Networks for Contextual Prediction of Diabetic Nephropathy Progression from Whole Slide Images

Diabetic nephropathy (DN) in the context of type 2 diabetes is the leading cause of end-stage renal disease (ESRD) in the United States. DN is graded based on glomerular morphology and has a spatially heterogeneous presentation in kidney biopsies that complicates pathologists' predictions of disease progression. Artificial intelligence and deep learning methods for pathology have shown promise for quantitative pathological evaluation and clinical trajectory estimation; but, they often fail to capture large-scale spatial anatomy and relationships found in whole slide images (WSIs). In this study, we present a transformer-based, multi-stage ESRD prediction framework built upon nonlinear dimensionality reduction, relative Euclidean pixel distance embeddings between every pair of observable glomeruli, and a corresponding spatial self-attention mechanism for a robust contextual representation. We developed a deep transformer network for encoding WSI and predicting future ESRD using a dataset of 56 kidney biopsy WSIs from DN patients at Seoul National University Hospital. Using a leave-one-out cross-validation scheme, our modified transformer framework outperformed RNNs, XGBoost, and logistic regression baseline models, and resulted in an area under the receiver operating characteristic curve (AUC) of 0.97 (95% CI: 0.90-1.00) for predicting two-year ESRD, compared with an AUC of 0.86 (95% CI: 0.66-0.99) without our relative distance embedding, and an AUC of 0.76 (95% CI: 0.59-0.92) without a denoising autoencoder module. While the variability and generalizability induced by smaller sample sizes are challenging, our distance-based embedding approach and overfitting mitigation techniques yielded results that sugest opportunities for future spatially aware WSI research using limited pathology datasets.

Consulting to nephrologist when starting continuous renal replacement therapy for acute kidney injury is associated with a survival benefit

BACKGROUND

Several studies suggest improved outcomes for patients with kidney disease who consult a nephrologist. However, it remains undetermined whether a consultation with a nephrologist is related to a survival benefit after starting continuous renal replacement therapy (CRRT) due to acute kidney injury (AKI).

METHODS

Data from 2,397 patients who started CRRT due to severe AKI at Seoul National University Hospital, Korea between 2010 and 2020 were retrospectively collected. The patients were divided into two groups according to whether they underwent a nephrology consultation regarding the initiation and maintenance of CRRT. The Cox proportional hazards model was used to calculate the hazard ratio (HR) of mortality during admission to the intensive care unit after adjusting for multiple variables.

RESULTS

A total of 2,153 patients (89.8%) were referred to nephrologists when starting CRRT. The patients who underwent a nephrology consultation had a lower mortality rate than those who did not have a consultation (HR = 0.47 [0.40-0.56]; P < 0.001). Subsequently, patients who had nephrology consultations were divided into two groups (i.e., early and late) according to the timing of the consultation. Both patients with early and late consultation had lower mortality rates than patients without consultations, with HRs of 0.45 (0.37-0.54) and 0.51 (0.42-0.61), respectively.

CONCLUSIONS

Consultation with a nephrologist may contribute to a survival benefit after starting CRRT for AKI.

Impact of intradialytic hypotension on mortality following the transition from continuous renal replacement therapy to intermittent hemodialysis

BACKGROUND

The transition of dialysis modalities from continuous renal replacement therapy (CRRT) to intermittent hemodialysis (iHD) is frequently conducted during the recovery phase of critically ill patients with acute kidney injury. Herein, we addressed the occurrence of intradialytic hypotension (IDH) after this transition, and its association with the mortality risk.

METHODS

A total of 541 patients with acute kidney injury who attempted to transition from CRRT to iHD at Seoul National University Hospital, Korea from 2010 to 2020 were retrospectively collected. IDH was defined as a discontinuation of dialysis because of hemodynamic instability plus a nadir systolic blood pressure <90 mm Hg or a decrease in systolic blood pressure ≥30 mm Hg during the first session of iHD. Odds ratios (ORs) of outcomes, such as in-hospital mortality and weaning from RRT, were measured using a logistic regression model after adjusting for multiple variables.

RESULTS

IDH occurred in 197 patients (36%), and their mortality rate (44%) was higher than that of those without IDH (19%; OR, 2.64; 95% confidence interval [CI], 1.70-4.08). For patients exhibiting IDH, the iHD sessions delayed successful weaning from RRT (OR, 0.62; 95% CI, 0.43-0.90) compared with sessions on those without IDH. Factors such as low blood pressure, high pulse rate, low urine output, use of mechanical ventilations and vasopressors, and hypoalbuminemia were associated with IDH risk.

CONCLUSIONS

IDH occurrence following the transition from CRRT to iHD is associated with high mortality and delayed weaning from RRT.

Spatially Aware Transformer Networks for Contextual Prediction of Diabetic Nephropathy Progression from Whole Slide Images

Diabetic nephropathy (DN) in the context of type 2 diabetes is the leading cause of end-stage renal disease (ESRD) in the United States. DN is graded based on glomerular morphology and has a spatially heterogeneous presentation in kidney biopsies that complicates pathologists' predictions of disease progression. Artificial intelligence and deep learning methods for pathology have shown promise for quantitative pathological evaluation and clinical trajectory estimation; but, they often fail to capture large-scale spatial anatomy and relationships found in whole slide images (WSIs). In this study, we present a transformer-based, multi-stage ESRD prediction framework built upon nonlinear dimensionality reduction, relative Euclidean pixel distance embeddings between every pair of observable glomeruli, and a corresponding spatial self-attention mechanism for a robust contextual representation. We developed a deep transformer network for encoding WSI and predicting future ESRD using a dataset of 56 kidney biopsy WSIs from DN patients at Seoul National University Hospital. Using a leave-one-out cross-validation scheme, our modified transformer framework outperformed RNNs, XGBoost, and logistic regression baseline models, and resulted in an area under the receiver operating characteristic curve (AUC) of 0.97 (95% CI: 0.90-1.00) for predicting two-year ESRD, compared with an AUC of 0.86 (95% CI: 0.66-0.99) without our relative distance embedding, and an AUC of 0.76 (95% CI: 0.59-0.92) without a denoising autoencoder module. While the variability and generalizability induced by smaller sample sizes are challenging, our distance-based embedding approach and overfitting mitigation techniques yielded results that suggest opportunities for future spatially aware WSI research using limited pathology datasets.

Hyperlactatemia is a predictor of mortality in patients undergoing continuous renal replacement therapy for acute kidney injury

BACKGROUND

Hyperlactatemia occurs frequently in critically ill patients, and this pathologic condition leads to worse outcomes in several disease subsets. Herein, we addressed whether hyperlactatemia is associated with the risk of mortality in patients undergoing continuous renal replacement therapy (CRRT) due to acute kidney injury.

METHODS

A total of 1,661 patients who underwent CRRT for severe acute kidney injury were retrospectively reviewed between 2010 and 2020. The patients were categorized according to their serum lactate levels, such as high (≥ 7.6 mmol/l), moderate (2.1-7.5 mmol/l) and low (≤ 2 mmol/l), at the time of CRRT initiation. The hazard ratios (HRs) for the risk of in-hospital mortality were calculated with adjustment of multiple variables. The increase in the area under the receiver operating characteristic curve (AUROC) for the mortality risk was evaluated after adding serum lactate levels to the Sequential Organ Failure Assessment (SOFA) and the Acute Physiology and Chronic Health Evaluation (APACHE) II score-based models.

RESULTS

A total of 802 (48.3%) and 542 (32.6%) patients had moderate and high lactate levels, respectively. The moderate and high lactate groups had a higher risk of mortality than the low lactate group, with HRs of 1.64 (1.22-2.20) and 4.18 (2.99-5.85), respectively. The lactate-enhanced models had higher AUROCs than the models without lactates (0.764 vs. 0.702 for SOFA score; 0.737 vs. 0.678 for APACHE II score).

CONCLUSIONS

Hyperlactatemia is associated with mortality outcomes in patients undergoing CRRT for acute kidney injury. Serum lactate levels may need to be monitored in this patient subset.

AB0053 LncRNA NUTM2A-AS1 ALLEVIATED OSTEOARTHRITIS BY REGULATING miR-183-5p/TGFA PATHWAY

Background

Osteoarthritis(OA) is a common comorbidity in the elderly, characterized by articular cartilage degeneration, hyperosteogeny and synovitis. Long non-coding RNAs (LncRNAs) have been shown to be involved in several human diseases, including OA. However, the effect of NUTM2A-AS1 on chondrocytes remains unknown.

Objectives

The purpose of this study was to evaluate the role of LncRNA NUTM2A-AS1 in OA pathological changes in vitro.

Methods

Chondrocyte cells were treated for 24 hours to mimic OA pathological conditions. The experimental center used chondrocytes with interleukin-1 beta (IL-1β) Intervention to simulate the pathological state of OA.The expression levels of LncRNA NUTM2A-AS1, miR-183-5p and TGFA mRNA were detected by quantitative real-time PCR (qRT-PCR), along with CCK-8 to determine cell viability. Inflammatory response was assessed by determining the release of pro-inflammatory factors such as TNF-α and IL-6 using ELISA kits. Cell apoptosis was examined by flow cytometry assay. The binding relationship between miR-183-5p and LncRNA NUTM2A-AS1 or TGFA was confirmed by dual-luciferase reporter assay.

Results

IL-1β induced chondrocytes to express LncRNA NUTM2A-AS1 and TGFA, and the miR-183-5p expression was decreased in IL-1β-treated cells. Low expression of LncRNA NUTM2A-AS1 or TGFA mitigated IL-1β-caused chondrocyte viability reduction and apoptosis promotion. miR-183-5p overexpression alleviated IL-1β-mediated chondrocyte apoptosis and inflammatory injury via decreasing TGFA expression. In addition, our work revealed that miR-183-5p is a target of LncRNA NUTM2A-AS1. Rescue experiments showed that TGFA overexpression could reverse the effects of low expression of LncRNA NUTM2A-AS1 on the pathological changes in IL-1β-induced chondrocytes.

Conclusion

Low expression of LncRNA NUTM2A-AS1 significantly mitigated the chondrocytes damage induced by IL-1β through regulating miR-183-5p/TGFA axis, which might be an important target to regulate the promotion of OA.

References

[1]Kong H, Sun ML, Zhang XA, Wang XQ. Crosstalk Among circRNA/lncRNA, miRNA, and mRNA in Osteoarthritis[J]. Front Cell Dev Biol, 2021, 9:77437

Disclosure of Interests

None declared

POS0055 TNF-α REGULATION OF mir-29b EXPRESSION IN CD14+PBMs AND ITS RELEASE OF PROINFLAMMATORY CYTOKINES IN RHEUMATOID ARTHRITIS

Background

It was found that the expression of mir-29b was significantly up-regulated in PBMs, and we tried to clarify TNF- α The production of proinflammatory cytokines was increased by inducing the overexpression of mir-29b in CD14 +PBMs in patients with rheumatoid arthritis (RA), and by using TNF-α that the expression of mir-29b was significantly urapy to reverse regulate mir-29b, and carry out relevant experiments to verify our scientific research hypothesis.

Objectives

By observing patients with RA treated with TNF-α significantly urapy to reverse regulate mir-29b, and carry out relevant experimenperipheral blood mononuclear cells (CD14+PBMs) and releases pro-inflammatory cytokines.

Methods

(1) Cell experiment: PBM cells from RA patients were collected and extracted for CD14+ cell expression labeling. Different doses of TNF-α blood mononuclear c500 ng/ml) were used for intervention. Meanwhile, the expression of mir-29b was analyzed by rt-qpcr at the level of TNF-α100 ng/ml for different periods of time (0,6,12,24h). In addition, the supernatant of cell culture was collected and human cytokines IL-1α, IL-1β, TNFα, IL-6, IFN-α and IL-8 were measured using a V-plex human cytokine 30-plex kit. (2)Grouping experiment of clinical intervention: 21 patients with RA diagnosis and 15 healthy volunteers were divided into three groups. TNF-α inhibitor group: RA patients treated with TNF-α inhibitor were collected (n=15); IL-6 monoclonal antibody group: RA patients treated with tocilizumab (n=6); Control group: healthy volunteers (n=15) were used as normal controls. PBMc was extracted from TNF-α inhibitor group for 6 months, IL-6 monoclonal antibody group for 6 months, and control group, respectively, to observe the difference of Mir-29b expression in CD14+PBMs of the three groups.

Results

The expression of miR-29b was dose-dependent and time-dependent with the incubation of TNF-α, and there was a significant difference (P < 0.05). Compared with the control group, the overexpression of miR-29b also led to an increase in the expression levels of a wide range of chemokines and proinflammatory cytokines (including IL-1α, IL-1β, TNFα, IL-6, IFN-α and IL-8) (P < 0.05). The expression of miR-29b in RA patients treated with TNF-α inhibitor was significantly reduced compared with that treated with Totuzumab (P<0.05).

Conclusion

TNF-α inflammatory factors can induce the overexpression of miR-29b in RA patients, and then producing a large number of proinflammatory cytokines, which can aggravate the inflammation mechanism of RA. In RA patients, TNF-α inhibitors may partially reduce the inflammatory response through the TNF-α/CD14+PBMs/ Mir-29b signaling pathway. Therefore, more attention should be paid to the expression of Mir-29b in TNF-α and CD14+PBMs in clinical practice, which may accurately indicate the state of immune disease in patients, and provide a basis for more accurate judgment of prognosis and the course of immunotherapy, as well as optimization of immunotherapy programs.

References

[1]Long L, Yu P, Liu Y, et al. Upregulated microRNA-155 expression in peripheral blood mononuclear cells and fibroblast-like synoviocytes in rheumatoid arthritis. Clin Dev Immunol. 2013;2013:296139.

Disclosure of Interests

None declared

System of integrating biosignals during hemodialysis: the CONTINUAL (Continuous mOnitoriNg viTal sIgN dUring hemodiALysis registry

Background

Appropriate monitoring of intradialytic biosignals is essential to minimize adverse outcomes because intradialytic hypotension and arrhythmia are associated with cardiovascular risk in hemodialysis patients. However, a continuous monitoring system for intradialytic biosignals has not yet been developed.

Methods

This study investigated a cloud system that hosted a prospective, open-source registry to monitor and collect intradialytic biosignals, which was named the CONTINUAL (Continuous mOnitoriNg viTal sIgN dUring hemodiALysis) registry. This registry was based on real-time multimodal data acquisition, such as blood pressure, heart rate, electrocardiogram, and photoplethysmogram results.

Results

We analyzed session information from this system for the initial 8 months, including data for some cases with hemodynamic complications such as intradialytic hypotension and arrhythmia.

Conclusion

This biosignal registry provides valuable data that can be applied to conduct epidemiological surveys on hemodynamic complications during hemodialysis and develop artificial intelligence models that predict biosignal changes which can improve patient outcomes.

Computational Integration of Renal Histology and Urinary Proteomics using Neural Networks

Histological image data and molecular profiles provide context into renal condition. Often, a biopsy is drawn to diagnose or monitor a suspected kidney problem. However, molecular profiles can go beyond a pathologist's ability to see and diagnose. Using AI, we computationally incorporated urinary proteomic profiles with microstructural morphology from renal biopsy to investigate new and existing molecular links to image phenotypes. We studied whole slide images of periodic acid-Schiff stained renal biopsies from 56 DN patients matched with 2,038 proteins measured from each patient's urine. Using Seurat, we identified differentially expressed proteins in patients that developed end-stage renal disease within 2 years of biopsy. Glomeruli, globally sclerotic glomeruli, and tubules were segmented from WSI using our previously published HAIL pipeline. For each glomerulus, 315 handcrafted digital image features were measured, and for tubules, 207 features. We trained fully connected networks to predict urinary protein measurements that were differentially expressed between patients who did/ did not progress to ESRD within 2 years of biopsy. The input to this network was either glomerular or tubular histomorphological features in biopsy. Trained network weights were used as a proxy to rank which morphological features correlated most highly with specific urinary proteins. We identified significant image feature-protein pairs by ranking network weights by magnitude. We also looked at which features on average were most significant in predicting proteins. For both glomeruli and tubules, RGB color values and variance in PAS+ areas (specifically basement membrane for tubules) were, on average, more predictive of molecular profiles than other features. There is a strong connection between molecular profile and image phenotype, which can be elucidated through computational methods. These discovered links can provide insight to disease pathways, and discover new factors contributing to incidence and progression.

Kidney VISTA prevents IFNγ-IL-9 axis-mediated tubulointerstitial fibrosis after acute glomerular injury

Severe glomerular injury ultimately leads to tubulointerstitial fibrosis that determines patient outcome, but the immunological molecules connecting these processes remain undetermined. The present study addressed whether V-domain Ig suppressor of T cell activation (VISTA), constitutively expressed in kidney macrophages, plays a protective role in tubulointerstitial fibrotic transformation after acute antibody-mediated glomerulonephritis. After acute glomerular injury using nephrotoxic serum, tubules in the VISTA-deficient (Vsir–/–) kidney suffered more damage than those in WT kidneys. When interstitial immune cells were examined, the contact frequency of macrophages with infiltrated T cells increased and the immunometabolic features of T cells changed to showing high oxidative phosphorylation and fatty acid metabolism and overproduction of IFN-γ. The Vsir–/– parenchymal tissue cells responded to this altered milieu of interstitial immune cells as more IL-9 was produced, which augmented tubulointerstitial fibrosis. Blocking antibodies against IFN-γ and IL-9 protected the above pathological process in VISTA-depleted conditions. In human samples with acute glomerular injury (e.g., antineutrophil cytoplasmic autoantibody vasculitis), high VISTA expression in tubulointerstitial immune cells was associated with low tubulointerstitial fibrosis and good prognosis. Therefore, VISTA is a sentinel protein expressed in kidney macrophages that prevents tubulointerstitial fibrosis via the IFN-γ/IL-9 axis after acute antibody-mediated glomerular injury.

Correction: Use of Deep Learning to Predict Acute Kidney Injury After Intravenous Contrast Media Administration: Prediction Model Development Study

[This corrects the article DOI: 10.2196/27177.].

Hyperchloremia is associated with poor renal outcome after coronary artery bypass grafting

Background

Hyperchloremia is associated with the risks of several morbidities and mortality. However, its relationship with acute kidney injury (AKI) and end-stage renal disease (ESRD) in patients undergoing coronary artery bypass grafting (CABG) remains unresolved.

Methods

A total of 2977 patients undergoing CABG between 2003 and 2015 were retrospectively reviewed from two tertiary hospitals. Patients were categorized by serum chloride levels into normochloremia (95–105 mmol/L), mild hyperchloremia (106–110 mmol/L), and severe hyperchloremia (> 110 mmol/L). The odds ratios (ORs) for AKI and hazard ratios (HRs) for ESRD were calculated after adjustment for multiple covariates. The death-adjusted risk of ESRD was additionally evaluated.

Results

Postoperative AKI occurred in 798 patients (26.5%). The hyperchloremia group had a higher risk of AKI than the normochloremia group, wherein the risk was incremental depending on the severity of hyperchloremia, as follows: ORs were 1.26 (1.06–1.51) and 1.95 (1.52–2.51) in the mild and severe hyperchloremia groups, respectively. During a median period of 7 years (maximum 15 years), 70 patients (2.3%) had ESRD. The severe hyperchloremia group was at an elevated risk of ESRD compared with the normochloremia group, with an HR of 2.43 (1.28–4.63). Even after adjusting for the competing risk of death, hyperchloremia was associated with the risk of ESRD.

Conclusions

Preoperative hyperchloremia is associated with poor renal outcomes such as AKI and ESRD after CABG. Accordingly, serum chloride should be monitored in patients undergoing CABG.

Use of Deep Learning to Predict Acute Kidney Injury After Intravenous Contrast Media Administration: Prediction Model Development Study

Background

Precise prediction of contrast media–induced acute kidney injury (CIAKI) is an important issue because of its relationship with poor outcomes.

Objective

Herein, we examined whether a deep learning algorithm could predict the risk of intravenous CIAKI better than other machine learning and logistic regression models in patients undergoing computed tomography (CT).

Methods

A total of 14,185 patients who were administered intravenous contrast media for CT at the preventive and monitoring facility in Seoul National University Hospital were reviewed. CIAKI was defined as an increase in serum creatinine of ≥0.3 mg/dL within 2 days or ≥50% within 7 days. Using both time-varying and time-invariant features, machine learning models, such as the recurrent neural network (RNN), light gradient boosting machine (LGM), extreme gradient boosting machine (XGB), random forest (RF), decision tree (DT), support vector machine (SVM), κ-nearest neighbors, and logistic regression, were developed using a training set, and their performance was compared using the area under the receiver operating characteristic curve (AUROC) in a test set.

Results

CIAKI developed in 261 cases (1.8%). The RNN model had the highest AUROC of 0.755 (0.708-0.802) for predicting CIAKI, which was superior to that obtained from other machine learning models. Although CIAKI was defined as an increase in serum creatinine of ≥0.5 mg/dL or ≥25% within 3 days, the highest performance was achieved in the RNN model with an AUROC of 0.716 (95% confidence interval [CI] 0.664-0.768). In feature ranking analysis, the albumin level was the most highly contributing factor to RNN performance, followed by time-varying kidney function.

Conclusions

Application of a deep learning algorithm improves the predictability of intravenous CIAKI after CT, representing a basis for future clinical alarming and preventive systems.

Can sodium fluorescein cause contrast-induced nephropathy?

BACKGROUND

Contrast-induced nephropathy (CIN) is a common cause of acute kidney injury (AKI), and can be diagnosed when the etiology of AKI is unclear other than via a contrast agent. Fluorescent angiography (FAG) with fluorescein sodium dye is generally considered to be safe for patients with kidney diseases. However, it remains unresolved whether or not FAG can induce CIN.

METHODS

Patients from two tertiary hospitals who underwent FAG and had serum creatinine results within 4 weeks before FAG and 3 days after FAG between 2001 and 2017 were retrieved. Cases with concurrent iodinated contrast imaging or undergoing dialysis were excluded from the analysis. CIN was defined by two criteria: CIN criteria as >0.5 mg/dL or >25% increase in serum creatinine (sCr) level within 3 days after FAG, and contrast-induced acute kidney injury (CIAKI) criteria as ≥0.3 mg/dL increase within 2 days or ≥50% increase within 7 days after FAG.

RESULTS

A total of 979 patients were screened, and we found 124 patients with AKI after FAG. After excluding 32 patients with clear causes of AKI other than FAG, the incidence rates of CIN were 7.3% by CIN criteria and 6.4% by CIAKI criteria. CIN incidence had a U-shaped distribution according to chronic kidney disease (CKD) stages in CIN criteria, while linear association between CIN incidence and CKD stages were found in CIAKI criteria. Kaplan-Meier curves showed the CIN group was significantly associated with end-stage renal disease (ESRD) progression (log-rank P < 0.001, in both CIN criteria and CIAKI criteria), and adjusted hazard ratios by multivariable Cox regression were 2.23 [95% confidence interval (CI) 1.468-3.378] in CIN criteria and 2.17 (95% CI 1.462-3.232) in CIAKI criteria.

CONCLUSIONS

According to CIN and CIAKI criteria, FAG may cause CIN and appeared to be a possible risk factor for ESRD progression. However, CIN or CIAKI criteria themselves may overestimate AKI and require meticulous attention to the interpretation of results.

Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis

BACKGROUND

Interstitial fibrosis, tubular atrophy (IFTA), and glomerulosclerosis are indicators of irrecoverable kidney injury. Modern machine learning (ML) tools have enabled robust, automated identification of image structures that can be comparable with analysis by human experts. ML algorithms were developed and tested for the ability to replicate the detection and quantification of IFTA and glomerulosclerosis that renal pathologists perform.

METHODS

A renal pathologist annotated renal biopsy specimens from 116 whole-slide images (WSIs) for IFTA and glomerulosclerosis. A total of 79 WSIs were used for training different configurations of a convolutional neural network (CNN), and 17 and 20 WSIs were used as internal and external testing cases, respectively. The best model was compared against the input of four renal pathologists on 20 new testing slides. Further, for 87 testing biopsy specimens, IFTA and glomerulosclerosis measurements made by pathologists and the CNN were correlated to patient outcome using classic statistical tools.

RESULTS

The best average performance across all image classes came from a DeepLab version 2 network trained at 40× magnification. IFTA and glomerulosclerosis percentages derived from this CNN achieved high levels of agreement with four renal pathologists. The pathologist- and CNN-based analyses of IFTA and glomerulosclerosis showed statistically significant and equivalent correlation with all patient-outcome variables.

CONCLUSIONS

ML algorithms can be trained to replicate the IFTA and glomerulosclerosis assessment performed by renal pathologists. This suggests computational methods may be able to provide a standardized approach to evaluate the extent of chronic kidney injury in situations in which renal-pathologist time is restricted or unavailable.

Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension

BACKGROUND AND OBJECTIVES

Intradialytic hypotension has high clinical significance. However, predicting it using conventional statistical models may be difficult because several factors have interactive and complex effects on the risk. Herein, we applied a deep learning model (recurrent neural network) to predict the risk of intradialytic hypotension using a timestamp-bearing dataset.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We obtained 261,647 hemodialysis sessions with 1,600,531 independent timestamps (i.e., time-varying vital signs) and randomly divided them into training (70%), validation (5%), calibration (5%), and testing (20%) sets. Intradialytic hypotension was defined when nadir systolic BP was <90 mm Hg (termed intradialytic hypotension 1) or when a decrease in systolic BP ≥20 mm Hg and/or a decrease in mean arterial pressure ≥10 mm Hg on the basis of the initial BPs (termed intradialytic hypotension 2) or prediction time BPs (termed intradialytic hypotension 3) occurred within 1 hour. The area under the receiver operating characteristic curves, the area under the precision-recall curves, and F1 scores obtained using the recurrent neural network model were compared with those obtained using multilayer perceptron, Light Gradient Boosting Machine, and logistic regression models.

RESULTS

The recurrent neural network model for predicting intradialytic hypotension 1 achieved an area under the receiver operating characteristic curve of 0.94 (95% confidence intervals, 0.94 to 0.94), which was higher than those obtained using the other models (P<0.001). The recurrent neural network model for predicting intradialytic hypotension 2 and intradialytic hypotension 3 achieved area under the receiver operating characteristic curves of 0.87 (interquartile range, 0.87-0.87) and 0.79 (interquartile range, 0.79-0.79), respectively, which were also higher than those obtained using the other models (P≤0.001). The area under the precision-recall curve and F1 score were higher using the recurrent neural network model than they were using the other models. The recurrent neural network models for intradialytic hypotension were highly calibrated.

CONCLUSIONS

Our deep learning model can be used to predict the real-time risk of intradialytic hypotension.

Target value of mean arterial pressure in patients undergoing continuous renal replacement therapy due to acute kidney injury

Background

Although patients undergoing continuous renal replacement therapy (CRRT) due to acute kidney injury (AKI) frequently have instability in mean arterial pressure (MAP), no consensus exists on the target value of MAP related to high mortality after CRRT.

Methods

A total of 2,292 patients who underwent CRRT due to AKI in three referral hospitals were retrospectively reviewed. The MAPs were divided into tertiles, and the 3^rd tertile group served as a reference in the analyses. The major outcome was all-cause mortality during the intensive care unit period. The odds ratio (OR) of mortality was calculated using logistic regression after adjustment for multiple covariates. The nonlinear relationship regression model was applied to determine the threshold value of MAP related to increasing mortality.

Results

The mean value of MAP was 80.7 ± 17.3 mmHg at the time of CRRT initiation. The median intensive care unit stay was 5 days (interquartile range, 2–12 days), and during this time, 1,227 (55.5%) patients died. The 1^st tertile group of MAP showed an elevated risk of mortality compared with the 3^rd tertile group (adjusted OR, 1.28 [1.03–1.60]; P = 0.029). In the nonlinear regression analysis, the threshold value of MAP was calculated as 82.7 mmHg. Patients with MAP < 82.7 mmHg had a higher mortality rate than those with ≥ 82.7 mmHg (adjusted OR, 1.21 [1.01–1.45]; P = 0.037).

Conclusions

Low MAP at CRRT initiation is associated with a high risk of mortality, particularly when it is < 82.7 mmHg. This value may be used for risk classification and as a potential therapeutic target.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-020-02227-4.

Blood Pressure and Renal Progression in Patients Undergoing Percutaneous Coronary Intervention

BACKGROUND

High blood pressure (BP) may impair renal function following percutaneous coronary intervention (PCI). However, the predictability of renal progression based on admission and discharge BP and BP threshold values remains unclear.

METHODS

A total of 8,176 adult patients who underwent PCI at Seoul National University Hospital from 2006 to 2016 were retrospectively analyzed. Renal progression was defined as a doubling of serum creatinine levels, ≥50% decrease of the estimated glomerular filtration rate, or development of end-stage renal disease. The risk of renal progression according to admission BP (any time) and discharge BP (8:00-10:00 am) was evaluated by multivariable Cox and additive generalized models with penalized splines.

RESULTS

During a median follow-up of 7 years (maximum: 13 years), 9.3% of patients (n = 758) reached renal progression. BP between admission and discharge showed a low correlation, and all BP parameters showed a nonlinear relationship with renal progression. Systolic BP at discharge (SBPd) was selected as the best predictor of renal progression because the delta for the Akaike information criterion from the baseline model to the model with BP parameters was the lowest. The risk of renal progression started to increase at SBPd ≥ 125 mm Hg. This increasing risk of renal progression with SBPd ≥ 125 mm Hg remained significant, despite adjusting for the competing risk of all-cause death.

CONCLUSIONS

High SBPd is associated with renal progression following PCI, particularly when it is ≥125 mm Hg. This can be used as a risk classification and potential target of renoprotective therapies.

MEST-C pathological score and long-term outcomes of child and adult patients with Henoch-Schönlein purpura nephritis

BACKGROUND

Henoch-Schönlein purpura nephritis (HSPN), a small-vessel vasculitis, shares renal pathological features with immunoglobulin A nephropathy. Oxford classification of immunoglobulin A nephropathy pathology has been updated to the MEST-C score, but its application in HSPN remains unresolved.

METHODS

Two hundred and thirteen patients with biopsy-proven HSPN were retrieved from the Seoul National University Hospital between 2000 and 2017. Renal outcome risks (i.e., end-stage renal disease or doubling of serum creatinine) were evaluated according to MEST-C scores after stratification by age: 113 children aged < 18 years (9.2 ± 3.6 years) and 100 adults aged ≥18 years (38.6 ± 18.3 years). We pooled our data with four previous cohort studies in which MEST or MEST-C scores were described in detail.

RESULTS

Twenty-one child (19%) and 16 adult (16%) patients reached the renal outcome during the median follow-up periods of 12 years and 13 years, respectively (maximum 19 years). In children, M1 and T1/T2 scores revealed worse renal outcomes than did M0 and T0 scores, respectively, whereas the T score was the only factor related to worse outcomes in adult patients after adjusting for multiple clinical and laboratory variables. The pooled data showed that M1, S1, and T1/T2 in children and E1 and T1/T2 in adults were correlated with poorer renal outcomes than those of their counterpart scores.

CONCLUSIONS

The Oxford classification MEST-C scores can predict long-term renal outcomes in patients with HSPN.

Immunoblot analysis of a 10 kDa antigen in cyst fluid of Taenia solium metacestodes

The diagnostic value of a 10 kDa subunit of 150 kDa protein in cyst fluid (CF) of Taenia solium metacestodes was evaluated. Immunoblot analysis revealed that most sera from patients with neurocysticercosis recognized the 10 kDa subunit strongly (209/217 cases, 84.6%), while a few sera from individuals with other parasitic diseases including alveolar echinococcosis (AE, 2/20, 10%) and cystic echinococcosis (CE, 2/25, 8%) showed faint reactions. Sera of cases with other parasitic diseases, especially AE and CE, exhibited cross reactions against other bands in CF. Both differential immunoblot and immunoprecipitation analyses showed that the 10 kDa subunit was the most specific to cysticercosis and highly antigenic, whereas other components at 20-40, 64, 95 and 106 kDa in CF were cross-reactive. IgG subclass ELISA and immunoblot demonstrated that both IgG4 and IgG1 reactions were predominant in neurocysticercosis and recognized the 10 kDa.

Designing manufacturing and the clinic application of the prosthesis for the patients with large ocular defects

PURPOSE

To design and manufacture the prosthesis of external type for patients with ocular large defects.

METHODS

Silicon and polymethyl methacrylate (PMMA) were chosen the chief materials: The main procedures are: taking the impression, color mixture, making primitive form of the prosthesis, modification and fixation.

RESULTS

Fifty patients with large ocular defects took the prosthesis, the cosmetic results are satisfactory. All the patients give satisfactory comments on the 5 main indexes: color 90%, stereo-feeling 100%, peripheral difference 86%, fidelity 98% and fixation 86%.

CONCLUSIONS

To make ocular prosthesis, materials with good quality and complicated technology are needed. The prosthesis made by silicon combined with PMMA show good results. Among the procedures of making prosthesis, color mixture and fixation are the most important.