The Biomarker TCONS_00016233 Drives Septic AKI by Targeting the miR-22-3p/AIFM1 Signaling Axis

Zhang, Pan; Lee, Yi; Qu, Siyuan; Dai, Jinzhong; Li, Xiaozhou; Liu, Bohao; Li, Huiling; Ai, Kai; Zheng, Peilin; Qiu, Shuangfa; Li, Yijian; Wang, Yinhuai; Xiang, Xudong; Dong, Zheng; Zhang, Dongshan

doi:10.2139/ssrn.3469729

Cited by 10 publications

(12 citation statements)

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“…For over a decade, the study of biomarkers and urine volumes that can recognize AKI early and more reliably than serum creatinine elevated has been the focus of research [23][24][25][26] The machine learning model improves the accuracy of predicting AKI stages, which may be of great signi cance for middle-aged and older patients treated in ICU, especially for the patients with severe AKI in the future. For example, the model predicts that the patient would develop to AKI stage3 after 48 hours, based on which doctors in ICU could intervene in advance, such as disabling kidney damage drugs, rehydration therapy, or early CRRT.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Prediction of AKI Among Middle-Aged and Older in ICU: A Retrospective and Machine Learning Study

Wang¹,

Deng²,

Zhang³

et al. 2020

Preprint

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BackgroundAcute Kidney Injury (AKI), a major public health problem，is responsible for two-thirds of intensive care unit patients’ cost, and aging is an independent risk factor for AKI and its associated mortality and morbidity. The early recognition of AKI helps ICU caregivers to guide fluid treatment and titrate the dosing of the nephrotoxic drug. Therefore, it is desirable to build models to predict their position. The study is to build models based machine learning to predict AKI stage after 24 hours and 48 hours among middle-aged and older patients respectively in ICU. Methods and FindingsWe used two real-world databases to build and test models. The Medical Information Mart for Intensive Care (MIMIC-III v1.4) database for training, funded by National Institutes of Health (NIIH) built by the Computational Physiology Laboratory of MIT, Beth Israel Dikon Medical Center, and Philips Medical. The eICU Collaborative Research Database (eICU-CRD v 2.0) for the test is open-access, de-identified data sets of patients admitted to ICUs. 26316 patients in the overall cohort were generally older (median age ranging from 57 to 79) and 54% were male. Here we present three models, using the support vector machine (SVM), Long short-term memory (LSTM), and convolutional LSTM ConvLSTM respectively. the ConvLSTM model had the best performance in the test data set, and it has good ability and surpasses any previous model to predict whether older patients have AKI or not. The area under the receiver operating characteristic curve (AUC) of 24-hour prediction AKI is 99.79%, 48-hours AKI 99.43% during the hospital. we demonstrate that deep learning can handle lots of variables which may be predictors and that the algorithm achieved robust and excellent performance.ConclusionsTo our knowledge, this study is the first to use large-scale data collected from electronic health record（EHR）to prove the contribution of big data and deep learning methods to the real-time prediction of AKI prognosis in middle-aged and elderly patients. The model performance is better than any previous models. This work provides novel evidence to change clinical practice and precise personalized interventions.

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Section: Discussionmentioning

confidence: 99%

Real-Time Prediction of AKI Among Middle-Aged and Older in ICU: A Retrospective and Machine Learning Study

Wang¹,

Deng²,

Zhang³

et al. 2020

Preprint

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“…Immunohistochemical analyses were performed using anti-DsbA-L (1:50), Col I (1:100), Col III (1:100), and α-SMA (1:100) according to the previous protocol 41 , 42 , 53 . The immunofluorescence of DsbA-L and Hsp90 was carried out following the standard procedure 20 , 54 – 56 . Mitochondrial staining followed the standard operating procedure.…”

Section: Methodsmentioning

confidence: 99%

DsbA-L mediated renal tubulointerstitial fibrosis in UUO mice

Pan

et al. 2020

Nat Commun

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Recent studies have reported that upregulation of disulfide-bond A oxidoreductase-like protein (DsbA-L) prevented lipid-induced renal injury in diabetic nephropathy (DN). However, the role and regulation of proximal tubular DsbA-L for renal tubulointerstitial fibrosis (TIF) remains unclear. In current study, we found that a proximal tubules-specific DsbA-L knockout mouse (PT-DsbA-L-KO) attenuated UUO-induced TIF, renal cell apoptosis and inflammation. Mechanistically, the DsbA-L interacted with Hsp90 in mitochondria of BUMPT cells which activated the signaling of Smad3 and p53 to produce connective tissue growth factor (CTGF) and then resulted in accumulation of ECM of BUMPT cells and mouse kidney fibroblasts. In addition, the progression of TIF caused by UUO, ischemic/reperfusion (I/R), aristolochic acid, and repeated acute low-dose cisplatin was also alleviated in PT-DsbA-L-KO mice via the activation of Hsp90 /Smad3 and p53/CTGF axis. Finally, the above molecular changes were verified in the kidney biopsies from patients with obstructive nephropathy (Ob). Together, these results suggest that DsbA-L in proximal tubular cells promotes TIF via activation of the Hsp90 /Smad3 and p53/CTGF axis.

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“…NEAT1 promoted NF-κB-mediated inflammatory responses by inhibiting the expression of miR-204 ( 53 ). Additionally, lncRNA TCONS_00016233 was observed to aggravate septic AKI via the modulation of the miR-22-3p/apoptosis-inducing factor mitochondrion-associated 1 axis, which indicated that TCONS_00016233 not only acted as a diagnostic marker but also as a new target for sepsis-induced AKI therapy ( 87 ). Similarly, lncRNA SNHG14 was overexpressed in septic patient plasma with AKI, and SNHG14 could interact with miR-495-3p and exert influence on cell apoptosis, proliferation, and inflammatory response, thereby exacerbates sepsis-induced AKI ( 82 ).…”

Section: Lncrnas In Sepsis-induced Renal Dysfunctionmentioning

confidence: 99%

Long Non-Coding RNAs as Biomarkers and Therapeutic Targets in Sepsis

et al. 2021

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Sepsis, an infection-induced systemic inflammatory disorder, is often accompanied by multiple organ dysfunction syndromes with high incidence and mortality rates, and those who survive are often left with long-term sequelae, bringing great burden to social economy. Therefore, novel approaches to solve this puzzle are urgently needed. Previous studies revealed that long non-coding RNAs (lncRNAs) have exerted significant influences on the process of sepsis. The aim of this review is to summarize our understanding of lncRNAs as potential sepsis-related diagnostic markers and therapeutic targets, and provide new insights into the diagnosis and treatment for sepsis. In this study, we also introduced the current diagnostic markers of sepsis and discussed their limitations, while review the research advances in lncRNAs as promising biomarkers for diagnosis and prognosis of sepsis. Furthermore, the roles of lncRNAs in sepsis-induced organ dysfunction were illustrated in terms of different organ systems. Nevertheless, further studies should be carried out to elucidate underlying molecular mechanisms and pathological process of sepsis.

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The Biomarker TCONS_00016233 Drives Septic AKI by Targeting the miR-22-3p/AIFM1 Signaling Axis

Cited by 10 publications

References 0 publications

Real-Time Prediction of AKI Among Middle-Aged and Older in ICU: A Retrospective and Machine Learning Study

Real-Time Prediction of AKI Among Middle-Aged and Older in ICU: A Retrospective and Machine Learning Study

DsbA-L mediated renal tubulointerstitial fibrosis in UUO mice

Long Non-Coding RNAs as Biomarkers and Therapeutic Targets in Sepsis

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