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Event: 1865

Key Event Title

The KE title should describe a discrete biological change that can be measured. It should generally define the biological object or process being measured and whether it is increased, decreased, or otherwise definably altered relative to a control state. For example “enzyme activity, decreased”, “hormone concentration, increased”, or “growth rate, decreased”, where the specific enzyme or hormone being measured is defined. More help

Acute kidney injury (AKI)

Short name
The KE short name should be a reasonable abbreviation of the KE title and is used in labelling this object throughout the AOP-Wiki. The short name should be less than 80 characters in length. More help
Acute kidney injury (AKI)

Biological Context

Structured terms, selected from a drop-down menu, are used to identify the level of biological organization for each KE. Note, KEs should be defined within a particular level of biological organization. Only KERs should be used to transition from one level of organization to another. Selection of the level of biological organization defines which structured terms will be available to select when defining the Event Components (below). More help

Organ term

Further information on Event Components and Biological Context may be viewed on the attached pdf.The biological context describes the location/biological environment in which the event takes place.  For molecular/cellular events this would include the cellular context (if known), organ context, and species/life stage/sex for which the event is relevant. For tissue/organ events cellular context is not applicable.  For individual/population events, the organ context is not applicable. More help
Organ term

Key Event Components

Further information on Event Components and Biological Context may be viewed on the attached pdf.Because one of the aims of the AOP-KB is to facilitate de facto construction of AOP networks through the use of shared KE and KER elements, authors are also asked to define their KEs using a set of structured ontology terms (Event Components). In the absence of structured terms, the same KE can readily be defined using a number of synonymous titles (read by a computer as character strings). In order to make these synonymous KEs more machine-readable, KEs should also be defined by one or more “event components” consisting of a biological process, object, and action with each term originating from one of 22 biological ontologies (Ives, et al., 2017; See List). Biological process describes dynamics of the underlying biological system (e.g., receptor signalling). The biological object is the subject of the perturbation (e.g., a specific biological receptor that is activated or inhibited). Action represents the direction of perturbation of this system (generally increased or decreased; e.g., ‘decreased’ in the case of a receptor that is inhibited to indicate a decrease in the signalling by that receptor).Note that when editing Event Components, clicking an existing Event Component from the Suggestions menu will autopopulate these fields, along with their source ID and description. To clear any fields before submitting the event component, use the 'Clear process,' 'Clear object,' or 'Clear action' buttons. If a desired term does not exist, a new term request may be made via Term Requests. Event components may not be edited; to edit an event component, remove the existing event component and create a new one using the terms that you wish to add. More help
Process Object Action
Acute kidney injury occurrence

Key Event Overview

AOPs Including This Key Event

All of the AOPs that are linked to this KE will automatically be listed in this subsection. This table can be particularly useful for derivation of AOP networks including the KE. Clicking on the name of the AOP will bring you to the individual page for that AOP. More help
AOP Name Role of event in AOP Point of Contact Author Status OECD Status
Cyclooxygenase inhibition leading to acute kidney injury AdverseOutcome Arthur Author (send email) Under development: Not open for comment. Do not cite


This is a structured field used to identify specific agents (generally chemicals) that can trigger the KE. Stressors identified in this field will be linked to the KE in a machine-readable manner, such that, for example, a stressor search would identify this as an event the stressor can trigger. NOTE: intermediate or downstream KEs in one AOP may function as MIEs in other AOPs, meaning that stressor information may be added to the KE description, even if it is a downstream KE in the pathway currently under development.Information concerning the stressors that may trigger an MIE can be defined using a combination of structured and unstructured (free-text) fields. For example, structured fields may be used to indicate specific chemicals for which there is evidence of an interaction relevant to this MIE. By linking the KE description to a structured chemical name, it will be increasingly possible to link the MIE to other sources of chemical data and information, enhancing searchability and inter-operability among different data-sources and knowledgebases. The free-text section “Evidence for perturbation of this MIE by stressor” can be used both to identify the supporting evidence for specific stressors triggering the MIE as well as to define broad chemical categories or other properties that classify the stressors able to trigger the MIE for which specific structured terms may not exist. More help

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) can be selected from an ontology. In many cases, individual species identified in these structured fields will be those for which the strongest evidence used in constructing the AOP was available in relation to this KE. More help
Term Scientific Term Evidence Link
human Homo sapiens High NCBI
mice Mus sp. High NCBI
rat Rattus norvegicus High NCBI
zebra fish Danio rerio Moderate NCBI
dog Canis lupus familiaris High NCBI
cat Felis catus High NCBI
Equus caballus Equus caballus Low NCBI
chicken Gallus gallus Low NCBI
duck Anas platyrhynchos Low NCBI

Life Stages

The structured ontology terms for life-stage are more comprehensive than those for taxa, but may still require further description/development and explanation in the free text section. More help
Life stage Evidence
All life stages High

Sex Applicability

The authors must select from one of the following: Male, female, mixed, asexual, third gender, hermaphrodite, or unspecific. More help
Term Evidence
Unspecific High

Key Event Description

A description of the biological state being observed or measured, the biological compartment in which it is measured, and its general role in the biology should be provided. For example, the biological state being measured could be the activity of an enzyme, the expression of a gene or abundance of an mRNA transcript, the concentration of a hormone or protein, neuronal activity, heart rate, etc. The biological compartment may be a particular cell type, tissue, organ, fluid (e.g., plasma, cerebrospinal fluid), etc. The role in the biology could describe the reaction that an enzyme catalyses and the role of that reaction within a given metabolic pathway; the protein that a gene or mRNA transcript codes for and the function of that protein; the function of a hormone in a given target tissue, physiological function of an organ, etc. Careful attention should be taken to avoid reference to other KEs, KERs or AOPs. Only describe this KE as a single isolated measurable event/state. This will ensure that the KE is modular and can be used by other AOPs, thereby facilitating construction of AOP networks. More help

Acute kidney injury (AKI), also known as acute renal failure (ARF), is the defined by the sudden (within 48 hours) reduction in kidney function (Mehta et al., 2007). The decrease in function is associated with structural damage of the kidneys as well as with loss of key kidney functions. AKI can be classified into three categories: pre-renal AKI, acute post-renal obstructive nephropathy, and lastly intrinsic AKI. Pre-renal and post-renal AKI are the result of extra-renal diseases that lead to a reduction in glomerular filtration rate (GFR) whereas, intrinsic AKI represents true kidney disease. If persisted, pre- and post-renal AKI may evolve into intrinsic renal disease (Makris & Spanou, 2016).

AKI can manifest in several cellular targets, notably damage to the plasma membrane of kidney epithelial cells. In cases of necrotic cell injury, the cell membrane integrity is compromised resulting in extrusion of cellular components. With AKI derived apoptosis, phospholipids from the inner and outer membrane leaflet undergo translocation and membrane blebbing (Agarwal et al., 2016). Changes in the composition of lipid microdomains important for preventing cell injury have also been reported in AKI (Zager & Kalhorn, 2000). Additionally, changes in cytoskeleton actin have also been associated with AKI. Actin is important in cell-cell interactions, cell-matrix interaction and as a result can influence GFR and kidney function. The kidney is one of the most mitochondria rich organs in the body, coming in second only to the heart (Agarwal et al., 2016). Mitochondria dense cells are required to support the movement of sodium and other solutes across strong electrochemical gradients in renal tubular cells. Mitochondrial injury is shared across all forms of human AKI and is key in the pathophysiology of kidney injury (Parekh et al., 2013; Perazella, 2010; Takasu et al., 2013).

AKI is often under-recognized due to rapid return of kidney function however both single and repeated events of AKI are associated with increased morbidity and mortality outcomes (Chertow et al., 2005; Makris & Spanou, 2016; Praught & Shlipak, 2005). AKI is typically followed by a recovery period within days of the initial initiating event. AKI recovery is defined by a return to baseline kidney function, often measured through serum creatinine, blood urea nitrogen and glomerular filtration rates. Comorbidities that may alter the ability to recover from and increase individual risk for developing AKI include older age, genetic factors, hypertension, diabetes mellitus, cardiac disease, and chronic kidney disease (Forni et al., 2017). Furthermore, AKI has been documented to increase the risk of developing chronic kidney disease (CKD) and end stage kidney disease (ESKD) (Coca, Singanamala, & Parikh, 2012; See et al., 2019), cardiovascular disease (Coca et al., 2012; Odutayo et al., 2017), repeated hospitalization (Sawhney et al., 2017) and long-term mortality (Coca et al., 2012; See et al., 2019).

How It Is Measured or Detected

One of the primary considerations in evaluating AOPs is the relevance and reliability of the methods with which the KEs can be measured. The aim of this section of the KE description is not to provide detailed protocols, but rather to capture, in a sentence or two, per method, the type(s) of measurements that can be employed to evaluate the KE and the relative level of scientific confidence in those measurements. Methods that can be used to detect or measure the biological state represented in the KE should be briefly described and/or cited. These can range from citation of specific validated test guidelines, citation of specific methods published in the peer reviewed literature, or outlines of a general protocol or approach (e.g., a protein may be measured by ELISA).Key considerations regarding scientific confidence in the measurement approach include whether the assay is fit for purpose, whether it provides a direct or indirect measure of the biological state in question, whether it is repeatable and reproducible, and the extent to which it is accepted in the scientific and/or regulatory community. Information can be obtained from the OECD Test Guidelines website and the EURL ECVAM Database Service on Alternative Methods to Animal Experimentation (DB-ALM). ?

Serum and urine creatinine

The Acute Kidney Injury Network modified RIFLE (Risk, Injury, Failure, Loss, and End-stage kidney disease) criteria to suit the diagnosis of AKI based on serum creatinine levels (Table 1). This criterion provides a non-invasive way to classify AKI into three stages (Mehta et al., 2007). The two main assays used to measure creatinine levels in serum and urine are the Roche Jaffe and enzymatic creatinine assay (Niazpour et al., 2019; Peake & Whiting, 2006). In the Roche Jaffe method, creatinine reacts with picrate ions formed in an alkaline media to give off a distinct red-orange color which is then measured using a spectrophotometer at a wavelength of 505 nm. Alternatively, the enzymatic creatinine assay converts creatinine to creatine and then sarcosine which is the oxidized by sarcosine oxidase. The oxidation of sarcosine produces hydrogen peroxide which is quantified in the presence of peroxidase at 550 nm by the formation of a colored dye (Peake & Whiting, 2006). Both methods are cost and time effective, allowing for easy evaluation of creatinine in sample. Notably, there is argument surrounding the specificity and sensitivity of creatinine levels in predicting AKI as levels may fluctuate due to numerous other renal pathologies (C. L. Edelstein, 2017; Ostermann et al., 2020).


Serum creatinine criteria

Urine output criteria


> 0.3mg/dl or 150-200% (1.5- to 2.0-fold) from baseline

< 0.5 mg/kg per hour for > 6 hours


> 200-300% (2.0- to 3.0-fold) from baseline

< 0.5 ml/kg per hour for >12 hours


> 300% (3-fold) from baseline or > 4.0 mg/dl with an acute increase of > 0.5 mg/dl

< 0.3 ml/kg per hour for 24 hours or anuria for 12 hours

Table 1- Classification and staging of AKT base upon serum and urine creatinine criteria. Adapted from Mehta et al. 2007.

Blood urea nitrogen to creatinine ratio (BCR):

Blood urine nitrogen to creatinine ratio (BCR) has been used since the 1940s to distinguish prerenal AKI (PR AKI) from intrinsic AKI (I AKI). In normal conditions human BCR should be less then 20, with concentrations typically expressed in mmol/L. This ratio will rise above 20 in cases of AKI as blood urea nitrogen (BUN) rises and plasma creatinine will fall (Manoeuvrier et al., 2017) . Typically, measurement of BUN is done using the diacetyl or Fearon reaction or by enzymatic methods. The Fearon reaction results in the development of a yellow chromogen that is quantified by a photometer. Enzymatic methods, use urease to convert urea to ammonia and carbonic acid which are then quantified by absorbance or by measuring conductivity of the solution urea is hydrolyzed in (Hosten, 1990). Creatinine can be measured by the Roche Jaffe and enzymatic creatinine assay previously described (Niazpour et al., 2019; Peake & Whiting, 2006). Recent studies have suggested that BCR may not be adequate at distinguishing PR AKI from I AKI, however this method is still readily used in the diagnosis of AKI (Manoeuvrier et al., 2017; Uchino et al., 2012).

Kidney Injury Molecule-1 (KIM-1)

Kidney injury molecule-1 (KIM-1) is the main biomarker used in diagnosis of AKI as well as for measuring disease activity and progression (Lim et al., 2013; Tanase et al., 2019). A systematic review found that the sensitivity of KIM-1 in predicting AKI ranged from 92-100% (Don-Wauchope, 2011). KIM-1 orthologues have been used for assessment of AKI in rodents, humans, zebrafish monkeys and dogs (Bonventre, 2009). An enzyme linked immunosorbent assays (ELISA) is typically used to measure KIM-1 directly in serum blood and urine samples. This method uses an anti-KIM-1 antibody linked to a reporter enzyme to quantify KIM-1 protein expression using a microplate reader. Sandwich ELISAs that measure KIM-1 between two layers of antibodies have also been developed to decrease the steps and time required to process samples (Chaturvedi, Farmer, & Kapke, 2009).

Potential biomarkers:

Given the arguably low specificity and sensitivity of serum creatinine and BUN in predicting AKI, other protein biomarkers have been researched to aid in early identification. Interleukin-18 (IL-18) is a cytokine with immunoregulatory properties that has been shown to increase in urine of AKI patients (Vaidya et al., 2008). Importantly, IL-18 was able to predict AKI 1-2 days earlier then changes in serum creatinine. IL-18 can be measured by use of ELISA and comes with commercially available antibodies (Parikh et al., 2005). Proximal tubule enzymes including α-glutathione-S-transferase (α-GST), γ-glutamyl transpeptidase (γGT), and N-acetyl-β-glucosaminidase (NAG) have also shown to be upregulated in AKI and may be measured by ELISA (Vaidya et al., 2008; Westhuyzen et al., 2003).

Domain of Applicability

This free text section should be used to elaborate on the scientific basis for the indicated domains of applicability and the WoE calls (if provided). While structured terms may be selected to define the taxonomic, life stage and sex applicability (see structured applicability terms, above) of the KE, the structured terms may not adequately reflect or capture the overall biological applicability domain (particularly with regard to taxa). Likewise, the structured terms do not provide an explanation or rationale for the selection. The free-text section on evidence for taxonomic, life stage, and sex applicability can be used to elaborate on why the specific structured terms were selected, and provide supporting references and background information.  More help

AKI is most commonly described in humans, particularly during hospitalization however other species are at risk of AKI development (Chertow et al., 2005; Makris & Spanou, 2016; Parekh et al., 2013). AKI is often studied in mice (Hueper et al., 2013) and rat (Brar et al., 2014) animal models to understand disease pathogenesis. Furthermore, zebrafish models have been developed to study the pathophysiology of AKI and have been used to augment mammalian models (Cirio et al., 2015; Kim et al., 2020; Wen et al., 2018). AKI has also been documented in dogs (Thoen & Kerl, 2011), cats (Cole et al., 2017), and horses (Siwinska et al., 2020) following ischemic injury. Death following AKI is also of emerging concern for poultry animals including chickens and ducks as well as other tropical birds such as budgies but AKI in avian models is not as well understood (Lierz, 2003; Swayne et al., 1994). Some studies suggest that elderly individuals and males have a higher risk of developing AKI (Nie et al., 2018). Despite this AKI it is a condition that can occur independent of sex and age, having widespread effects over entire populations (Dixit et al., 2010).

Regulatory Significance of the Adverse Outcome

An AO is a specialised KE that represents the end (an adverse outcome of regulatory significance) of an AOP. For KEs that are designated as an AO, one additional field of information (regulatory significance of the AO) should be completed, to the extent feasible. If the KE is being described is not an AO, simply indicate “not an AO” in this section.A key criterion for defining an AO is its relevance for regulatory decision-making (i.e., it corresponds to an accepted protection goal or common apical endpoint in an established regulatory guideline study). For example, in humans this may constitute increased risk of disease-related pathology in a particular organ or organ system in an individual or in either the entire or a specified subset of the population. In wildlife, this will most often be an outcome of demographic significance that has meaning in terms of estimates of population sustainability. Given this consideration, in addition to describing the biological state associated with the AO, how it can be measured, and its taxonomic, life stage, and sex applicability, it is useful to describe regulatory examples using this AO. More help

Numerous regulatory bodies including the U.S. Food and Drug Administration (FDA), Environmental Protection Agency (EPA), Health Canada, Environment and Climate Change Canada (ECCC) and the European Chemicals Agency (ECHA) have used the outcome of AKI in the construction of human and environmental health assessments (ECHA, 2020b, 2020a; FDA, 2021; Health Canada & ECCC, 2017; USEPA, 2018b, 2018a, 2018c). To provide some brief examples, acute renal failure in pediatric patients was used as evidence in the human health hazard assessment of melamine conducted by the ECHA. This report also considered the increased risk of mortality and morbidity associated with AKI diagnosis (ECHA, 2020b). AKI was also used in the draft screen assessment of carboxylic acid groups conducted by Health Canada, outlining two instances where star fruit toxicity had led to AKI (Helath Canada & ECCC, 2017). In FDA guidance for industry reports which are used as regulatory criteria, AKI is assessed in the hazard assessment of cisplatin. Furthermore, in a report investigating new drug application safety, the FDA also recommends recording clinical data that may correlate with the adverse event of AKI such as monitoring serum creatinine levels (FDA, 2021). Lastly, AKI and associated biomarkers including oliguria and elevated BUN have been used in the EPAs problem formulation of carbon tetrachloride, 1,4-dioxane, and 1-bromopropane to provide evidence of adverse effects on renal function (U.S. Environmental Protection Agency, 2018b, 2018a, 2018c). 


List of the literature that was cited for this KE description. Ideally, the list of references, should conform, to the extent possible, with the OECD Style Guide ( (OECD, 2015). More help

European Chemicals Agency (ECHA) (2020a). Committee for Risk Assessment RAC: proposing harmonised classification and labelling at EU level of barium diboron tetraoxide (CLH-O-0000002720-08-03 / F). Helsinki, Finland.

European Chemicals Agency (ECHA) (2020b). Committee for Risk Assessment RAC: proposing harmonised classification and labelling at EU level of melamine (CLH-O-0000006932-69-01/F). Helsinki,Finland.

U.S. Food and Drug Administration (FDA)(2021). S5 ( R3 ) Detection of Reproductive and Developmental Toxicity for Human Pharmaceuticals Guidance for Industry. Silver Spring, Maryland.

Agarwal, A., Dong, Z., Harris, R., Murray, P., Parikh, S. M., Rosner, M. H., … Ronco, C. (2016). Cellular and Molecular Mechanisms of AKI. Journal of the American Society of Nephrology : JASN, 27(5), 1288–1299.

Bonventre, J. V. (2009). Kidney injury molecule-1 (KIM-1): a urinary biomarker and much more. Nephrology Dialysis Transplantation, 24(11), 3265–3268.

Brar, R., Singh, J. P., Kaur, T., Arora, S., & Singh, A. P. (2014). Role of GABAergic activity of sodium valproate against ischemia–reperfusion-induced acute kidney injury in rats. Naunyn-Schmiedeberg’s Archives of Pharmacology, 387(2), 143–151.

Health Canada & Environment and Climate Change Canada (ECCC)(2017). Draft Screening Assessment: Carboxylic Acid Groups. file:///Users/owner/Desktop/English%20Draft%20Screening%20Assessment%20Carboxylic%20Acids%20Group2.pdf

Chaturvedi, S., Farmer, T., & Kapke, G. F. (2009). Assay validation for KIM-1: human urinary renal dysfunction biomarker. International Journal of Biological Sciences, 5(2), 128–134.

Chertow, G. M., Burdick, E., Honour, M., Bonventre, J. V, & Bates, D. W. (2005). Acute Kidney Injury, Mortality, Length of Stay, and Costs in Hospitalized Patients. Journal of the American Society of Nephrology, 16 (11), 3365 LP – 3370.

Cirio, M. C., de Caestecker, M. P., & Hukriede, N. A. (2015). Zebrafish Models of Kidney Damage and Repair. Current Pathobiology Reports, 3(2), 163–170.

Coca, S. G., Singanamala, S., & Parikh, C. R. (2012). Chronic kidney disease after acute kidney injury: a systematic review and meta-analysis. Kidney International, 81(5), 442–448.

Cole, L., Jepson, R., & Humm, K. (2017). Systemic hypertension in cats with acute kidney injury. Journal of Small Animal Practice, 58(10), 577–581.

Dixit, M., Doan, T., Kirschner, R., & Dixit, N. (2010). Significant acute kidney injury due to non-steroidal antiinflammatory drugs: Inpatient setting. Pharmaceuticals, 3(4), 1279–1285.

Don-Wauchope, Y. H. and A. C. (2011). The Clinical Utility of Kidney Injury Molecule 1 in the Prediction, Diagnosis and Prognosis of Acute Kidney Injury: A Systematic Review. Inflammation & Allergy - Drug Targets (Discontinued).

Edelstein, C. L. (2017). Chapter Six - Biomarkers in Acute Kidney Injury. Biomarkers of Kidney Disease (Second Edition) (pp. 241–315). Academic Press.

Forni, L. G., Darmon, M., Ostermann, M., Oudemans-van Straaten, H. M., Pettilä, V., Prowle, J. R., … Joannidis, M. (2017). Renal recovery after acute kidney injury. Intensive Care Medicine, 43(6), 855–866.

Hosten, A. O. (1990). Clinical Methods: The History, Physical, and Laboratory Examinations. Annals of Internal Medicine, 113(7), 563.

Hueper, K., Rong, S., Gutberlet, M., Hartung, D., Mengel, M., Lu, X., … Gueler, F. (2013). T2 Relaxation Time and Apparent Diffusion Coefficient for Noninvasive Assessment of Renal Pathology After Acute Kidney Injury in Mice: Comparison With Histopathology. Investigative Radiology, 48(12), 834–842.

Kim, M.-J., Moon, D., Jung, S., Lee, J., & Kim, J. (2020). Cisplatin nephrotoxicity is induced via poly(ADP-ribose) polymerase activation in adult zebrafish and mice. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 318(5), R843–R854.

Lierz, M. (2003). Avian renal disease: Pathogenesis, diagnosis, and therapy. Veterinary Clinics of North America - Exotic Animal Practice, 6(1), 29–55.

Lim, A. I., Tang, S. C. W., Lai, K. N., & Leung, J. C. K. (2013). Kidney injury molecule-1: More than just an injury marker of tubular epithelial cells? Journal of Cellular Physiology, 228(5), 917–924.

Makris, K., & Spanou, L. (2016). Acute Kideny Injury: Definition, Pathophysiology and Clinical Phenotypes. Clinical Biochemist Reviews, 37(2), 85–98.

Manoeuvrier, G., Bach-Ngohou, K., Batard, E., Masson, D., & Trewick, D. (2017). Diagnostic performance of serum blood urea nitrogen to creatinine ratio for distinguishing prerenal from intrinsic acute kidney injury in the emergency department. BMC Nephrology, 18(1), 1–7.

Mehta, R. L., Kellum, J. A., Shah, S. V, Molitoris, B. A., Ronco, C., Warnock, D. G., … Network, A. K. I. (2007). Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury. Critical Care (London, England), 11(2), R31–R31.

Niazpour, F., Bahiraee, A., Esfahani, E. N., Abdollahi, M., Bandarian, F., & Razi, F. (2019). Comparison of glomerular filtration rate estimation using Jaffé and enzymatic creatinine assays in diabetic patients. Journal of Diabetes and Metabolic Disorders, 18(2), 551–556.

Nie, S., Feng, Z., Tang, L., Wang, X., He, Y., Fang, J., … Chen, X. (2018). Risk factor analysis for AKI including laboratory indicators: A nationwide multicenter study of hospitalized patients. Kidney and Blood Pressure Research, 42(5), 761–773.

Odutayo, A., Wong, C. X., Farkouh, M., Altman, D. G., Hopewell, S., Emdin, C. A., & Hunn, B. H. (2017). AKI and long-term risk for cardiovascular events and mortality. Journal of the American Society of Nephrology, 28(1), 377–387.

Ostermann, M., Zarbock, A., Goldstein, S., Kashani, K., Macedo, E., Murugan, R., … Ronco, C. (2020). Recommendations on Acute Kidney Injury Biomarkers From the Acute Disease Quality Initiative Consensus Conference: A Consensus Statement. JAMA Network Open, 3(10), e2019209–e2019209.

Parekh, D. J., Weinberg, J. M., Ercole, B., Torkko, K. C., Hilton, W., Bennett, M., … Venkatachalam, M. A. (2013). Tolerance of the Human Kidney to Isolated Controlled Ischemia. Journal of the American Society of Nephrology, 24(3), 506 LP – 517.

Parikh, C. R., Abraham, E., Ancukiewicz, M., & Edelstein, C. L. (2005). Urine IL-18 is an early diagnostic marker for acute kidney injury and predicts mortality in the intensive care unit. Journal of the American Society of Nephrology, 16(10), 3046–3052.

Peake, M., & Whiting, M. (2006). Measurement of serum creatinine--current status and future goals. The Clinical Biochemist. Reviews, 27(4), 173–184. Retrieved from

Perazella, M. A. (2010). Tenofovir-induced kidney disease: an acquired renal tubular mitochondriopathy. Kidney International, 78(11), 1060–1063.

Praught, M. L., & Shlipak, M. G. (2005). Are small changes in serum creatinine an important risk factor? Current Opinion in Nephrology and Hypertension, 14(3). Retrieved from

Sawhney, S., Marks, A., Fluck, N., McLernon, D. J., Prescott, G. J., & Black, C. (2017). Acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions. BMC Nephrology, 18(1), 9.

See, E. J., Jayasinghe, K., Glassford, N., Bailey, M., Johnson, D. W., Polkinghorne, K. R., … Bellomo, R. (2019). Long-term risk of adverse outcomes after acute kidney injury: a systematic review and meta-analysis of cohort studies using consensus definitions of exposure. Kidney International, 95(1), 160–172.

Siwinska, N., Zak, A., Slowikowska, M., Niedzwiedz, A., & Paslawska, U. (2020). Serum symmetric dimethylarginine concentration in healthy horses and horses with acute kidney injury. BMC Veterinary Research, 16(1), 396.

Swayne, D. E., Radin, M. J., Hoepf, T. M., & Slemons, R. D. (1994). Acute Renal Failure as the Cause of Death in Chickens Following Intravenous Inoculation with Avian Influenza Virus A/Chicken/Alabama/7395/75 (H4N8). Avian Diseases, 38(1), 151–157.

Takasu, O., Gaut, J. P., Watanabe, E., To, K., Fagley, R. E., Sato, B., … Hotchkiss, R. S. (2013). Mechanisms of cardiac and renal dysfunction in patients dying of sepsis. American Journal of Respiratory and Critical Care Medicine, 187(5), 509–517.

Tanase, D. M., Gosav, E. M., Radu, S., Costea, C. F., Ciocoiu, M., Carauleanu, A., … Rezus, C. (2019). The Predictive Role of the Biomarker Kidney Molecule-1 (KIM-1) in Acute Kidney Injury (AKI) Cisplatin-Induced Nephrotoxicity. International Journal of Molecular Sciences .

Thoen, M. E., & Kerl, M. E. (2011). Characterization of acute kidney injury in hospitalized dogs and evaluation of a veterinary acute kidney injury staging system: Acute kidney injury in dogs. Journal of Veterinary Emergency and Critical Care (San Antonio, Tex. : 2000), n/a.

U.S. Environmental Protection Agency (USEPA). (2018a). Problem Formulation of the Risk Evaluation for 1,4-Dioxane (EPA-740-R1-7012).

U.S. Environmental Protection Agency (USEPA). (2018b). Problem Formulation of the Risk Evaluation for Carbon Tetrachloride (Methane, Tetrachloro-) (EPA-740-R1-7020). Retrieved from

U.S. Environmental Protection Agency (USEPA). (2018c). Problem Formulation of the Risk Evaluation for Perchloroethylene (EPA-740-R1-7019). Retrieved from

Uchino, S., Bellomo, R., & Goldsmith, D. (2012). The meaning of the blood urea nitrogen/creatinine ratio in acute kidney injury. Clinical Kidney Journal, 5, 187–191.

Vaidya, V. S., Ferguson, M. A., & Bonventre, J. V. (2008). Biomarkers of acute kidney injury. Annual Review of Pharmacology and Toxicology, 48, 463–493.

Wen, X., Cui, L., Morrisroe, S., Maberry  Donald, J., Emlet, D., Watkins, S., … Kellum, J. A. (2018). A zebrafish model of infection-associated acute kidney injury. American Journal of Physiology. Renal Physiology, 315(2), F291–F299.

Westhuyzen, J., Endre, Z. H., Reece, G., Reith, D. M., Saltissi, D., & Morgan, T. J. (2003). Measurement of tubular enzymuria facilitates early detection of acute renal impairment in the intensive care unit. Nephrology Dialysis Transplantation, 18(3), 543–551.

Zager, R. A., & Kalhorn, T. F. (2000). Changes in Free and Esterified Cholesterol: Hallmarks of Acute Renal Tubular Injury and Acquired Cytoresistance. The American Journal of Pathology, 157(3), 1007–1016.