Ultrasound molecular imaging in evaluating the severity of ischemia-reperfusion injury-induced acute kidney injury

Introduction

Acute kidney injury (AKI) is a global public health problem involving high morbidity, rapid progression, and high mortality (1,2). It affects approximately 10–15% of hospitalized patients and over 50% of those in intensive care units (3). Although mild AKI may resolve within seven days without complications, severe cases are often irreversible and carry a high risk of progressing to chronic kidney disease (CKD) (4-6). Therefore, early and accurate assessment of the degree of AKI is crucial for determining the prognosis of patients.

Currently, urine output and creatinine are commonly used in clinical practice to monitor the onset and progression of AKI. However, the specificity and sensitivity of urine volume and creatinine are low. Many factors affect them (7)—for example, urine volume is easily influenced by diuretics and urinary tract pathology. In contrast, creatinine is easily affected by age, gender, protein intake, drugs, and so on, and creatinine will be significantly elevated only when the glomerular filtration rate is lower than 50% (8). Therefore, creatinine and urine output cannot detect AKI in its early stages, nor can they accurately assess the severity of AKI. Early biomarkers, such as cystatin C (Cys-C), kidney injury molecule-1 (KIM-1), and neutrophil gelatinase-associated lipocalin (NGAL), have been proposed for monitoring AKI (9,10). However, their clinical implementation is constrained by high detection costs and inconsistent diagnostic thresholds. Therefore, there is a need to develop more sensitive and cost-effective methods for the early assessment of AKI.

Ischemia-reperfusion injury (IRI) represents a major cause of AKI and is predominantly mediated by the renal inflammatory response (11,12). Several studies have confirmed that the timely, effective, and accurate assessment of renal inflammatory response aids in identifying AKI (7). During the initial phase of AKI, dysfunction of renal endothelial cells leads to the upregulation of adhesion molecule expression (13,14). Vascular cell adhesion molecule-1 (VCAM-1) is highly expressed mainly on vascular endothelial cells and dendritic cells in AKI, whereas it is not expressed or is expressed only in trace amounts in normal cells (15). Therefore, real-time dynamic monitoring of VCAM-1 expression can enable early assessment of the renal inflammatory response in AKI.

Ultrasound molecular imaging (USMI) enhances ultrasound imaging at the molecular level by utilizing targeted ultrasound contrast agents that accumulate in specific tissues, providing real-time visualization of molecular expression changes within tissues (16,17). USMI, based on VCAM-1-targeted microbubbles, has been widely used to monitor atherosclerosis and inflammatory bowel disease (18). At the same time, a few studies have used it to monitor AKI (19). Still, studies have yet to report on its application to evaluate the inflammatory response of different degrees of AKI.

Therefore, in the present study, we established bilateral renal mild and severe IRI-AKI models in mice to monitor early inflammatory responses using USMI with VCAM-1-targeted microbubbles. We also evaluated the effectiveness of USMI in assessing early injury severity in AKI. We present this article in accordance with the ARRIVE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-12/rc).

Methods

Experimental animals

A total of 66 male C57BL/6J mice, aged 6–8 weeks and weighing 22–24 grams, were obtained from the Medical Laboratory Animal Center of the Chinese People’s Liberation Army (PLA) General Hospital. The animal experiments were approved by the Animal Ethics Committee of the Chinese PLA General Hospital (No. 2022-X18-91), in compliance with the institutional guidelines for the care and use of animals. The experiments were conducted after a 1-week acclimatization period. The mice were randomly assigned to a mild IRI-AKI group (m-AKI group, ischemia 22 min) and a severe IRI-AKI group (s-AKI group, ischemia 35 min). Both groups were further subdivided based on reperfusion time (0.5, 2, 6, 12, 24 h). A sham-operated group (sham group) was established as the control, comprising six mice in each group.

Establishment of renal IRI-AKI model

Following weighing, mice received an intraperitoneal injection of 100 µL of 1% sodium pentobarbital per 10 g body weight for anesthesia. Once anesthetized, the lateral abdominal skin was prepared bilaterally and disinfected with iodophor. Mice were maintained on a thermostatic blanket at 37–38 ℃. A small incision of 1–2 cm was made longitudinally in the left lateral abdomen at 1.5 cm from the spine. The skin and ventral muscles were incised to expose both kidneys. The renal pedicles were separated, and the renal arteries and veins were exposed. The renal arteries and veins were clamped with non-invasive arterial clips. The kidneys were observed to gradually turn a purplish-brown color. The kidneys were then placed back into the abdominal cavity and the start time of clamping the left kidney was recorded. The right renal artery and vein were clamped with the same method, and the start time of the right renal clamp was recorded. Simultaneously, the mouse was covered with gauze to maintain body temperature. After each group reached the corresponding ischemia duration, the non-damaging arterial clips were released. The kidneys reverted to a pale red color within seconds. The abdominal wall and muscle tissue were sutured. The sham group did not undergo vascular clamping; all other steps were identical to the other groups. This study was performed by the same researcher, ensuring entirely consistent experimental conditions across all groups.

USMI protocol

When the corresponding reperfusion time was reached, USMI was performed using a Mindray Resona R9 color Doppler ultrasound diagnostic instrument equipped with an L11-3U linear array probe (Mindray, Shenzhen, China). Targeted microbubbles were prepared using the biotin-avidin method and carrying VCAM-1 polypeptide. The detailed preparation procedures were as described in previous research (20), with a concentration of 5×10⁶/mL, which was injected into the tail vein of the mice.

After allowing microbubbles to circulate freely for 3 minutes, the maximum coronal slice of the right kidney was selected. The probe was fixed, then switched to contrast mode (mechanical index 0.072, center frequency 5.6 MHz, gain 65 dB, image depth 1.5 cm, frame rate 12 frames/second). A destructive pulse was applied with a high mechanical index of 0.503 to destroy all microbubbles within 3 seconds. Immediately following destruction, imaging was performed for 10 seconds to capture signals from newly circulating microbubbles. The dynamic images were retained throughout the process. Signal intensity prior to the destructive pulse represents the total signal from both target-bound and free-circulating microbubbles, whereas signal intensity after destruction is from reperfused free-circulating microbubbles only. USMI signal quantification was calculated as the difference between pre- and post-destruction signal intensities, expressed as normalized intensity difference [NID (%) = (pre-destruction signal intensity − post-destruction signal intensity)/pre-destruction signal intensity × 100%] (21-23). All mice were imaged under identical conditions, with the same operator administering VCAM-1-targeted microbubbles, and all examinations were performed on the same instrument (with fixed parameters) to minimize experimental variation.

Detection of serum indicators

After USMI, blood was collected from the inferior vena cava of mice. After centrifugation, the supernatants were collected, and serum creatinine (Scr), blood urea nitrogen (BUN), and serum Cys-C levels were measured using a fully automated biochemical analyzer (Hitachi, Tokyo, Japan). In addition, the serum KIM-1 and NGAL levels were measured using enzyme-linked immunosorbent assay (ELISA) kits according to the manufacturer’s instructions (Jiangsu Meimian Industrial Co., Ltd., Jiangsu, China).

Renal histopathology evaluation

Half of the kidneys were fixed in 4% paraformaldehyde, dehydrated, and embedded in paraffin. Sections were stained with periodic acid-Schiff (PAS). A total of 10 non-overlapping fields at 400× magnification, five from the cortical region and five from the cortical-medullary junction, were randomly selected. Sections were blindly scored for acute tubular injury, including loss of brush borders, tubular dilatation, cast formation, tubular necrosis, and neutrophil infiltration (24). Renal tubular injury was graded on a six-point scale: 0 (normal), 1 (mild, 0–10%), 2 (moderate, 11–25%), 3 (severe, 26–49%), 4 (highly severe, 50–75%), and 5 (extensive, >75%) (25). All evaluations were performed by two pathologists who were blinded to the experimental conditions.

Terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate-nick end labeling (TUNEL) is the most commonly used method for detecting apoptosis (26). According to the manufacturer’s instructions (Roche, Basel, Switzerland), apoptosis was detected by TUNEL staining of kidney paraffin sections. The number of apoptotic cells was counted by randomly selecting five non-overlapping fields under 400× magnification.

Tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) can reflect the degree of inflammation in model mice. According to the manufacturer’s instructions, the levels of TNF-α and IL-6 in kidney tissue were measured using ELISA kits (Jiangsu Meimian Industrial Co., Ltd, Jiangsu, China).

Paraffin-embedded tissue sections were subjected to immunohistochemical staining to analyze VCAM-1 expression in renal tissue. Following routine dewaxing, endogenous peroxidase was inactivated using a 3% hydrogen peroxide solution, followed by antigen retrieval. After blocking with goat serum at room temperature for 30 minutes, sections were incubated overnight at 4 °C with rabbit anti-mouse VCAM-1 antibody (Abcam, Waltham, MA, USA) diluted 1:500. After washing, horseradish peroxidase (HRP)-labeled goat anti-rabbit secondary antibody was added and incubated at room temperature for 50 minutes. Subsequently, diaminobenzidine (DAB) was added for color development and counterstain nuclei with hematoxylin. Following dehydration, transparency, and neutral gum sealing, the renal tissue expression of VCAM-1 protein was observed and compared among groups under a microscope. Five non-overlapping fields of view at 400× magnification were randomly selected. Two pathologists, blinded to the experimental conditions, quantified the area of VCAM-1-positive staining in each group, recorded the results, and performed comparisons.

Statistical analysis

Statistical analyses were performed using GraphPad Prism 8.0 (GraphPad Software, San Diego, CA, USA) and SPSS Statistics (version 25.0; IBM Corp., Armonk, NY, USA). The Kolmogorov-Smirnov test was used to test the normal distribution of the data. One-way analysis of variance (ANOVA) was used to compare and analyze all quantitative parametric indicators. When the homogeneity of variance test P>0.05, Tukey’s multiple comparison post-hoc test was used; otherwise, Dunnett’s T3 post-hoc test was used. Pearson’s correlation was used to analyze the relationship between NID and pro-inflammatory factors, as well as VCAM-1 protein expression. Receiver operating characteristic (ROC) curves were drawn to evaluate the efficacy of commonly used renal function evaluation indicators, early AKI biomarkers, and NID in diagnosing mild and severe AKI at various reperfusion time points. P<0.05 was taken to indicate a statistically significant difference. A protocol was prepared before the study without registration.

Results

Comparison of USMI for quantifying renal inflammatory response in AKI

Figure 1A displays USMI images and time-intensity curves for the sham, m-AKI, and s-AKI groups at each reperfusion time point. The quantitative analysis revealed that NID levels at all reperfusion time points were significantly higher in the s-AKI group compared to both the m-AKI and sham groups (P<0.05). The NID levels in the m-AKI group at 2, 6, 12, and 24 hours after reperfusion were significantly higher than those in the sham group (P<0.05) (Figure 1B).

Figure 1 Comparison of USMI for quantifying renal inflammatory response. (A) Representative USMI images at different reperfusion time points. Red circles represent kidney outlines. (B) Comparison of the NID (%) (n=6 per group). *, P<0.05. AKI, acute kidney injury; m-AKI, mild ischemia-reperfusion injury-AKI; NID, normalized intensity difference; s-AKI, severe ischemia-reperfusion injury-AKI; USMI, ultrasound molecular imaging.

Comparison of renal tubular injury and serological indicators

Figure 2 compares renal tubular damage and serological indicators between the m-AKI and s-AKI groups at each reperfusion time point. PAS staining showed that renal tubular damage was present in the m-AKI and s-AKI groups at 6 and 12 hours, and in the s-AKI group at 24 hours, relative to the sham group. Damage was more severe in the s-AKI group than it was in the m-AKI group (Figure 2A). Renal tubular injury scores at 6 and 12 hours in both m-AKI and s-AKI groups, and at 24 hours in the s-AKI group, were significantly higher than those in the sham group (P<0.05). The s-AKI group consistently had higher scores than the m-AKI group (P<0.05). No significant difference was found between the m-AKI and s-AKI groups at 0.5 and 2 hours of reperfusion (P>0.05) (Figure 2B).

Figure 2 Comparison of renal tubular injury and serological indicators. (A) Histopathologic images of the kidney using PAS staining (scale: 20 μm). (B) Comparison of the renal tubular injury score (n=6 per group). (C) Comparison of the Scr levels (n=6 per group). (D) Comparison of the BUN levels (n=6 per group). (E) Comparison of the serum Cys-C levels (n=6 per group). (F) Comparison of the serum KIM-1 levels (n=6 per group). (G) Comparison of the serum NGAL levels (n=6 per group). *, P<0.05. AKI, acute kidney injury; Cys-C, cystatin C; KIM-1, kidney injury molecule-1; m-AKI, mild ischemia-reperfusion injury-AKI; NGAL, neutrophil gelatinase-associated lipocalin; PAS, periodic acid-Schiff; s-AKI, severe ischemia-reperfusion injury-AKI; Scr, serum creatinine.

Regarding the comparison of Scr and BUN levels, the results showed that the levels of reperfusion at 6, 12, and 24 hours in the s-AKI group were significantly higher than those in the m-AKI group (P<0.05). At the same time, the levels of 0.5 and 2 hours of reperfusion were not statistically significant between the two groups (P>0.05) (Figure 2C,2D).

The results of the serum assay showed that the Cys-C levels at 12 and 24 hours of reperfusion in the s-AKI group were higher than those in the m-AKI group (P<0.05) (Figure 2E). The KIM-1 levels were higher in the s-AKI group than in the m-AKI group at 6, 12, and 24 hours of reperfusion, and the difference was statistically significant (P<0.05) (Figure 2F). The NGAL levels at 2 and 24 hours of reperfusion in the s-AKI group were significantly higher than those in the m-AKI group (P<0.05) (Figure 2G).

Comparison of histological indicators

Figure 3A shows TUNEL staining to detect apoptosis in each group. The amount of apoptosis in the s-AKI group at 2, 6, 12, and 24 hours after reperfusion was higher than that in the m-AKI group, and the difference was statistically significant (P<0.05) (Figure 3B).

Figure 3 Comparison of histological indicators. (A) Representative images of TUNEL staining for the detection of cell apoptosis (scale: 20 μm). (B) Comparison of the number of apoptotic cells (400×) (n=6 per group). (C) Comparison of the tissue TNF-α levels (n=6 per group). (D) Comparison of the tissue IL-6 levels (n=6 per group). (E) Pearson’s correlation between the NID and tissue TNF-α level. (F) Pearson’s correlation between the NID and tissue IL-6 level. *, P<0.05. AKI, acute kidney injury; IL-6, interleukin-6; m-AKI, mild ischemia-reperfusion injury-AKI; NID, normalized intensity difference; s-AKI, severe ischemia-reperfusion injury-AKI; TNF-α, tumor necrosis factor-alpha; TUNEL, transferase-mediated deoxyuridine triphosphate-nick end labeling.

Proinflammatory factors TNF-α and IL-6 were consistently higher in the s-AKI group at multiple time points (P<0.05; Figure 3C,3D). Pearson’s correlation analysis revealed a strong positive correlation between NID and both TNF-α and IL-6 levels (r=0.794 and 0.748, P<0.001; Figure 3E,3F).

Immunohistochemistry revealed higher VCAM-1 expression in the s-AKI group at all reperfusion time points (P<0.05; Figure 4A,4B). NID was also positively correlated with VCAM-1 protein expression (r=0.852, P<0.001; Figure 4C).

Figure 4 Immunohistochemical staining detection of VCAM-1 protein expression. (A) Immunohistochemical images of VCAM-1 protein expression (scale: 20 μm). (B) Quantitative analysis of VCAM-1 protein expression (n=6 per group). (C) Pearson’s correlation analysis between the NID and VCAM-1 protein expression. *, P<0.05. AKI, acute kidney injury; m-AKI, mild ischemia-reperfusion injury-AKI; NID, normalized intensity difference; s-AKI, severe ischemia-reperfusion injury-AKI; VCAM-1, vascular cell adhesion molecule-1.

Comparison of the diagnostic efficacy by ROC curves

Figure 5A-5F shows the ROC curves of six indicators (Scr, BUN, Cys-C, KIM-1, NGAL, and NID) for the diagnosis of mild and severe AKI at 0.5, 2, 6, 12, and 24 hours of reperfusion, respectively. The corresponding area under the ROC curves (AUCs) and 95% confidence intervals (CIs) are shown in Table 1. The results showed that NID had good diagnostic efficacy in determining the degree of AKI at each reperfusion time point (P<0.05).

Figure 5 Comparison of the diagnostic efficacy by ROC curves. The ROC curves of six indicators (Scr, BUN, Cys-C, KIM-1, NGAL, and NID) for the diagnosis of mild and severe AKI at different reperfusion time points [(A) reperfusion 0.5 h; (B) reperfusion 2 h; (C) reperfusion 6 h; (D) reperfusion 12 h; (E) reperfusion 24 h; (F) total reperfusion time points]. AKI, acute kidney injury; BUN, blood urea nitrogen; Cys-C, cystatin C; KIM-1, kidney injury molecule-1; NGAL, neutrophil gelatinase-associated lipocalin; NID, normalized intensity difference; ROC, receiver operating characteristic; Scr, serum creatinine.

Discussion

In this study, mild and severe IRI-AKI models of bilateral kidneys in mice were established. USMI, based on VCAM-1-targeted microbubbles, was used to monitor the inflammatory response in the early stage of AKI, and the value of USMI in the early assessment of AKI was discussed. The results showed that NID in the s-AKI group was higher than that in the m-AKI group at different reperfusion time points, and the difference was statistically significant. Moreover, NID showed a positive correlation with VCAM-1 protein expression, as well as with TNF-α and IL-6 levels. In addition, USMI was shown to have a higher diagnostic efficacy in assessing the degree of AKI than commonly used renal function indicators and early AKI biomarkers. In brief, USMI has good value for evaluating different degrees of injury in early AKI.

The emergence of USMI, based on the construction of specific targeted ultrasound contrast agents, is an essential milestone in medical imaging (27). USMI enables non-invasive and continuous monitoring of diseases, such as inflammation and tumors, at cellular and molecular levels (23). In recent years, considerable work has been conducted on utilizing USMI for the early detection of AKI, primarily focusing on the development of targeted microbubbles with distinct targets to facilitate an early assessment of renal inflammatory responses in AKI. The degree of injury in AKI is closely related to the prognosis of patients (2). However, few studies have utilized USMI to evaluate the inflammatory response associated with different degrees of injury in AKI. Therefore, this study established mild and severe IRI-AKI mouse models to evaluate the effectiveness of USMI in assessing the severity of early AKI.

Some studies have reported that changing the renal ischemia time can establish IRI-AKI models with varying degrees of injury. Most studies have concluded that ischemia lasting shorter than 25 minutes can cause mild AKI, whereas ischemia lasting longer than 30 minutes can cause severe AKI (28). In our research, through preliminary experiments, we ultimately selected mice with bilateral kidney ischemia for 22 minutes as the m-AKI group and ischemia for 35 minutes as the s-AKI group. In addition, to achieve real-time dynamic evaluation of the severity of AKI, multiple reperfusion time points (0.5, 2, 6, 12, and 24 hours) were established at the early stage of injury in both the m-AKI and s-AKI groups, respectively.

Previous studies have confirmed that AKI can cause the kidney to synthesize pro-inflammatory cytokines, such as IL-6 and TNF-α (29,30). TNF-α further induces the expression of VCAM-1 protein (31). In this study, NID showed a strong linear positive correlation with the levels of TNF-α, IL-6, and VCAM-1 protein expression in AKI kidney tissues, indicating that USMI can effectively monitor the renal inflammatory response in AKI. In addition, the NID in the s-AKI group consistently exceeded that observed in the m-AKI group across multiple reperfusion time points. The ROC curves demonstrated that the USMI exhibited good diagnostic efficacy at all reperfusion time points, particularly at 0.5 and 2 hours of reperfusion, which was significantly better than commonly used indicators of renal function and early AKI biomarkers. In conclusion, USMI based on VCAM-1-targeted microbubbles can be used for the early assessment of the inflammatory response of AKI at different levels of injury and has an excellent ability to diagnose mild and severe AKI.

This study has some limitations. Firstly, we did not use non-targeted microbubbles as a control because previous in vitro and in vivo experiments had confirmed that VCAM-1-targeted microbubbles specifically bind to the kidneys of AKI mice expressing VCAM-1 (20). Secondly, we assessed only one ischemia time point in the m-AKI and s-AKI groups. Future studies will include additional time points to better evaluate the value of USMI with VCAM-1-targeted microbubbles in assessing early injury. Thirdly, the mouse AKI model does not fully represent clinical AKI in patients. Validation in large animal models is needed to support clinical translation.

Conclusions

USMI with VCAM-1-targeted microbubbles enables the early assessment of renal inflammation across varying degrees of AKI. This approach outperforms standard renal function indicators and early biomarkers, offering valuable information for diagnosis and injury assessment.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the ARRIVE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-12/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-24-12/dss

Funding: This study was supported by the Beijing Chao-Yang Hospital Golden Seeds Foundation (No. CYJZ202411), and the Youth Independent Innovation Science Fund Project of the Chinese PLA General Hospital (No. 22QNFC081).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-12/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The animal experiments were approved by the Animal Ethics Committee of the Chinese PLA General Hospital (No. 2022-X18-91), in compliance with the institutional guidelines for the care and use of animals.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Ronco C, Bellomo R, Kellum JA. Acute kidney injury. Lancet 2019;394:1949-64. [Crossref] [PubMed]
2. Bhatraju PK, Prince DK, Mansour S, Ikizler TA, Siew ED, Chinchilli VM, Garg AX, Go AS, Kaufman JS, Kimmel PL, Coca SG, Parikh CR, Wurfel MM, Himmelfarb J. Integrated Analysis of Blood and Urine Biomarkers to Identify Acute Kidney Injury Subphenotypes and Associations With Long-term Outcomes. Am J Kidney Dis 2023;82:311-321.e1. [Crossref] [PubMed]
3. Zhao Y, Pu M, Wang Y, Yu L, Song X, He Z. Application of nanotechnology in acute kidney injury: From diagnosis to therapeutic implications. J Control Release 2021;336:233-51. [Crossref] [PubMed]
4. Birkelo BC, Pannu N, Siew ED. Overview of Diagnostic Criteria and Epidemiology of Acute Kidney Injury and Acute Kidney Disease in the Critically Ill Patient. Clin J Am Soc Nephrol 2022;17:717-35. [Crossref] [PubMed]
5. Joannidis M, Meersch-Dini M, Forni LG. Acute kidney injury. Intensive Care Med 2023;49:665-8. [Crossref] [PubMed]
6. Gharaie S, Lee K, Newman-Rivera AM, Xu J, Patel SK, Gooya M, Arend LJ, Raj DS, Pluznick J, Parikh C, Noel S, Rabb H. Microbiome modulation after severe acute kidney injury accelerates functional recovery and decreases kidney fibrosis. Kidney Int 2023;104:470-91. [Crossref] [PubMed]
7. Pickkers P, Darmon M, Hoste E, Joannidis M, Legrand M, Ostermann M, Prowle JR, Schneider A, Schetz M. Acute kidney injury in the critically ill: an updated review on pathophysiology and management. Intensive Care Med 2021;47:835-50. [Crossref] [PubMed]
8. Zhang WR, Parikh CR. Biomarkers of Acute and Chronic Kidney Disease. Annu Rev Physiol 2019;81:309-33. [Crossref] [PubMed]
9. Rossiter A, La A, Koyner JL, Forni LG. New biomarkers in acute kidney injury. Crit Rev Clin Lab Sci 2024;61:23-44. [Crossref] [PubMed]
10. De Vlieger G, Forni L, Schneider A. New diagnostics for AKI in critically ill patients: what to expect in the future. Intensive Care Med 2022;48:1632-4. [Crossref] [PubMed]
11. Geo HN, Murugan DD, Chik Z, Norazit A, Foo YY, Leo BF, Teo YY, Kadir SZSBSA, Chan Y, Chai HJ, Medel M, Abdullah NA, Johns EJ, Vicent MJ, Chung LY, Kiew LV. Renal Nano-drug delivery for acute kidney Injury: Current status and future perspectives. J Control Release 2022;343:237-54. [Crossref] [PubMed]
12. Raup-Konsavage WM, Wang Y, Wang WW, Feliers D, Ruan H, Reeves WB. Neutrophil peptidyl arginine deiminase-4 has a pivotal role in ischemia/reperfusion-induced acute kidney injury. Kidney Int 2018;93:365-74. [Crossref] [PubMed]
13. Gonsalez SR, Cortês AL, Silva RCD, Lowe J, Prieto MC, Silva Lara LD. Acute kidney injury overview: From basic findings to new prevention and therapy strategies. Pharmacol Ther 2019;200:1-12. [Crossref] [PubMed]
14. Kellum JA, Romagnani P, Ashuntantang G, Ronco C, Zarbock A, Anders HJ. Acute kidney injury. Nat Rev Dis Primers 2021;7:52. [Crossref] [PubMed]
15. Mota SMB, Albuquerque PLMM, Meneses GC, da Silva GB Junior, Martins AMC, De Francesco Daher E. Role of endothelial biomarkers in predicting acute kidney injury in Bothrops envenoming. Toxicol Lett 2021;345:61-6. [Crossref] [PubMed]
16. Wang G, Song L, Hou X, Kala S, Wong KF, Tang L, Dai Y, Sun L. Surface-modified GVs as nanosized contrast agents for molecular ultrasound imaging of tumor. Biomaterials 2020;236:119803. [Crossref] [PubMed]
17. Zhang Z, Miao X, Yao W, Ren J, Chen C, Li X, Yang J, You Y, Lin Y, Yin T, Hei Z. Molecular ultrasound imaging of neutrophil membrane-derived biomimetic microbubbles for quantitative evaluation of hepatic ischemia-reperfusion injury. Theranostics 2021;11:6922-35. [Crossref] [PubMed]
18. Manea SA, Vlad ML, Rebleanu D, Lazar AG, Fenyo IM, Calin M, Simionescu M, Manea A. Detection of Vascular Reactive Oxygen Species in Experimental Atherosclerosis by High-Resolution Near-Infrared Fluorescence Imaging Using VCAM-1-Targeted Liposomes Entrapping a Fluorogenic Redox-Sensitive Probe. Oxid Med Cell Longev 2021;2021:6685612. [Crossref] [PubMed]
19. Yang J, Miao X, Guan Y, Chen C, Chen S, Zhang X, Xiao X, Zhang Z, Xia Z, Yin T, Hei Z, Yao W. Microbubble Functionalization with Platelet Membrane Enables Targeting and Early Detection of Sepsis-Induced Acute Kidney Injury. Adv Healthc Mater 2021;10:e2101628. [Crossref] [PubMed]
20. Ren L, Zhao Y, Wang T, Tong Y, Zhao P, Nie F, Luo Y, Zhu L. Ultrasound molecular imaging for early detection of acute renal ischemia-reperfusion injury. Bioeng Transl Med 2024;9:e10638. [Crossref] [PubMed]
21. Qiu C, Yin T, Zhang Y, Lian Y, You Y, Wang K, Zheng R, Shuai X. Ultrasound Imaging Based on Molecular Targeting for Quantitative Evaluation of Hepatic Ischemia-Reperfusion Injury. Am J Transplant 2017;17:3087-97. [Crossref] [PubMed]
22. Miao X, Sha T, Zhang W, Zhou H, Qiu C, Deng H, You Y, Ren J, Zhang X, Zheng R, Yin T. Liver Fibrosis Assessment by Viewing Sinusoidal Capillarization: US Molecular Imaging versus Two-dimensional Shear-Wave Elastography in Rats. Radiology 2022;304:473-82. [Crossref] [PubMed]
23. Kosareva A, Abou-Elkacem L, Chowdhury S, Lindner JR, Kaufmann BA. Seeing the Invisible-Ultrasound Molecular Imaging. Ultrasound Med Biol 2020;46:479-97. [Crossref] [PubMed]
24. Kudose S, Hoshi M, Jain S, Gaut JP. Renal Histopathologic Findings Associated With Severity of Clinical Acute Kidney Injury. Am J Surg Pathol 2018;42:625-35. [Crossref] [PubMed]
25. Yang B, Lan S, Dieudé M, Sabo-Vatasescu JP, Karakeussian-Rimbaud A, Turgeon J, Qi S, Gunaratnam L, Patey N, Hébert MJ. Caspase-3 Is a Pivotal Regulator of Microvascular Rarefaction and Renal Fibrosis after Ischemia-Reperfusion Injury. J Am Soc Nephrol 2018;29:1900-16. [Crossref] [PubMed]
26. Liu H, Zhang H, Yin N, Zhang Y, Gou J, Yin T, He H, Ding H, Zhang Y, Tang X. Sialic acid-modified dexamethasone lipid calcium phosphate gel core nanoparticles for target treatment of kidney injury. Biomater Sci 2020;8:3871-84. [Crossref] [PubMed]
27. Wang S, Hossack JA, Klibanov AL. Targeting of microbubbles: contrast agents for ultrasound molecular imaging. J Drug Target 2018;26:420-34. [Crossref] [PubMed]
28. Fu Y, Tang C, Cai J, Chen G, Zhang D, Dong Z. Rodent models of AKI-CKD transition. Am J Physiol Renal Physiol 2018;315:F1098-106. [Crossref] [PubMed]
29. Abdelmageed MM, Kefaloyianni E, Arthanarisami A, Komaru Y, Atkinson JJ, Herrlich A. TNF or EGFR inhibition equally block AKI-to-CKD transition: opportunities for etanercept treatment. Nephrol Dial Transplant 2023;38:1139-50. [Crossref] [PubMed]
30. Farooqui N, Zaidi M, Vaughan L, McKee TD, Ahsan E, Pavelko KD, Villasboas JC, Markovic S, Taner T, Leung N, Dong H, Alexander MP, Herrmann SM. Cytokines and Immune Cell Phenotype in Acute Kidney Injury Associated With Immune Checkpoint Inhibitors. Kidney Int Rep 2023;8:628-41. [Crossref] [PubMed]
31. Ellermann SF, Jongman RM, Luxen M, Kuiper T, Plantinga J, Moser J, Scheeren TWL, Theilmeier G, Molema G, Van Meurs M. Pharmacological inhibition of protein tyrosine kinases axl and fyn reduces TNF-α-induced endothelial inflammatory activation in vitro. Front Pharmacol 2022;13:992262. [Crossref] [PubMed]