Next Article in Journal
Pentraxin-3 Is a Strong Biomarker of Sepsis Severity Identification and Predictor of 90-Day Mortality in Intensive Care Units via Sepsis 3.0 Definitions
Previous Article in Journal
STAT3 Activation in Psoriasis and Cancers
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 11
Issue 10
10.3390/diagnostics11101905
diagnostics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Levels of Circulating MicroRNAs at 6-Hour Cardiac Arrest Can Predict 6-Month Poor Neurological Outcome

by
Sang Hoon Oh
1,
Ho-Shik Kim
2,
Kyu Nam Park
1,
Sanghee Ji
2,
Ji-Young Park
2,
Seung Pill Choi
3,
Jee Yong Lim
1,
Han Joon Kim
1,* and
on behalf of CROWN Investigators
†
1
Department of Emergency Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
2
Department of Biochemistry, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
3
Department of Emergency Medicine, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Korea
*
Author to whom correspondence should be addressed.
†
Membership of Cerebral Resuscitation and Outcome Evaluation Within Catholic Network Investigators is provided in Acknowledgments section.
Diagnostics 2021, 11(10), 1905; https://doi.org/10.3390/diagnostics11101905
Submission received: 25 August 2021 / Revised: 9 October 2021 / Accepted: 12 October 2021 / Published: 15 October 2021
(This article belongs to the Section Pathology and Molecular Diagnostics)
Browse Figures
Review Reports Versions Notes

Abstract

:
Early prognostication in cardiac arrest survivors is challenging for physicians. Unlike other prognostic modalities, biomarkers are easily accessible and provide an objective assessment method. We hypothesized that in cardiac arrest patients with targeted temperature management (TTM), early circulating microRNA (miRNA) levels are associated with the 6-month neurological outcome. In the discovery phase, we identified candidate miRNAs associated with cardiac arrest patients who underwent TTM by comparing circulating expression levels in patients and healthy controls. Next, using a larger cohort, we validated the prognostic values of the identified early miRNAs by measuring the serum levels of miRNAs, neuron-specific enolase (NSE), and S100 calcium-binding protein B (S100B) 6 h after cardiac arrest. The validation cohort consisted of 54 patients with TTM. The areas under the curve (AUCs) for poor outcome were 0.85 (95% CI (confidence interval), 0.72–0.93), 0.82 (95% CI, 0.70–0.91), 0.78 (95% CI, 0.64–0.88), and 0.77 (95% CI, 0.63–0.87) for miR-6511b-5p, -125b-1-3p, -122-5p, and -124-3p, respectively. When the cut-off was based on miRNA levels predicting poor outcome with 100% specificity, sensitivities were 67.7% (95% CI, 49.5–82.6), 50.0% (95% CI, 32.4–67.7), 35.3% (95% CI, 19.7–53.5), and 26.5% (95% CI, 12.9–44.4) for the above miRNAs, respectively. The models combining early miRNAs with protein biomarkers demonstrated superior prognostic performance to those of protein biomarkers.

1. Introduction

Since positive results of two landmark studies were reported in 2002 [1,2], targeted temperature management (TTM) has been accepted as the intervention to improve outcomes for patients resuscitated from cardiac arrest (CA). Currently, the recommendations for TTM from the European Resuscitation Council include maintaining a target temperature for all comatose cardiac arrest survivors at a constant value between 32 °C and 36 °C for at least 24 h and avoiding fever for at least 72 h after the return of spontaneous circulation (ROSC) in patients who remain in a coma [3]. Most deaths in patients with TTM are caused by irreversible ischemic brain injury [4]. However, approximately one-third of deaths are due to non-neurological causes, which usually occur in the first 3 days after the return of spontaneous circulation (ROSC) [5]. Therefore, early detection of whether they already have hypoxic brain injury or not is important for allowing further advanced treatment decisions and guiding post-cardiac arrest management strategies. However, early prognosis is challenging for physicians. Among commonly used prognostic tools, neurological examination is not always accurate due to the use of medications during TTM [6]. The detection of ischemic changes on neuroimaging tests can be delayed [7], and the results of electrophysiologic studies such as electroencephalography (EEG) and somatosensory evoked potential (SSEP) are inconsistent during the first days after CA [8,9]. Furthermore, interrater variability is frequently demonstrated with these tools [10,11]. While EEG findings are described with a standardized terminology, the skill of EEG interpretation is learned primarily in a master–apprentice format [12]. In a recent study, full-length EEGs from 103 comatose cardiac arrest patients were interpreted by four EEG specialists who were blinded for patient outcome; there was moderate interrater agreement for malignant EEG patterns [10]. The main source of disagreement in SSEP interpretation was related to the noise levels [13]. When SSEPs were interpreted independently by experienced clinical neurophysiologists, moderate interobserver agreement was found [11,13]. In contrast, serum biomarkers are unlikely to be affected by medications and are easy to assess without interrater variability.
Neuron-specific enolase (NSE) and S100 calcium-binding protein B (S100B) are the only biomarkers that have been used for the prediction of outcomes after CA [14,15]. However, current guidelines do not recommend a biomarker to predict a poor outcome in the early phase. MicroRNAs (miRNAs) are noncoding RNA molecules composed of 19–24 nucleotides that regulate gene expression by inhibiting or inactivating target messenger RNAs [16]. Recently, results of advanced molecular biology techniques have revealed that miRNAs can be used as candidate biomarkers in CA patients. After CA, neuronal miRNAs cross the disrupted blood–brain barrier (BBB) and can be measured in the plasma [17,18,19]; recent studies have reported the prognostic utility of these miRNAs [20,21,22]. While previous studies have assessed the prognostic value of circulating brain-enriched miRNAs at 48 h after CA, cardiac-enriched miRNAs are released early after cardiac injury [23]; Wander et al. demonstrated that circulating miRNAs during CA are related to resuscitation outcome [24].
Therefore, in the present study, we hypothesized that in CA patients with TTM, circulating miRNA expression at 6 h after ROSC is associated with 6-month neurological outcomes. We performed miRNA sequencing technology in a small subset of samples and then validated the differentially expressed miRNAs in a larger cohort using quantitative real-time polymerase chain reaction (qRT-PCR).

2. Materials and Methods

2.1. Study Protocol and Subjects

This prospective observational study was conducted in a single tertiary hospital in Seoul, South Korea. The study consisted of two phases: a discovery phase and a validation phase. The discovery phase aimed to identify candidate miRNAs for neurological prediction after CA. From June to July 2016, a convenience sample of 11 adult (>18 years of age) patients with TTM at 6 h after ROSC and 4 healthy controls were examined. We identified miRNAs with significantly different serum expression between the two groups. For the validation phase, we measured the expression of candidate miRNAs and the serum levels of NSE and S100B at 6 h after ROSC in consecutive TTM-treated patients from September 2016 to July 2018 (n = 54). During the study period, all comatose patients with ROSC were considered eligible for TTM at 33 °C or 36 °C for 24 h. The exclusion criteria included unavailable serum samples. This study was approved by our Institutional Review Board, and written informed consent was obtained from each patient’s next of kin.

2.2. Small RNA Sequencing

Small RNA sequencing libraries were constructed using the NEXTflex Small RNA sample preparation protocol with 70 ng of total RNA as input. The adapters were directly ligated to the total RNA as follows. For the first step, NEXTflex 3’ 4N adenylated adapter (5′ rApp/NNNNTGGAATTCTCGGGTGCCAAGG/3ddC/) and NEXTflex 5’ 4N adapter (5’ GUUCAGAGUUCUACAGUCCGACGAUCNNNN) were ligated to each end of the total RNA samples. The 5′ and 3′ NEXTflex adapter-ligated products were reverse transcribed by M-MuLV reverse transcriptase in the presence of RNA RT primer (5′ GCCTTGGCACCCGAGAATTCCA) to produce single-stranded complementary DNA (cDNA). The cDNA was then PCR amplified using a universal primer (5’ AATGATACGGCGACCACCGAGATCTACACGTTCAGAGTTCTACAGTCCGA) and a primer containing barcode sequences for 18 cycles. The PCR cycles consisted of 20 s at 95 °C, 30 s at 60 °C, and 15 s at 72 °C. The amplified cDNA was separated on a 6% TBE gel (Invitrogen, Waltham, MA, USA), and the 140–160 bp bands were excised. The cDNA was then eluted from the gel and concentrated by ethanol precipitation. The quality and size distribution of the adapter-ligated RNAs and amplified libraries were confirmed by electrophoresis on Agilent Bioanalyzer High Sensitivity DNA microfluidic chips (Agilent, Santa Clara, CA, USA). Libraries were quantified using the KAPA Library Quantification Kit (KK4824, Kapa Biosystems, Wilmington, MA, USA).
Subsequently, the library was sequenced using an Illumina HiSeq2500 that was set to rapid run mode. Cluster generation, followed by 2 × 100 cycle sequencing reads separated by paired-end turnaround, was performed on the instrument. Image analysis was performed using HiSeq Control Software version 1.8.4. The high-quality reads were then mapped onto the human reference genome (ENSEMBL release 72) using bowtie with the following parameters: only one mismatch (−n 1), max 80 sum of mismatch quals across alignment, 30 seed length, and the suppression of the reads showing >5 alignments [25,26]. The miRNA reads were counted using HTSeq with the ‘intersection-nonempty’ mode based on miRBase release 20 [27,28]. Based on the read counts for each miRNA, EdgeR was applied to analyze the differential expression between regions [29]. Differentially expressed miRNAs were identified with a significant p-value of < 0.05.

2.3. qRT-PCR

Small RNA was extracted from plasma using the miRNeasy Serum/Plasma Kit (Qiagen, Hilden, Germany). Small RNA from three 200 µL-plasma aliquots was extracted according to the manufacturer’s instructions, and finally, RNA was eluted with RNase-free water. The purity and quantity of extracted RNA were measured using a NanoDrop ND-2000 (Thermo Fisher Scientific, Wilmington, DE, USA). Since appropriate endogenous reference genes are absent in plasma, UniSp6 RNA was added to the extracted RNA samples before the cDNA synthesis reaction. First-strand cDNA synthesis and qPCR were performed using a miRCURY LNA miRNA PCR Assay kit (Qiagen, Hilden, Germany). After first-strand cDNA synthesis was completed, the cDNA was amplified by miRNA-specific and LNA-enhanced primers using an ABI 7300 Real-Time PCR System (Applied Biosystems, Carlsbad, CA, USA). Each miRNA level was normalized to the UniSp6 RNA level in the same sample, and the relative changes across samples were expressed relative to the spike-in UniSp6 using the ΔΔCt method [30].

2.4. Laboratory Measurement

NSE and S100B measurements were performed with Roche Elecsys reagents specific to NSE and S100B (Roche Diagnostics, Mannheim, Germany) in serum at 6 h after ROSC. If the serum showed significant hemolysis, the results were discarded. The serum levels of troponin T, N-terminal pro-brain natriuretic peptide, and total bilirubin were measured at admission.

2.5. Outcome Measurement

The primary endpoint was a 6-month poor neurological outcome defined as a cerebral performance category (CPC) score of 3–5. A CPC score of 1–2 indicated a good outcome. Additionally, to evaluate the hemodynamic state, whether the patient had a shock on admission and needed vasopressor support at 6 h was assessed.

2.6. Statistical Analysis

All data are summarized and displayed as the number (percentage) of patients in each group for categorical variables and as the median with interquartile range (IQR) for continuous variables. Comparisons of categorical variables between groups were made using Fisher’s exact test. After testing for normal distribution, continuous variables were compared using Mann–Whitney U tests. The ability of miRNAs and NSE or S100B to predict poor outcomes was assessed based on their sensitivity and specificity using an exact binomial 95% confidence interval (CI) and receiver operating characteristic (ROC) curve analysis. Pairwise area under the curve (AUC) comparisons were also performed between two predictors using the nonparametric approach [31]. We also created combined models using several logistic regression models and compared the AUCs of single biomarkers. The Pearson correlation coefficients between biomarkers were calculated (low, 0.10–0.29; moderate, 0.30–0.69; strong, 0.70–1.00). All analyses were performed using SPSS 24.0 software (IBM, SPSS Inc., Chicago, IL, USA). A value of p < 0.05 was considered significant for all analyses.

3. Results

3.1. Discovery Phase

We first performed small RNA sequencing to profile the expression of miRNAs in 4 healthy controls and 11 CA patients (good outcome, 5; poor outcome, 6). Baseline characteristics were not significantly different between the CA and control groups (Table S1, Supplementary Materials). A median of 413 (IQR, 371–542) known miRNAs and 179 (IQR, 130–254) novel miRNAs were identified in the 15 samples (Table S2, Supplementary Materials).

3.2. Validation Phase

From the differentially expressed miRNAs identified by small RNA sequencing, we selected 10 miRNAs (miR-6511b-5p, -125b-1-3p, -122-5p, -124-3p, -18b-3p, -511-5p, -519a-3p, -3180-3p, -24-2-5p, and -590-3p) and validated these miRNAs using qRT-PCR. The validation cohort consisted of 54 CA patients treated with TTM, of whom 20 patients had a good outcome and 34 had a poor outcome. The patients’ characteristics are summarized in Table 1. There was no difference between the outcome groups with regard to age, sex, comorbidities, or bystander cardiopulmonary resuscitation and witnessed arrest. Patients with poor outcomes had a higher rate of non-cardiac etiology arrest, non-shockable initial rhythm, and longer time to ROSC than those with good outcomes (all p < 0.05).
The circulating levels of the 10 candidate miRNAs at 6 h after ROSC were measured using qRT-PCR. All miRNAs showed significant differential expression between the outcome groups (Table S3, Supplementary Materials). Among these, four miRNAs (miR-6511b-5p, -125b-1-3p, -122-5p, and -124-3p) for which the expression difference between the groups was most significant were selected for additional statistical analysis.
To explore the function of these miRNAs, we analyzed gene ontologies using the PANTHER resource (http://pantherdb.org/) (Figure S1, Supplementary Materials) [32].
The circulating levels of the miRNAs and the serum levels of NSE and S100B according to the outcome group are shown in Figure 1 and Figure 2, respectively. ROC analysis of miR-6511b-5p, -125b-1-3p, -122-5p, and -124-3p showed AUCs of 0.85 (95% CI, 0.72–0.93), 0.82 (95% CI, 0.70–0.91), 0.78 (95% CI, 0.64–0.88), and 0.77 (95% CI, 0.63–0.87), respectively (Figure 3). When the cutoff was based on miRNA levels predicting poor outcome with 100% specificity, the sensitivities for the above miRNAs were 67.7% (95% CI, 49.5–82.6), 50.0% (95% CI, 32.4–67.7), 35.3% (95% CI, 19.7–53.5), and 26.5% (95% CI, 12.9–44.4), respectively. In addition, the AUCs of NSE and S100B measured at 6 h were 0.72 (95% CI, 0.58–0.86) and 0.85 (95% CI, 0.75–0.96), respectively, and were not significantly different from the AUCs of the four miRNAs (all p > 0.05). A cutoff of >63.52 ng mL−1 for NSE and >0.379 ng mL−1 for S100B yielded 100% specificity and 26.5% and 32.6% sensitivities for a poor outcome, respectively.
According to the hemodynamic state, differences in the expression levels of miR-6511b-5p, -125b-1-3p, and -122-5p were also observed (Table S4, Supplementary Materials).

3.3. Correlations between Biomarkers

The levels of miR-6511b-5p, -125b-1-3p, and -124-3p moderately correlated with the levels of NSE (r = 0.502, r = 0.479, and r = 0.456, respectively) and S100B (r = 0.344, r = 0.428, and r = 0.432, respectively) (Figure 4). However, there was no correlation between miR-122-5p and NSE or S100B (r = 0.076, p = 0.584 and r = 0201, p = 0.144, respectively). Regarding inter-miRNA expression, the correlation between miR-6511b-5p and miR-125b-1-3p was the strongest (r = 0.920, p < 0.001), and there was no correlation between miR-122-5p and miR-124-3p (r = 0.070, p = 0.616). The correlation coefficients between miRNAs and other laboratory values are presented in Table S5 (Supplementary Materials).

3.4. Prognostic Performance of miRNAs Combined with Protein Biomarkers

The AUCs of the different logistic regression models with combinations of various miRNAs added to either or both NSE and S100B were calculated (Table 2). The AUCs to predict poor outcomes at 6 months increased when various miRNAs were added to NSE or S100B. In particular, adding miR-6511b-5p or -125b-1-3p significantly improved the AUC of NSE (0.87 (95% CI, 0.78–96) vs. 0.72 (95% CI, 0.58–0.83), p = 0.029 and 0.86 (95% CI, 0.76–0.96) vs. 0.72 (95% CI, 0.58–0.83), p = 0.027, respectively). However, miR-122-5p and -124-3p did not. This trend was similar to the results of combining miRNAs with S100B or both NSE and S100B, although the differences were not significant (all p > 0.05).

4. Discussion

Our results indicated that the early expression of several miRNAs in the serum at 6 h after ROSC can predict 6-month neurological outcome in CA patients treated with TTM. In particular, miR-6511b-5p and -125b-1-3p predicted poor outcomes, with moderate sensitivity. The models combining early miRNAs with early NSE or S100B demonstrated superior performance to those of either NSE or S100B alone.
As a biomarker of ischemic stroke, miR-124-3p, a brain-enriched miRNA, has been well characterized previously [33,34]. In CA, Gilje et al. presented that circulating levels of miR-124-3p were increased in patients with poor outcome compared to those with good outcome at 24 h and 48 h after CA [21]. Further large-scale studies conducted in 579 cohorts confirmed the prognostic value of miR-124-3p at 48 h [35]. We identified miR-124-3p as a possible predictor even at 6 h after ROSC. This result is supported by several other studies [17,19,35]. Stefanizzi et al. reported that the circulating levels of brain-enriched miRNAs at 48 h after CA correlated with NSE levels [36]. Interestingly, according to our results, the circulating levels of miR-124-3p were also correlated with the serum levels of NSE and S100B at 6 h after CA.
In one pioneering study, miR-122 was overexpressed in the serum of poor outcome patients at 48 h after CA [20]; however, the prognostic values of miR-122 are inconsistent among different studies. Gilje et al. were unable to confirm that miR-122 has prognostic value after CA and found a decrease in the plasma levels of miR-122 in both outcome groups [21]. Another large-scale study reported that patients with low levels of miR-122-5p were at high risk for poor outcomes [37]. miR-122 is generally regarded as a liver-specific miRNA and is involved in lipid metabolism [38]. Our results indicated that, although increased miR-122-5p expression predicted poor outcome, it was not correlated with NSE, S100B, or miR-124-3p expression. Several possibilities might account for these variable results. First, the characteristics of the included cohorts between studies were different. Our ratios of non-shockable rhythm arrest and non-cardiac etiology arrest were generally higher than those in other published reports with opposite results. Post-CA syndrome is a heterogeneous entity that involves multiple organs as well as the brain and causes death through a variety of mechanisms. In our study, miR-122-5p expression was associated with bilirubin levels and the presence of shock, which induces multiple organ damage, such as damage to the liver. However, it was observed that these associations were not specific to miR-122-5p. Second, the temperature at the timing of miRNA sampling could impact miRNA expression. Hypothermia regulates miRNA expression by enhancing the processing of pre-miRNAs by Dicer [39,40,41]. In the porcine cardiogenic shock model, mildly induced hypothermia decreased the plasma levels of miR-122 [42]. Therefore, miRNA expression at the early phase of hypothermia could be different from that of other studies.
The miRNAs identified as prognostic candidate biomarkers in our study deserve further acknowledgement. The prognostic values of miR-6511b-5p and -125b-1-3p were superior to those of well-known miRNA and protein biomarkers, and their high expressions had 100% specificity with moderate sensitivity for poor outcomes. miR-125b has been demonstrated to negatively regulate NR2A-containing N-methyl-D-aspartate receptor [43], while activation of this glutamate receptor exerts neuroprotective effects and promotes neuronal survival against excitotoxicity-mediated neuronal damage [44]. Meanwhile, miR-6511b-5p has been poorly characterized. Since their discovery in 2001, miRNAs have become potential biomarkers of neurological and cardiovascular disease. However, they have rarely been studied in CA. Further studies are warranted to confirm that miR-6511b-5p has good prognostic performance for these patients.
Early detection and stratification of brain injury can help clinicians optimize the dose of in-hospital treatment [45]. When combined with protein biomarkers, the miRNAs showed higher prognostic value. The combination of NSE, S100B, and miR-6511b-5p has the best AUC value, and interestingly, models adding a miRNA to NSE are slightly better than the S100B model. This might be due to the kinetics of S100b, which is an “earlier” biomarker than NSE, with a tendency to decrease over time, compared to NSE [46,47].
Strengths of this study include the various protein biomarkers available and systematic approach to discover various miRNA candidates. Because patients with non-neurological death could be categorized into the poor outcome group, we used NSE and S100B as surrogate markers of brain injury. We also performed miRNA sequencing and then validated the differentially expressed miRNAs. Through this approach, we identified several miRNAs for early prediction.
Our study should be interpreted in the context of the following limitations. First, this study included a relatively small number of patients. Therefore, we could not adjust variables to evaluate the mechanisms underlying these observed differences in miRNA expression. Second, although the aim of the present study was to investigate miRNA candidates as early biomarkers, we did not conduct serial measurements of miRNAs. The kinetics of release of brain-derived miRNAs after CA could not be accurately characterized. Finally, although miRNAs have been reported to have some potential to attenuate ischemic brain injury [48], we only focused on the prognostic value of miRNA with increased expression as a poor outcome predictor. Thus, further studies using a larger sample size are necessary to confirm our findings and determine the precise reasons for the observed differences in miRNA expression.

5. Conclusions

In this small sample study, we identified several miRNAs for the early prediction of 6-month neurological outcomes in TTM-treated CA patients. In particular, high expressions of miR-6511b-5p and -125b-1-3p at 6 h after ROSC predicted poor outcomes, with moderate sensitivities, and may complement other biomarkers. Our results need to be validated in larger cohorts.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/diagnostics11101905/s1. Figure S1: Gene ontology analysis of miR-6511b-5p, -125b-1-3p, -122-5p, and -124-3p; Table S1: Baseline characteristics of the discovery cohort; Table S2: Summary of small RNA sequencing in a small cohort; Table S3: Comparisons of miRNA expression levels between patients with good outcome and poor outcome; Table S4: miRNA expression levels according to the hemodynamic state; Table S5: The correlation coefficient between miRNAs and other laboratory values.

Author Contributions

Conceptualization, S.H.O., H.-S.K., and H.J.K.; methodology, H.-S.K. and K.N.P.; formal analysis, S.H.O.; investigation, S.J. and J.-Y.P.; data curation, J.Y.L.; writing—original draft preparation, S.H.O.; writing—review and editing, H.-S.K. and H.J.K.; supervision, S.P.C.; project administration, K.N.P. and H.J.K.; funding acquisition, H.J.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2016R1D1A1B03933862).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of the Catholic University of Korea, Seoul Saint Mary’s Hospital (KC16TISI0006), approved on 22/2/2016.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to legal restrictions.

Acknowledgments

The following investigators participated in the Cerebral Resuscitation and Outcome Evaluation Within Catholic Network: Kyu Nam Park, Young Min Kim, Han Joon Kim, Chun Song Youn, Sang Hoon Oh, Jee Yong Lim, Hyo Joon Kim, Hwan Song, Seung Pill Choi, Soo Hyun Kim, Jae Hun Oh, Joo Suk Oh, Won Jung Jeong, Ji Hoon Kim, Seon Hee Woo.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. The Hypothermia after Cardiac Arrest Study Group. Mild Therapeutic Hypothermia to Improve the Neurologic Outcome after Cardiac Arrest. N. Engl. J. Med. 2002, 346, 549–556. [Google Scholar] [CrossRef]
  2. Bernard, S.A.; Gray, T.W.; Buist, M.D.; Jones, B.M.; Silvester, W.; Gutteridge, G.; Smith, K. Treatment of Comatose Survivors of Out-of-Hospital Cardiac Arrest with Induced Hypothermia. N. Engl. J. Med. 2002, 346, 557–563. [Google Scholar] [CrossRef]
  3. Nolan, J.P.; Sandroni, C.; Böttiger, B.W.; Cariou, A.; Cronberg, T.; Friberg, H.; Genbrugge, C.; Haywood, K.; Lilja, G.; Moulaert, V.R.M.; et al. European Resuscitation Council and European Society of Intensive Care Medicine guidelines 2021: Post-Resuscitation Care. Intensiv. Care Med. 2021, 47, 369–421. [Google Scholar] [CrossRef]
  4. Witten, L.; Gardner, R.; Holmberg, M.J.; Wiberg, S.; Moskowitz, A.; Mehta, S.; Grossestreuer, A.V.; Yankama, T.; Donnino, M.W.; Berg, K.M. Reasons for Death in Patients Successfully Resuscitated from Out-of-Hospital and In-Hospital Cardiac Arrest. Resuscitation 2019, 136, 93–99. [Google Scholar] [CrossRef]
  5. Lemiale, V.; Dumas, F.; Mongardon, N.; Giovanetti, O.; Charpentier, J.; Chiche, J.D.; Carli, P.; Mira, J.P.; Nolan, J.; Cariou, A. In-tensive Care Unit Mortality after Cardiac Arrest: The Relative Contribution of Shock and Brain Injury in a Large Cohort. Intensive Care Med. 2013, 39, 1972–1980. [Google Scholar] [CrossRef]
  6. Chamorro, C.; Borrallo, J.M.; Romera, M.A.; Silva, J.A.; Balandín, B. Anesthesia and Analgesia Protocol during Therapeutic Hypo-Thermia after Cardiac Arrest: A Systematic Review. Anesth. Analg. 2010, 110, 1328–1335. [Google Scholar] [CrossRef]
  7. Wu, O.; Greer, D.M. Neuroimaging in Cardiac Arrest Prognostication. Semin. Neurol. 2017, 37, 66–74. [Google Scholar] [CrossRef]
  8. Cloostermans, M.C.; van Meulen, F.B.; Eertman, C.J.; Hom, H.W.; van Putten, M.J. Continuous Electroencephalography Monitoring for Early Prediction of Neurological Outcome in Postanoxic Patients after Cardiac Arrest: A Prospective Cohort Study. Crit. Care Med. 2012, 40, 2867–2875. [Google Scholar] [CrossRef]
  9. Oh, S.H.; Park, K.N.; Shon, Y.M.; Kim, Y.M.; Kim, H.J.; Youn, C.S.; Kim, S.H.; Choi, S.P.; Kim, S.C. Continuous Amplitude-Integrated Electroencephalographic Monitoring is a Useful Prognostic Tool for Hypothermia-Treated Cardiac Arrest Patients. Circulation 2015, 132, 1094–1103. [Google Scholar] [CrossRef]
  10. Westhall, E.; Rosén, I.; Rossetti, A.O.; van Rootselaar, A.-F.; Kjaer, T.W.; Friberg, H.; Horn, J.; Nielsen, N.; Ullén, S.; Cronberg, T. Interrater Variability of EEG Interpretation in Comatose Cardiac Arrest Patients. Clin. Neurophysiol. 2015, 126, 2397–2404. [Google Scholar] [CrossRef]
  11. Pfeifer, R.; Weitzel, S.; Günther, A.; Berrouschot, J.; Fischer, M.; Isenmann, S.; Figulla, H.R. Investigation of the Inter-Observer Var-Iability Effect on the Prognostic Value of Somatosensory Evoked Potentials of the Median Nerve (SSEP) in Cardiac Arrest Survivors Using an SSEP Classification. Resuscitation 2013, 84, 1375–1381. [Google Scholar] [CrossRef]
  12. Grant, A.C.; Abdel-Baki, S.G.; Weedon, J.; Arnedo, V.; Chari, G.; Koziorynska, E.; Lushbough, C.; Maus, D.; McSween, T.; Mortati, K.A.; et al. EEG Interpretation Reliability and Interpreter Confidence: A Large Single-Center Study. Epilepsy Behav. 2014, 32, 102–107. [Google Scholar] [CrossRef] [Green Version]
  13. Zandbergen, E.; Hijdra, A.; de Haan, R.; van Dijk, J.; de Visser, B.O.; Spaans, F.; Tavy, D.; Koelman, J. Interobserver Variation in the Interpretation of Sseps in Anoxic–Ischaemic Coma. Clin. Neurophysiol. 2006, 117, 1529–1535. [Google Scholar] [CrossRef]
  14. Callaway, C.W.; Donnino, M.W.; Fink, E.L.; Geocadin, R.G.; Golan, E.; Kern, K.B.; Leary, M.; Meurer, W.J.; Peberdy, M.A.; Thompson, T.M. Part 8: Post-Cardiac Arrest Care: 2015 American Heart Association Guidelines Update for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation 2015, 132, S465–S482. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Nolan, J.P.; Soar, J.; Cariou, A.; Cronberg, T.; Moulaert, V.R.; Deakin, C.D.; Bottiger, B.W.; Friberg, H.; Sunde, K.; Sandroni, C. European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015: Section 5 of the European Resuscitation Council Guidelines for Resuscitation 2015. Resuscitation 2015, 95, 202–222. [Google Scholar] [CrossRef]
  16. Mitchell, P.; Parkin, R.K.; Kroh, E.M.; Fritz, B.R.; Wyman, S.K.; Pogosova-Agadjanyan, E.L.; Peterson, A.; Noteboom, J.; O’Briant, K.C.; Allen, A.; et al. Circulating MicroRNAs as Stable Blood-Based Markers for Cancer Detection. Proc. Natl. Acad. Sci. USA 2008, 105, 10513–10518. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Rink, C.; Khanna, S. MicroRNA in Ischemic Stroke Etiology and Pathology. Physiol. Genom. 2011, 43, 521–528. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Gan, C.; Wang, C.; Tan, K. Short Communication Circulatory microRNA-145 Expression is Increased in Cerebral Ischemia. Genet. Mol. Res. 2012, 11, 147–152. [Google Scholar] [CrossRef] [PubMed]
  19. Jeyaseelan, K.; Lim, K.Y.; Armugam, A. MicroRNA Expression in the Blood and Brain of Rats Subjected to Transient Focal Ischemia by Middle Cerebral Artery Occlusion. Stroke 2008, 39, 959–966. [Google Scholar] [CrossRef] [Green Version]
  20. Stammet, P.; Goretti, E.; Vausort, M.; Zhang, L.; Wagner, D.R.; Devaux, Y. Circulating MicroRNAs after Cardiac Arrest*. Crit. Care Med. 2012, 40, 3209–3214. [Google Scholar] [CrossRef] [PubMed]
  21. Gilje, P.; Gidlöf, O.; Rundgren, M.; Cronberg, T.; Al-Mashat, M.; Olde, B.; Friberg, H.; Erlinge, D. The Brain-Enriched MicroRNA miR-124 in Plasma Predicts Neurological Outcome after Cardiac Arrest. Crit. Care 2014, 18, R40. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Boileau, A.; Somoza, A.S.; Dankiewicz, J.; Stammet, P.; Gilje, P.; Erlinge, D.; Hassager, C.; Wise, M.P.; Kuiper, M.; Friberg, H.; et al. Circulating Levels of miR-574-5p are Associated with Neurological Outcome after Cardiac Arrest in Women: A Target Temperature Management (TTM) Trial Substudy. Dis. Markers 2019, 2019, 1802879. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Devaux, Y.; Vausort, M.; Goretti, E.; Nazarov, P.; Azuaje, F.; Gilson, G.; Corsten, M.F.; Schroen, B.; Lair, M.-L.; Heymans, S.; et al. Use of Circulating MicroRNAs to Diagnose Acute Myocardial Infarction. Clin. Chem. 2012, 58, 559–567. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Wander, P.; Enquobahrie, D.; Pritchard, C.; McKnight, B.; Rice, K.; Christiansen, M.; Lemaitre, R.; Rea, T.; Siscovick, D.; Sotoodehnia, N. Circulating MicroRNAs and Sudden Cardiac Arrest Outcomes. Resuscitation 2016, 106, 96–101. [Google Scholar] [CrossRef] [Green Version]
  25. Flicek, P.; Ahmed, I.; Amode, M.R.; Barrell, D.; Beal, K.; Brent, S.; Carvalho-Silva, D.; Clapham, P.; Coates, G.; Fairley, S. Ensembl 2013. Nucleic Acids Res. 2013, 41, D48–D55. [Google Scholar] [CrossRef]
  26. Langmead, B.; Trapnell, C.; Pop, M.; Salzberg, S.L. Ultrafast and Memory-Efficient Alignment of Short DNA Sequences to the Human Genome. Genome Biol. 2009, 10, 1–10. [Google Scholar] [CrossRef] [Green Version]
  27. Anders, S.; Pyl, P.T.; Huber, W. HTSeq—A Python Framework to Work with High-Throughput Sequencing Data. Bioinformatics 2014, 31, 166–169. [Google Scholar] [CrossRef]
  28. Griffiths-Jones, S. miRBase: The MicroRNA Sequence. Database 2006, 342, 129–138. [Google Scholar] [CrossRef]
  29. Robinson, M.D.; McCarthy, D.; Smyth, G. EdgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data. Bioinformatics 2010, 26, 139–140. [Google Scholar] [CrossRef] [Green Version]
  30. Schmittgen, T.D.; Livak, K.J. Analyzing Real-Time PCR Data by the Comparative C(T) Method. Nat. Protoc. 2008, 3, 1101–1108. [Google Scholar] [CrossRef]
  31. DeLong, E.R.; DeLong, D.M.; Clarke-Pearson, D.L. Comparing the Areas under Two or More Correlated Receiver Operating Charac-Teristic Curves: A Nonparametric Approach. Biometrics 1988, 44, 837–845. [Google Scholar] [CrossRef] [PubMed]
  32. Mi, H.; Ebert, D.; Muruganujan, A.; Mills, C.; Albou, L.-P.; Mushayamaha, T.; Thomas, P.D. PANTHER Version 16: A Revised Family Classification, Tree-Based Classification Tool, Enhancer Regions and Extensive API. Nucleic Acids Res. 2021, 49, D394–D403. [Google Scholar] [CrossRef] [PubMed]
  33. Sun, Y.; Gui, H.; Li, Q.; Luo, Z.-M.; Zheng, M.-J.; Duan, J.-L.; Liu, X. MicroRNA-124 Protects Neurons Against Apoptosis in Cerebral Ischemic Stroke. CNS Neurosci. Ther. 2013, 19, 813–819. [Google Scholar] [CrossRef]
  34. He, X.-W.; Shi, Y.-H.; Liu, Y.-S.; Li, G.-F.; Zhao, R.; Hu, Y.; Lin, C.-C.; Zhuang, M.-T.; Su, J.-J.; Liu, J.-R. Increased Plasma Levels of Mir-124-3p, Mir-125b-5p and Mir-192-5p are Associated with Outcomes in Acute Ischaemic Stroke Patients Receiving Thrombolysis. Atherosclerosis 2019, 289, 36–43. [Google Scholar] [CrossRef]
  35. Devaux, Y.; Dankiewicz, J.; Salgado-Somoza, A.; Stammet, P.; Collignon, O.; Gilje, P.; Gidlöf, O.; Zhang, L.; Vausort, M.; Hassager, C. Association of Circulating MicroRNA-124-3p Levels with Outcomes after Out-of-Hospital Cardiac Arrest: A Substudy of a Randomized Clinical Trial. JAMA Cardiol. 2016, 1, 305–313. [Google Scholar] [CrossRef] [Green Version]
  36. Stefanizzi, F.M.; Nielsen, N.; Zhang, L.; Dankiewicz, J.; Stammet, P.; Gilje, P.; Erlinge, D.; Hassager, C.; Wise, M.P.; Kuiper, M.; et al. Circulating Levels of Brain-Enriched MicroRNAs Correlate with Neuron Specific Enolase after Cardiac Arrest—A Substudy of the Target Temperature Management Trial. Int. J. Mol. Sci. 2020, 21, 4353. [Google Scholar] [CrossRef]
  37. Devaux, Y.; Salgado-Somoza, A.; Dankiewicz, J.; Boileau, A.; Stammet, P.; Schritz, A.; Zhang, L.L.; Vausort, M.; Gilje, P.; Erlinge, D.; et al. Incremental Value of Circulating MiR-122-5p to Predict Outcome after Out of Hospital Cardiac Arrest. Theranostics 2017, 7, 2555–2564. [Google Scholar] [CrossRef]
  38. Zampetaki, A.; Mayr, M. MicroRNAs in Vascular and Metabolic Disease. Circ. Res. 2012, 110, 508–522. [Google Scholar] [CrossRef] [Green Version]
  39. Truettner, J.S.; Alonso, O.F.; Bramlett, H.M.; Dietrich, W.D. Therapeutic Hypothermia Alters Microrna Responses to Traumatic Brain Injury in Rats. Br. J. Pharmacol. 2012, 31, 1897–1907. [Google Scholar] [CrossRef]
  40. Pilotte, J.; Dupont-Versteegden, E.E.; Vanderklish, P.W. Widespread Regulation of MiRNA Biogenesis at the Dicer Step by the Cold-Inducible RNA-Binding Protein, RBM3. PLoS ONE 2011, 6, e28446. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Dresios, J.; Aschrafi, A.; Owens, G.C.; Vanderklish, P.W.; Edelman, G.M.; Mauro, V.P. Cold Stress-Induced Protein Rbm3 Binds 60S Ribosomal Subunits, Alters Microrna Levels, and Enhances Global Protein Synthesis. Proc. Natl. Acad. Sci. USA 2005, 102, 1865–1870. [Google Scholar] [CrossRef] [Green Version]
  42. Andersson, P.; Gidlöf, O.; Braun, O.; Götberg, M.; van der Pals, J.; Olde, B.; Erlinge, D. Plasma Levels of Liver-Specific MiR-122 is Massively Increased in a Porcine Cardiogenic Shock Model and Attenuated by Hypothermia. Shock 2012, 37, 234–238. [Google Scholar] [CrossRef]
  43. Edbauer, D.; Neilson, J.R.; Foster, K.A.; Wang, C.-F.; Seeburg, D.P.; Batterton, M.N.; Tada, T.; Dolan, B.M.; Sharp, P.A.; Sheng, M. Regulation of Synaptic Structure and Function by FMRP-Associated MicroRNAs miR-125b and MiR-132. Neuron 2010, 65, 373–384. [Google Scholar] [CrossRef] [Green Version]
  44. Liu, Y.; Wong, T.P.; Aarts, M.; Rooyakkers, A.; Liu, L.; Lai, T.W.; Wu, D.C.; Lu, J.; Tymianski, M.; Craig, A.M.; et al. NMDA Receptor Subunits have Differential Roles in Mediating Excitotoxic Neuronal Death Both In Vitro and In Vivo. J. Neurosci. 2007, 27, 2846–2857. [Google Scholar] [CrossRef] [Green Version]
  45. Geocadin, R.G. Moving Beyond One-Size-Fits-All Treatment for Patients After Cardiac Arrest. JAMA Netw. Open 2020, 3, e208809. [Google Scholar] [CrossRef] [PubMed]
  46. Stammet, P.; Collignon, O.; Hassager, C.; Wise, M.; Hovdenes, J.; Aneman, A.; Horn, J.; Devaux, Y.; Erlinge, D.; Kjaergaard, J.; et al. Neuron-Specific Enolase as a Predictor of Death or Poor Neurological Outcome After Out-of-Hospital Cardiac Arrest and Targeted Temperature Management at 33 °C and 36 °C. J. Am. Coll. Cardiol. 2015, 65, 2104–2114. [Google Scholar] [CrossRef]
  47. Stammet, P.; Dankiewicz, J.; Nielsen, N.; Fays, F.; Collignon, O.; Hassager, C.; Wanscher, M.; Undèn, J.; Wetterslev, J.; Pellis, T. Protein S100 as Outcome Predictor after Out-of-Hospital Cardiac Arrest and Targeted Temperature Management at 33 °C and 36 °C. Crit. Care 2017, 21, 153. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Khoshnam, S.E.; Winlow, W.; Farbood, Y.; Moghaddam, H.F.; Farzaneh, M. Emerging Roles of microRNAs in Ischemic Stroke: As Possible Therapeutic Agents. J. Stroke 2017, 19, 166–187. [Google Scholar] [CrossRef] [PubMed]
Diagnostics 11 01905 g001 550
Figure 1. Serum expression levels of various miRNAs at 6 h after return of spontaneous circulation in patients with good and poor neurological outcomes. (a) Serum expression levels of miR-6511b-5p. (b) Serum expression levels of miR-125b-1-3p. (c) Serum expression levels of miR-122-5p. (d) Serum expression levels of miR-124-3p. Horizontal lines represent the median and error bars indicate the interquartile range. * p < 0.05.
Figure 1. Serum expression levels of various miRNAs at 6 h after return of spontaneous circulation in patients with good and poor neurological outcomes. (a) Serum expression levels of miR-6511b-5p. (b) Serum expression levels of miR-125b-1-3p. (c) Serum expression levels of miR-122-5p. (d) Serum expression levels of miR-124-3p. Horizontal lines represent the median and error bars indicate the interquartile range. * p < 0.05.
Diagnostics 11 01905 g001
Diagnostics 11 01905 g002 550
Figure 2. Serum levels of NSE and S100B at 6 h after return of spontaneous circulation in patients with good and poor neurological outcomes. (a) Serum levels of NSE. (b) Serum levels of S100B. Horizontal lines represent the median and error bars indicate the interquartile range. NSE—neuron-specific enolase; S100B—S100 calcium-binding protein B. * p < 0.05.
Figure 2. Serum levels of NSE and S100B at 6 h after return of spontaneous circulation in patients with good and poor neurological outcomes. (a) Serum levels of NSE. (b) Serum levels of S100B. Horizontal lines represent the median and error bars indicate the interquartile range. NSE—neuron-specific enolase; S100B—S100 calcium-binding protein B. * p < 0.05.
Diagnostics 11 01905 g002
Diagnostics 11 01905 g003 550
Figure 3. Receiver operating characteristic curves for 6-month poor neurological outcome based on the expression levels of miRNAs and NSE or S100B. NSE—neuron-specific enolase; S100B—S100 calcium-binding protein B; ROSC—return of spontaneous circulation; AUC—area under the curve; CI—confidence interval.
Figure 3. Receiver operating characteristic curves for 6-month poor neurological outcome based on the expression levels of miRNAs and NSE or S100B. NSE—neuron-specific enolase; S100B—S100 calcium-binding protein B; ROSC—return of spontaneous circulation; AUC—area under the curve; CI—confidence interval.
Diagnostics 11 01905 g003
Diagnostics 11 01905 g004 550
Figure 4. The correlation between protein and miRNA biomarkers. Log-transformed values of biomarkers and miRNA expressions are represented. Since the axis is logarithmic, values equal to zero or lower than the lowest marked value on the axis are not included on the graph. NSE—neuron-specific enolase; S100B—S100 calcium-binding protein B.
Figure 4. The correlation between protein and miRNA biomarkers. Log-transformed values of biomarkers and miRNA expressions are represented. Since the axis is logarithmic, values equal to zero or lower than the lowest marked value on the axis are not included on the graph. NSE—neuron-specific enolase; S100B—S100 calcium-binding protein B.
Diagnostics 11 01905 g004
Table
Table 1. Characteristics of the included patients.
Table 1. Characteristics of the included patients.
Good Outcome
(n = 20)		Poor Outcome
(n = 34)		p-Value
Male12 (60.0)25 (73.5)0.369
Age, year48.0 (34.0–65.58)60.0 (44.8–72.0)0.142
Comorbidities
Hypertension5 (25.0)12 (35.3))0.549
Diabetes mellitus3 (15.0)13 (38.2)0.122
Ischemic heart disease2 (10.0)4 (11.8)1.000
Chronic heart failure1 (5.0)1 (2.9)1.000
Stroke0 (0.0)1 (2.9)1.000
Chronic obstructive pulmonary disease0 (0.0)2 (5.9)0.525
Chronic renal disease1 (5.0)5 (14.7)0.395
Liver failure0 (0.0)1 (2.9)1.000
Malignancy2 (10.0)1 (2.9)0.548
OHCA15 (75.0)32 (94.1)0.087
Cardiac cause16 (80.0)13 (38.2)0.004
Shockable rhythm13 (65.0))4 (11.8)<0.001
Witnessed16 (80.0)21 (61.8)0.229
Bystander CPR15 (75.0))18 (52.9)0.151
Time from arrest to ROSC, min13.0 (10.0–28.8)34.5 (16.0–46.8)0.002
Shock on admission2 (10.0)17 (50.0)0.003
Laboratory measurements
Troponin T, ng/mL0.1 (0.01–0.34)0.06 (0.01–0.20)0.706
NT-proBNP, pmol/L10.3 (5.2–58.3)24.2 (7.0–251.2)0.144
Bilirubin, μmol/L11.1 (7.4–13.7)8.6 (5.1–12.0)0.065
Coronary angiography14 (70.0)11 (32.4)0.011
Percutaneous coronary intervention7 (35.0)8 (23.5)0.530
Target temperature, 33 °C17 (85.0)32 (94.1)0.347
Need for vasopressor at 6 h7 (35.0)24 (70.6)0.021
Data are presented as n (%) for categorical variables and as the median with interquartile range (IQR) for continuous variables. OHCA, out-of-hospital cardiac arrest; CPR, cardiopulmonary resuscitation; ROSC, return of spontaneous circulation; NT-proBNP, N-terminal pro-brain natriuretic peptide.
Table
Table 2. Prognostic performances of the different models predicting 6-month poor neurological outcome.
Table 2. Prognostic performances of the different models predicting 6-month poor neurological outcome.
AUC (95% CI)p-Value
NSE0.72 (0.58–0.83)N/A
NSE + miR-6511b-5p0.87 (0.78–0.96)0.029
NSE + miR-125b-1-3p0.86 (0.76–0.96)0.027
NSE + miR-122-5p0.81 (0.70–0.92)0.121
NSE + miR-124-3p0.75 (0.62–0.88)0.478
S100B0.85 (0.73–0.94)N/A
S100B + miR-6511b-5p0.91 (0.83–0.98)0.281
S100B + miR-125b-1-3p0.89 (0.80–0.97)0.448
S100B + miR-122-5p0.86 (0.76–0.96)0.867
S100B + miR-124-3p0.85 (0.74–0.95)0.742
NSE + S100B0.84 (0.74–0.95)N/A
NSE + S100B + miR-6511b-5p0.92 (0.85–0.99)0.055
NSE + S100B + miR-125b-1-3p0.91 (0.83–0.98)0.074
NSE + S100B + miR-122-5p0.86 (0.76–0.96)0.455
NSE + S100B + miR-124-3p0.85 (0.75–0.95)0.629
p-value indicates difference between protein biomarkers and the combination model with miRNA added. AUC—area under the curve; CI—confidence interval; N/A—not applicable; NSE—neuron-specific enolase; S100B—S100 calcium-binding protein B.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Oh, S.H.; Kim, H.-S.; Park, K.N.; Ji, S.; Park, J.-Y.; Choi, S.P.; Lim, J.Y.; Kim, H.J.; on behalf of CROWN Investigators. The Levels of Circulating MicroRNAs at 6-Hour Cardiac Arrest Can Predict 6-Month Poor Neurological Outcome. Diagnostics 2021, 11, 1905. https://doi.org/10.3390/diagnostics11101905

AMA Style

Oh SH, Kim H-S, Park KN, Ji S, Park J-Y, Choi SP, Lim JY, Kim HJ, on behalf of CROWN Investigators. The Levels of Circulating MicroRNAs at 6-Hour Cardiac Arrest Can Predict 6-Month Poor Neurological Outcome. Diagnostics. 2021; 11(10):1905. https://doi.org/10.3390/diagnostics11101905

Chicago/Turabian Style

Oh, Sang Hoon, Ho-Shik Kim, Kyu Nam Park, Sanghee Ji, Ji-Young Park, Seung Pill Choi, Jee Yong Lim, Han Joon Kim, and on behalf of CROWN Investigators. 2021. "The Levels of Circulating MicroRNAs at 6-Hour Cardiac Arrest Can Predict 6-Month Poor Neurological Outcome" Diagnostics 11, no. 10: 1905. https://doi.org/10.3390/diagnostics11101905

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop