Predictive Value of Apoptotic Factor M30 for Negative Left Ventricular Remodeling in Patients Undergoing Primary Percutaneous Coronary Intervention

    

    

    Figure 1: Flow chart of study design.
    

In our study, LVNR was defined as increase in left ventricle end diastolic volume (LVEDV) by ≥20% at 6 months after PPCI [12]. The patients were divided into two groups on the basis of 6 month post PPCI follow-up findings: non-LVNR group (Group A) and LVNR group (Group B). The study protocol was approved by the hospital ethical committee and each participant provided written informed consent. All the clinical details of the patients were recorded in a predesigned proforma.

PPCI

PPCI was performed in all patients within 12 hours of symptoms onset by experienced interventionists. Decision of thrombus aspiration, ballooning, stenting, and use of Glycoprotein (GpIIb/ IIIa) inhibitors was on operator’s discretion. Final Thrombolysis in Myocardial Infarction (TIMI)-3 flow in LAD with less than 20% residual stenosis was defined as successful PCI. The final TIMI-flow of less than 3 was defined as no-reflow in our study [13], which was assessed by two expert angiographers unaware of each other’s result or of the patient’s data. All patients were discharged on Guideline Directed Medical Therapy (GDMT) [14]. Angiotensin Converting Enzyme Inhibitors (ACEIs) or Angiotensin Receptor Blockers (ARBs) were continued if patient was already on therapy or initiated in all patients with systolic blood pressure greater than 100 mmHg. Beta blockers were similarly continued or initiated in all patients including patients with LVEF ≤40%, with no other contraindication for beta blocker therapy (reactive airway diseases, advanced AV block). Similarly aldosterone antagonists (eplerenone/spironolactone) were initiated in all patients with LVEF ≤40% in background of ACEIs/ ARBs and beta blocker therapy if they were diabetic or had symptoms of heart failure at presentation (Table 1).

Echocardiographic evaluation

All TTE initially and on follow-up were performed by an experienced blinded echocardiographer using PHILIPS ECHO machine (Model number UTAP20W) with 2.5-3.5 MHz transducer. All patients underwent TTE at 24 hours and at 6 months after PPCI. LVNR was defined as ≥20% increase in Left Ventricular End Diastolic Volume (LVEDV) at 6 months after PPCI based on repeated measurement in each patient and on the upper 95% confidence limit of the intraobserver variability [12]. In each patient the LVEDV, Left Ventricular End Systolic Volume (LVESV) and Left Ventricular Ejection Fraction (LVEF) were measured by the modified Simpson’s rule. The LV Wall Motion Score Index (WMSI) was assessed according to the definitions of the American Society of Echocardiography [15]. In order to assess intraobserver variability, echocardiographic parameters were measured offline in 30 patients a week after the first measurement. The intraobserver variability was calculated by difference between the two sets of measurements by the mean of the observations.

Assessment of biomarkers

Apart from routine biochemical investigations done on admission, biomarkers like M30, M65 and N-terminal-pro-B-type natriuretic peptide (NT-proBNP) were assessed at 24 hours from symptom onset. CPK-MB is the conventional biomarker of necrosis, which is routinely assessed in our institute. CPK-MB in the present study was assessed at 6 hourly intervals following PPCI till 24 hours as was done in other trials [4,16]. The peak value of CPK-MB amongst these four samples was taken as predictor of maximal myocardial necrosis. Apart from this, M30 (a marker of apoptosis), M65 (a marker of necrosis) and NT-proBNP (a marker of LV wall stress) were also assessed at 24 hours after symptom onset as peak level of M30 is achieved at this time following PPCI [10] with no significant change reported in peak level of M65 upto 48 hours [11].

M30 and M65 measurement: Blood samples were collected at 24 hours after symptom onset and were analyzed in duplicate in a blinded manner. The samples were centrifuged at 5000g for 10 minutes and serum aliquots were stored at -80C until analysis. Serum level of M30 and M65 were determined by commercially available immunoassays [M30-Apoptosense Enzyme Linked Immunosorbant Assay (ELISA) kit and M65 ELISA kit (Peviva AB, Bromma, Sweden)] according to manufacturer’s instructions. The M65 ELISA kit measures natural soluble CK18 (M65 antigen) whereas the M30 ELISA kit measures the level of CK18-Asp396 neoepitope (M30 antigen). Briefly the samples were placed in wells coated with a mouse monoclonal antibody as a catcher. After washing, a horse radish peroxidase conjugated antibody (M30 or M65) was used for detection. Reference concentration of M30 antigen or M65 antigen was used to prepare assay calibration. The absorbance was determined by an ELISA reader at 450 nm.

Follow-up

After discharge, patients were followed-up by hospital visits or telephonically at 1 and 3 months and a compulsory hospital visit at 6 months for final clinical evaluation and echocardiographic recording.

Statistical analysis

Statistical analysis was carried out using SPSS (Statistical Package for the Social Science) version 18 (SPSS, Inc, Chicago, Illinois, USA) software. Data are expressed as mean ± Standard Deviation (SD) or median with Inter-Quartile Range (IQR). Continuous variables were assessed by the Student t test and categorical variables by Mann- Whitney test. Correlation of the studied biomarker with various other biomarkers was assessed by Spearman’s correlation analysis. The univariate test was applied to assess the predictors that might be associated with LVNR. As number of subjects were small, only significant factors on univariate analysis (p<0.05) were selected for multivariate regression analysis. A logistic regression analysis was done to find out the independent predictors of LVNR. A stepwise backward likelihood method was applied for it. The optimal M30 cut off point for predicting LVNR was calculated using receiver operating characteristics (ROC) curve analysis. The Area Under the Curve (AUC) value was calculated as a measure of accuracy of the test. A two tailed p value of less than 0.05 was considered as statistically significant.

Results

Baseline clinical findings

There were 56 patients in Group A and 44 patients in Group B. No significant difference in age, sex, BMI and in risk factors like dyslipidemia, hypertension or smoking was seen between the two groups (Table 1). However, percentage of patients with diabetes mellitus (p=0.049) was significantly higher in Group B.

Table 1: Baseline characteristics.

  

    
Variables
    
Group    A (n=56) 
    
Group    B (n=44) 
    
p    –value
  
  

    
Age (years) 
    
52.98±11.52 
    
51.09±12.808 
    
0.442 
  
  

    
Systolic blood pressure (mmHg) 
    
121.86±18.03 
    
119.86±22.58 
    
0.621 
  
  

    
Diastolic blood pressure (mmHg) 
    
81.18±9.34 
    
77.86±9.11 
    
0.322 
  
  

    
Weight (Kg) 
    
72.74 ±8.85 
    
72.34±9.03 
    
0.412 
  
  

    
Height (cm) 
    
171.55±3.67 
    
171.43±3.64 
    
0.462 
  
  

    
BMI (Kg/m2) 
    
24.76±2.85 
    
24.64±2.90 
    
0.418 
  
  

    
Killip class 
    

    

    

  
  

    
Killip-I 
    
46 (82.14) 
    
36 (81.81) 
    
1 
  
  

    
Killip-II 
    
8 (14.28) 
    
6 (13.63) 
  
  

    
Killip-III 
    
2 (3.57) 
    
2 (4.54) 
  
  

    
Hemoglobin (g/dl) 
    
13.99±2.02 
    
13.72±2.058 
    
0.52 
  
  

    
S. creatinine (mg/dl) 
    
1.012±0.26 
    
1.005±0.27 
    
0.882 
  
  

    
Blood urea (mg/dl) 
    
29.72±12.40 
    
25.61±7.78 
    
0.069 
  
  

    
Random blood sugar (mg/dl) 
    
165.04±63.08 
    
165.14±77.64 
    
0.992 
  
  

    
High density lipoprotein (mg/dl) 
    
38.30±5.17 
    
38.55±4.97 
    
0.812 
  
  

    
Total cholesterol (mg/dl) 
    
172.98±30.17 
    
173.39±30.63 
    
0.942 
  
  

    
Triglycerides (mg/dl) 
    
204.11±42.99 
    
190.73±45.43 
    
0.135 
  
  

    
Diabetes 
    
4 (7.14) 
    
9 (20.45) 
    
0.049 
  
  

    
Hypertension 
    
10 (17.85) 
    
13 (29.54) 
    
0.162 
  
  

    
Smoking 
    
22 (46.4) 
    
16 (31.6) 
    
0.658 
  
  

    
Medications at time of discharge 
    

    

    

  
  

    
Aspirin 
    
56 (100) 
    
44 (100) 
    
1 
  
  

    
P2Y12inhibitors 
    
56 (100) 
    
44 (100) 
    
1 
  
  

    
Statins 
    
56 (100) 
    
44 (100) 
    
1 
  
  

    
ACEIs or ARBs 
    
49 (87.5) 
    
40 (90.9) 
    
0.751 
  
  

    
Beta blockers 
    
50 (89.28) 
    
41 (93.18) 
    
0.727 
  
  

    
Aldosterone    antagonists 
    
13 (23.21) 
    
15 (34.09) 
    
0.266 
  
  

    
% of patients on GDMT or at least on 50% of target dose of GDMT 
    

    

    

  
  

    
Aspirin 
    
56 (100) 
    
44 (100) 
    
1 
  
  

    
P2Y12inhibitors 
    
55 (98.21) 
    
43 (97.72) 
    
1 
  
  

    
Statins 
    
54 (96.42) 
    
44 (100) 
    
0.502 
  
  

    
ACEIs or ARBs 
    
35 (62.5) 
    
26 (59.09) 
    
0.836 
  
  

    
Beta blockers 
    
40 (71.42) 
    
30 (68.18) 
    
0.826 
  
  

    
Aldosterone    antagonists 
    
13 (23.21) 
    
15 (34.09) 
    
0.266 
  
  

    
BMI=Body    Mass Index, GDMT=Guideline Directed Medical Therapy. Values are mean±standard    deviation or n (%). 
  

Table 1: Baseline characteristics.

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Angiographic and Interventional findings

The number of patients with symptom to balloon time of more than 6 hours was significantly higher in Group B compared to Group A (Table 2). There was no significant difference between the two groups with regard to stent diameter/number/length, use of thrombus aspiration, use of Glycoprotein IIb/IIIa inhibitors, incidence of concomitant multivessel disease along with baseline and residual Syntax score. Patients in group B had significantly higher percentage of patients with no-reflow than in Group A (Table 2).

Table 2: Angiographic findings of the two groups.

  

    
Angiographic findings 
    
Group A (n=56) 
    
Group B (n=44) 
    
p –value 
  
  

    
Symptom onset to balloon time (>6hours) [n (%)] 
    
19 (33.9) 
    
29 (63.9) 
    
0.001 
  
  

    
Multivessel involvement [n (%)] 
    
19 (33.92) 
    
13 (29.54) 
    
0.53 
  
  

    
Total stent length (mm) 
    
22.1±3.6 
    
22.4±4.5 
    
0.67 
  
  

    
Stent diameter (mm) 
    
3.3±0.29 
    
3.4±1.1 
    
0.56 
  
  

    
Thrombus aspiration (%) 
    
11 (19.64) 
    
10 (22.72) 
    
0.81 
  
  

    
Glycoprotein IIb/IIIa inhibitors [n (%)] 
    
8 (14.28) 
    
8 (18.18) 
    
0.78 
  
  

    
Initial Syntax 
    
16.9±5.1 
    
17.8±4.4 
    
0.55 
  
  

    
Final Syntax 
    
12.45±5.9 
    
13.1±4.6 
    
0.66 
  
  

    
No-reflow [n (%)] 
    
2(3.6) 
    
8(18.2) 
    
0.016 
  

Table 2: Angiographic findings of the two groups.

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Biomarkers of necrosis, apoptosis and LV wall stress

There was significant difference in the level of necrotic biomarker CPK-MB and M65, apoptotic biomarker M30 and NT-proBNP between Group A and B (Table 3).

Table 3: Biomarkers of necrosis,apoptosis and LV stress.

  

    
Biomarker 
    
Group A    (n=56)
    
Group B    (n=44)
    
p-value
  
  

    
CPK-MB [ng/ml]
    
162.0 [144.0-187.2]
    
240.0 [180.0-342.9]
    
<0.001
  
  

    
NT-proBNP [pg/ml]
    
258.5 [136.5-365.9]
    
490.0 [327.3-1014.0]
    
<0.001
  
  

    
M30 [U/l]
    
71.84 [58.45-100.63]
    
165.17 [93.68-285.7]
    
<0.001
  
  

    
M65 [U/l]
    
91.12 [78.66-404.0]
    
116.4 [96.7-404.0]
    
<0.001
  
  

    
Values    are in median with interquartile range 
  

Table 3: Biomarkers of necrosis,apoptosis and LV stress.

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A very good correlation was found between the apoptotic marker M30 with necrotic biomarker M65, CPK-MB and NT-proBNP (Table 4). There was also a good correlation between M30 level and change in LV volume (baseline to 6 months) as assessed by echocardiography (Table 4).

Table 4: Correlation of M30 with other variables.

  

    
Variable 
    
Spearman's correlation    (r) 
    
p-value 
  
  

    
M65 
    
0.781 
    
<0.001 
  
  

    
CPK-MB 
    
0.62 
    
<0.001 
  
  

    
NT-proBNP 
    
0.454 
    
<0.001 
  
  

    
Symptom onset to    balloon time 
    
0.223 
    
0.026 
  
  

    
*Change    in LV ESV 
    
0.713 
    
<0.001 
  
  

    
*Change    in LVEDV 
    
0.789 
    
<0.001 
  
  

    
*Change    in LVEF 
    
0.085 
    
0.535 
  
  

    
*Change from baseline to 6 months, LVESV:Left Ventricular    End Systolic Volume; LVEDV:Left Ventricular End Diastolic Volume, LVEF:Left    Ventricular Ejection Fraction. 
  

Table 4: Correlation of M30 with other variables.

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Echocardiographic findings at baseline and on follow-up

A comparison of the echocardiographic findings indexed to body surface area is shown in Table 5. At baseline, there was no significant difference in LVEDV, LVESV and LVEF between the two groups. However, there was progressive increase in both LVEDV and LVESV in group B compared to the group A resulting in significant decrease in LVEF in group B patients (at the end of 6 months) compared to group A. The intra-observer variability in the evaluation of LVEDV and LVESV were 2.2±1.8 % and 2.9±2.1 % respectively.

Table 5: Echocardiographic findings.

  

    
Echocardiographic    findings 
    
Group A (n=56) 
    
Group B (n=44) 
    
p-value 
  
  

    
LVEDV at baseline    (ml/m2) 
    
61.91±13.93 
    
64.72±12.17 
    
0.11 
  
  

    
LVEDV at 6 month (ml/m2) 
    
58.80±10.50 
    
81.61±17.76 
    
< 0.001 
  
  

    
LVESV at baseline    (ml/m2) 
    
41.45±10.85 
    
44.34±11.25 
    
0.097 
  
  

    
LVESV at 6 month (ml/m2) 
    
37.42±11.36 
    
58.80±14.69 
    
<0.001 
  
  

    
LVEF at Baseline (%) 
    
43.25±5.6 
    
40.45±9.7 
    
0.06 
  
  

    
LVEF at 6 months (%) 
    
46±3.8 
    
29±4.1 
    
<0.001 
  
  

    
Number of segments    affected 
    
3.2±1.3 
    
3.7±2.1 
    
0.07 
  
  

    
LVEDV: Left    Ventricular End-Diastolic Volume; LVESV=Left Ventricular End-Systolic Volume;    LVEF: Left Ventricular Ejection Fraction; WMSI: Wall Motion Score Index. 
  

Table 5: Echocardiographic findings.

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Predictors of LVNR in patients with Anterior wall STEMI

In our study, the univariate test was first applied to predict the clinical and biochemical markers that may be associated with LVNR. Since numbers of subjects were small, only significant predictors (p≤0.05) at univariate analysis (Table 1,2 and 3) were selected for multivariate logistic regression analysis. The backward elimination model was applied to assess the independent predictors of LVNR. The bivariate correlation between M30 and M65 was 0.78 and it was collinear. Thus, M30 was included in the model (excluding M65) along with other biomarkers. The Hosmer-Leme show test for final model was applied for assessing the goodness of fit prediction model in multivariate analysis. Diabetes (p=0.032), symptom onset to balloon time (p=0.059), CPK-MB (p=0.007) and M30 (p=0.012) were found to be independent predictors of LVNR (Table 6).c

Table 6: Independent predictors of negative remodelling.

  

    
Variable 
    
Coefficient (SE) 
    
Odds ratio (95% CI) 
    
P-value 
  
  

    
Diabetes 
    
2.11 (0.982) 
    
8.259 (1.206-56.57) 
    
0.032 
  
  

    
Symptom onset to balloon time (> 6hours) 
    
1.29 (0.682) 
    
3.63 (0.954-13.82) 
    
0.059 
  
  

    
CPK-MB [ng/ml] 
    
0.018 (0.007) 
    
1.018 (1.005-1.032) 
    
0.007 
  
  

    
M30 [U/I] 
    
0.016 (0.0016) 
    
1.016 (1.003-1.028) 
    
0.012 
  

Table 6: Independent predictors of negative remodelling.

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ROC curve analysis

The cutoff value of M30 for predicting LVNR on the basis of ROC curve (Figure 2) was 81.183 U/l with positive and negative predictive value of 70.4% and 87% respectively (Table 7). As CPK-MB is used in our hospital to assess the size of infarct and was also an independent predictor of LVNR, we constructed an ROC curve to predict LVNR by combining CPK-MB and M30. The optimal cutoff value of CPKMB was 122.5ng/ml with positive and negative predictive value of 84.8% and 76.1% (Table 7).

Figure 2: ROC curve for M30 biomarker.

    

    

    Figure 2: ROC curve for M30 biomarker.
    

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Table 7: Predictive value of various biomarkers.

  

    
Biomarker 
    
AUC with 95% CI 
    
p value 
    
Sensitivity 
    
Specificity 
    
PPV 
    
NPV 
  
  

    
M30 
    
85.3 [77.9-92.8 
    
<0.001 
    
88.63% 
    
71.42% 
    
70.40% 
    
87% 
  
  

    
CPK-MB 
    
83.2 [75-91.5] 
    
<0.001 
    
63.60% 
    
91.10% 
    
84.80% 
    
76.10% 
  
  

    
M30+CPK-MB 
    
87.3 [80.1-94.6] 
    
<0.001 
    
72.70% 
    
91% 
    
86.50% 
    
81% 
  
  

    
AUC:Area    Under the Curve: CI:Confidence Interval; NPV:Negative Predictive Value; PPV:Positive    Predictive Value 
  

Table 7: Predictive value of various biomarkers.

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Binary logistic regression analysis was used to assess the combined effect of CPK-MB and M30. The area under ROC of the combined biomarkers was 87.3 [95% Confidence Interval (CI); 80.1 to 94.6] with positive and negative predictive values of 86.5% and 81% respectively. The area under the ROC curve increased from 83.2 (for CPK-MB only) to 87.3 (for CPK-MB + M30) as shown in figure 3. The additional discriminate power of M30 was found to be statistically significant (p<0.001) using likelihood ratio test in binary logistic regression with CPK-MB only and with CPK-MB + M30.

Figure 3: ROC curve to predict LV remodeling by combined CPK-MB and M30.

    

    

    Figure 3: ROC curve to predict LV remodeling by combined CPK-MB and
M30.
    

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Follow-up data

None of our patients who had survived for 24 hours following successful PPCI expired or were lost to follow-up. None of our patients during follow-up required re-hospitalization for recurrent coronary event, symptoms of heart failure or stroke. However, fourteen patients in LVNR group complained of worsening of dyspnoea at 3 months for which a loop diuretic had to be initiated. While eight patients improved symptomatically with one tablet of furosemide (40 mg), in six patients, dose had to be increased to two tablets daily after which they became asymptomatic till 6 months of follow-up. However, none of the patients required intravenous diuretics or hospital admission for worsening symptoms of heart failure. On further analysis, we found that six of these patients had no reflow following angioplasty, eight were diabetic and ten had symptom onset to balloon time of more than 6 hours. At end of 6 months, there was no significant difference in percentage of patients receiving GDMT between the two groups.

Discussion

To the best of our knowledge, this is the first study from Southeast Asia assessing the level of the apoptotic marker M30 in patients of STEMI undergoing PPCI to predict LVNR. We have used the same kit as used by Turkoglu et al. [5] in their study in the Turkish population to assess the cut-off value of M30 in a similar group of patients (first episode of anterior wall STEMI presenting within 12 hours of symptoms and undergoing PPCI) to predict LVNR. The cut-off value of M30 in the study by Turkoglu was significantly higher (144.9 U/l vs 81.183 U/l) in comparison to our study. This is due to the fact that the mean BMI of patients in their study was significantly higher 28.3 kg/m² (grade-1 obesity) [17] compared to mean of 24.64 kg/m² [overweight (BMI 23.0-24.9 kg/m²)] [18] in our study implying higher cardiac mass in the Turkish population than our studied population.

A study from Tiawan [19], has shown significantly higher M30 in healthy subjects with higher BMI (marker of obesity) than those with normal or lower BMI. Accordingly, the cutoff value of M30 in the patients of our study to predict LVNR was significantly lower than that of the study [5] in the Turkish population. Secondly in our study, the other biomarker which was independent predictor of LVNR was CPK-MB (a marker of volume of myocardial necrosis) but not NT-proBNP. The fact that a biomarker like CPK-MB was also independently associated with the changes in LVEDV demonstrate that our study had adequate power for detecting association of biomarkers depicting irreversible myocardial injury following myocardial infarction with LVNR.

Further due to a larger sample size, our study showed very good correlation of M30 with peak CPK-MB level unlike the study by Dincer Y et al. [20] who failed to show any correlation. In our study, NT-proBNP level assessed at 24 hours following PPCI failed to predict LVNR which is in agreement with the study by Heack DE et al. [21] who have shown that NT-proBNP assessed at 3 to 6 months is a better predictor of LVNR than that measured during the acute event in patients of anterior wall STEMI.

Apart from M30 (a marker of apoptosis), we had also assessed the level of M65 (a marker of necrosis) in our study. There was very good correlation between level of M30 and M65 in our study (r=0.781, p<0.001) showing that degree of necrosis parallels the degree of apoptosis following ischemic injury in patients of STEMI.

Clinical implication: Apart from well known factors like diabetes [22,23], symptom onset to balloon time [24] and peak CPK-MB level [25], M30 has also been found to be an independent predictor of LVNR in our study. In our study, both the group of patients had similar LVEF at time of discharge (Table 5) but Group B patients due to LVNR had significantly lower ejection fraction at the end of 6 months. The percentage of patients who were on recommended dose or at least 50% of dose of GDMT at six month were similar (Table 1). This shows the limitation of present medical therapy in preventing LVNR. There have been animal studies where anti-apoptotic agents have been shown to attenuate the extent of LVNR [26,27] but no definitive therapy targeting apoptosis has yet evolved.

Limitations

• Single centre study with small sample size which needs to be confirmed in larger studies.

• LVNR was assessed by echocardiography while cardiovascular magnetic resonance imaging is considered the gold standard.

Conclusion

LVNR can be predicted early after acute STEMI by assessing the apoptotic biomarker M30 along with conventional biomarker of necrosis like CPK-MB. This can assist in better risk stratification of patients after successful PPCI and identify the subgroup of patients who require more intensive medical follow up with anti-remodeling drugs to attenuate the development of LVNR.

Acknowledgement

We are thankful to Dr Rajeev Kumar Malhotra, scientist (statistician), Delhi cancer registry, Dr B.R.A. IRCH, AIIMS, Delhi for statistical analysis.

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Citation:Yusuf J, Mukhopadhyay S, Viadya PN, Gautam A, Mehta V, Gupta MD, et al. Predictive Value of Apoptotic Factor M30 for Negative Left Ventricular Remodeling in Patients Undergoing Primary Percutaneous Coronary Intervention. Austin Cardiol. 2021; 6(1): 1028.

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