You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction {#s1}
============

Atherosclerosis is driven by sustained inflammation leading to chronic alterations of inflammatory mediators, like CRP, pro-inflammatory cytokines, metalloproteinases (MMPs), adhesion molecules and selectins [@pone.0038376-Ross1], [@pone.0038376-Geisler1]. The interaction of endothelial cells, monocytes, macrophages and platelets plays a crucial role in upholding a pro-inflammatory and prothrombotic milieu leading to plaque instability and subsequent atherothrombotic events [@pone.0038376-Gawaz1]. Additionally, there have been *in-vitro* data supporting the linkage of platelet activation, hyperaggregability and up-regulation of cytokines and chemokines involved in recruitment of inflammatory cells [@pone.0038376-May1]. ACS are triggered by inflammatory response and plaque degradation as indicated by increased inflammatory processes at site of intimal rupture and increased circulating levels of MMPs during the event [@pone.0038376-Gresele1], [@pone.0038376-vanderWal1]. MIF is an integral component of the host antimicrobial alarm system and stress response that promotes the pro-inflammatory functions of immune cells. A growing body of literature suggests that MIF is involved in pathomechanisms of sepsis, inflammatory and autoimmune diseases and atherosclerosis [@pone.0038376-Roger1]--[@pone.0038376-Lue1]. Additionally, MIF has previously been reported to be associated with signs of plaquae instability [@pone.0038376-Schmeisser1]. Hence, MIF might be a potential biomarker to predict the severity and the risk of ACS and to suggest therapies that target MIF pathways as attractive treatment options for cardiovascular diseases. To date little is known about the clinical impact of MIF in CAD. Hence, the aim of the present study was to further evaluate the association of MIF in a consecutive population of patients with CAD and its association with established inflammatory markers, angiographic findings and its shortterm prognostic impact.

Materials and Methods {#s2}
=====================

Study Population and Data Acquisition {#s2a}
-------------------------------------

A consecutive pilot study was performed in 286 unselected patients recruited at the University Hospital Tübingen to evaluate the association of MIF, inflammatory markers and platelet function under antiplatelet therapy. All patients underwent coronary stent implantation (bare-metal and drug-eluting stents) due to symptomatic CAD. Platelet function analysis was performed in all subjects before and after receiving a 600-mg clopidogrel loading dose in addition to aspirin (ASA) pre-treatment. The study protocol was approved by the local ethical committee. Inclusion criteria comprised written informed consent and an age \>18 years. A standard maintenance dose of 75 mg/day Clopidogrel + ASA 100 mg/day was prescribed for all patients in the further course. ACS was diagnosed in the presence of one of the following criteria: unstable angina (clinical symptoms and new ECG changes, but no markers of myocardial necrosis), acute myocardial infarction with markers of myocardial necrosis (TnI or creatine kinase (CK)) including ST-elevation myocardial infarction (STEMI) and non--ST-elevation myocardial infarction (NSTEMI).

Biochemical Measurements {#s2b}
------------------------

Peripheral venous blood samples were obtained in EDTA collection tubes at the time of PCI, centrifuged at room temperature (1500×g) for 15 min, and stored as EDTA-plasma at −80°C, until MIF levels and inflammatory markers were analysed. To assess MIF concentrations in the obtained plasma samples we used anti-human MIF ELISA, R&D Systems, Inc., Minneapolis, USA. The assay was performed according to the instruction booklet provided by R&D systems, Inc.

IL-6, MCP-1 and RANTES were determined using the Bio-Plex^©^ cytokine assay (Bio-Rad Laboratories Inc., Hercules, CA, USA) according to manufacturer's instructions. Samples were tested for CRP using an immunoturbidimetric assay (ADVIA 1800 chemistry analyzer, Siemens Medical Solutions, Germany).

Angiographic Analysis {#s2c}
---------------------

Coronary angiography was performed in all patients. All angiograms were analyzed by two independent blinded investigators. Plaque rupture was angiographically defined by thrombus containing "culprit" lesion without significant angiographic stenosis.

Statistical Analysis {#s2d}
--------------------

A chi-square test was performed to evaluate the distribution of categorical data and a Fisher's exact test for dichotomous analysis. A Shapiro-Wilk test was used to assess for normality of continuous variables. Normally distributed continuous data are expressed as mean ± standard deviation. For these variables means between two categories were compared with a two-tailed unpaired t-test. Non-normally distributed continuous variables are presented as median and interquartile range. Differences of non-parametric variables among categories were tested by using a Mann-Whitney-U-Test. An univariate analysis of covariance (ANCOVA) was used to estimate the effects of different factors on MIF expression. Main effects of relevant demographic factors and factors with influence on MIF expression in univariate analysis were entered into the model. All analyses were two-sided and a p-value \<0.05 was considered statistically significant. Statistical analysis was performed with SPSS, Version 15 for Windows (SPSS, Inc., Chicago, IL).

Results {#s3}
=======

Study Cohort and Association of MIF-levels with Demographic Factors {#s3a}
-------------------------------------------------------------------

Baseline demographics of the whole study cohort are shown and stratified according to levels of MIF under and beyond the median in [table 1](#pone-0038376-t001){ref-type="table"}. Patients with high MIF levels were in general older, had more often arterial hypertension, hyperlipidemia, were more often treated with ASA, clopidogrel, statins and AT1-inhibitors and had a more preserved left-ventricular function compared to patients with low MIF-levels (≤1.96 ng/mL). Moreover, patients with high MIF levels showed significantly higher numbers of monocytes in the peripheral blood (p = 0.047).

10.1371/journal.pone.0038376.t001

###### Baseline characteristics of the studied patients stratified according to median of MIF levels.

![](pone.0038376.t001){#pone-0038376-t001-1}

  Characteristics                                                                           Total,     Low to moderate MIF (≤1.96 ng/mL)   High MIF(\>1.96 ng/mL)     p-value
  -------------------------------------------------------------------------------------- ------------ ----------------------------------- ------------------------ -------------
  Age [\*](#nt101){ref-type="table-fn"}                                                   67.9±13.0                65.3±14.0                     70.4±11.5          **\<0.001**
  Female gender                                                                           84 (29.4)                38 (26.8)                     46 (32.4)             0.298
  Glomerular Filtration Rate (MDRD, mL/min/1.73 m^2^)[\*](#nt101){ref-type="table-fn"}    35.2±17.4                31.2±14.0                     39.0±19.5          **\<0.001**
  Leucocyte count (10^3^/µl)                                                               8.7±3.1                  8.4±3.2                       8.8±3.1              0.136
  Monocyte count (% of leucocytes)                                                         5.0±0.2                  4.5±3.6                       5.3±3.1            **0.047**
  **Cardiovascular risk factors -- no (%)**                                                                                                                        
  Arterial Hypertension                                                                   221 (77.3)               94 (66.2)                     125 (88.0)         **\<0.001**
  Diabetes                                                                                88 (30.8)                36 (25.4)                     50 (35.2)             0.071
  Hyperlipidemia                                                                          168 (58.7)               71 (50.0)                     95 (66.9)           **0.004**
  Tobacco use                                                                             105 (36.7)               48 (33.8)                     57 (40.1)             0.269
  Acute coronary syndrome                                                                 119 (41.9)               44 (31.0)                     75 (52.8)          **\<0.001**
  LV-Function                                                                                                                                                      
  Slightly reduced (EF\<55%)                                                              70 (24.5)                24 (16.9)                     45 (31.7)           **0.002**
  Moderately reduced (EF 35--45%)                                                         55 (19.2)                31 (21.8)                     24 (16.9)         
  Severely reduced (EF\<35%)                                                              49 (17.1)                34 (23.9)                     15 (10.6)         
  **Comedication -- no (%)**                                                                                                                                       
  ASA                                                                                     189 (66.1)               84 (59.2)                     103 (72.5)          **0.017**
  Clopidogrel                                                                             87 (30.4)                35 (24.6)                     51 (64.1)           **0.039**
  Phenprocoumon                                                                            20 (7.0)                 9 (6.3)                       11 (7.7)             0.643
  ACE-Inhibitors                                                                          177 (61.9)               93 (65.5)                     83 (58.8)             0.222
  AT1-Blockers                                                                            48 (16.8)                35 (24.6)                      13 (9.2)          **\<0.001**
  Beta-Blockers                                                                           214 (74.8)              106 (74.6)                     107 (75.4)            0.891
  Diuretics                                                                               153 (53.5)               80 (56.3)                     71 (50.0)             0.285
  Statins                                                                                 140 (49.0)               59 (41.5)                     80 (56.3)           **0.013**

mean value ± standard deviation.

Hyperlipidemia was defined as triglycerides ≥175 mg/dl and/or LDL-cholesterol≥100 mg/dl and/or taking any of lipid lowering drugs.

ASA: aspirin.

Patients with ACS had significant higher levels of MIF compared to patients with stable CAD and healthy volunteers (median 2.85 ng/ml, IQR 3.52 versus median 1.22 ng/ml, IQR 2.99, versus median 0.1, IQR 0.09, p\<0.001, [figure 1](#pone-0038376-g001){ref-type="fig"}).

![Boxplots showing MIF-levels according to acuity of CAD compared with healthy volunteers.](pone.0038376.g001){#pone-0038376-g001}

Univariate Analysis of Covariance for MIF-levels and Possible Confounders for ACS {#s3b}
---------------------------------------------------------------------------------

Baseline MIF-levels were independently associated with ACS in unvariate analysis of covariance (F-value 11.96, p\<0.001) after inclusion of relevant variables in univariate analysis. Additionally, renal function correlated with MIF plasma levels, decreased glomerular filtration rate (F-value 23.35, p = 0.004) was independently associated with low MIF-levels. Arterial hypertension on the other hand was independently associated with higher MIF-levels ([table 2](#pone-0038376-t002){ref-type="table"}).

10.1371/journal.pone.0038376.t002

###### Results of univariate analysis of covariance (ANCOVA) for MIF and possible confounders in symptomatic CAD.

![](pone.0038376.t002){#pone-0038376-t002-2}

  Coefficients                                      mean      95% Confidence Interval     F     Mean   Square Sig          
  --------------------------------------------- ------------ ------------------------- ------- ------ ------------ ------- -------------
  **Age**                                          0.925               2.591            0.337                              
  **Gender**                                      **Male**             2.29             1.87    2.70      1.49      4.18       0.223
                                                 **female**            2.59             2.06    3.12                       
  **Arterial Hypertension**                        **No**              2.16             1.58    2.75      3.98      11.21    **0.047**
                                                  **Yes**              2.71             2.31    3.11                       
  **Acute coronary syndrome**                                          2.81             2.31    3.31     11.21      31.59   **\<0.001**
  **Stable CAD**                                                       2.06             1.64    2.48                       
  **Diabetes**                                     **No**              2.47             2.07    2.88      0.13      0.37       0.72
                                                  **Yes**              2.40             1.87    2.93                       
  **Hyperlipidemia:**                              **No**              2.21             1.73    2.69      3.77      10.62      0.05
                                                  **Yes**              2.67             2.20    3.13                       
  **Renal function** (MDRD, mL/min/1.73 m^2^)                                                             6.55      18.45    **0.01**
  **Reduced LV- function**                         **No**              2.36             1.86    2.87      1.95      0.70       0.41
                                                  **Yes**              2.67             2.24    3.11                       
  **AT1-Blockers**                                 **No**              2.41             2.06    2.76      0.03      0.07       0.87
                                                  **Yes**              2.46             1.83    3.10                       
  **Statins**                                      **No**              2.35             1.87    2.83      0.51      1.43       0.48
                                                  **Yes**              2.52             2.04    3.01                       
  **ASA**                                          **No**              2.46             1.92    2.99     0.026      0.07       0.87
                                                  **Yes**              2.41             1.98    2.85                       
  **Clopidogrel**                                  **No**              2.39             1.99    2.80      0.04      0.11       0.84
                                                  **Yes**              2.48             1.93    3.04                       

Hyperlipidemia was defined as triglycerides ≥175 mg/dl and/or LDL-cholesterol≥100 mg/dl and/or taking any of lipid lowering drugs.

ASA: aspirin.

Correlation of MIF with Inflammatory Markers and TnI Release {#s3c}
------------------------------------------------------------

Patients with a higher amount of MIF showed significantly increased levels of CRP and IL-6, and trendwise increased levels of RANTES and MCP-1 ([figure 2](#pone-0038376-g002){ref-type="fig"}).

![Association of inflammatory markers IL-6, RANTES, MCP-1 and CRP with MIF levels in the study cohort.](pone.0038376.g002){#pone-0038376-g002}

The extent of maximum TnI release after PCI moderately correlated with MIF-expression in the patient cohort (spearman rank coefficient: 0.31, p\<0.001, [figure 3](#pone-0038376-g003){ref-type="fig"}).

![Correlation of the extent of maximum TnI release after PCI in the study cohort.](pone.0038376.g003){#pone-0038376-g003}

High Plasma Levels of MIF are Associated with the Presence of Plaque Ruptures in ACS Patients {#s3d}
---------------------------------------------------------------------------------------------

Patients with ACS were also analyzed regarding the presence of plaque rupture as verified by coronary angiography. Remarkably, we found that patients with an angiographically verified "culprit" lesion due to vulnerable plaques showed significantly higher plasma levels of MIF compared to patients with ACS caused by flow limiting stenotic lesions (p = 0.002, [figure 4](#pone-0038376-g004){ref-type="fig"}). In ROC analysis, we identified a cut off-value of 1.22 ng/mL with a sensitivity of 82% and a specifity of 56% and of 1.7 ng/mL with a sensitivity of 67% and a specifitiy of 62% for the diagnosis of ACS compared to patients with stable CAD and healthy controls. For the diagnosis of plaque rupture in ACS patients we found a high specifity (92%) for MIF levels \>5.1 ng/mL.

![Patients with ACS due to plaque rupture showed higher plasma levels of MIF than patients with flow limiting stenotic lesions in coronary angiography.](pone.0038376.g004){#pone-0038376-g004}

Discussion {#s4}
==========

In the present study we demonstrate that the expression of MIF is significantly enhanced in ACS at an early phase during the event. This effect was independent from other risk factors that were reported to influence the incidence and prognosis of ACS. There is growing evidence that recruitment and migration of inflammatory cells into the vessel wall lead to promotion of atherosclerosis and support the development of unstable atherosclerotic lesions. The different phases of monocyte and macrophage recruitment, adhesion and transmigration are regulated by specifically acting chemokines [@pone.0038376-Zernecke1], [@pone.0038376-Soehnlein1]. MIF has previously been decribed in many other proinflammatory diseases such as arthritis [@pone.0038376-Calandra2], septic shock [@pone.0038376-Bernhagen1], colitis and hypersensitivity syndrome [@pone.0038376-Schober1]. MIF is a chemokine-like protein and therefore takes over many chemokine like functions regarding the recruitment and chemotaxis of inflammatory cells and the evolvement of atherosclerotic lesions. Association of MIF with extensive lesion development was previously described in animal models of hypercholesterolemic rabbits [@pone.0038376-Lin1]. In clinical setting, there are only few reports investigating the association of MIF-expression with markers of plaque instability. The formation of vulnerable plaques in coronary arteries is a critical stage of the atherosclerotic disease with a high risk for acute myocardial infarction. Therefore, it is crucial to understand its mechanisms. Vulnerable plaques are characterized through high inflammatory activity indexed by a high content of inflammatory cells such as monocytes/macrophages and various chemokines that critically contribute to plaque rupture. In a Chinese population, a significant correlation between MIF and activator protein-1 (AP-1) and MMP-9 and -2 concentrations was found [@pone.0038376-Yang1].

We could show that high plasma levels of MIF are found in ACS patients with "culprit" lesions followed by plaque rupture. These results may suggest that MIF plays an important role in plaque development and stability. Therefore, MIF may serve as a marker of early ACS and of plaque instability.

We also found an association of higher MIF-levels with the expression of established inflammatory markers. Amplification of the inflammatory response is critical for the development of acute coronary events. Additionally, inflammatory response is associated with the outcome after PCI. Previous studies demonstrated an association between the degree of inflammatory response measured by inflammatory markers, platelet hyperactivity and outcome in PCI-patients. We further investigated the role of MIF on cardiac necrosis marker release. Thus, we found a relevant correlation of initial MIF-levels and subsequent maximum amounts of TnI after PCI.

We are aware that the present study is barely hypothesis generating and has its limitations. First of all, it is a cross-sectional study and we did not perform repeated measurements to investigate the stability and time course of MIF plasma concentration. Secondly, we neither investigated markers of plaque instability, nor the prognostic impact of MIF-levels.

In conclusion, the present data underscore the necessity to further characterize the role of MIF for risk marker analysis and its prognostic role in patients with symptomatic CAD undergoing PCI. We demonstrate that there is a highly significant and independent association of increased plasma amounts of MIF in ACS. Additionally, we show that MIF expression correlates with inflammatory response and the extent of cardiac necrosis markers. This finding is of utmost clinical importance as a linkage of inflammatory response, platelet function and adverse cardiovascular outcome including stent thrombosis has been described previously [@pone.0038376-Mller1]. Thus, multimodal strategies are supported to addresss the question, if anti-inflammatory and plaque-stabilizing approaches can lead to improved prognosis in patients with symptomatic CAD undergoing PCI [@pone.0038376-Saltzman1]. Hence, identification of MIF expression can support risk-stratification in multimarker approaches for ACS. Effects of MIF-modulation on cardiovascular outcome should be investigated in the future. Thus, specific molecules targeting MIF have been recently described [@pone.0038376-OuertataniSakouhi1] and might move into the focus of CAD treatment in the future.

We greatly appreciate the excellent technical help of Jadwiga Kwiatkowska.

**Competing Interests:**The authors have the following interests to declare: Material for platelet aggregation studies with the Multiplate device was kindly provided free of charge by Dynabyte (Munich, Germany). There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials, as detailed online in the guide for authors.

**Funding:**Material for platelet aggregation studies with the Multiplate device was kindly provided free of charge by Dynabyte (Munich, Germany). The company had no influence on design, conduct of the study, interpretation of the data and the whole manuscript process. The study was in part funded by the Deutsche Forschungsgemeinschaft (Transregio-SFB19), and the Klinische Forschergruppe (KFO 274): Platelets--Molecular Mechanisms and Translational Implications. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding received for this study.

[^1]: Conceived and designed the experiments: IIM MG. Performed the experiments: KALM MS HS AK. Analyzed the data: KALM HS AK TG IIM. Contributed reagents/materials/analysis tools: TG KALM BB RJ. Wrote the paper: TG IIM KALM MG.