Novel Biomarker Identification for Acute Coronary Syndrome via Integrating WGCNA and Machine Learning

Background: There is a great deal of mortality and morbidity associated with various cardiovascular diseases that comprise acute coronary syndrome (ACS). One crucial factor in the development of ACS is inflammation of the coronary plaque. Cell adhesion molecules play a key role in the inflammatory cascade. The vascular endothelium is directly affected by elevated levels of pro-inflammatory cytokines and other systemic inflammatory markers in ACS related to atherogenesis. This causes an increase in the expression of adhesion molecules, such as selectins. Objective: This study aims to document the inflammatory response after acute coronary syndrome by evaluating the association between serum E-selectin levels and the risk and severity of acute coronary syndrome. Materials and Methods: A case-control study involving 120 male subjects aged 41–70 years, who were divided into two groups: 60 ACS patients and 60 healthy individuals as a control. Serum E-selectin levels were measured using an ELISA technique. Results: The study revealed a significant increase in serum E-selectin levels when comparing patients to the healthy control group (216.07±20.26 pg/ml Vs 179.74±53 pg/ml, P ≤ 0.0001) respectively. The analysis of the receiver operating curve (ROC) for E-selectin showed a sensitivity of 85%, a specificity of 70%, a 95% confidence interval (CI) of 0.673-0.863, and the area under the curve (AUC) was 0.768. The cut-off point was set at 197.37 pg/ml or higher. Conclusion: Elevated serum E-selectin levels in ACS patients suggest a potential role for adhesion molecules in the pathogenesis of ACS. Adhesion molecules could be considered as a biochemical marker for assessing ACS.

Acute coronary syndrome (ACS) is a leading cause of death worldwide. Prompt and accurate diagnosis of acute myocardial infarction (AMI) or ACS is crucial for improved management and prognosis of patients. The rapid growth of machine learning (ML) research has significantly enhanced our understanding of ACS. Most studies have focused on applying ML to detect ACS, predict prognosis, manage treatment, identify risk factors, and discover potential biomarkers, particularly using data from electrocardiograms (ECGs), electronic medical records (EMRs), imaging, and omics as the main data modality. Additionally, integrating ML with smart devices such as wearables, smartphones, and sensor technology enables real-time dynamic assessments, enhancing clinical care for patients with ACS. This review provided an overview of the workflow and key concepts of ML as they relate to ACS. It then provides an overview of current ML algorithms used for ACS diagnosis, prognosis, identification of potential risk biomarkers, and management. Furthermore, we discuss the current challenges faced by ML algorithms in this field and how they might be addressed in the future, especially in the context of medicine.

Emergency physician threshold to test for acute coronary syndrome (ACS) is directly related to ACS diagnosis rate and inversely related to ACS missed diagnosis rate. Feedback to emergency physicians of information on their prior patients whose ACS diagnosis was not identified may improve physician diagnostic performance. A critical pathway for evaluation of patients for ACS was modified to include feedback to physicians on their cases who had a return visit and did not have their ACS diagnosis identified at their prior emergency department visit. Feedback included case-specific details, discussion of the case at the monthly Morbidity and Mortality conference, and a yearly a report to each physician comparing their performance to their peers (ACS evaluation rate, ACS diagnosis rate, and ACS missed diagnosis rate). Cases were identified, and physician-specific performance was calculated from a computerized encounter database at 2 community teaching hospitals. During the study period, 29 emergency physicians evaluated 295,758 patients and identified 6472 ACS cases. During the study, the yearly ACS evaluation rate for individual physician ranged from 19% to 70% (average 40.3%; 95% confidence interval [CI], 39.5%-41.1%), the yearly ACS diagnosis rate for individual physician ranged from 1.1% to 4.2% (average 1.7%; 95% CI, 1.65%-1.75%), and the yearly missed ACS diagnosis rate for individual physician ranged from 0% to 17% (average 2.8%; 95% CI, 2.3%-3.3%). Individual physician ACS evaluation rate was directly related to physician ACS diagnosis rate (r 0.76, P = 0.00012) and was inversely related to that physician missed ACS rate (r 0.45, P = 0.001). During the study, implementation of the critical pathway increased the ACS evaluation rate from 30% to 48% and decreased the ACS missed diagnosis rate from 1.5% to 0.3%. Emergency physicians with lower threshold for ACS evaluation more frequently diagnose patients with ACS and less frequently miss the diagnosis of ACS. Feedback to emergency physicians of information on their patient's return visits and their own diagnostic performance may improve outcome for patients with ACS.

Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions. The current review aimed to highlight algorithms and important predictor variables through examining those studies which used machine learning algorithms for predicting major adverse cardiovascular events in patients with acute coronary syndrome. To predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. In doing so, PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The checklist "Quality assessment of machine learning studies" was used to assess the quality of eligible studies. The findings of the studies are presented in the form of a narrative synthesis of evidence. In total, among 2,558 retrieved articles, 22 studies were qualified for analysis. Major adverse cardiovascular events and mortality were predicted in 5 and 17 studies, respectively. According to the results, 14 (63.64%) studies did not perform external validation and only used registry data. The algorithms used in this study comprised, inter alia, Regression Logistic, Random Forest, Boosting Ensemble, Non-Boosting Ensemble, Decision Trees, and Naive Bayes. Multiple studies (N = 20) achieved a high area under the ROC curve between 0.8 and 0.99 in predicting mortality and major adverse cardiovascular events. The predictor variables used in these studies were divided into demographic, clinical, and therapeutic features. However, no study reported the integration of machine learning model into clinical practice. Machine learning algorithms rendered acceptable results to predict major adverse cardiovascular events and mortality outcomes in patients with acute coronary syndrome. However, these approaches have never been integrated into clinical practice. Further research is required to develop feasible and effective machine learning prediction models to measure their potentially important implications for optimizing the quality of care in patients with acute coronary syndrome.

The spleen is an important immune organ in fish. MicroRNAs (miRNAs) have been shown to play an important role in the regulation of immune function. However, miRNA expression profiles and their interaction networks associated with the postnatal late development of spleen tissue are still poorly understood in fish. The grass carp (Ctenopharyngodon idella) is an important economic aquaculture species in China. Here, two small RNA libraries were constructed from the spleen tissue of healthy grass carp at one-year-old and three-year-old. A total of 324 known conserved miRNAs and 9 novel miRNAs were identified by using bioinformatic analysis. Family analysis showed that 23 families such as let-7, mir-1, mir-10, mir-124, mir-8, mir-7, mir-9, and mir-153 were highly conserved between vertebrates and invertebrates. In addition, 14 families such as mir-459, mir-430, mir-462, mir-7147, mir-2187, and mir-722 were present only in fish. Expression analysis showed that the expression patterns of miRNAs in the spleen of one-year-old and three-year-old grass carp were highly consistent, and the percentage of miRNAs with TPM > 100 was above 39%. Twenty significant differentially expressed (SDE) miRNAs were identified. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that these SDE miRNAs were primarily involved in erythrocyte differentiation, lymphoid organ development, immune response, lipid metabolic process, the B cell receptor signaling pathway, the T cell receptor signaling pathway, and the PPAR signaling pathway. In addition, the following miRNA-mRNA interaction networks were constructed: immune and hematopoietic, cell proliferation and differentiation, and lipid metabolism. This study determined the miRNA transcriptome as well as miRNA-mRNA interaction networks in normal spleen tissue during the late development stages of grass carp. The results expand the number of known miRNAs in grass carp and are a valuable resource for better understanding the molecular biology of the spleen development in grass carp.

The spleen is an important immune organ in fish. MicroRNAs (miRNAs) have been shown to play an important role in the regulation of immune function. However, miRNA expression profiles and their interaction networks associated with the postnatal late development of spleen tissue are still poorly understood in fish. The grass carp (Ctenopharyngodon idella) is an important economic aquaculture species in China. Here, two small RNA libraries were constructed from the spleen tissue of healthy grass carp at one-year-old and three-year-old. A total of 324 known conserved miRNAs and 9 novel miRNAs were identified by using bioinformatic analysis. Family analysis showed that 23 families such as let-7, mir-1, mir-10, mir-124, mir-8, mir-7, mir-9, and mir-153 were highly conserved between vertebrates and invertebrates. In addition, 14 families such as mir-459, mir-430, mir-462, mir-7147, mir-2187, and mir-722 were present only in fish. Expression analysis showed that the expression patterns of miRNAs in the spleen of one-year-old and three-year-old grass carp were highly consistent, and the percentage of miRNAs with TPM > 100 was above 39%. Twenty significant differentially expressed (SDE) miRNAs were identified. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that these SDE miRNAs were primarily involved in erythrocyte differentiation, lymphoid organ development, immune response, lipid metabolic process, the B cell receptor signaling pathway, the T cell receptor signaling pathway, and the PPAR signaling pathway. In addition, the following miRNA-mRNA interaction networks were constructed: immune and hematopoietic, cell proliferation and differentiation, and lipid metabolism. This study determined the miRNA transcriptome as well as miRNA-mRNA interaction networks in normal spleen tissue during the late development stages of grass carp. The results expand the number of known miRNAs in grass carp and are a valuable resource for better understanding the molecular biology of the spleen development in grass carp.

Prompt diagnosis of acute coronary syndrome (ACS) using a 12-lead electrocardiogram (ECG) is a critical task for emergency physicians. While computerized algorithms for ECG interpretation are limited in their accuracy, machine learning (ML) models have shown promise in several areas of clinical medicine. We performed a systematic review to compare the performance of ML-based ECG analysis to clinician or non-ML computerized ECG interpretation in the diagnosis of ACS for emergency department (ED) or prehospital patients. We searched Medline, Embase, Cochrane Central, and CINAHL databases from inception to May 18, 2022. We included studies that compared ML algorithms to either clinicians or non-ML based software in their ability to diagnose ACS using only a 12-lead ECG, in adult patients experiencing chest pain or symptoms concerning for ACS in the ED or prehospital setting. We used QUADAS-2 for risk of bias assessment. Prospero registration CRD42021264765. Our search yielded 1062 abstracts. 10 studies met inclusion criteria. Five model types were tested, including neural networks, random forest, and gradient boosting. In five studies with complete performance data, ML models were more sensitive but less specific (sensitivity range 0.59-0.98, specificity range 0.44-0.95) than clinicians (sensitivity range 0.22-0.93, specificity range 0.63-0.98) in diagnosing ACS. In four studies that reported it, ML models had better discrimination (area under ROC curve range 0.79-0.98) than clinicians (area under ROC curve 0.67-0.78). Heterogeneity in both methodology and reporting methods precluded a meta-analysis. Several studies had high risk of bias due to patient selection, lack of external validation, and unreliable reference standards for ACS diagnosis. ML models have overall higher discrimination and sensitivity but lower specificity than clinicians and non-ML software in ECG interpretation for the diagnosis of ACS. ML-based ECG interpretation could potentially serve a role as a "safety net", alerting emergency care providers to a missed acute MI when it has not been diagnosed. More rigorous primary research is needed to definitively demonstrate the ability of ML to outperform clinicians at ECG interpretation.

Platelet-bound P-selectin expression in patients with coronary artery disease: impact on clinical presentation and myocardial necrosis, and effect of diabetes mellitus and anti-platelet medication.

Coronary artery disease (CAD) remains the leading cause of mortality worldwide, especially in developing countries, with dyslipidemia being a major risk factor. This study aimed to evaluate lipid parameters and inflammatory biomarkers—E-selectin and tumor necrosis factor-alpha (TNF-α)—to understand their roles in the pathogenesis of acute coronary syndrome (ACS). A case-control design was used, involving 120 participants: 60 patients diagnosed with ACS and 60 healthy controls, enrolled between January and December 2024. Blood samples were analyzed to assess lipid profiles, including total cholesterol, triglycerides, HDL, LDL, and VLDL, using a SMART-120 chemistry analyzer. Serum levels of TNF-α and E-selectin were measured using enzyme-linked immunosorbent assay (ELISA). Results showed significant differences in lipid profiles between ACS patients and controls, supporting the impact of dyslipidemia on ACS development. E-selectin levels were significantly elevated in ACS patients (213.26 ± 2.72 pg/mL) compared to controls (175.11 ± 2.71 pg/mL), with P < 0.0001. Similarly, TNF-α levels were higher in patients (83.20 ± 3.88 pg/mL) than controls (45.65 ± 1.79 pg/mL), also with P < 0.0001. ROC curve analysis demonstrated that E-selectin had 96% sensitivity and specificity at a cutoff of 73.44 pg/mL, while TNF-α had 93% sensitivity and 86% specificity at a cutoff of 188.65 pg/mL. Both biomarkers positively correlated with body mass index (r = 0.572, P < 0.0001).The findings suggest that TNF-α and E-selectin are potential diagnostic biomarkers for ACS and play key.

The 2011 European Society of Cardiology guidelines state that acute coronary syndrome (ACS) may be excluded with a rapid 3 h high-sensitivity troponin T (HsTnT) sampling protocol. We aimed to evaluate the diagnostic and prognostic performance of HsTnT in patients with chest pain admitted with possible ACS in routine care. A total of 773 consecutive patients admitted for in-hospital care for chest pain suspicious of ACS were included retrospectively. HsTnT values at admission and at 3-4 and 6-7 h were analysed for diagnostic performance. In addition, prognostic performance towards a combined 60-day endpoint of ACS, nonelective revascularization or death of all causes was studied. Twenty-three per cent of the patients had ACS during the hospital stay and 1.6% had an endpoint after discharge but within 60 days. The sensitivity of HsTnT for ACS at admission, 3-4 and 6-7 h was only 68, 79 and 81%, respectively. Sensitivity and negative predictive value for acute myocardial infarction alone were 80 and 93% on admission and 97 and 99% at 3-4 h. Among patients aged 75 years and older, 63% had a positive HsTnT on admission, but only 39% of these had an ACS during hospital stay. Our data confirm that prolonged testing with HsTnT after 3-4 h does not improve diagnostic performance for ACS. However, although sensitivity for acute myocardial infarction was very good, sensitivity for ACS was insufficient to rule out ACS even at 6-7 h. Less than half of all recorded positive HsTnT were true positives. On the basis of these results, we find it unlikely that HsTnT has improved the diagnosis of ACS in the emergency care setting.

Objective To investigate the predictive effects of small dense low density lipoprotein cholesterol (sdLDL-C) level and sdLDL-C/high density lipoprotein cholesterol (HDL-C) ratio on the occurrence in patients with acute coronary syndrome (ACS). Methods Two hundred and sixty-eight patients with acute chest pain and diagnosed as ACS according to the clinical symptoms, changes in electrocardiogram and myocardial enzymes, and coronary angiography from November 2017 to April 2018 were enrolled. One hundred and thirty-four cases of unstable angina (UA) and 134 cases of acute myocardial infarction (AMI) were included. Meanwhile, 66 patients with non-ACS were selected as the control group. They baseline data were matched with those of ACS in the same period. Results The sdLDL-C levels and sdLDL-C/HDL-C of ACS patients were significantly increased [0.88(0.70, 1.09) mmol/L vs. 0.61(0.41, 0.84) mmol/L, 0.98(0.72, 1.30) vs. 0.58(0.40, 0.86)]. The sdLDL-C levels and sdLDL-C/HDL-C of AMI group were higher than those of UA group [0.94(0.82, 1.21) mmol/L vs. 0.78 (0.61, 0.98) mmol/L, 1.10(0.79, 1.40) vs. 0.86 (0.62, 1.19)], while those of UA group were also higher than those of the control group [0.78(0.61, 0.98) mmol/L vs. 0.61(0.41, 0.84) mmol/L, 0.86(0.62, 1.19) vs. 0.58(0.40, 0.86)]. There were significant differences (P<0.01). Logistic regression analysis showed that sdLDL-C level was an independent risk factor for ACS prediction. Compared with those of the control group, the OR values of ACS group, UA group and AMI group were respectively 26.85, 15.19 and 74.40. Correlation analysis showed that sdLDL-C was significantly positively correlated with TC and LDL-C levels (r=0.697, 0.684, P<0.01), while it controlled TC and LDL-C levels, and sdLDL-C levels were still significantly positively correlated with ACS (r=0.185, P=0.001). ROC analysis revealed that sdLDL-C ≥ 0.613 mmol/L had a sensitivity of 86.6% and specificity of 51.5%, and a sdLDL-C/HDL-C ≥ 0.938 mmol/L had a sensitivity of 53.7% and specificity of 87.9%. ROC curve was used to analyze AMI in ACS group, and the best threshold sdLDL-C=0.732 mmol/L divided the cases into low-risk groups and high-risk groups. Logistic regression analysis showed that, compared with the low-risk groups, the relative risk estimates of the AMI in the high-risk group was 4.84, after other indicators were adjusted. Conclusions sdLDL-C levels and sdLDL-C/HDL-C are closely related to the occurrence of ACS. As independent risk factors, they are risk assessment predictors for ACS. Key words: Coronary disease; Small dense low density lipoprotein cholesterol; High density lipoprotein cholesterol

The inflammatory etiology of atherosclerosis has prompted a search for biomarkers of inflammation that predict risk for coronary artery disease and its sequelae. Within the acute coronary syndromes (ACS), inflammatory biomarkers may provide independent information regarding pathophysiology, prognosis, and optimal therapeutic strategies. On the basis of the hypothesis that different pathophysiological processes provide nonoverlapping information regarding risk stratification and disease management, this review series addresses biomarkers for each step in the inflammatory process that leads to ACS. Part I reviewed cytokines; this part reviews acute-phase reactants and biomarkers of endothelial cell activation; subsequent parts will address biomarkers of oxidative stress, angiogenesis, extracellular matrix degradation, and platelet activation. In the acute-phase response, cytokines drive production of acute-phase reactants, defined by >25% change in circulating concentration during an inflammatory response. These markers may also remain elevated chronically because of continuing inflammatory stimuli (Table). View this table: Inflammatory Biomarkers in ACS ### C-Reactive Protein C-reactive protein (CRP) is a pentraxin acute-phase protein, members of which are evolutionarily conserved in most vertebrates.1 Hepatocytes and possibly smooth muscle cells and macrophages transcriptionally activate production of CRP in response to inflammatory cytokines, including interleukin-1 and interleukin-6.2 CRP is a robust clinical marker because of its stability, reproducible results, and ease of assay. Although it was originally proposed as a nonspecific marker of inflammation, several reports suggest that CRP may play a direct pathophysiological role in the development and progression of atherosclerosis. Proposed mechanisms include induction of endothelial dysfunction,3 promotion of foam cell formation,4 inhibition of endothelial progenitor cell survival and differentiation,5 and activation of complement in atherosclerotic plaque intima6 and ischemic myocardium.7 Patients with ACS have elevations in CRP in association with their presenting symptoms. There appears to be a bimodal CRP response among patients with ACS. In some patients, CRP may remain elevated for …

Identification of risk factors for acute coronary syndrome (ACS) is important for both diagnostic and prognostic purposes. Among the platelet parameters- mean platelet volume (MPV) and platelet distribution width (PDW) are thought to be risk factors of ACS. This quasi- experimental study was conducted from September 2011 to August 2012 in the Department of Clinical Pathology, in collaboration with Departments of Cardiology, Bangbandhu Sheikh Mujib Medical University (BSMMU), Dhaka and Bangladesh Institute of Research and Rehabilitation in Diabetes, Endocrine and Metabolic Disorders (BIRDEM) . It was enrolled 79 patients with ACS, diagnosed based on clinical history, electrocardiographic changes and increased cardiac markers especially troponin I, and 63 subjects were enrolled as control. For determination of platelet parameters, the blood samples were obtained from all patients of ACS before anti-platelet therapy when patient attended in the cardiac emergency and after 5th day of ongoing anti-platelet therapy; and from control group on the 1st day and 5th day at outpatient department. The blood samples were taken properly and processed in haematology autoanalyser. In this study, the baseline characteristics of study patients were measured. Platelet counts were 273.1±50.15 x 109/L in patients with ACS and 290.78±74.86 x 109/L in control subjects in 1st sample and 284.56±41.93 x 109/L in patients with ACS in 2nd sample. In 1st samples, platelet counts were slightly low in patients with ACS compared to controls and 2nd samples. There were no statistical significant differences between the groups and the samples. MPV was 12.48±1.17 fl and 10.45±0.66 fl in patients with ACS and controls and 11.55±1.08 fl in 2nd sample in ACS cases. PDW was 16.23±2.56 fl, 11.89±1.42 fl and 14.29±2.11 fl in patients with ACS, controls and 2nd sample of ACS cases respectively. Both MPV and PDW were statistically significant between the groups and the samples (p&lt;0.001). The sensitivity, specificity, positive and negative predictive value of platelet parameters of ACS cases were obtained from ROC curve and compared with controls. The best cut off value of platelet count, MPV and PDW were &gt;225 x 109/L, &gt; 10.7 fl and &gt;12.7 fl respectively. The sensitivity, specificity, accuracy, positive and negative predictive value of platelet counts, MPV and PDW were 83%, 28.1%, 42.3%, 37.6%, 64%; 90.6%, 49.4%, 64.8%, 51.6%, 89.8%; and 94.3%,52.8%, 69%,54.9%, 94.1% respectively. The study showed that PDW had higher sensitivity and specificity in contrast to MPV. Platelet parameters were increased in patients with ACS before anti-platelet therapy and gradually decreased after anti-platelet therapy. These two markers may used as predictor for early detection of ACS and risk stratification, when other cardiac biomarkers are negative.

Objective To investigate the value of detecting HEART score and HEARTS3 score in risk stratification and prognosis of acute coronary syndrome (ACS) in patients with non-ST segment elevation chest pain in emergency department (ED). Methods Clinical data of case-control retrospective study of 775 patients with non-ST segment elevation chest pain in ED were collected from July 2011 to March 2015. The patients were estimated and risk stratification was made with HEART score and HEARTS3 score. After follow-up visiting by telephone for 30 days, outcomes were found to be ACS and myocardial infarction (MI). And the patients were categorized with score into low, intermediate and high risk groups .The correlation between the ACS and risk score in three groups was analyzed. Comparison of capability of performance in predicting 30-day ACS between the HEART score and HEARTS3 risk score. Statistical analyses were performed using SPSS13.0. Enumeration variables were expressed as percentage. For comparison of predictive value of the two sets of scores, area under the receiver operating curve (auROC) was calculated and compared by Z test. Results There were 92 cases with 30-day ACS. The rate of ACS had a trend of increase with increase in HEART score and HEARTS3 score. The patients with higher scores of HEART and HEARTS3, higher incidence of ACS in 30 days .Especially, the high-risk patients with score≥7 of HEART score and≥8 of HEARTS3 score had higher rate of ACS. And there was significant difference in predicting high-risk patients between two sets of scoring (P<0.05). The HEARTS3 score outperformed the HEART score as determined by comparison of areas under the ROC curve for MI (0.952 vs 0.813; P=0.028), 30-day ACS (0.913 vs. 0.815; P=0.034). Conclusions HEART score and HEARTS3 score both can be used to evaluate and perform risk stratification for non-ST segment elevation chest pain patients in ED .But HEARTS3 score can more precisely stratify high-risk patients with chest pain for 30-day ACS. Key words: HEART score; HEARTS3 score; Chest pain; Risk Stratification; Acute Coronary Syndrome(ACS); Prognosis

The concept of vulnerable plaques has been long described since some atherosclerotic lesions rupture suddenly causing myocardial infarctions and strokes while others remain quiescent or stable for many years. Two potentially feasible approaches were used here in an attempt to identify these vulnerable plaques: imaging and urine proteomics. Imaging: Several intravascular imaging techniques have been investigated to identify vulnerable plaques without definitive success yet, demanding better understanding of pathophysiology of these lesions and more reliable imaging methods. We described the near infrared range (NIR) intrinsic fluorescent activity to be a property unique to the unstable plaques, using a well-established mouse model of tandem stenosis as well as human carotid endarterectomy samples. The source of NIR autofluorescence is shown to be intraplaque haemorrhage where haem degradation products were intermingled with various chemicals of the necrotic core. We also demonstrated that changes in the plaque burden were reflected by the changes in NIR fluorescent intensity using haem oxygenase enzyme modulation which played a complex role in the pathophysiology of plaque progression and vulnerability. NIR autofluorescence in the areas of intraplaque haemorrhage, a critical element of plaque vulnerability, provides a much needed new foundation in the field of intravascular imaging for unstable plaques. Although NIR fluorescent imaging is still in the pre-clinical stage, it should be further explored with the aim of developing intravascular probe to be applied in clinical studies. Urine proteomics: A pilot proteomic study aimed at the identification of secreted urinary peptide biomarkers and the modelling of a prognostic classifier for acute coronary syndrome (ACS) is reported in this thesis. The urinary proteome profile data of 126 individuals who had suffered from ACS up to 5 years post urine sampling and proteome data of 126 controls without ACS were analysed. The initial statistical comparison of proteome profile data of 84 individuals with an ACS and 84 matched controls resulted in the discovery of 75 potential ACS-specific prognostic peptide biomarkers. Based on these peptide biomarkers we established the support vector machine-modelled prognostic ACS classifier ACSP75. The performance of the classifier was assessed using sensitivity, specificity and discrimination (c-statistics) and was compared the performance of the Framingham risk score (FHS), and to an algorithm combining the classifier, age and BMI. In the validation data set, the classifier identified individuals with an ACS with a sensitivity of 73.8% and demonstrated reasonable discrimination (c statistic=0.664). The classifier showed similar performance compared to FHS (C-statistics: 0.664 vs 0.644 [p=0.692] for classifier and FHS, respectively). In a model where we combined the classifier with other traditional risk factors (BMI and age), the algorithm showed good discrimination (c-statistic=0.707), but was not significantly better than the classifier itself (p=0.213). The sensitivity (83.3 %) and specificity (78.6 %) of the composite classifier was better than the classifier on its own. We demonstrate that the proteomic classifier ACSP75 based on urinary peptide biomarkers has the potential to predict future ACS events. The classifier and the composite classifier should be validated in a large cohort.