Plasma and aqueous levels of subfatin, preptin and betatrophin in patients with diabetic retinopathy.

AIM: To examine subfatin, preptin and betatrophin levels in plasma and aqueous in patients with diabetes mellitus (DM) (with and without retinopathy). MATERIAL AND METHOD: Sixty patients, who were similar in terms of age and gender, and were scheduled for operation due to cataract, were included in the study. The patients were divided into three groups as Group C (20 weeks without diabetes and comorbidity), Group DM (20 patients with DM but no retinopathy) and Group DR (20 patients with diabetic retinopathy). The preoperative body mass index (BMI), fasting plasma glucose, HbA1c, lipid profile levels of all patients in the groups were examined. Blood samples were also taken for plasma subfatin, preptin and betatrophin levels. At the beginning of the cataract surgery, 0.1 ml of aqueous fluid was taken from the anterior chamber. Plasma and aqueous subfatin, preptin and betatrophin levels were analyzed by ELISA (enzyme-linked immunosorbent assays) method. RESULTS: In our study results, there was a significant difference in BMI, fasting plasma glucose and hemoglobin A1c levels (p < 0.05 for all parameters). Plasma and aqueous subfatin levels were higher in Group DR compared to Group C (p < 0.001, p = 0.036, respectively). Plasma and aqueous preptin levels were higher in group DR and group DM than in group C (p = 0.001, p = 0.002, p < 0.001, p = 0.001, respectively). Plasma and aqueous betatrophin levels were higher in Group DR compared to group C (p = 0.001, p = 0.010, respectively). CONCLUSION: Subfatin, preptin and betatrophin molecules may have an important role in the pathogenesis of diabetic retinopathy.

Effect of altitude on ticagrelor-induced dyspnea in patients with acute coronary syndrome.

OBJECTIVE: This study aimed to define the association between altitude and ticagrelor-associated dyspnea in patients with acute coronary syndrome (ACS). METHODS: We studied consecutive patients with de novo ACS who were admitted to two centers at a low altitude (18 and 25 m, n = 65) and two centers at a high altitude (1313 and 1041 m, n = 136). We managed them with ticagrelor between May 2017 and September 2017. Patients with ACS underwent an interventional procedure within <90 minutes in those with ST elevation and within <3 hours in those without ST elevation. We recorded the incidence of dyspnea in patients with ACS receiving ticagrelor therapy. RESULTS: The mean age was 59.5 ± 10 years, and the mean ejection fraction was 43% ± 18%. A total of 110 (56.7%) patients had ST elevation and 84 (43.3%) did not. There were no significant differences in cardiac risk factors, concurrent medications, or procedural variables between the two groups. Dyspnea developed during hospitalization in 53 (38%) patients from high-altitude centers and in 13 (20%) patients from low-altitude centers (66 patients represented 32% of the total ACS cohort). CONCLUSIONS: Dyspnea is a common multifactorial symptom in patients following development of ACS. Ticagrelor-induced dyspnea appears to be associated with altitude.

Evaluation of the Effects of Dual Antiplatelet Therapy on Guided Bone Regeneration in Peri-Implant Bone Defect.

In this study, the authors aim to investigate the effect of dual antiplatelet agents on peri-implant-guided bone regeneraation by studying a sample of rats with titanium implants in their tibias. The rats were randomly divided into 5 groups: acetylsalicylic acid (ASA) (n=10), treated with 20 mg/kg of ASA; ASA+CLPD (Clopidogrel): (n=10), treated with 20 mg/kg of ASA and 30 mg/kg of clopidogrel; ASA+PRSG (Prasugrel): (n=10), treated with 20 mg/kg of ASA and 15 mg/kg of prasugrel; ASA+TCGR (Ticagrelor): (n=10), treated with 20 mg/kg of ASA and 300 mg/kg of ticagrelor; and a control group (n=10) received no further treatment after implant surgery. Bone defects created half of the implant length circumferencial after implant insertion and defects filled with bone grafts. After 8 weeks experimental period, the rats sacrified and implants with surrounding bone tissues were collected to histologic analysis; bone filling ratios of defects (%) and blood samples collected to biochemical analysis (urea, creatinine, aspartate aminotransferase, alanine aminotransferase, phosphorus, magnesium, alkaline phosphatase, calcium, and parathormone). A statistically significant difference was not detected between the groups for all parameters ( P >0.05). When the percentage of new bone formation was examined, it was found that there was no statistically significant difference between the groups ( P >0.05). Antiplatelet therapy may not adversely affect guided bone regeneration in peri-implant bone defects.

Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals.

Myocardial infarction (MI) is detected using electrocardiography (ECG) signals. Machine learning (ML) models have been used for automated MI detection on ECG signals. Deep learning models generally yield high classification performance but are computationally intensive. We have developed a novel multilevel hybrid feature extraction-based classification model with low time complexity for MI classification. The study dataset comprising 12-lead ECGs belonging to one healthy and 10 MI classes were downloaded from a public ECG signal databank. The model architecture comprised multilevel hybrid feature extraction, iterative feature selection, classification, and iterative majority voting (IMV). In the hybrid handcrafted feature (HHF) generation phase, both textural and statistical feature extraction functions were used to extract features from ECG beats but only at a low level. A new pooling-based multilevel decomposition model was presented to enable them to create features at a high level. This model used average and maximum pooling to create decomposed signals. Using these pooling functions, an unbalanced tree was obtained. Therefore, this model was named multilevel unbalanced pooling tree transformation (MUPTT). On the feature extraction side, two extractors (functions) were used to generate both statistical and textural features. To generate statistical features, 20 commonly used moments were used. A new, improved symmetric binary pattern function was proposed to generate textural features. Both feature extractors were applied to the original MI signal and the decomposed signals generated by the MUPTT. The most valuable features from among the extracted feature vectors were selected using iterative neighborhood component analysis (INCA). In the classification phase, a one-dimensional nearest neighbor classifier with ten-fold cross-validation was used to obtain lead-wise results. The computed lead-wise results derived from all 12 leads of the same beat were input to the IMV algorithm to generate ten voted results. The most representative was chosen using a greedy technique to calculate the overall classification performance of the model. The HHF-MUPTT-based ECG beat classification model attained excellent performance, with the best lead-wise accuracy of 99.85% observed in Lead III and 99.94% classification accuracy using the IMV algorithm. The results confirmed the high MI classification ability of the presented computationally lightweight HHF-MUPTT-based model.

DKPNet41: Directed knight pattern network-based cough sound classification model for automatic disease diagnosis.

PROBLEM: Cough-based disease detection is a hot research topic for machine learning, and much research has been published on the automatic detection of Covid-19. However, these studies are useful for the diagnosis of different diseases. AIM: In this work, we collected a new and large (n=642 subjects) cough sound dataset comprising four diagnostic categories: 'Covid-19', 'heart failure', 'acute asthma', and 'healthy', and used it to train, validate, and test a novel model designed for automatic detection. METHOD: The model consists of four main components: novel feature generation based on a specifically directed knight pattern (DKP), signal decomposition using four pooling methods, feature selection using iterative neighborhood analysis (INCA), and classification using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation. Multilevel multiple pooling decomposition combined with DKP yielded 41 feature vectors (40 extracted plus one original cough sound). From these, the ten best feature vectors were selected. Based on each vector's misclassification rate, redundant feature vectors were eliminated and then merged. The merged vector's most informative features automatically selected using INCA were input to a standard kNN classifier. RESULTS: The model, called DKPNet41, attained a high accuracy of 99.39% for cough sound-based multiclass classification of the four categories. CONCLUSIONS: The results obtained in the study showed that the DKPNet41 model automatically and efficiently classifies cough sounds for disease diagnosis.

An accurate valvular heart disorders detection model based on a new dual symmetric tree pattern using stethoscope sounds.

BACKGROUND AND PURPOSE: Valvular heart disease (VHD) is an important cause of morbidity and mortality. Echocardiography is the reference standard for VHD diagnosis but is not universally accessible. Manual cardiac auscultation is inadequate for screening VHD. Many machine learning models using heart sounds acquired with an electronic stethoscope may improve the accuracy of VHD diagnosis. We aimed to develop an accurate sound classification model for VHD diagnosis. MATERIALS AND METHODS: A new large stethoscope sound dataset containing 10,366 heart sounds divided into ten categories (nine VHD and one healthy) were prospectively collected. We developed a handcrafted learning model that comprised multilevel feature extraction based on a dual symmetric tree pattern (DSTP) and multilevel discrete wavelet transform (DWT), feature selection, and classification. The multilevel DWT was used to create subbands to extract features at both high and low levels. Then, iterative neighborhood component analysis was used to select the most discriminative 512 features from among the extracted features in the generated feature vector. In the classification phase, a support vector machine (SVM) was used with 10-fold cross-validation (CV) and leave-one-subject-out (LOSO) CV. RESULTS: Our proposed DSTP-based model attained 99.58% and 99.84% classification accuracies using SVM classifier with 10-fold CV and LOSO CV, respectively. CONCLUSIONS: The presented DSTP-based classification model attained excellent multiclass classification performance on a large prospective heart sound dataset at a low computational cost.

Multi-classification deep CNN model for diagnosing COVID-19 using iterative neighborhood component analysis and iterative ReliefF feature selection techniques with X-ray images.

Background: The acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease seriously affected worldwide health. It remains an important worldwide concern as the number of patients infected with this virus and the death rate is increasing rapidly. Early diagnosis is very important to hinder the spread of the coronavirus. Therefore, this article is intended to facilitate radiologists automatically determine COVID-19 early on X-ray images. Iterative Neighborhood Component Analysis (INCA) and Iterative ReliefF (IRF) feature selection methods are applied to increase the accuracy of the performance criteria of trained deep Convolutional Neural Networks (CNN). Materials and methods: The COVID-19 dataset consists of a total of 15153 X-ray images for 4961 patient cases. The work includes thirteen different deep CNN model architectures. Normalized data of lung X-ray image for each deep CNN mesh model are analyzed to classify disease status in the category of Normal, Viral Pneumonia and COVID-19. The performance criteria are improved by applying the INCA and IRF feature selection methods to the trained CNN in order to improve the analysis, forecasting results, make a faster and more accurate decision. Results: Thirteen different deep CNN experiments and evaluations are successfully performed based on 80-20% of lung X-ray images for training and testing, respectively. The highest predictive values are seen in the analysis using INCA feature selection in the VGG16 network. The means of performance criteria obtained using the accuracy, sensitivity, F-score, precision, MCC, dice, Jaccard, and specificity are 99.14%, 97.98%, 99.58%, 98.80%, 97.81%, 98.83%, 97.68%, and 99.56%, respectively. This proposed study is indicated the useful application of deep CNN models to classify COVID-19 in X-ray images.

Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds.

COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds.

Effects of Systemic Propranolol Application on the New Bone Formation in Periimplant Guided Bone Regeneration.

OBJECTIVES: The aim of this experimental animal study was to evaluate the effects of systemic propranolol on new bone formation in peri-implant bone defects. MATERIAL AND METHODS: Implant slots were created 4mm long and 2.5 mm wide. After the titanium implants were placed in the sockets, 2 mm defects were created in the neck of the implants. Bone grafts were placed in these defects. Then the rats were randomly divided into three equal groups: control (n = 8), propranolol dose-1 (PRP-1) (n = 8), and propranolol dose-2 (PRP-2) (n = 8) groups. In the control group, the rats received no further treatment during the eight-week experimental period after the surgery. The rats in the PRP-1 and PRP-2 groups were given 5 mg/kg and 10 mg/kg propranolol, respectively, every three days for the eight-week experimental period after the surgery. At the end of the experimental period, the rats were euthanized. Blood serum was collected for biochemical analysis, and the implants and surrounding bone tissues were used for the histological analysis. RESULTS: There were no significant differences in the histological analysis results and the biochemical parameters (alkaline phosphatase, calcium, creatinine and phosphorus) of the groups (P > 0.05). Also, in the test groups, there was numerically but not statistically more new bone formation detected compared with the controls. CONCLUSIONS: Within the limitations of this study, propranolol did not affect the new bone formation in peri-implant defects.

Coronary Angiography Print: An Automated Accurate Hidden Biometric Method Based on Filtered Local Binary Pattern Using Coronary Angiography Images.

BACKGROUND AND PURPOSE: Biometrics is a commonly studied research issue for both biomedical engineering and forensics sciences. Besides, the purpose of hidden biometrics is to discover hidden biometrics features. This work aims to demonstrate the biometric identification ability of coronary angiography images. MATERIAL AND METHOD: A new coronary angiography images database was collected to develop an automatic identification model. The used database was collected from 51 subjects and contains 2156 images. The developed model has to preprocess; feature generation using local binary pattern; feature selection with neighborhood component analysis; and classification phases. In the preprocessing phase; image rotations; median filter; Gaussian filter; and speckle noise addition functions have been used to generate filtered images. A multileveled extractor is presented using local binary pattern and maximum pooling together. The generated features are fed to neighborhood component analysis and the selected features are classified using k nearest neighbor classifier. RESULTS: The presented angiography image identification method attained 99.86% classification accuracy on the collected database. CONCLUSIONS: The obtained findings demonstrate that the angiography images can be utilized as biometric identification. Moreover, we discover a new hidden biometric feature using coronary angiography images and name of this hidden biometric is coronary angiography print.

Correction to: Evaluation of Index of Cardio-Electrophysiological Balance in Patients With Atrial Fibrillation on Antiarrhythmic-Drug Therapy.

[This corrects the article DOI: 10.14740/cr1185.].

Evaluation of Index of Cardio-Electrophysiological Balance in Patients With Atrial Fibrillation on Antiarrhythmic-Drug Therapy.

BACKGROUND: Index of cardio-electrophysiological balance (iCEB) has been described as a novel risk marker for predicting malignant ventricular arrhythmia. There remains limited evidence on the effects of amiodarone and propafenone used for sinus rhythm maintenance on iCEB in patients with atrial fibrillation (AF). The aim of this study was to evaluate iCEB in patients with AF on antiarrhythmic-drug therapy. METHODS: A total of 108 patients with AF (68 patients using amiodarone and 40 patients using propafenone) and 50 healthy subjects were included in the study. All groups underwent a standard 12-lead surface electrocardiogram. QRS duration, QT, T wave peak-to-end (Tp-e) intervals, iCEB (QT/QRS) and iCEBc (heart rate-corrected QT (QTc)/QRS) rates were calculated from the electrocardiogram and compared between groups. RESULTS: QT, Tp-e intervals and Tp-e/QT ratio were significantly longer in the amiodarone group than the propafenone and control groups (P < 0.001, for all). iCEB was similar in the amiodarone and control groups (4.4 ± 0.6 and 4.2 ± 0.4; P > 0.05), while iCEB values in the propafenone group were significantly lower than the amiodarone group and control groups (3.9 ± 0.5; P < 0.001). There was a significantly difference in iCEBc values among the amiodarone, control and propafenone groups (4.8 ± 0.6, 4.6 ± 0.4 and 4.3 ± 0.6; P < 0.001, respectively). CONCLUSIONS: In this study, higher iCEBc parameters were observed in patients using amiodarone, while iCEBc values were lowest among patients with AF using propafenone. Further studies are needed to determine whether these electrophysiological changes are associated with ventricular arrhythmias for patients with AF on antiarrhythmic-drug therapy.

Can we define migraine patients with blood high-sensitivity C-reactive protein and galectin-3 levels in the emergency department?

INTRODUCTION: Migraine, the most widespread cause of headache in young adults, is a frequent reason for presentation to emergency departments. The aim of this study was to determine the diagnostic value of serum galectin-3 and high-sensitivity C-reactive protein (hsCRP) levels in migraine patients. MATERIAL AND METHODS: Serum galectin-3 and hsCRP levels were measured using enzyme-linked immunosorbent assay in 70 migraine patients and 70 healthy control subjects. RESULTS: The mean values of hsCRP and galectin-3 levels were significantly higher in the migraine group than those in the control group (1.67 ±0.49, 1.12 ±0.38 mg/l, p < 0.001; and 3.76 ±1.45, 2.05 ±0.29 ng/ml, p < 0.001, respectively). When the diagnostic values of the biomarkers were assessed in the migraine attack and control groups, the sensitivity and specificity of the test for hsCRP at the cut-off point of 1.32 mg/l were 70% and 73% (AUC = 0.805, 95% CI: 0.74-0.88, p < 0.001), and for galectin-3 at the cut-off point of 2.4 ng/ml, 89% and 90% (AUC = 0.97, 95% CI: 0.95-0.99, p < 0.001), respectively. CONCLUSIONS: This study showed the presence of a relation between high serum levels of hsCRP and galectin-3 in migraine patients. However, well-controlled, carefully executed longitudinal studies are required to confirm these findings.

Tuberculous pericarditis: a case report.

Pericardial effusion is common disease and difficult to diagnose. Tuberculosis accounts for up to 4% of acute pericarditis and 7% of cardiac tamponade cases. Quick treatment can be lifesaving but requires accurate diagnosis. We report a case of a 65-year-old man who presented with a 3-week history of fever with chills, non-productive cough and dyspnea. The case was diagnosed by positivity of acid-fast staining, culture and polymerase chain reaction (PCR) of the aspirated pericardial fluid and treated promptly with antituberculosis drugs. The patient showed complete recovery.

The awareness, efficacy, safety, and time in therapeutic range of warfarin in the Turkish population: WARFARIN-TR.

OBJECTIVE: The awareness, time in therapeutic range (TTR), and safety of warfarin therapy were investigated in the adult Turkish population. METHODS: This multicenter prospective study includes 4987 patients using warfarin and involved regular international normalized ratio (INR) monitoring between January 1, 2014 and December 31, 2014. TTR was calculated according to F.R. Roosendaal's algorithm. Awareness was evaluated based on the patients' knowledge of warfarin's affect and food-drug interactions. RESULTS: The mean TTR of patients was 49.52±22.93%. The patients with hypertension (55.3%), coronary artery disease (23.2%), congestive heart failure (24.5%), or smoking habit (20.8%) had significantly lower TTR levels than the others. Of the total number of patients, 42.6% had a mechanical valve, 38.4% had non-valvular atrial fibrillation (AF), and 19% had other indications for warfarin. Patients with other indications had lower TTR levels than those with mechanical valve and non-valvular AF (p=0.018). Warfarin awareness decreased in higher age groups. The knowledge of warfarin's food-drug interactions was 55%. People with higher warfarin awareness had higher TTR levels. Patients with ≤8 INR monitoring/year had lower TTR levels (46.4±25.3 vs. 51.1±21.3, respectively, p<0.001) and lower awareness (44.6% vs. 60.6%, p<0.001) than patients with ≥8 INR monitoring/year. In this study, 20.1% of the patients had a bleeding event (major bleeding 15.8%, minor bleeding 84.2%) within a year. CONCLUSION: Both the mean TTR ratios and awareness of the Turkish population on warfarin therapy were found to be low. It was thought that low TTR levels of the Turkish population may be caused by the low awareness of warfarin, warfarin's food-drug interactions, and high rates of concomitant diseases.

An epidemiological study to evaluate the use of vitamin K antagonists and new oral anticoagulants among non-valvular atrial fibrillation patients in Turkey- AFTER-2 study design.

OBJECTIVES: Atrial fibrillation (AF) is one of the most common causes of preventable ischemic stroke and is related to increased cardiovascular morbidity and mortality. There is a lack of data in Turkey on the use of new oral anticoagulants (NOACs), and time in therapeutic INR range (TTR) in vitamin K antagonist users and AF management modality. In this multi-center trial, we aimed to analyze, follow and evaluate the epidemiological data in non-valvular AF patients. STUDY DESIGN: Four thousand one hundred consecutive adult patients from 42 centers with at least one AF attack identified on electrocardiography will be included in the study. Patients with rheumatic mitral valve stenosis and prosthetic valve disease will be excluded from the study. At the end of one year, the patients will be evaluated in terms of major cardiac end points (death, transient ischemic attack, stroke, systemic thromboembolism, major bleeding and hospitalization). RESULTS: First results are expected in June 2015. Data about major cardiovascular end-points will be available in January 2016. CONCLUSION: The rates and kind of oral anticoagulant use, TTR in vitamin K antagonist users and main management modality applied in non-valvular AF patients will be determined by AFTER-2 study. In addition, the rate of major adverse events (MACEs) and the independent predictors of these MACEs will be detected (AFTER-2 Study ClinicalTrials.gov number, NCT02354456.).

QT interval dispersion in the patients with central serous chorioretinopathy.

AIM: To evaluate QT dispersion (QTD) in patients with central serous chorioretinopathy (CSC). METHODS: This clinical, comperative, case-control study included 30 patients with CSC at acute phase (Group 1) and 30 age- and sex-matched healthy subjects (Group 2, the control group). From all subjects, a 12-lead surface electrocardiography was obtained. The heart rate (HR), QT maximum (QTmax), QT minimum (QTmin), QT corrected (QTc), QTD and Tmean were manually measured and analyzed. Student's t-test and Pearson's method of correlation were used for statistical analysis. RESULTS: The patient and control groups were matched for age, smoking status (rate and duration) and gender. There were no significant differences with regard to these among the groups (P>0.05). The participants included 19 men (63.3%) and 11 women (36.7%) in Group 1, 20 men (66.7%) and 10 women (33.3%) in Group 2. QTmax, QTD and QTc were significantly higher than those of healthy controls (P<0.001 for QTmax, P=0.01 for QTD and P=0.001 for QTc). QTmin, Tmean and HR did not differ significantly between the study groups (P=0.28 for QTmin, P=0.56 for Tmean and P>0.05 for HR). No significant correlation was found between duration of the disorder and QTD values (r=0.13, P>0.05). CONCLUSION: These findings suggest that CSC may be associated with an increase in QTD and that the patients might be at risk for ventricular arrhythmia.

Evaluation of coronary sinus strain in patients with dipper and nondipper hypertension.

OBJECTIVE: Hypertension has been reported to affect both the left and the right ventricular functions, but its effect on the coronary sinus has not been investigated. The aim of this study was to investigate the effect of systemic hypertension on the cardiac venous system by evaluating the coronary sinus strain (CSS). METHODS: One-hundred and twelve hypertensive patients without diabetes and 44 healthy individuals (the control group) were evaluated consecutively at the outpatient clinic and enrolled in the study. CSS was evaluated by echocardiography in all the participants before blood pressure evaluations. Twenty-four-hour ambulatory blood pressure monitoring enabled the study population to be divided into two groups: 52 patients with dipper pattern hypertension and 60 with nondipper pattern hypertension. RESULTS: Nondipper pattern patients had significantly lower values of CSS compared with the participants in the control group (140.8±54.2 and 193.9±48.1, P<0.001). Similarly, dipper pattern patients had significantly lower values of CSS values compared with the controls (164±68.4 and 193.9±48.1, P=0.036). On comparing the three groups, the CSS values showed a progressive decrease from normal individuals to dipper and nondipper hypertension patients. Correlation analysis indicated a positive correlation between the aortic strain and the CSS (r=0.247, P=0.002). There was a weak correlation between left ventricular mass and CSS (r=-164, P=0.041). CONCLUSION: Our study suggests that systemic hypertension may affect the cardiac venous system as well as the arterial system, which has been reported in many papers. The effect on the venous system may be more pronounced in nondipper pattern hypertension.

Decreased saliva/serum irisin concentrations in the acute myocardial infarction promising for being a new candidate biomarker for diagnosis of this pathology.

Irisin is a muscle-secreted protein. Cardiac muscle produces more irisin than skeletal muscle in response to acute exercise, and is associated with myocardial infarction (MI) in an experimental model induced by isoproterenol in rats. The timing and significance of its release in patients with acute myocardial infarction (AMI) needs further investigation. We have studied the relationship between serum/saliva irisin concentration and AMI in humans. Serum and saliva samples were taken within 3 days of admission in 11 patients with AMI and in 14 matched controls. Salivary gland irisin was detected immunohistochemically, and serum and saliva levels were measured by ELISA. The three major paired salivary glands (submandibular, sublingual and parotid) produce and release irisin into saliva. Troponin-I, CK, CK-MB concentrations in the AMI group gradually increased from up to 12h, while saliva and serum irisin gradually decreased from up to 48 h, compared with the control group (P<0.05). After 12h, troponin-I, CK, CK-MB started to decrease, while saliva and serum irisin started to increase at 72 h. Serum irisin levels correlated with age, while troponin I, CK-MB, and CK were correlated and with saliva irisin in AMI patients. Besides cardiac troponin and CK-MB, irisin adds new diagnostic information in AMI patients, and the gradual decrease of saliva/serum irisin over 48 h could be a useful biomarker.

The relationship between platelet collagen receptor glycoprotein VI and no-reflow phenomenon in patients with acute ST-segment elevation myocardial infarction.

OBJECTIVES: The purpose of this study was to determine whether admission soluble glycoprotein VI (sGP-VI) level is associated with no-reflow phenomenon (NRP) after primary percutaneous coronary intervention (P-PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI). METHODS: A total of 178 consecutive patients admitted to our hospital for a first STEMI and undergoing P-PCI within 12 hours of onset of symptoms were enrolled. The patients were divided into 2 groups (NRP group and reflow group). Admission sGP-VI plasma levels were measured by enzyme-linked immunosorbent assay. RESULTS: Of the 178 patients who underwent P-PCI, 41 patients (23%) developed NRP. The patients in the reflow group had higher levels of sGP-VI compared with the patients in the NRP group (38.5 ± 21.0 vs 21.9 ± 11.9 ng/mL, P < 0.001). The sensitivity and specificity values of the sGP-VI levels were 90% and 49%, respectively (cutoff value was ≤ 25). In the multivariate logistic regression analyses, sGP-VI levels of 25 ng/mL or lower, higher peak troponin T levels and body mass index value, amount of opaque of greater than 250 mL, and lesion length of greater than 13.5 mm were independent predictors of angiographic NRP. CONCLUSIONS: Lower admission sGP-VI levels are associated with NRP in patients with STEMI undergoing P-PCI. This outcome may open new therapeutic facility in the setting of P-PCI.