Research Article
Volume 2 Issue 1 - 2015
Episodes of T-Wave and QRS Complex Alternans in Haemodialysis Patients
Iana I Simova1*, Giovanni Bortolan2, Lilyana G Kambova3, Ivaylo I Christov4 and Dsci TM Katova1
1Department of Non invasive Cardiovascular Imaging and Functional Diagnostics, National Cardiology Hospital, Bulgaria
2Institute of Biomedical Engineering ISIB - CNR, Italy
3Department of Haemodialysis, National Cardiology Hospital, Bulgaria
4Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Bulgaria
*Corresponding Author: Iana I Simova, Department of Noninvasive Cardiovascular Imaging and Functional Diagnostics, National Cardiology Hospital, 65 Koniovitsa Str, Sofia 1309, Bulgaria.
Received: July 13, 2015; Published: August 17, 2015
Citation: Iana I Simova., et al. “ Episodes Of T-Wave And QRS Complex Alternans In Haemodialysis Patients”. EC Cardiology 2.1 (2015): 60-67.
Objective: To detect occurrence of T-wave and QRS alternans, to evaluate their independent predictors, and to estimate alternans change during haemodialysis (HD).
Methods: We studied 58 patients, mean age 59 ± 13 years, 52% males. We performed 1-minute ECG-recording before and after HD determining episodes of alternans.
Results: T-wave alternans episodes were present in 21% and 28% of patients pre-HD and post-HD, respectively, without significant change during the procedure. Pre-HD values were independently predicted by body mass index (BMI) (negative correlation) and number of cigarettes smoked (positive correlation). Post-HD values were predicted by usage of acetyl salicylic acid (ASA) and ferric substitutes (negative correlation) and baseline heart rate (positive correlation).
Episodes of QRS alternans were evident in 7 patient’s pre-HD and one post-HD: significant decrease during HD (from 0.26 ± 0.85 to 0.07 ± 0.53 number of alternans episodes, p = 0.015). Independent predictors for baseline QRS alternans values were BMI and ASA usage (negative correlation), baseline heart rate and calcium channel blocker usage (positive correlation).
Conclusion: Both T-wave and QRS alternans are present in HD-patients. T-wave alternans episodes did not change significantly during HD-procedure, while episodes of QRS alternans showed significant decrease. Regression analysis suggested protective effects for higher BMI and ASA usage in this group.
Keywords: Electrocardiogram; Haemodialysis; QRS complex alternans; QRS alternans; T-wave alternans
Abbreviations: ASA: Acetyl salicylic acid; BMI: Body mass index; CAD: Coronary artery disease; CCB: Calcium channel blockers; ECG: Electrocardiogram; HD: Haemodialysis; IQR: Inter quartile range; PCA: Principal Component Analysis; SCD: Sudden cardiac death; SD: Standard deviation; TWA: T wave alternans
Repolarization abnormalities are a major risk factor for sudden cardiac death (SCD). Although many methods have been developed to assess the degree of repolarization heterogeneity, the optimal ECG characterization of ventricular repolarization is still a matter of debate. There are static measurements and dynamic repolarization markers. Electrical alternans belongs to the latter group and is defined as alternating patterns in QRS (QRS alternans) or ST segment and/or T wave (T-wave alternans) in consecutive cardiac beats. Electrical alternans occurs at the level of a single cardiomyocyte and is the result of intracellular disturbance in calcium handling. QRS and T-wave alternans reflect instability of cardiac depolarization and repolarization, respectively [1]. T-wave alternans is a continuous variable with higher values implementing greater risk for cardiovascular death and SCD [2,3]. The current recommendation is that it is reasonable to consider TWA evaluation whenever there is suspicion of vulnerability to lethal cardiac arrhythmias [4]. Electrical alternans of the QRS complex is an electrocardiographic phenomenon seen in different clinical situation - mainly supraventricular and ventricular tachyarrhythmia [5, 6]. Its clinical significance however has been less well studied [5-8].
A variety of algorithms and particular signal processing methods for detecting and quantifying TWA have been proposed, employing techniques as spectral analysis, complex demodulation, zero-crossings counting in a series of correlation coefficients, Karhunen - Loève transform, low-pass Capon filtering, Poincaré mapping, periodicity transforms, statistical tests, modified moving average, Laplacian likelihood ratio, etc. A review by Martínez and Olmos [9] highlights the need for methodological systematization effort in characterization and comparison of the different methods. But it remains very difficult to validate or to compare any of these algorithms, even the commercially available ones, since no generally accepted objective criteria exist for measuring TWA, and no generally available set of validation data exists as a basis for comparison. In an attempt to investigate this problem, PhysioNet and Computers in Cardiology organized a Challenge in 2008: “Detecting and quantifying T-wave alternans” [10].
Haemodialysis (HD) is the most commonly applied method used to treat end-stage renal disease patients. It removes waste products and free water from the blood, restoring the homeostasis with a proper balance of electrolytes. The process is related to a significant shift in extracellular water and blood volume and consequently to substantial changes in the electrical activity of the heart, observed by analysis of ECG. Before HD some of the patients present with significant electrolyte abnormalities which is an accepted risk factor for ventricular arrhythmias. Also there is epidemiological evidence that risk of SCD is increased immediately after HD [11]. Our aim was to evaluate T-wave and QRS complex alternans episodes in HD patients and to compare electrical alternans before and after an HD procedure.
Materials and Methods
Patient group
We studied 58 patients on chronic HD at a mean age of 59 years. Baseline characteristics of the study group are presented in table 1. Medical therapy is described in table 2. Digital ECGs (1-minute duration, 12-standard leads, and 500 ‎Hz sampling rate) were recorded before and after HD sessions. Serum electrolytes (potassium-K, sodium-Na, phosphorus-Ph and calcium-Ca), urea and creatinine levels were evaluated before and after HD. We calculated also mean HD clearance.
Variable Distribution
Age (years) - mean ± SD 59.3 ± 12.5
Gender: male/female - number (%) 30 (52%)/28 (48%)
BMI (kg/m2) - mean ± SD 25 ± 4.2
Duration of renal disease (years) - mean ± SD 9.7 ± 6.7
Duration of HD (years) - mean ± SD 5.2 ± 4.4
Arterial hypertension - number (%) 55 (95%)
Systolic blood pressure (mmHg) - mean ± SD 139.5 ± 15.8
Diastolic blood pressure (mmHg) - median (IQR) 80 (80-90)
Heart rate (beats per minute) - mean ± SD 78.2 ± 15
Hemoglobin (g/l) - mean ± SD 92.5 ± 10.6
Hematocrit (%) - mean ± SD 26.3 ± 4.6
Erythrocytes (x1012/l) - mean ± SD 2.82 ± 0.4
Albumin (g/l) - mean ± SD 35.7 ± 3.5
Urea (mmol/l) - mean ± SD 19.6 ± 4.5
Creatinine (μmol/l) - mean ± SD 699.2 ± 155.2
Sodium (mmol/l) - mean ± SD 136.4 ± 3.4
Potassium (mmol/l) - mean ± SD 5.24 ± 0.72
Calcium (mmol/l) - mean ± SD 2.27 ± 0.19
Phosphorus (mmol/l) - mean ± SD 2.01 ± 0.52
Diabetes mellitus - number (%) 7 (12%)
Dyslipidemia - number (%) 16 (28%)
Present smokers - number (%) 20 (35%)
Ex-smokers - number (%) 11 (19%)
Years of smoking history - mean ± SD 26.9 ± 12.8
Number of cigarettes per day - mean ± SD 14.9 ± 7.9
Family history of CAD - number (%) 6 (10%)
CAD - number (%) 8 (14%)
History of myocardial infarction - number (%) 7 (12%)
History of coronary intervention - number (%) 5 (9%)
History of heart failure - number (%) 7 (12%)
History of peripheral artery disease - number (%) 3 (5%)
History of cerebrovascular disease - number (%) 5 (9%
Table 1: Baseline characteristics of the study group.
SD: standard deviation; IQR: interquartile range; BMI: body mass index; CAD: coronary artery disease.
Drug Class Distribution n (%)
Beta blockers 26 (45%)
CCB 26 (45%)
ACE inhibitors 10 (17%)
ARB 7 (12%)
Centrally acting antihypertensive agents 28 (48%)
Thiazide diuretics 2 (3%)
Loop diuretics 3 (5%)
Ferric substitutes 48 (83%)
Phosphate binders 8 (14%)
Vitamin D supplements 31 (53%)
Calcium supplements 18 (31%)
Erythropoietin 44 (76%)
ASA 45 (78%)
Folic acid 3 (5%)
Table 2:Medical therapy in the studied population.
CCB: calcium channel blockers; ACE: angiotensin-converting enzyme; ARB: angiotensin receptor blockers; ASA: acetyl salicylic acid.
Alternans detection
The method for T-wave alternans detection successfully participated in the Physionet/Computers in Cardiology Challenge, 2008, reaching the best score [12-14]. Further it was expanded for QRS alternans detection and quantification. First the ECG signals were pre-processed to eliminate or suppress the power line interference [15], the drift [16] and the electromyographic noise [17]. QRS detection was applied [18], onsets and offsets of the QRS complex and T wave were automatically delineated [19].
Later a multi-lead approach has been followed in order to extract single indices of the amplitude and the morphology of the wave from the entire ECG record. We used the ratio 2nd/1st eigenvalues of the Principal Component Analysis (PCA) for quantifying the complexity morphology index. The amplitude index was measured from the 3-D spatial vector calculated from the X, Y and Z vectors derived by the standard 12-leads. The PCA and amplitude indices were computed for the QRS and T wave intervals. The alternans detection was performed in an overlapping time window of 60 hearth beats (overlapping step = 10 beats) by separation of the parameters from odd and even RR intervals and the consequent statistical analysis on the two series with the non parametric paired-sampled Wilcoxon signed rank test. The 5% significance level of that statistical test is the threshold to determine the episode as alternans ‘yes/no’. The process is repeated for every interval and the result is the number of the detected episodes with alternans. All the RR intervals were analyzed, independently of the presence of noise or artifact, and the heart rate was not considered.
All patients signed an informed consent for personal data analysis. The study protocol was approved by the local ethical committee (Protocol No 3/30.03.2010) and is in accordance with the Declaration of Helsinki.
Statistical analysis
We tested the distribution of continuous variables using the Kolmogorov-Smirnov test. Normally distributed data were presented as mean ± standard deviation (SD), whereas non-normally distributed data - as median and interquartile range (IQR) (the difference between the 25th and 75th percentile). Categorical variables were presented in percentage terms. QRS and T-wave alternans were considered as continuous variables, as well as transformed to categorical variables based on the presence or absence of alternans. We compared patients’ characteristics and baseline laboratory parameters between groups with and without QRS and T-wave alternans using one sample t test for normally distributed data and the Mann-Whitney U test for non-normally distributed data. We performed a multivariable analysis using Linear Regression Model with the stepwise procedure with a criterion to enter ≤ 0.05 and criterion to remove ≥ 0.1. To determine independent predictors for post-HD T-wave alternans we both pre- and post-HD values of electrolytes, heart rate and blood pressure, along with other possible confounding variables. A two-tailed p value < 0.05 was considered statistically significant. Statistical analysis was performed using SPSS statistical software for Windows version 13.0.
T-wave alternans
Before HD T-wave alternans episodes were present in 12 patients (21%) and absent in 46 (79%). After HD we found episodes of T-wave alternans in 16 patients (28%). In the group of 12 patients with pre-HD T-wave alternans episodes, such episodes of electrical alternans persisted after HD in 5 of them (9% of the whole group and 42% of the subgroup with T-wave alternans before HD) and disappeared in 7 (12% of the whole group and 58% of the subgroup). T-wave alternans episodes as a new finding after haemodialysis appeared in 11 subjects (19% of the whole group). The mean value of all local episodes of detected T-wave alternans did not change significantly during HD: from 0.5 ± 1.84 to 0.78 ± 1.62 detected episodes, p = 0.17 table 3.
  Pre-HD Post-HD
T-wave alternans - n (%)    
  1 episode 9 (16%) 4 (7%)
  2 episodes 1 (2%) 4 (7%)
  3 episodes 0 4 (7%)
  4 episodes ≥ 5 episodes 0 2 (4%) 3 (5%) 1 (2%)
T-wave alternans mean number of episodes 0.5 ± 1.84 0.78 ± 1.62
T-wave alternans presence 12 (21%) 16 (28%)
Table 3: Prevalence and distribution of T wave alternans before and after HD.
Characteristics of patients with carotid sinus hypersensitivity (CHS) and control subject. Previous diagnosis of diabetes and treatment for hypertension are given. Comparisons between groups were made with Student’s T-test. And results are given as means +/- standard deviations (SD).
When we compared baseline characteristics and laboratory values between two subgroups of patients with presence (12 subjects) or absence (46 subjects) of T-wave alternans episodes before HD, we found that the two groups are very similar to each other. The only differences in the baseline characteristics were the following: patients with episodes of T-wave alternans had lower BMI (22.2 ± 3.2 vs 25.7 ± 4.2 kg/m2, p = 0.008) and systolic blood pressure (123.3 ± 18.1 vs 140.1 ± 23.2 mmHg, p = 0.019) as compared to those without T-wave alternans episodes.
Multivariable regression analysis showed that independent predictors for pre-HD T-wave alternans value were number of cigarettes smoked per day and BMI (R Square for the model combining both parameters - 0.308). The correlation with the number of cigarettes per day was positive (i.e. the more cigarettes smoked, the higher T-wave alternans) while with BMI there was a negative correlation (i.e. the lower the BMI, the higher T-wave alternans values). Post-HD T-wave alternans values were independently predicted by the use of acetyl salicylic acid (ASA), ferric substitutes and heart rate before initiation of HD (R Square for the model combining all parameters - 0.721). There was a negative correlation for the use of ASA and ferric substitutes with T-wave alternans (i.e. those who used them had lower T-wave alternans values), while heart rate was positively correlated (i.e. the higher pre-HD heart rate, the higher T-wave alternans).
QRS alternans
Seven patients (12%) had episodes of QRS alternans before HD, mean number of episodes for the whole group - 0.26 ± 0.85. After HD QRS alternans episodes diminished significantly to a mean value of 0.07 ± 0.53, p = 0.015) and disappeared in all but one of the previously positive patients table 4.
  Pre-HD Post-HD
QRS alternans - n (%)    
   1 episode 3 (5%) 0
   2 episodes 2 (4%) 0
   3 episodes 1 (2%) 0
   4 episodes 0 1 (2%)
   5 episodes 1 (2%) 0
QRS alternans mean number of episodes 7 (12%) 1 (2%)
QRS alternans presence 0.26 ± 0.85 0.07 ± 0.53
Table 4: Prevalence and distribution of QRS complex alternans before and after HD.
When we compared groups with or without pre-HD QRS alternans episodes, there was not a significant difference in any of the demographic characteristics, renal disease or HD duration, risk factors for or evidence of cardiovascular disease, baseline hemodynamic parameters, electrolyte concentration, urea and creatinine levels. There was, however, difference in the uptake of some medication classes: patients with episodes of QRS alternans were less likely to take ASA (p < 0.001) and more likely to receive centrally acting antihypertensive agents (p = 0.048) and angiotensine-receptor blockers (p = 0.032) compared to those without episodes of QRS alternans.
Multivariable linear regression analysis showed that independent predictors for the number of pre-HD QRS alternans episodes were the use of ASA and calcium channel blockers (CCB), baseline heart rate and BMI. Again there was negative correlation between ASA usage and BMI and QRS alternans (i.e. protective effect of ASA and higher BMI). Correlation of QRS alternans with CCB usage and baseline heart rate was a positive one (i.e. subjects who took CCB and had higher baseline heart rate, had higher QRS alternans values). 
History of CAD was present in 8 patients (14%). None of the patients had evidence of acute myocardial ischemia during haemodialysis, as evidenced by ECG and absence of symptoms. Presence of CAD did not correlate significantly with, and was not an independent predictor of, episodes of T-wave or QRS complex alternans pre- or post-haemodialysis.
Analyzing a group of HD patients we were able to find that T-wave alternans episodes are present in a considerable number of subjects but did not change significantly after an HD session. Independent predictors for higher pre-HD T-wave alternans values were more cigarettes smoked per day and lower BMI, and for higher post-HD T-wave alternans values - higher baseline heart rate and non-usage of ASA and ferric substitutes. Episodes of QRS alternans diminished significantly after HD as compared to pre-HD values. Higher baseline QRS alternans values were independently predicted by lower BMI, increase in baseline heart rate, non-usage of ASA and usage of CCB.
Our results are in accordance with a study of Green., et al. [20] - in a group of 19 HD-patients the authors found that TWA did not change significantly after HD, nor did it correlate with any laboratory or echocardiographic parameters. Other studies have found high prevalence of T-wave alternans in HD subject (higher than what we have found in our analysis) but without variability between recognized periods of variable risk [21]. We were not able to find any studies evaluating QRS alternans during HD and in chronic kidney disease patients. There is, however, a significantly small study (15 participants) by Ojanen., et al. [22] showing that QRS vector difference and ST segment vector magnitude change significantly during HD, giving a false impression of myocardial ischemia. Simova., et al. [23] have also reported a significant increase of the QRS area and maximal vector in VCG as result of HD. These VCG dynamics were related mostly to a change in extracellular water and blood volume. 
The positive correlation between microvolt T-wave alternans and heart rate is well known. In the present study we found a similar positive correlation between QRS alternans and heart rate. This means that until data of the exact relation of QRS alternans and heart rate in different populations (especially in those with established risk of sudden death) become available, the clinical interpretation of QR alternans remains unknown.
An interesting finding in our study (and also persistent in different settings) was the protective effect of higher BMI - predicting lower T-wave and QRS alternans values. Obesity of course is a well-known and accepted risk factor for cardio-vascular disease but probably in the setting of advanced disease (such as end-stage renal disease) increased body weight could play a protective role. This is in line with the so called “obesity paradox” hypothesis that has been largely discussed in several other disease settings [24,25].
“Obesity paradox” has received a lot of attention recently. On one side there are indisputable evidence and impressive consistency on the effects of obesity on the incidence of diabetes [26], cardiovascular disease [27], and much other morbidity [26], which could be involved in the pathogenesis of chronic kidney disease. On the other hand there are data that for patients with chronic kidney disease, end-stage renal disease and on HD having more fat mass results in better survival, i.e. obesity paradox [28,29]. Our study however is too small to have a significant influence in confirming the presence of obesity paradox in this population.
The other consistent finding that ASA usage is related to lower T-wave and QRS alternans values is not a surprise, since ASA has been proven long ago to protect against cardiovascular mortality, including SCD, in secondary and also in many primary prevention settings [30]. We did not, however, find any studies evaluating the effect of ASA usage on T wave or QRS complex alternans.
This is a single center study and we have included only patients on a maintenance HD in a single HD center. That explains the relatively small study group (58 subjects). Multivariable linear regression analysis was carried out on even smaller subgroups of patients (12 patients - 21% with T-wave alternans episodes before HD, 16 patients - 28% with T-wave alternans episodes after HD and 7 patients - 12% with episodes of QRS alternans before HD) which could compromise the reliability of our results.
Another limitation is fact MMA alternans measurement was done on one-minute ECG recordings instead of the more accepted method of 24 (48)-hour ambulatory Holter ECG recording.
Electrical alternans is present in a significant number of patients undergoing HD. Number of patients with episodes of T-wave alternans did not change significantly during HD, while episodes of QRS alternans showed a significant decrease after an HD procedure. Regression analysis suggested protective effects (lower T-wave and QRS alternans values) for higher BMI and ASA usage in this group.
The study is supported by the “Short Term Mobility 2014” Program of CNR, Italy.
  1. Verrier RL., et al. “Microvolt T-Wave Alternans. Physiological Basis, Methods of Measurement, and Clinical Utility—Consensus Guideline by International Society for Holter and Noninvasive Electrocardiology”. Journal of American College of Cardiology 58.13 (2011): 1309-1324.
  2. Slawnych MP., etal. “Post-exercise assessment of cardiac repolarization alternans in patients with coronary artery disease using modified moving average the method”. Journal of American College of Cardiology53.13 (2009): 1130-1137.
  3. Minkkinen M., et al. ”Enhanced Predictive Power of Quantitative TWA during Routine Exercise Testing in the Finnish Cardiovascular Study”. Journal of Cardiovascular Electrophysiology 20.4 (2009): 408-415.
  4. Verrier   R., et al. “Microvolt T-Wave Alternans Testing Has a Role in Arrhythmia Risk Stratification”.Journal of the American College of Cardiology 59.17 (2012): 1572-1573.
  5. Morady F. “Significance of QRS alternans during narrow QRS tachycardias”. Pacing and ClinicalElectrophysiology14.12 (1991): 2193-2198.
  6. Maury P and Metzger J. “Alternans in QRS amplitude during ventricular tachycardia”. Pacing ClinicalElectrophysiology 25.2 (2002): 142-150.
  7. Das MK and El Masry H. “Fragmented QRS and other depolarization abnormalities as a predictor of mortality and sudden cardiac death”. Current Opinion in Cardiology 25.1 (2010): 59-64.
  8. Christov I., et al. “T wave and QRS complex alternans during standard diagnostic stress ECG test”. Computing in Cardiology  37 (2010): 1039-1042.
  9. Martínez JP and Olmos S. “Methodological principles of T-wave alternans analysis: A unified framework”. IEEE Transactions on Biomedical Engineering 52.4 (2005): 599-613.
  10. Moody GB. “The PhysioNet/Computers in Cardiology Challenge 2008: T-Wave Alternans”. Computing in Cardiology35 (2008): 505-508.
  11. Franczyk-Skóra B., etal. “Prevention of sudden cardiac death in patients with chronic kidney disease”. BMC Nephrology 13 (2012): 162.
  12. Bortolan G and Christov II. “Principal component analysis for the detection and assessment of T-wave alternans”. Computing in Cardiology 35 (2008): 521-524.
  13. Bortolan G and Christov I. “T-wave alternans detection by a combined method of principal component analysis and T-wave amplitude”. Physiological Measurement 33.3 (2012): 333-343.
  14. Christov I., et al. “T wave and QRS complex alternans during stress ECG testing according to the presence or absence of diabetes mellitus”. Journal of Clinical Endocrinology & Metabolism 2.1 (2012): 32-38.
  15. Levkov Ch., et al. “Removal of power-line interference from the ECG: A review of the subtraction procedure”. BioMedical Engineering OnLine4 (2005): 50.
  16. Daskalov IK., et al. “Developments in ECG acquisition preprocessing parameter measurement and recording”. IEEE Transactions on Biomedical Engineering 17.2 (1998): 50-58.
  17. Christov I and Daskalov IK. “Filtering of electromyogram artifacts from the electrocardiogram”. Medical Engineering & Physics 21.10 (1999): 731-736.
  18. Christov II. “Real time electrocardiogram QRS detection using combined adaptive threshold”. BioMedical Engineering OnLine 3 (2004): 28.
  19. Christov I and Simova I. “Q-onset and T-end delineation: Assessment of the performance of an automated method with the use of a reference database”. Physiological Measurement28.2 (2007): 213-221.
  20. Green D., etal. “Dialysis-dependent changes in ventricular repolarization”. Pacing and Clinical Electrophysiology 35.6(2012): 703-710.
  21. Secemsky EA., et al. “High prevalence of cardiac autonomic dysfunction and T-wave alternans in dialysis patients”. Heart Rhythm 8.4 (2011): 592-608.
  22. Ojanen S., et al. “Hemodialysis causes changes in dynamic vectorcardiographic ischemia monitoring parameters”. Clinical Nephrology 54.3 (2000): 227-233.
  23. Simova I., et al. “QRS and T Loops Area Changes During Haemodialysis”. Computing in Cardiology 41 (2014): 409-412.
  24. Stevens J., et al. “Obesity Paradox should not interfere with public health efforts”. International Journal of Obesity  39.1(2015): 80-81.
  25. Cepeda-Valery B., etal. “Association between obesity and severity of coronary artery disease at the time of acute myocardial infarction: Another piece of the puzzle in the "obesity paradox"”. International Journal of cardiology 176.1 (2014): 247-249.
  26. Guh DP., et al. “The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis”. BMC Public Health 9 (2009): 88.
  27. Jensen MD., et al.“2013 AHA/ACC/TOS Guideline for the Management of Overweight and Obesity in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society”. Journal of American College of Cardiology63.25 Pt B (2014): 2985-3023.
  28. Mohebi R., etal. “Obesity Paradox and Risk of Mortality Events in Chronic Kidney Disease Patients: A Decade of Follow-up in Tehran Lipid and Glucose Study”. Journal of Renal Nutrition 25.4 (2015): 345-50.
  29. Park J., et al. “Obesity paradox in end-stage kidney disease patients”. Progress in Cardiovascular Diseases56.4 (2014): 415-425.
  30. Hjemdahl-Monsen CE., et al. “Role of antithrombotic therapy in unstable angina, myocardial infarction and sudden death”. Journal of American College of Cardiology 8 (1986): 67B-75B.
Copyright: © 2015 Iana I Simova., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PubMed Indexed Article

EC Pharmacology and Toxicology
LC-UV-MS and MS/MS Characterize Glutathione Reactivity with Different Isomers (2,2' and 2,4' vs. 4,4') of Methylene Diphenyl-Diisocyanate.

PMID: 31143884 [PubMed]

PMCID: PMC6536005

EC Pharmacology and Toxicology
Alzheimer's Pathogenesis, Metal-Mediated Redox Stress, and Potential Nanotheranostics.

PMID: 31565701 [PubMed]

PMCID: PMC6764777

EC Neurology
Differences in Rate of Cognitive Decline and Caregiver Burden between Alzheimer's Disease and Vascular Dementia: a Retrospective Study.

PMID: 27747317 [PubMed]

PMCID: PMC5065347

EC Pharmacology and Toxicology
Will Blockchain Technology Transform Healthcare and Biomedical Sciences?

PMID: 31460519 [PubMed]

PMCID: PMC6711478

EC Pharmacology and Toxicology
Is it a Prime Time for AI-powered Virtual Drug Screening?

PMID: 30215059 [PubMed]

PMCID: PMC6133253

EC Psychology and Psychiatry
Analysis of Evidence for the Combination of Pro-dopamine Regulator (KB220PAM) and Naltrexone to Prevent Opioid Use Disorder Relapse.

PMID: 30417173 [PubMed]

PMCID: PMC6226033

EC Anaesthesia
Arrest Under Anesthesia - What was the Culprit? A Case Report.

PMID: 30264037 [PubMed]

PMCID: PMC6155992

EC Orthopaedics
Distraction Implantation. A New Technique in Total Joint Arthroplasty and Direct Skeletal Attachment.

PMID: 30198026 [PubMed]

PMCID: PMC6124505

EC Pulmonology and Respiratory Medicine
Prevalence and factors associated with self-reported chronic obstructive pulmonary disease among adults aged 40-79: the National Health and Nutrition Examination Survey (NHANES) 2007-2012.

PMID: 30294723 [PubMed]

PMCID: PMC6169793

EC Dental Science
Important Dental Fiber-Reinforced Composite Molding Compound Breakthroughs

PMID: 29285526 [PubMed]

PMCID: PMC5743211

EC Microbiology
Prevalence of Intestinal Parasites Among HIV Infected and HIV Uninfected Patients Treated at the 1o De Maio Health Centre in Maputo, Mozambique

PMID: 29911204 [PubMed]

PMCID: PMC5999047

EC Microbiology
Macrophages and the Viral Dissemination Super Highway

PMID: 26949751 [PubMed]

PMCID: PMC4774560

EC Microbiology
The Microbiome, Antibiotics, and Health of the Pediatric Population.

PMID: 27390782 [PubMed]

PMCID: PMC4933318

EC Microbiology
Reactive Oxygen Species in HIV Infection

PMID: 28580453 [PubMed]

PMCID: PMC5450819

EC Microbiology
A Review of the CD4 T Cell Contribution to Lung Infection, Inflammation and Repair with a Focus on Wheeze and Asthma in the Pediatric Population

PMID: 26280024 [PubMed]

PMCID: PMC4533840

EC Neurology
Identifying Key Symptoms Differentiating Myalgic Encephalomyelitis and Chronic Fatigue Syndrome from Multiple Sclerosis

PMID: 28066845 [PubMed]

PMCID: PMC5214344

EC Pharmacology and Toxicology
Paradigm Shift is the Normal State of Pharmacology

PMID: 28936490 [PubMed]

PMCID: PMC5604476

EC Neurology
Examining those Meeting IOM Criteria Versus IOM Plus Fibromyalgia

PMID: 28713879 [PubMed]

PMCID: PMC5510658

EC Neurology
Unilateral Frontosphenoid Craniosynostosis: Case Report and a Review of the Literature

PMID: 28133641 [PubMed]

PMCID: PMC5267489

EC Ophthalmology
OCT-Angiography for Non-Invasive Monitoring of Neuronal and Vascular Structure in Mouse Retina: Implication for Characterization of Retinal Neurovascular Coupling

PMID: 29333536 [PubMed]

PMCID: PMC5766278

EC Neurology
Longer Duration of Downslope Treadmill Walking Induces Depression of H-Reflexes Measured during Standing and Walking.

PMID: 31032493 [PubMed]

PMCID: PMC6483108

EC Microbiology
Onchocerciasis in Mozambique: An Unknown Condition for Health Professionals.

PMID: 30957099 [PubMed]

PMCID: PMC6448571

EC Nutrition
Food Insecurity among Households with and without Podoconiosis in East and West Gojjam, Ethiopia.

PMID: 30101228 [PubMed]

PMCID: PMC6086333

EC Ophthalmology
REVIEW. +2 to +3 D. Reading Glasses to Prevent Myopia.

PMID: 31080964 [PubMed]

PMCID: PMC6508883

EC Gynaecology
Biomechanical Mapping of the Female Pelvic Floor: Uterine Prolapse Versus Normal Conditions.

PMID: 31093608 [PubMed]

PMCID: PMC6513001

EC Dental Science
Fiber-Reinforced Composites: A Breakthrough in Practical Clinical Applications with Advanced Wear Resistance for Dental Materials.

PMID: 31552397 [PubMed]

PMCID: PMC6758937

EC Microbiology
Neurocysticercosis in Child Bearing Women: An Overlooked Condition in Mozambique and a Potentially Missed Diagnosis in Women Presenting with Eclampsia.

PMID: 31681909 [PubMed]

PMCID: PMC6824723

EC Microbiology
Molecular Detection of Leptospira spp. in Rodents Trapped in the Mozambique Island City, Nampula Province, Mozambique.

PMID: 31681910 [PubMed]

PMCID: PMC6824726

EC Neurology
Endoplasmic Reticulum-Mitochondrial Cross-Talk in Neurodegenerative and Eye Diseases.

PMID: 31528859 [PubMed]

PMCID: PMC6746603

EC Psychology and Psychiatry
Can Chronic Consumption of Caffeine by Increasing D2/D3 Receptors Offer Benefit to Carriers of the DRD2 A1 Allele in Cocaine Abuse?

PMID: 31276119 [PubMed]

PMCID: PMC6604646

EC Anaesthesia
Real Time Locating Systems and sustainability of Perioperative Efficiency of Anesthesiologists.

PMID: 31406965 [PubMed]

PMCID: PMC6690616

EC Pharmacology and Toxicology
A Pilot STEM Curriculum Designed to Teach High School Students Concepts in Biochemical Engineering and Pharmacology.

PMID: 31517314 [PubMed]

PMCID: PMC6741290

EC Pharmacology and Toxicology
Toxic Mechanisms Underlying Motor Activity Changes Induced by a Mixture of Lead, Arsenic and Manganese.

PMID: 31633124 [PubMed]

PMCID: PMC6800226

EC Neurology
Research Volunteers' Attitudes Toward Chronic Fatigue Syndrome and Myalgic Encephalomyelitis.

PMID: 29662969 [PubMed]

PMCID: PMC5898812

EC Pharmacology and Toxicology
Hyperbaric Oxygen Therapy for Alzheimer's Disease.

PMID: 30215058 [PubMed]

PMCID: PMC6133268

News and Events

November Issue Release

We always feel pleasure to share our updates with you all. Here, notifying you that we have successfully released the November issue of respective journals and the latest articles can be viewed on the current issue pages.

Submission Deadline for Upcoming Issue

ECronicon delightfully welcomes all the authors around the globe for effective collaboration with an article submission for the upcoming issue of respective journals. Submissions are accepted on/before December 15, 2022.

Certificate of Publication

ECronicon honors with a "Publication Certificate" to the corresponding author by including the names of co-authors as a token of appreciation for publishing the work with our respective journals.

Best Article of the Issue

Editors of respective journals will always be very much interested in electing one Best Article after each issue release. The authors of the selected article will be honored with a "Best Article of the Issue" certificate.

Certifying for Review

ECronicon certifies the Editors for their first review done towards the assigned article of the respective journals.

Latest Articles

The latest articles will be updated immediately on the articles in press page of the respective journals.