Predictive factors of cardiovascular changes depending on the type and intensity of physical activity in professional athletes

Aim. To determine the early predictive factors of cardiovascular changes in professional athletes, depending on the type and intensity of physical activity.Material and methods. A total of 136 male athletes were examined. Of these, 116 were professional athletes (age, 22,07±4,1 years) as follows: freestyle wrestling, judo (n=30), cross-country skiing, biathlon (n=27), powerlifting (n=33), volleyball (n=26). Control group included 20 athletes (age, 17,95±1,5 years) with a history of training less than 3 years. All participants underwent electrocardiography (ECG), echocardiography, cycle ergometry (CE) with assessment of physical performance at a heart rate of 170 bpm (PWC170) and maximum oxygen consumption (MOC). When creating predictive models of early cardiovascular changes, we used logistic regression, stepwise regression and Wald statistics. Differences were considered significant at p<0,05.Results. Predictive models of logistic regression using ROC analysis showed high sensitivity and specificity, a high percentage of correct predictions using data from echocardiography — 86,8%, CE — 80,9%, ECG and other indicators — 83,1%. A stepwise algorithm was used to select prognostic factors determining early cardiovascular changes in young athletes, depending on the stage of sports training, the intensity and type of dynamic and/or static exercise: left ventricular posterior wall thickness (p=0,008), left ventricular mass (p=0,001), stroke volume (p=0,002), end-systolic volume (p=0,001), PWC170 (p=0,025), MOC (p=0,003), recovery time of heart rate (HR) (p=0,029) and blood pressure (p=0,032) after submaximal exercise on a cycle ergometer, body mass index (p=0,029), heart rate (p=0,034), office systolic blood pressure (p=0,009), intraventricular (bundle) block (p=0,046), left ventricular repolarization abnormalities (p=0,010), mild cardiac connective tissue anomalies (p=0,035).Conclusion. The early prognostic factors established by the logistic regression affect the characteristics and risk of cardiovascular changes in each group of young athletes. This demonstrates the need to develop individual medical support programs, further monitoring, evaluation, correction and prevention of identified disorders, taking into account the type of sports, intensity and exercise.

Continuous gastric saline perfusion elicits cardiovascular responses in freshwater rainbow trout (Oncorhynchus mykiss)

When in seawater, rainbow trout (Oncorhynchus mykiss) drink to avoid dehydration and display stroke volume (SV) mediated elevations in cardiac output (CO) and an increased proportion of CO is diverted to the gastrointestinal tract as compared to when in freshwater. These cardiovascular alterations are associated with distinct reductions in systemic and gastrointestinal vascular resistance (RSys and RGI, respectively). Although increased gastrointestinal blood flow (GBF) is likely essential for osmoregulation in seawater, the sensory functions and mechanisms driving the vascular resistance changes and other associated cardiovascular changes in euryhaline fishes remain poorly understood. Here, we examined whether internal gastrointestinal mechanisms responsive to osmotic changes mediate the cardiovascular changes typically observed in seawater, by comparing the cardiovascular responses of freshwater-acclimated rainbow trout receiving continuous (for 4 days) gastric perfusion with half-strength seawater (½ SW, ~ 17 ppt) to control fish (i.e., no perfusion). We show that perfusion with ½ SW causes significantly larger increases in CO, SV and GBF, as well as reductions in RSys and RGI, compared with the control, whilst there were no significant differences in blood composition between treatments. Taken together, our data suggest that increased gastrointestinal luminal osmolality is sensed directly in the gut, and at least partly, mediates cardiovascular responses previously observed in SW acclimated rainbow trout. Even though a potential role of mechano-receptor stimulation from gastrointestinal volume loading in eliciting these cardiovascular responses cannot be excluded, our study indicates the presence of internal gastrointestinal milieu-sensing mechanisms that affect cardiovascular responses when environmental salinity changes.

Cafeteria Diet-Induced Metabolic and Cardiovascular Changes in Rats: The Role of Piper nigrum Leaf Extract

Background. Cafeteria diet is known to induce excessive body fat accumulation (obesity) that could cause metabolic and cardiovascular changes and even death. The increase in prevalence over time and the failure in treatment options make obesity a real public health problem. The present study assessed the preventive effect of the hydro-ethanolic extract of the Piper nigrum leaf on the development of metabolic and cardiovascular changes in cafeteria diet fed Wistar rats. Methods. Thirty-six male rats were divided into 5 groups of 6 rats each: a normal control group (Nor.), a negative control group (Neg.), two groups administered different doses of extract in mg/kg (E250 and E500), and a group administered atorvastatin 10 mg/kg (Ator., reference drug). The animals were fed with experimental diets (standard and cafeteria) for a period of 5 weeks. Food and water intake were assessed daily, and the body weight assessed weekly. At the end of the feeding, plasma lipid profile and markers of hepatic and renal function were assessed. Furthermore, the relative weights of the adipose tissue and the organs were assessed. The liver, kidneys, and heart homogenates were assessed for markers of oxidative stress while the aorta was histopathologically examined. Results. Cafeteria diet-induced weight gain of 30% and increased triglyceride, total cholesterol, and low-density lipoprotein cholesterol level of more than 50%. Equally, an increase in the relative weight of accumulated adipose tissues of more than 90%, oxidative stress, and alteration in the organ structure were visible in cafeteria diet fed rats (Neg). Treatment with P. nigrum extract significantly prevented weight gain, dyslipidemia, oxidative stress, and alteration in the architecture of the aorta. The effect of P. nigrum extract was comparable to that of the reference drug. Conclusion. Piper nigrum leaf may prevent weight gain and possess cardioprotective activity with a strong antioxidant activity.

Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning

Background: Coronavirus disease 2019 (COVID-19) has become a pandemic since its first appearance in late 2019. Deaths caused by COVID-19 are still increasing day by day and early diagnosis has become crucial. Since current diagnostic methods have many disadvantages, new investigations are needed to improve the performance of diagnosis.Methods: A novel method is proposed to automatically diagnose COVID-19 by using Electrocardiogram (ECG) data with deep learning for the first time. Moreover, a new and effective method called hexaxial feature mapping is proposed to represent 12-lead ECG to 2D colorful images. Gray-Level Co-Occurrence Matrix (GLCM) method is used to extract features and generate hexaxial mapping images. These generated images are then fed into a new Convolutional Neural Network (CNN) architecture to diagnose COVID-19.Results: Two different classification scenarios are conducted on a publicly available paper-based ECG image dataset to reveal the diagnostic capability and performance of the proposed approach. In the first scenario, ECG data labeled as COVID-19 and No-Findings (normal) are classified to evaluate COVID-19 classification ability. According to results, the proposed approach provides encouraging COVID-19 detection performance with an accuracy of 96.20% and F1-Score of 96.30%. In the second scenario, ECG data labeled as Negative (normal, abnormal, and myocardial infarction) and Positive (COVID-19) are classified to evaluate COVID-19 diagnostic ability. The experimental results demonstrated that the proposed approach provides satisfactory COVID-19 prediction performance with an accuracy of 93.00% and F1-Score of 93.20%. Furthermore, different experimental studies are conducted to evaluate the robustness of the proposed approach.Conclusion: Automatic detection of cardiovascular changes caused by COVID-19 can be possible with a deep learning framework through ECG data. This not only proves the presence of cardiovascular changes caused by COVID-19 but also reveals that ECG can potentially be used in the diagnosis of COVID-19. We believe the proposed study may provide a crucial decision-making system for healthcare professionals.Source Code: All source codes are made publicly available at: https://github.com/mkfzdmr/COVID-19-ECG-

Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning

Background Coronavirus disease 2019 (COVID-19) has become a pandemic since its first appearance in late 2019. Deaths caused by COVID-19 are still increasing day by day and early diagnosis has become crucial. Since current diagnostic methods have many disadvantages, new investigations are needed to improve the performance of diagnosis. Methods A novel method is proposed to automatically diagnose COVID-19 by using Electrocardiogram (ECG) data with deep learning for the first time. Moreover, a new and effective method called hexaxial feature mapping is proposed to represent 12-lead ECG to 2D colorful images. Gray-Level Co-Occurrence Matrix (GLCM) method is used to extract features and generate hexaxial mapping images. These generated images are then fed into a new Convolutional Neural Network (CNN) architecture to diagnose COVID-19. Results Two different classification scenarios are conducted on a publicly available paper-based ECG image dataset to reveal the diagnostic capability and performance of the proposed approach. In the first scenario, ECG data labeled as COVID-19 and No-Findings (normal) are classified to evaluate COVID-19 classification ability. According to results, the proposed approach provides encouraging COVID-19 detection performance with an accuracy of 96.20% and F1-Score of 96.30%. In the second scenario, ECG data labeled as Negative (normal, abnormal, and myocardial infarction) and Positive (COVID-19) are classified to evaluate COVID-19 diagnostic ability. The experimental results demonstrated that the proposed approach provides satisfactory COVID-19 prediction performance with an accuracy of 93.00% and F1-Score of 93.20%. Furthermore, different experimental studies are conducted to evaluate the robustness of the proposed approach. Conclusion Automatic detection of cardiovascular changes caused by COVID-19 can be possible with a deep learning framework through ECG data. This not only proves the presence of cardiovascular changes caused by COVID-19 but also reveals that ECG can potentially be used in the diagnosis of COVID-19. We believe the proposed study may provide a crucial decision-making system for healthcare professionals. Source code All source codes are made publicly available at: https://github.com/mkfzdmr/COVID-19-ECG-Classification

Polymorphisms rs4673 and rs28714259 as predictors of anthracycline-mediated cardiotoxicity in patients with breast cancer.

12005 Background: Numerous pharmacogenetic studies have led to the identification of genetic polymorphisms associated not only with the development of cardiovascular disease, but also increase the risk of complications due to the use of anthracycline drugs, widely used in the treatment of cancer. The purpose of this study was to study the frequency of rs4673 and rs28714259 and possible associations with the risk of cardiovascular changes in patients with breast cancer during anthracycline therapy (anthracycline-mediated cardiotoxicity — AMC). Methods: The study included 256 Caucasian patients (median age - 55 years) with a diagnosis of breast cancer without diagnosed cardiovascular changes, who were treated with anthracyclines at the National Medical Research Center of Oncology in 2019-2020. For genotyping of rs4673 and rs28714259, DNA was extracted from blood using DNA-sorb-B (AmpliSens, Russia) and HRM-PCR was performed on a CFX96 amplifier (Bio-Rad, USA). The presence of polymorphisms was confirmed by Sanger sequencing on a Genetic Analyzer 3500 (ABI, USA). Results: During the follow-up period 21 (8.2%) patients were diagnosed with signs of subacute (changes developed within several weeks after the last course of therapy) or early chronic AMC (changes developed within a year after completion of anthracycline therapy). In the group of patients without AOC the allelic frequency of rs4673 (c.214T > C CYBA) was 0.38, the frequency of genotypes C/C – 0.4, C/T – 0.43, and T/T – 0.17. In the same group, the frequency of the A allele rs28714259 was 0.07, the frequency of the G/G genotypes – 0.87, G/A – 0.13, and A/A – 0. The prevalence of genotypes T/T rs4673 and allele G rs28714259 in a cohort of Russian patients differed from the European population (p = 0.014 and p = 0.05, respectively). The risk of cardiovascular changes on the background of anthracycline therapy increased in the presence of the rs4673 polymorphic allele by 6.49 times, in the case of the G/A and A/A rs28714259 genotypes - by 3.27 times. The results of the ROC-analysis suggested high quality of the tests based on the dominant models rs4673 and rs28714259 (AUC was 71.9% and 76.3% correspondingly). Conclusions: In this study the prognostic efficiency of the genetic markers rs4673 and rs28714259 was shown for the prompt detection of the risks of AMC development in the management of cancer patients. However, population characteristics should be taken into consideration for risk assessment.