Effects of carazolol on electrocadiographic and trace element status in sheeps
Published on: 9th April, 2018
OCLC Number/Unique Identifier: 7666282308
Carazolol is a non-specific β-adrenargic reseptor blocking agent. It ıs structurally analogous to catecholamins, in that, when administered, it forms reversible bonds with β-adrenergic, however, induce adrenergic effects, and it inhibits the actions of the catecholamins in times of stres by saturing their sites of operation. The purpose of the research was to investigate the effects of carazolol on some serum enzymes, trace elements and cardiovascular status in sheep. Seven sheeps (age 6 months, 35 ± 10 kg) were used in this study. Carazolol administered by the intramuscular route at the dose of 0.01 mg/kg. Serum levels of urea, creatinin, ALT, AST, G-GT, LDH, T. protein, Ca, P, Mg, Cu, Fe, Zn, Se were investigated. Although all serum enzymes did not show any difference, serum Fe and Zn levels were decreased. Important results were obtained in electrocardiography (abnormal T wave and ST segment depression).
These results suggest that carazolol may increase incidence rate of myocardiyal ischemia risk in sheeps and it investigated by new researches.
Automatic heartbeat monitoring system
Published on: 30th September, 2019
OCLC Number/Unique Identifier: 8286584056
The proliferation and popularity of open source hardware and software, such as Arduino and Raspberry PI, together with IoT and Embedded System, has brought the health industry to rapid evolution, creating portable and low-cost medical devices for monitoring vital signals. Electrocardiographic (ECG) equipment plays a vital role for diagnosis of cardiac disease. However, the cost of this equipment is huge and the operation is too much complex which cannot offer better services to a large population in developing countries. In this paper, I have designed and implemented a low cost fully portable ECG monitoring system using android smartphone and Arduino. The results obtained by the device were tested comparing them with those obtained from a traditional ECG used in clinical practice on 70 people, in resting and under-activity conditions. The values of beats per minute (BPM), ECG waveform and ECG parameters were identical, and presented a sensitivity of 97.8% and a specificity of 78.52%.
A Wolff-Parkinson-White (WPW) Electrocardiographic Pattern in Asymptomatic Patient – State-of-the-Art-Review
Published on: 7th June, 2022
A comprehensive approach to asymptomatic adults with Wolff-Parkinson-White (WPW) pattern discovered incidentally on routine electrocardiography (ECG) is debatable. The objective of this review article is to update the most recent evidence on the management of young patients with asymptomatic WPW patterns. A substantial proportion of adults with WPW patterns on ECG may remain asymptomatic but the lifetime risk for fatal arrhythmias still exists. The inherent properties of the accessory pathway determine the risk of sudden cardiac death. A low-risk pathway is considered when the pre-excitation is intermittent on ambulatory monitoring or when it disappears completely or abruptly during exercise testing. On the other hand, a high-risk pathway in EP study is suggested by the presence of the shortest pre-excited RR interval (SPERRI) during atrial fibrillation of ≤ 250 ms or accessory pathway effective refractory period (APERP) ≤ 240 ms. The cardiac evaluation may thus be considered in asymptomatic patients with WPW to determine the individual risk for future symptomatic arrhythmia. A shared-decision making must be performed before offering catheter ablation whose procedural success rate is high.
Noninvasive Cuffless Blood Pressure Monitoring. How Mechanism-Driven and Data-Driven Models Can Help in Clinical Practice
Published on: 12th July, 2023
Continuous noninvasive cuffless blood pressure (BP) monitoring is essential for early detection and treatment of hypertension. In this paper, we provide an overview of the recent advancements in cuffless BP sensors. These include contact wearable sensors such as electrocardiography (ECG), photoplethysmography (PPG), contact non-wearable sensors such as ballistocardiography (BCG), and contactless sensors such as video plethysmography (VPG). These sensors employ different measuring mechanisms such as pulse arrival time (PAT), pulse transit time (PTT), and pulse wave analysis (PWA) to estimate BP. However, challenges exist in the effective use and interpretation of signal features to obtain clinically reliable BP measurements. The correlations between signal features and BP are obtained by mechanism-driven models which use physiological principles to identify mathematical correlations, and data-driven models which use machine learning algorithms to analyze observational data to identify multidimensional correlations. On the one hand, applying mechanism-driven models to non-linear scenarios and incomplete or noisy data is challenging On the other hand, data-driven models require a large amount of data in order to prevent physically inconsistent predictions, resulting in poor generalization. From this perspective, this paper proposes to combine the strengths of mechanism-driven and data-driven approaches to obtain a more comprehensive approach, the physiology-informed machine-learning approach, with the goal of enhancing the accuracy, interpretability, and scalability of continuous cuffless BP monitoring. This holds promise for personalized clinical applications and the advancement of hypertension management.
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