THE REGULATION OF ARTERIAL BLOOD PRESSURE

1946 ◽  
Vol 146 (3) ◽  
pp. 410-421 ◽  
Author(s):  
J. P. Holt ◽  
W. J. Rashkind ◽  
R. Bernstein ◽  
J. C. Greisen
Keyword(s):  
Blood Pressure ◽  
Arterial Blood Pressure ◽  
Arterial Blood

Cardiovascular Effects of Micromeria graeca (L.) Benth. ex Rchb in Normotensive and Hypertensive Rats

2020 ◽  
Vol 20 (8) ◽  
pp. 1253-1261
Author(s):  
Mourad Akdad ◽  
Mohamed Eddouks
Keyword(s):  
Blood Pressure ◽  
Cardiovascular System ◽  
Arterial Blood Pressure ◽  
Antihypertensive Activity ◽  
Computer Assisted ◽  
Hypertensive Rats ◽  
Vasorelaxant Effect ◽  
Arterial Blood

Aims: The present study was performed in order to analyze the antihypertensive activity of Micromeria graeca (L.) Benth. ex Rchb. Background: Micromeria graeca (L.) Benth. ex Rchb is an aromatic and medicinal plant belonging to the Lamiaceae family. This herb is used to treat various pathologies such as cardiovascular disorders. Meanwhile, its pharmacological effects on the cardiovascular system have not been studied. Objective: The present study aimed to evaluate the effect of aqueous extract of aerial parts of Micromeria graeca (AEMG) on the cardiovascular system in normotensive and hypertensive rats. Methods: In this study, the cardiovascular effect of AEMG was evaluated using in vivo and in vitro investigations. In order to assess the acute effect of AEMG on the cardiovascular system, anesthetized L-NAME-hypertensive and normotensive rats received AEMG (100 mg/kg) orally and arterial blood pressure parameters were monitored during six hours. In the sub-chronic study, rats were orally treated for one week, followed by blood pressure assessment during one week of treatment. Blood pressure was measured using a tail-cuff and a computer-assisted monitoring device. In the second experiment, isolated rat aortic ring pre-contracted with Epinephrine (EP) or KCl was used to assess the vasorelaxant effect of AEMG. Results: Oral administration of AEMG (100 mg/kg) provoked a decrease of arterial blood pressure parameters in hypertensive rats. In addition, AEMG induced a vasorelaxant effect in thoracic aortic rings pre-contracted with EP (10 μM) or KCl (80 mM). This effect was attenuated in the presence of propranolol and methylene blue. While in the presence of glibenclamide, L-NAME, nifedipine or Indomethacin, the vasorelaxant effect was not affected. Conclusion: This study showed that Micromeria graeca possesses a potent antihypertensive effect and relaxes the vascular smooth muscle through β-adrenergic and cGMP pathways.

scholarly journals Arterial blood pressure in adult Nigerians with sickle cell anemia

2010 ◽  
Vol 56 (3) ◽  
pp. 326-331 ◽  
Author(s):  
N.I. Oguanobi ◽  
B.J.C. Onwubere ◽  
O.G. Ibegbulam ◽  
S.O. Ike ◽  
B.C. Anisiuba ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Arterial Blood Pressure ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Arterial Blood

scholarly journals A METHOD TO DETERMINE THE PERIPHERAL ARTERIAL BLOOD PRESSURE IN THE MOUSE

1941 ◽  
Vol 74 (1) ◽  
pp. 29-40 ◽  
Author(s):  
Philip D. McMaster
Keyword(s):  
Blood Pressure ◽  
Systolic Blood Pressure ◽  
Arterial Blood Pressure ◽  
Close Agreement ◽  
Carotid Arteries ◽  
Systolic Pressure ◽  
Arterial Blood ◽  
Relative Transparency ◽  
Direct Measurements ◽  
Peripheral Arterial

Advantage has been taken of the relative transparency of the claw of the mouse to devise a method, here described, to measure the blood pressure in the animal's leg. Direct measurements of the systolic blood pressure from the carotid arteries of anesthetized mice have also been made. Simultaneous blood pressure readings by both these methods applied to the same animal showed close agreement. The systolic pressure ranged from 60 to 126 mm. Hg, according to the conditions.

scholarly journals A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction

2021 ◽  
Author(s):  
Annunziata Paviglianiti ◽  
Vincenzo Randazzo ◽  
Stefano Villata ◽  
Giansalvo Cirrincione ◽  
Eros Pasero
Keyword(s):  
Blood Pressure ◽  
Neural Networks ◽  
Deep Learning ◽  
Arterial Blood Pressure ◽  
Blood Pressure Measurement ◽  
National Standards ◽  
Detection Methods ◽  
Physiological Parameter ◽  
Arterial Blood ◽  
Learning Techniques

AbstractContinuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world’s population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118 mmHg on and 2.228 mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di Torino.

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