Nycthemeral Rhythm of the Frequency and Biomechanical Energy of High Frequency Intraocular Pressure Fluctuations

Author(s):  
Vincent Libertiaux ◽  
William P. Seigfreid ◽  
Massimo A. Fazio ◽  
Juan F. Reynaud ◽  
Claude F. Burgoyne ◽  
...  

The optic nerve head (ONH) is the site of insult in glaucoma, the second leading cause of blindness worldwide. Intraocular pressure (IOP) is commonly regarded as a major factor in the onset and progression of the disease1 and lowering IOP is the only clinical treatment that has been shown to retard the onset and progression of glaucoma2. However, many patients continue to progress even at an epidemiologically-determined normal level of IOP3. This suggests that in addition to the mean value of IOP, IOP fluctuations could be a factor in glaucomatous pathophysiology. The importance of low frequency fluctuations of clinically-measured mean IOP remains controversial. These studies all rely on snapshot measurements of mean IOP at each time point, and those measurements are taken at relatively infrequent intervals (hourly at the most frequent, but usually monthly or longer). Recently however, there has been some interest in ocular pulse amplitude, or the fluctuation in IOP associated with the cardiac cycle, which can be measured by Dynamic Contour Tonometry (DCT). DCT provides continuous measurement of IOP, but only for a period of tens of seconds in which a patient can tolerate corneal contact without blinking or eye movement, which ironically are two of the most common sources of large high frequency IOP fluctuations according to our telemetric data collected from monkeys4 and previous human studies. In a recent report, continuous IOP telemetry was used in three nonhuman primates to characterize IOP dynamics at multiple time scales for multiple 24-hour periods5.

2006 ◽  
Vol 31 (10) ◽  
pp. 851-862 ◽  
Author(s):  
Omar S. Punjabi ◽  
Hoai-Ky V. Ho ◽  
Christoph Kniestedt ◽  
Alan G. Bostrom ◽  
Robert L. Stamper ◽  
...  

2021 ◽  
Vol 62 (9) ◽  
pp. 1235-1242
Author(s):  
Gyeong Min Lee ◽  
Seung Joo Ha

Purpose: To compare the intraocular pressure reduction and changes in ocular pulse amplitude of travoprost 0.003% and tafluprost 0.0015%. Methods: We assessed patients who were diagnosed with open-angle glaucoma from January 2017 to July 2019 for the first time at our hospital. Forty-two eyes were assigned to the travoprost group (23 patients) and 26 eyes were assigned to the tafluprost group (14 patients). Changes in intraocular pressure were measured by Goldmann applanation tonometry (GAT), and corrected ocular pulse amplitude (cOPA) was measured using dynamic contour tonometry. Changes in these parameters were observed and compared for 1 year. Results: No significant differences were observed between the GAT measurements and the cOPA of patients treated with travoprost and tafluprost for 1 year (p = 0.512, p = 0.105). The change in initial intraocular pressure on GAT observed after 1 week was -5.32 ± 2.63 mmHg for travoprost and -3.79 ± 3.19 mmHg for tafluprost (p = 0.0457). The initial change in cOPA was +0.04 ± 0.9 mmHg in the travoprost group and -0.76 ± 0.97 mmHg in the tafluprost group (p = 0.0028). Conclusions: Travoprost and tafluprost reached the targeted intraocular pressure with no difference in the long-term effects of reduced intraocular pressure. However, travoprost was initially better at lowering intraocular pressure faster, and tafluprost had a greater effect on lowering OPA. Prostaglandin analogs can be selected individually by considering the aforementioned factors.


2016 ◽  
Vol 28 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Maryam Shafaei ◽  
Jan Adamowski ◽  
Ahmad Fakheri-Fard ◽  
Yagob Dinpashoh ◽  
Kazimierz Adamowski

Abstract Given its importance in water resources management, particularly in terms of minimizing flood or drought hazards, precipitation forecasting has seen a wide variety of approaches tested. As monthly precipitation time series have nonlinear features and multiple time scales, wavelet, seasonal auto regressive integrated moving average (SARIMA) and hybrid artificial neural network (ANN) methods were tested for their ability to accurately predict monthly precipitation. A 40-year (1970–2009) precipitation time series from Iran’s Nahavand meteorological station (34°12’N lat., 48°22’E long.) was decomposed into one low frequency subseries and several high frequency sub-series by wavelet transform. The low frequency sub-series were predicted with a SARIMA model, while high frequency subseries were predicted with an ANN. Finally, the predicted subseries were reconstructed to predict the precipitation of future single months. Comparing model-generated values with observed data, the wavelet-SARIMA-ANN model was seen to outperform wavelet-ANN and wavelet-SARIMA models in terms of precipitation forecasting accuracy.


2007 ◽  
Vol 16 (8) ◽  
pp. 700-703 ◽  
Author(s):  
Jennifer S. Weizer ◽  
Sanjay Asrani ◽  
Sandra S. Stinnett ◽  
Leon W. Herndon

2016 ◽  
Vol 248 ◽  
pp. 204-210 ◽  
Author(s):  
Marian Sikora

The purpose of this study was to develop a model of the dynamic behavior of a hydraulic vehicle double-tube shock absorber. The model accounts for the effects of compressibility, valve stiction, inertia, etc. and can be suitable for use in the analyses on flow-induced pressure fluctuations in the device. The author highlights all major variables to influence the output of the shock absorber, and then proceeds by performing a series of simulations using the developed model. The model is demonstrated to operate well in the large amplitude and low frequency range as well as the small amplitude and high frequency excitation operation regimes. The results are presented in the form of time histories of pressures in each fluid volume of the damper, flow rates through the valves, piston rod acceleration and force. Fast Fourier Transform (FFT) graphs are presented, too, in order to identify major components of the pressure fluctuation phenomena in frequency domain.


2018 ◽  
Vol 53 (3) ◽  
pp. 215-221 ◽  
Author(s):  
Edsel Ing ◽  
Christian Pagnoux ◽  
Felix Tyndel ◽  
Arun Sundaram ◽  
Seymour Hershenfeld ◽  
...  

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