Intraocular pressure fluctuations of growing chick eyes are suppressed in constant light conditions

2016 ◽  
Vol 148 ◽  
pp. 52-54 ◽  
Author(s):  
Christina Wahl ◽  
Tong Li ◽  
Howard C. Howland
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.


2017 ◽  
Vol 26 (10) ◽  
pp. 923-928 ◽  
Author(s):  
Ronald M.P.C. de Crom ◽  
Carroll A.B. Webers ◽  
Marina A.W. van Kooten-Noordzij ◽  
Agnes C. Michiels ◽  
Jan S.A.G. Schouten ◽  
...  

1999 ◽  
Vol 30 (3) ◽  
pp. 212-215 ◽  
Author(s):  
Moshe Snir ◽  
Ruth Axer-Siegel ◽  
J Chalimi ◽  
B Shalev ◽  
Yuval Yassur

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Dominik Schneider ◽  
Laura S. Lopez ◽  
Meng Li ◽  
Joseph D. Crawford ◽  
Helmut Kirchhoff ◽  
...  

Abstract Background Over the last years, several plant science labs have started to employ fluctuating growth light conditions to simulate natural light regimes more closely. Many plant mutants reveal quantifiable effects under fluctuating light despite being indistinguishable from wild-type plants under standard constant light. Moreover, many subtle plant phenotypes become intensified and thus can be studied in more detail. This observation has caused a paradigm shift within the photosynthesis research community and an increasing number of scientists are interested in using fluctuating light growth conditions. However, high installation costs for commercial controllable LED setups as well as costly phenotyping equipment can make it hard for small academic groups to compete in this emerging field. Results We show a simple do-it-yourself approach to enable fluctuating light growth experiments. Our results using previously published fluctuating light sensitive mutants, stn7 and pgr5, confirm that our low-cost setup yields similar results as top-prized commercial growth regimes. Moreover, we show how we increased the throughput of our Walz IMAGING-PAM, also found in many other departments around the world. We have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets. We provide detailed instructions on how to build and functionally test each setup. Conclusions With material costs well below USD$1000, it is possible to setup a fluctuating light rack including a constant light control shelf for comparison. This allows more scientists to perform experiments closer to natural light conditions and contribute to an emerging research field. A small addition to the IMAGING-PAM hardware not only increases sample throughput but also enables larger-scale plant phenotyping with automated data analysis.


2013 ◽  
Vol 2 (4) ◽  
pp. 313-320 ◽  
Author(s):  
Lenneke de Winter ◽  
Anne J. Klok ◽  
Maria Cuaresma Franco ◽  
Maria J. Barbosa ◽  
René H. Wijffels

Ophthalmology ◽  
2002 ◽  
Vol 109 (7) ◽  
pp. 1367-1371 ◽  
Author(s):  
Tony Realini ◽  
Laurie Barber ◽  
Diana Burton

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