Negative-pressure wound therapy (NPWT) has revolutionised wound care. Yet, it is still not understood how hypobaric tissue pressure accelerates wound healing. There is very little reported on the relevant physics of any substance subjected to suction in this manner. The common assumption is that applying suction to a substance is likely to result in a reduction of pressure in that substance. Although more than 250 research articles have been published on NPWT, there are little data verifying whether suction increases or decreases the pressure of the substance it is applied to. Clarifying this basic question of physics is the first step in understanding the mechanism of action of these dressings. In this study, pressure changes were recorded in soft plasticene and processed meat, using an intracranial tissue pressure microsensor. Circumferential, non-circumferential and cavity NPWT dressings were applied, and pressure changes within the underlying substance were recorded at different suction pressures. Pressures were also measured at 1cm, 2 cm and 3 cm from the NPWT placed in a cavity. In all three types of NPWT dressings, the underlying substance pressure was increased (hyperbaric) as suction pressure increased. Although there was a substantial pressure increase at 1cm, the rise in pressure at the 2-cm and 3-cm intervals was minimal. Substance pressure beneath all types of NPWT dressing is hyperbaric in inanimate substances. Higher suction pressures generate greater substance pressures; however, the increased pressure rapidly dissipates as the distance from the dressing is increased. The findings of this study on inanimate objects suggest that we may need to review our current perception of the physics underlying NPWT dressings. Further research of this type on living tissues is warranted.
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http://dx.doi.org/10.1016/j.bjps.2008.08.037 | DOI Listing |
J Neuroophthalmol
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Departments of Ophthalmology (DB, G-SY, GTL, RAA) and Neurology (DB, GTL, RAA), Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; and Division of Ophthalmology (AG, GTL, RAA), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
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Changchun Sci-Tech University, JiLin ChangChun 130600, China.
This study assesses gender inequality in education and employment in BRICS (Brazil, Russia, India, China, and South Africa) countries between the 2000 and 2021, using data from the World Bank database. A descriptive data analysis was carried out as well as graphical representations to compare among countries. The results showed that that female education achieved significant success, especially at secondary and tertiary levels, through specific policies including financial support and affirmative actions.
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Division of Maternal-Fetal Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, L1, Boston, MA, 02115, USA. Electronic address:
Preeclampsia is a life-threatening complication that develops in 2-8% of pregnancies. It is characterized by elevated blood pressure after 20 weeks of gestation and may progress to multiorgan dysfunction, leading to severe maternal and fetal morbidity and mortality. The only definitive treatment is delivery, and efforts are focused on early risk prediction, surveillance, and severity mitigation.
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Department of Criminology, University of Ottawa, Ottawa, ON, Canada.
While the early mental health of girls and boys is similar, as children age, girls tend to report worse mental health than boys. Explanations for these gendered disparities remain elusive. This study seeks to understand the social context in which mental health experiences are shaped.
View Article and Find Full Text PDFInt J Numer Method Biomed Eng
January 2025
College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.
The accurate non-invasive detection and estimation of central aortic pressure waveforms (CAPW) are crucial for reliable treatments of cardiovascular system diseases. But the accuracy and practicality of current estimation methods need to be improved. Our study combines a meta-learning neural network and a physics-driven method to accurately estimate CAPW based on personalized physiological indicators.
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