Background: High-flow oxygen therapy via tracheostomy (HFT) can be used in tracheostomized patients during ventilator disconnection. The physiologic effects of this technique are unknown. We hypothesized that HFT would reduce inspiratory effort and improve breathing pattern compared to conventional oxygen therapy via T-tube. This study aimed to evaluate the physiologic effects of HFT compared to conventional O in patients with prolonged mechanical ventilation.
Methods: A randomized crossover physiologic study was conducted in adult tracheostomized patients who experienced temporary periods of ventilator disconnection. Subjects were ventilated with pressure support ventilation (PSV) for 15 min and were then randomly assigned to HFT or conventional O via T-tube for 30 min. After a washout period, subjects were switched to the other system. Esophageal pressure (P), breathing frequency, blood pressure, heart rate, [Formula: see text], and transcutaneously measured pressure of carbon dioxide ([Formula: see text]) were recorded. The primary outcome was inspiratory effort as determined by the simplified esophageal pressure-time product (sPTP). Secondary outcomes were P swing, breathing frequency, heart rate, mean arterial pressure, [Formula: see text], and [Formula: see text] between groups.
Results: Twenty-two subjects were enrolled: sPTP per minute was significantly higher with HFT and conventional O compared to PSV (153.5 ± 97.9, 163.5 ± 111.3, and 86.8 ± 51.1 cm HO × s/min, respectively, = .001), but it was not different between HFT and conventional O ( = .72). Breathing frequency increased significantly after switching from PSV to HFT and conventional O (23 ± 4 vs 26 ± 6 and 23 ± 4 vs 27 ± 5 breaths/min, respectively, = .001). [Formula: see text] was higher with conventional O compared to HFT ( = .02). No differences in [Formula: see text], mean arterial pressure, or heart rate were observed between HFT and conventional O.
Conclusions: Inspiratory effort and breathing frequency increased significantly during unassisted breathing compared to PSV in tracheostomized subjects, but HFT via tracheostomy provided no measurable additional physiologic benefit compared to O therapy via T-tube.
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http://dx.doi.org/10.4187/respcare.08585 | DOI Listing |
Proc Natl Acad Sci U S A
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Department of Earth Sciences, University of Oregon, Eugene, OR 97403.
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January 2025
Institute for Advanced Study, Tsinghua University, Beijing 100084, China.
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Beijing National Laboratory for Molecular Sciences, CAS Laboratory of Colloid and Interface and Thermodynamics, CAS Research/Education Centre for Excellence in Molecular Sciences, Centre for Carbon Neutral Chemistry, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China; School of Chemistry, University of Chinese Academy of Sciences, Beijing 100049, China; Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China. Electronic address:
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Saint Petersburg State University, St. Petersburg, 198504, Russia.
Using angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT), an experimental and theoretical study of changes in the electronic structure (dispersion dependencies) and corresponding modification of the energy band gap at the Dirac point (DP) for topological insulator (TI) [Formula: see text] have been carried out with gradual replacement of magnetic Mn atoms by non-magnetic Ge atoms when concentration of the latter was varied from 10% to 75%. It was shown that when Ge concentration increases, the bulk band gap decreases and reaches zero plateau in the concentration range of 45-60% while trivial surface states (TrSS) are present and exhibit an energy splitting of 100 and 70 meV in different types of measurements. It was also shown that TSS disappear from the measured band dispersions at a Ge concentration of about 40%.
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January 2025
Department of Mathematical Sciences, Faculty of Science, Somali National University, Mogadishu Campus, Mogadishu, Somalia.
In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy.
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