Purpose: Carbogen has long been under investigation as an adjuvant to radiotherapy of tumors. A major factor confounding its evaluation is its inconsistency in raising blood partial pressure of CO(2) (pCO(2)). We investigated whether a new partial rebreathing method would provide better control of pCO(2) than carbogen.
Methods And Materials: We compared the efficacy of each method in 10 healthy volunteers. Volunteers breathed 1.5, 3 and 5% carbogen in 5-min stages via the usual non-rebreathing circuit. All the volunteers then breathed 100% O(2) through a commercial sequential gas delivery (SGD) circuit modified by attaching a reservoir to its exhalation port. Hypercarbia was induced by step reductions in oxygen flow to the SGD circuit. We monitored minute ventilation and end-tidal pCO(2) (ETpCO(2)) as a surrogate for its arterial value.
Results: Inhalation of 1.5 and 3% carbogen did not increase ETpCO(2) from baseline (40 +/- 1.5 mmHg); 5% carbogen increased ETpCO(2) to 45 +/- 1.6 mmHg (p < 0.001). With the SGD circuit, reducing O(2) flow to 4.3 +/- 0.7 l/min increased ETpCO(2) in all subjects from 41 +/- 2.0 mmHg (baseline) to 46 +/- 2.1 mmHg (p < 0.001). Voluntary hyperventilation reduced ETpCO(2) with 5% carbogen but not with SGD (p = 0.379).
Conclusions: We confirm previous observations that carbogen inhalation does not result in a predictable rise in ETpCO(2) and suggest that a precise and stable target ETpCO(2) can instead be induced by simply controlling O(2) flow into a modified SGD circuit. We hoped that the reliable control of pCO(2) will enable studies that address first, the efficacy of raising ETpCO(2) on specific tumor blood flow, and eventually, its benefit as an adjuvant to radiotherapy.
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BMC Med Imaging
September 2023
Department of Computer Science, Lancashire College of Further Education, Accrington, BB5 OHJ, UK.
Hybrid quantum systems have shown promise in image classification by combining the strengths of both classical and quantum algorithms. These systems leverage the parallel processing power of quantum computers to perform complex computations while utilizing classical algorithms to handle the vast amounts of data involved in imaging. The hybrid approach is intended to improve accuracy and speed compared to traditional classical methods.
View Article and Find Full Text PDFCirc Heart Fail
March 2022
Department of Surgery and Division of Cardiovascular Medicine, Department of Internal Medicine (A.E.A., J.C.F., S.G.D., D.C.), University of Utah, Salt Lake City.
Background: Extrinsic control of cardiomyocyte metabolism is poorly understood in heart failure (HF). FGF21 (Fibroblast growth factor 21), a hormonal regulator of metabolism produced mainly in the liver and adipose tissue, is a prime candidate for such signaling.
Methods: To investigate this further, we examined blood and tissue obtained from human subjects with end-stage HF with reduced ejection fraction at the time of left ventricular assist device implantation and correlated serum FGF21 levels with cardiac gene expression, immunohistochemistry, and clinical parameters.
Neural Netw
August 2020
Centre for Artificial Intelligence, University of Technology Sydney, Ultimo, NSW 2007, Australia. Electronic address:
Neural networks implemented with traditional hardware face inherent limitation of memory latency. Specifically, the processing units like GPUs, FPGAs, and customized ASICs, must wait for inputs to read from memory and outputs to write back. This motivates memristor-based neuromorphic computing in which the memory units (i.
View Article and Find Full Text PDFFront Neurosci
March 2018
Institute of Microelectronics, Tsinghua University, Beijing, China.
The parallel updating scheme of RRAM-based analog neuromorphic systems based on sign stochastic gradient descent (SGD) can dramatically accelerate the training of neural networks. However, sign SGD can decrease accuracy. Also, some non-ideal factors of RRAM devices, such as intrinsic variations and the quantity of intermediate states, may significantly damage their convergence.
View Article and Find Full Text PDFACS Nano
July 2016
Key Laboratory for the Physics and Chemistry of Nanodevices and Department of Electronics, Peking University, Beijing 100871, People's Republic of China.
A nano self-gating diode (SGD) based on nanoscale semiconducting material is proposed, simulated, and realized on semiconducting carbon nanotubes (CNTs) through a doping-free fabrication process. The relationships between the performance and material/structural parameters of the SGD are explored through numerical simulation and verified by experiment results. Based on these results, performance optimization strategy is outlined, and high performance CNT SGDs are fabricated and demonstrated to surpass other published CNT diodes.
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