We compared loss of pulse oximetry signal (dropout rates) for both finger and forehead sensors in postanesthesia patients. Pulse oximetry is a widely practiced method for measuring oxygen saturation. Several studies in various patient populations have demonstrated that low flow states, patient movement, and hypothermia may result in poor signal quality with the use of finger oximetry sensors. These clinical conditions are common in patients as they emerge from anesthesia. New forehead sensors may reduce signal dropout. A method-comparison design was used to compare finger and forehead oximetry signal dropout rates. Of 48 subjects studied, only three had a signal dropout. Overall, there were seven episodes of signal dropout; six of seven occurred with the finger sensor. Signal dropout occurred rarely in PACU subjects. Use of finger sensors for routine postanesthesia monitoring should be adequate in the majority of patients.
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http://dx.doi.org/10.1016/j.jopan.2008.07.009 | DOI Listing |
Med Biol Eng Comput
January 2025
Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
Positron emission tomography (PET) imaging plays a pivotal role in oncology for the early detection of metastatic tumors and response to therapy assessment due to its high sensitivity compared to anatomical imaging modalities. The balance between image quality and radiation exposure is critical, as reducing the administered dose results in a lower signal-to-noise ratio (SNR) and information loss, which may significantly affect clinical diagnosis. Deep learning (DL) algorithms have recently made significant progress in low-dose (LD) PET reconstruction.
View Article and Find Full Text PDFBehav Res Methods
January 2025
Methods Center, Eberhard Karls University of Tübingen, Haußerstr. 11, 72076, Tübingen, Germany.
Due to the increased availability of intensive longitudinal data, researchers have been able to specify increasingly complex dynamic latent variable models. However, these models present challenges related to overfitting, hierarchical features, non-linearity, and sample size requirements. There are further limitations to be addressed regarding the finite sample performance of priors, including bias, accuracy, and type I error inflation.
View Article and Find Full Text PDFFunctional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Preprocessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts.
View Article and Find Full Text PDFCancer Med
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Department of Pediatric Oncology/Hematology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Background: CREB binding protein (CREBBP) is a key epigenetic regulator, altered in a fifth of relapsed cases of acute lymphoblastic leukemia (ALL). Selectively targeting epigenetic signaling may be an effective novel therapeutic approach to overcome drug resistance. Anti-tumor effects have previously been demonstrated for GSK-J4, a selective H3K27 histone demethylase inhibitor, in several animal models of cancers.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240 Shanghai, China.
Identifying spatial domains is critical for understanding breast cancer tissue heterogeneity and providing insights into tumor progression. However, dropout events introduces computational challenges and the lack of transparency in methods such as graph neural networks limits their interpretability. This study aimed to decipher disease progression-related spatial domains in breast cancer spatial transcriptomics by developing the three graph regularized non-negative matrix factorization (TGR-NMF).
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