Preoperative and postoperative psychological factors, postoperative pain, analgesic consumption, treatment satisfaction were compared in patients treated with intravenous patient-controlled analgesia (IV-PCA) or intramuscular analgesics after laparoscopic ovarian cystectomy. Thirty-one women with laparoscopically operated benign ovarian cysts were recruited in Zonguldak Karaelmas University Faculty of Medicine, Department of Obstetrics and Gynecology. Postoperatively sixteen women received morphine delivered by IV-PCA pump system and 15 women were prescribed another opioid (meperidine) intramuscularly. Two weeks before and one day after the surgery, Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI) were self-administered. Afterwards, the operation visual analog scale (VAS) and satisfaction with pain control scale were recorded. Preoperative BDI and BAI scores of both groups were comparable. Postoperative BDI (7.9 +/- 7.2 versus 13.8 +/- 6.9, P = 0.03) and BAI (11.4 +/- 9.1 versus 17.4 +/- 6.2, P = 0.045) scores were significantly lower in the IV-PCA group. Morphine usage with PCA resulted in significantly higher pain scores than equivalent doses of meperidine administered intramuscularly (2.94 +/- 1.0 versus 1.67 +/- 0.7, P = 0.001). Although higher pain scores were obtained from IV-PCA group, self-reported satisfaction rates were higher in this group (8.3 +/- 1.1 versus 7.4 +/- 1.1, P = 0.04). Involvement of patients in their pain management might increase the awareness of pain but their satisfaction about the control of postoperative pain was significantly improved.
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Sci Rep
December 2024
Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh.
Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ab initio calculations. In this work, an effective and robust deep learning-based model is proposed by incorporating persistent homology with graph neural network which offers an accuracy of and an F1 score of in classifying topological versus non-topological materials, outperforming the other state-of-the-art classifier models.
View Article and Find Full Text PDFNPJ Parkinsons Dis
December 2024
Univ. Bordeaux, CNRS, Institut des Maladies Neurodégénératives, UMR 5293, F-33000, Bordeaux, France.
α-synucleinopathies progression involves the spread of α-synuclein aggregates through the extracellular space (ECS). Single-particle tracking studies showed that α-synuclein-induced neurodegeneration increases ECS molecular diffusivity. To disentangle the consequences of neuronal loss versus α-synuclein-positive intracellular assemblies formation, we performed near-infrared single-particle tracking to characterise ECS rheology in the striatum of mouse models of α-synucleinopathies.
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December 2024
Harman International, HarmanX Neurosense, 30001 Cabot Dr, Novi, MI, 48377, USA.
Cognitive load (CL) is one of the leading factors moderating states and performance among drivers. Heavily increased CL may contribute to the development of mental stress. Averaged heart rate (HR) and heart rate variability (HRV) indices are shown to reflect CL levels in different tasks.
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December 2024
Creative Robotics Lab, UNSW, Sydney, 2021, Australia.
Unlike the conventional, embodied, and embrained whole-body movements in the sagittal forward and vertical axes, movements in the lateral/transversal axis cannot be unequivocally grounded, embodied, or embrained. When considering motor imagery for left and right directions, it is assumed that participants have underdeveloped representations due to a lack of familiarity with moving along the lateral axis. In the current study, a 32 electroencephalography (EEG) system was used to identify the oscillatory neural signature linked with lateral axis motor imagery.
View Article and Find Full Text PDFNat Commun
December 2024
Department of Psychology, Cornell University, Ithaca, NY, USA.
Subjective feelings are thought to arise from conceptual and bodily states. We examine whether the valence of feelings may also be decoded directly from objective ecological statistics of the visual environment. We train a visual valence (VV) machine learning model of low-level image statistics on nearly 8000 emotionally charged photographs.
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