Scientific reports
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Repeated mild traumatic brain injury (rmTBI), the most common type of traumatic brain injuries, can result in neurological dysfunction and cognitive deficits. However, the molecular mechanisms and the long-term consequence of rmTBI remain elusive. In this study, we developed a modified rmTBI mouse model and found that rmTBI-induced transient neurological deficits and persistent impairments of spatial memory function. ⋯ Microarray analysis of whole genome gene expression showed that rmTBI significantly altered the expression level of 87 genes which are involved in apoptosis, stress response, metabolism, and synaptic plasticity. The results indicate the potential mechanism underlying rmTBI-induced acute neurological deficits and its chronic effect on memory impairments. This study suggests that long-term monitoring and interventions for rmTBI individuals are essential for memory function recovery and reducing the risk of developing neurodegenerative diseases.
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Arterial oxygen partial pressure can increase during inspiration and decrease during expiration in the presence of a variable shunt fraction, such as with cyclical atelectasis, but it is generally presumed to remain constant within a respiratory cycle in the healthy lung. We measured arterial oxygen partial pressure continuously with a fast intra-vascular sensor in the carotid artery of anaesthetized, mechanically ventilated pigs, without lung injury. ⋯ These arterial oxygen partial pressure respiratory oscillations can be modelled from a single alveolar compartment and a constant oxygen uptake, without the requirement for an increased shunt fraction during expiration. Our results are likely to contribute to the interpretation of arterial oxygen respiratory oscillations observed during mechanical ventilation in the acute respiratory distress syndrome.
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Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. ⋯ Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.