Background: The number of geriatric traumatic brain injury (TBI) patients is increasing every year due to the population’s aging in most of the developed countries. Unfortunately, there is no widely recognized tool for specifically evaluating the prognosis of geriatric TBI patients. We designed this study to compare the prognostic value of different machine learning algorithm-based predictive models for geriatric TBI. Methods: TBI patients aged ≥65 from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were eligible for this study. To develop and validate machine learning algorithm-based prognostic models, included patients were divided into a training set and a testing set, with a ratio of 7:3. The predictive value of different machine learning based models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy and F score. Results: A total of 1123 geriatric TBI patients were included, with a mortality of 24.8%. Non-survivors had higher age (82.2 vs. 80.7, p = 0.010) and lower Glasgow Coma Scale (14 vs. 7, p < 0.001) than survivors. The rate of mechanical ventilation was significantly higher (67.6% vs. 25.9%, p < 0.001) in non-survivors while the rate of neurosurgical operation did not differ between survivors and non-survivors (24.3% vs. 23.0%, p = 0.735). Among different machine learning algorithms, Adaboost (AUC: 0.799) and Random Forest (AUC: 0.795) performed slightly better than the logistic regression (AUC: 0.792) on predicting mortality in geriatric TBI patients in the testing set. Conclusion: Adaboost, Random Forest and logistic regression all performed well in predicting mortality of geriatric TBI patients. Prognostication tools utilizing these algorithms are helpful for physicians to evaluate the risk of poor outcomes in geriatric TBI patients and adopt personalized therapeutic options for them.
<b><i>Background and Purpose:</i></b> Increased researches focus into pathophysiological mechanisms of spinal cord injury (SCI), particularly toward the relationship between relevant biomarkers and the degree of SCI and prognosis. Circular ribonucleic acids (circRNAs) possess microRNA (miRNA) binding sites that can play the role of miRNA sponges and thus participate in the expression of parental gene modification. This study focused on rat SCI models and explore the relationship between circRNAs and SCI at a genomic level. <b><i>Methods:</i></b> We first established a rat SCI model and extracted the target spinal cord tissue according to 4 time points. Then investigated the alterations in the circRNA expression by high-throughput whole transcriptome sequencing, analyzed data by gene ontology and the Kyoto Encyclopedia of Genes and Genomes, and constructed the circRNA-miRNA network. <b><i>Results:</i></b> A total of 178 circRNAs were dysregulated (89 upregulated/89 downregulated). Differential circRNAs were found to be mainly involved in the composition of specific organelles in the cytoplasm and are mainly involved in the energy transfer process associated with electron transfer (and similar activities). In all the signaling pathways identified in this study, the MAPK, Wnt, and mTOR signaling pathways are intimately associated with the pathophysiological process of rats post-SCI. In this study, 10 circRNAs with obvious dysregulation were selected for prediction, 26 miRNAs with additional interactions were obtained, and a network diagram of circRNAs-miRNAs was constructed. In this manner, one can understand in further detail the pathogenesis of SCI and to provide new strategies for the prevention, diagnosis, and treatment of SCI-related injuries at the genetic level.
The weight reduction of UAVs can reduce power consumption, improve the carrying capacity and range, and is of great significance to the green development of the civil aviation industry. Based on the topology optimization theory, the finite element analysis and lightweight design of the UAV fuselage are carried out. Through the optimization and comparison of five schemes, i.e., symmetry, bidirectional draft, unidirectional draft, bidirectional draft+symmetry and squeeze, the comparison results show that the symmetrical optimization is the best scheme. After the optimization of the UAV fuselage model, the mass reduction ratio is 50.24%, the maximum Mises equivalent stress of the model under the load condition is 10.82Mp, the maximum displacement is 0.7512mm, and the minimum safety factor is 4.2, which meets the strength and lightweight design requirements of the parts, and provides a new idea and feasible scheme for the lightweight design of the UAV.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.