RESUMEN
Consumption of saturated fat-enriched diets during adolescence has been closely associated with the reduction of hippocampal synaptic plasticity and the impairment of cognitive function. Nevertheless, the effect of long-term intake of these foods has not yet been studied. In the present study, we have investigated the effect of a treatment, lasting for 40 weeks, with a diet enriched in saturated fat (SOLF) on i) spatial learning and memory, ii) hippocampal synaptic transmission and plasticity, and iii) hippocampal gene expression levels in aged male and female mice. Our findings reveal that SOLF has a detrimental impact on spatial memory and synaptic plasticity mechanisms, such as long-term potentiation (LTP), and downregulates Gria1 expression specifically in males. In females, SOLF downregulates the gene expression of Gria1/2/3 and Grin1/2A/2B glutamate receptor subunits as well as some proinflammatory interleukins. These findings highlight the importance of considering sex-specific factors when assessing the long-term effects of high-fat diets on cognition and brain plasticity.
RESUMEN
Artificial intelligence (AI) systems are already being used in various healthcare areas. Similarly, they can offer many advantages in hospital emergency services. The objective of this work is to demonstrate that through the novel use of AI, a trained system can be developed to detect patients at potential risk of infection in a new pandemic more quickly than standardized triage systems. This identification would occur in the emergency department, thus allowing for the early implementation of organizational preventive measures to block the chain of transmission. MATERIALS AND METHODS: In this study, we propose the use of a machine learning system in emergency department triage during pandemics to detect patients at the highest risk of death and infection using the COVID-19 era as an example, where rapid decision making and comprehensive support have becoming increasingly crucial. All patients who consecutively presented to the emergency department were included, and more than 89 variables were automatically analyzed using the extreme gradient boosting (XGB) algorithm. RESULTS: The XGB system demonstrated the highest balanced accuracy at 91.61%. Additionally, it obtained results more quickly than traditional triage systems. The variables that most influenced mortality prediction were procalcitonin level, age, and oxygen saturation, followed by lactate dehydrogenase (LDH) level, C-reactive protein, the presence of interstitial infiltrates on chest X-ray, and D-dimer. Our system also identified the importance of oxygen therapy in these patients. CONCLUSIONS: These results highlight that XGB is a useful and novel tool in triage systems for guiding the care pathway in future pandemics, thus following the example set by the well-known COVID-19 pandemic.
