Non-alcoholic steatohepatitis (NASH), one of the deleterious stages of non-alcoholic fatty liver disease, remains a significant cause of liver-related morbidity and mortality worldwide. In the current work, we used an exploratory data analysis to investigate time-dependent cellular and mitochondrial effects of different supra-physiological fatty acids (FA) overload strategies, in the presence or absence of fructose (F), on human hepatoma-derived HepG2 cells. We measured intracellular neutral lipid content and reactive oxygen species (ROS) levels, mitochondrial respiration and morphology, and caspases activity and cell death. FA-treatments induced a time-dependent increase in neutral lipid content, which was paralleled by an increase in ROS. Fructose, by itself, did not increase intracellular lipid content nor aggravated the effects of palmitic acid (PA) or free fatty acids mixture (FFA), although it led to an up-expression of hepatic fructokinase. Instead, F decreased mitochondrial phospholipid content, as well as OXPHOS subunits levels. Increased lipid accumulation and ROS in FA-treatments preceded mitochondrial dysfunction, comprising altered mitochondrial membrane potential (ΔΨm) and morphology, and decreased oxygen consumption rates, especially with PA. Consequently, supra-physiological PA alone or combined with F prompted the activation of caspase pathways leading to a time-dependent decrease in cell viability. Exploratory data analysis methods support this conclusion by clearly identifying the effects of FA treatments. In fact, unsupervised learning algorithms created homogeneous and cohesive clusters, with a clear separation between PA and FFA treated samples to identify a minimal subset of critical mitochondrial markers in order to attain a feasible model to predict cell death in NAFLD or for high throughput screening of possible therapeutic agents, with particular focus in measuring mitochondrial function.
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http://dx.doi.org/10.3390/nu13051723 | DOI Listing |
Am J Emerg Med
January 2025
Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.
Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.
Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables.
PLoS One
January 2025
Faculty of Philosophy, Philosophy of Science and the Study of Religion, Ludwig Maximilian University of Munich, München, Germany.
Many visualisations used in the climate communication field aim to present the scientific models of climate change to the public. However, relatively little research has been conducted on how such data are visually processed, particularly from a behavioural science perspective. This study examines trends in visual attention to climate change predictions in world maps using mobile eye-tracking while participants engage with the visualisations.
View Article and Find Full Text PDFJ Bronchology Interv Pulmonol
January 2025
Division of Thoracic Surgery and Interventional Pulmonology, Beth Israel Deaconess Medical Center, Harvard Medical School.
Background: Open window thoracostomy (OTW) is the standard of care for debilitated patients with chronic pleural infection and nonexpandable lungs (NEL) who are not candidates for major surgical intervention. Tunneled pleural catheters (TPC) offer tremendous treatment potential in this setting based on their efficacy in malignant pleural effusion and NEL. We aim to assess the efficacy, safety, and health care utilization of TPC in this setting.
View Article and Find Full Text PDFCells
December 2024
Department of Herbal Pharmacology, College of Korean Medicine, Gachon University, 1342 Seongnamdae-ro, Sujeong-gu, Seongnam-si 13120, Republic of Korea.
The NLRP3 inflammasome, plays a critical role in the pathogenesis of rheumatoid arthritis (RA) by activating inflammatory cytokines such as IL1β and IL18. Targeting NLRP3 has emerged as a promising therapeutic strategy for RA. In this study, a multidisciplinary approach combining machine learning, quantitative structure-activity relationship (QSAR) modeling, structure-activity landscape index (SALI), docking, molecular dynamics (MD), and molecular mechanics Poisson-Boltzmann surface area MM/PBSA assays was employed to identify novel NLRP3 inhibitors.
View Article and Find Full Text PDFHealthcare (Basel)
December 2024
Department of Computer Science, School of Arts, Humanities and Social Sciences, University of Roehampton, London SW15 5PH, UK.
: Diabetes is a metabolic disorder characterized by increased blood sugar levels. Early detection of diabetes could help individuals to manage and delay the progression of this disorder effectively. Machine learning (ML) methods are important in forecasting the progression and diagnosis of different medical problems with better accuracy.
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