An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model

Severity Patterns in COVID-19 Hospitalised Patients in Spain: I-MOVE-COVID-19 Study

In the frame of the I-MOVE-COVID-19 project, a cohort of 2050 patients admitted in two Spanish reference hospitals between March 2020 and December 2021 was selected and a range of clinical factor data were collected at admission to assess their impact on the risk COVID-19 severity outcomes through a multivariate adjusted analysis and nomograms. The need for ventilation and intensive care unit (ICU) admission were found to be directly associated with a higher death risk (OR 6.9 and 3.2, respectively). The clinical predictors of death were the need for ventilation and ICU, advanced age, neuromuscular disorders, thrombocytopenia, hypoalbuminemia, dementia, cancer, elevated creatin phosphokinase (CPK), and neutrophilia (OR between 1.8 and 3.5), whilst the presence of vomiting, sore throat, and cough diminished the risk of death (OR 0.5, 0.2, and 0.1, respectively). Admission to ICU was predicted by the need for ventilation, abdominal pain, and elevated lactate dehydrogenase (LDH) (OR 371.0, 3.6, and 2.2, respectively) as risk factors; otherwise, it was prevented by advanced age (OR 0.5). In turn, the need for ventilation was predicted by low oxygen saturation, elevated LDH and CPK, diabetes, neutrophilia, obesity, and elevated GGT (OR between 1.7 and 5.2), whilst it was prevented by hypertension (OR 0.5). These findings could enhance patient management and strategic interventions to combat COVID-19.

CYP2E1 in 1,4-dioxane metabolism and liver toxicity: insights from CYP2E1 knockout mice study

1,4-Dioxane (DX), an emerging water contaminant, is classified as a Group 2B liver carcinogen based on animal studies. Understanding of the mechanisms of action of DX liver carcinogenicity is important for the risk assessment and control of this environmental pollution. Previous studies demonstrate that high-dose DX exposure in mice through drinking water for up to 3 months caused liver mild cytotoxicity and oxidative DNA damage, a process correlating with hepatic CYP2E1 induction and elevated oxidative stress. To access the role of CYP2E1 in DX metabolism and liver toxicity, in the current study, male and female Cyp2e1-null mice were exposed to DX in drinking water (5000 ppm) for 1 week or 3 months. DX metabolism, redox and molecular investigations were subsequently performed on male Cyp2e1-null mice for cross-study comparisons to similarly treated male wildtype (WT) and glutathione (GSH)-deficient Gclm-null mice. Our results show that Cyp2e1-null mice of both genders were resistant to DX-induced hepatocellular cytotoxicity. In male Cyp2e1-null mice exposed to DX for 3 months, firstly, DX metabolism to β-hydroxyethoxyacetic acid was reduced to ~ 36% of WT levels; secondly, DX-induced hepatic redox dysregulation (lipid peroxidation, GSH oxidation, and activation of NRF2 antioxidant response) was substantially attenuated; thirdly, liver oxidative DNA damage was at a comparable level to DX-exposed WT mice, accompanied by suppression of DNA damage repair response; lastly, no aberrant proliferative or preneoplastic lesions were noted in DX-exposed livers. Overall, this study reveals, for the first time, that CYP2E1 is the main enzyme for DX metabolism at high dose and a primary contributor to DX-induced liver oxidative stress and associated cytotoxicity. High dose DX-induced genotoxicity may occur via CYP2E1-independent pathway(s), potentially involving impaired DNA damage repair.

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.