Artificial intelligence-based techniques for predicting outcomes in COVID-19 patients
Introduction
Currently, extensive research has shown that almost all published prediction models are poorly studied and have significant limitations, leading to their predictive performance often being overestimated. Additionally, there is still no universally accepted scoring system, primarily due to the need for adaptation to heterogeneous patient samples (including patient numbers, clinical profiles, and risk factors) and/or ongoing differences in the organization of healthcare systems across various countries.
Material and methods
This is a narrative literature review. A bibliographic search was conducted in the PubMed, Hinari, SpringerLink, National Center for Biotechnology Information, and Medline databases. Articles published between 2000 and 2024 were selected based on keyword combinations such as "artificial intelligence", "prediction model", "algorithm", "machine learning", and "COVID-19". Information on machine learning predictive models was selected and processed to identify characteristics that can be used to predict diagnosis, severity, length of hospital stay, ICU admission, treatment, vaccination, and mortality in COVID-19 patients. After processing the data according to the search criteria, 125 full-text articles were identified. The final bibliography includes 52 relevant sources, which were considered representative of the literature on this synthesis article topic.
Results
Artificial intelligence techniques are increasingly being used to predict outcomes in COVID-19 patients, particularly in estimating mortality among individuals infected with SARS-CoV-2, which can rapidly and effectively support clinical decision-making. According to the analysis of multiple studies, strong predictors of mortality in COVID-19 patients include advanced age, male gender, comorbidities, reduced levels of calcium, albumin, red blood cells, and oxygen saturation, as well as lymphopenia, elevated blood urea nitrogen, creatinine, lactate dehydrogenase, D-dimers, neutrophils, interleukin-6, procalcitonin, bilirubin, ferritin, aspartate aminotransferase, and troponin.
Conclusions
Artificial intelligence techniques provide potential advantages over conventional assessment methods. The information obtained from machine learning and deep learning algorithms, including easily accessible and interpretable data, can assist healthcare workers in making accurate decisions for the appropriate and timely care of COVID- 19 patients. This can improve patient outcomes, reduce the burden on healthcare systems, and ultimately decrease mortality rates.
Current affairs in the use of medical ozone. Biological effects. Mechanisms of action
Introduction
Oxygen-ozone therapy stands as a medically endorsed practice confirmed by numerous international clinical studies. Various authors have illustrated the beneficial clinical outcomes of ozone therapy in terms of its capacity to regulate redox balance, cellular inflammatory responses, and adaptation to ischemia/reperfusion processes. Ozone therapy extends to encompass a range of viral infections, inflammatory disorders, and degenerative ailments, used as both monotherapy and as an adjunct to unified conventional therapies.
Materials and methods
Narrative literature review study. Bibliographic search was conducted using the PubMed, Hinari, and SpringerLink databases, as well as the National Center of Biotechnology Information and Medline. Articles published between 1990 and 2022 were selected using various combinations of keywords, including “ozone”, “ozone therapy”, “mechanisms of ozone action”, “biological effects of ozone”, “antioxidant effect”, “anti-inflammatory effect” and “immunomodulatory effect.” Information regarding ozone's mechanisms of action was identified and processed. Following the database information processing and search criteria, a total of 475 full-text articles were found. The final bibliography consists of 52 relevant sources that were deemed representative of the materials published on the topic of this synthesis article.
Results
The effects of ozone on oxygen metabolism are explained by changes in the rheological properties of blood, including inhibition of erythrocyte aggregation and stimulation of 2,3-diphosphoglycerate in erythrocytes, favoring the transport and delivery of oxygen to tissues while facilitating the substantial elimination of nitric oxide and increasing blood flow. Intracellular triatomic oxygen enhances the oxidative carboxylation of pyruvate, stimulating ATP production, which also contributes to reducing peripheral vascular resistance.
Conclusions
Ozone generates a moderate oxidative stress. Yet, it can set off several beneficial biochemical mechanisms that reactivate both the intra- and extracellular antioxidant systems and reverse chronic oxidative stress in various inflammatory and degenerative processes. Ozone induces a mild activation of the immune system by triggering neutrophil activation and stimulating the synthesis of certain cytokines (IL-2, TNF-α, IL-6, and IFN-γ), thereby initiating a complete cascade of immune responses. Ozone therapy yields the following biological reactions: optimization of blood circulation and oxygen delivery to ischemic tissue, regulation of cellular antioxidant enzymes, initiation of a slight immune system activation, and enhancing the release of growth factors.