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Oxygen-ozone (O2-O3) immunoceutical therapy for patients with COVID-19. Preliminary evidence reported.

ABSTRACT: OBJECTIVE:This study evaluated the potential efficacy of a novel approach to treat COVID-19 patients, using an oxygen-ozone (O2-O3) mixture, via a process called Oxygen-Ozone- Immunoceutical Therapy. The methodology met the criteria of a novel, promising approach to treat successfully elderly COVID-19 patients, particularly when hospitalized in intensive care units (ICUs) Experimental design: We investigated the therapeutic effect of 4 cycles of O2-O3 in 50 hospitalized COVID-19 subjects suffering from acute respiratory disease syndrome (ARDS), aged more than 60 years, all males and undergoing non invasive mechanical ventilation in ICUs. RESULTS:Following O2-O3 treatment a significant improvement in inflammation and oxygenation indexes occurred rapidly and within the first 9 days after the treatment, despite the expected 14-20 days. A significant reduction of inflammatory and thromboembolic markers (CRP, IL-6, D-dimer) was observed. Furthermore, amelioration in the major respiratory indexes, such as respiratory and gas exchange markers (SatO2%, PaO2/FiO2 ratio), was reported. CONCLUSION:Our results show that O2-O3 treatment would be a promising therapy for COVID-19 patients. It leads patients to a fast recovery from ARDS via the improvement of major respiratory indexes and blood gas parameters, following a relatively short time of dispensed forced ventilation (about one to two weeks). This study may encourage the scientific community to further investigate and evaluate the proposed method for the treatment of COVID-19 patients.

SUBMITTER: Franzini M

PROVIDER: S-EPMC7414302 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Oxygen-ozone (O2-O3) immunoceutical therapy for patients with COVID-19. Preliminary evidence reported.

Franzini Marianno M Valdenassi Luigi L Ricevuti Giovanni G Chirumbolo Salvatore S Depfenhart Markus M Bertossi Dario D Tirelli Umberto U

International immunopharmacology 20200808

<h4>Objective</h4>This study evaluated the potential efficacy of a novel approach to treat COVID-19 patients, using an oxygen-ozone (O2-O3) mixture, via a process called Oxygen-Ozone- Immunoceutical Therapy. The methodology met the criteria of a novel, promising approach to treat successfully elderly COVID-19 patients, particularly when hospitalized in intensive care units (ICUs) Experimental design: We investigated the therapeutic effect of 4 cycles of O2-O ...[more]

PMID: 32795898

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Project description:IntroductionUsing ozone therapy to manage COVID-19 patients has been accompanied by conflicting results in prior studies. Therefore, we aimed to widely assess the effects of ozone as adjuvant therapy in COVID-19 patients.MethodsPubMed, Scopus, Web of Science, Cochrane, ProQuest, Springer, and Sage journals were searched systematically until April 2022. Mortality rate, ICU admission, hospital-length stay, negative PCR, pulmonary, renal, and hepatic functions, as well as inflammatory and blood systems were pooled to compare the efficacy of ozone as adjacent therapy (OZ) and standard treatment (ST). Analyses were run with the random/fixed models, sub-group analysis, funnel plot, and sensitivity analysis using comprehensive meta-analysis (CMA) software version 2.0.ResultsThe results of four randomized clinical trials (RCTs) and four case-control studies with a total of 371 COVID-19 positive patients were analyzed. The OZ group patients had a shorter length of hospital stay (P > 0.05), lower ICU admissions (P > 0.05), and lower mortality rates (P < 0.05) than the ST group cases. After treatment, 41% more COVID-19 patients had negative PCR tests than the ST group (P < 0.05). Serum creatinine and urea levels were not modified in either group (P > 0.05). Moreover, except for albumin serum levels, which decreased significantly in the OZ group, serum bilirubin, ALT, and AST were not modified in either group (P > 0.05). Both arms did not show a decrease in C-reactive protein blood levels (P > 0.05), but the OZ group showed a significant modification in LDH serum levels (P < 0.05). Unlike the d-dimer and WBC serum levels (P > 0.05), platelet levels were increased in the OZ group (P < 0.05). No negative side effects were demonstrated in either group.ConclusionOzone therapy was effective significantly on PCR test and LDH serum levels, as well as mortality based on overall estimation. Concerning the length of hospital stay and ICU admissions, although the results were insignificant, their effect sizes were notable clinically. More RCT studies are needed to show the efficacy of ozone therapy on other studied variables.

Project description:The lack of available biomarkers for diagnosing and predicting different stages of coronavirus disease 2019 (COVID-19) is currently one of the main challenges that clinicians are facing. Recent evidence indicates that the plasma levels of specific miRNAs may be significantly modified in COVID-19 patients. Large-scale deep sequencing analysis of small RNA expression was performed on plasma samples from 40 patients hospitalized for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (between March and May 2020) (median 13.50 [IQR 9–24] days since symptoms initiation) and 21 healthy noninfected individuals. Patients were categorized as hospitalized not requiring oxygen therapy (n = 6), hospitalized requiring low-flow oxygen (n = 23), and hospitalized requiring high-flow oxygen support (n = 11). A total of 1218 different micro(mi)RNAs were identified. When compared with healthy noninfected donors, SARS-CoV-2 infected patients showed significantly (fold change [FC] >1.2 and adjusted p [padj] <0.05) altered expression of 190 miRNAs. The top 10 differentially expressed (DE) miRNAs were miR-122-5p, let-7b-5p, miR-146a-5p, miR-342-3p, miR-146b-5p, miR-629-5p, miR-24-3p, miR-12136, let-7a-5p, and miR-191-5p, which displayed FC and padj values ranging from 153 to 5 and 2.51 × 10-32 to 2.21 × 10-21, respectively, which unequivocally diagnosed SARS-CoV-2 infection. No differences in blood cell counts and biochemical plasma parameters, including interleukin 6, ferritin and D-dimer, were observed between COVID-19 patients on high-flow oxygen therapy, low-flow oxygen therapy, or not requiring oxygen therapy. Notably, 31 significantly deregulated miRNAs were found when patients on high- and low-flow oxygen therapy were compared. Similarly, 6 DE miRNAs were identified between patients on high flow and those not requiring oxygen therapy. SARS-CoV-2 infection generates a specific miRNA signature in hospitalized patients. Furthermore, specific miRNA profiles are associated with COVID-19 prognosis in severe patients.

Project description:BackgroundPatients with severe Coronavirus disease 19 (COVID-19) typically require supplemental oxygen as an essential treatment. We developed a machine learning algorithm, based on deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critically ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice.MethodsWe modeled the oxygen flow trajectory of COVID-19 patients and their health outcomes as a Markov decision process. Based on individual patient characteristics and health status, an optimal oxygen control policy is learned by using deep deterministic policy gradient (DDPG) and real-time recommends the oxygen flow rate to reduce the mortality rate. We assessed the performance of proposed methods through cross validation by using a retrospective cohort of 1372 critically ill patients with COVID-19 from New York University Langone Health ambulatory care with electronic health records from April 2020 to January 2021.ResultsThe mean mortality rate under the RL algorithm is lower than the standard of care by 2.57% (95% CI: 2.08-3.06) reduction (P < 0.001) from 7.94% under the standard of care to 5.37% under our proposed algorithm. The averaged recommended oxygen flow rate is 1.28 L/min (95% CI: 1.14-1.42) lower than the rate delivered to patients. Thus, the RL algorithm could potentially lead to better intensive care treatment that can reduce the mortality rate, while saving the oxygen scarce resources. It can reduce the oxygen shortage issue and improve public health during the COVID-19 pandemic.ConclusionsA personalized reinforcement learning oxygen flow control algorithm for COVID-19 patients under intensive care showed a substantial reduction in 7-day mortality rate as compared to the standard of care. In the overall cross validation cohort independent of the training data, mortality was lowest in patients for whom intensivists' actual flow rate matched the RL decisions.

Project description:BackgroundPatients with severe Coronavirus disease 19 (COVID-19) typically require supplemental oxygen as an essential treatment. We developed a machine learning algorithm, based on deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critically ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice.MethodsWe modeled the oxygen flow trajectory of COVID-19 patients and their health outcomes as a Markov decision process. Based on individual patient characteristics and health status, an optimal oxygen control policy is learned by using deep deterministic policy gradient (DDPG) and real-time recommends the oxygen flow rate to reduce the mortality rate. We assessed the performance of proposed methods through cross validation by using a retrospective cohort of 1,372 critically ill patients with COVID-19 from New York University Langone Health ambulatory care with electronic health records from April 2020 to January 2021.ResultsThe mean mortality rate under the RL algorithm is lower than the standard of care by 2.57% (95% CI: 2.08-3.06) reduction (P<0.001) from 7.94% under the standard of care to 5.37 % under our proposed algorithm. The averaged recommended oxygen flow rate is 1.28 L/min (95% CI: 1.14-1.42) lower than the rate delivered to patients. Thus, the RL algorithm could potentially lead to better intensive care treatment that can reduce the mortality rate, while saving the oxygen scarce resources. It can reduce the oxygen shortage issue and improve public health during the COVID-19 pandemic.ConclusionsA personalized reinforcement learning oxygen flow control algorithm for COVID-19 patients under intensive care showed a substantial reduction in 7-day mortality rate as compared to the standard of care. In the overall cross validation cohort independent of the training data, mortality was lowest in patients for whom intensivists' actual flow rate matched the RL decisions.

Dataset Information

Oxygen-ozone (O2-O3) immunoceutical therapy for patients with COVID-19. Preliminary evidence reported.

Publications

Oxygen-ozone (O<sub>2</sub>-O<sub>3</sub>) immunoceutical therapy for patients with COVID-19. Preliminary evidence reported.

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