Is hydrogen peroxide an effective mouthwash for reducing the viral load of SARS-CoV-2 in dental clinics?

BACKGROUND: Previous studies have demonstrated that SARS-CoV-2 is mainly transmitted by inhalation of aerosols and can remain viable in the air for hours. Viruses can spread in dental settings and put professionals and patients at high risk of infection due to proximity and aerosol-generating procedures, and poor air ventilation. OBJECTIVES: The aim of this study was to investigate the effects of a 1% hydrogen peroxide (H2O2) mouth rinse on reducing the intraoral SARS-CoV-2 load. METHODS: Portable air cleaners with HEPA filters exposed for 3 months were analysed to test for virus presence in a waiting room (where patients wore a face mask but did not undergo mouth rinsing) and three treatment rooms (where patients wore no mask but carried out mouth rinsing). As CO2 is co-exhaled with aerosols containing SARS-CoV-2 by COVID-19 infected people, we also measured CO2 as a proxy of infection risk indoors. Specific primer and probe RT-PCR were applied to detect viral genomes of the SARS-CoV-2 virus in the filters. Specifically, we amplified the nucleocapsid gene (Nuclv) of SARS-CoV-2. RESULTS: CO2 levels ranged from 860 to 907 ppm, thus indicating low ventilation and the risk of COVID-19 transmission. However, we only found viral load in filters from the waiting room and not from the treatment rooms. The results revealed the efficiency of 1-minute mouth rinsing with 1% H2O2 since patients rinsed their mouths immediately after removing their mask in the treatment rooms. CONCLUSIONS: Our findings suggest that dental clinics would be safer and more COVID-19 free by implementing mouth rinsing 1 min with 1% H2O2 immediately after the patients arrive at the clinic.

Incidence and predictors of mortality among COVID-19 patients admitted to treatment centers in North West Ethiopia; A retrospective cohort study, 2021.

Background: Currently, coronavirus disease 2019 (COVID-19) is the leading cause of death and the rate of mortality is rapidly increasing over time. There is a paucity of information regarding the incidence and predictors of mortality among COVID-19 patients from low-income countries, particularly in Ethiopia. Objective: To assess incidence and predictors of mortality among COVID-19 patients admitted to treatment centers in North West Ethiopia. Methods: An institution-based retrospective cohort study was conducted among 552 laboratory-confirmed COVID-19 cases at Debre Markos University and Tibebe Ghion Hospital COVID-19 treatment centers in North West Ethiopia from March 2020 to March 2021. Data were collected from patients' medical records using a structured data extraction tool. Cox-proportional hazards regression models was fitted to identify significant predictors of mortality. Result: The overall mortality rate of COVID-19 was 4.7, (95 % CI: 3.3-6.8) per 1000 person day observations. Older age (AHR: 4.9; 95% CI: 1.8, 13.5), rural residence (AHR: 0.18; 95% CI: 0.05, 0.64), presence of hypertension (AHR: 3.04; 95% CI: 1.18, 7.8), presence of diabetes mellitus (AHR: 8.1; 95% CI: 2.9, 22.4) and cardiovascular disease (AHR: 5.2; 95% CI: (1.69, 16.2) were significantly associated with mortality. Conclusions: The rate of mortality among hospitalized COVID-19 patients in this study was low. COVID-19 patients from urban residences, older patients, and patients with comorbidity have a high risk of death. These high risk groups should be prioritized for COVID-19 vaccinations, and early screening and appropriate intervention should be established on presentation to health facility.

Recovery scenario and immunity in COVID-19 disease: A new strategy to predict the potential of reinfection.

Background: The recent ongoing outbreak of coronavirus disease 2019 (COVID-19), still is an unsolved problem with a growing rate of infected cases and mortality worldwide. The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is targeting the angiotensin-converting enzyme 2 (ACE2) receptor and mostly causes a respiratory illness. Although acquired and resistance immunity is one of the most important aspects of alleviating the trend of the current pandemic; however, there is still a big gap of knowledge regarding the infection process, immunopathogenesis, recovery, and reinfection. Aim of Review: To answer the questions regarding "the potential and probability of reinfection in COVID-19 infected cases" or "the efficiency and duration of SARS-CoV-2 infection-induced immunity against reinfection" we critically evaluated the current reports on SARS-CoV-2 immunity and reinfection with special emphasis on comparative studies using animal models that generalize their finding about protection and reinfection. Also, the contribution of humoral immunity in the process of COVID-19 recovery and the role of ACE2 in virus infectivity and pathogenesis has been discussed. Furthermore, innate and cellular immunity and inflammatory responses in the disease and recovery conditions are reviewed and an overall outline of immunologic aspects of COVID-19 progression and recovery in three different stages are presented. Finally, we categorized the infected cases into four different groups based on the acquired immunity and the potential for reinfection. Key Scientific Concepts of Review: In this review paper, we proposed a new strategy to predict the potential of reinfection in each identified category. This classification may help to distribute resources more meticulously to determine: who needs to be serologically tested for SARS-CoV-2 neutralizing antibodies, what percentage of the population is immune to the virus, and who needs to be vaccinated.

Prevalence and 30-Day Mortality in Hospitalized Patients With Covid-19 and Prior Lung Diseases.

INTRODUCTION: Patients with pre-existing respiratory diseases in the setting of COVID-19 may have a greater risk of severe complications and even death. METHODS: A retrospective, multicenter, cohort study with 5847 COVID-19 patients admitted to hospitals. Patients were separated in two groups, with/without previous lung disease. Evaluation of factors associated with survival and secondary composite end-point such as ICU admission and respiratory support, were explored. RESULTS: 1,271 patients (22%) had a previous lung disease, mostly COPD. All-cause mortality occurred in 376 patients with lung disease (29.5%) and in 819 patients without (17.9%) (p < 0.001). Kaplan-Meier curves showed that patients with lung diseases had a worse 30-day survival (HR = 1.78; 95%C.I. 1.58-2.01; p < 0.001) and COPD had almost 40% mortality. Multivariable Cox regression showed that prior lung disease remained a risk factor for mortality (HR, 1.21; 95%C.I. 1.02-1.44; p = 0.02). Variables independently associated with all-cause mortality risk in patients with lung diseases were oxygen saturation less than 92% on admission (HR, 4.35; 95% CI 3.08-6.15) and elevated D-dimer (HR, 1.84; 95% CI 1.27-2.67). Age younger than 60 years (HR 0.37; 95% CI 0.21-0.65) was associated with decreased risk of death. CONCLUSIONS: Previous lung disease is a risk factor for mortality in patients with COVID-19. Older age, male gender, home oxygen therapy, and respiratory failure on admission were associated with an increased mortality. Efforts must be done to identify respiratory patients to set measures to improve their clinical outcomes.

INTRODUCCIÓN: Los pacientes con enfermedades respiratorias preexistentes pueden tener en el contexto de la covid-19 un mayor riesgo de complicaciones graves e incluso de muerte. MÉTODOS: Estudio de cohortes multicéntrico y retrospectivo de 5.847 pacientes con covid-19 ingresados en hospitales. Los pacientes se separaron en 2 grupos, sin y con enfermedad pulmonar previa. Se evaluaron factores asociados con la supervivencia y criterios combinados de valoración secundarios, como el ingreso en la UCI y la necesidad de asistencia respiratoria. RESULTADOS: Mil doscientos setenta y un (1.271) pacientes (22%) tenían una enfermedad pulmonar previa, principalmente EPOC. La mortalidad por todas las causas ocurrió en 376 pacientes con enfermedad pulmonar (29,5%) y en 819 pacientes sin enfermedad pulmonar (17,9%; p < 0,001). Las curvas de Kaplan-Meier mostraron que los pacientes con enfermedades pulmonares tenían una peor supervivencia a los 30 días (HR: 1,78; IC del 95%: 1,58-2,01; p < 0,001) y la EPOC tenía una mortalidad de casi el 40%. La regresión de Cox multivariante mostró que la enfermedad pulmonar previa seguía siendo un factor de riesgo de mortalidad (HR: 1,21; IC del 95%: 1,02-1,44; p = 0,02). Las variables asociadas de forma independiente con el riesgo de muerte por todas las causas en pacientes con enfermedades pulmonares fueron la saturación de oxígeno inferior al 92% al ingreso (HR: 4,35; IC del 95%: 3,08-6,15) y el dímero D elevado (HR: 1,84; IC del 95%: 1,27-2,67). La edad menor de 60 años (HR: 0,37; IC del 95%: 0,21-0,65) se asoció con una disminución del riesgo de muerte. CONCLUSIONES: La enfermedad pulmonar previa es un factor de riesgo de muerte en pacientes con covid-19. La edad avanzada, el sexo masculino, la oxigenoterapia domiciliaria y la insuficiencia respiratoria al ingreso se asociaron con un aumento de la mortalidad. Se deben realizar esfuerzos para identificar a los pacientes respiratorios y establecer medidas para mejorar sus resultados clínicos.

COVID-19 pneumonia accurately detected on chest radiographs with artificial intelligence.

PURPOSE: To investigate the diagnostic performance of an Artificial Intelligence (AI) system for detection of COVID-19 in chest radiographs (CXR), and compare results to those of physicians working alone, or with AI support. MATERIALS AND METHODS: An AI system was fine-tuned to discriminate confirmed COVID-19 pneumonia, from other viral and bacterial pneumonia and non-pneumonia patients and used to review 302 CXR images from adult patients retrospectively sourced from nine different databases. Fifty-four physicians blind to diagnosis, were invited to interpret images under identical conditions in a test set, and randomly assigned either to receive or not receive support from the AI system. Comparisons were then made between diagnostic performance of physicians working with and without AI support. AI system performance was evaluated using the area under the receiver operating characteristic (AUROC), and sensitivity and specificity of physician performance compared to that of the AI system. RESULTS: Discrimination by the AI system of COVID-19 pneumonia showed an AUROC curve of 0.96 in the validation and 0.83 in the external test set, respectively. The AI system outperformed physicians in the AUROC overall (70% increase in sensitivity and 1% increase in specificity, p < 0.0001). When working with AI support, physicians increased their diagnostic sensitivity from 47% to 61% (p < 0.001), although specificity decreased from 79% to 75% (p = 0.007). CONCLUSIONS: Our results suggest interpreting chest radiographs (CXR) supported by AI, increases physician diagnostic sensitivity for COVID-19 detection. This approach involving a human-machine partnership may help expedite triaging efforts and improve resource allocation in the current crisis.