Elevator Cabin Decontamination With ACTIVE Particle Control™ Technology

Effectively reducing contamination and aerosolized bioburden may limit the risk of disease transmission in closed settings when social distancing is not possible. Unlike uncontrolled ionization and oxidation devices ACTIVE Particle Control™ conditions particles in a highly controlled fashion which provides effective air purification without the generation of ozone or other toxic by-products. The purpose of this study was to determine the impact of ACTIVE Particle Control™ on elevator cabin particle load compared to standard ventilation. The intervention trial utilized particle mass tools to determine the difference in particle clearance between standard elevator cabin ventilation and ACTIVE Particle Control™ technology. Cabin particulate contaminants were significantly reduced using ACTIVE Particle Control™ technology in an operating elevator.

Meta Analysis: Relationship of Tuberculosis Patient Contact, Density And Ventilation Area With Tuberculosis Events

Tuberculosis is one of 10 causes of death in the world. In 2018 TB sufferers in Indonesia reached 840 thousand people, the third-highest figure in the world after India and China. The purpose of this study was to analyze the relationship between contact with tuberculosis patients, occupancy density and ventilation area with tuberculosis’ incidence. This study used meta-analysis, the articles’ sources were from Google Scholar, PubMed and DOAJ published from 2011-2020. There were 12 articles that met the conditions for contact-free variables with tuberculosis patients, 12 articles of occupancy density, and 10 articles of ventilation area variable. The results were contacting with tuberculosis patients had 5.93 times more of getting tuberculosis compared to people who had no contact with tuberculosis patients, people who lived in densely populated areas were 2.41 times more getting tuberculosis compared to people living in occupancy that is not crowded, people who live in dwellings with a non-standard ventilation area were 2.14 times more getting tuberculosis when compared to people who live in an area where the ventilation area meets the standard. The conclusion of this study is tuberculosis patient contact, occupancy density, and ventilation area with the incidence of tuberculosis have a significant relationship.

A novel pre-clinical strategy to deliver antimicrobial doses of inhaled nitric oxide

Effective treatment of respiratory infections continues to be a major challenge. In high doses (≥160 ppm), inhaled Nitric Oxide (iNO) has been shown to act as a broad-spectrum antimicrobial agent, including its efficacy in vitro for coronavirus family. However, the safety of prolonged in vivo implementation of high-dose iNO therapy has not been studied. Herein we aim to explore the feasibility and safety of delivering continuous high-dose iNO over an extended period of time using an in vivo animal model. Yorkshire pigs were randomized to one of the following two groups: group 1, standard ventilation; and group 2, standard ventilation + continuous iNO 160 ppm + methylene blue (MB) as intravenous bolus, whenever required, to maintain metHb <6%. Both groups were ventilated continuously for 6 hours, then the animals were weaned from sedation, mechanical ventilation and followed for 3 days. During treatment, and on the third post-operative day, physiologic assessments were performed to monitor lung function and other significative markers were assessed for potential pulmonary or systemic injury. No significant change in lung function, or inflammatory markers were observed during the study period. Both gas exchange function, lung tissue cytokine analysis and histology were similar between treated and control animals. During treatment, levels of metHb were maintained <6% by administration of MB, and NO2 remained <5 ppm. Additionally, considering extrapulmonary effects, no significant changes were observed in biochemistry markers. Our findings showed that high-dose iNO delivered continuously over 6 hours with adjuvant MB is clinically feasible and safe. These findings support the development of investigations of continuous high-dose iNO treatment of respiratory tract infections, including SARS-CoV-2.

Effects of airway management and tidal volume feedback ventilation during pediatric resuscitation in piglets with asphyxial cardiac arrest

To compare the effect on the recovery of spontaneous circulation (ROSC) of early endotracheal intubation (ETI) versus bag-mask ventilation (BMV), and expiratory real-time tidal volume (VTe) feedback (TVF) ventilation versus without feedback or standard ventilation (SV) in a pediatric animal model of asphyxial cardiac arrest. Piglets were randomized into five groups: 1: ETI and TVF ventilation (10 ml/kg); 2: ETI and TVF (7 ml/kg); 3: ETI and SV; 4: BMV and TVF (10 ml/kg) and 5: BMV and SV. Thirty breaths-per-minute guided by metronome were given. ROSC, pCO2, pO2, EtCO2 and VTe were compared among groups. Seventy-nine piglets (11.3 ± 1.2 kg) were included. Twenty-six (32.9%) achieved ROSC. Survival was non-significantly higher in ETI (40.4%) than BMV groups (21.9%), p = 0.08. No differences in ROSC were found between TVF and SV groups (30.0% versus 34.7%, p = 0.67). ETI groups presented lower pCO2, and higher pO2, EtCO2 and VTe than BMV groups (p < 0.05). VTe was lower in TVF than in SV groups and in BMV than in ETI groups (p < 0.05). Groups 1 and 3 showed higher pO2 and lower pCO2 over time, although with hyperventilation values (pCO2 < 35 mmHg). ETI groups had non significantly higher survival rate than BMV groups. Compared to BMV groups, ETI groups achieved better oxygenation and ventilation parameters. VTe was lower in both TVF and BMV groups. Hyperventilation was observed in intubated animals with SV and with 10 ml/kg VTF.

Air Germs Prediction Factors Analysis for Elementary School In Banyumas Regency 2020

Background Schools and formal education can be a bridge for airborne disease to spread caused by air germs. Measurement of air germs result, shows that class 4 (9482 CFU/m3) and class 5(2371 CFU/m3) in SDN 5 Teluk, Purwokerto Selatan district. The average air gems rate is 1685.33 CFU/m3 in SDN Karangmangu, Baturaden district. The aims of this study was to analyze predictive factors for air germs number in public elementary schools in Banyumas Regency. Methods This research is observational study with cross sectional analytic approach. The independent variables or predictive variables are temperature, humidity, lighting, occupancy density, occupant behavior, cleaning frequency, and ventilation area. The dependent variable is the number of air germs. The sample size was 46 classrooms. The analysis used simple and multiple regression. Research Resulth average temperature (29.9130C), humidity (74.087%), lighting (225.304 lux), occupancy density (2.050 m2 / person), cleaning frequency (2.5 times / day), occupant behavior (53.470% active), ventilation area (9,171%), air germ rate (3425,130 CFU / m3), wind speed (not detected by tools). Prediction of temperature with the number of air germs, Y = 1026.505 + 80.187 X, R = 0.169, p = 0.262. Prediction of humidity with the number of air germs, Y = 2719.038 + 9.531 X, R = 0.083, p = 0.585. Prediction of exposure with air germ count, Y = 3343.684 + 0.361 X, R = 0.059, p = 0.696. Prediction of occupancy density with air germ numbers, Y = 3959.041 + (-260.389) X, R = - 0.386, p = 0.008. Prediction of cleaning frequency with air germ count, Y = 3204.664 + 88.187 X, R = 0.150, p = 0.320. Prediction of occupant behavior with air germ count, Y = 3632.488 + (-3.878) X, R = - 0.160, p = 0.289. Prediction of ventilation area with air germ count, Y = 3965.421 + (-58.911) X, R = -0.427, p = 0.003. Simultaneously predict temperature, humidity, lighting, occupancy density, cleaning frequency, occupant behavior and ventilation area with air germ count, Y = (-1267.495) + (-194.907) (density p = 0.049) + (-42.019) ( Ventilation p = 0.061) + 148.449 (Temperature p = 0.072) + 90.826 (Cleaning p = 0.379) + 12.187 (Humidity p = 0.543) + (-2.205) (Behavior p = 0.561) + 0.111 (Exposure p = 0.913), R = 0.5850. Conclusion , predictive factors for occupancy density, ventilation and temperature are significant in predicting the number of airborne germs. Suggestions need to regulate the number of students in each class, the availability standard ventilation, and the addition of an Exhauster.

The Build-Up of Aerosols Carrying the SARS-CoV-2 Coronavirus, in Poorly Ventilated, Confined Spaces

A model of the distribution of respiratory droplets and aerosols by Lagrangian turbulent air-flow is developed and used to show how the SARS-CoV-2 Coronavirus can be dispersed by the breathing of an infected person. It is shown that the concentration of viruses in the exhaled cloud can increase to infectious levels with time (grow linearly), in a confined space where the air re-circulates. The model is used to analyze the air-flow and SARS-CoV-2 Coronavirus build-up in a restaurant in Guangzhou, China [32, 30]. It is concluded that the outbreak of Covid-19 pandemic in the restaurant in January 2020, is due to the build-up of the airborne droplets and aerosols carrying the SARS-CoV-2 Coronavirus and would not have been prevented by standard ventilation. A comparison with standard models for aerosol concentration shows that, in the absence of ventilation, the decay of the aerosol concentration is also controlled by the decay time of the virions in aerosols. This decay time is very long, with low relative humidity, and a steady state is not achieved in the time-frame of the contagion. Instead the concentration exhibits a polynomial increase and reaches infectious levels in a relatively short time, explaining the outbreak in the restaurant in Guangzhou.

Functional Pathophysiology of SARS-CoV-2 Induced Acute Lung Injury and Clinical Implications

The worldwide pandemic caused by the SARS-CoV-2 virus has resulted in over 84,407,000 cases with over 1,800,000 deaths when this paper was submitted, with comorbidities such as gender, race, age, body mass, diabetes, and hypertension greatly exacerbating mortality. This review will analyze the rapidly increasing knowledge of COVID-19 induced lung pathophysiology. Although controversial, the acute respiratory distress syndrome (ARDS) associated with COVID-19 (CARDS) seems to present as two distinct phenotypes: Type-L and Type-H. The 'L' refers to Low elastance, ventilation/perfusion ratio, lung weight, and recruitability, and the 'H' refers to High pulmonary elastance, shunt, edema, and recruitability. However, the LUNG SAFE and ESICM Trials Groups has shown that ~13% of the mechanically ventilated non-COVID-19 ARDS patients have the Type-L phenotype. However, other studies have shown that CARDS and ARDS respiratory mechanics overlap and that standard ventilation strategies apply to these patients. The mechanisms causing alterations in pulmonary perfusion could be caused by some combination of: 1) renin-angiotensin system (RAS) dysregulation, 2) thrombosis caused by loss of endothelial barrier, 3) endothelial dysfunction causing loss of hypoxic pulmonary vasoconstriction (HPV) perfusion control, and 4) hyper-perfusion of collapsed lung tissue that has been directly measured and supported by a computational model. A flow chart has been constructed highlighting the need for personalized and adaptive ventilation strategies, such as the time controlled adaptive ventilation (TCAV) method to set and adjust the airway pressure release ventilation (APRV) mode, which recently was shown effective at improving oxygenation and reducing FiO2, vasopressors, and sedation in COVID-19 patients.

The Build-Up of Aerosols Carrying the SARS-CoV-2 Coronavirus, in Poorly Ventilated, Confined Spaces

A model of the distribution of respiratory droplets and aerosols by Lagrangian turbulent air-flow is developed and used to show how the SARS-CoV-2 Coronavirus can be dispersed by the breathing of an infected person. It is shown that the concentration of viruses in the exhaled cloud can increase to infectious levels with time (grow linearly), in a confined space where the air re-circulates. The model is used to analyze the air-flow and SARS-CoV-2 Coronavirus build-up in a restaurant in Guangzhou, China [23, 21]. It is concluded that the outbreak of Covid-19 pandemic in the restaurant in January 2020, is due to the build-up of the airborne droplets and aerosols carrying the SARS-CoV-2 Coronavirus and would not have been pre- vented by standard ventilation. A comparison with standard models for aerosol concentration shows that, in the absence of ventilation, the decay of the aerosol concentration is also con- trolled by the decay time of the virions in aerosols. This decay time is very long and a steady state is not achieved in the time-frame of the contagion. Instead the concentration exhibits a polynomial increase and reaches infectious levels in a relatively short time, explaining the outbreak in the restaurant in Guangzhou.

Quantifying proximity, confinement, and interventions in disease outbreaks: a decision support framework for air-transported pathogens

The inability to communicate how infectious diseases are transmitted in human environments has triggered avoidance of interactions during the COVID-19 pandemic. We define a metric, Effective ReBreathed Volume (ERBV), that encapsulates how infectious pathogens transport in air. This measure distinguishes environmental transport from other factors in the chain of infection, thus allowing quantitative comparisons of the riskiness of different situations for any pathogens transported in air, including SARS-CoV-2. Particle size is a key factor in transport, removal onto surfaces, and elimination by mitigation measures, so ERBV is presented for a range of exhaled particle diameters: 1 μm, 10 μm, and 100 μm. Pathogen transport is enhanced by two separate but interacting effects: proximity and confinement. Confinement in enclosed spaces overwhelms proximity after 10-15 minutes for all but the largest particles. Therefore, we review plausible strategies to reduce the confinement effect. Changes in standard ventilation and filtration can reduce person-to-person transport of 1-μm particles (ERBV1) by 13-85% in residential and commercial situations. Deposition to surfaces competes with intentional removal for 10-μm and 100-μm particles, so the same interventions reduce ERBV10 by only 3-50%, and ERBV100 is unaffected. Determining transmission modes is critical to identify intervention effectiveness, and would be accelerated with prior knowledge of ERBV. When judiciously selected, the interventions examined can provide substantial reduction in risk, and the conditions for selection are identified. The framework of size-dependent ERBV supports analysis and mitigation decisions in an emerging situation, even before other infectious parameters are well known.

The Build-Up of Aerosols Carrying the SARS-CoV-2 Coronavirus, in Poorly Ventilated, Confined Spaces

A model of the distribution of respiratory droplets and aerosols by Lagrangian turbulent air-flow is developed and used to show how the SARS-CoV-2 Coronavirus can be dispersed by the breathing of an infected person. It is shown that the concentration of viruses in the exhaled cloud can increase to infectious levels with time (grow linearly), in a confined space where the air re-circulates. The model is used to analyze the air-flow and SARS-CoV-2 Coronavirus build-up in a restaurant in Guangzhou, China [30, 28]. It is concluded that the outbreak of Covid-19 pandemic in the restaurant in January 2020, is due to the build-up of the airborne droplets and aerosols carrying the SARS-CoV-2 Coronavirus and would not have been prevented by standard ventilation. A comparison with standard models for aerosol concentration shows that, in the absence of ventilation, the decay of the aerosol concentration is also controlled by the decay time of the virions in aerosols. This decay time is very long and a steady state is not achieved in the time-frame of the contagion. Instead the concentration exhibits a polynomial increase and reaches infectious levels in a relatively short time, explaining the outbreak in the restaurant in Guangzhou.