Correlating physicochemical and biological properties to define critical quality attributes of a recombinant AAV vaccine candidate

Recombinant adeno-associated viruses (rAAVs) are a preferred vector system in clinical gene transfer. A fundamental challenge to formulate and deliver rAAVs as stable and efficacious vaccines is to elucidate interrelationships between the vector's physicochemical properties and biological potency. To this end, we evaluated an rAAV-based COVID-19 vaccine candidate which encodes the Spike antigen (AC3) and is produced by a commercially viable process. First, state-of-the-art analytical techniques were employed to determine key structural attributes of AC3 including primary and higher-order structures, particle size, empty/full capsid ratios, aggregates and multi-step thermal degradation pathway analysis. Next, several quantitative potency measures for AC3 were implemented and data were correlated with the physicochemical analyses on thermal-stressed and control samples. Results demonstrate links between decreasing AC3 physical stability profiles, in vitro transduction efficiency in a cell-based assay, and importantly, in vivo immunogenicity in a mouse model. These findings are discussed in the general context of future development of rAAV-based vaccines candidates as well as specifically for the rAAV vaccine application under study.

SARS-CoV2 in public spaces in West London, UK during COVID-19 pandemic.

BACKGROUND: Spread of SARS-CoV2 by aerosol is considered an important mode of transmission over distances >2 m, particularly indoors. OBJECTIVES: We determined whether SARS-CoV2 could be detected in the air of enclosed/semi-enclosed public spaces. METHODS AND ANALYSIS: Between March 2021 and December 2021 during the easing of COVID-19 pandemic restrictions after a period of lockdown, we used total suspended and size-segregated particulate matter (PM) samplers for the detection of SARS-CoV2 in hospitals wards and waiting areas, on public transport, in a university campus and in a primary school in West London. RESULTS: We collected 207 samples, of which 20 (9.7%) were positive for SARS-CoV2 using quantitative PCR. Positive samples were collected from hospital patient waiting areas, from hospital wards treating patients with COVID-19 using stationary samplers and from train carriages in London underground using personal samplers. Mean virus concentrations varied between 429 500 copies/m3 in the hospital emergency waiting area and the more frequent 164 000 copies/m3 found in other areas. There were more frequent positive samples from PM samplers in the PM2.5 fractions compared with PM10 and PM1. Culture on Vero cells of all collected samples gave negative results. CONCLUSION: During a period of partial opening during the COVID-19 pandemic in London, we detected SARS-CoV2 RNA in the air of hospital waiting areas and wards and of London Underground train carriage. More research is needed to determine the transmission potential of SARS-CoV2 detected in the air.

Impact of ionizers on prevention of airborne infection in classroom.

Infectious diseases (e.g., coronavirus disease 2019) dramatically impact human life, economy and social development. Exploring the low-cost and energy-saving approaches is essential in removing infectious virus particles from indoors, such as in classrooms. The application of air purification devices, such as negative ion generators (ionizers), gains popularity because of the favorable removal capacity for particles and the low operation cost. However, small and portable ionizers have potential disadvantages in the removal efficiency owing to the limited horizontal diffusion of negative ions. This study aims to investigate the layout strategy (number and location) of ionizers based on the energy-efficient natural ventilation in the classroom to improve removal efficiency (negative ions to particles) and decrease infection risk. Three infected students were considered in the classroom. The simulations of negative ion and particle concentrations were performed and validated by the experiment. Results showed that as the number of ionizers was 4 and 5, the removal performance was largely improved by combining ionizer with natural ventilation. Compared with the scenario without an ionizer, the scenario with 5 ionizers largely increased the average removal efficiency from around 20% to 85% and decreased the average infection risk by 23%. The setup with 5 ionizers placed upstream of the classroom was determined as the optimal layout strategy, particularly when the location and number of the infected students were unknown. This work can provide a guideline for applying ionizers to public buildings when natural ventilation is used. Electronic Supplementary Material ESM: the Appendix is available in the online version of this article at 10.1007/s12273-022-0959-z.

Atmospheric Boundary Layer Over Ahmedabad, Western Indian Region: Impact of COVID-19 Nationwide Lockdown.

The government of India imposed a nationwide lockdown to tackle the outbreak of COVID-19 in 2020. This period witnessed record low anthropogenic activity, which had severe socio-economic impacts but also had orthogonal effects on the ambient air quality of the atmosphere. This study focuses on the variations in the atmospheric boundary layer (ABL) over a western Indian urban region in the light of COVID-19. Continuous backscatter recorded by a ceilometer, stationed at Ahmedabad, was used in this study to monitor the ABL during the national lockdown (NLD) in 2020 and state restrictions in 2021, and compared with the control year of 2019. In parallel, improvement in air quality during the NLD was observed by the SAFAR air quality station at Ahmedabad, with decreased particulate matter concentrations. The ground-based observations were substantiated by the ERA5 reanalysis dataset. A decline in the ABL height was recorded during the NLD, which showed improvement in 2021 but which was shy of the ABL in 2019. This was correlated with rain events during the observational period, recorded by an automatic weather station.

A parent-school initiative to assess and predict air quality around a heavily trafficked school.

Many primary schools in the UK are situated in close proximity to heavily-trafficked roads, yet long-term air pollution monitoring around such schools to establish factors affecting the variability of exposure is limited. We co-designed a study to monitor particulate matter in different size fractions (PM1, PM2.5, PM10), gaseous pollutants (NO2, O3 and CO) and meteorological parameters (ambient temperature, relative humidity) over a period of one year. The period included phases of national COVID-19 lockdown and its subsequent easing and removal. Statistical analysis was used to assess the diurnal patterns, pollution hotspots and underlying factors driving changes. A pollution episode was observed early in January 2021 when, owing to new year celebration fireworks, the daily average PM2.5 was around three-times the World Health Organisation limit. PM2.5 and NO2 exceeded the threshold limits on 15 and 10 days, respectively, as the lockdown eased and the school reopened, despite the predominant wind direction often being away from the school towards the roads. The peak concentration levels for all pollutants occurred during morning drop-off hours; however, some weekends showed higher or comparable concentrations to those during weekdays. The strong disproportional Pearson correlation between CO and temperature demonstrated the possible contribution of local sources through biomass burning. The impact of lifting restrictions after removing the weather impact showed that the average pollution levels were low in the beginning and increased by up to 22.7 % and 4.2 % for PM2.5 and NO2, respectively, with complete easing of lockdown. The Prophet algorithm was implemented to develop a prediction model using an NO2 dataset that performed moderately (R2, 0.48) for a new monthly dataset. This study was able to build a local air pollution database at a school gate, which enabled an understanding of the air pollution variability across the year and allowed evidence-based mitigation strategies to be devised.

Did unprecedented air pollution levels cause spike in Delhi's COVID cases during second wave?

The onset of the second wave of COVID-19 devastated many countries worldwide. Compared with the first wave, the second wave was more aggressive regarding infections and deaths. Numerous studies were conducted on the association of air pollutants and meteorological parameters during the first wave of COVID-19. However, little is known about their associations during the severe second wave of COVID-19. The present study is based on the air quality in Delhi during the second wave. Pollutant concentrations decreased during the lockdown period compared to pre-lockdown period (PM2.5: 67 µg m-3 (lockdown) versus 81 µg m-3 (pre-lockdown); PM10: 171 µg m-3 versus 235 µg m-3; CO: 0.9 mg m-3 versus 1.1 mg m-3) except ozone which increased during the lockdown period (57 µg m-3 versus 39 µg m-3). The variation in pollutant concentrations revealed that PM2.5, PM10 and CO were higher during the pre-COVID-19 period, followed by the second wave lockdown and the lowest in the first wave lockdown. These variations are corroborated by the spatiotemporal variability of the pollutants mapped using ArcGIS. During the lockdown period, the pollutants and meteorological variables explained 85% and 52% variability in COVID-19 confirmed cases and deaths (determined by General Linear Model). The results suggests that air pollution combined with meteorology acted as a driving force for the phenomenal growth of COVID-19 during the second wave. In addition to developing new drugs and vaccines, governments should focus on prediction models to better understand the effect of air pollution levels on COVID-19 cases. Policy and decision-makers can use the results from this study to implement the necessary guidelines for reducing air pollution. Also, the information presented here can help the public make informed decisions to improve the environment and human health significantly.

Prediction and comparison of psychological health during COVID-19 among Indian population and Rajyoga meditators using machine learning algorithms

Issues of providing mental health support to people with emerging or current mental health disorders are becoming a significant concern throughout the world. One of the biggest effects of digital psychiatry during COVID-19 is its capacity for early identification and forecasting of a person's mental health decline resulting in chronic mental health issues. Therefore, through this study aims at addressing the hological problems by identifying people who are more likely to acquire mental health issues induced by COVID-19 epidemic. To achieve this goal, this study includes 1) Rajyoga practitioners' perceptions of psychological effects, levels of anxiety, stress, and depression are compared to those of the non practitioners 2) Predictions of mental health disorders such as stress, anxiety and depression using machine learning algorithms using the online survey data collected from Rajyoga meditators and general the population. Decision tree, random forest, naive bayeBayespport vector machine and K nearest neighbor algorithms were used for the prediction as they have been shown to be more accurate for predicting psychological disorders. The support vector machine showed the highest accuracy among all other algorithms. The f1 score was also the highest for support vector machine.

SARS-CoV2 and Air Pollution Interactions: Airborne Transmission and COVID-19

A link between outdoor pollution of particulate matter (PM) and the mortality from COVID-19 disease has been reported. The potential interaction of SARS-CoV2 emitted from an infected subject in the form of droplets or as an aerosol with PM2.5 (PM of 2.5 μm or less in aerodynamic diameter) may modulate SARS-CoV2 replication and infectivity. This may represent an important airborne route of transmission, which could lead to pneumonia and a poor outcome from COVID-19. Further studies are needed to assess the potential infectivity and severity of such transmission.

Active Air Monitoring for Understanding the Ventilation and Infection Risks of SARS-CoV-2 Transmission in Public Indoor Spaces

Indoor, airborne, transmission of SARS-CoV-2 is a key infection route. We monitored fourteen different indoor spaces in order to assess the risk of SARS-CoV-2 transmission. PM2.5 and CO2 concentrations were simultaneously monitored in order to understand aerosol exposure and ventilation conditions. Average PM2.5 concentrations were highest in the underground station (261 ±62.8 μgm-3), followed by outpatient and emergency rooms in hospitals located near major arterial roads (38.6 ±20.4 μgm-3), the respiratory wards, medical day units and intensive care units recorded concentrations in the range of 5.9 to 1.1 μgm-3. Mean CO2 levels across all sites did not exceed 1000 ppm, the respiratory ward (788 ±61 ppm) and the pub (bar) (744 ±136 ppm) due to high occupancy. The estimated air change rates implied that there is sufficient ventilation in these spaces to manage increased levels of occupancy. The infection probability in the medical day unit of hospital 3, was 1.6-times and 2.2-times higher than the emergency and outpatient waiting rooms in hospitals 4 and 5, respectively. The temperature and relative humidity recorded at most sites was below 27 °C, and 40% and, in sites with high footfall and limited air exchange, such as the hospital medical day unit, indicate a high risk of airborne SARS-CoV-2 transmission.

Impact of ionizers on prevention of airborne infection in classroom

Infectious diseases (e.g., coronavirus disease 2019) dramatically impact human life, economy and social development. Exploring the low-cost and energy-saving approaches is essential in removing infectious virus particles from indoors, such as in classrooms. The application of air purification devices, such as negative ion generators (ionizers), gains popularity because of the favorable removal capacity for particles and the low operation cost. However, small and portable ionizers have potential disadvantages in the removal efficiency owing to the limited horizontal diffusion of negative ions. This study aims to investigate the layout strategy (number and location) of ionizers based on the energy-efficient natural ventilation in the classroom to improve removal efficiency (negative ions to particles) and decrease infection risk. Three infected students were considered in the classroom. The simulations of negative ion and particle concentrations were performed and validated by the experiment. Results showed that as the number of ionizers was 4 and 5, the removal performance was largely improved by combining ionizer with natural ventilation. Compared with the scenario without an ionizer, the scenario with 5 ionizers largely increased the average removal efficiency from around 20% to 85% and decreased the average infection risk by 23%. The setup with 5 ionizers placed upstream of the classroom was determined as the optimal layout strategy, particularly when the location and number of the infected students were unknown. This work can provide a guideline for applying ionizers to public buildings when natural ventilation is used. Electronic Supplementary Material (ESM) the Appendix is available in the online version of this article at 10.1007/s12273-022-0959-z.

An evaluation of the risk of airborne transmission of COVID-19 on an inter-city train carriage.

Experiments were conducted in an UK inter-city train carriage with the aim of evaluating the risk of infection to the SARS-CoV-2 virus via airborne transmission. The experiments included in-service CO2 measurements and the measurement of salt aerosol concentrations released within the carriage. Computational fluid dynamics simulations of the carriage airflow were also used to visualise the airflow patterns, and the efficacy of the HVAC filter material was tested in a laboratory. Assuming an infectious person is present, the risk of infection for a 1-h train journey was estimated to be 6 times lower than for a full day in a well-ventilated office, or 10-12 times lower than a full day in a poorly ventilated office. While the absolute risk for a typical journey is likely low, in the case where a particularly infectious individual is on-board, there is the potential for a number of secondary infections to occur during a 1-h journey. Every effort should therefore be made to minimize the risk of airborne infection within these carriages. Recommendations are also given for the use of CO2 sensors for the evaluation of the risk of airborne transmission on train carriages.

Efficacy of facemasks in mitigating respiratory exposure to submicron aerosols.

We designed a novel experimental set-up to pseudo-simultaneously measure size-segregated filtration efficiency (ηF), breathing resistance (ηP) and potential usage time (tB) for 11 types of face protective equipment (FPE; four respirators; three medical; and four handmade) in the submicron range. As expected, the highest ηF was exhibited by respirators (97 ± 3%), followed by medical (81 ± 7%) and handmade (47 ± 13%). Similarly, the breathing resistance was highest for respirators, followed by medical and handmade FPE. Combined analysis of efficiency and breathing resistance highlighted trade-offs, i.e. respirators showing the best overall performance across these two indicators, followed by medical and handmade FPE. This hierarchy was also confirmed by quality factor, which is a performance indicator of filters. Detailed assessment of size-segregated aerosols, combined with the scanning electron microscope imaging, revealed material characteristics such as pore density, fiber thickness, filter material and number of layers influence their performance. ηF and ηP showed an inverse exponential decay with time. Using their cross-over point, in combination with acceptable breathability, allowed to estimate tB as 3.2-9.5 h (respirators), 2.6-7.3 h (medical masks) and 4.0-8.8 h (handmade). While relatively longer tB of handmade FPE indicate breathing comfort, they are far less efficient in filtering virus-laden submicron aerosols compared with respirators.

Central Retinal Vein Occlusion Following the First Dose of COVID Vaccine.

The reports of vascular adverse events in the eye following COVID-19 vaccination are infrequent. We report the case of a healthy male who developed central retinal vein occlusion in his left eye three days following administration of the first dose of Covishield vaccine. As the underlying systemic and ocular risk factors were absent and laboratory investigations were normal, vein occlusion appeared to probably result from the vaccine. The patient developed retinal hemorrhages and non-perfusion ischemic areas all over the fundus. The macular edema was reduced with intravitreal triamcinolone acetonide, but the visual gain was not much, which appears to be due to the time lag in his initial presentation to the Ophthalmology Department. A close watch should be kept for ophthalmic adverse events to have an early intervention.

Reliability and factor analysis of Hindi version of IES-R scale: Effect of Rajyoga meditation on perceiving the impact of COVID-19.

The purpose of this study is to present the Hindi translation and validation of the Impact of Event Scale-Revised and to evaluate psychometric qualities of this scale in a sample of regular Rajyoga meditators to examine the psychological impact of Coronavirus on them. The convenience sampling method was used to collect the data from 801 Rajyoga meditators through online survey. Data were analysed using SPSS 26.0. The Hindi version of IES-R demonstrated good internal consistency with the value of alpha coefficient being 0.91 for the scale and ranging between 0.81 to 0.83 for the subscales. The correlations between the subscales varied between 0.55 and 0.66. Principal components analysis using Varimax rotation was run with three-factor solution based on eigen value greater than one. This solution explained 54 percent of the total variance. It generated mainly two factors, an intrusion hyperarousal factor and an avoidance factor and third factor with one item only. Only 4.7 percent of the meditators rated the outbreak's psychological impact as moderate or severe. The mean score of IES-R was 10.01 (with an S.D. of 11.107). Significant positive correlations were found among IES-R scores and the presence of COVID symptoms. Thus, in clinical and research contexts, the scale appears to be a valid measure of post-trauma occurrences. The present study was conducted to generate a validated Hindi version of the IES-R that is easier and more compatible for use in the Indian population.

Source terms for benchmarking models of SARS-CoV-2 transmission via aerosols and droplets.

There is ongoing and rapid advancement in approaches to modelling the fate of exhaled particles in different environments relevant to disease transmission. It is important that models are verified by comparison with each other using a common set of input parameters to ensure that model differences can be interpreted in terms of model physics rather than unspecified differences in model input parameters. In this paper, we define parameters necessary for such benchmarking of models of airborne particles exhaled by humans and transported in the environment during breathing and speaking.

Noise and air pollution during Covid-19 lockdown easing around a school site.

During the Covid-19 pandemic and resulting lockdowns, road traffic volumes reduced significantly leading to reduced pollutant concentrations and noise levels. Noise and the air pollution data during the lockdown period and loosening of restrictions through five phases in 2021 are examined for a school site in the United Kingdom. Hourly and daily average noise level as well as the average over each phase, correlations between noise and air pollutants, variations between pollutants, and underlying reasons explaining the temporal variations are explored. Some strong linear correlations were identified between a number of traffic-sourced air pollutants, especially between the differently sized particulates PM1, PM2.5, and PM10 (0.70 < r <0.98) in all phases and an expected inverse correlation between nitrogen dioxide (NO2) and ground-level ozone (O3) (-0.68 < r < -0.78) as NO2 is a precursor of O3. Noise levels exhibit a weak correlation with the measured air pollutants and moderate correlation with meteorological factors, including wind direction, temperature, and relative humidity. There was a consistent and significant increase in noise levels (p < 0.01) of up to 3 dB with initial easing, and this was maintained through the remaining phases.

Air pollution exposure assessment simulation of babies in a bike trailer and implication for mitigation measures

Young children are a vulnerable population cohort. They receive higher exposure to particulate matter than adults in outdoor roadside environments, necessitating research on an unexplored area of exposure to young children in electric bike trailers. We simulated the exposure profiles of an adult cyclist and young children sitting in a bike-trailer attached to it for multiple air pollutants – particulate matter ≤10µm in aerodynamic diameter (PM10), ≤2.5µm (PM2.5;fine particles), ≤1µm (PM1), BC, and CO2 – during the school run in the morning and afternoon hours. We assessed the differences in their exposure concentrations and analysed the impact of trailer covers and COVID-19 lockdown restrictions via simultaneous measurements under six settings forming three scenarios: (i) bike-trailer versus adult cyclist height;(ii) bike-trailer with and without the cover;and (iii) exposure during the lockdown and eased-lockdown periods. We carried out a total of 82 single runs covering a length of 176 km. These runs were repeated on a 2.1 km long predefined route between an origin (University campus) and destination (a local school) to simulate morning drop-off (08:00-10:00h;local time) and afternoon pick-up (15:00-17:00h) times of school children. Substantial variability was observed in concentrations of measured pollutants within each run (e.g., up to 97% for BC) and between different runs (e.g., ∼93% for PM2.5 during morning versus afternoon) in bike-trailer. Compared with cyclist height, the average bike-trailer concentration of fine and coarse particles was higher by up to 14% and 18%, respectively, during both morning and afternoon runs. The lockdown restrictions when schools were closed led to a reduction in bike-trailer PM2.5 concentrations by up to 91% compared with eased lockdown period when schools re-opened in March 2021. Trailer covers led up to 50% (fine particles) and 24% (BC;a component of PM2.5) reductions in concentrations compared with trailers without cover. Young children carried in bike trailers are exposed to higher air pollution concentrations compared with the cyclist, particularly during peak morning periods at urban pollution hotspots such as traffic lights.

How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future.

The clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid-19) have led the World Health Organization to declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) and machine learning. AI-based model systems could improve pattern recognition of disease spread in populations and predictions of outbreaks in different geographical locations. A variable and a minimal amount of data are available for the signs and symptoms of Covid-19, allowing a composite of maximum likelihood algorithms to be employed to enhance the accuracy of disease diagnosis and to identify potential drugs. AI-based forecasting and predictions are expected to complement traditional approaches by helping public health officials to select better response and preparedness measures against Covid-19 cases. AI-based approaches have helped address the key issues but a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid-19 pandemic.

COVID-19 pandemic: What can we learn for better air quality and human health?

The COVID-19 lockdown resulted in improved air quality in many cities across the world. With the objective of what could be the new learning from the COVID-19 pandemic and subsequent lockdowns for better air quality and human health, a critical synthesis of the available evidence concerning air pollution reduction, the population at risk and natural versus anthropogenic emissions was conducted. Can the new societal norms adopted during pandemics, such as the use of face cover, awareness regarding respiratory hand hygiene, and physical distancing, help in reducing disease burden in the future? The use of masks will be more socially acceptable during the high air pollution episodes in lower and middle-income countries, which could help to reduce air pollution exposure. Although post-pandemic, some air pollution reduction strategies may be affected, such as car-pooling and the use of mass transit systems for commuting to avoid exposure to airborne infections like coronavirus. However, promoting non-motorized modes of transportation such as cycling and walking within cities as currently being enabled in Europe and other countries could overshadow such losses. This demand focus on increasing walkability in a town for all ages and populations, including for a differently-abled community. The study highlighted that for better health and sustainability there. is also a need to promote other measures such as work-from-home, technological infrastructure, the extension of smart cities, and the use of information technology.