Monoclonal antibodies against SARS-CoV-2: potential game-changer still underused

Even several months after the start of a massive vaccination campaign against COVID-19, mortality and hospital admission are still in considerable numbers in many nations. Monoclonal antibodies are the ideal complement to vaccination in high-risk subjects who have been infected by SARS-CoV-2 and are at high risk of developing severe disease. Combining data provided by clinal trials and demographics of SARS-CoV-2 infections, this analysis tries to predict the benefits of an extensive use of monoclonal antibodies to reduce hospital admissions, deaths, and costs.

Pleiotropic effect of Lactoferrin in the prevention and treatment of COVID-19 infection: in vivo, in silico and in vitro preliminary evidences

Lactoferrin, a multifunctional cationic glycoprotein, secreted by exocrine glands and neutrophils, possesses an antiviral activity extendable to SARS-CoV-2. We performed in vitro assays proving lactoferrin antiviral activity through direct attachment to both virus and cell surface components. This activity varied according to concentration (100/500g/ml), multiplicity of infection (0.1/0.01) and cell type (Vero E6/Caco-2 cells). Interestingly, the in silico results strongly supported the hypothesis of a direct recognition between the lactoferrin and the Spike S glycoprotein, thus hindering the viral entry into the cells. Hence, we conducted a clinical trial to investigate effect and tolerability of a liposomal lactoferrin formulation as a supplementary nutraceutical agent in mild-to-moderate and asymptomatic COVID-19 patients. A total of 92 mild-to-moderate (67/92) and asymptomatic (25/92) COVID-19 patients were recruited and divided in 3 groups according to the administered regimen. Thirty-two patients, 14 hospitalised and 18 in home-based insolation received oral and intranasal liposomal bovine lactoferrin (bLf), 32 hospitalised patients were treated with standard of care treatment (hydroxychloroquine, azitromicin and lopinavir/darunavir), and 28, in home-based isolation, did not take any medication. Furthermore, 32 COVID-19 negative, not-treated, healthy subjects were added as a control group for ancillary analysis. bLf-supplemented COVID-19 patients obtained an earlier and significant (p < 0,0001.) median rRT-PCR SARS-COV-2 RNA negative conversion than standard of care-treated and non-treated COVID-19 patients (14.25 vs 27.13 vs 32.61 days, respectively). In addition, bLf-supplemented COVID-19 patients showed significant fast clinical symptoms recovery than standard of care-treated and non-treated COVID-19 patients. Moreover, in bLf-supplemented patients, a significant decrease of either serum ferritin or IL-6 levels or host iron overload, all parameters characterizing inflammatory processes, were observed. Serum D-dimers was also found significantly decreased following bLf supplement. No adverse events were reported. These in vitro and in vivo observations led us to speculate a potential and safe supplementary role of Blf in the management of mild-to-moderate and asymptomatic COVID-19 patients.

The Role of Air Conditioning in the Diffusion of Sars-CoV-2 in Indoor Environments: a First Computational Fluid Dynamic Model, based on Investigations performed at the Vatican State Childrens Hospital.

BackgroundAbout 15 million people worldwide were affected by the Sars-Cov-2 infection, which already caused 600,000 deaths. This virus is mainly transmitted through exhalations from the airways of infected persons, so that Heating, Ventilation and Air Conditioning (HVAC) systems might play a role in increasing or reducing the spreading of the infection in indoor environments. MethodsWe modelled the role of HVAC systems in the diffusion of the contagion through Computational Fluid Dynamics (CFD) simulations of cough at the "Bambino Gesu" Vatican State Childrens Hospital. Both waiting and hospital rooms were modeled as indoor scenarios. A specific Infection-Index ({eta}) parameter was used to estimate the amount of contaminated air inhaled by each person present in the simulated indoor scenarios. The potential role of exhaust air ventilation systems placed above the coughing patients mouth was also assessed. ResultsOur CFD-based simulations of the waiting room show that HVAC air-flow remarkably enhances infected droplets diffusion in the whole indoor environment within 25 seconds from the cough event, despite the observed dilution of saliva particles containing the virus. At the same time also their number is reduced due to removal through the HVAC system or deposition on the surfaces. The proper use of Local Exhaust Ventilation systems (LEV) simulated in the hospital room was associated to a complete reduction of infected droplets spreading from the patients mouth in the first 0.5 seconds following the cough event. In the hospital room, the use of LEV system completely reduced the {eta} index computed for the patient hospitalized at the bed next to the spreader, with a decreased possibility of contagion. ConclusionsCFD-based simulations for indoor environment can be useful to optimize air conditioning flow and to predict the contagion risk both in hospitals/ambulatories and in other public/private settings.

SARS-Cov-2 RNA Found on Particulate Matter of Bergamo in Northern Italy: First Preliminary Evidence

In previous communications, we have hypothesized the possibility that SARS-CoV-2 virus could be present on particulate matter (PM) during the spreading of the infection, consistently with evidence already available for other viruses. Here, we present the first results of the analyses that we have performed on 34 PM10 samples of outdoor/airborne PM10 from an industrial site of Bergamo Province, collected with two different air samplers over a continuous 3-weeks period, from February 21st to March 13th. We can confirm to have reasonably demonstrated the presence of SARS-CoV-2 viral RNA by detecting highly specific RtDR gene on 8 filters in two parallel PCR analyses. This is the first preliminary evidence that SARS-CoV-2 RNA can be present on outdoor particulate matter, thus suggesting that, in conditions of atmospheric stability and high concentrations of PM, SARS-CoV-2 could create clusters with outdoor PM and, by reducing their diffusion coefficient, enhance the persistence of the virus in the atmosphere. Further confirmations of this preliminary evidence are ongoing, and should include real-time assessment about the vitality of the SARS-CoV-2 as well as its virulence when adsorbed on particulate matter. At the present, no assumptions can be made concerning the correlation between the presence of the virus on PM and COVID-19 outbreak progression. Other issues to be specifically addressed are the average concentrations of PM eventually required for a potential boost effect of the contagion (in case it is confirmed that PM might act as a carrier for the viral droplet nuclei), or even the theoretic possibility of immunization consequent to minimal dose exposures at lower thresholds of PM.

The Potential role of Particulate Matter in the Spreading of COVID-19 in Northern Italy: First Evidence-based Research Hypotheses

BackgroundAn epidemic model based only on respiratory droplets and close contact could not fully explain the regional differences in the spread of the recent severe acute respiratory syndrome COVID-19 in Italy, which was fast and dramatic only in Lombardy and Po Valley. On March 16th 2020, we presented a Position Paper proposing a research hypothesis concerning the association between higher mortality rates due to COVID-19 observed in Northern Italy and the peaks of particulate matter concentrations, frequently exceeding the legal limit of 50 {micro}g/m3 as PM10 daily average MethodsTo assess environmental factors related to the spread of the COVID-19 in Italy from February 24th to March 13th (the date when the lockdown has been imposed over Italy), official daily data relevant to ambient PM10 levels were collected from all Italian Provinces between February 9th and February 29th, taking into account the average time (estimated in 17 days) elapsed between the initial infection and the recorded COVID positivity. In addition to the number of exceedances of PM10 daily limit value, we considered also population data and daily travelling information per each Province. ResultsPM10 daily limit value exceedances appear to be a significant predictor (p < .001) of infection in univariate analyses. Less polluted Provinces had a median of 0.03 infection cases over 1000 residents, while most polluted Provinces had a median of 0.26 cases over 1000 residents. Thirty-nine out of 41 Northern Italian Provinces resulted in the category with highest PM10 levels, while 62 out of 66 Southern Provinces presented low PM10 concentrations (p< 0.001). In Milan, the average growth rate before the lockdown was significantly higher than Rome (0.34 vs. 0.27 per day, with a doubling time of 2.0 days vs. 2.6), suggesting a basic reproductive number R0>6.0, comparable with the highest values estimated for China.

Forecasting Covid-19 Outbreak Progression in Italian Regions: A model based on neural network training from Chinese data

BackgroundEpidemiological figures of Covid-19 epidemic in Italy are worse than those observed in China. MethodsWe modeled the Covid-19 outbreak in Italian Regions vs. Lombardy to assess the epidemics progression and predict peaks of new daily infections and total cases by learning from the entire Chinese epidemiological dynamics. We trained an artificial neural network model, a modified auto-encoder with Covid-19 Chinese data, to forecast epidemic curve of the different Italian regions, and use the susceptible-exposed-infected-removed (SEIR) compartment model to predict the spreading and peaks. We have estimated the basic reproduction number (R0) - which represents the average number of people that can be infected by a person who has already acquired the infection - both by fitting the exponential growth rate of the infection across a 1-month period, and also by using a day by day assessment, based on single observations. ResultsThe expected peak of SEIR model for new daily cases was at the end of March at national level. The peak of overall positive cases is expected by April 11th in Southern Italian Regions, a couple of days after that of Lombardy and Northern regions. According to our model, total confirmed cases in all Italy regions could reach 160,000 cases by April 30th and stabilize at a plateau. ConclusionsTraining neural networks on Chinese data and use the knowledge to forecast Italian spreading of Covid-19 has resulted in a good fit, measured with the mean average precision between official Italian data and the forecast.

Covid-19 Outbreak Progression in Italian Regions: Approaching the Peak by March 29th

BackgroundItaly and especially the Lombardy region is experiencing a heavy burden of Covid-19 infection. The peak of the epidemics has not yet been reached and it is expected to be delayed in Central and Southern Italian regions compared to Northern ones. We have modeled the Covid-19 outbreak progression in Italian Regions vs. Lombardy. MethodsIn our models, we have estimated the basic reproduction number (R0) -which represents the average number of people that can be infected by a person who has already acquired the infection - both by fitting the exponential growth rate of the infection across a 1-month period and also by using day by day assessment, based on single observations. We used the susceptible-exposed-infected-removed (SEIR) compartment model to predict the spreading of the pandemic in Italy. ResultsThe two methods provide agreements of values, although the first method based on exponential fit should provide a better estimation, being computed on the entire time series. Taking into account the growth rate of the infection across a 1-month period, in Lombardy each infected person has involved other 5 people (4.94 base on data of March 22nd vs. 5.07 based on data of March 19th) compared to a value of R0 = 2.68 reported in the Chinese city of Whuan. According to our model and Piedmont, Veneto, Emilia Romagna, Tuscany and Marche reach an R0 value up to 4. The R0 is 3.7 for Lazio and 3.6 for Campania region, where this latter shows the highest value among the Southern Italian regions, followed by Apulia (3.5), Sicily (3.4), Abruzzo (3.4), Calabria (3.1), Basilicata (2.5) and Molise (2.4). The value of R0 is decreasing in Lombardy and Northern Regions, while it is increasing in Central and Southern Regions. ConclusionThe expected peak of SEIR model can be forecast by the last week of March at national level, and by the first weeks of April in Southern Italian Regions. These kind of models can be useful for adoption of all the possible preventive measures, and to assess the epidemics progression across Southern regions as opposed to the Northern ones.