'sars-cov2' in healthcare technology

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Corona SEIR Workbench

From www.codeproject.com - June 15, 2021 10:44 PM

Pandemic SEIR and SEIRV modelling software and infrastructure for the Corona SARS-COV-2 COVID-19 disease with data from Johns-Hopkins-University CSSE, Robert Koch-Institute and vaccination data from Our World In Data.

The SARS-COV-2 pandemic has been affecting our lives for months. The effectiveness of measures against the pandemic can be tested and predicted by using epidemiological models. The Corona SEIR Workbench uses a SEIR model and combines a graphical output of the results with a simple parameter input for the model. Modelled data can be compared country by country with the SARS-COV-2 infection data of the Johns Hopkins University. Additionally, the R₀ values of the Robert Koch Institute can be displayed for Germany. Vaccination data is used from Our World In Data.

view on Github : https://github.com/logiclink/corona_seir_workbench

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Which mRNA vaccines are researchers working on that aren't for COVID-19 ?

From theconversation.com - April 24, 2021 3:01 AM

mRNA is a type of genetic material that tells your body how to make proteins. The two mRNA vaccines for SARS-CoV-2, the coronavirus that causes COVID-19, deliver fragments of this mRNA into your cells.

The world’s first mRNA vaccines — the COVID-19 vaccines from Pfizer/BioNTech and Moderna — have made it in record time from the laboratory, through successful clinical trials, regulatory approval and into people’s arms.

The high efficiency of protection against severe disease, the safety seen in clinical trials and the speed with which the vaccines were designed are set to transform how we develop vaccines in the future.

We have two mRNA COVID-19 vaccines so far. But what else can this technology do?

Once researchers have set up the mRNA manufacturing technology, they can potentially produce mRNA against any target. Manufacturing mRNA vaccines also does not need living cells, making them easier to produce than some other vaccines.

So mRNA vaccines could potentially be used to prevent a range of diseases, not just COVID-19.

Flu vaccine

Moderna is already turning its attention to an mRNA vaccine against seasonal influenza. This would target the four seasonal strains of the virus the WHO predicts will be circulating.

But the holy grail is a universal flu vaccine. This would protect against all strains of the virus (not just what the WHO predicts) and so wouldn’t need to be updated each year.

The same researchers who pioneered mRNA vaccines are also working on a universal flu vaccine.

Malaria vaccine

There is no vaccine for Malaria. However, US researchers working with pharmaceutical company GSK have filed a patent for an mRNA vaccine against malaria.

The mRNA in the vaccine codes for a parasite protein called PMIF. By teaching our bodies to target this protein, the aim is to train the immune system to eradicate the parasite.

This malaria mRNA vaccine is an example of a self-amplifying mRNA vaccine. This means very small amounts of mRNA need to be made, packaged and delivered, as the mRNA will make more copies of itself once inside our cells. This is the next generation of mRNA vaccines after the “standard” mRNA vaccines seen so far against COVID-19.

Cancer vaccines

We already have vaccines that prevent infection with viruses that cause cancer. For example, hepatitis B vaccine prevents some types of liver cancer and the human papillomavirus (HPV) vaccine prevents cervical cancer.

But the flexibility of mRNA vaccines lets us think more broadly about tackling cancers not caused by viruses.

Some types of tumours have antigens or proteins not found in normal cells. If we could train our immune systems to identify these tumour-associated antigens then our immune cells could kill the cancer.

Cancer vaccines can be targeted to specific combinations of these antigens. BioNTech is developing one such mRNA vaccine that shows promise for people with advanced melanoma. CureVac has developed one for a specific type of lung cancer, with results from early clinical trials.

Then there’s the promise of personalised anti-cancer mRNA vaccines. If we could design an individualised vaccine specific to each patient’s tumour then we could train their immune system to fight their own individual cancer. Several research groups and companies are working on this.

read the unedited original article at https://theconversation.com/3-mrna-vaccines-researchers-are-working-on-that-arent-covid-157858

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Estimating COVID Severity Based on Mutations in the SARS-CoV-2 Genome

From www.biorxiv.org - May 12, 2021 4:09 AM

Numerous studies demonstrate frequent mutations in the genome of SARS-CoV-2. Our goal was to statistically link mutations to severe disease outcome.

We found that automated machine learning, such as the method of Tsamardinos and coworkers used here, is a versatile and effective tool to find salient features in large and noisy databases, such as the fast growing collection of SARS-CoV-2 genomes.

In this work we used machine learning techniques to select mutation signatures associated with severe SARS-CoV-2 infections. We grouped patients into 2 major categories (“mild” and “severe”) by grouping the 179 outcome designations in the GISAID database.

A protocol combined of logistic regression and feature selection algorithms revealed that mutation signatures of about twenty mutations can be used to separate the two groups. The mutation signature is in good agreement with the variants well known from previous genome sequencing studies, including Spike protein variants V1176F and S477N that co-occur with DG14G mutations and account for a large proportion of fast spreading SARS-CoV-2 variants. UTR mutations were also selected as part of the best mutation signatures. The mutations identified here are also part of previous, statistically derived mutation profiles.

An online prediction platform was set up that can assign a probabilistic measure of infection severity to SARS-CoV-2 sequences, including a qualitative index of the strength of the diagnosis. The data confirm that machine learning methods can be conveniently used to select genomic mutations associated with disease severity, but one has to be cautious that such statistical associations – like common sequence signatures, or marker fingerprints in general – are by no means causal relations, unless confirmed by experiments.

Our plans are to update the predictions server in regular time intervals. While this project was underway more than 100 thousand sequences were deposited in public databases, and importantly, new variants emerged in the UK and in South Africa that are not yet included in the current datasets. Also, in addition to mutations, we plan to include also insertions and deletions which will hopefully further improve the predictive power of the server.

The study was funded by the Hungarian Ministry for Innovation and Technology (MIT) , within the framework of the Bionic thematic programme of the Semmelweis University.

Read the entire study at https://www.biorxiv.org/content/10.1101/2021.04.01.438063v1.full

Access the online portal mentioned above at https://covidoutcome.com/

nrip's insight:

I love studies like this. Each one builds upon the value provided by the previous one. AI in Healthcare keeps getting better. and that opens up the door for Healthcare to become more accurate, and eventually faster.

Key takeaways -

Artificial intelligence is an effective tool for uncovering hidden associations in large medical datasets.

The mutation signature of the virus be used as an indicator of the severity of the disease

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Analysis reveals SARS-CoV-2 infection causes deregulation of lung cell metabolism

From journals.plos.org - April 14, 2021 9:12 AM

A model has been developed by researchers at Indian Institute of Technology ,Kharagpur predicting alteration in metabolic reaction rates of lung cells post SARS-CoV-2 infection.

"We have used the gene expression of normal human bronchial cells infected with SARS-CoV-2 along with the macromolecular make-up of the virus to create this integrated genome-scale metabolic model. The growth rate predicted by the model showed a very high agreement with experimentally and clinically reported effects of SARS-CoV-2," said Dr Amit Ghosh, Assistant Professor, School of Energy Science and Engineering, IIT Kharagpur who coauthored the paper

The research would lead to a better understanding of metabolic reprogramming and aid the development of better therapeutics to deal with viral pandemics,

Summary:

Metabolic flux analysis in disease biology is opening up new avenues for therapeutic interventions. Numerous diseases lead to disturbance in the metabolic homeostasis and it is becoming increasingly important to be able to quantify the difference in interaction under normal and diseased condition.

While genome-scale metabolic models have been used to study those differences, there are limited methods to probe into the differences in flux between these two conditions. Our method of conducting a differential flux analysis can be leveraged to find which reactions are altered between the diseased and normal state.

We applied this to study the altered reactions in the case of SARS-CoV-2 infection. We further corroborated our results with other multi-omics studies and found significant agreement.

read the paper at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008860

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