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

Analysis reveals SARS-CoV-2 infection causes deregulation of lung cell metabolism | healthcare technology | Scoop.it

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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Tips for the responsible use of AI in Healthcare

Tips for the responsible use of AI in Healthcare | healthcare technology | Scoop.it

Artificial intelligence(AI) is slowly demonstrating its ability to improve healthcare. Typical examples are

  • Predicting health outcomes
  • Improving workflow inefficiencies
  • Assisting in Triaging  

 

However,  questions remain about how to ensure these technologies and tools are developed, implemented and maintained responsibly

 

A NAM report published in JAMA Viewpoint column, “Artificial Intelligence in Health Care: A Report From the National Academy of Medicine.”  recommends that people developing, using, implementing and regulating health care AI do  seven key things.

 

 

Promote population-representative data with accessibility, standardization and quality is imperative.

[to ensure accuracy for all populations]

 

Prioritize ethical, equitable and inclusive medical AI while addressing explicit and implicit bias.

[to understand the potential of the Underlying biases to worsen or address existing inequity ]

 

Contextualize the dialogue of transparency and trust, which means accepting differential needs.

[to clarify the level of transparency needed across a AI developers, implementation teams, users and regulators]

 

Focus in the near term on augmented intelligence rather than AI autonomous agents.

[supporting data synthesis, interpretation and decision-making by clinicians and patients is where opportunities are now]

 

Develop and deploy appropriate training and educational programs.

[Training programs must be multidisciplinary and should engage AI developers, implementers, health care system leadership, frontline clinical teams, ethicists, humanists, patients and caregivers]

 

Leverage frameworks and best practices for learning health care systems, human factors and implementation science.

[Have a robust and mature IT governance strategy in place before Health delivery systems use AI formally]

 

Balance innovation with safety through regulation and legislation to promote trust.

[evaluate deployed clinical AI for effectiveness and safety based on clinical data.]

 

read more at https://www.ama-assn.org/practice-management/digital/7-tips-responsible-use-health-care-ai

 

nrip's insight:

I am very optimistic about AI’s potential use within the medical and clinical business, both in terms of making these disciplines more efficient and effective, as well as in the long term changing their meaning to the rest of the world. My worry though is that its being promoted more as a fad in todays times. By promoting unrealistic expectations based on biased data, we run the risk of creating low levels of trust in the mindset of the users, much like digital health has suffered for years with

GlobalTrustopedia's comment, March 25, 2021 4:51 AM
Nice one tho
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Scientists threaten to boycott Human Brain Project

Scientists threaten to boycott Human Brain Project | healthcare technology | Scoop.it

Researchers say European commission-funded initiative to simulate human brain suffers from 'substantial failures'


The world's largest project to unravel the mysteries of the human brain has been thrown into crisis with more than 100 leading researchers threatening to boycott the effort amid accusations of mismanagement and fears that it is doomed to failure.


More than 80 European and international research institutions signed up to the 10-year project.


But it proved controversial from the start. Many researchers refused to join on the grounds that it was far too premature to attempt a simulation of the entire human brain in a computer. Now some claim the project is taking the wrong approach, wastes money and risks a backlash against neuroscience if it fails to deliver.


In an open letter to the European commission on Monday, more than 130 leaders of scientific groups around the world, including researchers at Oxford, Cambridge, Edinburgh and UCL, warn they will boycott the project and urge others to join them unless major changes are made to the initiative.


The researchers urge EC officials who are now reviewing the plans to take a hard look at the science and management before deciding on whether to renew its funding. They believe the review, which is due to conclude at the end of the summer, will find "substantial failures" in the project's governance, flexibility and openness.


Central to the latest controversy are recent changes which sidelined cognitive scientists who study high-level brain functions, such as thought and behaviour. Without them, the brain simulation will be built from the bottom up, drawing on more fundamental science, such as studies of individual neurons. The brain, the most complex object known, has some 86bn neurons and 100tn connections.



Sir Colin Blakemore, professor of neuroscience at the University of London, who is not one of the signatories to the letter, said: "It's important that the review should be thorough and, if necessary, critical. But it would be unfortunate if this high-profile project were to be abandoned. There's enough flexibility in the plans to allow the project to be refocused and re-energised.


"The most important thing is that the goals should be realistic. If they promise the politicians cures for dementia or miraculous breakthroughs in artificial intelligence, but don't really deliver them, it might have a negative impact on the whole funding of neuroscience in the future – and that would be a disaster.".

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Using digital twins to help with infection control

Using digital twins to help with infection control | healthcare technology | Scoop.it

No current tool can predict the course of disease and help a doctor decide on the most appropriate treatment for an individual COVID-19 patient.

 

Digital twins are software replicas of the dynamic function and failure of engineered products and processes. The medical analog, patient-specific digital twins, could integrate known human physiology and immunology with real-time patient-specific clinical data to produce predictive computer simulations of viral infection and immune response. Such medical digital twins could be a powerful addition to our arsenal of tools to fight future pandemics, combining mechanistic knowledge, observational data, medical histories, and the power of artificial intelligence (AI).

 

Although medical digital twins are much more difficult to develop than those for engineered devices, they have begun to find applications in improving human health.

Examples include the “artificial pancreas” for type 1 diabetes patients

 

Building a personalized digital twin

Data from multiple scales are needed to build computational representations of biological processes and body systems that are affected by viral infection. These submodels are integrated and personalized with clinical data from individual patients. The digital twin can then be used to derive predictions about diagnosis, prognosis, and efficacy and optimization of therapeutic interventions.

 

Digital twins describing infection and treatment require the development, validation, and integration of numerous component submodels in the context of a rapidly developing scientific understanding of biological behaviors and continual generation of new experimental and clinical data.

 

Although individual laboratories may construct submodels, the development of comprehensive digital twins will require laboratories and research groups around the world to integrate and validate submodels independently, with only limited central coordination.

 

Enabling such parallel development requires a flexible simulation architecture that uses a multiscale map of all the relevant components of a patient's response to viral infection, as well as responses to available treatments.

 

read the paper  at https://science.sciencemag.org/content/371/6534/1105.full

 

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Case study: Big data improves cardiology diagnoses by 17%

Case study: Big data improves cardiology diagnoses by 17% | healthcare technology | Scoop.it

Big data analytics technology has been able to find patterns and pinpoint disease states more accurately than even the most highly-trained physicians.


The human brain may be nature’s finest computer, but artificial intelligences fed on big data are making a convincing challenge for the crown. In the realm of healthcare, natural language processing, associative intelligence, and machine learning are revolutionizing the way physicians make decisions and diagnose complex patients, significantly improving accuracy and catching deadly issues before symptoms even present themselves.


In this case study examining the impact of big data analytics on clinical decision making, Dr. Partho Sengupta, Director of Cardiac Ultrasound Research and Associate Professor of Medicine in Cardiology at the Mount Sinai Hospital, has used an associative memory engine from Saffron Technology to crunch enormous datasets for more accurate diagnoses.


Using 10,000 attributes collected from 90 metrics in six different locations of the heart, all produced by a single, one-second heartbeat, the analytics technology has been able to find patterns and pinpoint disease states more quickly and accurately than even the most highly-trained physicians.


more at http://healthitanalytics.com/2014/07/07/case-study-big-data-improves-cardiology-diagnoses-by-17/


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