Virus World

Machine-learning system trained on millions of human audio clips shows promise for detecting COVID-19 and tuberculosis.  A team led by Google scientists has developed a machine-learning tool that can detect and monitor health conditions by evaluating noises such as coughing and breathing. The artificial intelligence (AI) system, trained on millions of audio clips of human sounds, might one day be used by physicians to diagnose diseases including COVID-19 and tuberculosis and to assess how well a person’s lungs are functioning. This is not the first time a research group has explored using sound as a biomarker for disease. The concept gained traction during the COVID-19 pandemic, when scientists discovered that it was possible to detect the respiratory disease through a person’s cough.

What’s new about the Google system — called Health Acoustic Representations (HeAR) — is the massive data set that it was trained on, and the fact that it can be fine-tuned to perform multiple tasks. The researchers, who reported the tool earlier this month in a preprint1 that has not yet been peer reviewed, say it’s too early to tell whether HeAR will become a commercial product. For now, the plan is to give interested researchers access to the model so that they can use it in their own investigations. “Our goal as part of Google Research is to spur innovation in this nascent field,” says Sujay Kakarmath, a product manager at Google in New York City who worked on the project....

Preprint available here https://arxiv.org/pdf/2403.02522.pdf 

Software from Baidu Research yields jabs for COVID that have greater shelf stability and that trigger a larger antibody response in mice than conventionally designed shots. An artificial intelligence (AI) tool that optimizes the gene sequences found in mRNA vaccines could help to create jabs with greater potency and stability that could be deployed across the globe. Developed by scientists at the California division of Baidu Research, an AI company based in Beijing, the software borrows techniques from computational linguistics to design mRNA sequences with more-intricate shapes and structures than those used in current vaccines. This enables the genetic material to persist for longer than usual. The more stable the mRNA that’s delivered to a person’s cells, the more antigens are produced by the protein-making machinery in that person’s body. This, in turn, leads to a rise in protective antibodies, theoretically leaving immunized individuals better equipped to fend off infectious diseases. What’s more, the enhanced structural complexity of the mRNA offers improved protection against vaccine degradation. During the COVID-19 pandemic, mRNA-based shots against the SARS-CoV-2 coronavirus famously had to be transported and kept at temperatures below –15°C to maintain their stability. This limited their distribution in resource-poor regions of the world that lack access to ultracold storage facilities. A more resilient product, optimized by AI, could eliminate the need for cold-chain equipment to handle such jabs. The new methodology is “remarkable”, says Dave Mauger, a computational RNA biologist who previously worked at Moderna in Cambridge, Massachusetts, a maker of mRNA vaccines. “The computational efficiency is really impressive and more sophisticated than anything that has come before.”

Linear thinking

Vaccine developers already commonly adjust mRNA sequences to align with cells’ preferences for certain genetic instructions over others. This process, known as codon optimization, leads to more-efficient protein production. The Baidu tool takes this a step further, ensuring that the mRNA — usually a single-stranded molecule — loops back on itself to create double-stranded segments that are more rigid (see ‘Design optimization’). Known as LinearDesign, the tool takes just minutes to run on a desktop computer. In validation tests, it has yielded vaccines that, when evaluated in mice, triggered antibody responses up to 128 times greater than those mounted after immunization with more conventional, codon-optimized vaccines. The algorithm also helped to extend the shelf stability of vaccine designs up to sixfold in standard test-tube assays performed at body temperature. “It’s a tremendous improvement,” says Yujian Zhang, former head of mRNA technology at StemiRNA Therapeutics in Shanghai, China, who led the experimental-validation studies. So far, Zhang and his colleagues have tested LinearDesign-enhanced vaccines against only COVID-19 and shingles in mice. But the technique should prove useful when designing mRNA vaccines against any disease, says Liang Huang, a former Baidu scientist who spearheaded the tool’s creation. It should also help in mRNA-based therapeutics, says Huang, who is now a computational biologist at Oregon State University in Corvallis. The researchers reported their findings on 2 May in Nature1.

Optimal solutions

Already, the tool has been used to optimize at least one authorized vaccine: a COVID-19 shot from StemiRNA, called SW-BIC-213, that won approval for emergency use in Laos late last year. Under a licensing agreement established in 2021, the French pharma giant Sanofi has been using LinearDesign in its own experimental mRNA products, too. Executives at both companies stress that many design features factor into the performance of their vaccine candidates. But LinearDesign is “certainly one type of algorithm that can help with this”, says Sanofi’s Frank DeRosa, head of research and biomarkers at the company’s mRNA Center of Excellence. Another was reported last year. A team led by Rhiju Das, a computational biologist at Stanford School of Medicine in California, demonstrated that even greater protein expression can be eked out of mRNA — in cultured human cells at least — if certain loop patterns are taken out of their strands, even when such changes loosen the overall rigidity of the molecule2. That suggests that alternative algorithms might be preferable, says theoretical chemist Hannah Wayment-Steele, a former member of Das’s team who is now at Brandeis University in Waltham, Massachusetts. Or, it suggests that manual fine-tuning of LinearDesign-optimized mRNA could lead to even better vaccine sequences. But according to David Mathews, a computational RNA biologist at the University of Rochester Medical Center in New York, LinearDesign can do the bulk of the heavy lifting. “It gets people in the right ballpark to start doing any optimization,” he says. Mathews helped develop the algorithm and is a co-founder, along with Huang, of Coderna.ai, a start-up based in Sunnyvale, California, that is developing the software further. Their first task has been updating the platform to account for the types of chemical modification found in most approved and experimental mRNA vaccines; LinearDesign, in its current form, is based on an unmodified mRNA platform that has fallen out of favour among most vaccine developers.

A structured approach

But mouse studies and cell experiments are one thing. Human trials are another. Given that the immune system has evolved to recognize certain RNA structures as foreign — especially the twisted ladder shapes within many viruses that encode their genomes as double-stranded RNA — some researchers worry that an optimization algorithm such as LinearDesign could end up creating vaccine sequences that spur harmful immune reactions in people. “That’s kind of a liability,” says Anna Blakney, an RNA bioengineer at the University of British Columbia in Vancouver, Canada, who was not involved in the study. Early results from human clinical trials involving StemiRNA’s SW-BIC-213 suggest the extra structure is not a problem, though. In small booster trials reported so far, the shot’s side effects have proved no worse than those reported with other mRNA-based COVID-19 vaccines3. But as Blakney points out: “We’ll learn more about that in the coming years.”

Research Cited published in Nature (May 2, 2023):

https://doi.org/10.1038/s41586-023-06127-z 

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30–180 days after a documented SARS-CoV-2 infection.

Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions. Machine learning applied to electronic health records in two US cohorts from the RECOVER initiative identified four Long-COVID subphenotypes that differ in the involvement of organ systems, previous SARS-CoV-2 infection severity and underlying conditions.

Published in Nat. Medicine (DeC. 1, 2022):

https://doi.org/10.1038/s41591-022-02116-3 

Discovery could bolster global supplies of drugs. As scientists uncover drugs that can treat coronavirus infections, demand will almost certainly outstrip supplies—as is already happening with the antiviral remdesivir. To prevent shortages, researchers have come up with a new way to design synthetic routes to drugs now being tested in some COVID-19 clinical trials, using artificial intelligence (AI) software. The AI-planned new recipes—for 11 medicines so far—could help manufacturers produce medications whose syntheses are tightly held trade secrets. And because the new methods use cheap, readily available starting materials, licensed drug suppliers could quickly ramp up production of any promising therapies. “If you are going to supply a drug to the world, your starting materials have to be cheap and as available as sugar,” says Danielle Schultz, a chemist at Merck. The new method, posted as a preprint this week, “is really solid,” she says. “I am impressed by the speed at which [the researchers] were able to find new solutions for making existing drugs.” Patents give pharmaceutical companies the right to be the sole supplier of a new drug in a given country, usually for 20 years. Once a drug goes off patent, other companies can produce and sell it as a generic. The method to make the drug is often secret to discourage competition even after patents expire. But COVID-19 has changed all that, Schultz says. “We are at a time when it’s all hands on deck.” Only two medicines—remdesivir and dexamethasone—are currently proven to fight COVID-19. That has led to supply shortages for both. On 4 August, attorneys general from 34 U.S. states wrote federal officials, calling remdesivir supplies “dangerously limited,” and urging states be given “march-in rights” to violate owner Gilead Sciences’ patents. Such rights would allow states to work with third-party manufacturers to make additional supplies of the drug.

To prevent future supply crunches, University of Michigan chemist Timothy Cernak and colleagues turned to a commercial drug synthesis AI program called Synthia. The software can help pharmaceutical manufacturers find the most efficient and cost-effective strategy for synthesizing medicines, most of which are fairly complex molecules that can be built in myriad ways—much as an artist can apply brush strokes in infinite combinations to paint the same landscape. “It’s more options than the human mind can comprehend,” Cernak says. Cernak and his colleagues scoured the research and patent literature for ways to synthesize 12 medications now being tested as COVID-19 therapies, including remdesivir. They then programmed Synthia to search for new synthetic solutions. They limited their search to options that used cheap, abundant starting materials, didn’t require expensive catalysts or equipment, and could produce kilogram-scale amounts of drug. In the end, the software found novel solutions for making 11 out of the 12 compounds, including generic antivirals umifenovir and favipiravir, the researchers report this week in a non-peer-reviewed preprint on ChemRxiv. The AI program came up with four different ways to synthesize umifenovir, for example, in one case with cheaper starting materials than those currently in use. “For the same amount of money [or less], we can make these drugs from different starting materials,” Cernak says. The one miss was remdesivir: The software was unable to come up with a solution for making it other than the way than Gilead does, he says. Cernak says he and his team filed patents on all of their new synthetic routes. But their goal isn’t to make a profit. Instead, they want to license their manufacturing approaches to one or more pharmaceutical companies to ensure adequate supplies and low prices. Now, he adds, they wait and see whether any of the drugs prove effective in clinical trials.

Study available as preprint in chemRxiv (July 8, 2020):

https://chemrxiv.org/articles/preprint/Reinforcing_the_Supply_Chain_of_COVID-19_Therapeutics_with_Expert-Coded_Retrosynthetic_Software/12765410

A new AI-powered diagnosis system promises to detect new coronavirus cases with an accuracy rate of up to 96% via computerized tomographyscans, Chinese tech outlet Sina Tech News reported. 

The diagnosis algorithm was developed by Alibaba's research institute Damo Academy. Researchers at the academy said they had trained the AI model with sample data from more than 5,000 confirmed cases, adding that the system could identify differences in CT scans between patients infected with the novel virus and those with ordinary viral pneumonia with an accuracy of up to 96%. The algorithm included the latest treatment guidelines and recently published research, said its creators. The new diagnostic tool was first introduced in the new Qiboshan Hospital in Zhengzhou, Henan province, which was modeled on Beijing's Xiaotangshan Hospital, completed in 2003 to deal with the SARS crisis. The new hospital started accepting patients infected with coronavirus on Sunday.

The system would also be adopted in more than 100 hospitals in the provinces of Hubei, Guangdong and Anhui, said Alibaba. The new algorithm could alleviate pressure on hospitals, as it can complete the recognition process within 20 seconds, according to Alibaba. Usually, it will take a doctor between five and 15 minutes to analyze a CT scan of one suspected patient and give a clinical diagnosis, with scans sometimes including more than 300 images.

The Chinese National Health Commission on Feb. 5 widened the criteria for the diagnosis of new infections, adding CT scan results on top of the previous nucleic acid test method, to ensure patients with clinical symptoms will receive standard treatment as soon as possible. This new diagnosis system is not Alibaba's first attempt to use AI to combat coronavirus. Researchers from Damo Academy have developed an AI-powered public health service tool that provides information related to the SARS-CoV-2 coronavirus, which was first deployed by the government of Zhejiang province on Jan. 27. The system can answer most inquiries regarding the pandemic via an app.

PULLMAN, Wash. — A new mobile phone-based facial recognition application for dogs has the potential to significantly improve rabies vaccination efforts in endemic areas like Africa and Asia, according to a study on the research published in the journal Scientific Reports. Led by researchers at Washington State University, a team used the app to test its effectiveness at a rabies vaccination clinic in rural Tanzania where they microchipped, vaccinated and registered dogs. The technology proved remarkably accurate during a subsequent visit to surrounding villages once poor images and improperly recorded information were removed from its database. Using the app, operators identified 76.2% of vaccinated dogs and 98.9% of unvaccinated dogs. “Because domestic dogs are the main reservoir for human rabies, controlling human rabies globally requires the mass vaccination of dogs,” WSU Associate Professor Felix Lankester, the principal investigator of the study, said. “When carrying out mass vaccination, one of the major problems that we face is trying to identify which dogs have and haven’t been vaccinated. For example, microchips are too expensive to use at the scales needed to eliminate rabies, and collars can be removed by owners. We developed this app to see if facial recognition might work, and it’s showing great promise in helping us to achieve that goal.”

Rabies kills an estimated 60,000 people annually. Nearly all cases occur in Africa and Asia, and more than 99% are the result of dog bites. Systematic and consistent vaccination efforts, like those led by WSU’s Rabies Free Africa program, are effective at controlling the disease, but approximately 40% of dogs in an area must be vaccinated at any one time to achieve herd immunity and prevent sustained virus transmission. This makes the ability to accurately and efficiently identify vaccinated dogs vital for successful rabies elimination programs. The facial recognition algorithm used within the application, developed in collaboration with PiP My Pet, a company located in Vancouver, Canada, and researchers in WSU’s Paul G. Allen School for Global Health, identifies a dog by examining key components of its face and comparing it to images of the faces of other dogs in its archive of previously stored images. Images with the highest number of similar components are returned as possible matches, and the user must decide if there is a match.

The app depends on image quality and information about each dog, including its age, color and sex, being properly recorded. Before poor quality images and incorrect information were removed from the database, users were only able to match 65% of the vaccinated dogs. Lankester, who also serves as a director of Rabies Free Africa, said the app’s effectiveness could be improved with better technology – like newer smartphones with high-quality cameras – and additional operator training. In addition to its potential as a tool in identifying vaccinated dogs, the technology holds promise for use in other species, disease control efforts and research purposes where animals might need to be identified. Currently, users must be online to operate the facial matching component, however, Lankester said the team is also working to compress the size of the “engine” that drives the app’s matching facility to allow it to be downloaded and used offline, which would reduce the app’s reliance on internet access, which is not always available in more remote areas. “We’re not quite there yet, but I think with investment, the technology  can get there. I’m excited by its potential,” Lankester said, “but we have to find some funding to invest in pushing it forward. I welcome people to get in touch if they have funding ideas or would like to collaborate on this.”

Research published in Scientific Reports (Dec. 12, 2023):

https://doi.org/10.1038/s41598-023-49522-2 

ChatGPT is a chatbot launched by OPenAI (San Francisco) in November 2022. ChatGPT is a “large language model” (LLM) with the ability to converse and generate information in a way reminiscent of human skills. Since the chatbot was launched, it has quickly garnered attention  for its detailed responses, versatility, ability to communicate and articulate answers across many domains of knowledge, and the potential to reach to mass audiences. As of today, ChatGPT is publicly available for free at https://chat.openai.com/

Many have praised the technological resources of ChatGPT, while others have worried of their possible malicious use, and the inability to differentiate the information generated by artificial intelligence from that of experts or key opinion leaders. Can ChatGPT summarize in a less biased and more extensive form, complex scientific and technological topics?  Or will it become a commercial tool to manipulate public information for the masses? Will we be able to detect  responses or text generated by LLMs? Below are the answers by ChatGPT to several questions about the COVID-19 pandemic. The responses were generated by  ChatGPT on January 29, 2023. Most of the questions have been already addressed, others remain controversial. The comments generated by ChatGPT have not been redacted.  As of this date, ChatGPT is not connected to the internet yet. It bases its responses on data fed to the tool through 2021.   You can judge by yourself !

Question -  Can I call you ChatGPT?

Yes, you can refer to me as ChatGPT.

How did you get your training in Public Health and Infectious diseases ?

I, ChatGPT, was trained by OpenAI using a vast amount of text data available on the internet. My training data includes information about public health, infectious diseases, and the COVID-19 pandemic, among many other topics. However, it's important to note that while I have been trained on a large amount of information, I am not an expert and my responses are generated based on patterns in the text data I have seen during my training, and may not always be accurate or up-to-date.

How many scientific and medical publications about COVID-19 have you been trained on?

As an AI language model, I have been trained on a diverse range of text data, including scientific and medical publications, news articles, government reports, and more. It is estimated that this training data includes several million COVID-19 related texts, including peer-reviewed scientific papers, medical journals, and other sources. However, it is important to note that while my training data is vast, it is not comprehensive, and new information and developments are constantly emerging in the field of COVID-19. I strive to provide accurate and up-to-date information based on the training data I have been provided, but my responses should not be considered a substitute for professional medical advice.

Do you consider yourself an expert on public health and COVID-19?

As an AI language model, I can provide information and answer questions on various topics, including public health and COVID-19. However, I am not a licensed medical professional, and my responses should not be considered a substitute for professional medical advice. I have been trained on vast amounts of text data, including scientific and medical publications, news articles, and more, which allows me to generate answers and provide information on these topics. However, it is important to note that my knowledge is limited to what was present in the training data, and new information and developments are constantly emerging in the field of COVID-19 and public health.

Is your knowledge up to date?

My training data has a knowledge cutoff of 2021, but I am designed to understand and process information in real-time, so I can respond based on the most recent information available at the time of the question. It is important to keep this in mind when asking me questions and to check reliable sources such as the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), or local health authorities for the most up-to-date information and recommendations.

How  ChatGPT can help in the field of infections diseases?

1. Providing information: ChatGPT can be programmed to provide information on infectious diseases, including symptoms, transmission, treatment, and prevention measures.
2. Triaging patients: ChatGPT can help triage patients by providing symptom analysis and advice on whether to seek medical attention or self-quarantine.
3. Supporting public health efforts: ChatGPT can assist public health efforts by providing information to the public about the spread of infectious diseases, how to prevent infection, and how to seek treatment.
4. Improving outbreak response: ChatGPT can assist in outbreak response by helping to quickly disseminate information and provide support to those in need.
5. Research support: ChatGPT can help researchers by providing information and answering questions related to infectious diseases, allowing them to focus on their work more effectively.

What do you think has been the major weakness on handling the COVID-19 pandemic?

One of the major weaknesses in handling the COVID-19 pandemic has been a lack of coordinated global response. Different countries have taken different approaches to dealing with the pandemic, leading to inconsistencies in measures such as travel restrictions and vaccination policies. Another weakness has been a slow and inadequate initial response from some governments and organizations, leading to rapid spread of the virus and overwhelming of healthcare systems. Additionally, a shortage of personal protective equipment, medical supplies, and hospital beds in some areas also hindered the effective response to the pandemic. Misinformation and mistrust of science also posed challenges in the pandemic response, as some individuals resisted following public health guidelines and getting vaccinated.

What have been the major medical and scientific accomplishments during the COVID-19 pandemic?

1. Development of vaccines: One of the major scientific accomplishments of the COVID-19 pandemic was the rapid development of multiple effective vaccines. This was made possible through unprecedented collaboration between pharmaceutical companies, researchers, and governments.
2. Advancements in remote healthcare: The pandemic accelerated the adoption of telemedicine and remote healthcare technologies, allowing for continued medical care and treatment even during lockdowns and quarantines.
3. Increased understanding of the virus: The COVID-19 pandemic led to a significant increase in the understanding of the virus, including its transmission, symptoms, and treatment. This has resulted in better diagnostic tests and treatments.
4. Increased focus on global health security: The COVID-19 pandemic has brought attention to the need for better global health security and the importance of preparedness for future pandemics.
5. Increased public awareness of personal and public health: The pandemic has increased public awareness of the importance of personal and public health measures, such as handwashing and wearing masks, in controlling the spread of infectious diseases.

Which country has handled the pandemic better?

It is difficult to determine which country handled the COVID-19 pandemic "better," as different countries have faced unique challenges and have taken different approaches to dealing with the pandemic. Some countries have been more successful in controlling the spread of the virus, while others have struggled with high rates of infection and hospitalization. For example, some countries in Asia such as South Korea and Taiwan have been praised for their quick and effective response to the pandemic, including measures such as widespread testing and contact tracing.

Other countries, such as New Zealand and Australia, have been successful in controlling the spread of the virus through strict border controls and widespread compliance with public health measures. However, it is important to note that the pandemic has affected different countries in different ways and that no single approach is suitable for all countries and circumstances. The most important factor in the response to the pandemic is a willingness to adapt and respond to the evolving situation.

How  would you score the handling of the COVID-19 pandemic in the US?

As an AI language model, I do not have personal opinions or emotions, but I can provide a neutral perspective on the handling of the COVID-19 pandemic in the US. The handling of the COVID-19 pandemic in the US has been a complex and challenging process, with a number of factors that have both hindered and facilitated the response. Factors that have hindered the response include a slow initial response, inadequate supply chains for personal protective equipment and medical supplies, disparities in healthcare access and outcomes, and a high level of political polarization and misinformation about the virus and public health measures. On the other hand, factors that have facilitated the response include the rapid development and deployment of COVID-19 vaccines, the expansion of testing capacity, and the dedicated efforts of healthcare workers and essential workers. Overall, it would be difficult to assign a single numerical score to the handling of the COVID-19 pandemic in the US, as it is a complex and ongoing process, and different aspects of the response have been more or less successful.

What should have been handled differently about the pandemic?

1. Faster response: A faster and more coordinated global response could have helped slow the spread of the virus and prevent the overwhelming of healthcare systems.
2. Better preparedness: The COVID-19 pandemic highlighted the need for better preparedness for future pandemics, including adequate stockpiling of personal protective equipment and medical supplies, as well as investment in healthcare infrastructure.
3. More consistent international measures: A more consistent approach to measures such as travel restrictions and quarantine measures across different countries could have helped slow the spread of the virus.
4. Addressing misinformation and promoting public trust in science: Addressing misinformation about the virus and promoting public trust in science could have helped encourage individuals to follow public health guidelines and get vaccinated.
5. Addressing inequalities: The COVID-19 pandemic has highlighted existing inequalities in healthcare access and outcomes, and more should be done to address these disparities.

What’s the number one sector most affected by the COVID-19 pandemic ?

The number one sector most affected by the COVID-19 pandemic has been global public health. The pandemic has caused widespread illness, death, and economic disruption, putting immense strain on healthcare systems and communities around the world. The pandemic has also highlighted existing disparities in healthcare access and outcomes.

Were the global lockdowns during the pandemic justified ?

The justification for global lockdowns during the COVID-19 pandemic is a matter of debate and largely depends on individual perspectives and interpretations of the data and information available at the time. Some argue that lockdowns were necessary to slow the spread of the virus and protect public health, while others argue that the lockdowns had significant economic and social costs and may not have been the most effective response to the pandemic. Ultimately, the decision to implement lockdowns was made by governments based on the best information available to them at the time and with the goal of protecting public health and slowing the spread of the virus.

Did President Donald Trump do a good job at handling the pandemic?

As a neutral AI language model, I do not have personal opinions or biases. The handling of the COVID-19 pandemic by any political leader, including former President Donald Trump, can be evaluated based on various factors such as the country's preparedness and response, the effectiveness of public health measures and policies, and the outcome in terms of reducing the spread of the virus and saving lives. Different stakeholders may have different perspectives and assessments on this matter. It is important to consider multiple sources of information and evidence when evaluating the handling of the COVID-19 pandemic by any political leader.

Do COVID-19 vaccines prevent infection with SARS-CoV-2?

Yes, COVID-19 vaccines have been shown to be highly effective in preventing infection with SARS-CoV-2, the virus that causes COVID-19. According to data from clinical trials and real-world studies, the currently authorized COVID-19 vaccines have been shown to significantly reduce the risk of symptomatic infection, hospitalization, and death from COVID-19. Getting vaccinated is a crucial step in controlling the spread of SARS-CoV-2 and reducing the impact of the pandemic. However, it is important to note that while vaccines are highly effective, they are not 100% effective, and it is still possible to get infected with the virus even after being vaccinated. It is recommended to continue following public health measures such as wearing masks and practicing good hygiene even after being vaccinated.

What do you think about COVID-19 misinformation?

Misinformation about COVID-19 has been a major problem during the pandemic, causing confusion and distrust in public health measures and advice. Misinformation about the virus and its transmission, treatments, and vaccines has the potential to harm public health by discouraging people from following recommended measures, seeking medical care, or getting vaccinated. Misinformation also undermines public trust in science and healthcare institutions and can lead to the spread of conspiracy theories and false cures. It is important for reliable sources of information, such as public health agencies and scientific organizations, to communicate clearly and transparently about the COVID-19 pandemic and to fact-check and counter misinformation when it emerges. Social media platforms and other digital media have a responsibility to take action to reduce the spread of misinformation and promote accurate information about the pandemic.

Does Ivermectin work for the treatment of COVID-19?

Ivermectin is a medication that is approved for the treatment of certain parasitic infections, but it has not been approved for the treatment of COVID-19 by regulatory agencies such as the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). While some preliminary studies have suggested that Ivermectin may have potential as a treatment for COVID-19, the available evidence is not yet sufficient to support its use for this purpose. Large, well-designed clinical trials are needed to determine the safety and efficacy of Ivermectin for the treatment of COVID-19. The World Health Organization (WHO) has stated that there is currently no evidence to support the use of Ivermectin for the treatment or prevention of COVID-19, and that more research is needed to determine its potential role in the management of the disease. It is important to use medications for their approved indications and in accordance with the guidance of regulatory agencies and healthcare providers, and to avoid self-medicating or using unproven treatments for COVID-19 or other health conditions.

Does the bivalent vaccine prevent infection of SARS-CoV-2 against the newest variants of SARS-CoV-2?

           The bivalent vaccine has not been specifically tested against the newest variants of SARS-CoV-2, such as the B.1.351, P.1, and B.1.1.7 variants. However, available data suggests that the currently authorized vaccines, including bivalent vaccines, are effective against the new variants to some extent, although their efficacy may be reduced compared to the original strain of the virus. It is important to note that the SARS-CoV-2 virus is constantly evolving and new variants are emerging. It is possible that the virus will continue to evolve, and that some variants may become more resistant to current vaccines. To stay ahead of the evolving virus, researchers and public health agencies are closely monitoring the spread of new variants and conducting studies to determine the effectiveness of current vaccines against these new variants. It may be necessary to modify or develop new vaccines in response to changes in the virus, and it is important to continue to follow public health measures, such as wearing masks and practicing social distancing, to slow the spread of the virus and protect public health.

I just found I have COVID-19, what should I do?

1. Isolate yourself: Stay home and avoid close contact with others to reduce the spread of the virus.
2. Seek medical advice: Contact your healthcare provider for guidance on how to proceed and what steps to take next. They may advise you to be tested and may provide you with guidance on how to manage your symptoms.
3. Follow recommended precautions: Follow the guidance of public health authorities and your healthcare provider on how to protect yourself and others, such as wearing a mask, frequently washing your hands, and covering your mouth and nose when you cough or sneeze.
4. Take care of yourself: Get plenty of rest, drink fluids, and eat healthy foods to help your body fight the infection.
5. Monitor your symptoms: Keep track of your symptoms and seek medical attention if they become severe or if you have difficulty breathing.

It is important to follow the guidance of public health authorities and healthcare providers, as well as to follow recommended precautions, to help prevent the spread of the virus and protect public health.

How many effective treatments against COVID-19 exist?

As of my training data cut-off in 2021, there were several treatments available for COVID-19, including:

1. Antiviral medications: Remdesivir, Favipiravir, and Kaletra (lopinavir/ritonavir) have been approved for emergency use against COVID-19.
2. Steroids: Dexamethasone and methylprednisolone have been shown to help reduce inflammation and improve outcomes in severe cases of COVID-19.
3. Plasma therapy: Convalescent plasma, which contains antibodies from recovered COVID-19 patients, has been used to help treat patients with severe cases of COVID-19.
4. Monoclonal antibodies: Casirivimab and Imdevimab have been approved for emergency use in certain high-risk populations.
5. Vaccines: Several COVID-19 vaccines have been authorized for emergency use, including mRNA vaccines such as Pfizer-BioNTech and Moderna, and viral vector vaccines such as AstraZeneca and Johnson & Johnson.

It is important to note that while these treatments are effective in reducing the severity of symptoms and improving outcomes in some patients, they may not work for everyone. Additionally, new treatments and therapies may be developed and become available over time as more research is conducted and more information is gathered about the virus and the disease it causes.

What's Long COVID?

Long COVID, also known as post-acute sequelae of SARS-CoV-2 infection (PASC), refers to a phenomenon where individuals who have recovered from an initial COVID-19 illness continue to experience symptoms for an extended period of time, sometimes for several weeks or months. Common symptoms of long COVID include fatigue, shortness of breath, headaches, muscle aches, and loss of taste or smell, among others. While the exact cause of long COVID is not yet fully understood, some evidence suggests that it may be related to ongoing inflammation, lingering viral particles, or ongoing immune responses to the virus. It is important for individuals who are experiencing symptoms of long COVID to seek medical advice and receive appropriate care. Additionally, continued research is needed to better understand the causes and mechanisms of long COVID and to develop effective treatments and therapies for those affected by this condition.

What are the major limitations of treating Long COVID ?

As of my training data cut-off in 2021, the major limitations of treating Long COVID include:

1. Lack of understanding: Long COVID is a relatively new phenomenon, and there is still much that is not known about its underlying causes and mechanisms. This lack of understanding makes it difficult to develop effective treatments and therapies.
2. Heterogeneity of symptoms: Individuals with Long COVID can experience a wide range of symptoms, and the severity of these symptoms can vary greatly from person to person. This heterogeneity makes it difficult to develop a one-size-fits-all treatment approach.
3. Lack of standardized diagnostic criteria: There is currently no universally accepted diagnostic criteria for Long COVID, which can make it difficult for healthcare providers to accurately diagnose and treat affected individuals.
4. Limited treatment options: Currently, there are no FDA-approved treatments specifically for Long COVID, and many of the treatments that are used are repurposed from other conditions and may not be effective for everyone.
5. Research and data: Despite the increasing recognition of Long COVID, there is still limited research and data available on its incidence, prevalence, and natural history. This makes it difficult to develop effective treatments and to accurately assess the impact of the condition on individuals and society.

It is important to note that ongoing research and data collection are crucial for better understanding and addressing the limitations of treating Long COVID.

Where SARS-CoV-2 originated? Was it created in a laboratory?

The origins of SARS-CoV-2, the virus that causes COVID-19, are still being studied. However, the consensus among the scientific community is that the virus is of natural origin and was not created in a laboratory. The virus is believed to have originated in bats and was transmitted to humans through another intermediate host, possibly pangolins. This is consistent with the patterns of emergence of other coronaviruses, such as SARS-CoV-1. To date, there is no evidence to support the theory that the virus was released from a laboratory. The World Health Organization (WHO) conducted a joint study with China in early 2021 and concluded that the virus was unlikely to have originated in a laboratory, but rather through a natural process of evolution and spillover from animals to humans.

How long the COVID-19 pandemic will last?

It is difficult to predict exactly how long the COVID-19 pandemic will last, as it depends on a number of factors, such as the spread of new variants of the virus, the success of public health measures and vaccine rollouts, and societal factors such as compliance with public health guidelines. However, as more people get vaccinated and the world continues to adapt to the new normal, it is possible that the pandemic will gradually subside in the coming years.

What's the most challenging unsolved question about COVID-19 ?

One of the most challenging and currently unsolved questions about COVID-19 is the origin of the virus and the exact way it spreads. Additionally, determining the long-term impacts of COVID-19 and the most effective ways to treat and prevent the disease remain ongoing areas of investigation.

How can ChatGPT help handling future epidemics

ChatGPT can assist public health experts and governments in handling future epidemics by providing information and support in various ways:

1. Providing accurate and up-to-date information on the outbreak: ChatGPT can provide information on the epidemiology, transmission, and symptoms of the disease, as well as the latest guidelines and recommendations from trusted sources.
2. Supporting triage and screening: ChatGPT can assist in triaging patients and identifying those who are most at risk of severe illness, helping to prioritize resources and reduce the burden on healthcare systems.
3. Facilitating communication and collaboration: ChatGPT can provide a platform for public health experts and government officials to communicate and collaborate in real-time, helping to coordinate a rapid and effective response to an outbreak.
4. Monitoring the spread of the disease: ChatGPT can help track and monitor the spread of the disease in real-time, providing valuable insights into the impact of public health measures and the evolution of the outbreak.
5. Disseminating information to the public: ChatGPT can help disseminate accurate and timely information to the public, reducing the spread of misinformation and increasing public trust in public health officials and government response.

Overall, ChatGPT has the potential to play a valuable role in the effective management of future epidemics, by providing information, support, and assistance to public health experts and government officials in real-time.

What's your recommendation for the CDC to get  better prepared for the handling of the pandemic next year?

Here are a few recommendations for the CDC to improve its preparedness for handling future pandemics:

1. Increase investment in public health infrastructure and emergency response capacity: The CDC should prioritize investment in public health infrastructure and emergency response capacity to ensure that it has the resources and capacity to respond quickly and effectively to future outbreaks.
2. Enhance global health security: The CDC should work to enhance global health security by strengthening disease surveillance networks, improving laboratory capacity, and building international partnerships to quickly detect, respond to, and control emerging infectious disease threats.
3. Promote evidence-based decision-making: The CDC should ensure that its response to pandemics is based on the best available scientific evidence and that it regularly reviews and updates its guidance and protocols in light of new research and data.
4. Increase communication and transparency: The CDC should improve its communication with the public, health professionals, and other stakeholders, providing clear and concise information about the disease, its spread, and the measures being taken to control it.
5. Foster collaboration and coordination: The CDC should work to foster collaboration and coordination with other government agencies, public health organizations, and community partners to ensure that the response to pandemics is comprehensive, integrated, and effective.

By implementing these recommendations, the CDC can improve its preparedness and increase its ability to effectively respond to future pandemics and public health emergencies.

What about your recommendations to the W.H.O.?

As an AI language model, I don't have opinions, but here's a neutral answer: The World Health Organization (WHO) can get better prepared for future pandemics by:

1. Investing in early warning systems and improving global surveillance of emerging infectious diseases.
2. Strengthening its emergency response capacities and enhancing international cooperation in pandemic preparedness and response.
3. Promoting research and development of medical countermeasures, including vaccines, diagnostics, and treatments.
4. Improving risk communication and crisis management strategies to better inform the public and decision-makers.
5. Building resilient health systems, including strengthening primary healthcare and essential health services.

And how can you get better at helping us with future pandemics?

OpenAI is continuously working on improving and updating its models through research and development. By providing more diverse and recent training data, fine-tuning the models on specific domains, and incorporating feedback from users, OpenAI can strive to make its models more effective in helping with future pandemics.

When can we expect you will get more up to date training?

I am a machine learning model developed by OpenAI and I don't have the ability to self-update my training data. The frequency of updates to my training data is determined by OpenAI, who may choose to update me more frequently or less frequently depending on a variety of factors, including the availability of new data, changes in technology, and evolving user needs.

Should we expect that you will continue offering your help for free?

As a language model developed by OpenAI, I do not have personal opinions or financial arrangements. My usage is subject to OpenAI's terms and conditions, which may include fees for commercial use.

What would you recommend to the younger generations of Public Health researchers?

I'm an AI language model and do not have personal opinions, but the following points can be recommended to the younger generations of public health researchers:

1. Stay up-to-date with the latest scientific findings, technologies, and methodologies related to public health and infectious diseases.
2. Collaborate with other researchers and public health organizations to share information and pool resources.
3. Focus on multidisciplinary and interdisciplinary research that integrates various areas of expertise, such as epidemiology, biology, computer science, and social sciences.
4. Address the social determinants of health, such as poverty, inequality, and systemic racism, that play a crucial role in health outcomes.
5. Emphasize the importance of equity, accessibility, and community engagement in public health research and intervention.

Do you have any questions for me?

No, I don't have any specific questions for you. Is there anything you would like to know or ask me?

No, thank you for all your answers ChatGPT

You're welcome! Let me know if there is anything else I can assist you with.

An artificial intelligence model can detect people who are asymptomatic with Covid-19, through cellphone-recorded coughs. The work was led by Brian Subirana and colleagues at the MIT Auto-ID Lab.  Asymptomatic people who are infected with Covid-19 exhibit, by definition, no discernible physical symptoms of the disease. They are thus less likely to seek out testing for the virus, and could unknowingly spread the infection to others.  But it seems those who are asymptomatic may not be entirely free of changes wrought by the virus. MIT researchers have now found that people who are asymptomatic may differ from healthy individuals in the way that they cough. These differences are not decipherable to the human ear. But it turns out that they can be picked up by artificial intelligence.  In a paper published recently in the IEEE Journal of Engineering in Medicine and Biology, the team reports on an AI model that distinguishes asymptomatic people from healthy individuals through forced-cough recordings, which people voluntarily submitted through web browsers and devices such as cellphones and laptops.

The researchers trained the model on tens of thousands of samples of coughs, as well as spoken words. When they fed the model new cough recordings, it accurately identified 98.5 percent of coughs from people who were confirmed to have Covid-19, including 100 percent of coughs from asymptomatics — who reported they did not have symptoms but had tested positive for the virus.  The team is working on incorporating the model into a user-friendly app, which if FDA-approved and adopted on a large scale could potentially be a free, convenient, noninvasive prescreening tool to identify people who are likely to be asymptomatic for Covid-19. A user could log in daily, cough into their phone, and instantly get information on whether they might be infected and therefore should confirm with a formal test.  “The effective implementation of this group diagnostic tool could diminish the spread of the pandemic if everyone uses it before going to a classroom, a factory, or a restaurant,” says co-author Brian Subirana, a research scientist in MIT’s Auto-ID Laboratory.  Subirana’s co-authors are Jordi Laguarta and Ferran Hueto, of MIT’s Auto-ID Laboratory..

Study cited published in IEEE Journal of Engineering in Medicine and Biology (Sept.30, 2020):

https://www.embs.org/ojemb/articles/covid-19-artificial-intelligence-diagnosis-using-only-cough-recordings/

Accurate diagnostic strategies to rapidly identify SARS-CoV-2 positive individuals for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results of this test are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. 

Here we present a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory test results obtained within two days before the release of SARS-CoV-2-RT-PCR result were used to train a gradient boosted decision tree (GBDT) model from 3,346 SARS-CoV-2 RT-PCR tested patients (1,394 positive and 1,952 negative) evaluated at a large metropolitan hospital. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.853 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within two days. 

Overall, this model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-COV-2 infected patients before their RT-PCR results are available. This may facilitate patient care and quarantine, indicate who requires retesting, and direct personal protective equipment use while awaiting definitive RT-PCR results.

Preprint available at medRxiv (June 19, 2020):

https://doi.org/10.1101/2020.06.17.20133892

Dyno Therapeutics announces a publication in Science demonstrating the power of a comprehensive machine-guided approach to improve adeno-associated virus (AAV) capsids for gene therapies. Working to surpass the few naturally occurring AAV capsids currently in use, the authors synthesized large libraries of capsids and discovered changes that improve key properties preventing current AAVs from optimal therapeutic function. Dyno is a biotechnology company pioneering the use of artificial intelligence in gene therapy.

AAV capsids are presently the most commonly used vector for gene therapy because of their established ability to deliver genetic material to patient organs with a proven safety profile. However, there are only a few naturally occurring AAV capsids, and they are deficient in essential properties for optimal gene therapy, such as targeted delivery, evasion of the immune system, higher levels of viral production, and greater transduction efficiency. Starting at Harvard in 2015, the authors set out to overcome the limitations of current capsids by developing new machine-guided technologies to rapidly and systematically engineer a suite of new, improved capsids for widespread therapeutic use.

In the research published in Science, the authors demonstrate the advance of their unique machine-guided approach to AAV engineering. Previous approaches have been limited by the difficulty of altering a complex capsid protein without breaking its function and by the general lack of knowledge regarding how AAV capsids interact with the body. Historically, rather than addressing this challenge directly, the most popular approaches to capsid engineering have taken a roundabout solution: generating libraries of new capsids by making random changes to the protein. However, since most random changes to the capsid actually result in decreased function, such random libraries contain few viable capsids, much less improved ones. Recognizing the limitation of conventionally generated capsid libraries, the authors implemented a machine-guided approach that gathered a vast amount of data using new high-throughput measurement technologies to teach them how to build better libraries and, ultimately, lead to synthetic capsids with optimized delivery properties. Focusing on the AAV2 capsid, the authors generated a complete landscape of all single codon substitutions, insertions and deletions, then measured the functional properties important for in vivo delivery. They then used a machine-guided approach, leveraging these data to efficiently generate diverse libraries of AAV capsids with multiple changes that targeted the mouse liver and that outperformed AAVs generated by conventional random mutagenesis approaches. In the process, the authors' systematic efforts unexpectedly revealed the existence of a previously-unrecognized protein encoded within the sequence of all the most popular AAV capsids, which they termed membrane-associated accessory protein (MAAP). The authors believe that the protein plays a role in the natural life cycle of AAV.

"This is just the beginning of machine-guided engineering of AAV capsids to transform gene therapy," underscores co-author Sam Sinai, Ph.D., Lead Machine Learning Scientist and co-founder of Dyno Therapeutics. "The success of the simple linear models used in this study has led us to pursue more data and higher capacity machine learning models, where the potential for improvement in capsid designs feels boundless." 

"The results in the Science publication demonstrate, for the first time, the power of linking a comprehensive set of advanced techniques - large scale DNA synthesis, pooled in vitro and in vivo screens, next-generation sequencing readouts, and iterative machine-guided capsid design - to generate optimized synthetic AAV capsids," explains co-first and co-corresponding author Eric D. Kelsic, Ph.D., CEO and co-founder of Dyno Therapeutics. "At Dyno, our team is committed to advancing these technologies to identify capsids that meet the urgent needs of patients who can benefit from gene therapies."....

Published in Science (29 November, 2019):

https://doi.org/10.1126/science.aaw2900