A group of researchers has developed a machine learning method which processes massive amounts of data to help determine which existing drugs could improve outcomes in diseases for which they are not prescribed.

New cheaper uses

The drug reuse It is an interesting activity because it could reduce the risk associated with safety testing of new drugs and dramatically reduce the time it takes to bring a drug to market for clinical use.

But arriving at those new uses usually involves a combination of serendipity and expensive, time-consuming randomized clinical trials.

To combat this problem, researchers at Ohio State University created a model that combines huge data sets related to patient care with high-powered computing. to reach repurposed drug candidatess and the estimated effects of those existing medications on a defined set of outcomes.

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The research team used insurance claims data in nearly 1.2 million heart disease patients, providing information on assigned treatment, disease outcomes, and other values.

The deep learning algorithm can also take into account the passage of time in each patient's experience, for each visit, prescription and diagnostic test. Model input for medications is based on their active ingredients.

Although this study focused on the proposed repurposing of medications to prevent heart failure and stroke in patients with coronary artery disease, the model is flexible and could be applied to most diseases.

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New machine learning algorithm is helping determine which drugs can be repurposed for other conditions

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Sergio Parra

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According to a new study published in Lancet and which has involved hundreds of patients in the Chinese city of Wuhan, more than three quarters of people hospitalized with COVID-19 still had at least one symptom after six months.

This highlights the need for more research into the lingering effects of the coronavirus.

At least one symptom

The World Health Organization has said the virus poses a risk to some people of ongoing serious effects, even among young, otherwise healthy people who were not hospitalized.

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The new study included 1,733 patients with COVID-19 discharged from Jinyintan Hospital in Wuhan between January and May last year.

The patients, who had an average age of 57 years, were visited between June and September and answered questions about their symptoms and health-related quality of life. The researchers also performed physical examinations and laboratory tests.

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The study thus found that fatigue or muscle weakness were the most common symptoms, while people also reported having difficulty sleeping. As explained by the main author Bin Cao, from the National Center for Respiratory Medicine:

Because COVID-19 is such a new disease, we are just beginning to understand some of its long-term effects on patients' health. Our work also underscores the importance of conducting longer follow-up studies in larger populations to understand the full spectrum of effects COVID-19 can have on people.

The study also examined 94 patients whose blood antibody levels were recorded at the height of infection as part of another trial. When these patients were reexamined after six months, their neutralizing antibody levels were 52.5 percent lower. This raises concerns about the possibility of reinfection with COVID-19.

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The 76% of patients with COVID-19 still present symptoms six months after infection

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Sergio Parra

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Men and women who tend to endorse “hegemonic masculinity” (a culturally idealized form of masculinity that dictates that men should be strong, tough, and dominant) are more likely to vote for Donald Trump and have positive feelings towards him.

This is what it suggests a new study from Penn State.

New masculinity for new politics

Because American politics is largely dominated by men, researchers have noted that political campaigns often emphasize traditionally masculine characteristics to convince voters of a candidate's competence and ability.

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In the United States, idealized forms of masculinity suggest that men should have high power, status, and dominance, while also being physically, mentally, and emotionally strong.

To test this, the researchers recruited a total of 2,007 participants for seven different studies. In the first six studies, participants answered questions about their support for hegemonic masculinity, trust in government, sexism, racism, homophobia, and xenophobia. They also indicated their political affiliation, how they voted in the 2016 presidential election, and their evaluations of Trump and Hillary Clinton.

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In a seventh and final study, participants answered similar questions but also provided information about how they were going to vote in the 2020 presidential election, as well as their evaluations of Trump and Biden.

Nathaniel Schermerhorn warns that the findings, published in Proceedings of the National Academy of Sciences, suggest that while American society appears ready for a female president, an active rejection of hegemonic masculinity may need to take place first.

The success of Donald Trump's 2016 campaign shows that even if we, as a society, have made progress in saying that discrimination and prejudice are undesirable, we have not, as a society, fully questioned the systematic ways in which those prejudices are upheld. .

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Those who believe in a culturally idealized masculinity tend to support Donald Trump

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Xataka Science

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Sergio Parra

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