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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 increasing cost of medicines is not only due to the difficulty in developing them

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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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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More people have voted for Trump because left-wing identity politics and political correctness create aversion

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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Sergio Parra

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English Wikipedia articles are slightly more politically biased than Encyclopedia Britannica articles, and most of this bias is in favor of Democratic views. That is, in almost all cases, Wikipedia leans more to the left than Britannica.

The bias disappears as more edits are made. Something to keep in mind after having attended that kind of granguiñolesca performance what was he storming of the capitol.

Objective/subjective information

One of the biggest problems of encyclopedias is to objectively present the knowledge they contain. involves subjective, unverifiable or controversial information. Even today's issues like immigration, gun control, abortion, and foreign policy are open to fervent debate depending on who is giving an opinion.

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Using data from Encyclopædia Britannica, written by experts, and Wikipedia, an encyclopedia produced by an online community, in A study compared the bias and bias of pairs of articles on identical topics in American politics.

The lean measure is less (more) than zero when an article leans toward Democratic (Republican) views, while the bias is the absolute value of the lean.

What was found is that Wikipedia articles lean more toward Democratic views than Britannica articles, and they are also more biased. All in all, perhaps the most interesting finding of the research is that the more times an article is reviewed on Wikipedia, the less bias it is likely to show.

However, the number of revisions needed to start showing this effect is quite large (at least 2000 issues) and the articles most read by users are not necessarily the most reviewed by editors.

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What is stigmergia?

These results highlight the pros and cons of each knowledge production model, help identify the extent of empirical generalizability of previous studies comparing the information quality of the two production models, and offer implications for organizations managing knowledge. crowd-based knowledge production.

If Wikipedia wants to improve its objectivity, the study's authors note, is recommended to encourage editors to review the most read stories first, as well as encourage people with different political leanings to edit the same article. Only in this way, perhaps, will we blur a little the increasingly opaque border between Democrats and Republicans:

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Wikipedia is a little more politically biased than Encyclopedia Britannica (in favor of Democrats)

was originally published in

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Sergio Parra

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According to A study conducted by researchers at the Max Planck Institute of Psychiatry, led by Nikolaos Koutsouleris, a combination of human and artificial intelligence optimizes prediction of mental health problems, including psychosis.

The study combined psychiatric evaluations with machine learning models that analyze clinical and biological data.

Predictions of the course of the disease

Although psychiatrists can make very accurate predictions about the positive outcomes of mental illness, they may underestimate the frequency of adverse cases that lead to relapses.

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Therefore, algorithmic pattern recognition helps doctors to better predict the course of the disease.

The results of the study show that it is the combination of artificial and human intelligence that optimizes the prediction of mental illnesses, and not just one or the other. With the algorithm, then, doctors can identify at an early stage those patients who need therapeutic intervention and those who do not. As Koutsouleris explains:

This algorithm allows us to improve the prevention of psychosis, especially in young patients at high risk or with emerging depression, and to intervene in a more specific and timely manner.

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The psychologist who suffered a disorder that made him write a 'bible' in a week

Therefore, the algorithm does not replace treatment by medical professionals; rather, it aids decision making and provides recommendations on whether further testing should be performed on an individual basis.

The results of our study could help drive a reciprocal and interactive process of clinical validation and improve prognostic tools in real-world screening services.

Norman Bates I would celebrate it.

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This algorithm is used to predict if you are going to suffer psychosis (if mixed with human intelligence)

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Sergio Parra

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According to a new study published in Nature, non-cognitive skills (such as curiosity, motivation, persistence, determination, self-control, growth mindset...) represent more than half of the overall genetic influence on educational attainment and obtaining better jobs and higher incomes.

Specifically, the study identified 157 significant genome-wide loci and a polygenic architecture that accounts for 57% of the genetic variance in educational attainment.

genetic architecture of traits

According to the study cited, the non-cognitive genetics was enriched in the same brain tissues and cell types as cognitive performance, but showed different associations with brain gray matter volumes.

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Overall, the genetics of non-cognitive abilities were associated with greater risk tolerance, greater willingness to forego immediate gratification, less risky health behavior, and a delay in fertility.

The researchers also observed that the genetics of non-cognitive skills were associated with a constellation of personality traits linked to success in relationships and at work, such as being curious and eager to learn, being more emotionally stable, and being more hardworking and tidy.

The genetics of noncognitive skills that were associated with educational attainment were also related to an increased risk of schizophrenia, bipolar disorder, obsessive-compulsive disorder and anorexia nervosa.

The research provides evidence for the idea that inheriting genes that affect things other than cognitive ability is important for understanding differences in people's life outcomes.

GWAS

The researchers applied a new statistical method to develop an understanding of the essence of non-cognitive abilities and how genetic correlations with non-cognitive abilities diverged from genetic correlations with cognitive abilities, as measured by standardized IQ tests: genome-wide association (GWAS) of educational achievement.

In the GWAS, samples are taken from all people and all their DNA is examined in search of something that is a match in all of them. This coincidence does not necessarily tell us something about the ultimate cause, but rather that there is a common genetic characteristic that could provide the trait.

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The first GWAS Was published very recently, 2005, after scanning the genome of 93 people who suffered from a retinal disease. On paper, GWAS seems like an elegant solution, but the problem is that the amount of data is overwhelming. For example, simply to look for genes related to height, an examination of 183,727 different people was carried out. The study required the participation of 280 authors. The results They appeared published in Nature in 2010, suggesting that hundreds of genetic variants were involved in height alone, and furthermore those variants only explained 10 percent of the differences in height.

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The 57% of total genetic influence on educational and occupational achievement has nothing to do with cognitive abilities

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Sergio Parra

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The procrastination It is the habit of putting off things we should do, getting bogged down in less important tasks or even deliberately spending our time on things that we force ourselves to believe are more urgent.

But this defect is not only found in human beings, but other animals.

Procrastinating pigeons

James Mazur, Harvard-trained psychologist, has managed to indirectly demonstrate procrastination in animals, specifically in pigeons: he trained a group of them for two different work schedules and gave them the possibility of choosing the one they preferred.

Both had a candy as a reward after the same deadline, but the first one started with a little work and came after a long delay, while the second one started with a long delay and ended with much more work (up to four times more).

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What is procrastination and why do we tend to it?

Basically, the pigeons had to choose between working a little first, then resting, or taking it easy first, and then face harder work.

We already know what most of us would do, but curiously the pigeons also opted for the same strategy, as explained Piers Steel in his book Procrastination:

They postponed the afternoon despite the fact that much more arduous work awaited them to obtain the reward in the end. (…) The birds leave it until later and even the chimpanzees at the zoo leave it until later.

When it comes to procrastinating, then, almost all animals are cut from the same cloth.

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Animals can also procrastinate just like humans do.

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Sergio Parra

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