David Goggins
Former Navy Seal
Career Development
Bryan Cranston
Critical Thinking
Liv Boeree
International Poker Champion
Emotional Intelligence
Amaryllis Fox
Former CIA Clandestine Operative
Chris Hadfield
Retired Canadian Astronaut & Author
from the world's big
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A new hydrogel might be strong enough for knee replacements

Duke University researchers might have solved a half-century old problem.

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  • Duke University researchers created a hydrogel that appears to be as strong and flexible as human cartilage.
  • The blend of three polymers provides enough flexibility and durability to mimic the knee.
  • The next step is to test this hydrogel in sheep; human use can take at least three years.
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Predicting PTSD symptoms becomes possible with a new test

An algorithm may allow doctors to assess PTSD candidates for early intervention after traumatic ER visits.

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  • 10-15% of people visiting emergency rooms eventually develop symptoms of long-lasting PTSD.
  • Early treatment is available but there's been no way to tell who needs it.
  • Using clinical data already being collected, machine learning can identify who's at risk.

The psychological scars a traumatic experience can leave behind may have a more profound effect on a person than the original traumatic experience. Long after an acute emergency is resolved, victims of post-traumatic stress disorder (PTSD) continue to suffer its consequences.

In the U.S. some 30 million patients are annually treated in emergency departments (EDs) for a range of traumatic injuries. Add to that urgent admissions to the ED with the onset of COVID-19 symptoms. Health experts predict that some 10 percent to 15 percent of these people will develop long-lasting PTSD within a year of the initial incident. While there are interventions that can help individuals avoid PTSD, there's been no reliable way to identify those most likely to need it.

That may now have changed. A multi-disciplinary team of researchers has developed a method for predicting who is most likely to develop PTSD after a traumatic emergency-room experience. Their study is published in the journal Nature Medicine.

70 data points and machine learning

nurse wrapping patient's arm

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Study lead author Katharina Schultebraucks of Columbia University's Department Vagelos College of Physicians and Surgeons says:

"For many trauma patients, the ED visit is often their sole contact with the health care system. The time immediately after a traumatic injury is a critical window for identifying people at risk for PTSD and arranging appropriate follow-up treatment. The earlier we can treat those at risk, the better the likely outcomes."

The new PTSD test uses machine learning and 70 clinical data points plus a clinical stress-level assessment to develop a PTSD score for an individual that identifies their risk of acquiring the condition.

Among the 70 data points are stress hormone levels, inflammatory signals, high blood pressure, and an anxiety-level assessment. Says Schultebraucks, "We selected measures that are routinely collected in the ED and logged in the electronic medical record, plus answers to a few short questions about the psychological stress response. The idea was to create a tool that would be universally available and would add little burden to ED personnel."

Researchers used data from adult trauma survivors in Atlanta, Georgia (377 individuals) and New York City (221 individuals) to test their system.

Of this cohort, 90 percent of those predicted to be at high risk developed long-lasting PTSD symptoms within a year of the initial traumatic event — just 5 percent of people who never developed PTSD symptoms had been erroneously identified as being at risk.

On the other side of the coin, 29 percent of individuals were 'false negatives," tagged by the algorithm as not being at risk of PTSD, but then developing symptoms.

Going forward

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Schultebraucks looks forward to more testing as the researchers continue to refine their algorithm and to instill confidence in the approach among ED clinicians: "Because previous models for predicting PTSD risk have not been validated in independent samples like our model, they haven't been adopted in clinical practice." She expects that, "Testing and validation of our model in larger samples will be necessary for the algorithm to be ready-to-use in the general population."

"Currently only 7% of level-1 trauma centers routinely screen for PTSD," notes Schultebraucks. "We hope that the algorithm will provide ED clinicians with a rapid, automatic readout that they could use for discharge planning and the prevention of PTSD." She envisions the algorithm being implemented in the future as a feature of electronic medical records.

The researchers also plan to test their algorithm at predicting PTSD in people whose traumatic experiences come in the form of health events such as heart attacks and strokes, as opposed to visits to the emergency department.

Consumer advocacy groups are mostly funded by Big Pharma, according to new research

An article in Journal of Bioethical Inquiry raises questions about the goal of these advocacy groups.

Image by Jukka Niittymaa / Pixabay
  • Two-thirds of American consumer advocacy groups are funded by pharmaceutical companies.
  • The authors of an article in Journal of Bioethical Inquiry say this compromises their advocacy.
  • Groups like the National Alliance on Mental Illness act more like lobbyists than patient advocates.

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New research suggests biases play a role in FDA drug approval

Innovative drugs are sometimes held up due to old-fashioned human biases.

Photo by Noel Celis/AFP via Getty Images
  • When new drugs are similar to popular drugs on the market, FDA approval takes up to 75 percent longer.
  • Texas McCombs Professor Francisco Polidoro Jr. reviewed 291 drugs over a 35-year period.
  • Polidoro believes that potential coronavirus treatments or vaccines could help the FDA improve upon this longstanding bias.
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Why science research still focuses mostly on males

In spite of a government mandate, females are often treated as afterthoughts in scientific research.

Image source: Louis Reed/Unsplash
  • A new study finds that though more females are included in experiments, sex-specific data often goes un-analyzed.
  • Only about a third of studies analyzed published participant breakdown by sex.
  • Some researchers say considering females more fully as research subjects is logistically too challenging.

In 2016, the National Institutes of Health (NIH) issued a directive that scientists receiving NIH funding must consider sex as a biological variable in pre-clinical research on vertebrate animals and human cells and tissues. According to a new study published in eLife that looked at over 700 journal articles, the number of women included as participants in pre-clinical research has jumped from 28 percent in 2009 to 49 percent in 2019. However, it's also unfortunately still the case that few studies actually consider sex as a biological influence that may potentially affect outcomes, and that data from women participants continues to be simply combined with data from men.

Study co-author Nicole C. Woitowich of Northwestern University's Feinberg School of Medicine tells INSIDE Higher Ed, "In the last 10 years, there has been a major in increase in sex inclusion, but it's still not where it's needs to be."

What's missing in current research

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Woitowich and others see two particularly problematic aspects to the continuing disregard of sex as a meaningful biological research variable.

First, female-specific data is rarely considered in study conclusions, despite the fact that it may have implications for women's health. According to L. Syd M Johnson of SUNY Update Medical University, who was not involved with the study, "This becomes highly problematic both scientifically and ethically, because women, children, and the elderly also need medical care, and they shouldn't be treated as if they have adult, male bodies. When they are excluded from research, and from the reported results, treatment for them becomes, effectively, off-label.

Second, Woitowich tells INSIDE Higher Ed it's, "troublesome to me as a scientist [that] a little under one-third [of studies] did not even report the number of males and females used as subjects." This makes it impossible for scientists to replicate the results. "If I don't have all the information," Woitowich says, "I'm left guessing."

On top of that, Woitowich laments that too much of the female-focused research that is undertaken is what's been called "bikini science," research surrounding issues related to female reproductive organs.

Why is this happening?

doctor giving female patient a pill and water

Image source: Image Point Fr/Shutterstock

"Many scientists, I don't even know if this is on their radar," says Woitowich. She proposes, therefore, that in the short term it may be the research gatekeepers — the funding entities, journal editors, and peer reviewers — who will have to step up and demand more inclusive science. She expresses surprise that they aren't already doing more to enforce the NIH's mandate. In the longer term, training for medical students should include a fuller awareness of the role that can be played by sex differences in research.

In a 2014 letter to the journal Nature, Janine A. Clayton and Francis S. Collins of the NIH admitted the problem even extends to female researchers. Noting that roughly half of the scientists doing NIH-funded research are women: "There has not been a corresponding revolution in experimental design and analyses in cell and animal research — despite multiple calls to action."

Another possible explanation

microscopes in a lab

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There are some researchers who feel that a greater inclusion of women and their data in studies would unnecessarily complicate the problems inherent in designing research and getting it funded.

In a 2015 letter to the journal Science, a group of researchers wrote that sex considerations added an additional investigational layer to research, one that was often irrelevant to the purpose of a research project. They asserted that, "nonhypothesis-driven documentation of sex differences in basic laboratory research is more likely to introduce conceptual and empirical problems in research on sex and gender than bring new clarity to differences in men's and women's health outcomes."

The writers also suggested that sex may be less of a biological variable than gender and weight. If, for example, women are more likely to be taking multiple pharmaceuticals than men and tend to be lighter in weight, these factors may be more influential on experiment outcomes than sex. Reluctant to commit to considering sex as a variable, they suggested instead two generalized studies to determine if it should be, writing, "we see a stronger empirical basis for directed funding initiatives in two areas: scientific validation of preclinical models for studying human sex differences, and human studies of the interaction of sex- and gender-related variables in producing health outcomes that vary by sex."


researcher wearing gloves and writing on a chart

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A 2019 analysis by Harvard University's GenderSci Lab found that basic science researchers, "repeated again and again that their experiments were in large part constrained by practicalities of various sorts. These practicalities were often used to explain why they don't or can't account for sex in their research," says the lab's Annika Gompers. Among the practicalities noted were the acquisition of study materials such as cells from deceased patients, test animals, fat from cosmetic surgery patients, and so on. Gompers said researchers often simply work with what they can get.

She adds, "While my participants recognize that considering sex can be important for the generalizability of results, in practice it is often impractical if not impossible to incorporate sex as a variable into biomedical research. Such a finding is consistent with scholars who have long looked at science as practice and observed how practicalities — as mundane as the availability of materials — are often central to the reduction of complexity into 'doable problems.'"

As far as sample composition goes, the choice of subjects may have to do with researchers wanting to avoid the constraints and costs of the safety regulations that accompany studies of pregnant women, women of child-bearing age whom may become pregnant, children, and the elderly.

Finally, though it may be that having enough females in a sample to draw valid conclusions would likely require larger participant cohorts. Woitowich's co-author, Smith College's Anneliese Beery, says that fears of doubled sample sizes are overblown, asserting that such increases in participant numbers would be "not actually necessary."

Avoiding wasted research opportunities

One of the authors of that Science letter was Harvard's Sarah S. Richardson, who suggests a sort of middle path, though it does give researchers license to ignore the NIH requirement as they see fit. Richardson proposes something she calls "sex contextualism," which is the "simple view that the definition of sex and sex-related variables, and whether they are relevant in biological research, depends on the research context."

Science journalist Angela Saini agrees , saying, "While it's valuable to include a broad spectrum of people in studies, it doesn't necessarily follow that the sex differences will be significant or important. So disaggregating for sex, while useful sometimes, doesn't always matter."

The above points, however, don't seem to acknowledge the potential for findings important specifically to female health, and seem more concerned with protecting the efficacy of studies that benefit males.

In any event, Woitowich finds that things are progressing more slowly than the NIH and others may have hoped. While Beery says it's "exciting to see increased inclusion of female subjects across so many different fields of biology," there are potentially meaningful scientific insights being lost. The disinclination toward fully collecting and analyzing female data for research experiments "means we are still missing out on the opportunity to understand when there are sex differences and losing statistical power when sex differences go unnoticed."