Interesting study about molecular interactions that could impact future medicine discussed by Phys.org
This news story was published in Phys.Org, a science, research, and technology news aggregator. Most of the content tends to be republished from other news agencies, for example, this specific article was directly taken from the University of Minnesota. The news story highlights a research study published in November 2019, where researchers developed a mathematical model to represent multivalent interactions between proteins as a result of proteins having multiple binding sites. Parameters could be manipulated to understand the interactions and predict how the proteins would bind. The research article was published in a highly reputable journal, Proceedings of the National Academy of Sciences (PNAS), which has an impact factor of 9.58 (2018).
The research article’s claims focused mostly on how the model could benefit understanding molecular interactions. Meanwhile, the news story took these results one step further to saying that the model will help make developing new medicine easier and more efficient, especially for diseases of high concern like HIV, cancer, and autoimmune diseases. While there is some merit to this claim, as the direct quotes from the researchers of the original study suggested implications in accelerating the production of new therapies, the news story author seems to overemphasize this implication with too much certainty. As well, because the original news story was provided from the University of Minnesota, there is no direct link to the original author and their information. In fact, the author hyperlink only takes you to the University of Minnesota website.
That said, the language used is well-suited to the target audience, as Phys.Org is intended for current scientists and researchers. The author does a satisfactory job of simplifying certain concepts that may be difficult for someone without a background in mathematical modeling or computational biochemistry to understand. Besides a little bit of overemphasis on the implications for this model to be used in medicine, the author describes the research in a neutral style and states some of the main highlights of the research article and mathematical model. Thus, I still am able to give this news story a 5 out of 5.
This Phys.org article describes recent findings published in PNAS regarding the development of a novel computational model to study molecular interactions. Biomedical engineers designed a mathematical framework to be able to predict how a molecule will bind where they are able to adjust parameters and see the effects on the molecular interaction. This model has many potential applications in biology as well as medicine seeing as molecular binding is known to play a role in many diseases.
The author successfully lays out the research process and manages to make a relatively complicated topic accessible to a broad audience without any unexplained jargon; however, they could have added a few more details on the mechanisms of molecular binding as well as how they play a role in diseases to add more context to the article. The author remains neutral throughout and clearly describes the potential applications of this research, and the link to the original study is available at the bottom of the page.
This news article does have a few shortcomings, mainly the author’s unjustified use of certainty. The article’s findings are presented in a way that leads the reader to believe that they will undoubtedly lead to breakthrough medical advances. These claims are not entirely unwarranted seeing as there are direct quotes from the researchers highlighting the potential applications of their work in the treatment of diseases. But the author seems to overemphasize this by stating without hesitation that the findings “will make it easier and more efficient” to develop new therapies “for diseases such as cancer, HIV and autoimmune diseases.”
In addition, there is no information available about the author, making it very difficult to identity any potential bias or conflict of interest on their part, and the sources used are all from within the original research paper.
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