Researchers ‘zoom’ in for an ultra-magnified peek at shark skin

Florida Atlantic University

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Scanning electron images show four types of denticle shapes found in bonnethead shark skin, arranged from least to most pointed (A–D). Samples come from juvenile and mature female sharks, revealing how denticle shape varies with size and maturity.

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Credit: Florida Atlantic University

Have you ever wondered what makes shark skin so tough and sleek? It’s dermal denticles – tiny, tooth-like structures that cover a shark’s skin. Made of the same material as teeth and shaped like small scales with grooves, these microscopic armor plates aren’t just for show. Dermal denticles serve important roles in helping sharks glide effortlessly, and protect their skin, especially during mating.

Although much is known, researchers still lack a full understanding of how dermal denticle shape changes across different parts of the shark’s body as it grows and if there are differences between males and females.

To solve this mystery, researchers from Florida Atlantic University turned to high-resolution imaging to examine bonnethead sharks (Sphyrna tiburo) – the pint-sized cousins of hammerhead sharks. Using advanced scanning electron microscopy, they were able to capture detailed images of the sharks’ skin, focusing on minute features like denticle shape, size and ridge patterns – details far too small to be seen with standard microscopes.

The team studied skin samples from 24 bonnethead sharks across various life stages. These sharks were an ideal subject, as their skin denticles undergo noticeable changes as they grow and show distinct features between males and females, especially in areas linked to mating.

Findings, published in the journal Integrative and Comparative Biology, provide an ultra-magnified peek into the hidden world of shark skin, revealing how evolution fine-tunes this natural armor for survival and reproduction.

Results of the study showed that denticle morphology changes significantly as bonnethead sharks mature, supporting the idea that these changes improve swimming efficiency and skin protection. Younger sharks had fewer ridges on the denticles, less overlap between them, and smaller ridge angles compared to older sharks. However, the overall length of the denticles stayed about the same at all stages. These changes likely help sharks swim better and protect their skin as they mature.   

“Shark skin is far more dynamic than people realize,” said Marianne E. Porter, Ph.D., senior author and an associate professor of biological sciences in FAU’s Charles E. Schmidt College of Science. “Our study shows that as bonnethead sharks grow, their skin doesn’t just get bigger – it transforms in ways that improve swimming performance and provide greater protection. These changes help reduce drag in the water and strengthen the skin against physical challenges like predators or mating-related injuries. It’s a remarkable example of how nature fine-tunes biological structures to meet the changing demands of an animal’s life.”

Although previous studies found that female sharks often have thicker, tougher skin with higher denticle density – possibly to protect against male bites during mating – this study found minimal differences between the sexes. The only denticle trait that showed sexual dimorphism was ridge angle, which was slightly larger in males. There were also no significant differences in denticle features across the dorsal, medial and ventral parts of the abdominal region studied.

“This research is relevant because gaining an understanding of the developmental aspects of a shark’s dermal denticles can provide extraordinary insights into their evolutionary role in facilitating survival locomotion and reproductive materials,” said Hannah Epstein, corresponding author, a recent graduate of FAU High School and a current student in FAU’s Harriet L. Wilkes Honors College. “We can also apply these quantifications of shark skin to other fields, such as bioengineering, to specifically design materials that can help someone swim faster, just as denticles help a shark swim faster.” 

The patterns observed in this study mirrors findings in other species, such as Portuguese dogfish sharks, which have 11 different denticle shapes that appear at different developmental stages. Past research has also shown that juveniles tend to have smaller denticles than adults, a trend that held true for bonnethead sharks in this study. 

“The advanced imaging and analysis tools we have at the Marcus Research and Innovation Center were essential for this research,” said Tricia Meredith, Ph.D., co-author, director of research for Florida Atlantic Laboratory Schools, and an assistant research professor in FAU’s College of Education. “Using scanning electron microscopy and precise morphometric software allowed us to see and measure the tiny details of shark denticles like never before. This technology opens up new possibilities to understand how these structures function and evolve, giving us a clearer picture of shark biology and biomechanics.”

The Berlin Family Bioimaging Lab is a one-of-a-kind research laboratory that provides students access to high-tech equipment to work on complex research projects, including cancer treatment research, vaccine development, and prosthetic creation, among others. Students like Epstein can research some of the world’s most challenging problems at an early age and can share that research and publish it in peer-reviewed journals. The lab includes a micro computed tomography scanner; scanning electron microscope; histology suite; inverted compound microscope; and stereoscope and is available to researchers of all levels at FAU.  

Scanning electron images of embryonic bonnethead shark skin show detailed denticle structure. Image A displays the raw scanning electron microscopy view; Image B highlights five central denticles used for morphometric measurements, which were averaged to represent the sample.

(From left) Janeisy Davila, an alumna of FAU; Marianne Porter, Ph.D., Hannah Epstein (seated); and Jamie Knaub, FAU imaging lab assistant and a Ph.D. candidate.

Credit

Alex Dolce, Florida Atlantic University

Study co-author is Madeleine E. Hagood, a Ph.D. student of integrative biology at FAU.

The research was supported by a National Science Foundation CAREER Award grant, awarded to Porter, and an FAU Office of Undergraduate Research and Inquiry grant awarded to Epstein.

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About Florida Atlantic University:

Florida Atlantic University serves more than 32,000 undergraduate and graduate students across six campuses along Florida’s Southeast coast. Recognized as one of only 21 institutions nationwide with dual designations from the Carnegie Classification - “R1: Very High Research Spending and Doctorate Production” and “Opportunity College and University” - FAU stands at the intersection of academic excellence and social mobility. Ranked among the Top 100 Public Universities by U.S. News & World Report, FAU is also nationally recognized as a Top 25 Best-In-Class College and cited by Washington Monthly as “one of the country’s most effective engines of upward mobility.” As a university of first choice for students across Florida and the nation, FAU welcomed its most academically competitive incoming class in university history in Fall 2025. To learn more, visit www.fau.edu.

 

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Journal

Integrative and Comparative Biology

DOI

10.1093/icb/icaf115 

Method of Research

Imaging analysis

Subject of Research

Animals

Article Title

Zooming in on Bonnetheads: Quantifying Impacts of Maturity on Denticle Morphology

 

AI system finds crucial clues for diagnoses in electronic health records

The Mount Sinai Hospital / Mount Sinai School of Medicine

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New York, NY [October 15, 2025]—Doctors often must make critical decisions in minutes, relying on incomplete information. While electronic health records contain vast amounts of patient data, much of it remains difficult to interpret quickly—especially for patients with rare diseases or unusual symptoms.

Now, researchers at the Icahn School of Medicine at Mount Sinai and collaborators have developed an artificial intelligence system, called InfEHR, that links unconnected medical events over time, creating a diagnostic web that reveals hidden patterns. Published in the September 26 online issue of Nature Communications, the study shows that Inference on Electronic Health Records (InfEHR) transforms millions of scattered data points into actionable, patient-specific diagnostic insights.

"We were intrigued by how often the system rediscovered patterns that clinicians suspected but couldn't act on because the evidence wasn't fully established," says senior corresponding author Girish N. Nadkarni, MD, MPH, Chair of the Windreich Department of Artificial Intelligence and Human Health, Director of the Hasso Plattner Institute for Digital Health, the Irene and Dr. Arthur M. Fishberg Professor of Medicine at the Icahn School of Medicine at Mount Sinai, and the Chief AI Officer of the Mount Sinai Health System. "By quantifying those intuitions, InfEHR gives us a way to validate what was previously just a hunch and opens the door to entirely new discoveries." 

Most medical artificial intelligence (AI), no matter how advanced, applies the same diagnostic process to every patient. InfEHR works differently by tailoring its analysis to each individual. The system builds a network from a patient’s specific medical events and their connections over time, allowing it to not only provide personalized answers but also to ask personalized questions. By adapting both what it looks for and how it looks, InfEHR brings personalized diagnostics within reach, the investigators say.

In the study, InfEHR analyzed deidentified, privacy-protected electronic records from two hospital systems (Mount Sinai in New York and UC Irvine in California). The investigators turned each patient’s medical timeline—visits, lab tests, medications, vital signs—into a network that showed how events connected over time. The AI studied many of these networks to learn which combinations of clues tend to appear when a hidden condition is present.

With a small set of doctor-confirmed examples to calibrate it, the system checked whether it could correctly flag two real-world problems: newborns who develop sepsis despite negative blood cultures and patients who develop a kidney injury after surgery. Its performance in identifying patients with the diagnosis was compared with current clinical rules and validated across both hospitals. Notably, the system could also signal when the record lacked sufficient information, allowing it to respond “not sure” as a safety feature.

The study found that InfEHR can detect disease patterns that are invisible when examining isolated data. For neonatal sepsis without positive blood cultures—a rare, life-threatening condition—InfEHR was 12–16 times more likely to identify affected infants than current methods. For postoperative kidney injury, the system flagged at-risk patients 4–7 times more effectively. Importantly, InfEHR achieved this without needing large amounts of training data, learning directly from patient records and adapting across hospitals and populations.

“Traditional AI asks, ‘Does this patient resemble others with the disease?’ InfEHR takes a different approach: ‘Could this patient’s unique medical trajectory result from an underlying disease process?’ It’s the difference between simply matching patterns and uncovering causation,” says lead author Justin Kauffman, MS, Senior Data Scientist at the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine.

Importantly, in addition, InfEHR flags how confident it is in its predictions. Unlike other AI that may give a wrong answer with certainty, InfEHR knows when to say, ‘I don’t know’—a key safety feature for real-world clinical use, say the investigators.

The team is making the coding of InfEHR available to other researchers as it continues to study uses of the system. For example, the team will next explore how InfEHR could personalize treatment decisions by learning from clinical trial data and extending those insights to patients whose specific characteristics or symptoms were not fully represented in the original trials. 

“Clinical trials often focus on specific populations, while doctors care for every patient,” Mr. Kauffman says. “Our probabilistic approach helps bridge that gap, making it easier for clinicians to see which research findings truly apply to the patient in front of them.”

The paper is titled “InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records.” The study’s authors, as listed in the journal, are Justin Kauffman, Emma Holmes, Akhil Vaid, Alexander W. Charney, Patricia Kovatch, Joshua Lampert, Ankit Sakhuja, Marinka Zitnik, Benjamin S. Glicksberg, Ira Hofer, and Girish N. Nadkarni.

This work was supported in part by the National Institutes of Health grant UL1TR004419, and the Clinical and Translational Science Awards grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this publication was also supported by the Office of Research Infrastructure of the National Institutes of Health under awards S10OD026880 and S10OD030463.

For more Mount Sinai artificial intelligence news, visit: https://icahn.mssm.edu/about/artificial-intelligence. 

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About Mount Sinai's Windreich Department of AI and Human Health  

Led by Girish N. Nadkarni, MD, MPH—an international authority on the safe, effective, and ethical use of AI in health care—Mount Sinai’s Windreich Department of AI and Human Health is the first of its kind at a U.S. medical school, pioneering transformative advancements at the intersection of artificial intelligence and human health. 

The Department is committed to leveraging AI in a responsible, effective, ethical, and safe manner to transform research, clinical care, education, and operations. By bringing together world-class AI expertise, cutting-edge infrastructure, and unparalleled computational power, the department is advancing breakthroughs in multi-scale, multimodal data integration while streamlining pathways for rapid testing and translation into practice. 

The Department benefits from dynamic collaborations across Mount Sinai, including with the Hasso Plattner Institute for Digital Health at Mount Sinai—a partnership between the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, and the Mount Sinai Health System—which complements its mission by advancing data-driven approaches to improve patient care and health outcomes. 

At the heart of this innovation is the renowned Icahn School of Medicine at Mount Sinai, which serves as a central hub for learning and collaboration. This unique integration enables dynamic partnerships across institutes, academic departments, hospitals, and outpatient centers, driving progress in disease prevention, improving treatments for complex illnesses, and elevating quality of life on a global scale. 

In 2024, the Department's innovative NutriScan AI application, developed by the Mount Sinai Health System Clinical Data Science team in partnership with Department faculty, earned Mount Sinai Health System the prestigious Hearst Health Prize. NutriScan is designed to facilitate faster identification and treatment of malnutrition in hospitalized patients. This machine learning tool improves malnutrition diagnosis rates and resource utilization, demonstrating the impactful application of AI in health care. 

For more information on Mount Sinai's Windreich Department of AI and Human Health, visit: ai.mssm.edu 

 

About the Hasso Plattner Institute at Mount Sinai 

At the Hasso Plattner Institute for Digital Health at Mount Sinai, the tools of data science, biomedical and digital engineering, and medical expertise are used to improve and extend lives. The Institute represents a collaboration between the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, and the Mount Sinai Health System.  

Under the leadership of Girish Nadkarni, MD, MPH, who directs the Institute, and Professor Lothar Wieler, a globally recognized expert in public health and digital transformation, they jointly oversee the partnership, driving innovations that positively impact patient lives while transforming how people think about personal health and health systems. 

The Hasso Plattner Institute for Digital Health at Mount Sinai receives generous support from the Hasso Plattner Foundation. Current research programs and machine learning efforts focus on improving the ability to diagnose and treat patients. 

 

About the Icahn School of Medicine at Mount Sinai

The Icahn School of Medicine at Mount Sinai is internationally renowned for its outstanding research, educational, and clinical care programs. It is the sole academic partner for the seven member hospitals* of the Mount Sinai Health System, one of the largest academic health systems in the United States, providing care to New York City’s large and diverse patient population.  

The Icahn School of Medicine at Mount Sinai offers highly competitive MD, PhD, MD-PhD, and master’s degree programs, with enrollment of more than 1,200 students. It has the largest graduate medical education program in the country, with more than 2,600 clinical residents and fellows training throughout the Health System. Its Graduate School of Biomedical Sciences offers 13 degree-granting programs, conducts innovative basic and translational research, and trains more than 560 postdoctoral research fellows. 

Ranked 11th nationwide in National Institutes of Health (NIH) funding, the Icahn School of Medicine at Mount Sinai is among the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges.  More than 4,500 scientists, educators, and clinicians work within and across dozens of academic departments and multidisciplinary institutes with an emphasis on translational research and therapeutics. Through Mount Sinai Innovation Partners (MSIP), the Health System facilitates the real-world application and commercialization of medical breakthroughs made at Mount Sinai.

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* Mount Sinai Health System member hospitals: The Mount Sinai Hospital; Mount Sinai Brooklyn; Mount Sinai Morningside; Mount Sinai Queens; Mount Sinai South Nassau; Mount Sinai West; and New York Eye and Ear Infirmary of Mount Sinai

  

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Journal

Nature Communications

Method of Research

Data/statistical analysis

Subject of Research

People

Article Title

InfEHR: Clinical phenotype resolution through deep geometric learning on electronic health records

 

Podcasts now count towards research impact in world first for Altmetric

Altmetric adds podcasts as an attention source, offering a more complete view of research influence

Digital Science

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Discover how Altmetric tracks research impact in podcasts.

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Credit: Digital Science/Altmetric.

In a major step forward for tracking the real-world impact of research, Digital Science today announces that Altmetric has added a new attention source: Podcasts.

Altmetric is the first in the world to include podcasts among its measures of research impact.

Podcasts will now be reflected in the distinctive Altmetric Badges – appearing as a purple color – as well as in Altmetric Attention Scores, with more detail displayed in Altmetric Explorer.

In addition to podcasts, Altmetric’s many attention sources include select social media channels, news, blogs, public policy sites, patents, clinical guidelines, and more.

A complete view of research influence

Miguel Garcia, VP of Product, Digital Science, said: “Altmetric is about tuning in to where research conversations are really happening, and understanding how that research is being received, discussed, debated, and shared. A complete view of research influence isn’t possible without podcasts.

“With Altmetric podcast tracking, we recognize that these real-world conversations play a critical role in shaping public understanding and acceptance of research. Podcasts add rich, narrative-driven evidence to the impact story, offering a more complete view of research influence across scholarly, professional, and public domains.

“With more than half a billion people listening to podcasts for information, and at a time when podcasts are growing as a communication and educational platform, we feel the moment is right to include these conversations as an attention source. Publishers, academics, industry, governments, and funders will all now benefit from better understanding the impact of research.”

Benefits of podcast tracking

By adding podcasts as an attention source, Altmetric will enable users to:

• Strengthen reporting on research impact
• Capture a broader, more complete attention landscape
• Gain deeper public engagement insights
• Diversify research impact data sources

All user segments within the research ecosystem will benefit from Altmetric’s podcast tracking:

• Academics: Strengthen submissions that demonstrate the real-world impact and influence of research
• Enterprise: Identify emerging Key Opinion Leaders (KOLs) and track therapeutic-area conversations, even outside traditional publishing
• Publishers: Highlight where journals are discussed in accessible, mainstream forums that boost author engagement
• Funders: Ensure research funded is making an impact in broader public discourse, justifying investment

Find out more about Altmetric’s podcast tracking

See a YouTube video about Podcasts in Altmetric

 

About Altmetric

Altmetric is a leading provider of alternative research metrics, helping everyone involved in research gauge the impact of their work. We serve diverse markets including universities, institutions, government, publishers, corporations, and those who fund research. Our powerful technology searches thousands of online sources, revealing where research is being shared and discussed. Teams can use our powerful Altmetric Explorer application to interrogate the data themselves, embed our dynamic ‘badges’ into their webpages, or get expert insights from Altmetric’s consultants. Altmetric is part of the Digital Science group, dedicated to making the research experience simpler and more productive by applying pioneering technology solutions. Find out more at  altmetric.com and follow @altmetric on X and @altmetric.com on Bluesky.

About Digital Science

Digital Science is an AI-focused technology company providing innovative solutions to complex challenges faced by researchers, universities, funders, industry and publishers. We work in partnership to advance global research for the benefit of society. Through our brands – Altmetric, Dimensions, Figshare, IFI CLAIMS Patent Services, metaphacts, Overleaf, ReadCube, Symplectic, and Writefull – we believe when we solve problems together, we drive progress for all. Visit digital-science.com and follow Digital Science on Bluesky, on X or on  LinkedIn.

Media Contact

David Ellis, Press, PR & Social Manager, Digital Science: Mobile +61 447 783 023, d.ellis@digital-science.com