Thursday, February 27, 2025

Do starchy carbs cause cavities?



 It’s common knowledge that sugar causes cavities, but new Cornell University research provides evidence that – depending on your genetic makeup – starches could also be a contributing factor.

The study, published in Microorganisms, explores the response of the oral microbiome to starch, finding that the number of copies of a particular gene, AMY1, in combination with starch, alters the complex composition of bacteria that play a role in oral health.

“Most people have been warned that if you eat a bunch of sugar, make sure you brush your teeth,” said Angela Poole, senior author and assistant professor of molecular nutrition. “The takeaway finding here is that depending on your AMY1 copy number, you may want to be just as vigilant about brushing your teeth after eating those digestible starches.”

AMY1 codes for the salivary amylase enzyme, which helps break down starch in the mouth. Previous studies have associated AMY1 with cavities and periodontal disease. Poole, in prior studies, found that a high AMY1 copy number is associated with higher levels of the species Porphyromonas endodontalis, which is strongly associated with periodontitis and gum disease.

But how the salivary amylase enzyme interacts with its main substrate, starch, to alter the oral microbiome and increase disease risk was unclear.

“That’s what we wanted to know in this experiment,” Poole said. “What’s going on in the mouth if someone eats starch, and is the answer different if their copy number is high or if it’s low? What we found was that there are other bacteria involved in these processes and that the changes depended on AMY1.”

The researchers also found evidence that the oral microbiome has co-evolved in response to increasing copies of AMY1, which is found in higher numbers in populations where there’s a long history of agriculture and starch consumption. In the pool of 31 samples, taken in Ithaca, N.Y., the AMY1 number ranged from two to 20 copies.

“The populations that historically had greater access to starch tend to have more copies,” Poole said, “which makes sense from a practical standpoint, because it would have given you a survival advantage when food is scarce, to be able to break down those starches more efficiently.”

In saliva samples with a high AMY1 copy number, the researchers saw increased populations of bacteria, like Streptococcus, that feed off the starch’s sugars.

“If someone has a high copy number, they break down starch efficiently, and bacteria that like those sugars are going to grow more in that person’s mouth,” Poole said. “So you can have species behave differently based on the different substrates. It’s pretty incredible – how we adapt and these microbes turn around and adapt, too.”


 

Thursday, February 6, 2025

Revolutionizing dental surgery with AI

 


Researchers in engineering are exploring smart dental implant surgery planning for personalized care with predictable outcomes

Grant and Award Announcement

Texas A&M University

Texas A&M University researchers Dr. Yuxiao Zhou, assistant professor in the J. Mike Walker ’66 Department of Mechanical Engineering, and Dr. Jaesung Lee, assistant professor in the Wm Michael Barnes ’64 Department of Industrial and Systems Engineering, have been awarded the 2024 Seed Program for AI, Computing, and Data Science award. 

Their project, “Toward Smart Orthopedic Surgery Planning by using Physics-Informed Machine Learning,” was selected as one of the top ten proposals in a competitive cycle featuring 39 submissions from researchers across three institutions.

Dental implant surgeries are critical for enhancing the quality of life, particularly among the aging population. However, their success depends on achieving optimal mechanical stress levels in the surrounding bone during chewing to prevent bone loss from insufficient loading and avoid bone fracture due to excessive loading. 

Implant success is complicated by challenges such as delayed bone healing and age-related bone loss in older individuals and varying bone stiffness. Current methods for measuring bone stiffness are often invasive, computationally costly, or lack accuracy, creating a need for innovative and practical patient-specific solutions.

To address this, Dr. Zhou and Dr. Lee are developing a hybrid biomechanical physics-informed machine learning model. Their approach combines experimentally measured bone deformation data with governing physics and a robust machine learning framework, enabling precise, personalized predictions of mechanical stress in the bone. This innovation provides an efficient tool for patient-specific dental surgery planning, optimizing bone healing and ensuring long-term implant success.

“Our model will revolutionize surgical planning by delivering personalized, computationally efficient treatment plans with predictable outcomes,” said Dr. Zhou.

The project also highlights interdisciplinary collaboration, leveraging Dr. Lee’s expertise in machine learning for healthcare systems to address a long-standing clinical challenge. The success of this work has the potential to extend beyond dental implants, offering advancements for other surgical applications in healthcare.

This award highlights Texas A&M’s commitment to research in AI, computing, and data science that drive solutions with real-world impacts.

By Maddy Busby, Texas A&M Engineering