Featured Post from University of Delaware

Discovery pinpoints potential Achilles’ Heel in HIV, opening new frontier in drug development

Feb 19, 2026

2 min

Scientists have long known that detecting HIV early is crucial in slowing and treating the virus. During the acute stage of infection, a single human cell can produce as many as 10,000 new HIV particles. A discovery led by the University of Delaware's Juan Perilla offers hope: A new drug target early in the virus's life cycle that could save millions of lives.

In the surprising discovery, published Feb. 18 in Nature, Perilla and collaborators in the U.S. and the United Kingdom, have revealed a previously unknown role for the viral protein integrase. Scientists already knew that integrase helps HIV insert itself into human DNA. But this new study provides the first direct evidence that integrase plays a critical structural role earlier on in HIV’s life cycle — when the virus matures into an infectious force.

Using high-resolution cryo-electron microscopy (cryo-EM), the research team – which also included UD doctoral student Juan S. Rey – found that integrase proteins form gluey filaments that line the inside of the capsid. Each segment of the filament slots neatly into the capsid’s hexagon-shaped tiles, while gripping tightly to HIV’s RNA genome. This zipper-like arrangement organizes and packs the virus, preparing it to hijack a cell and start making copies of itself.

“Integrase plays a structural role inside the HIV capsid — nobody expected that,” Perilla said. “This protein forms filaments that anchor the RNA to the capsid. Without these filaments, the virus is non-infective.”

Seeing inside HIV is no small feat. The capsid is only about 120 nanometers wide —roughly 1/800th the thickness of a human hair. It is incredibly small, fragile, densely packed and constantly changing, Perilla said.

To reveal its hidden architecture, the researchers relied on deep collaboration and a combination of sophisticated microscopy, molecular modeling and experimentation. Read more about the science behind the study here.

“The thing with HIV is that people are chronically living with it,” Perilla said. “Treatments are effective, but patients always need new therapeutics. We want to help develop the next generation of inhibitors and hope to have a significant contribution.”

To reach Perilla directly and arrange an interview, visit his profile page. Interested reporters can also email MediaRelations@udel.edu.

Connect with:

Juan Perilla

Assistant Professor

Juan Perilla develops physical and chemical based methodologies for the understanding of biological process related to life and disease

Computational PhysicsComputational ChemistryData ScienceMachine LearningMolecular Virology

Find Experts

Jun 23, 2026

3 min

Decoding epilepsy, one brainwave at a time

Epilepsy isn’t always easy to diagnose. Seizures often don't occur during routine brain-wave recordings, leaving doctors without the direct observation they need to make a clear diagnosis. In a proof-of-concept study in mice, University of Delaware researchers and collaborators showed that using artificial intelligence to detect early warning signs hidden in the brain's electrical rhythms can identify subtle EEG differences linked to a genetic form of epilepsy, even when no visible seizures occurred. The findings, published in the Journal of Neural Engineering, set the stage for the next phase of the research, which will test the method on EEGs from children being evaluated for epilepsy at Nemours Children's Health. A dictionary of brain waves Neurologists often use EEGs to help diagnose epilepsy, but routine recordings offer only about a 20-minute snapshot of brain activity. Without a seizure captured during that window, clinicians must look for far subtler clues that can be difficult to detect visually. That's where AI comes in. “Our machine-learning approach lets the algorithm learn the brain’s ‘language’ of waveforms, spotting subtle patterns humans might miss during manual review,” said Austin Brockmeier, assistant professor in electrical and computer engineering and computer and information sciences. Starting small with a mouse model When Brockmeier presented his computational neuroscience research at a seminar, he caught the attention of Amanda Hernan, an affiliated associate professor of psychological and brain sciences and biomedical engineering at UD and senior research scientist at Nemours Children’s Health. Hernan studies how variations in brain activity affect thinking and learning in children with epilepsy. The two decided to put machine learning to the test using EEGs from mice with epilepsy-causing variations in the TSC1 gene. The researchers used a panel of more than 40 mice, including animals with and without the gene variation, across three different genetic backgrounds, or strains. They extracted EEG segments from five days of recordings from each mouse for analysis. Because the EEG segments contained no seizure activity, the algorithm had to detect differences in the brain's baseline activity alone. It was able to distinguish between the mouse strains and to detect the TSC1 gene variation with high accuracy in two of the three strains. “These results show that EEG patterns contain measurable signals of neurological differences, even without visible seizures,” Hernan said. Taking it to the clinic Now, Brockmeier and Hernan will next apply their approach to EEG recordings from children being evaluated for epilepsy at Nemours Children's Health. Pediatric EEGs are shorter than the multi-day recordings used in the mouse study, and children present with many different types of epilepsy. But the researchers are optimistic. “The goal is to identify biomarkers that flag underlying changes in the brain’s electrical activity before seizures occur,” Hernan said. Earlier detection could lead to earlier treatment and less uncertainty for families. That uncertainty, Hernan said, takes a toll. “Seizures follow natural cycles, but without a way to know where you are in that cycle, the anticipation can be incredibly anxiety-provoking,” she explained. Better pattern recognition could also improve treatment decisions. For example, if a new medication is introduced during a natural lull in seizure activity, its benefits could be overestimated. Looking further ahead, the researchers envision a future where wearable EEG devices allow continuous, real-time monitoring for those with high risk of seizures. Similar approaches could eventually be applied to other neurological conditions, including autism and ADHD. "This is a step toward precision medicine," Brockmeier said. "Brain-wave typing could help identify which interventions will work best for a given patient." For families navigating the daily uncertainty of epilepsy, that kind of precision could make a huge difference. To speak with Brockmeier and Hernan, please reach out to mediarelations@udel.edu.

Jun 18, 2026

1 min

UD’s happiness expert appears on NPR's Hidden Brain to explain importance of a helping hand in a stressed-out America

Happiness isn’t just about chasing big, exciting moments. A lot of the science points to the smaller, everyday things that help people feel connected, calm and grounded. Simple habits like helping others when we see them struggling create a bigger impact than we often expect. University of Delaware's resident "happiness expert" Amit Kumar, a psychologist and assistant professor of marketing in UD's Lerner College of Business & Economics, appeared on NPR's Hidden Brain to discuss that very topic. Kumar discusses why sometimes it feels like we can't help others and how we can surmount those fears to build strong connections and also feel a greater sense of happiness. To speak with Kumar about this topic, click his profile.

Jun 11, 2026

1 min

Concussions in soccer featured

University of Delaware professor Tom Kaminski leads FIFA’s research on header safety and avoiding concussions. NBC10 Delaware Bureau reporter Tim Furlong tells us more about his findings.