Category: 8. Health

Florida Atlantic University researchers have secured two key grants to investigate targeting a gene for the first time as a new approach to treat glioblastoma, a very aggressive and fast-growing type of brain cancer.

Malignant gliomas, such as glioblastoma multiforme and astrocytomas, are the most common type of primary brain tumor in the United States. They make up about 78% of all malignant brain tumors. Although these tumors are not very common, especially in the U.S., they cause a high number of cancer deaths because they are so aggressive.

These awards, granted by the Florida Department of Health’s Cancer Connect program for $562,000 and the Palm Health Foundation for $50,000, support a distinctive collaboration among FAU researchers that unites complementary expertise in neuroscience and cancer biology. Together, this partnership aims to advance highly innovative projects that have the potential to make a meaningful impact on cancer therapy.

The grants will explore a promising new target – a gene called MBLAC1 – whose properties encouraged a collaboration between two FAU research labs, one focused on molecular neuroscience and the other on cancer mechanisms and treatment.

MBLAC1 plays a key role in regulating copper levels within cells, which affects mitochondrial function and oxidative stress – both critical factors in cancer cell survival and growth. Since glioblastoma cells rely heavily on mitochondrial energy production and protection against oxidative damage, targeting MBLAC1 could disrupt these processes and slow tumor progression.

Investigators of this grant are Randy D. Blakely, Ph.D., executive director of the FAU Stiles-Nicholson Brain Institute, the David J.S. Nicholson Distinguished Professor in Neuroscience and a professor of biomedical science within FAU’s Charles E. Schmidt College of Medicine; and Gregg B. Fields, Ph.D., FAU vice president for research and executive director of the FAU Institute for Human Health and Disease Intervention (I-Health).

By combining our expertise in cancer biology and neuroscience, we are approaching glioblastoma with a fresh and powerful perspective. By identifying how MBLAC1 supports tumor growth and testing drugs that block its function, our team hopes to lay the groundwork for developing new, effective therapies that could improve outcomes for patients with glioblastoma and possibly other cancers.”

Gregg B. Fields, Ph.D., FAU vice president for research and executive director of the FAU Institute for Human Health and Disease Intervention 

The project will investigate how MBLAC1 influences glioblastoma invasion and copper homeostasis using advanced 3D tumor models and genetically engineered mice lacking the gene.

“I’m excited to bring my background in neuroscience to explore new frontiers in cancer research,” said Blakely. “Our work centers on how a specific gene regulates copper – a vital micronutrient – in brain cells, which directly influences how these cells generate energy and manage stress. Because cancer cells depend heavily on energy to grow and spread, uncovering and interrupting this process could transform treatment. This collaboration is a perfect example of how combining different scientific perspectives can spark innovation and accelerate progress in the fight against cancer.”

The Blakely lab discovered that MBLAC1, a previously unstudied gene, plays a crucial role in brain cell function by regulating copper, which is essential for cell energy production and protection against oxidative stress. Lower levels of MBLAC1 have been linked to better survival in patients, suggesting it could be a key target for new treatments.

Researchers will investigate whether MBLAC1 from support brain cells or from the cancer cells themselves drives tumor invasion. Using 3D tumor models that closely mimic real tumors, they will observe how blocking MBLAC1 or copper affects tumor growth and spread. They are also developing a new test to quickly identify drugs that specifically inhibit MBLAC1 activity.

Blakely is collaborating on this project with cancer biologists Fields and Ania Knapinska, Ph.D., principal investigator and a research professor at FAU I-Health.

“Our project breaks new ground by uncovering a molecular pathway that actively promotes cancer through a gene linked to copper balance and glioblastoma risk,” said Knapinska. “We’re also pioneering novel genetic models to study glioblastoma in ways never done before. Our discoveries that mutations in this gene reduce mitochondrial function and increase cellular stress have driven us to explore its fundamental role in copper regulation and metabolism in living systems. Because this gene is highly targetable by drugs, our research opens promising new avenues for developing effective glioblastoma treatments.”

Glycogen is typically thought of as a reserve energy source stored in the liver and muscles. While small amounts also exist in the brain, particularly in support cells called astrocytes, its role in neurons has long been dismissed as negligible. “This new study challenges that view, and it does so with striking implications,” says Professor Pankaj Kapahi, PhD, senior scientist on the study. “Stored glycogen doesn’t just sit there in the brain; it is involved in pathology.”

The research team, led by postdoc Sudipta Bar, PhD, discovered that in both fly and human models of tauopathy (a group of neurodegenerative diseases including Alzheimer’s), neurons accumulate excessive glycogen. More importantly, this buildup appears to contribute to disease progression. Bar says tau, the infamous protein that clumps into tangles in Alzheimer’s patients, appears to physically bind to glycogen, trapping it and preventing its breakdown.

When glycogen can’t be broken down, the neurons lose an essential mechanism for managing oxidative stress, a key feature in aging and neurodegeneration. By restoring the activity of an enzyme called glycogen phosphorylase (GlyP) — which kicks off the process of glycogen breakdown — the researchers found they could reduce tau-related damage in fruit flies and human stem cell-derived neurons.

Rather than using glycogen as a fuel for energy production, these enzyme-supported neurons rerouted the sugar molecules into the pentose phosphate pathway (PPP) — a critical route for generating NADPH (nicotinamide adenine dinucleotide phosphate) and Glutathione, molecules that protect against oxidative stress. “By increasing GlyP activity, the brain cells could better detoxify harmful reactive oxygen species, thereby reducing damage and even extending the lifespan of tauopathy model flies,” said Bar.

Even more promising, the team demonstrated that dietary restriction (DR) — a well-known intervention to extend lifespan — naturally enhanced GlyP activity and improved tau-related outcomes in flies. They further mimicked these effects pharmacologically using a molecule called 8-Br-cAMP, showing that the benefits of DR might be reproduced through drug-based activation of this sugar-clearing system. “This work could explain why GLP-1 drugs, now widely used for weight loss, show promise against dementia, potentially by mimicking dietary restriction,” said Kapahi.

Researchers also confirmed similar glycogen accumulation and protective effects of GlyP in human neurons derived from patients with frontotemporal dementia (FTD), strengthening the potential for translational therapies. Kapahi says the study emphasizes the power of the fly as a model system in uncovering how metabolic dysregulation impacts neurodegeneration. “Work in this simple animal allowed us to move into human neurons in a much more targeted way,” he said.

Kapahi also acknowledges the Buck’s highly collaborative atmosphere as a major factor in the work. His lab, with expertise in fly aging and neurodegeneration, took advantage of proteomics expertise in the Schilling lab and the Seyfried lab (at Emory University) as well as the Ellerby lab which has expertise in human iPSCs and neurodegeneration.

Kapahi says this study not only highlights glycogen metabolism as an unexpected hero in the brain but also opens up a new direction in the search for treatments against Alzheimer’s and related diseases. “By discovering how neurons manage sugar, we may have unearthed a novel therapeutic strategy: one that targets the cell’s inner chemistry to fight age-related decline,” he says. “As we continue to age as a society, findings like these offer hope that better understanding — and perhaps rebalancing — our brain’s hidden sugar code could unlock powerful tools for combating dementia.”

Coauthors: Additional Buck collaborators include Kenneth A. Wilson, Tyler A.U. Hilsabeck, Sydney Alderfer, Jordan B Burton, Samah Shah, Anja Holtz, Enrique M. Carrera, Jennifer N. Beck, Jackson H Chen, Grant Kauwe, Tara E. Tracy, Birgit Schilling, and Lisa M. Ellerby. Other collaborators include Eric B. Dammer, Fatemeh Seifar and Nicholas T. Seyfried, Emory Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA as well as Ananth Shantaraman, Department of Biochemistry, Emory University School of Medicine, Atlanta, GA

Acknowledgments: The work was supported by NIH grants R01AG038688, R21AG054121, AG045835, R01AG071995, R01AG070193, T32AG000266-23, R01AG061879, P01AG066591 and 1S10 OD016281. Other support came from the Hevolution Foundation, American Federation of Aging Research, the Larry L. Hillblom Foundation and the Catalyst^X award from Alex and Bob Griswold

New research from the University of Pittsburgh School of Medicine and La Jolla Institute for Immunology, published today in Nature Microbiology, reveals an opportunity for developing a therapy against cytomegalovirus (CMV), the leading infectious cause of birth defects in the United States.

Researchers discovered a previously unappreciated mechanism by which CMV, a herpes virus that infects the majority of the world’s adult population, enters cells that line the blood vessels and contributes to vascular disease. In addition to using molecular machinery that is shared by all herpes viruses, CMV employs another molecular “key” that allows the virus to sneak through a side door and evade the body’s natural immune defenses.

The finding might explain why efforts to develop prophylactic treatments against CMV have, so far, been unsuccessful. This research also highlights a new potential avenue for the development of future antiviral drugs and suggests that other viruses of the herpes family, such as Epstein-Barr and chickenpox, could use similar molecular structures to spread from one infected cell to the next while avoiding immune detection.

If we don’t know what weapons the enemy is using, it is hard to protect against it. We found a missing puzzle piece that represents one possible reason why immunization efforts against CMV have been unsuccessful.”

Jeremy Kamil, Ph.D., senior author, associate professor of microbiology and molecular genetics at Pitt

In the United States, approximately one in every 200 babies is born with congenital CMV infection. Of the babies infected, one in five will have birth defects, such as hearing loss, or go on to have long-term health challenges. For most adults, CMV infections are asymptomatic. But a CMV infection during pregnancy presents significant health risks to the unborn child and could be deadly for people who are immunosuppressed, including organ transplant recipients.

Because of the large size of its genome and its complicated molecular machinery, CMV long evaded attempts to develop prophylactic treatments. Similar to other herpes viruses, CMV relies on a protein called gH to enter cells of the vessel lining. But unlike other herpes viruses, which use a protein partner called gL to facilitate infection, the new study found that CMV replaces gL with another partner called UL116 and recruits a protein called UL141. The resulting complex of gH-UL116-UL141, called GATE by the authors, then becomes an alternative tool for breaking into cells lining the blood vessels and causing internal damage while simultaneously preventing the body’s own immune system from recognizing the signs of infection.

The newly discovered GATE could become a potential vaccine target for CMV and other herpes viruses.

“Previous attempts to generate a CMV vaccine have failed, but that was before we identified the GATE complex. We hope that new strategies targeting GATE will improve our chances to combat CMV infection, and also perhaps cleanse our bodies of this lifelong infection,” said Chris Benedict, Ph.D., associate professor at La Jolla Institute for Immunology and co-senior author of the study with Kamil and LJI professor, president & CEO Erica Ollmann Saphire, Ph.D., MBA. “If we can develop antiviral drugs or vaccines that inhibit CMV entry, this will allow us to combat the many diseases this virus causes in developing babies and immune-compromised people.”

Other authors of this research are Michael Norris, Ph.D., of the University of Toronto; Lauren Henderson, Mohammed Siddiquey, Ph.D., both of Louisiana State University Health Shreveport; and Jieyun Yin, Ph.D., Kwangsun Yoo, Ph.D., Simon Brunel, Ph.D., Michael Mor, Ph.D., and Erica Ollmann Saphire, Ph.D., all of La Jolla Institute for Immunology.

This research was supported by the National Institutes of Health (grants AI11685, AI139749, AI101423 and T32HL155022) and by ARPA-H APECx contract 1AY1AX000055.

GA, UNITED STATES, June 30, 2025 /EINPresswire.com/ — A new study reveals that the protein Lcn2, secreted by lung macrophages, plays a central role in exacerbating severe pneumonia caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Researchers developed a lethal pneumonia mouse model using an adapted viral strain and found that elevated levels of Lcn2 directly correlate with disease severity. Lcn2 not only enhanced inflammatory signaling and neutrophil adhesion but also disrupted endothelial barriers, leading to heightened lung damage. This research sheds light on a critical pathway—NLRP3-mediated Lcn2 secretion—that drives the escalation of inflammation in the lungs. The findings suggest Lcn2 as a potential diagnostic marker and therapeutic target for severe respiratory infections such as COVID-19.

Since the emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), understanding the mechanisms underlying severe pneumonia has remained a major research challenge. Although mouse models exist, most mimic only mild to moderate disease, limiting the ability to study life-threatening respiratory outcomes. Emerging data show that immune overactivation, especially through macrophage-driven inflammation, plays a decisive role in worsening COVID-19. Among many inflammatory mediators, lipocalin 2 (Lcn2) has been increasingly associated with respiratory disease severity, yet its regulatory mechanisms and pathological consequences are not well defined. Based on these challenges, there is a pressing need to investigate the upstream pathways and downstream effects of Lcn2 in virus-induced lung inflammation.

In a letter-style study published on August 24, 2024, in Protein & Cell, researchers from the Institute of Laboratory Animal Science, CAMS & PUMC, etc., reported that macrophage-secreted Lcn2 significantly worsens SARS-CoV-2-induced pneumonia in mice. By adapting the Beta variant to wild-type BALB/c mice, the team established a model of severe pneumonia, enabling detailed investigation into immune responses. The researchers identified Lcn2 as a key proinflammatory mediator activated through the NLRP3 signaling pathway, linking it directly to alveolar injury and systemic inflammation in viral lung infections.

To create a more accurate model of severe COVID-19 pneumonia, the researchers developed a mouse-adapted SARS-CoV-2 strain (P7) that induced intense lung pathology and high mortality. P7-infected mice exhibited elevated levels of inflammatory cytokines and profound lung tissue damage. Transcriptomic and proteomic analyses revealed that Lcn2 expression was markedly upregulated following P7 infection. Further studies showed that macrophages were the main source of Lcn2, and that its production was driven by the NLRP3 signaling pathway. Inhibiting NLRP3 significantly reduced Lcn2 levels, confirming the pathway’s central regulatory role. Functionally, Lcn2 was shown to amplify pulmonary inflammation by stimulating endothelial cells to express adhesion molecules (e.g., VCAM1), increasing neutrophil adhesion to endothelial cells, and weakening intercellular junctions. This resulted in compromised vascular integrity and greater immune cell infiltration. The team also discovered that a specific W682R mutation near the furin cleavage site in the viral spike protein may contribute to the enhanced infectivity and inflammation observed in the P7 strain. These findings provide crucial mechanistic insights into how viral evolution and host immune responses interact to produce severe lung pathology.

This study identifies Lcn2 as a key inflammatory mediator that drives severe lung damage during viral infection, said Dr. Linlin Bao, corresponding author of the study. By establishing a wild-type mouse model that closely mimics severe pneumonia, the researchers were able to uncover how the NLRP3-Lcn2 axis contributes to the pathogenesis. This opens new doors for understanding disease mechanisms and for targeting inflammation at its source, potentially leading to novel treatment strategies for severe COVID-19 and related respiratory diseases.

The discovery of Lcn2’s central role in promoting severe pneumonia has broad implications. It positions Lcn2 not only as a biomarker for early detection of disease severity but also as a candidate for therapeutic intervention. Targeting the NLRP3-Lcn2 axis may offer a new strategy to mitigate lung injury in severe respiratory infections. Moreover, the established mouse model provides a valuable tool for testing antiviral and anti-inflammatory treatments. As new SARS-CoV-2 variants continue to emerge, understanding host-pathogen interactions like these will be critical for preparing for future public health threats.

References
DOI
10.1093/procel/pwae045

Original Source URL
https://doi.org/10.1093/procel/pwae045

Funding information
This work was supported by the National Research and Development Project of China (grant no. 2023YFF0724800), the CAMS Initiative for Innovative Medicine of China (grant no. 2021-I2M-1-035), the Sector Fund (2060302), and Young Elite Scientists Sponsorship Program by CAST (YESS) (grant no: 2020QNRC001).

Lucy Wang
BioDesign Research
email us here

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Now, a team of Northwestern Medicine scientists has developed an AI tool called iSeg that not only matches doctors in accurately outlining lung tumors on CT scans but can also identify areas that some doctors may miss, reports a large new study.

Unlike earlier AI tools that focused on static images, iSeg is the first 3D deep learning tool shown to segment tumors as they move with each breath — a critical factor in planning radiation treatment, which half of all cancer patients in the U.S. receive during their illness.

“We’re one step closer to cancer treatments that are even more precise than any of us imagined just a decade ago,” said senior author Dr. Mohamed Abazeed, chair and professor of radiation oncology at Northwestern University Feinberg School of Medicine.

“The goal of this technology is to give our doctors better tools,” added Abazeed, who leads a research team developing data-driven tools to personalize and improve cancer treatment and is a member of the Robert H. Lurie Comprehensive Cancer Center of Northwestern University.

The study was published today (June 30) in the journal npj Precision Oncology.

How iSeg was built and tested

The Northwestern scientists trained iSeg using CT scans and doctor-drawn tumor outlines from hundreds of lung cancer patients treated at nine clinics within the Northwestern Medicine and Cleveland Clinic health systems. That’s far beyond the small, single-hospital datasets used in many past studies.

After training, the AI was tested on patient scans it hadn’t seen before. Its tumor outlines were then compared to those drawn by physicians. The study found that iSeg consistently matched expert outlines across hospitals and scan types. It also flagged additional areas that some doctors missed — and those missed areas were linked to worse outcomes if left untreated. This suggests iSeg may help catch high-risk regions that often go unnoticed.

“Accurate tumor targeting is the foundation of safe and effective radiation therapy, where even small errors in targeting can impact tumor control or cause unnecessary toxicity,” Abazeed said.

“By automating and standardizing tumor contouring, our AI tool can help reduce delays, ensure fairness across hospitals and potentially identify areas that doctors might miss — ultimately improving patient care and clinical outcomes,” added first author Sagnik Sarkar, a senior research technologist at Feinberg who holds a Master of Science in artificial intelligence from Northwestern.

Clinical deployment possible ‘within a couple years’

The research team is now testing iSeg in clinical settings, comparing its performance to physicians in real time. They are also integrating features like user feedback and working to expand the technology to other tumor types, such as liver, brain and prostate cancers. The team also plans to adapt iSeg to other imaging methods, including MRI and PET scans.

“We envision this as a foundational tool that could standardize and enhance how tumors are targeted in radiation oncology, especially in settings where access to subspecialty expertise is limited,” said co- author Troy Teo, instructor of radiation oncology at Feinberg.

“This technology can help support more consistent care across institutions, and we believe clinical deployment could be possible within a couple of years,” Teo added.

This study is titled “Deep learning for automated, motion- resolved tumor segmentation in radiotherapy.”

In radiation therapy, precision can save lives. Oncologists must carefully map the size and location of a tumor before delivering high-dose radiation to destroy cancer cells while sparing healthy tissue. But this process, called tumor segmentation, is still done manually, takes time, varies between doctors – and can lead to critical tumor areas being overlooked.

Now, a team of Northwestern Medicine scientists has developed an AI tool called iSeg that not only matches doctors in accurately outlining lung tumors on CT scans but can also identify areas that some doctors may miss, reports a large new study.

Unlike earlier AI tools that focused on static images, iSeg is the first 3D deep learning tool shown to segment tumors as they move with each breath – a critical factor in planning radiation treatment, which half of all cancer patients in the U.S. receive during their treatment.

“We’re one step closer to cancer treatments that are even more precise than any of us imagined just a decade ago,” said senior author Dr. Mohamed Abazeed, chair and professor of radiation oncology at Northwestern University Feinberg School of Medicine.

“The goal of this technology is to give our doctors better tools,” added Abazeed, who leads a research team developing data-driven tools to personalize and improve cancer treatment and is a member of the Robert H. Lurie Comprehensive Cancer Center of Northwestern University.

The study was published today (June 30) in the journal npj Precision Oncology.

How iSeg was built and tested

The Northwestern scientists trained iSeg using CT scans and doctor-drawn tumor outlines from hundreds of lung cancer patients treated at nine clinics within the Northwestern Medicine and Cleveland Clinic health systems. That’s far beyond the small, single-hospital datasets used in many past studies.

After training, the AI was tested on patient scans it hadn’t seen before. Its tumor outlines were then compared to those drawn by physicians. The study found that iSeg consistently matched expert outlines across hospitals and scan types. It also flagged additional areas that some doctors missed – and those missed areas were linked to worse outcomes if left untreated. This suggests iSeg may help catch high-risk regions that often go unnoticed.

“Accurate tumor targeting is the foundation of safe and effective radiation therapy, where even small errors in targeting can impact tumor control or cause unnecessary toxicity,” Abazeed said.

“By automating and standardizing tumor contouring, our AI tool can help reduce delays, ensure fairness across hospitals and potentially identify areas that doctors might miss – ultimately improving patient care and clinical outcomes,” added first author Sagnik Sarkar, a senior research technologist at Feinberg who holds a Master of Science in artificial intelligence from Northwestern.

Clinical deployment possible ‘within a couple years’

The research team is now testing iSeg in clinical settings, comparing its performance to physicians in real time. They are also integrating features like user feedback and working to expand the technology to other tumor types, such as liver, brain and prostate cancers. The team also plans to adapt iSeg to other imaging methods, including MRI and PET scans.

“We envision this as a foundational tool that could standardize and enhance how tumors are targeted in radiation oncology, especially in settings where access to subspecialty expertise is limited,” said co- author Troy Teo, instructor of radiation oncology at Feinberg.

“This technology can help support more consistent care across institutions, and we believe clinical deployment could be possible within a couple of years,” Teo added.

This study is titled “Deep learning for automated, motion- resolved tumor segmentation in radiotherapy.”

A low-fat vegan diet significantly decreases dietary acid load compared to a Mediterranean diet, finds the Physicians Committee for Responsible Medicine. The randomized crossover trial also affirms that this diet — consisting of leafy greens, berries, and legumes — promotes weight loss and a healthy gut microbiome.

“Eating acid-producing foods like meat, eggs, and dairy can increase the dietary acid load, or the amount of acids consumed, causing inflammation linked to weight gain,” says lead author Hana Kahleova, MD, Ph.D., director of clinical research at the Physicians Committee. 

“Replacing animal products with plant-based foods like leafy greens, berries, and legumes can help promote weight loss and create a healthy gut microbiome.”

Animal products linked to health risks

Published in Frontiers in Nutrition, the study examined 62 overweight adults who either followed a Mediterranean or a low-fat vegan diet for 16 weeks. They first went through a four-week cleansing period, followed by an additional 16 weeks on the alternate diet.

Those eating animal products — meat, fish, eggs, and cheese — produced more acid, increasing dietary acid load. The researchers say this is tied to chronic inflammation and metabolism disruption that can lead to increased body weight. 

They add that plant diets are more alkaline and linked to weight loss, improved insulin sensitivity, and lower blood pressure.

Early this year, the U.S. News and World Report scored the Mediterranean diet as the “most highly rated” out of 38 diets examined, based on nutritional completeness, health risks and benefits, long-term sustainability and evidence-based effectiveness.

Vegan diets lead to weight loss

Researchers used the Potential Renal Acid Load (PRAL) and Net Endogenous Acid Production (NEAP) scores to calculate dietary acid load. Higher scores show higher acid load.

PRAL and NEAP scores were seen to decrease significantly on the vegan diet, while no significant changes were seen on the Mediterranean diet.

Lower dietary acid is linked to weight loss, which was seen even after adjusting changes in energy intake, say the researchers.

The study revealed participants’ body weight decreased by 13.2 pounds on the vegan diet compared to no changes in the Mediterranean diet. 

Researchers add that top alkalizing foods include vegetables, particularly leafy greens, broccoli, beets, asparagus, garlic, carrots, and cabbage. 

It also includes fruits, such as berries, apples, cherries, apricots, or cantaloupe; legumes, like lentils, chickpeas, peas, beans or soy; and grains, such as quinoa or millet.

By: Cassidy Delamarter, University Communications and Marketing

Diagnosing post-traumatic stress disorder in children can be notoriously difficult.
Many, especially those with limited communication skills or emotional awareness, struggle
to explain what they’re feeling. Researchers at the University of South Florida are
working to address those gaps and improve patient outcomes by merging their expertise
in childhood trauma and artificial intelligence. 

Led by Alison Salloum, professor in the USF School of Social Work, and Shaun Canavan, associate professor in the Bellini Center for Artificial Intelligence, Cybersecurity and Computing, the interdisciplinary team is building a system that could provide clinicians with
an objective, cost-effective tool to help identify PTSD in children and adolescents,
while tracking their recovery over time.

Traditionally, diagnosing PTSD in children relies on subjective clinical interviews
and self-reported questionnaires, which can be limited by cognitive development, language
skills, avoidance behaviors or emotional suppression. 

“This really started when I noticed how intense some children’s facial expressions
became during trauma interviews,” Salloum said. “Even when they weren’t saying much,
you could see what they were going through on their faces. That’s when I talked to
Shaun about whether AI could help detect that in a structured way.”

Canavan, who specializes in facial analysis and emotion recognition, repurposed existing
tools in his lab to build a new system that prioritizes patient privacy. The technology
strips away identifying details and only analyzes de-identified data, including head
pose, eye gaze and facial landmarks, such as the eyes and mouth. 

“That’s what makes our approach unique,” Canavan said. “We don’t use raw video. We
completely get rid of the subject identification and only keep data about facial movement,
and we factor in whether the child was talking to a parent or a clinician.”

The study, published in Science Direct, is the first of its kind to incorporate context-aware PTSD classification while
fully preserving participant privacy. The team built a dataset from 18 sessions with
children as they shared emotional experiences. With more than 100 minutes of video
per child and each video containing roughly 185,000 frames, Canavan’s AI models extracted
a range of subtle facial muscle movements linked to emotional expression.

The findings revealed distinct patterns are detectable in the facial movements of
children with PTSD.  The researchers also found that facial expressions during clinician-led
interviews were more revealing than parent-child conversations. This aligns with existing
psychological research showing children may be more emotionally expressive with therapists
and may avoid sharing distress with parents due to shame or their cognitive abilities.

“That’s where the AI could offer a valuable supplement,” Salloum said. “Not replacing
clinicians, but enhancing their tools. The system could eventually be used to give
practitioners real-time feedback during therapy sessions and help monitor progress
without repeated, potentially distressing interviews.”

The team hopes to expand the study to further examine any potential bias from gender,
culture and age, especially preschoolers, where verbal communication is limited and
diagnosis relies almost entirely on parent observation. 

Though the study is still in its early stages, Salloum and Canavan feel the potential
applications are far-reaching. Many of the current participants had complex clinical
pictures, including co-occurring conditions like depression, ADHD or anxiety, mirroring
real-world cases and offering promise for the system’s accuracy. 

“Data like this is incredibly rare for AI systems, and we’re proud to have conducted
such an ethically sound study. That’s crucial when you’re working with vulnerable
subjects,” Canavan said. “Now we have promising potential from this software to give
informed, objective insights to the clinician.”

If validated in larger trials, USF’s approach could redefine how PTSD in children
is diagnosed and tracked, using everyday tools like video and AI to bring mental health
care into the future.

Robert Volpe, a professor of applied psychology, says help is available for adults with ADHD, but self-diagnosis is dangerous

Attention problems and overactivity have long been associated with childhood.  

It was first included in the Diagnostic and Statistical Manual of the American Psychiatric Association in 1968 — at that time, it was called hyperkinetic reaction of childhood, says Robert Volpe, a professor of applied psychology at Northeastern and an expert on Attention Deficit Hyperactivity Disorder. 

It was understood to refer to the type of ants-in-your-pants behavior that distinguished children with severe motor activity, restlessness, distractibility and short attention spans.

These days, many adults are wondering whether their distractibility and difficulty with managing the tasks of everyday life stem from undiagnosed ADHD. Online discussion boards buzz with their concerns, and diagnostic internet quizzes are available with the click of a keyboard.

“Self-diagnosis is a risky thing to do,” says Volpe, who adds that only licensed psychologists and medical doctors, such as psychiatrists, are qualified to diagnose the condition and recommend treatment. 

There’s a danger people may misdiagnose themselves with ADHD when they need to be treated for anxiety or depression, he says.

But adult ADHD is a real phenomenon, Volpe says. And understanding what ADHD is — and isn’t — can be the first step in getting help.

ADHD is a neurodevelopmental disorder

Volpe says ADHD is a neurodevelopmental disorder that is associated with a complex set of interactions between genes and the environment, but for which there is no biological test.

“There are certainly some genetics involved, but it’s not one gene. It’s a combination of genes,” he says. The environment also must be factored in, as is the case with most psychiatric disorders.

There are different types of ADHD

In the 1980s, the disorder was referred to as simply attention deficit disorder. 

There are three subtypes now of ADHD now, Volpe says.

“Hyperactivity only is very rare and only found in young children. Hyperactivity is also very rare in adults. The two common subtypes for children and adolescents are ADHD Predominantly Inattentive Type and ADHD Combined Type.”  

Two sets of nine symptoms

There are two sets of symptom criteria, one for inattentive ADHD and one for hyperactive/impulsive ADHD, Volpe says.

People with the disorder must have at least six out of nine symptoms for one or both, he says.

The Cleveland Clinic says symptoms for inattentive ADHD include trouble paying attention to detail, listening to others or staying focused on long-winded tasks such as reading or listening to a presentation.

The symptom list also includes lack of follow-through on tasks and obligations as well as difficulty keeping track of everyday items such as pencils, wallets and keys, or staying in the moment without distractions.