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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.