Category: 8. Health

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.