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In a preclinical study, researchers led by City of Hope have discovered that a type of immune cell in the human body, known to be important for allergy and other immune responses, can also attack cancer. The cells, called human type 2 innate lymphoid cells (ILC2s), can be expanded outside of the body and applied in larger numbers to overpower a tumor’s defenses and eliminate malignant cells in mouse models with cancer.

The findings are published in Cell in an article titled, “Therapeutic application of human type 2 innate lymphoid cells via induction of granzyme B-mediated tumor cell death.”

“The City of Hope team has identified human ILC2 cells as a new member of the cell family capable of directly killing all types of cancers, including blood cancers and solid tumors,” said Jianhua Yu, PhD, a professor in the department of hematology and hematopoietic cell transplantation at City of Hope and the study’s senior author. “In the future, these cells could be manufactured, preserved by freezing, and then administered to patients. Unlike T cell-based therapies, such as CAR T cells, which necessitate using the patient’s own cells due to their specific characteristics, ILC2s might be sourced from healthy donors, presenting a distinct potential therapeutic approach as an allogeneic and ‘off-the-shelf’ product.”

Last week, researchers at the West Virginia University Rockefeller Neuroscience Institute reported that by using focused ultrasound to open the blood-brain barrier, they improved delivery of a new Alzheimer’s treatment and sped up clearance of the sticky plaques that are thought to contribute to some of the cognitive and memory problems in people with Alzheimer’s by 32%.

For this issue of The Checkup, we’ll explore some of the ways scientists are trying to disrupt the blood-brain barrier.

In the West Virginia study, three people with mild Alzheimer’s received monthly doses of aducanumab, a lab-made antibody that is delivered via IV. This drug, first approved in 2021, helps clear away beta-amyloid, a protein fragment that clumps up in the brains of people with Alzheimer’s disease. (The drug’s approval was controversial, and it’s still not clear whether it actually slows progression of the disease.) After the infusion, the researchers treated specific regions of the patients’ brains with focused ultrasound, but just on one side. That allowed them to use the other half of the brain as a control. PET scans revealed a greater reduction in amyloid plaques in the ultrasound-treated regions than in those same regions on the untreated side of the brain, suggesting that more of the antibody was getting into the brain on the treated side.

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OHSU becomes the first in Oregon to offer focused ultrasound to immediately relieve symptoms of essential tremor, tremor-dominant Parkinson’s disease.

Discovered mechanism provides potential therapeutic targets to slow aging and age-related neurodegenerative diseases.

One approach to AI uses a process called machine learning. In machine learning, a computer model is built to predict what may happen in the real world. The model is taught to analyze and recognize patterns in a data set. This training enables the model to then make predictions about new data. Some AI programs can also teach themselves to ask new questions and make novel connections between pieces of information.

“Computer models and humans can really work well together to improve human health,” explains Dr. Grace C.Y. Peng, an NIH expert on AI in medicine. “Computers are very good at doing calculations at a large scale, but they don’t have the intuitive capability that we have. They’re powerful, but how helpful they’re going to be lies in our hands.”

Researchers are exploring ways to harness the power of AI to improve health care. These include assisting with diagnosing and treating medical conditions and delivering care.

Part 3: This is the last of a three-part series on how Stanford Medicine researchers are designing vaccines that protect people from not merely individual viral strains but broad ranges of them. The ultimate goal: a vaccine with coverage so broad it can protect against viruses never before encountered.

Until now, vaccine efforts have mainly focused on stimulating B cells, described and discussed in Part 1 and Part 2. These antibody-producing immune cells’ virtue of being highly specific in what they target is also a vice. An antibody against influenza is unlikely to ever bind to, say, a coronavirus or a rabies virus.

Even when a virus mutates in some small way that distorts or disguises one of its biochemical bull’s-eyes, antibodies that worked before (because they aimed at that particular bull’s-eye) are now unemployed.

As we turn our attention from the front of the eye to the back, we also look to the future. Many studies have combined oculomics with AI tools to predict biological age from retinal biomarkers, such as retinal vasculature [1, 6], and even linked this to chronic disease risk, such as cardiovascular disease and cancer [7]. High resolution imaging tools also enable direct visualisation of the neural layers within the retina, which can show signs of neurodegenerative diseases, such as Alzheimer’s disease [1, 6], Parkinson’s disease [8], multiple sclerosis [6, 9], and even rare conditions, such as Lafora disease [10]. In many cases, the oculomic signs are present before symptoms arise. For example, it has been shown that proteins related to Alzheimer’s disease (such as amyloid-beta) accumulate at least one decade prior to cognitive decline [11] and these proteins also accumulate in the retina [12]. This is particularly pertinent to clinical research and drug development, as it enables identification of those who may benefit from intervention before irreversible damage has taken place.

Advances in imaging technology mean that we can now detect biomarkers at cellular resolution. We are continually finding new applications for imaging techniques to detect disease before it takes hold, providing the opportunity to intervene and potentially avoid disease altogether. It’s definitely an exciting time for oculomics research!

Crystallomancy has come a long way since Ancient Roman times, and it makes one wonder whether the scryers of the past could have predicted the transformation of orb-gazing from a mystical art to a rigorous science. Not only does Oculomics enable us to look into your past and present, but also has the potential to look into your future, providing you the opportunity to change your “fate”. Although we cannot be sure what form the advancements in imaging and AI tools will take over the coming years, we can be sure of one thing – that oculomics has a promising future in the quest for longevity.

A previously unidentified genetic mutation in a small protein provides significant protection against Parkinson’s disease and offers a new direction for exploring potential treatments, according to a new USC Leonard Davis School of Gerontology study.

The variant, located in a mitochondrial microprotein dubbed SHLP2, was found to be highly protective against Parkinson’s disease; individuals with this mutation are half as likely to develop the disease as those who do not carry it. The variant form of the protein is relatively rare and is found primarily in people of European descent.

The findings appear in the journal Molecular Psychiatry.

A new artificial intelligence tool that interprets medical images with unprecedented clarity does so in a way that could allow time-strapped clinicians to dedicate their attention to critical aspects of disease diagnosis and image interpretation.

The tool, called iStar (Inferring Super-Resolution Tissue Architecture), was developed by researchers at the Perelman School of Medicine at the University of Pennsylvania, who believe they can help clinicians diagnose and better treat cancers that might otherwise go undetected.

The imaging technique provides both highly detailed views of individual cells and a broader look at the full spectrum of how people’s genes operate, which would allow doctors and researchers to see cancer cells that might otherwise have been virtually invisible. This tool can be used to determine whether safe margins were achieved through cancer surgeries and automatically provide annotation for microscopic images, paving the way for molecular disease diagnosis at that level.