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Hemorrhagic strokes account for just 13% of the nearly 800,000 strokes that occur in the U.S. each year, yet they are responsible for more than 40% of all stroke-related deaths. These devastating brain bleeds have long lacked standardized surgical treatment protocols, limiting patient recovery options.

In response, the American Heart Association, devoted to changing the future of health for all, is launching a new nationwide initiative to accelerate the adoption of effective surgical approaches, including minimally invasive techniques, to improve outcomes for people affected by intracerebral hemorrhage (ICH).

The Hemorrhagic Stroke Surgical Quality Improvement Initiative, supported financially by Stryker, will identify and amplify national models of effective ICH care, including the use of minimally invasive parafascicular surgery (MIPS). MIPS is a minimally invasive procedure that uses a tubular retractor to navigate through the brain’s natural folds, along with a powered instrument designed to remove clots and help prevent damage. Research has shown that when performed within 24 hours of a brain bleed, MIPS may lead to better recovery outcomes at six months compared to standard treatments.

The ability to share data and accelerate learning is critical to improving stroke care. Through this initiative, we hope to reduce the devastating outcomes associated with hemorrhagic stroke by improving access to surgical interventions proven to improve patient recovery.”

Kevin Sheth, M.D., FAHA, American Heart Association volunteer, chair of the Association’s ICH Science Advisory Group and director of the Yale Center for Brain & Mind Health

Fifteen hospitals across the U.S. will participate in a learning collaborative, using data from the Association’s Get With The Guidelines^® – Stroke registry, which tracks approximately three-quarters of the nation’s stroke hospitalizations. These selected sites will analyze ICH care data and clinical workflows to identify successful models that could be scaled nationally:

• Aurora St. Luke’s Medical Center in Milwaukee
• Cleveland Clinic in Cleveland
• DMC Detroit Receiving Hospital in Detroit
• Eden Medical Center in Castro Valley, California
• Hackensack Meridian Health Jersey Shore University Medical Center in Neptune, New Jersey
• Indiana University Health Methodist Hospital in Indianapolis
• Massachusetts General Hospital in Boston
• Memorial Hermann-Texas Medical Center in Houston
• Montefiore Medical Center-Moses Campus in Bronx, New York
• Penn State Health Milton S. Hershey Medical Center in Hershey, Pennsylvania
• SSM Health Saint Louis University Hospital in St. Louis
• The University of Kansas Health System in Kansas City, Kansas
• UCI Medical Center in Orange, California
• University Medical Center of El Paso in El Paso, Texas
• Yale-New Haven Hospital in New Haven, Connecticut

Criteria for site selection included demonstrated leadership in MIPS, infrastructure readiness and interest in expanding ICH surgical options.

As cancer cases have increased worldwide, the disease has become more complex, presenting challenges to scientific advances in diagnosis and treatment. In this context, artificial intelligence (AI) has emerged as a valuable tool for predicting and detecting cases. A tool developed by Brazilian and Polish researchers may contribute to this process.

The machine learning model can predict the aggressiveness of certain tumors by identifying specific proteins. It generates a stemness index ranging from zero to one, with zero indicating low aggressiveness and one indicating high aggressiveness. As the index increases, the cancer tends to become more aggressive and resistant to drugs and more likely to recur.

The degree of stemness indicates how closely tumor cells resemble pluripotent stem cells, which can transform into almost any type of cell in the human body. As the disease progresses, malignant cells become less and less similar to the tissue from which they originated. These cells self-renew and exhibit an undifferentiated phenotype.

The scientists developed the tool using data sets from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) for 11 types of cancer. They then developed the protein expression-based stemness index (PROTsi). They analyzed more than 1,300 samples of breast, ovarian, lung (squamous cell carcinoma and adenocarcinoma), kidney, uterine, brain (pediatric and adult), head and neck, colon, and pancreatic cancers.

By integrating PROTsi with proteomic data from 207 pluripotent stem cells, the group identified proteins that drive the aggressiveness of some types of these tumors. These molecules may be potential targets for new general or specific therapies. Thus, the tool contributes not only to advancing the clinical development of treatments but also to the personalization of cancer therapy.

The study’s findings and validation results were published in the scientific journal Cell Genomics.

“Many of these proteins are already targets of drugs available on the market for cancer patients and other diseases. They can be tested in future studies based on this identification. We arrived at them by associating the stemness phenotype with tumor aggressiveness,” explained Professor Tathiane Malta, of the Multiomics and Molecular Oncology Laboratory at the Ribeirão Preto Medical School of the University of São Paulo (FMRP-USP) in Brazil, when speaking with Agência FAPESP.

Malta is the corresponding author of the article, along with Professor Maciej Wiznerowicz from Poznan University of Medical Sciences in Poland. She is supported by FAPESP through the Young Investigators Grant program (projects 19/14928-1 and 18/00583-0).

The professor was one of the winners of an award in 2022 that aims to promote and recognize women’s participation in science, in recognition of her work over the years.

In 2018, she was the first author of an article published in Cell, the result of her postdoctoral research. In the article, her group developed a stemness index that can objectively measure the similarity between tumor samples and pluripotent stem cells.

At the time, we developed the machine learning-based algorithm using the public tumor database maintained by The Cancer Genome Atlas [TCGA] in the United States. We relied on gene expression data, quantifying RNA, and epigenomics data, with DNA methylation. Now, we’re working with the CPTAC database, based on proteomics, and we’ve updated our work with analyses of protein, a functional molecule that can be applied to treatment possibilities and clinical application.”

Professor Tathiane Malta, of the Multiomics and Molecular Oncology Laboratory at the Ribeirão Preto Medical School of the University of São Paulo 

Based on the results obtained thus far, PROTsi has a positive correlation with stemness scores derived from previously published transcriptomes, including the 2018 model. PROTsi was more effective in distinguishing between tumor and non-tumor samples, for example.

Renan Santos Simões, Malta’s advisor and co-first author of the article with Iga Kołodziejczak-Guglas from the International Institute for Molecular Oncology in Poznan, Poland, says that the progress made in characterizing stemness and considering protein levels and their modifications paves the way for a deeper understanding of tumor progression and mechanisms of resistance to current therapies.

“Science advances slowly, carefully, and is built by many hands. It’s gratifying to realize that we’re contributing to this process. That’s what motivates us: knowing that what we do today can make a real difference for patients, improving treatments and quality of life,” says Simões, a FAPESP scholarship recipient. Brazilian researcher Emerson de Souza Santos, who is also a student of Malta, participated in the research as well.

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On the last World Cancer Day on February 4, the World Health Organization (WHO) warned that 40 people worldwide are diagnosed with cancer every minute and require treatment.

Tumors are one of the leading causes of death and affect the young population the most. A 2023 study published in BMJ Oncology revealed that the incidence of early-onset cancer in adults under 50 increased by 79% between 1990 and 2019, along with a 28% rise in cancer-related deaths. The study analyzed 29 types of cancer in 204 countries.

The National Cancer Institute (INCA) in Brazil estimates that there will be 704,000 new cancer cases per year during the period from 2023 to 2025. According to 2023 Estimate – Cancer Incidence in Brazil, the most common malignant tumors are non-melanoma skin cancer (31% of total cases), followed by breast cancer in females (10.5%), prostate cancer (10%), colon and rectal cancer (6.5%), lung cancer (4.6%), and stomach cancer (3%).

Results

During the validation process, PROTsi demonstrated consistent performance across multiple data sets. It clearly distinguished between stem and differentiated cells, with different tumors falling at various intermediate levels. PROTsi demonstrated predictive ability in cases of uterine and head and neck cancer, for example.

Additionally, the tool was more effective at differentiating higher-grade tumors in adenocarcinoma, uterine, pancreatic, and pediatric brain cancer samples. “We sought to build a model that can be applied to any cancer, but we found that it works better for some than for others. We’re making a data source available for future work,” says Malta.

According to the professor, the USP group is testing additional computational models in an effort to improve predictions.

University of New Mexico researchers have received funding to launch an early-stage clinical trial of a vaccine engineered to clear pathological tau protein from the brains of patients suffering from Alzheimer’s dementia.

The Phase 1a/1b trial, supported in part by a $1 million grant from the Alzheimer’s Association’s Part the Cloud initiative, will test the novel vaccine, which was developed by UNM School of Medicine scientists, said Kiran Bhaskar, PhD, professor in the Departments of Molecular Genetics & Microbiology and Neurology.

The primary endpoint of this study is safety and tolerability. Can these subjects take these vaccinations without any anticipated side effects or adverse events? The second endpoint is the immunogenicity – can they make antibodies to tau?”

Kiran Bhaskar, PhD, Professor, Departments of Molecular Genetics & Microbiology and Neurology, UNM School of Medicine

If all goes well, the next step would be a Phase 2 study to assess whether the vaccine – which has shown promising results in rodents and monkeys – is effective in human patients, Bhaskar said.

In a normal brain, the tau protein stabilizes the microtubules that form the “skeleton” of neurons. But when it undergoes a process called phosphorylation, it is ejected from neurons, creating tangles that are characteristic of Alzheimer’s and other neurodegenerative diseases.

The FDA has approved several drugs in recent years that reduce levels of amyloid beta, another protein implicated in Alzheimer’s pathology, but they barely slow progression of the disease, leading many to wonder whether targeting tau might be a better bet.

The new trial will be conducted under the direction of Janice Knoefel, MD, at the UNM Center for Memory & Aging, which recently won designation as a federal Alzheimer’s Disease Research Center. The trial will likely start enrolling participants early in 2026 and is expected to last for 12 months, Bhaskar said.

In the double-blinded study, half the subjects will receive the active vaccine and half will get an inert placebo, he said. Blood tests, antibody tests, cognitive assessments and other biomarkers will be collected at various points during the trial.

UNM scientists created the vaccine by attaching pT181, a region of the altered tau protein that serves as an Alzheimer’s biomarker, to a virus-like particle (VLP) platform developed by Bhaskar’s colleagues Bryce Chackerian, PhD, and David Peabody, PhD.

VLPs are essentially viruses that have had their genetic material removed, leaving only their outer protein shell. Without a genome, they can’t reproduce, but the body’s immune system still recognizes them as invaders and manufactures antibodies to attack them – along with the proteins attached to their surface.

In this case, the pT181 protein segment attached to the surface of the VLP has been shown to trigger an immune response, leading to the elimination of the tau tangles in animal study subjects, which also saw improvements in their cognitive performance.

The antibodies from the monkeys bound to the human version of the tau protein. The same thing happened when the monkey sera was exposed to tau in brain tissue from people who had died from Alzheimer’s.

But animal models and test tube experiments aren’t sufficient to prove that the vaccine will work in human subjects. “The bottom line is, yes, we have some immunological data, and it does seem to do well, but we have to move with caution,” Bhaskar said.

The Alzheimer’s vaccine will be produced in partnership with TheraVac Biologics, a Canadian biotech company that holds an exclusive license to the technology, Bhaskar said. To receive FDA approval for the study, the vaccine must be produced using the agency’s “good manufacturing practices” standard.

Phase 1 clinical studies, the next step after animal research, typically determine whether a drug is safe for humans and help establish the dosage that might be needed to trigger a therapeutic response, Bhaskar said.

Major funding for the trial will be provided by Part the Cloud, an Alzheimer’s Association initiative led by California philanthropist Michaela Hoag, who lost both of her parents to the disease.

Through Hoag’s efforts, Part the Cloud has raised nearly $90 million since 2012 for research to accelerate translation of laboratory findings through trials into possible therapies – filling the gap in Alzheimer’s drug development by providing essential support for early-phase clinical trials.

Bhaskar said his team will need to raise additional funding to pay for the expensive process of manufacturing the vaccine. Grant applications have been submitted in the U.S. and Canada, he said.

A combination of routine eye scans and AI has created a powerful new way for measuring short-sightedness to better predict people’s long-term risk of serious retinal damage, a new study suggests.

Using the new way, people whose retinas looked more short-sighted than their glasses prescription indicated were more likely to develop retinal detachment or tears than those with the same prescription.

Experts say the approach offers a more tailored way to assess short-sightedness by taking into account subtle changes in an individual’s retina missed by glasses prescription, which could help identify people at higher risk of complications associated with short-sightedness.

The new measure of short-sightedness, called fundus refraction offset (FRO), could help clinicians better identify vulnerable patients, leading to more tailored management to prevent vision loss, researchers add.

More than one billion people worldwide are short-sighted, with nearly 50 per cent of the global population projected to be affected by 2050. People with strong short-sighted prescriptions are generally 13 times more likely to have retinal detachment than people without short-sightedness. However, this risk can vary greatly even among individuals with similar prescriptions, experts say.

In the first study of its kind, researchers from the University of Edinburgh analysed health data and retinal images from more than 9,300 UK Biobank participants aged 40–69 with no previous retinal problems.

FRO uses AI to compare how a person’s retina looks structurally to how it should normally look according to their glasses prescription.

The researchers used eye scan images to calculate the FRO score. They found that for each step lower in the score – meaning the retina looked more short-sighted than expected – the risk of developing retinal detachment or tears over 12 years increased by about 50 per cent, even among individuals with similar glasses prescription, age, sex, and other factors at baseline.

Experts say the FRO score offers a new, more accurate way to identify individuals at elevated risk of the sight-threatening condition.

The approach could also help guide decisions around popular elective procedures, such as laser eye surgery and clear lens exchange, by identifying patients whose retinas may be more vulnerable to certain surgeries, even if their glasses prescriptions are the same.

The study’s principal investigator, Fabian Yii, from the University of Edinburgh’s Robert O Curle Ophthalmology Suite in the Institute of Regeneration and Repair, said: “Our study supports moving beyond glasses prescription alone and taking into account retinal information when describing myopia severity. Given the limitations of current descriptors of myopia severity, most notably glasses prescription, in effectively capturing an individual’s risks of myopic complications, this research represents an important step in allowing care to be targeted more effectively for the increasing number of people affected by myopia.“

Emeli Chatterjee, PhD, of the Cardiovascular Research Center at Massachusetts General Hospital, is the lead author and Saumya Das, MD, PhD, of the Division of Cardiology at Massachusetts General Hospital, is the senior author of a paper published in Cell Genomics, “The extracellular vesicle transcriptome provides tissue-specific functional genomic annotation relevant to disease susceptibility in obesity.”

Q: How would you summarize your study for a lay audience?

Our study examines how RNA in extracellular vesicles (EVs) can provide insights into metabolic phenotypes related to obesity. By using functional genomics approaches, we found a high representation of genes and regulatory elements previously associated with obesity and type 2 diabetes represented by the RNA cargo within these EVs. Notably, these EV transcripts represent regulatory elements and transcriptionally active genes in adipose tissue and are associated with metabolic phenotypes, including body mass index and type 2 diabetes.

We provide a roadmap to integrate EV transcript profiling with genetic approaches that could ultimately inform the development of novel diagnostic and therapeutic strategies, improving our ability to predict, prevent and treat obesity-related diseases.

Q: What question were you investigating?

We aimed to understand the circulating EV landscape and how it differed between obese and lean individuals. Our focus was on how the EV RNA cargo varies between obese and lean individuals and how integrating with large population-based genetic and transcriptomic databases can offer insights into obesity biology.

Q: What methods or approach did you use?

We optimized methods to culture adipose tissues explants from bariatric surgery for EV harvesting and isolation of circulating EVs and transcriptomic data generation. Computational innovations were made in collaboration with Dr. Ravi Shah and Dr. Eric Gamazon, both of Vanderbilt University Medical Center, to integrate EV transcriptomics with population ‘omics’, such as a genome-wide association study (GWAS), a transcriptome-wide association study (TWAS) and a phenome-wide association study (PheWAS).

Q: What did you find?

Our findings demonstrate that differences in EV-RNA in obesity are driven primarily by EVs released from adipose tissues, and that the RNAs within these EVs arise from different cell-types within adipose tissue. By using functional genomics approaches, we identify 282 transcripts (corresponding to 277 unique gene symbols) differentially expressed within circulating EVs between lean and obese individuals.

Q: What are the implications?

Our study outlines a method to integrate EV transcriptomics with large scale genetic studies. It also demonstrates that EV-RNAs, which are differentially expressed in obesity, are linked with genes associated with metabolic phenotypes, such as obesity and type 2 diabetes. These EV-RNAs represent regulatory elements and transcriptionally active genes in adipose tissue, and are associated with metabolic phenotypes.

Q: What are the next steps?

We are currently investigating how these EV transcripts change with weight loss (either induced by surgery or drugs, like GLP-1 agonists). We are also studying how adipose EVs signal to other cells, such as cardiac cells, liver cells and brain cells, to mediate obesity-related comorbidities. Our functional genomics study has yielded targets that may be used to treat some of these conditions.