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New AI tool can predict pancreatic cancer three years in advance



A new artificial intelligence tool may be able to successfully identify people at risk of pancreatic cancer up to three years in advance.

The breakthrough AI model used patients’ medical records and information from previous scans to detect those at risk of the deadly disease, which can be hard to catch early, difficult to treat, and kills more than half of sufferers within five years of diagnosis.

Research led by investigators at Harvard Medical School and the University of Copenhagen in collaboration with VA Boston Healthcare System, Dana-Farber Cancer Institute, and the Harvard TH Chan School of Public Health, suggests AI was able to determine a person’s risk of developing pancreatic cancer with astounding accuracy.

The findings published in Nature Medicine implies AI-based population screening could be valuable in finding those at elevated risk of developing the disease, which has an average age of diagnosis of 72 and affects both men and women.

Currently, there are no population-based tools to screen broadly for pancreatic cancer, which in its early stages can have no specific symptoms.

Those with a family history and certain genetic mutations that predispose them to the disease are screened in a targeted fashion. But such focused screenings can miss other cases that fall outside of those categories, the researchers said.

Study co-senior investigator Chris Sander, faculty member in the Department of Systems Biology in the Blavatnik Institute at Harvard Medical School, said: ” One of the most important decisions clinicians face day to day is who is at high risk for a disease, and who would benefit from further testing, which can also mean more invasive and more expensive procedures that carry their own risks.

“An AI tool that can zero in on those at highest risk for pancreatic cancer who stand to benefit most from further tests could go a long way toward improving clinical decision-making.”

Applied at scale, such an approach could expedite detection of pancreatic cancer, lead to earlier treatment, and improve outcomes and prolong patients’ life spans, Dr Sander added.

According to World Cancer Research, pancreatic cancer is the 12th most common in the world with nearly 500,000 new cases in 2020. Globally, the incidence of the disease is projected to increase to 18.6% per 100,000 by 2050, meaning it will pose a significant public health burden.

Søren Brunak, professor of disease systems biology and director of research at the Novo Nordisk Foundation Center for Protein Research at the University of Copenhagen, said: “Many types of cancer, especially those hard to identify and treat early, exert a disproportionate toll on patients, families and the healthcare system as a whole.

“AI-based screening is an opportunity to alter the trajectory of pancreatic cancer, an aggressive disease that is notoriously hard to diagnose early and treat promptly when the chances for success are highest.”

In the study the researchers applied an AI algorithm to clinical date from nine million patient records from the US and Denmark. They asked the AI model to look for tell tale signs based on the data contained in the records.

Based on combinations of disease codes and the timing of their occurrence, the model was able to predict which patients are likely to develop pancreatic cancer in the future. Notably, many of the symptoms and disease codes were not directly related to or stemming from the pancreas.

The researchers tested different versions of the AI models for their ability to detect people at elevated risk for disease development within different time scales — six months, one year, two years, and three years – and found their methods were “substantially more accurate at predicting who would develop pancreatic cancer than current population-wide estimates of disease incidence.”

Among 22 people with lung nodules that eventually were diagnosed with the cancer, the AI flagged 18 as having a high risk of developing the disease.

The researchers said they believe the model is at least as accurate in predicting disease occurrence as are current genetic sequencing tests that are usually available only for a small subset of patients in data sets.

Relatively simple screening tests have become common for many other cancers like breast, cervix, and prostate. By picking up signs of disease early, they have transformed the outcome for patients.

But pancreatic cancer is harder and more expensive to screen and test for.

Medics look mainly at family history and the presence of genetic mutations, which, while important indicators of future risk, often miss many patients.

The researchers say one particular advantage of the AI tool is that it could be used on any and all patients for whom health records and medical history are available – not just in those with a known family history or genetic predisposition for the disease.

This is especially important, the researchers add, because many patients at high risk may not even be aware of their genetic predisposition or family history.

In the absence of symptoms and without a clear indication that someone is at high risk for pancreatic cancer, clinicians may be cautious to recommend more sophisticated and  expensive testing, such as CT scans, MRI or endoscopic ultrasound.

When these tests are used and suspicious lesions discovered, the patient must undergo a procedure to obtain a biopsy. Positioned deep inside the abdomen, the organ is hard to access and easy to provoke and inflame. Its irritability has earned it the nickname ‘the angry organ.’

An AI tool that identifies those at the highest risk for pancreatic cancer would ensure that clinicians test the right population, while sparing others unnecessary testing and additional procedures, the researchers said.

About 44% of people diagnosed in the early stages of pancreatic cancer survive five years after diagnosis, but only 12% of cases are diagnosed that early. Researchers estimate the survival rate drops to between 2-9% in those whose tumours have grown beyond their site of origin.

Dr Sander said: “That low survival rate is despite marked advances in surgical techniques, chemotherapy, and immunotherapy. So, in addition to sophisticated treatments, there is a clear need for better screening, more targeted testing, and earlier diagnosis, and this is where the AI-based approach comes in as the first critical step in this continuum.”

AI has come in for criticism recently due to fears about privacy, intellectual property rights, bias and ethics.

Many scientists are also worried that the technology could soon outperform humans.

But it is being embraced in medicine where it is already being used to help develop new drugs, in surgery and for personalising treatment.

In March this year researchers in Canada announced that artificial intelligence had developed a treatment for the most common type of liver cancer, hepatocellular carcinoma, in just 30 days and could predict a patient’s survival rate.


Tai chi outperforms conventional exercise for seniors



New findings from 12 studies involving 2,901 participants have demonstrated that tai chi outperforms conventional exercise in improving mobility and balance in seniors.

While tai chi is understood to be beneficial for functional mobility and balance in older adults, such benefits are not well understood due to large variance in research study protocols and observations.

This new review and analysis has now shown that tai chi can induce greater improvement in functional mobility and balance in relatively healthy older adults compared to conventional exercise.

The findings showed the following performance results:

  • The time to complete 50-foot walking was 1.84 seconds faster. 
  • The time to maintain a one-leg stance was 6 seconds longer when eyes were open and 1.65 seconds longer when eyes were closed. 
  • Individuals improved their timed-up-and-go test performance by 0.18 points, indicating quicker standing, walking, and sitting.
  • Individuals taking the functional reach test showed significant improvement with a standardised mean difference of 0.7, suggesting a noteworthy positive impact on the ability to reach and perform daily activities.

Secondary analyses revealed that the use of tai chi with relatively short duration of less than 20 weeks, low total time of less than 24 total hours, and/or focusing on the Yang-style of this ancient form of Chinese martial arts were particularly beneficial for functional mobility and balance as compared to conventional exercise.

“This systematic literature review and meta-analysis are exciting because they provide strong evidence that tai chi is a more efficient strategy to improve functional mobility and balance in relatively healthy older adults, as compared to conventional exercise,” said Brad Manor, Ph.D., director of the Mobility and Falls Program at Hebrew SeniorLife’s Hinda and Arthur Marcus Institute for Aging Research, and associate professor of medicine, Harvard Medical School and Beth Israel Deaconess Medical Center.

“This research suggests that tai chi should be carefully considered in future studies and routines of rehabilitative programs for balance and mobility in older adults,” said Bao Dapeng, professor at Beijing Sport University.

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New standards for biomarkers of ageing



A paper has put forward a new framework for standardising the development and validation of biomarkers of ageing to better predict longevity and quality of life.

Led by Harvard researchers, the team has zeroed in on biomarkers of ageing using omic data from population-based studies. 

The team included ageing and longevity expert Alex Zhavoronkov, PhD, founder and CEO of AI-driven drug discovery company Insilico Medicine, and the findings appeared in Nature Medicine

Ageing is associated with a number of biological changes including increased molecular and cellular damage, however, researchers do not yet have a standardised means to evaluate and validate biomarkers related to ageing. 

In order to create those standards as well as actionable clinical tools, the team analysed population-based cohort studies built on omic data (data related to biological molecules which can include proteomics, transcriptomics, genomics, and epigenomics) of blood-based biomarkers of ageing. The researchers then compared the predictive strength of different biomarkers, including study design and data collection approaches, and looked at how these biomarkers presented in different populations. 

In order to better assess the impact of ageing using biomarkers, the researchers found that clinicians needed to expand their focus to consider not only mortality as an outcome, but also how biomarkers of aging are associated with numerous other health outcomes, including functional decline, frailty, chronic disease, and disability. They also call for the standardisation of omic data to improve reliability. 

“Omics and biomarkers harmonisation efforts, such as the Biolearn project, are instrumental in validation of biomarkers of aging” said co-first author Mahdi Moqri, PhD, of the Division of Genetics. 

Biolearn is an open-source project for biomarkers of aging and is helping to harmonise existing ageing biomarkers, unify public datasets, and provide computational methodologies.

The team also emphasised the importance of continued collaborations among research groups on “large-scale, longitudinal studies that can track long-term physiological changes and responses to therapeutics in diverse populations”, and that further work is required to understand how implementation of biomarker evaluation in clinical trials might improve patient quality of life and survival.

“If we hope to have clinical trials for interventions that extend healthy lifespan in humans, we need reliable, validated biomarkers of ageing,” said co-first author Jesse Poganik, PhD, of the Division of Genetics. 

“We hope that our framework will help prioritise the most promising biomarkers and provide health care providers with clinically valuable and actionable tools.”

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Healthy aging research to receive $115 million



Global non-profit Hevolution Foundation has announced $115 million in funding that makes up 49 new awards under its Geroscience Research Opportunities (HF-GRO) programme.  

As part of Hevolution’s mission to catalyse the healthspan scientific ecosystem and drive transformative breakthroughs in healthy aging, HF-GRO is funding promising pre-clinical research in aging biology and geroscience. 

Through this first wave of HF-GRO awards, Hevolution will invest up to $115 million in this first cohort of 49 selected projects over the next five years. Its second call for proposals under HF-GRO will be announced later this year, offering an additional $115 million to address the significant funding gaps in aging research.  

Dr. Felipe Sierra, Hevolution’s Chief Scientific Officer stated: “These 49 important research projects represent a significant step forward in deepening our understanding of healthy aging. Hevolution’s prime objective is to mobilise greater investment around uncovering the foundational mechanisms behind biological aging. 

“We are steadfast in our belief that by examining the root causes of aging, rather than solely focusing on its associated diseases, we can usher in a brighter future for humanity.” 

HF-GRO awardees include researchers at prestigious institutions across the United States, Canada, and Europe, including the U.S. National Institute on Aging, Brigham and Women’s Hospital, the Buck Institute, the Mayo Clinic, New York University, and the University of California San Francisco, among many others. 

The American Federation for Aging Research is providing programmatic support for the HF-GRO program, with grantees selected through a rigorous two-stage peer-review process involving 100 experts in aging biology and geroscience. 

Dr Berenice Benayoun, an HF-GRO grant recipient at the University of Southern California, stated: “I am extremely honored and excited that Hevolution selected our project for funding. This is a project close to my heart, which aims at understanding why and how the female and male innate immune aging differs. 

“This funding will support us as we start laying the foundation for a lasting improvement of women’s health throughout aging.” 

To date, Hevolution has committed approximately $250 million to transform the healthy aging sector, including the $40 million for specialised research and development in healthspan science recently announced at Hevolution’s Global Healthspan Summit. 

Hevolution is ramping up its investments to enable healthier aging for all and is now the second largest funder of aging biology research worldwide.  

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