ARTIFICIAL INTELLIGENCE PREDICTS FUTURE PANCREATIC CANCER

According to the findings of a new study that was carried out under the direction of researchers from Harvard Medical School and the University of Copenhagen, an artificial intelligence tool was able to successfully identify individuals who were at the highest risk for pancreatic cancer up to three years prior to the diagnosis of the disease by utilizing only the medical records of the patients Manmade intelligence based populace screening could be significant in tracking down those at raised risk for the illness and could speed up the determination of a condition found over and over again at cutting edge stages when treatment is less successful and results are bleak

One of the most deadly cancers in the world is pancreatic cancer, and its death toll is expected to rise. There are currently no population-based pancreatic cancer screening tools Those with a family ancestry and certain hereditary changes that incline them toward pancreatic malignant growth are separated into a designated style Yet, such designated screenings can miss different cases that fall beyond those classes.

"Quite possibly the main choice clinicians face everyday is who is at high risk for a sickness, and who might profit from additional testing, which can likewise mean more obtrusive and more costly methodology that convey their own dangers," said co-senior specialist Chris Sander. "Clinical decisionmaking could greatly benefit from an AI tool that can pin point individuals most likely to benefit from additional tests and who are at greatest risk for pancreatic cancer. Sander added that if scaled up, such a strategy could speed up the detection of pancreatic cancer, enable earlier treatment, improve outcomes, and extend patients' lives.

"Many kinds of disease, particularly those hard to distinguish and treat early, apply an unbalanced cost for patients, families and the medical services framework overall," said co-senior specialist Soren Brunak "Pancreatic cancer is an aggressive disease that is notoriously difficult to diagnose early and treat promptly when the chances of success are highest AI-based screening presents an opportunity to alter its course The AI algorithm was trained on two distinct patients in the new study to predict which patients are most likely to develop pancreatic cancer in the future Prominently, a large number of the side effects and sickness codes were not straightforwardly connected with or coming from the pancreas.

The specialists tried various adaptations of the man-made intelligence models for their capacity to recognize individuals at raised risk for infection advancement inside various time scales: a half year, one year, two years, and three years. In general, each version of the AI algorithm was significantly more accurate at predicting who would develop pancreatic cancer than the current population-wide estimates of disease incidence, which are based on the frequency with which a condition develops in a population over a given time period Current genetic sequencing tests, which are typically only available for a small subset of patients in data sets, are said to be at least as accurate in predicting disease occurrence as the model

The "irate organ" evaluating for specific normal diseases like those of the bosom, cervix, and prostate organ depends on moderately straight forward and profoundly successful procedures: a mammogram, a Pap smear, and a blood test, separately By ensuring early detection and intervention in the disease's most treatable stages, these screening methods have altered outcomes.

By comparison, pancreatic cancer is harder and more expensive to screen and test for Physicians look mainly at family history and the presence of genetic mutations, which, while important indicators of future risk, often miss many patients. 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 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 understandably cautious to recommend more sophisticated and more 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 moniker "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. The survival rate drops to 2-9% in those whose tumors have grown beyond their site of origin

"That low survival rate is despite marked advances in surgical techniques, chemotherapy, and immunotherapy," Sander said. "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."

Previous diagnoses portend future risk

The AI model was trained on the health records of 6 2 million patients from Denmark's national health system over a 41 year period by the researchers for the current study. 23,985 of those patients eventually developed pancreatic cancer During the preparation, the calculation perceived designs characteristic of future pancreatic malignant growth risk in light of illness directions, that is to say, whether the patient had specific circumstances that happened in a specific grouping over the long run

For instance, within three years of evaluation, diagnosis of gallstones, anemia, type 2 diabetes, and other gastrointestinal issues indicated an increased risk of pancreatic cancer Inflammation of the pancreas was a less surprising predictor of future pancreatic cancer within a shorter time frame of just two years The researchers warn that none of these diagnoses alone should be taken as a guarantee of developing pancreatic cancer in the future On the other hand, the pattern and order in which they occur over time can be used as a starting point for an AI-based surveillance model. This could encourage doctors to closely monitor those who are at greater risk or to test accordingly Then, the scientists tried the best performing calculation on a completely new arrangement of patient records that had not recently experienced a US Veterans Wellbeing Organization informational index of almost 3 million records crossing 21 years and containing 3,864 people determined to have pancreatic malignant growth. On the US data set, the tool's predictive accuracy was slightly lower. This was probably because the US dataset was collected in a shorter amount of time and had slightly different patient population profiles than the Danish population as a whole in the Danish data set and the Veterans' Affairs data set, which included current and former military personnel. At the point when the calculation was retrained without any preparation on the US dataset, its prescient precision moved along. This, the analysts said, highlights two significant focuses: In the first place, guaranteeing that simulated intelligence models are prepared on top notch and rich information Second, the requirement for admittance to enormous delegate datasets of clinical records is collected broadly and universally AI models should be trained on local health data in the absence of globally valid models to ensure that their training reflects the peculiarities of local populations.

WHO: Grow food, not tobacco n the occasion of World’s No Tobacco Day, the World Health Organization ( WHO) has called upon governments worldwide to cease subsidizing tobacco farming and instead extend support towards cultivating sustainable crops that could address global hungerWhile tobacco farming has predominantly been a concern in Asia and South America, recent data reveals that tobacco companies are expanding their operations in Africa. Since 2005, there has been a nearly 20% increase in tobacco farming land across the African continent.

In addition to its detrimental effects on global food security, tobacco farming poses severe health risks to farmers themselves They are exposed to chemical pesticides, tobacco smoke, and nicotine levels equivalent to smoking 50 cigarettes, leading to chronic lung conditions and nicotine poisoning. Disturbingly, it is estimated that more than 1 million child laborers work on tobacco farms, depriving them of educational opportunities.

Dr. Tedros Adhanom Ghebreyesus, the WHO Director-General, emphasized the alarming fact that tobacco is responsible for 8 million deaths annually, despite governments across the world allocating substantial funds to support tobacco farms He stated, "By choosing to grow food instead of tobacco, we prioritize health, preserve ecosystems, and strengthen food security for all "

The global community currently faces acute food insecurity, affecting more than 300 million people Paradoxically, over 3 million hectares of land in more than 120 countries are dedicated to cultivating lethal tobacco, even in regions where communities suffer from starvation

The recently released WHO report titled "Grow food, not tobacco" sheds light on the detrimental impacts of tobacco cultivation and highlights the advantages of transitioning to sustainable food crops The report exposes the tobacco industry's role in ensnaring farmers in a cycle of debt, promoting tobacco growing through exaggerated economic benefits, and leveraging farming front groups for lobbying purposes.

In support of the Tobacco Free Farms initiative, WHO, the Food and Agriculture Organization, and the World Food Programme are aiding more than 5,000 farmers in Kenya and Zambia to transition from tobacco to sustainable food crop cultivation. World No Tobacco Day(May 31st) annually recognizes individuals who make a difference in tobacco control This year, Ms. Sprina Robi Chacha, a female farmer from Kenya, is being honored for her commendable efforts in shifting from tobacco farming to cultivating high-protein beans. Additionally, she has trained hundreds of fellow farmers on making this transition, contributing to the creation of a healthier community.

Currently, 182 Parties to the WHO Framework Convention on Tobacco Control have committed to promoting economically viable alternatives for tobacco workers and growers. An essential aspect of fulfilling this commitment is ending subsidies for tobacco farming and providing support for the cultivation of healthier crops

- NSH

The UN Environment Programme ( UNEP) report states that countries and companies can achieve a significant reduction of 80% in plastic pollution by 2040 through taking strong actions and implementing effective policies. The report, titled “Turning off the Tap: How the world can end plastic pollution and create a circular economy,” provides practical solutions and recommendations for governments and businesses.

The current way we produce, use, and dispose of plastics is causing severe pollution in our ecosystems, posing risks to human health, and contributing to climate change. The UNEP report offers a roadmap to address these issues by adopting a circular approach that keeps plastics out of the environment, out of our bodies, and in the economy By following this roadmap, we can achieve significant economic, social, and environmental benefits.

The report highlights three key market shifts that are necessary to achieve an 80% reduction in plastic pollution by 2040 First, we need to eliminate problematic and unnecessary plastics to reduce the scale of the problem. Second, we should promote reuse options, such as refillable bottles and packaging takeback schemes, which can reduce 30% of