YouTube's new guidelines for health content will fall under three categories: prevent, treatment and denial misinformation. Prevent will allegedly review and remove videos that oppose guidelines set out by trusted authorities or contradict vaccine safety and efficacy (the platform banned content with vaccine misinformation in 2021). Treatment should center on taking down any misinformation about — unsurprisingly — treating medical conditions, including unproven remedies. The platform claims that denial will focus on removing any content that makes a false claim, such as that people didn't die due to COVID-19.

"To determine if a condition, treatment or substance is in scope of our medical misinformation policies, we'll evaluate whether it's associated with a high public health risk, publicly available guidance from health authorities around the world, and whether it's generally prone to misinformation," YouTube's Director and Global Head of Healthcare and Public Health Partnerships Dr. Garth Graham and its VP and Global Head of Trust and Safety Matt Halprin said in the joint release outlining the new policies.

Starting now, YouTube says it will be removing videos specifically about cancer which violate any of these policies — an effort it claims will ramp up more in the coming weeks. For example, if a video states that garlic cures cancer, it's coming down. YouTube is also sharing a playlist of science-backed cancer-related videos and teaming up with Mayo Clinic to create even more informational videos about cancer.

These policies come less than two months after YouTube announced it would "stop removing content that advances false claims that widespread fraud, errors, or glitches occurred in the 2020 and other past US Presidential elections" because it curtailed political speech. So misinformation is allowed when it threatens democracy, just not across every category on the site — cool. Though, YouTube does say that it will allow some health videos with falsehoods to remain if the context is right, such as public interest. The platform says in some cases, content will be allowed to stay up but will be given an age restriction.

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During development, researchers from Brigham and Women’s Hospital designed the device specifically to help test treatments in patients with brain cancers or gliomas, a type of tumor that originates in the brain or spinal cord. The device is designed to only remain implanted in a patient for about two to three hours while it delivers microdoses of the respective drug that is under observation. It can observe the impact of up to 20 drugs on the market for cancerous tumors, according to the researchers. Once the device is removed (sometime before the surgery ends), the surrounding tissue is returned to the lab for analysis.

In a statement published Wednesday, Pierpaolo Peruzzi, co-principal investigator and assistant professor in the Department of Neurosurgery at Brigham and Women’s Hospital said that knowing the impact of cancer drugs on these tumors is critical. “We need to be able to understand, early on, which drug works best for any given patient,” he said.

During the development process, researchers at the Brigham and Women’s Hospital ran a clinical trial to observe the actual impact of the implant on real patients. The study found that none of the patients in the trial experienced any adverse effects. The researchers were able to collect biological data from the devices, such as what molecular changes happened when each drug was administered. While the study demonstrated that the implant could be easily incorporated into surgical practice, the researchers are still determining how the data it can gather should be used to optimize tumor therapy.

The researchers are now conducting another study that focuses on implanting the device through a minimally invasive procedure 72 hours before their main surgery. Advancements in the cancer treatment space continue to expand, with new iterations of drug cocktails and viruses that can fight cancer cells emerging in the biotech space. Implants like the one developed by the Brigham and Women’s Hospital bring scientists one step closer to better being able to use tools and data to provide more personalized care treatment plans for cancer patients.

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The UK approval of the novel therapy is informed by two previous global clinical trials that indicated the treatment's efficacy. 97 percent of patients using Casgevy were relieved of severe pain associated with the blood disorders for at least 12 months after treatment during the trials. The results suggest that the gene editing treatment could replace the current standard for care. Stem cell therapy and bone marrow transplants are currently the only pathways to cure sickle cell disease and beta thalassemia, however, they involve a lot of risks.

Both sickle cell disease and beta thalassemia are blood disorders characterized by defective red blood cells that can’t carry oxygen, and require patients to get monthly blood transfusions that can be costly and time-consuming. Casgevy works by specifically targeting the genes in the bone marrow stem cells that produce faulty blood cells. For the treatment to work, a patient’s stem cells need to be extracted from their bone marrow, edited in a lab and then re-infused into the patient.

Despite its promising outlook, CRISPR-based therapies may not be easily available to the general public. Gene editing is an expensive endeavor. The Innovative Genomics Institute (IGI) estimates that the average CRISPR-based therapy will cost between $500,000 and $2 million per patient. The IGI has built out an ‘Affordability Task Force’ to tackle the issue of expanding access to these novel therapies.

Aside from costliness, gene editing therapies offer huge promise to innovate treatment pathways for rare conditions including neurodegenerative diseases, cancer and muscular atrophy. More importantly, this landmark approval for Casgevy “opens the door for further applications of CRISPR therapies in the future,” Prof Dame Kay Davies, a scientist from the University of Oxford, said. And new iterations of gene editing technologies may even surpass CRISPR in the future.

Casgevy is still being reviewed by regulatory agencies for safety standards in other countries, including the United States and Saudi Arabia. A marketing application, the first step towards approval for the therapy, was recently validated by the European Medicines Agency.

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The current standard PDAC screening criteria catches about 10 percent of cases in patients examined by professionals. In comparison, MIT’s PRISM was able to identify PDAC cases 35 percent of the time.

While using AI in the field of diagnostics is not an entirely new feat, MIT’s PRISM stands out because of how it was developed. The neural network was programmed based on access to diverse sets of real electronic health records from health institutions across the US. It was fed the data of over 5 million patient’s electronic health records, which researchers from the team said “surpassed the scale” of information fed to an AI model in this particular area of research. “The model uses routine clinical and lab data to make its predictions, and the diversity of the US population is a significant advancement over other PDAC models, which are usually confined to specific geographic regions like a few healthcare centers in the US,” Kai Jia, MIT CSAIL PhD senior author of the paper said.

MIT’s PRISM project started over six years ago. The motivation behind developing an algorithm that can detect PDAC early has a lot to do with the fact that most patients get diagnosed in the later stages of the cancer’s development — specifically about eighty percent are diagnosed far too late.

The AI works by analyzing patient demographics, previous diagnoses, current and previous medications in care plans and lab results. Collectively, the model works to predict the probability of cancer by analyzing electronic health record data in tandem with things like a patient’s age and certain risk factors evident in their lifestyle. Still, PRISM is still only able to help diagnose as many patients at the rate the AI can reach the masses. At the moment, the technology is bound to MIT labs and select patients in the US. The logistical challenge of scaling the AI will involve feeding the algorithm more diverse data sets and perhaps even global health profiles to increase accessibility.

Nonetheless, this isn't MIT’s first stab at developing an AI model that can predict cancer risk. It notably developed a way to train models how to predict the risk of breast cancer among women using mammogram records. In that line of research, MIT experts confirmed, the more diverse the data sets, the better the AI gets at diagnosing cancers across diverse races and populations. The continued development of AI models that can predict cancer probability will not only improve outcomes for patients if malignancy is identified earlier, it will also lessen the workload of overworked medical professionals. The market for AI in diagnostics is so ripe for change that it is piquing the interest of big tech commercial companies like IBM, which attempted to create an AI program that can detect breast cancer a year in advance.

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