medicine

iv-infusion-enables-editing-of-the-cystic-fibrosis-gene-in-lung-stem-cells

IV infusion enables editing of the cystic fibrosis gene in lung stem cells

Biology, CRISPR, Cystic Fibrosis, gene editing, gene therapy, Genetics, medicine, Science / /

Right gene in the right place —

Approach relies on lipid capsules like those in the mRNA vaccines.

–

Abstract drawing of a pair of human hands using scissors to cut a DNA strand, with a number of human organs in the background.

The development of gene editing tools, which enable the specific targeting and correction of mutations, hold the promise of allowing us to correct those mutations that cause genetic diseases. However, the technology has been around for a while now—two researchers were critical to its development in 2020—and there have been only a few cases where gene editing has been used to target diseases.

One of the reasons for that is the challenge of targeting specific cells in a living organism. Many genetic diseases affect only a specific cell type, such as red blood cells in sickle-cell anemia, or specific tissue. Ideally, to limit potential side effects, we’d like to ensure that enough of the editing takes place in the affected tissue to have an impact, while minimizing editing elsewhere to limit side effects. But our ability to do so has been limited. Plus, a lot of the cells affected by genetic diseases are mature and have stopped dividing. So, we either need to repeat the gene editing treatments indefinitely or find a way to target the stem cell population that produces the mature cells.

On Thursday, a US-based research team said that they’ve done gene editing experiments that targeted a high-profile genetic disease: cystic fibrosis. Their technique largely targets the tissue most affected by the disease (the lung), and occurs in the stem cell populations that produce mature lung cells, ensuring that the effect is stable.

Getting specific

The foundation of the new work is the technology that gets the mRNAs of the COVID-19 mRNA vaccines inside cells. The nucleic acids of an mRNA are large molecules with a lot of charged pieces, which makes it difficult for them to cross a membrane to get inside of a cell. To overcome that problem, the researchers package the mRNA inside a bubble of lipids, which can then fuse with cell membranes, dumping the mRNA inside the cell.

This process, as the researchers note, has two very large advantages: We know it works, and we know it’s safe. “More than a billion doses of lipid nanoparticle–mRNA COVID-19 vaccines have been administered intramuscularly worldwide,” they write, “demonstrating high safety and efficacy sustained through repeatable dosing.” (As an aside, it’s interesting to contrast the research community’s view of the mRNA vaccines to the conspiracies that circulate widely among the public.)

There’s one big factor that doesn’t matter for vaccine delivery but does matter for gene editing: They’re not especially fussy about what cells they target for delivery. So, if you want to target something like blood stem cells, then you need to alter the lipid particles in some way to get them to preferentially target the cells of your choice.

There are a lot of ideas on how to do this, but the team behind this new work found a relatively simple one: changing the amount of positively charged lipids on the particle. In 2020, they published a paper in which they describe the development of selective organ targeting (SORT) lipid nanoparticles. By default, many of the lipid particles end up in the liver. But, as the fraction of positively charged lipids increases, the targeting shifts to the spleen and then to the lung.

So, presumably, because they know they can target the lung, they decided to use SORT particles to send a gene editing system specific to cystic fibrosis, which primarily affects that tissue and is caused by mutations in a single gene. While it’s relatively easy to get things into the lung, it’s tough to get them to lung cells, given all the mucus, cilia, and immune cells that are meant to take care of foreign items in the lung.

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ancient-egyptian-skull-shows-evidence-of-cancer,-surgical-treatment

Ancient Egyptian skull shows evidence of cancer, surgical treatment

Archaeology, forensic archaeology, medicine, paleo-oncology, paleopathology, Science / /

“We could not believe what was in front of us.” —

“An extraordinary new perspective in our understanding of the history of medicine.”

–

Skull and mandible 236, dating from between 2687 and 2345 BCE, belonged to a male individual aged 30 to 35.

Tondini, Isidro, Camarós, 2024.

The 4,000-year-old skull and mandible of an Egyptian man show signs of cancerous lesions and tool marks, according to a recent paper published in the journal Frontiers in Medicine. Those marks could be signs that someone tried to operate on the man shortly before his death or performed the ancient Egyptian equivalent of an autopsy to learn more about the cancer after death.

“This finding is unique evidence of how ancient Egyptian medicine would have tried to deal with or explore cancer more than 4,000 years ago,” said co-author Edgard Camarós, a paleopathologist at the University of Santiago de Compostela. “This is an extraordinary new perspective in our understanding of the history of medicine.”

Archaeologists have found evidence of various examples of primitive surgery dating back several thousand years. For instance, in 2022, archaeologists excavated a 5,300-year-old skull of an elderly woman (about 65 years old) from a Spanish tomb. They determined that seven cut marks near the left ear canal were strong evidence of a primitive surgical procedure to treat a middle ear infection. The team also identified a flint blade that may have been used as a cauterizing tool. By the 17th century, this was a fairly common procedure to treat acute ear infections, and skulls showing evidence of a mastoidectomy have been found in Croatia (11th century), Italy (18th and 19th centuries), and Copenhagen (19th or early 20th century).

Cranial trepanation—the drilling of a hole in the head—is perhaps the oldest known example of skull surgery and one that is still practiced today, albeit rarely. It typically involves drilling or scraping a hole into the skull to expose the dura mater, the outermost of three layers of connective tissue, called meninges, that surround and protect the brain and spinal cord. Accidentally piercing that layer could result in infection or damage to the underlying blood vessels. The practice dates back 7,000 to 10,000 years, as evidenced by cave paintings and human remains. During the Middle Ages, trepanation was performed to treat such ailments as seizures and skull fractures.

Just last year, scientists analyzed the skull of a medieval woman who once lived in central Italy and found evidence that she experienced at least two brain surgeries consistent with the practice of trepanation. Why the woman in question was subjected to such a risky invasive surgical procedure remains speculative, since there were no lesions suggesting the presence of trauma, tumors, congenital diseases, or other pathologies. A few weeks later, another team announced that they had found evidence of trepanation in the remains of a man buried between 1550 and 1450 BCE at the Tel Megiddo archaeological site in Israel. Those remains (of two brothers) showed evidence of developmental anomalies in the bones and indications of extensive lesions—signs of a likely chronic debilitating disease, such as leprosy or Cleidocranial dysplasia.

Ancient Egypt also had quite advanced medical knowledge for treating specific diseases and traumatic injuries like bone trauma, according to Camarós and his co-authors. There is paleopathological evidence of trepanation, prosthetics, and dental fillings, and historical sources describe various therapies and surgeries, including mention of tumors and “eating” lesions indicative of malignancy. They thought that cancer may have been much more prevalent in ancient Egypt than previously assumed, and if so, it seemed likely that Egyptians would have developed methods for therapy or surgery to treat those cancers.

  • Skull E270, dating from between 663 and 343 BCE, belonged to a female individual who was older than 50 years.

    Tondini, Isidro, Camarós, 2024

  • The skulls were examined using microscopic analysis and CT scanning.

    Tondini, Isidro, Camarós, 2024

  • CT Scan of skull.

    Tondini, Isidro, Camarós, 2024

  • Cutmarks found on skull 236, probably made with a sharp object.

    Tondini, Isidro, Camarós, 2024

  • Several of the metastatic lesions on skull 236 display cutmarks.

    Tondini, Isidro, Camarós, 2024

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what-do-threads,-mastodon,-and-hospital-records-have-in-common?

What do Threads, Mastodon, and hospital records have in common?

AI, Computer science, fediverse, health, medicine / /
A medical technician looks at a scan on a computer monitor.

It’s taken a while, but social media platforms now know that people prefer their information kept away from corporate eyes and malevolent algorithms. That’s why the newest generation of social media sites like Threads, Mastodon, and Bluesky boast of being part of the “fediverse.” Here, user data is hosted on independent servers rather than one corporate silo. Platforms then use common standards to share information when needed. If one server starts to host too many harmful accounts, other servers can choose to block it.

They’re not the only ones embracing this approach. Medical researchers think a similar strategy could help them train machine learning to spot disease trends in patients. Putting their AI algorithms on special servers within hospitals for “federated learning” could keep privacy standards high while letting researchers unravel new ways to detect and treat diseases.

“The use of AI is just exploding in all facets of life,” said Ronald M. Summers of the National Institutes of Health Clinical Center in Maryland, who uses the method in his radiology research. “There’s a lot of people interested in using federated learning for a variety of different data analysis applications.”

How does it work?

Until now, medical researchers refined their AI algorithms using a few carefully curated databases, usually anonymized medical information from patients taking part in clinical studies.

However, improving these models further means they need a larger dataset with real-world patient information. Researchers could pool data from several hospitals into one database, but that means asking them to hand over sensitive and highly regulated information. Sending patient information outside a hospital’s firewall is a big risk, so getting permission can be a long and legally complicated process. National privacy laws and the EU’s GDPR law set strict rules on sharing a patient’s personal information.

So instead, medical researchers are sending their AI model to hospitals so it can analyze a dataset while staying within the hospital’s firewall.

Typically, doctors first identify eligible patients for a study, select any clinical data they need for training, confirm its accuracy, and then organize it on a local database. The database is then placed onto a server at the hospital that is linked to the federated learning AI software. Once the software receives instructions from the researchers, it can work its AI magic, training itself with the hospital’s local data to find specific disease trends.

Every so often, this trained model is then sent back to a central server, where it joins models from other hospitals. An aggregation method processes these trained models to update the original model. For example, Google’s popular FedAvg aggregation algorithm takes each element of the trained models’ parameters and creates an average. Each average becomes part of the model update, with their input to the aggregate model weighted proportionally to the size of their training dataset.

In other words, how these models change gets aggregated in the central server to create an updated “consensus model.” This consensus model is then sent back to each hospital’s local database to be trained once again. The cycle continues until researchers judge the final consensus model to be accurate enough. (There’s a review of this process available.)

This keeps both sides happy. For hospitals, it helps preserve privacy since information sent back to the central server is anonymous; personal information never crosses the hospital’s firewall. It also means machine/AI learning can reach its full potential by training on real-world data so researchers get less biased results that are more likely to be sensitive to niche diseases.

Over the past few years, there has been a boom in research using this method. For example, in 2021, Summers and others used federated learning to see whether they could predict diabetes from CT scans of abdomens.

“We found that there were signatures of diabetes on the CT scanner [for] the pancreas that preceded the diagnosis of diabetes by as much as seven years,” said Summers. “That got us very excited that we might be able to help patients that are at risk.”

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