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New Therapy Shrinks Five Types of Pediatric Cancers in Mice

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Cancerous pediatric brain tumors are some of the most aggressive cancers to affect children, and are frequently fatal. They’re difficult to treat due to their proximity to sensitive brain tissue in tiny brains, and children’s bodies can rarely tolerate the side effects of the levels of chemotherapy and radiation necessary to shrink tumors.

But recently, researchers at Stanford Medicine, the Lucile Packard Children’s Hospital, and several other institutions successfully tested a promising immunotherapy treatment that shrank multiple tumor types in mouse models. Immunotherapy treatments harness the body’s own immune system to fight the cancer, and usually come with few to no side effects compared to chemotherapy drugs and radiation.

The collaborative study, published in Science Translational Medicine, showed results on the five most common types of pediatric tumors: Group 3 medulloblastomas (MB), atypical teratoid rhabdoid tumors (ATRT), primitive neuroectodermal tumors (PNET), pediatric glioblastoma (PG), and diffuse intrinsic pontine glioma (DIPG).

The Stanford researchers designed their study after the recent discovery of a molecule known as CD47, a protein expressed on the surface of all cells. CD47 sends a “don’t eat me” signal to the immune system’s macrophages—white blood cells whose job it is to destroy abnormal cells. “Think of the macrophages as the Pac-Man of the immune system,” Samuel Cheshier, lead study author and assistant professor of neurosurgery at Stanford Medicine, tells mental_floss.

Cancer cells have adapted to express high amounts of CD47, essentially fooling the immune system into not destroying their cells, which allows tumors to flourish. Cheshier and his team theorized that if they could block the CD47 signals on cancer cells, the macrophages would identify the cells on the cancerous tumors and eat them—without any toxicity to healthy cells. To do so, they used an antibody known as anti-CD47, which, as its name implies, blocks CD47 on the cancer from binding to a receptor on the macrophage called SIRP-alpha.

“It is this binding that tells the macrophage, 'Don't eat the tumor,'” he says. The anti-CD47 fits perfectly into the binding pocket where CD47 and SIRP-alpha interact, “like a jigsaw puzzle,” helping the macrophage correctly identify the tumor as something to be removed. “Anti-CD47 is the big power pill in Pac-Man that makes him able to eat the ghosts,” says Cheshier.

Even better, not only does anti-CD47 block the “don’t eat me” signal, it has the rare ability to pass the blood brain barrier, making it “very effective against all brain tumor types,” Cheshier says.

His team tested anti-CD47 on each of the five tumor types both in vitro (in live tissue cultured in a dish) and in vivo (human cancer cells implanted inside living mice). For the initial in vitro studies, Cheshier explains, they developed the cancer cells “in a way that preserves the cancer stem cells and allows them to grow.” Then the researchers introduced macrophages and added anti-CD47. Excitingly, the scientists “watched the macrophages eat the tumors,” he says.

Next, for each of the five tumor types, they isolated two separate lines of cancer cells taken from separate patients and cultured all 10 in the lab. Then they injected each of these different lines of tumor cells directly into the brains of 10 to 20 mice, so that a minimum of 20 animals per tumor type were tested. The tumor cells had been modified with firefly luciferase genes, making the tumors light up under scans so the scientists could track the cells’ progress. “Once we confirmed the tumor was growing, we gave some mice anti-CD47,” Cheshier says, while the control mice received none. “Only the mice that received anti-CD47 lived,” he explains.

Additionally, the scientists created an experiment where they also injected healthy human brain stem cells into mouse brains, in addition to tumor cells, and then treated some of the mice with anti-CD47. “In the mice that received anti-CD47, the normal brain cells grew normally. So there was no effect on [the healthy cells] even in the context of very active tumor killing.” Cheshier finds this result exciting because “this is the first time in any study where anyone put normal human cells with the cancer and then showed that anti-CD47 wasn’t toxic in an animal.”

Depending on the tumor type, and the amount of anti-CD47 injected, the tumors visibly shrank over the course of one week to six months—even disappearing altogether in some cases. While not every tumor was completely eliminated, Cheshier says this is most likely a matter of the length of the experiment and the amount of anti-CD47 given. “We could achieve [elimination] in every tumor type,” he says.

Of course, Cheshier warns that humans might react differently than mice, but these initial results are promising: He is most excited that a single therapy worked in all five tumor types. “You can imagine a situation where instead of giving different types of drugs for different tumors, we can just say, ‘Here is the treatment. It’s universal.’”

The pre-clinical study took four years to complete, and now the therapy has moved to phase one of a human clinical trial process, in order to test for toxicity. He has plans for phase two, which will ask the question, “Does it actually work in treating the tumor [in humans]?” Cheshier says. And phase three will be the randomized, double-blind clinical trial that hopefully proves anti-CD47 will be superior to current treatments. Meanwhile, other studies will look at its combined effects with other cancer treatments.

While there’s much further work to be done, Cheshier is very optimistic that this therapy will be both “more effective and less toxic than current standards of care. I think anti-CD47 will be part of an armamentarium where we’re using the immune system to treat cancer instead of toxic chemicals and radiation beams.”

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Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Nick Briggs/Comic Relief
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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]

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