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A Blood Test May Help Pinpoint the Right Antidepressant for You

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When doctors determine the best medication for a person with depression, they generally rely upon little more than guesswork and patient self-reports, due to insufficient medical evidence. Research out of UT Southwestern Medical Center (UTSMC) previously suggested that such practices were insufficient, and a new study, published in Psychoneuroendocrinology, provides additional diagnostic information that may change the way depression is treated.

The research team drew upon a large body of research that links low levels of inflammation in the body with depression. They say a blood test for an inflammatory biomarker, known as C-reactive protein (CRP), can significantly improve the success rate of two common antidepressants for depressed patients.

Lead author Madhukar Trivedi, a professor of psychiatry at UTSMC and director of the Center for Depression Research and Clinical Care, says doctors typically pick an antidepressant for their patients in one of three ways: personal experience; matching the perceived benefits of one drug with a certain type of patient’s needs; or having the patient pick a drug by ruling out the unwanted side effects of other drugs. “There isn’t a strong evidence base to support one way [of choosing an antidepressant] over another,” he tells mental_floss.

Trivedi says that because many doctors are pressed for time and overloaded with patients, they don't thoroughly address a depressed patient’s needs. “If you have diabetes, the doctor spends a lot of time explaining that it’s a serious illness—there are consequences for ignoring it, and there are treatments you need to do. In depression, that does not happen as much. Patient engagement is not that strong,” he says.

Trivedi led a landmark study more than a decade ago that revealed how serious the medication problem is: Up to one-third of depressed patients don’t see an improvement in their first month of medication, and approximately 40 percent of people who take antidepressants quit within the first three months.

This failure rate is exacerbated by the lingering social stigma accompanying the illness. “It is not fashionable to say, ‘I have depression,’ so people around you may put in their uninformed advice … 'Just go for a walk,' or 'Why are you depressed?'” says Trivedi.

The CRP blood test is traditionally used as a measure of inflammation for such diseases as cardiovascular disease, diabetes, and rheumatoid arthritis, among others, where doctors are looking for high levels of C-reactive protein—approximately 3 to 5 milligrams per blood liter. In the new study, which Trivedi refers to as a “secondary analysis” of a study he led in 2011 (the Co-MED trial), he says, “Our hypothesis was that for depression there may be stress related inflammation in lower levels.”

Trivedi’s lab measured depression remission rates of 106 patients, culled from 440 patients involved in the 2011 study, each of whom had given blood samples. Fifty-one of them had been prescribed only escitalopram (Lexapro), while 55 of them had been prescribed escitalopram plus bupriopion (Wellbutrin), both commonly prescribed SSRI antidepressant drugs.

After analyzing blood samples, the researchers found that for patients whose CRP levels were less than 1 milligram per liter of blood, escitalopram alone was more effective—patients experienced a 57 percent remission rate of their depression versus 30 percent on the other drug. For patients with higher CRP levels, escitalopram plus bupropion was more effective. These patients experienced a 51 percent remission rate, compared to 33 percent on only escitalopram.

Not only do these SSRI antidepressant drugs promote higher levels of retention of the “feel good” neurotransmitters serotonin and dopamine, they trigger an immune response that blocks inflammatory molecules called cytokines.

“The magnitude of the effect was really thrilling,” Trivedi says. “The bottom line in depression is we have not had objective tests that help us with any component of diagnosis or treatment matching—and this is a very solid first step.”

His next step will be to do a clinical trial in which researchers will go to primary care practices and randomize patients, so that half of the participants will get “the best care the provider is willing to do,” he says, and the other half will do the blood test and then get matched with one of the two drug approaches. “We want to show that if you have the treatment matching based on the blood tests, that group of patients will have significantly better outcomes than those who do usual care.”

He hopes that other studies will use the CRP test with other antidepressant drugs, as well. “It’s not a perfect solution for 100 percent of patients, but it helps.”

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iStock // Ekaterina Minaeva
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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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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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