We do know of some drugs that are really good at getting through this barrier, either by slipping in or by breaking the barrier down. A new study, currently published as a pre-print, has found that giving rats a drug that easily crosses the blood-brain barrier helps other drugs get into the brain, even if they aren’t normally able to. 

What is this barrier breaking drug? Methamphetamine, also known as meth. Meth’s powerful effects are partly due to its ability to get into your brain. At low doses, it can increase a process called fluid phase transcytosis, in which a drug is packaged and transported into the brain by the cells lining blood vessels. While previous studies have shown this is how low dose meth enters the brain, this new study is the first time it’s been shown that it can bring other drugs along for the ride.

Researchers gave low doses of meth to rats, along with therapeutic drugs that don't easily cross the blood brain barrier. They then looked at the rats' brains to see what molecules were present. The therapeutic drugs got into the brain far more easily if meth was given at the same time. In another experiment, meth was able to help a chemotherapy drug enter the brain, which increased survival in a mouse model of brain cancer. 

Meth isn’t often associated with medicine, but it is FDA approved for some uses. In cases such as aggressive brain tumors, it may be worth using a little meth for a lot of chemotherapeutic. 

A recent study published from researchers at the University of Washington showed that language ability and problem solving skills best predict how quickly people learn Python, a popular programming language. Their research, published in Scientific Reports, used behavioral tests and measures of brain activity to see how they correlated with how fast and well participants learned programming. 

For the study, 42 participants were recruited to try a popular online coding course through Codeacademy. They were asked to complete ten 45-minute lessons of the "Learn Python" course. From the 36 participants who completed the study, they were able to determine rate of learning and how well the students learned the lessons. 

Before doing online classes, participants did a battery of tests designed to look at math skills, working memory, problem solving, and second language learning ability. During their online programming course, the researchers were able to track how quickly they learned and how well they did in the quizzes built into the online software. They also completed a quiz and coding task at the end of the study to look at their overall coding knowledge.

The researchers where then able to compare the test results from before and after the Python course. The goal was to determine how much of the differences in participant Python learning could be explained by their performance on the different pre-tests: how much did memory, problem solving, and an aptitude for numbers or languages influence how quickly they learned to code?

The participants learned Python at different rates, and had different programming abilities at the end of the study. The researchers looked at the relationship between the skills covered in the pre-test skills and the variability in how participants learned Python. They found that how well students learned Python was mostly explained by general cognitive abilities (problem solving and working memory), while how quickly they learned was explained by both general cognitive skills and language aptitude.

Language aptitude explained almost 20% of the difference in how quickly people learned Python. In contrast, performance on the math pre-test only explained 2% of the variability in how quickly students learned, and didn't correlate at all with how well they learned. Learning to code depended much more on language skills than it did on numerical skills.

Additional evidence for the importance of language skills came from electroencephalography (EEG) data. EEG is a method of measuring brain activity through electrical patterns that can be recorded through the skull. Prior to their online learning tasks, participants were given a resting state EEG, which measures patterns in the brain when the subjects were relaxed and doing nothing. 

 Electrical activity at rest has different patterns. One of these patterns is slow waves of electrical activity called beta oscillations. Past research has shown that high levels of beta oscillations at rest are linked with the ability to learn a second language. 

In this study, high levels of these beta oscillations were associated with faster learning and more programming knowledge. While this finding gives additional support to the connection between language learning and learning to code, it's not clear (yet) how beta oscillations are related to learning outcomes, and more research is needed.

Taken together, these result make the case for language skills being an integral aspect of learning programming (or at least of learning Python), while math skills weren't very predictive of how well or quickly participants learned. This idea has important implications for the perceptions surrounding programming, which is often viewed as a "math intensive" field.

There are many assumptions about programmers, especially about who makes a good programmer. Women often feel they don't fit with the idea of a "typical" computer programmer. However, girls typically have higher language skills than boys on average. Since language abilities were shown to predict ability to learn programming, perhaps women should have more of  a reputation for being "good" at programming.

It's true that some fields require both math and programming skills, but those aren't necessarily the majority of programming jobs available. Based on this study, the requirements for advanced math classes for every computer science major seem unnecessary, and increased flexibility over math requirements could help recruit and retain students.

Explicitly connecting language skills to programming and providing education options that don't require advanced math may help improve diversity, while still teaching students the programming skills they need. Indeed, "bootcamp" style options that are rapidly growing in popularity lead to programming careers without forcing calculus on their participants. 

As programming becomes a pre-requisite for many jobs, it's time to question long held assumptions about pre-requisites for learning programming. Based on the results from this new study, universities and individuals should rethink how they characterize learning programming and what abilities play a role. There's a lot of people out there who "aren't math people," but they just might be computer science people. 

But for many of us, we still want to do more. As research labs close, there’s an enormous potential workforce with the skills needed to run diagnostic tests, though many lack formal certification. Some opportunities are appearing, including a call for volunteers at University of Washington and the University of California at Berkley, and increased hiring by private companies. Michael F Wells, a postdoctoral fellow at Harvard, is creating a database of scientists who want to help. While that’s a step in the right direction, it can take quite a while for new volunteers to get up to speed. For example, a call-out from the Innovative Genomics Center at UC Berkeley cites a two to three week training period — precious time in a pandemic. In a pandemic, weeks matter. 

This likely won’t be the last epidemic or pandemic. It may be worth investing in a system of training for these situations. While a Medical Reserve Corps does exist, the corps focuses on the medical and public health aspects of potential emergencies, without a specific role for scientists.

Scientists and students could be valuable help on the front lines. An organized, nationwide “Scientific Reserve Corps” could help. Scientists and students could complete training (and mandatory refreshers) on how to perform a variety of common tests, many of which could be similar to tests from their own research. They could train in collecting samples with proper PPE, analyzing data and data-sharing. For this to work, a Scientific Reserve Corps could encourage governments to plan for specific needs — coordinating types of test kits, extraction kits, and software — so that people could train before pandemic hits. 

With financial aid or compensation, this reserve system could also help students, who often struggle to make ends meet, The motto of the army reserve is “twice the citizen.” Perhaps it’s time for twice the scientist. 

One that you may have heard of is Neuralink, a company owned by Elon Musk. Neuralink has rapidly been moving toward selling a BMI, with the hope of using an implantable device to record brain signals, allowing people to control computers and other machines with just their thoughts. In July, the company revealed their new device and surgical set-up in a scientific pre-print and presentation, sparking a myriad of headlines and discussions about the implications of BMI technology. 

But while there has rightfully been a lot of focus on the ethical, legal and technological ramifications of BMIs, less attention has been paid to their biological demands. In order to record signals, these devices have to literally be inside your head—and the only way to get there is via invasive surgery. So before getting carried away with philosophical considerations, it's important to consider the more pragmatic medical concerns of surgical infections and device failures. 

These concerns are often neglected in both the conversations about, and the research into, BMIs. It's unclear if Neuralink consulted or collaborated with any neurosurgeons or other medical experts on either the potential risks of the implantation procedure or surgical best practices. The only author on the Neuralink paper, published in BioRxiv, is Elon Musk, who has no formal medical training. And articles submitted to BioRxiv are not peer-reviewed, meaning the work has not been evaluated by other experts in the field. Considering that the companies making BMIs, including Neuralink, are pitching them primarily as clinical tools to help people with spinal cord injuries and neuromuscular diseases like cerebal palsy, the lack of clinical considerations are surprising. 

Neuralink has given technological details for the implants and surgery, as well as examples of the brain signals the devices can record. But critical questions remain unanswered. From the information that's been made public, we don't know whether the BMI implants might damage nervous tissue, how long they will work, rates of infection at the implant sites, or even if the materials used will be compatible with human tissue. These are all basic questions that matter as much or more than how well the probes can record signals from the brain.

While Neuralink's paper claims that their system of flexible implantable probes is less invasive than the traditional, inflexible probes currently used for research and medical purposes, they don't provide any evidence their BMI causes less damage to the brain. There are many metrics that can be used to assess brain health, including brain cell death and markers that show inflammation and immune responses — none of those metrics have been quantified by Neuralink.

And just being less invasive may not make a practical difference in surgical risk and implantation outcomes. Neuralink is building on the existing body of research on implantable brain devices, such as deep brain stimulators (DBS), which have been used for decades. DBS uses electrical pulses to regulate function in areas of the brain damaged by diseases such as Parkinson's disease, and also involve surgery. The main risks of DBS are infections and device failures. One study of patients at a Shanghai-based hospital found that these outcomes occurred in 4.6% of patients. There are also risks associated with the surgery itself — including, in rare cases, hemorrhaging. BMIs, for all their technical advances, are susceptible to similar biological and surgical limitations: Even new technology still has to contend with the ancient enemy of infection.

Like with DBS, the risks of BMI implantation include post-operative infections and device failures. Infections are not often fatal, but they do necessitate antibiotics and removal of the device. While taking antibiotics might seem like a simple fix, emerging "superbugs" able to resist the majority of antibiotics, are becoming increasingly common. Hardware removal requires additional surgery, and we have no information on how removing Neuralink probes may alter brain tissue. It is safe to say that extracting hundreds of probes from your brain, no matter how flexible they are, probably causes damage. Silicon Valley may love disruption, but your tissues do not.

The current plan for Neuralink is to help patients with spinal cord injuries or paralysis, but Musk's grand plan is to pave the way for humans to "merge" with AI. As he said in the Neuralink presentation, "We are a brain in a vat, and that vat is our skull." Musk describes Neuralink as something even healthy people might potentially choose to augment their capabilities with. The social and ethical ramifications can be debated, but it seems beyond debate that the implantation will involve some level of risk. With this in mind, we might want to think twice before letting Elon Musk into our brains.

Now is the time to ask questions now about the biological impacts of these implants. BMIs may someday allow patients with spinal cord injuries to interact with the world in ways that were previously impossible—an improvement in life-quality that may be worth the risk to some people. The rest of us might be better off sticking to the brain-interfacing technology that evolution spent years perfecting: the human body. 

As the number of professional science journalists has declined, scientists have fulfilled the important task of communicating science to the public. As a scientist writing for Massive, it’s reassuring to me to see data confirming that scientists can write in a way that engages the general public, and that the public responds positively. 

The study authors note that while scientists can inform and share science news, they are not independent outsiders and have other limitations on their time and knowledge. Increasing the number of scientists writing articles may accelerate the decline of science journalists, and increase reliance on unpaid, outside sources. Despite these caveats, scientists can still play an important role in science communication. It only benefits society to hear their unique points of view.

An illuminating example are 18 candidate genes that are ostensibly linked to depression, which upon a second, more thorough look may have nothing to do with depression at all. These genes have been heavily researched, all based on foundation of research that turned out to be nothing more than sand. 

Graduate students are uniquely harmed by these unreplicatable experiments. While there are real fiscal and societal costs, there are also unseen personal costs. The “waste of 1000 studies” is a waste of many more years that graduate students spent trying to make these experiments work. 

We have five or so years to learn and work on a project. And critically, we often have five years to publish said project. If you’re starting a project on a foundation that doesn’t exist, you’re set up for failure. And in cases like the one the article discusses, where the evidence seems strong, that failure feels so personal. The logical assumption is that it’s your fault, as the lowly graduate student. 

It can get worse when the thing you’re seeking to build off and replicate is from past research in your own lab. Poor mentorship might mean you’re not just unable to fight the zombie but are also forced to reanimate it. 

It doesn’t help that research is a job that relies on zeal and curiosity, which are often fragile things. Intellectual zombies might not eat your brain but they eat away at your faith in the system.