One potentially positive outcome from the COVID pandemic is that it was a wakeup call – if there was any doubt previously about the fact that we all live in one giant interconnected world, it should not have survived the recent pandemic. This is particularly true when it comes to infectious disease. A bug that breaks out on the other side of the world can make its way to your country, your home, and cause havoc. It’s also not just about the spread of infectious organisms, but the breeding of these organisms.

One source of infectious agents is zoonotic spillover, where viruses, for example, can jump from an animal reservoir to a human. So the policies in place in any country to reduce the chance of this happening affect the world. The same is true of policies for laboratories studying potentially infectious agents.

It’s also important to remember that infectious agents are not static – they evolve. They can evolve even within a single host as they replicate, and they can evolve as they jump from person to person and replicate some more. The more bugs are allows to replicate, the greater the probability that new mutations will allow them to become more infectious, or more deadly, or more resistant to treatment. Resistance to treatment is especially critical, and is more likely to happen in people who are partially treated. Give someone an antibiotic to kill 99.9% of the bacteria that’s infecting them, but stop before the infection is completely wiped out, and then the surviving bacteria can resume replication. Those surviving bacteria are likely to be the most resistant bugs to the antibiotic. Bacteria can also swap antibiotic resistant genes, and build up increasing resistance.

In short, controlling infectious agents is a world-wide problem, and it requires a world-wide response. Not only is this a humanitarian effort, it is in our own best self-interest. The rest of the world is a breeding ground for bugs that will come to our shores. This is why we really need an organization, funded by the most wealthy nations, to help establish, fund, and enforce good policies when it comes to identifying, treating, and preventing infectious illness. This includes vaccination programs, sanitation, disease outbreak monitoring, drug treatment programs, and supportive care programs (like nutrition). We would also benefit from programs that target specific hotspots of infectious disease in poor countries that do not have the resources to adequately deal with them, like HIV in sub-Saharan Africa, and tuberculosis in Bangladesh.

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If you think about the human hand as a work of engineering, it is absolutely incredible. The level of fine motor control is extreme. It is responsive and precise. It has robust sensory feedback. It combines both rigid and soft components, so that it is able to grip and lift heavy objects and also cradle and manipulate soft or delicate objects. Trying to replicate this functionality with modern robotics have been challenging, to say the least. But engineers are making steady incremental progress.

I like to check it on how the technology is developing, especially when there appears to be a significant advance. There are two basic applications for robotic hands – for robots and for prosthetics for people who have lost their hand to disease or injury. For the latter we need not only advances in the robotics of the hand itself, but also in the brain-machine interface that controls the hand. Over the years we have seen improvements in this control, using implanted brain electrodes, scalp surface electrodes, and muscle electrodes.

We have also seen the incorporation of sensory feedback, which greatly enhances control. Without this feedback, users have to look at the limb they are trying to control. With sensory feedback, they don’t have to look at it, overall control is enhanced, and the robotic limb feels much more natural. Another recent addition to this technology has been the incorporation of AI, to enhance the learning of the system during training. The software that translates the electrical signals from the user into desired robotic movements is much faster and more accurate than without AI algorithms.

A team at Johns Hopkins is trying to take the robotic hand to the next level – A natural biomimetic prosthetic hand with neuromorphic tactile sensing for precise and compliant grasping. They are specifically trying to mimic a human hand, which is a good approach. Why second-guess millions of years of evolutionary tinkering? They call their system a “hybrid” robotic hand because it incorporates both rigid and soft components. Robotic hands with rigid parts can be strong, but have difficulty handling soft or delicate objects. Hands made of soft parts are good for soft objects, but tend to be weak. The hybrid approach makes sense, and mimics a human hand with internal bones covered in muscles and then soft skin.  Continue Reading »

For my entire career as a neurologist, spanning three decades, I have been hearing about various kinds of stem cell therapy for Parkinson’s Disease (PD). Now a Phase I clinical trial is under way studying the latest stem cell technology, autologous induced pluripotent stem cells, for this purpose. This history of cell therapy for PD tells us a lot about the potential and challenges of stem cell therapy.

PD has always been an early target for stem cell therapy because of the nature of the disease. It is caused by degeneration in a specific population of neurons in the brain – dopamine neurons in the substantial nigra pars compacta (SNpc). These neurons are part of the basal ganglia circuitry, which make up the extrapyramidal system. What this part of the brain does, essentially, is to modulate voluntary movement. One way to think about it is that is modulates the gain of the connection between the desire the move and the resulting movement – it facilitates movement. This circuitry is also involved in reward behaviors.

When neurons in the SNpc are lost the basal ganglia is less able to facilitate movement; the gain is turned down. Patients with PD become hypokinetic – they move less. It becomes harder to move. They need more of a will to move in order to initiate movement. In the end stage, patients with PD can become “frozen”.

The primary treatment for PD is dopamine or a dopamine agonist. Sinemet, which contains L-dopa, a precursor to dopamine, is one mainstay treatment. The L-dopa gets transported into the brain where it is made into dopamine.  These treatments work as long as there are some SNpc neurons left to convert the L-dopa and secrete the dopamine. There are also drugs that enhance dopamine function or are direct dopamine agonists. Other drugs are cholinergic inhibitors, as acetylcholine tends to oppose the action of dopamine in the basal ganglia circuits. These drugs all have side effects because dopamine and acetylcholine are used elsewhere in the brain. Also, without the SNpc neurons to buffer the dopamine, end-stage patients with PD go through highly variable symptoms based upon the moment-to-moment drug levels in their blood. They become hyperkinetic, then have a brief sweet-spot, and then hypokinetic, and then repeat that cycle with the next dose.

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In 2006 (yes, it was that long ago – yikes) the International Astronomical Union (IAU) officially adopted the definition of dwarf planet – they are large enough for their gravity to pull themselves into a sphere, they orbit the sun and not another larger body, but they don’t gravitationally dominate their orbit. That last criterion is what separates planets (which do dominate their orbit) from dwarf planets. Famously, this causes Pluto to be “downgraded” from a planet to a dwarf planet. Four other objects also met criteria for dwarf planet – Ceres in the asteroid belt, and three Kuiper belt objects, Makemake, Haumea, and Eris.

The new designation of dwarf planet came soon after the discovery of Sedna, a trans-Neptunian object that could meet the old definition of planet. It was, in fact, often reported at the time as the discovery of a 10th planet. But astronomers feared that there were dozens or even hundreds of similar trans-Neptunian objects, and they thought it was messy to have so many planets in our solar system. That is why they came up with the whole idea of dwarf planets. Pluto was just caught in the crossfire – in order to keep Sedna and its ilk from being planets, Pluto had to be demoted as well. As a sort-of consolation, dwarf planets that were also trans-Neptunian objects were named “plutoids”. All dwarf planets are plutoids, except Ceres, which is in the asteroid belt between Mars and Jupiter.

So here we are, two decades later, and I can’t help wondering – where are all the dwarf planets? Where are all the trans-Neptunian objects that astronomers feared would have to be classified as planets that the dwarf planet category was specifically created for? I really thought that by now we would have a dozen or more official dwarf planets. What’s happening? As far as I can tell there are two reasons we are still stuck with only the original five dwarf planets.

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Remember CRISPR (clustered regularly interspaced short palindromic repeats) – that new gene-editing system which is faster and cheaper than anything that came before it? CRISPR is derived from bacterial systems which uses guide RNA to target a specific sequence on a DNA strand. It is coupled with a Cas (CRISPR Associated) protein which can do things like cleave the DNA at the targeted location. We are really just at the beginning of exploring the potential of this new system, in both research and therapeutics.

Well – we may already have something better than CSRISP: TIGR-Tas. This is also an RNA-based system for targeting specific sequences of DNA and delivering a TIGR associated protein to perform a specific function. TIGR (Tandem Interspaced Guide RNA) may have some useful advantages of CRISPR.

As presented in a new paper, TIGR is actually a family of gene editing systems. It was discovered not by happy accident, but by specifically looking for it. As the paper details: “through iterative structural and sequence homology-based mining starting with a guide RNA-interaction domain of Cas9”. This means they started with Cas9 and then trolled through the vast database of phage and parasitic bacteria for similar sequences. They found what they were looking for – another family of RNA-guided gene editing systems.

Like CRISPR, TIGR is an RNA guided system, and has a modular structure. Different Tas proteins can be coupled with the TIGR to perform different actions at the targeted site. But there are several potential advantages for TIGR over CRISPR. Like CRISPR it is RNA guided, but TIGR uses both strands of the DNA to find its target sequence. This “dual guided” approach may lead to fewer off-target errors. While CRISPR works very well, there is a trade-off in CRISPR systems between speed and precision. The faster it works, the greater the number of off-target actions – like cleaving the DNA in the wrong place. The hope is that TIGR will make fewer off-target mistakes because of better targeting.

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