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Engineers' capsule aims to deliver drugs to GI patients – Today's Medical Developments

The new capsule incorporates 3D printing, spring action, and Wi-Fi commands to direct medication.
A 3D printed drug-delivery system using a sensor, heating element and wave spring sounds like a machine wheeled into the doctor’s office, not something popped in your mouth.
Yet a University of Maryland (UMD) engineering team has developed a futuristic new capsule that can be ingested, guided, and activated to detect, monitor, and treat chronic problems in the gastrointestinal (GI) tract.
This new research, published in the journal Advanced Materials Technologies, demonstrates a tiny spring actuator that can anchor the capsule, allowing it to deliver a drug deposit to planned locations in the GI tract. With the ability to stay in place for a sustained time, the capsule can deliver multiple doses of medication as needed.
“Our innovation is an early example of using hybrid fabrication approaches that merge 3D printing with traditional microfabrication to create new and impactful devices,” says first author Joshua Levy, a materials science and engineering doctoral student. “We expect our work will help form the foundation of new forms of treatment, and that these devices eventually will lead to better therapies.”
He is part of Professor Reza Ghodssi’s (ECE/ISR) MEMS Sensors and Actuators Laboratory, which has been working on capsule development for five years. Others in the A. James Clark School of Engineering who contributed to the research included bioengineering doctoral student Michael Straker, electrical and computer engineering doctoral student Justin Stine, UMD research associate Luke Beardslee, electrical and computer engineering alum Vivian Borbash ’22 and Ghodssi. All the graduate students are associated with the Robert E. Fischell Institute for Biomedical Devices.
Some 3.1 million people in the United States suffer from chronic GI autoimmune disorders such as inflammatory bowel disease, Crohn’s disease, and ulcerative colitis. Medical science has made substantial advances in the last few decades, largely through systemic therapies such as pills, injections, and infusions. Unfortunately, as these therapies diffuse throughout the body, their effectiveness also diminishes. Medicine can’t be targeted to the inflammatory lesions that characterize these gut diseases, and the treatments produce substantial side effects.
“The GI tract is a passage through the body that influences who we are through its direct connections to the outside world,” Ghodssi says. “This unique organ is susceptible to a number of health grand challenges, from cancer to IBD to neurodegenerative diseases, and mental health problems and metabolic diseases as well.”
Capsules can perform GI imaging, gas sensing, lesion biopsy and drug delivery, and they can be commanded remotely through Wi-Fi and a phone app. Still, one problem has persisted: how to keep the capsule in place to deliver medicine amid the constant churning of the digestive system.
This new research introduces the thermomechanical 3D-printable spring actuator, a mechanism that works with existing ingestible capsule-based sensing and communication technologies and enables treatment based on detected GI biomarkers and external commands, which can be delivered via Bluetooth.
The actuator is combined with the first application of the Ghodssi’s biomimetic barbed microneedle technology, known as SMAD, for Spiny Microneedle Anchoring drug Deposit. When it’s time to deploy the spring and propel its payload of therapeutic drugs, the capsule’s tiny resistive heating element melts a material called polycaprolactone that holds it in place. The SMAD is then released from the spring to provide prolonged dissolving therapeutic drug delivery to specific lesions.
“We hope that our emerging noninvasive capsule technology will be able to put another tool in the medical kit, one with fewer side effects and better targeted efficacy,” Ghodssi says. “Our work addresses only one of the promising research areas for this technology. We believe developing ingestible capsules is a frontier of research that requires an interdisciplinary team of doctors, engineers, biologists, and data analysts to solve.”
An algorithm for automatic assembly of products is accurate, efficient, and generalizable to a wide range of complex real-world assemblies.
The manufacturing industry (largely) welcomed artificial intelligence (AI) with open arms. Less of the dull, dirty, and dangerous? Say no more. Planning for mechanical assemblies still requires more than scratching out some sketches, of course – it’s a complex conundrum that means dealing with arbitrary 3D shapes and highly constrained motion required for real-world assemblies.
Human engineers, understandably, need to jump in the ring and manually design assembly plans and instructions before sending the parts to assembly lines, and this manual nature translates to high labor costs and the potential for error.
In a quest to ease some of said burdens, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Autodesk Research, and Texas A&M University came up with a method to automatically assemble products that’s accurate, efficient, and generalizable to a wide range of complex real-world assemblies. Their algorithm efficiently determines the order for multipart assembly, and then searches for a physically realistic motion path for each step.
The team cooked up a Spartan-level large-scale dataset with thousands of physically valid industrial assemblies and motions to test their method. The proposed method can solve almost all of them, especially outperforming previous methods by a large margin on rotational assemblies, like screws and puzzles. Also, it’s a bit of a speed demon in that it solves 80-part assemblies within several minutes.
“Instead of one assembly line specifically designed for one specific product, if we can automatically figure out ways to sequence and move, we can use a fully adaptive setup,” says Yunsheng Tian, a PhD student at MIT CSAIL and lead author on the paper. “Maybe one assembly line can be used for tons of different products. We think of this as low-volume, high-mixed assembly, opposed to traditional high-volume, low-mixed assembly, which is very specific to a certain product.”
Given the objective of assembling a screw attached to a rod, for example, the algorithm would find the assembly strategy through two stages: disassembly and assembly. The disassembly planning algorithm searches for a collision-free path to disassemble the screw from the rod. Using physics-based simulation, the algorithm applies different forces to the screw and observes the movement. As a result, a torque rotating along the rod’s central axis moves the screw to the end of the rod, then a straight force pointing away from the rod separates the screw and the rod. In the assembly stage, the algorithm reverses the disassembly path to get an assembly solution from individual parts.
“Think about IKEA furniture – it has step-by-step instructions with the little white book. All of those must be manually authored by people today, so now we can figure out how to make those assembly instructions,” says Karl D.D. Willis, a senior research manager at Autodesk Research. “You can imagine how, for people designing products, this could be helpful for building up those types of instructions. Either it’s for people, as in laying out these assembly plans, or it could be for some kind of robotic system right down the line.”
The disassemble/assemble dance
With current manufacturing, in a factory or assembly line, everything is typically hard-coded. If you want to assemble a given product, you must precisely control or program instructions to assemble or disassemble a product. Which part should be assembled first? Which part should be assembled next? And how are you going to assemble this?
Previous attempts have been mostly limited to simple assembly paths, like a very straight translation of parts – nothing too complicated. To move beyond this, the team used a physics-based simulator – a tool commonly used to train robots and self-driving cars – to guide the search for assembly paths, which makes things much easier and more generalizable. 
“Let’s say you want to disassemble a washer from the shaft, which is very tightly geometrically assembled. The status quo would simply try to sample a bunch of different ways to separate them, and it’s very possible you can’t create a simple path that’s perfectly collision-free. Using physics, you don’t have this limitation. You can try, for example, adding a simple downward force, and the simulator will find the correct motion to disassemble the washer from the shaft,” Tian says.
While the system handled rigid objects with ease, it remains in future work to plan for soft, deformable assemblies.
One avenue of work the team is looking to explore is making a physical robotic setup to assemble items. This would require more work in terms of robotic control and planning to be integrated with the team’s system, as a step toward their broader goal: to make an assembly line that can adaptively assemble everything without humans.
“The long-term vision here is, how do you take any object in the world and be able to either put that together from the parts, using automation and robotics,” Willis says. “Inversely, how do we take any object in the world that’s made up of many different types of materials and pull it apart so that we can recycle and get them into the correct waste streams? The step we’re taking is looking at how we can use some advanced simulation to be able to begin to pull apart those parts, and eventually get to the point where we can test that in the real world.”
“Assembly is a longstanding challenge in the robotics, manufacturing, and graphics communities,” says Yashraj Narang, senior robotics research scientist at NVIDIA. “This work is an important step forward in simulating mechanical assemblies and solving assembly planning problems. It proposes a method that is a clever combination of solving the computationally simpler disassembly problem, using force-based actions in a custom simulator for contact-rich physics, and using a progressively deepening search algorithm. Impressively, the method can discover an assembly plan for a 50-part engine in a few minutes. In the future, it will be exciting to see other researchers and engineers build upon this excellent work, perhaps allowing robots to perform the assembly operations in simulation and then transferring those behaviors to real-world industrial settings.”
MIT professor and CSAIL principal investigator Wojciech Matusik is a senior author on the paper, with PhD students Yunsheng Tian, Jie Xu (now a research scientist at NVIDIA) and Yichen Li also noted as CSAIL authors. Research scientists from Autodesk Research Jieliang Luo, Hui Li, Karl D.D. Willis, and assistant professor of computer science at Texas A&M University Shinjiro Sueda also worked on the paper. The team will present their findings at SIGGRAPH Asia 2022, with the paper also being published in ACM Transactions on Graphics. Their research was supported in part by the National Science Foundation.
How product innovation works in healthcare and how it leads to better patient treatment adherence and, ultimately, better health outcomes.
In the era of value-based care, providers across the care spectrum are being measured (and compensated) on patient health outcomes. While nobody is arguing with the premise of that focus, many providers are struggling to adapt to the new measurement system while also delivering compassionate, effective care.
One problem is that, as the industry scrambles to adapt to evolving value-based care metrics, providers and healthcare systems are being bombarded with tech tools purporting to make life easier.
But too many of these tools fall short of the mark. One common reason is that they only accommodate one of two necessary participants. Digitized medical charts, for example, make it easier for physicians’ practices to track and access patient data, but can be frustrating to patients if one provider’s electronic health record (HER) can’t easily share data with another’s.
Or else the reverse happens: smart health devices like the Apple Watch may track all kinds of health-related activity that patients rely on – but most of it can’t easily transfer to an EHR. What we need to drive better health outcomes are tools that take the needs and desires of both providers and patients into consideration.
The good news is that there’s an established way to do that – while also accounting for business viability and technical feasibility. It’s called product innovation. In this piece, I’ll explain how product innovation works in healthcare and how it leads to better patient treatment adherence and, ultimately, better health outcomes.
What is product innovation?
I realize that “product innovation” sounds a little buzzy. What it describes is a value system that balances three considerations when creating new things: user desirability, business viability, and technical feasibility.
In the context of healthcare, that means creating new products (or improving existing ones) in such a way that…
Maybe the simplest way to explain how product innovation is different from a traditional approach to building new products is this: when you embrace product innovation, there’s significantly less risk that you will produce a flop. That is, you’ll never invest lots of time and resources into building something that users don’t adopt or that the business can’t maintain for the long term.
In non-healthcare settings, that’s compelling because it means businesses can avoid months of expensive development that leads nowhere. In healthcare, the stakes are much higher: when you embrace product innovation, you avoid investing in solutions that have no effect on patient health (or, worse, that actually harm people).
There are a few key principles involved in developing in this way:
Now let’s take a look at some use cases to illustrate how this way of working can yield digital health products that inspire patient adherence and so lead to better outcomes.
Accounting for emotions in treating overactive bladder
When we partnered with a digital therapeutics company to develop a product to deliver behavioral therapy to people diagnosed with overactive bladder (OAB), we started the engagement by talking with potential users – that is, people with OAB.
One of the most striking things we noticed during those conversations was that many people started by assuring us their OAB symptoms weren’t a big deal. And then they’d tell story after story of how these symptoms had disrupted their lives.
That sparked an ah-ha moment for us: we saw that people tended to feel a lot of embarrassment about OAB, which signaled that we had to find ways to alleviate that embarrassment in whatever we built.
Subsequent prototype testing led us to develop a chatbot to accompany a digital bladder leak diary. CeCe, the chatbot, uses friendly, non-technical language. The imagery and language of the app are bright, cheerful, and matter-of-fact. When users record a bladder leak and the conditions in which it happened, CeCe offers them context about how many other people with OAB have similar experiences, which eases embarrassment by communicating to users that they’re not alone. With feasibility in mind, we built the product as a simple web app vs. a native app. This work was for beta testing that will eventually lead to a product that applies for FDA approval.
Zoom out, and the implications are significant: almost 50 million people have at least one chronic pelvic health disorder, with treatment costs greater than $100 billion per year. What’s more, many of these people live four hours or more from a practitioner who can offer specialized treatment. A digital health solution available on a smart device could greatly improve symptoms, prevent progression of conditions, improve the quality of life for patients, and help manage the cost of the condition.
Replacing patient memory with data in spinal fusion recovery
The unreliability of memory is well established. It can be particularly hard to remember pain, especially when trying to gauge the pain you’re feeling today versus the pain you felt three weeks ago. That makes the work of spinal surgeons tricky: a key metric to track after spinal fusion surgery is whether a patient’s pain is decreasing.
Without an accurate assessment, it’s hard to know how recovery is going, what to recommend, and when (and whether) to change course.
When we talked to spine surgeons about tracking patient recovery, they expressed a desire for more objective data, both about patients’ pain and about their adherence to recovery protocols, like wearing a bone stimulator and getting regular moderate activity.
When we talked to patients recovering from spinal fusion surgery, we learned that they often avoided activity during the recovery phase because it caused pain, and they were concerned that pain was a signal they should stop and be still.
In fact, moderate activity, while painful in the moment, tends to improve recovery and lead to less pain in the long term. We used all this information to develop an app to accompany a post-surgery bone stimulator. The device had a built-in pedometer, so we built an app that pulled daily step counts and daily device use and sent users a daily prompt to assess their pain.
Over time, patients could see their pain trend down as they used the bone stimulator and maintained regular activity levels – hugely motivating to continue adhering to both protocols.
During their follow-up visits, surgeons were able to look at their data as a PDF, seeing at a glance how well they were adhering and how their pain was trending, and make recommendations based on that data.
To build products users use, build products users love
It sounds obvious once you say it: build products users love, and they’re more likely to use them. In healthcare, that means they’re also more likely to adhere to treatment protocols associated with those products and therefore enjoy improved health outcomes.
Product innovation begins with the end in mind, focusing on developing products that work for both patients and providers who will be using them – while also taking into account technical constraints and business goals.
As health systems look for more cost-effective ways to deliver personalized care to patients, digital tools and products will no doubt play a bigger role in treatment. Embracing product innovation will help ensure efficient use of business resources in development and maintenance, enthusiastic adoption among patients and providers, and strong health outcomes overall.
Start 2023 off with insights from Terry Wohlers about what's new in additive manufacturing.
As the additive manufacturing (AM) industry continues to mature, many companies are looking to AM for where it can help shorten lead time and create new opportunities. However, AM is not a one-size-fits-all solution and still isn’t right for mass production in many areas. With AM being used for an array of applications, it can complement a company’s capabilities for some production applications.
Register for this free webinar – Advances and challenges in additive manufacturing – taking place on Wednesday, Jan. 11, 2023 12:00PM ET.
Learn about many of the most interesting developments in the industry, while recognizing the challenges that many face. Hear how AM can be scaled for mass production – and where that’s happening today. And find out some of the most thought-provoking trends in the AM industry.
As the company continues to grow and invest in Kentucky machine tool production operations, a 27,000 sq ft building has been added to the campus.
To support the production of another new Kentucky-designed and built line of machine tools, Mazak has expanded its Florence manufacturing campus with the addition of a new SYNCREX Assembly Plant. The 27,000ft2 state-of-the-art building combines engineering, production and applications support for the company’s recently launched SYNCREX Series of Swiss-style machine tools designed specifically for precision high production of small parts.
With an output capacity of up to 10 machines per month, the SYNCREX building features all the necessary overhead cranes and equipment its employees need to produce 16 different models within the SYNCREX Series for serving the North American market. The machines come in four bar stock capacities (20/25/32/38) and four different axis configurations up to a 9X model with full B-axis contouring capabilities. Production flow through the building starts with a machined base that progresses through assembly operations and on to alignment, testing, inspection and runoff procedures prior to shipping.
“While the building’s name is the SYNCREX Assembly Plant, a lot more than just assembly happens there,” says Kevin Sekerak, plant manager at Mazak. “Within the new building’s production flow, we’ve incorporated applications support, which is extremely critical for this particular type of machine. That support entails integrating various forms of automation and other ancillary systems together with the machines, then proving them out to make sure they all operate to customer performance requirements and specifications. Often, applications specialists will work side by side with assembly technicians during customer runoffs of a machine.”
The sliding-headstock SYNCREX machines are all constructed on Mazak High Damping Composite Castings (HDCC) produced in the United States and machined at Mazak Kentucky. The unique high rigidity base provides greater vibration damping characteristics, less thermal growth, and greater part surface capabilities when compared to a cast iron base machine. Mazak also produces SYNCREX spindles, headstocks, sheet metal, and many other key components in Kentucky to ensure every machine provides the highest possible accuracy and repeatability.
Launching the all-new SYNCREX line, Mazak further expanded its machine tool technology portfolio – in particular those models designed and produced in Florence, Kentucky. Among its Kentucky-built products is the company’s well-received Ez Series of machines that provide today’s job shops affordability without sacrificing machine performance.


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