Protege Undergraduate Research Program

The usage of Robots for surgery has increased substantially in recent years and involves the use of robotic systems to perform surgical procedures. The primary aim is to enhance the capabilities of surgeons, providing greater precision, flexibility, and control. Some common applications of robotic surgery include cardiac surgery, gynecological surgery, urological surgery, colorectal surgery, head and neck surgery, orthopedic surgery, ENT (ear, nose and throat) surgery, neurosurgery, and pediatric surgery (see few examples in Figure 1).

Specifically, our team has funding secured to explore extension of robotic surgery for space applications as NASA ventures towards extended crewed Mars missions. Figure 2 provides an illustration of the main issues associated with surgery beyond low earth orbit.

Research Tasks Proposed: (1) Acquiring an understanding of a robotic surgery control systems (literature review); (2) Introduction to Fuzzy Logic Systems and MATLAB toolbox; (3) Conventional (Linear Quadratic Regulator) Control and Fuzzy Logic controllers and MATLAB implementation/simulation; (4) Application to a idealized rubric surgery model and sensitivity studies; (5) Potential for research publications and presentations at the local and national levels.

Research Location: This research project will be completed in Prof. Cohen’s AI Bio Lab at Digital Futures, Suite #300. 

The Gas Dynamics and Propulsion Laboratory (GDPL) is located on campus, at a satellite research park, and in the Medical Sciences Building, and affords the researcher exceptional experimental and computational facilities.  The laboratory occupies a high bay area with over 10,000 square feet of floor space.  The most advanced flow and propulsion diagnostics are available, in addition to state-of-the-art computational capabilities.  A brief description of the research topics performed at GDPL follows.

Glioblastoma multiforme (GBM) is the most common primary brain cancer and of the most lethal with almost certain patient death within two years of diagnosis¹. These tumors can escape the natural host immune surveillance and subvert the physiological role of multiple brain cell types including vascular endothelial cells of the blood-brain barrier (BBB) and immune cells^2,3. Standard therapy for GBM consists of surgical resection of the main tumoral mass followed by radiation and chemotherapy⁴. Except for a few exceptions, tumors initially respond to these treatments, however, no current regimen can overcome tumor recurrence after which the chances of patient survival drop drastically to 6 months. While the exact mechanisms underpinning drug resistance are still largely unclear, the current leading hypothesis is that a small percentage of cancer cells with self-renewal potential, also named glioblastoma stem cells (GSC)⁵, could survive and be selected during the therapy. Such a limited population of resistant GSCs may eventually spread undetected through the brain and regenerate the tumoral mass during recurrences. Targeting GSCs and their specific niches represents a promising strategy for curing GBM, however, the field lacks robust translational models able to capture the complex set of molecular and biomechanical cues that are thought to support GSCs and that are normally generated by the healthy cells of the brain.

In our lab, we have recently established an enabling 3D model of GBM designed to recapitulate peculiar aspects of the brain tumor microenvironment that are known to modulate cancer resistance in vivo and include tissue-specific components of the extracellular matrix as well as human brain cells. This project will consist in conducting a proof-of-concept study to assess drug resistance of patient-derived tumors against existing and novel therapies. We will test the hypothesis that our new in vitro system supports patient-derived GSCs’ survival and maintenance of self-renewal markers when cancer cells are exposed to therapeutics used in the clinic. The project will include training in the use of advanced bioprinting methods, cell culture, and microscopy imaging.

References.

1.            Lin, D. et al. Trends in Intracranial Glioma Incidence and Mortality in the United States, 1975-2018. Front. Oncol. 11, 748061 (2021).

2.            Pacioni, S. et al. Brain Invasion along Perivascular Spaces by Glioma Cells: Relationship with Blood–Brain Barrier. Cancers 12, 18 (2019).

3.            Himes, B. T. et al. Immunosuppression in Glioblastoma: Current Understanding and Therapeutic Implications. Front. Oncol. 11, 770561 (2021).

4.            Stupp, R. et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol. 10, 459–466 (2009).

5.            Gilbertson, R. J. & Rich, J. N. Making a tumour’s bed: glioblastoma stem cells and the vascular niche. Nat. Rev. Cancer 7, 733–736 (2007).

6.            Dogan, E. et al. Cancer Stem Cells in Tumor Modeling: Challenges and Future Directions. Adv. NanoBiomed Res. 1, 2100017 (2021).

Large-scale injuries to bone tissue can have many causes, including traumatic injury, birth defects, or as a side effect for cancer treatment. In particular, bone defects greater than 2cm in length are termed “critically-sized” since they often cannot heal spontaneously. These large  bone injuries are often treated with autologous bone grafts, or bone tissue harvested from the patient’s own body, though these procedures are limited by donor site morbidity and a limited amount of harvestable bone. To overcome these shortcomings seen with bone grafts, our lab is seeking to develop synthetic, “off-the-shelf” systems to regenerate these large bone injuries. We are currently developing an injectable drug delivery system made from coated microparticles that can release multiple therapies in sequence to promote the growth of new blood vessels and new bone tissue. The ~10µm particles, made from the polymer poly(propylene sulfide) (PPS), can be loaded with bone-regenerating drugs during fabrication but are small enough to be injected from a syringe. These particles can also be coated with a separate vessel-regenerating drug (Figure 1A). Importantly, both the drug coating and drug-loaded particles are selectively degraded by cell-generated reactive oxygen species (ROS). This allows for sequential drug release from the injected microparticles after triggering by the local cells at the bone injury site (Figure 1B-D).

For this proposed project, the Protégé Scholar will be heavily involved in the development of these coated microparticles. The Protégé Scholar will assist in optimizing the particle coating process, measuring drug release kinetics, and evaluating the biological response of this system in preparation for future pre-clinical testing in more complex biological models

Dr. Hoilett is an Assistant Professor of Biomedical Engineering. He graduated from Purdue University in 2021 with a PhD in Biomedical Engineering and from Vanderbilt University in 2014 with a Bachelor of Engineering also in Biomedical Engineering. His academic interests involve developing wearable sensors for mobile health platforms and engaging engineering design activities for undergraduate and graduate students. He has several years of experience with analog circuit design and embedded systems. His previous work includes developing a wearable opioid overdose monitor for substance use disorder patients and developing an impedance analyzer for a microfluidic blood-brain barrier model among other projects. On the education side, he has developed several lab activities for first year and junior level Biomedical Engineering students. He was instrumental in developing the Milestones Program at Purdue, a certificate-granting series of workshops providing students professional engineering skills utilized in rapid-prototyping and industrial applications. At the University of Cincinnati, Dr. Hoilett’s lab will continue developing low-cost diagnostics and sensors for improving patient-care both in high-resource and low-resource settings, tackling healthcare disparities and unmet clinical needs worldwide.

The Protégé student will be paired with a Ph.D. student and assist in the research and development of continuous electrochemical aptamer biosensors for chronic disease monitoring.  Current focus is on developing sensors related to heart failure, insulin for diabetes patients, and cortisol for stress monitoring.  The sensors are adapted to work with existing continuous glucose monitor wearables (also known as ‘CGM’). The work involves basic wet-chemistry, device fabrication, electronic testing, and data analysis/presentation.  Students begin with gold electrodes, and the place down special DNA-based chemistry (aptamers) that can react with and measure the molecule to be measured, providing an electrical signal that can then be reported on a smart phone to the user.  A goal for the Protégé includes co-authorship on at least one publication resulting from the work with the Ph.D. student.

The vascular smooth muscle cells (VSMCs) that comprise the medial layer of arteries exhibit two distinct phenotypes: contractile and synthetic. Contractile VSMCs regulate blood pressure in the artery but can dedifferentiate into the synthetic phenotype in response to injury. We are hypothesizing a potential third phenotype of VSMCs initiated by mechanical strain caused by hypertension, known as the silent killer. We propose that this third phenotype happens through the Wnt signaling pathway as VSMCs become osteoblast-like cells and deposit mineral into the arterial extra cellular matrix. This project would provide new knowledge by confirming the continued plasticity of VSMCs caused by mechanical strain activating the canonical Wnt signaling pathway. By understanding the signaling pathway associated with this phenotypic modulation of VSMCs, we could work to develop targeted therapeutics to treat vascular calcification at the cellular and molecular level.

My research group has optimized and validated an in vitro model approach for inducing phenotypic modulation and calcification, we studied the calcium content in primary aortic smooth muscle cells. Inorganic sodium phosphate was used because it is known to induce phenotypic modulation and calcification in vascular smooth muscle cells. The calcium content for the phosphate-treated groups was significantly increased compared to the control groups. To further confirm the model’s ability to induce phenotypic modulation and calcification, we performed scanning electron microscopy (SEM) and energy-dispersive x-ray spectroscopy (EDS) to visualize and quantify the calcification present in the samples [Figure 1]. Given our ability to induce phenotypic modulation through phosphate supplemented media and literature hypothesizing this phenotypic switch occurs through the canonical Wnt signaling pathway, we want to examine this pathway as a therapeutic target. This protégé project would involve confirming the role of Wnt signaling in synthetic vascular Smooth muscle cells.

Experimental Plan:

1)    Induce smooth muscle cells into a synthetic phenotype.

2)    Examine the stability of the synthetic phenotype.

3)    Confirm Wnt signaling as essential for phenotypic modulation.

Training provided:

• Polymer synthesis
• Materials characterization
• Aseptic technique
• Cell culture
• Potential for research publications and presentations at local and national conferences

Current efforts in the area of ocular or intra-articular drug delivery include frequent direct injections, which is not only invasive and inconvenient for patients but also may increase the risk of complications. Therefore, development of stable drug delivery systems which have ability to be released in a controlled manner for the long term is necessary.

This research project is about developing dose-controllable implants, which release drug on-demand, and determining drug release kinetics. Drug inside the implant capsule made of biodegradable polymer is released upon exposure to light. During the summer, you will have experience on developing polymer capsules and examining characteristics of drug delivery.

The advisor will provide basic principles in soft materials, drug delivery, and various analytical technique.

Basic chemistry lab experience is required.

Fig. 1: Contrasting a. traditional PCR-based molecular diagnostic workflow with the proposed b. novel heating mechanism. A rotating disc with embedded conductors surrounded by magnets induces eddy currents that can raise temperature of fluidic samples. The spinning platform can also leverage centrifuge-based sample preparation workflows to enable pre-PCR nucleic acid extraction. Integrating a smartphone-based fluorescence detection of post PCR analysis enables inexpensive and portable “sample-to-answer” molecular diagnostic platform. 

Problem statement: The recent global pandemic (COVID-19) has exposed some of the key limitations in our current infectious disease management systems, particularly when applied in remote underdeveloped areas. Existing approaches (like the Polymerase Chain Reaction (PCR) tests) are highly resource-intensive, relying on people depositing their biological samples to collections centers where trained personnel process these samples in dedicated laboratories for further analysis. These inefficient channels introduce a considerable time lag between sample collection, diagnosis, and implementation of countermeasures. A need, therefore, exists for developing inexpensive and simple tools that can be broadly deployed to accelerate diagnosis, and provide real-time data to better inform decision making.

Project Description: The Protégé student will work in Dr. Priye’s laboratory to develop inexpensive and simplified instruments/protocols that can replace traditional bulky, expensive and power intensive laboratory instruments required for infectious disease diagnostics. One of the major challenges associated with making PCR tests more accessible is the need for thermal cyclers which repeatedly heat and cool the biological samples using power consuming Peltier heating elements. We will explore unconventional and novel techniques of heating samples that are more efficient and inexpensive such as heat generation via eddy currents and lasers. Detection of PCR tests is accomplished using expensive fluorometers. We will explore simpler optical setups such as the use of smartphone cameras or raspberry pi cameras to read fluorescent signals from PCR tests.    

Learning outcomes: Along with gaining research experience in the laboratory, the protégé student will learn and apply 3D printing, CNC machining, Arduino and python programming, Microfluidics technology and physics-based modeling. Students are encouraged to document their results and successful completion of the project can result in a journal publication.

Developing sustainable aviation fuel (SAF)from waste biomass has great potential to reduce U.S. dependence on fossil fuel while improving national energy security and addressing the environmental issues. The Renewable Fuel Standard mandates 36 billion gallons of biofuels should be produced annually by 2022, with 16 billion gallons coming from lignocellulosic biomass. Although ethanol has been the main candidate of transportation biofuels, butanol can be easily upgraded into SAF. SAF is one of the promising advanced biofuels being pursued by the Department of Energy (DOE) and National Science foundation (NSF) for the next generation of alternative fuels. However, one of the major bottlenecks impeding production of SAF from renewable biomass is the lack of cost-effective processes for converting biomass into biofuels including butanol. 

Biomass pretreatment is needed to break down the recalcitrant structure of the biomass cell wall for subsequent enzymatic hydrolysis and fermentation. However, the pretreatment processes generate inhibitors from the degradation of cellulose, hemicellulose and lignin, many of which significantly reduce the microbial growth and fermentation productivity during the fermentation process. Detoxification methods are required to reduce the toxicity of hydrolysates for biofuels fermentation, but they increase the total production cost significantly. We hypothesize that nucleophiles can react with carbonyl inhibitors in biomass pretreatment process and enhance butanol production. To verify this hypothesis, we will design new detoxification processes with the addition of nucleophilic amino acids and proteins for in situ removing the inhibitors in hydrolysates and optimizing the microbial fermentation in bioconversion processes. The protégé students will work on process design with hands-on experience in process design and microbial fermentation. 

Neuromorphic Computing and Artificial Intelligence: Artificial Intelligence (AI) has caught significant research attention. However, AI algorithms are compute resource intensive. Therefore, much emphasis has been devoted on developing specialized neuromorphic System on Chip (SoC) for accelerating AI recently. Some examples of these neuromorphic-SoC include  Google’s TPU, Intel’s Nirvana and Loihi, and IBM’s TrueNorth. In spite of these significant advances, the performance of these processors are limited by the lack of novel reconfigurable memory devices that can mimic the adaptive characteristics of biological synapses. MIND lab researchers have invented new type of artificial synaptic devices, referred to as, Low-Energy Evolutionary Channel Electronic Synapses (LEECHES) that can provide lucrative options for neuromorphic SoC. The student working on this project will work on this cutting-edge technology and develop neuromorphic SoC utilizing these novel memory devices. The student will perform the following tasks: 

o Testing and Modeling of LEECHES in Verilog/VHDL Biological computing elements are mimicked by developing novel nanoelectronic artificial neurons and synapses and assembled in brain-inspired cortical circuitries and architectures to create artificial brains and neuromorphic

o Modeling and simulation of neural circuit modules around LEECHES

o Implementation of learning algorithms and performance evaluation of this novel neuromorphic SoC

o Publication and presentations.  

o Training data collection on drones fitted with cameras and multiple sensors.  

Trusted and Secured Microelectronics Hardware: Due to complex supply-chain of microelectronics integrated circuits (IC) design and fabrication, hardware security has emerged out to be one of the prime concerns. This project seeks to develop novel ways to design and fabricate secure microelectronics circuits using emerging nanoelectronics logic and memory devices. The students will be involved in the design of ICs, simulations, fabrication, and testing.  A fundamental question we will try to answer is how can design itself be made more trust-worthy and resilient? Can emerging nanoelectronic memory and logic devices offer any inherent benefit in this area?

Advanced Semiconductor Logic and Memory Devices: With the end of Moore’s Law of conventional transistors scaling, novel logic and memory components are needed to address the demands of the next generation of computing. This project will train students on fabrication and testing of next generation of logic and memory devices. DNA based computing and data storage approaches will also be explored. 

Dr. Rashmi Jha, who is the director of MIND lab, has more that 15 years of experience in the areas of nanoelectronic logic and memory devices, neuromorphic devices, circuits and architecture, and microelectronics in industry (IBM Research) and academia.   MIND lab provides an excellent research environment to the students and is currently supporting 5 PhD, 5 MS, 4 UG students.  The students will work in the team of graduate, and undergraduate students, and PI in these stimulating research areas. Students will be mentored via weekly group meeting and one-to-one interactions with PI and encourage to publish work and attend conference. 

The next release problem (NRP) is rooted in the practical challenge of determining which subset of the candidate features should be added to the software system as part of the next release, and which other features would not be delivered in the upcoming version. Solving the NRP is critical for balancing diverse stakeholders’ evolving needs and software implementation’s resource constraints. For example, among the ten Zoom features listed below, if only five features could be released in the next version, which five should Zoom work on? This example illustrates the NRP, and software projects adopting agile development processes face the challenges associated with the NRP on a continuous basis. This Protégé research project is aimed at leveraging machine learning algorithms to solve the NRP.  The Protégé student will: (1) studying relevant literature on machine learning, NRP, and natural language processing (NLP), (2) developing algorithms to process the textual requirements data and learn to solve the NRP, and (3) experimentally evaluating the algorithms’ performances.

Can you help Zoom to decide which five features to release next, and which features to be postponed beyond the next version? The Protégé project is aimed at exploiting machine learning techniques to solve this kind of problems with real-world data from the software industry.

Topic 1: 3D Microfluidic Devices for Neural Studies: A neural circuit is a group of neurons interconnected to each other to perform certain function, while interconnected neural circuits form larger brain/neural networks.  The study of neural circuits is important to help understand brain functions and the causes of neurological disorders such as autism spectrum disorder and schizophrenia.  Traditionally, neural circuit characterizations are done by in vivo magnetic resonance imaging (MRI), which typically can only identify the circuit locations in the brain. 

Blood-brain barrier (BBB) refers to the biochemical, metabolic and protective interface between the brain and the capillary blood vessels within it. It prevents irregular changes in the environment like neurotoxic components from reaching the brain but allows nutrition and oxygen to go through to support the brain.  Study of BBB is of great interest to scientists and clinicians as the integrity of BBB is essential for the functionality and health of the brain. BBB dysfunctions are implicated in a collection of neurological disorders, e.g., Alzheimer’s disease. In vitro reconstruction of BBB is of high interest but has been challenging at least partially due to the difficulty in reproducing the circulation environment required for BBB to form at a size scale that suits organoids.

Microfluidic devices with separate compartments and interconnecting channels have been used to culture neurons and study the neural circuitry.  Similar devices have also been formed to culture blood vessel cells under continuous flows.  However, these existing devices are typically made in a planar 2D configuration that is suitable for cells but with limited capabilities or necesary 3D features to address the needs for larger brain tissues and organoids. 

This summer project will allow the Protege students to participate in the work on the development of the 3D neural study devices with graduate students and be exposed to interdisciplinary research in the field of MEMS and microfluidics, collaborating with Cincinnati Children’s Hospital Medical Center and contributing to research tasks including literature review, design, 3D printing, hands-on microfabrication using the cleanroom facility, and testing in vitro of the fabricated devices. Multiple students can be accommodated.

Topic 2: Wireless Magnetoelastic Sensors for Biomedical Applications: Magnetoelastic (ME) materials have been universally used as anti-theft security tags in shops.  These materials exhibit the magnetostriction behavior in which the magnetic property of the material changes under mechanical deformation (strain).  The application of such materials as sensors, particularly implantable microsensors for putting into the human body for biomedical applications, is of high interest due to its inherent passive wireless nature, which eliminates the needs for battery or antenna, leading to implanted devices with reduced complexity, size and cost.  The micro electro-discharge machining (µEDM) is a computer-controlled, effective micromachining technique for patterning ME materials for sensor fabrication with the micron-scale feature sizes.  This tool is available in house at the MEMS and Autonomous Integrated Microsystem (AIM) Laboratory.

This summer project will allow the Protege students to work on the development of a ME sensor for biomarker detection with graduate students and be exposed to interdisciplinary research in the field of MEMS and microsystems, collaborating with UC School of Medicine and contributing to research tasks including literature review, design and simulation, hands-on microfabrication using the cleanroom facility, and testing in vitro of the fabricated implantable microsensors.  Multiple students can be accommodated.

A side-channel attack can infer the secret key on a device (e.g., a microcontroller, a secure chip on a credit card, or an IoT device) by analyzing power consumption when the device runs encryption algorithms, such as Advanced Standard Encryption (AES). It is one of the primary threats to the security of embedded systems. While countermeasures (e.g., random delays, hiding, and masking) have been proposed to defend against traditional side-channel attacks, recent deep-learning-based side-channel attacks can defeat these existing countermeasures. Despite the promising results reported in recent studies, deep-learning-based side-channel attacks are not robust as they are sensitive to discrepancy between training data and test data. For instance, a deep neural network trained with data collected from one microcontroller (e.g., 8-bit XMEGA) may not derive high accuracy over data collected from another microcontroller (e.g., 8-bit XMEGA) due to minute changes in power measurements. 

This project aims to enhance the robustness of deep-learning-based side channel attacks. The students in this project will have the opportunities to (1) Study research papers related to deep-learning-based side-channel attacks; (2) Explore new architectures of neural networks that can be more robust in side-channel attacks; (3) Examine different target devices, including microcontrollers and FPGAs; (3) Learn cybersecurity and machine learning knowledge and skills related to this project; (4) Have access to GPU machines in Dr. Wang’s lab for training neural networks; (5) Have access to data collection platform (Chipwhisperer) and corresponding microcontrollers in Dr. Wang’s lab to collect power and EM (electromagnetic) traces of AES encryption for analysis. This opportunity is open to students in Computer Engineering, Computer Science, or Cybersecurity Engineering. 

Data collection setup in Dr. Wang's lab

UAV MASTER Lab and CDS Lab

Unmanned Aerial Vehicles (UAVs) or drones have generated a lot of interest recently among the research community due to their potential to revolutionize some of the applications in civilian domain such as emergency management, law enforcement, infrastructure inspection, precision agriculture, package delivery, and imaging/surveillance.  While there has been a lot of progress in drone applications in outdoor environments, indoor usage of drone has been limited even though there are several applications such as warehouse management and indoor emergency response. The objective of this project is to build a drone that can deliver telehealth services to people inside their homes. Operation of drones in indoor environment is particularly challenging due to several reasons including loss of access to GPS information, and proximity with obstacles and humans. For safe operation of UAVs in indoor environment, some level of autonomy is needed to allow UAVs to avoid collisions and carry out basic maneuvers without any assistance from humans. Incorporating autonomy in drones operating in indoor environment is often challenging due to the fact that positional feedback from GPS is intermittent or unavailable. The UAV needs to solve the problem of localization, i.e., finding its position and orientation using onboard sensors such as vision, laser, or other proximity sensor such as ultrasound sensors, via a process called Simultaneous Localization and Mapping (SLAM). Furthermore, the UAV should be able to use its positional estimate to perform navigation in a precise manner to enable safe operation. The objective of this project is to develop hardware and software means to carry out the process of SLAM and autonomous navigation of UAVs in indoor environments. To alleviate the safety issue arising due to operating close to humans, a prototype is being developed that can be remotely controlled via cellular network. This prototype will have inbuilt intelligence for obstacle avoidance, autonomous landing, communication delay, and lost communication. Apart from the hardware and software components, the project would involve carrying out flight tests in indoor and outdoor environments. This is a part of ongoing National Science Foundation funded project that involves researchers from multiple disciplines including College of Nursing and College of Allied Health Sciences. The student will be working in a multidisciplinary team of researchers and graduate students.

The production of metals is responsible for approximately 30% of greenhouse gas emissions, and it is projected that demand for metallic materials will continue to increase until at least the year 2060 to meet demand for renewable energy installations. The Department of Energy is investing billions of dollars to bring green energy innovations to market and create clean energy jobs in the United States. Improving useful life and properties while simultaneously controlling costs is key to reaching sustainable utilization of metallic materials. In this project, the student will contribute to the design and development of alloys for the circular economy, considering end-of-life recyclability and reuse. Students will gain exposure to:

• Synthesis of metallic specimens using vacuum arc melting
• Deformation processing of metals
• Prediction of solidification behavior and microstructure of alloys using commercial software packages
• Operation of microscopes and microstructure characterization techniques
• Mechanical testing of materials
• Scientific methods for alloy development
• Industrial-academic collaborations to solve significant challenges for sustainability
• Conducting a literature review

3D Printing, or Additive Manufacturing, has the great potential to revolutionize the industry, and thus make big impact on our economy. 3D printing usually follows a layer-wise building process, until the desired height is achieved. Therefore, products of arbitrary shape can be built without difficulty by using this innovative manufacturing method. One pressing issue with 3D printing is the lack of understanding on safety and hazards in various processes related to 3D printing, which include, but are not limited to, handling and process of 3D printing raw materials, building and removal of parts, and post processing of 3D printed parts. The typical safety and hazards concerns include emissions, laser or ultraviolet radiation, noise and ergonomic hazards, gas and material exposures, in particular nanomaterials. However, a comprehensive understanding on this important issue has been lacking. 

In this project, the student will be working with Dr. Jing Shi on developing a methodology by using data mining tools to survey the current status on safety and hazards research of 3D printing. A critical analysis will also be conducted to illuminate the current gaps and future research directions. Dr. Jing Shi of MME department also serves as the deputy director of OSHE (Occupational Safety and Health Engineering) program under NIOSH Education and Resource Center at UC. The undergraduate students will have opportunity to learn data mining and visualization tools for literature survey, and perform the formal analysis, which is expected to lead to a peer reviewed research paper. The experience and skills to be learned can be applied to many other areas and problem settings. In addition, the students will not only be nurtured by the professor on a regular basis, but also have the opportunity to interact with the Ph.D. students in Dr. Shi’s research group. 

Professor Murali Sundaram

Director ­­– Micro and Nano Manufacturing Laboratory

Department of Mechanical and Material Engineering

631 Rhodes Hall

Cincinnati, OH 45221-0072

Tel: (513) 556-2791

Fax: (513) 556-3390

Email: murali.sundaram@uc.edu

A current barrier to facilitating early tracheotomy is operating room access limitations and patient transportation challenges in ICU patients. Effectively transitioning tracheostomy from an operating room procedure to one readily completed in the Intensive Care Unit (ICU) or the Emergency Department or even in place of a cricothyrotomy by paramedics or first responders remains a technical challenge, and improper placement can be devastating. Percutaneous tracheotomy has grown in popularity as it can readily be performed at the bedside by an intensivist, obviating the need for a surgeon and/or valuable and limited operating room time; however, percutaneous tracheotomy is not appropriate in all patients, as those with morbid obesity, challenging neck anatomy from prior head and neck radiation or kyphosis, in need of an emergency airway, and on blood thinners are more suitable for open tracheotomy. To date, we have developed an artificial intelligence program capable of identifying space between tracheal rings more than 90% of the time that can reliably drive a needle into the airway in a semi-automated fashion in a cadaveric model. The objective of this proposal is to develop an Autonomous Ultrasound Robotic Assistance for Tracheotomy (AURA-Trach) with improved reliability, refined biomechanics, and enhanced outcomes by leveraging our current tracheostomy AI and ultrasound-guided miniature robotic prototype to allow accurate and reliable tracheotomy placement by a single person, securing a safe, patent airway. We hypothesize AI-assisted ultrasound can significantly improve the procedure guidance of percutaneous tracheotomy by reliably detecting critical anatomical landmarks around the neck across diverse populations, and miniature robotics can reduce the dexterity needed to safely place a tracheostomy tube by performing actuated needle, blade and dilator insertion. Specific opportunities on this project are improving ultrasounds-based AI algorithms, test model development, and mechanical testing.