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Posts Tagged ‘Technology’


 A Revolution in Medicine: Medical 3D BioPrinting

Curated by : Irina Robu, PhD

Imagine a scenario, where years from now, one of your organs stop working properly. What would you do?  The current option is to wait in line for a transplant, hoping that the donor is a match. But what if you can get an organ ready for you with no chance of rejection? Even though it may sound like science fiction at the current moment, organ 3D bioprinting can revolutionize medicine and health care.

I have always found the field of tissue engineering and 3D bioprinting fascinating. What interests me about 3D bioprinting is that it has the capacity to be a game changer, because it would make organs widely available to those who need them and it would eliminate the need for a living or deceased donor.  Moreover, it would be beneficial for pediatric patients who suffer specific problems that the current bio-prosthetic options might not address. It would minimize the risk of rejection as well as the components would be customized to size.

There have been advancements in the field of 3D bioprinting and one such advancement is using a 3D printed cranium by neurosurgeons at the University Medical Centre Utrecht. The patient was a young woman who suffered from a chronic bone disorder. The 3D reconstruction of her skull would minimize the brain damage that might have occurred if doctors used a durable plastic cranium.

So, what exactly is bioprinting? 3D bioprinting is an additive manufacturing procedure where biomaterials, such as cells and growth factors, are combined to generate tissue-like structures that duplicate natural tissues. At its core, bioprinting works in a similar way to conventional 3D printing. A digital model becomes a physical 3D object layer-by-layer.  However, in the case of bioprinting, a living cell suspension is used instead of a thermoplastic.

The procedure mostly involves preparation, printing, maturation and application and can be summarized in three steps:

  1. Pre-bioprinting step which includes creating a digital model obtained by using computed tomography (CT) and magnetic resonance imaging (MRI) scans which are then fed to the printer.
  2. Bioprinting step where the actual printing process takes place, where the bioink is placed in a printer cartridge and deposition occurs based on the digital model.
  3. Post-bioprinting step is the mechanical and chemical stimulation of printed parts in order to create stable biostructures which can ultimately be implanted.

3D bioprinting allows suitable microarchitectures that provide mechanical stability and promote cell ingrowth to be produced while preventing any homogeneity issues that occur after conventional cell seeding, such as cell placement. Immediate vascularization of implanted scaffolds is critical, because it provides an influx of nutrients and outflow of by-products preventing necrosis. The benefits of homogeneous seeded scaffolds are that it allows them to integrate faster into the host tissue, uniform cell growth in vivo and lower risk of rejection.

However, in order to address the limitations of the commercially available technology for producing tissue implants, researchers are working to develop a 3D bioprinter that can fit into a laminar flow hood, ultra-low cost and customizable for different organs. Bioprinting can be applied in a clinical setting where the ultimate goal is to implant 3D bioprinted structures into the patients, it is necessary to maintain sterile printing solutions and ensure accuracy in complex tissues, needed for cell-to-cell distances and correct output.

The final aim of bioprinting is to promote an alternative to autologous and allogeneic tissue implants, which will replace animal testing for the study of disease and development of treatments.  We know that for now a short-term goal for 3D bioprinting is to create alternatives to animal testing and to increase the speed of drug testing. The long-term goal is to change the status quo, to develop a personalized organ made from patient’s own cells. However, some ethical challenges still exist regarding the ownership of the organ.

A powerful starting point is the creation of tissue components for heart, liver, pancreas, and other vital organs.  Moreover, each small progress in 3D bioprinting will allow 3D bioprinting to make organs widely available to those who need them, instead of waiting years for a transplant to become available.

I invite you to read a biomedical e-book that I had the pleasure to author along with several other scientists, called Medical 3D BioPrinting – The Revolution in Medicine Technologies for Patient-centered Medicine: From R&D in Biologics to New Medical Devices (Series E: Patient-Centered Medicine Book 4).

 

 

 

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Using A.I. to Detect Lung Cancer gets an A!

Reporter: Irina Robu, PhD

Google researchers hypothesized that computers are as good or better than doctors at detecting tiny lung cancers on CT scans, since CT scan combines data from several X-rays to produce a detailed image of a structure inside the body. CT scans produce 2-dimensional images of a slice of the body and the data can also be used to construct 3-D images.

However, the technology published in Nature Medicine offers input in the future of artificial intelligence in medicine. By feeding vast amounts of data from medical imaging into systems called artificial neural networks, scientists can teach computers to identify patterns linked to a specific condition, like pneumonia, cancer or a wrist fracture that would be hard for a person to see. The system trails an algorithm, or set of instructions, and learns as it goes. The more data it receives, the better it becomes at interpretation.

The process, known as deep learning enables computers to identify objects and understand speech but it also created systems to help pathologists read microscope slides to diagnose cancer, and to help ophthalmologists detect eye disease in people with diabetes. In their recent study, the scientist used artificial intelligence to CT scans used to screen people for lung cancer, which caused 160,000 deaths in the United States last year, and 1.7 million worldwide. The scans are recommended for people at high risk because of a long history of smoking.

Screening studies showed that it can reduce the risk of dying from lung cancer and can also identify spots that might later become cancer, so that radiologists can categorize patients into risk groups and decide whether they need biopsies or more frequent follow-up scans to keep track of the suspect regions.

However, the test has errors. It can miss tumors or mistake benign spots for malignancies and shove patients into invasive, risky procedures like lung biopsies or surgery.

SOURCE

https://www.nytimes.com/2019/05/20/health/cancer-artificial-intelligence-ct-scans.html

Other related articles were published in this Online Scientific Open Access Journal including the following:

https://pharmaceuticalintelligence.com/2019/07/21/multiple-barriers-identified-which-may-hamper-use-of-artificial-intelligence-in-the-clinical-setting/

https://pharmaceuticalintelligence.com/2019/06/28/ai-system-used-to-detect-lung-cancer/

 

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Artificial throat may give voice to the voiceless

Reporter
Irina Robu, PhD

Flexible sensors have fascinated more and more attention as a fundamental part of anthropomorphic robot research, medical diagnosis and physical health monitoring. The fundamental mechanism of the sensor is based on triboelectric effect inducing electrostatic charges on the surfaces between two different materials. Just like a plate capacitor, current is produced while the size of the parallel capacitor fluctuations caused by the small mechanical disturbances and therefore the output current/voltage is produced.

Chinese scientists combine ultra sensitive motion detectors with thermal sound-emitting technology invented an “artificial throat” that could enable speech in people with damaged or non-functioning vocal cords. Team members from University in Beijing, fabricated a homemade circuit board on which to build out their dual-mode system combining detection and emitting technologies.

Graphene is a wonder material because it is thinnest material in the universe and the strongest ever measured. And graphene is only a one-atom thick layer of graphite and possess a high Young’s modulus as well as superior thermal and electrical conductivities. Graphene-based sensors have attracted much attention in recent years due to their variety of structures, unique sensing performances, room-temperature working conditions, and tremendous application prospects.

The skin like device, wearable artificial graphene throat (WAGT) is as similar as a temporary tattoo, at least as perceived by the wearer. In order to make the device functional and flexible, scientists designed a laser-scribed graphene on a thin sheet of polyvinyl alcohol film. The device is the size of two thumbnails side by side and can use water to attach the film to the skin over the volunteer’s throat and connected to electrodes to a small armband that contained a circuit board, microcomputer, power amplifier and decoder. At the development phase, the system transformed subtle throat movements into simple sounds like “OK” and “No.” During the trial of the device, volunteers imitated throat motions of speech and the device converted these movements into single-syllable words.

It is believed that this device, would be able to train mute people to generate signals with their throats and the device would translate signals into speech.

SOURCE
https://www.aiin.healthcare/topics/robotics/artificial-throat-may-give-voice-voiceless?utm_source=newsletter

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AI System Used to Detect Lung Cancer

Reporter: Irina Robu, PhD

Lung cancer is characterized by uncontrolled cell growth in tissues of the lung. The growth spreads beyond the lung by metastasis into nearby tissues. The most common symptoms are coughing (including coughing up blood), weight loss, shortness of breath, and chest pains. The two main types of lung cancer are small-cell lung carcinoma(SCLC) and non-small-cell lung carcinoma (NSCLC). Lung cancer may be seen on chest radiographs and computed tomography(CT) scans. However, computers seem to be as good or better than regular doctors at detecting tiny lung cancers on CT scans according to scientists from Google.

The AI designed by Google was able to interpret images using the same skills as humans to read microscope slides, X-rays, M.R.I.s and other medical scans by feeding huge amounts of data from medical imaging into the systems. It seems that the researchers were able to train computers to recognize patterns linked to a specific condition.
In a new Google study, the scientists applied artificial intelligence to CT scans used to screen people for lung cancer. Current studies have shown that screening can reduce the risk of dying from lung cancer and can also identify spots that might later become malignant.

The researchers created a neural network with multiple layers of processing and trained the AI by giving it many CT scans from patients whose diagnoses were known. This allows radiologists to sort patients into risk groups and decide whether biopsies are needed or follow up to keep track of the suspected regions. Even though the technology seems promising, but it can have pitfalls such as missing tumors, mistaken benign spots for malignancies and push patients into risky procedures.

Yet, the ability to process vast amounts of data may make it imaginable for artificial intelligence to recognize subtle patterns that humans simply cannot see. It is well understood that the systems should be studied extensively before using them for general public use. The lung-screening neural network is not ready for the clinic yet.

SOURCE

A.I. Took Test To Detect Lung Cancer And Smashed It

 

 

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Sepsis Detection using an Algorithm More Efficient than Standard Methods

Reporter : Irina Robu, PhD

Sepsis is a complication of severe infection categorized by a systemic inflammatory response with mortality rates between 25% to 30% for severe sepsis and 40% to 70% for septic shock. The most common sites of infection are the respiratory, genitourinary, and gastrointestinal systems, as well as the skin and soft tissue. The first manifestation of sepsis is fever with pneumonia being the most common symptom of sepsis with initial treatment which contains respiratory stabilization shadowed by aggressive fluid resuscitation. When fluid resuscitation fails to restore mean arterial pressure and organ perfusion, vasopressor therapy is indicated.

However, a machine-learning algorithm tested by Christopher Barton, MD from UC-San Francisco has exceeded the four typical methods used for catching sepsis early in hospital patients, giving clinicians up to 48 hours to interfere before the condition has a chance to begin turning dangerous. The four standard methods were Systemic Inflammatory Response Syndrome (SIRS) criteria, Sequential (Sepsis-Related) Organ-Failure Assessment (SOFA) and Modified Early Warning System (MEWS). The purpose of dividing the data sets between two far-flung institutions was to train and test the algorithm on demographically miscellaneous patient populations.

The patients involved in the study were admitted to hospital without sepsis and all had at least one recording of each of six vital signs such as oxygen levels in the blood, heart rate, respiratory rate, temperature, systolic blood pressure and diastolic blood pressure. Even though they were admitted to the hospital without it, some have contracted sepsis during their stay while others did not. Researchers used their algorithm detection versus the standard methods applied at sepsis onset at 24 hours and 48 hours prior.
Even though sepsis affects at least 1.7 million adults mostly outside of the hospital settings, nearly 270,000 die. Researchers are hoping that the algorithm would allow clinicians to interfere prior to the condition becoming deadly.

SOURCE
https://www.aiin.healthcare/topics/diagnostics/sepsis-diagnosis-machine-learning

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AI App for People with Digestive Disorders

Reporter: Irina Robu, PhD

Artificial intelligence (AI) constitutes machine learning and deep learning, which allows computers to learn without being clearly programmed every step of the way. The basic principle decrees that AI is machine intelligence leading to the best outcome when given a problem. This sets up AI well for life science applications, which states that AI can be taught to differentiate cells, be used for higher quality imaging techniques, and analysis of genomic data.

Obviously, this type of technology which serves a function and removes the need for explicit programming. It is clear that digital therapeutics will have an essential role in treatment of individuals with gastrointestinal disorders such as IBS. Deep learning is a favorite among the AI facets in biology. The structure of deep learning has its roots in the structure of the human brain which connect to one another through which the data is passed. At each layer, some data is extracted. For example, in cells, one layer may analyze cell membrane, the next some organelle, and so on until the cell can be identified.

A Berlin-based startup,Cara Care uses AI to help people manage their chronic digestive problems and intends to spend the funding raised getting the app in the hands of gastrointestinal patients in the U.S. The company declares its app has already helped up to 400,000 people in Germany and the U.S. manage widespread GI conditions such as reflux, irritable or inflammatory bowel, food intolerances, Crohn’s disease and ulcerative colitis “with a 78.8% treatment success rate.” Cara Care will also use the funding to conduct research and expand collaborations with companies in the pharmaceutical, diagnostics and food-production industries.

SOURCE
https://www.aiin.healthcare/topics/connected-care/ai-app-digestive-disorders-raises-7m?utm_source=newsletter

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First Surgical Robot Making Surgeon’s Life More Efficient

Reporter : Irina Robu, PhD

A team of microsurgeons and engineers, developed a high-precision robotic assistant called MUSA which is clinically and commercially available. The high precision robotic assistant is compatible with current operating techniques, workflow, instruments and other or instrument.   Microsure is a medical device company in The Netherlands founded by Eindhoven University of Technology and Maastricht University Medical Center in 2016. Microsure’s focus is to improve patients’ quality of life through developing robot systems for microsurgery.

The Microsure’s MUSA enhances surgical performance by stabilizing and scaling down the surgeon’s movements during complex microsurgical procedures on sub-millimeter scale. The surgical robot, allows lymphatic surgery on lymph vessels smaller than 0.3 mm in diameter. Microsure received the ISO 13485 certificate which assures that Microsure is adhering to the highest standards in quality management and regulatory compliance procedures to develop, manufacture, and test its products and services.

MUSA provides superhuman precision for microsurgeons, enabling new interventions that are currently impossible to perform by hand.

SOURCE

https://www.businesswire.com/news/home/20190607005175/en/

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