According to Berkeley University, Bioengineering is a discipline that applies engineering principles of design and analysis to biological systems and biomedical technologies.
This discipline involves different areas, all of them obtaining impressive results nowadays, and I have classified them as following:
Due to the advances on technology, scientishs are working on creating new tissues that could replace injured ones or even try to solve important diseases.
One of the most promising examples are the experiments being done by Cincinnati Children´s Hospital Medical center. Using embryonic-stage progenitor cells, along with other genetic and biochemical material they have created pancreatic cells disposed on islets. After transplanting them into mice affected by diabetes, they were cured. For being able to reproduce this experiment in humans they still have to solve the problem of the lack of vascularization that happens on humans when transplating external tissues and evolves in a rejection.
Another technique for producing new tissues are 3D printers which are becoming essential on medicine, not only for designing personalized implants for certain patients, but also for creating human organs. Scientish from the Wake Forest Regenerative Medicine Institute from California have been able to print an human ear and implant it on mice. After two months, the ear was in perfect conditions and also has developed cartilage.
Medical drugs production.
New techniques are improving the process by which medical drugs are synthesized. Standford University researchers have boosted production of a cough suppressant by using genetic-engineering techniques to turn the yeast into a “living factory” for producing the drug noscapine. Making this process much simpler and more efficient would result in an expanded supply and lower prices.
Nowadays cochlear implants are really extended, consisting on an electronic medical device that replaces the function of the damaged inner ear. It consists of a sound processor that captures sound and turns it into digital code transmitted to the implant that converts this signal into electrical impulses sent along the electrode array in order to stimulate the cochlea´s hearing nerve. In this way the signal reaches the brain and it can be interpreted as sound.
But there are other important advances in this area. On 2016 Hugh Herr, a MIT professor and leader of a biomechanical research group has obtained the Princess of Asturias Award for his bionic prothesis, which allow injured people to recover 100% their movements.
Also since 2008, there have been several experiments showing how monkeys with tiny sensors implanted in their brains have learned to control a mechanical arm with just their thoughts, using it to reach for and grab food and even to adjust for the size and stickiness of morsels when necessary in a report released on Nature journal. This was the beginning of the so called brain-machine interface technology, that is developing really fast and nowadays we could found several videos on YouTube showing monkeys riding vehicles with their minds.
Machine learning and big data applications.
There are plenty applications for both techniques and the most important ones are:
· Diseases identification and diagnosis. Google’s DeepMind Health are developing technology to address macular degeneration in aging eyes. But moreover, a prototype of an Augmented Reality Microscope (ARM) platform has been described recently in a congress. The platform consists of a modified light microscope that enables real-time image analysis and presentation of the results of machine learning algorithms directly into the field of view. Modern computational components and deep learning models, such as those built upon TensorFlow, will allow a wide range of pre-trained models to run on this platform.
· Personalized Treatment. In this field, IBM Watson Oncology is using patient medical information to optimize the selection of treatment options. Indeed with the increased use of micro biosensors and devices, as well as mobile apps with more sophisticated health-measurement and remote monitoring capabilities, it will be posible to improve treatment efficacy.
· Drug Discovery. By using machine learning, some biopharma companies are developing initial screenings of drug compounds to predicted success rate based on biological factors. For example the MIT Clinical Machine Learning Group, is focusing on the development of algorithms to better understand disease processes and design for effective treatment of diseases like Type 2 diabetes.
· Epidemic Outbreak Prediction. Applying machine learning and AI to monitor and predict epidemic otubreaks could be done based on information collected from satellites, historical information on the web, real-time social media updates, and other sources. Support vector machines and artificial neural networks have been used, for example, to predict malaria outbreaks, taking into account data such as temperature, average monthly rainfall, total number of positive cases, and other data points.
Robotics and AI as future caretakers.
Elderly people dealing with social isolation and loneliness are at increased risk of a variety of ailments, from cardiovascular disease and elevated blood pressure to cognitive deterioration and infection. This is the reason why several research groups are working on robots to improve the elderly’s movement but also to keeping them socially, emotionally and mentally engaged as well.
To conclude, technology is not only going to democratized the access to medical resources but also to make it posible to stop important diseases and make our live long more and in better conditions.