Goto

Collaborating Authors

Stem Cells


Scientists have built the world's first living, self-healing robots

#artificialintelligence

Scientists have created the world's first living, self-healing robots using stem cells from frogs. Named xenobots after the African clawed frog (Xenopus laevis) from which they take their stem cells, the machines are less than a millimeter (0.04 inches) wide -- small enough to travel inside human bodies. They can walk and swim, survive for weeks without food, and work together in groups. These are "entirely new life-forms," said the University of Vermont, which conducted the research with Tufts University's Allen Discovery Center. Stem cells are unspecialized cells that have the ability to develop into different cell types.


Scientists have built the world's first living, self-healing robots

#artificialintelligence

"These are novel living machines," said Joshua Bongard, one of the lead researchers at the University of Vermont, in the news release. "They're neither a traditional robot nor a known species of animal. Xenobots don't look like traditional robots -- they have no shiny gears or robotic arms. Instead, they look more like a tiny blob of moving pink flesh. The researchers say this is deliberate -- this "biological machine" can achieve things typical robots of steel and plastic cannot.


My Journey South: Tracing developments on Artificial Intelligence (AI) in Latin America and the Caribbean – SRC

#artificialintelligence

While some still consider AI to be beyond the grasp of developing countries, our South American neighbours have been shattering that stereotype. AI is being deployed in a number of their endeavours: to speed up artefact findings in Peru; to increase crop yields in Colombian rice fields through AI-powered platforms; to boost security and enhance customer service in Brazil's banking sector; to create vegan alternatives with the same taste and texture as animal-based foods in Chile's food industry; to predict school dropouts and teenage pregnancy in Argentina; and to forecast crimes in Uruguay. Some of the push in AI adoption in these countries has come from academics and researchers, like the ones at the University of Sao Paulo who are developing AI to determine the susceptibility of patients to disease outbreaks; or Peru's National Engineering University where robots are being used for mine exploration to detect gases; or Argentina's National Scientific and Technical Research Council where AI software is predicting early onset pluripotent stem cell differentiation. These and other truths were revealed to me at a Latin America and Caribbean (LAC) Workshop on AI organized by Facebook and the Inter-American Development Bank in Montevideo, Uruguay, in November this year. I was the lone Caribbean participant in attendance, presenting my paper entitled: AI & The Caribbean: A Discussion on Potential Applications & Ethical Considerations, on behalf of the Shridath Ramphal Centre (UWI, Cave Hill).


Machine learning programme used to predict stem cell growth

#artificialintelligence

Researchers have used machine learning to predict the conditions needed for stem cells to develop a certain way, which could be used to grow 3D organ models. Researchers have used a computational model to learn how to manipulate stem cell arrangement, including those that may eventually be useful in generating personalised organs. According to the team, their discovery could be used to develop model organs grown from a patient's own cells, which could'revolutionise' how diseases are treated by increasing disease understanding or testing drugs. The study was conducted by a team from Gladstone Institutes, in collaboration with Boston University, both US. Induced pluripotent stem (iPS) cells, similar to the stem cells found in an embryo, have the potential to become nearly every type of cell in the body.


Machine learning successfully replicates cell architecture 7wData

#artificialintelligence

A new study published in the journal Cell Systems on November 20, 2019, reports the use of machine learning to help form complex cell architectures from pluripotent stem cells, a sophisticated technology that could solve multiple issues that currently hampers the production of artificial tissues and organs. Medical scientists faced with irreparably damaged organs have long wanted to know how to stimulate their regeneration or to replace them with new ones, to prolong survival and to provide improved quality of life. Another equally important area of research involves creating artificial tissues which are identical to those in the body, in order to help understand how disease processes evolve and which drugs can be used to treat such disorders. This means that scientists must know how to direct the development of stem cells in the desired pattern to form multiple tissues in the right way. Pluripotent ('capable of multiple tasks') stem cells are cells that can divide indefinitely or can develop into any of the three germ layers found in the early embryo.


Machine learning successfully replicates cell architecture

#artificialintelligence

A new study published in the journal Cell Systems on November 20, 2019, reports the use of machine learning to help form complex cell architectures from pluripotent stem cells, a sophisticated technology that could solve multiple issues that currently hampers the production of artificial tissues and organs. Medical scientists faced with irreparably damaged organs have long wanted to know how to stimulate their regeneration or to replace them with new ones, to prolong survival and to provide improved quality of life. Another equally important area of research involves creating artificial tissues which are identical to those in the body, in order to help understand how disease processes evolve and which drugs can be used to treat such disorders. This means that scientists must know how to direct the development of stem cells in the desired pattern to form multiple tissues in the right way. Pluripotent ('capable of multiple tasks') stem cells are cells that can divide indefinitely or can develop into any of the three germ layers found in the early embryo.


Stem Cells and AI: Better Together

#artificialintelligence

One day in the future when you need medical care, someone will examine you, diagnose the problem, remove some of your body's healthy cells, and then use them to grow a cure for your ailment. The therapy will be personalized and especially attuned to you and your body, your genes, and the microbes that live in your gut. This is the dream of modern medical science in the field of "regenerative medicine." There are many obstacles standing between this dream and its implementation in real life, however. Cells often differ so much from one another and differ in so many ways that scientists have a hard time predicting what the cells will do in any given therapeutic scenario.


Deep Learning Algorithms Identify Structures in Living Cells

#artificialintelligence

These issues were on biomedical engineer Greg Johnson's mind when he joined the Allen Institute for Cell Science in Seattle in 2016. Johnson, whose doctoral work at Carnegie Mellon University had focused on creating computational tools to model cellular structures (see "Robert Murphy Bets Self Driving Instruments Will Crack Biology's Mysteries" here), was hired as part of a group of researchers working to build a 3-D model of a cell. According to Johnson, one of the key aims of the project, dubbed the "Allen Integrated Cell," was to develop a tool to help visualize changes in the spatial organization of cells as they move from one state to another--for example, from a pluripotent stem cell to a differentiated heart cell.


JCI - Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy

#artificialintelligence

Increases in the number of cell therapies in the preclinical and clinical phases have prompted the need for reliable and non-invasive assays to validate transplant function in clinical biomanufacturing. We developed a robust characterization methodology composed of quantitative bright-field absorbance microscopy (QBAM) and deep neural networks (DNNs) to non-invasively predict tissue function and cellular donor identity. The methodology was validated using clinical-grade induced pluripotent stem cell derived retinal pigment epithelial cells (iPSC-RPE). QBAM images of iPSC-RPE were used to train DNNs that predicted iPSC-RPE monolayer transepithelial resistance, predicted polarized vascular endothelial growth factor (VEGF) secretion, and matched iPSC-RPE monolayers to the stem cell donors. DNN predictions were supplemented with traditional machine learning algorithms that identified shape and texture features of single cells that were used to predict tissue function and iPSC donor identity.


NIH, NIST researchers use artificial intelligence for quality control of stem cell-derived tissues

#artificialintelligence

Technique key to scale up manufacturing of therapies from induced pluripotent stem cells. Researchers used artificial intelligence (AI) to evaluate stem cell-derived "patches" of retinal pigment epithelium (RPE) tissue for implanting into the eyes of patients with age-related macular degeneration (AMD), a leading cause of blindness. The proof-of-principle study helps pave the way for AI-based quality control of therapeutic cells and tissues. The method was developed by researchers at the National Eye Institute (NEI) and the National Institute of Standards and Technology (NIST) and is described in a report appearing online today in the Journal of Clinical Investigation. NEI is part of the National Institutes of Health.