Automated robots now have the tools to grow imitation, simplified human organs out of stem cells. Thankfully, we weren't transported to a sci-fi dystopia where the machines have risen up and started to farm humans, but rather a world where pharmaceutical and other biomedical research just became much easier and faster. Give these robots some pluripotent stem cells (stem cells that can become any type of cell), and 21 days later they'll have finished a complicated experiment testing out the effects of a drug or genetic manipulation on some human-like, lab-grown kidneys. According to research published yesterday, May 17, in Cell: Stem Cell, the process is much faster and more reliable than when humans grow the same mini-organs. "Ordinarily, just setting up an experiment of this magnitude would take a researcher all day, while the robot can do it in 20 minutes," said researcher Benjamin Freedman in a press release.
Mini-organs grown in the lab by robots could be the next'secret weapon' in the fight against disease, researchers say. Scientists have developed a system to automate the production of organoids from human stem cells, using liquid-handling robots that, unlike humans, don't'get tired and make mistakes.' A team in the US has demonstrated how the system can successfully introduce stem cells into plates containing hundreds of wells, to cultivate thousands of miniature kidneys in less than a month. Scientists have developed a system to automate the production of organoids from human stem cells. A microwell plate containing kidney organoids grown by the robots is shown above.
The advance promises to greatly expand the use of mini-organs in basic research and drug discovery, according to Benjamin Freedman, assistant professor of medicine, Division of Nephrology, at the UW School of Medicine, who led the research effort. "This is a new'secret weapon' in our fight against disease,' said Freedman, who is a scientist at the UW Institute for Stem Cell and Regenerative Medicine, as well as at the Kidney Research Institute, a collaboration between the Northwest Kidney Centers and UW Medicine. A report describing the new technique will be published online May 17 in the journal Cell Stem Cell. The lead authors were research scientists Stefan Czerniecki, and Nelly Cruz from the Freedman lab, and Dr. Jennifer Harder, assistant professor of internal medicine, Division of Nephrology at the University of Michigan School of Medicine, where she is a kidney disease specialist. The traditional way to grow cells for biomedical research, Freeman explained, is to culture them as flat, two-dimensional sheets, which are overly simplistic.
Looking at human cells isn't as simple as it sounds, especially when scientists want to examine clearly, in 3D, how multiple structures change over a time while cells are alive. Now, the Allen Institute for Cell Science has created a new tool, enabled by machine learning, that ticks all those boxes. Meet the Allen Integrated Cell, "a new way to see inside living human cells," as Rick Horwitz, Ph.D., executive director of the Allen Institute for Cell Science, put it. Funded by Microsoft co-founder and billionaire philanthropist Paul Allen, the Allen Institute previously created a large collection of human-induced pluripotent stem cells (hiPSCs), gene-edited with fluorescent tags that illuminate major structures. Scientists took pictures of tens of thousands of these glowing cells and trained two artificial intelligence algorithms to understand them.
People use artificial intelligence – or AI – any time they ask Siri, Alexa or Google to help them find something. But AI is also changing how health care providers treat patients. "Finding data that's faster, that works continuously like computers do to help make rare diagnoses or faster diagnoses," Dr. David Freeman, a Mayo Clinic neurologist, says. Dr. Freeman has helped develop AI that could soon improve outcomes for people who suffer from a certain kind of stroke called an intracerebral hemorrhage, or ICH. Right now, patients with an ICH go to a hospital with symptoms, get a CAT scan, then have to wait for results and for doctors to figure out how to address it.
A fully automated artificial intelligence (AI)-based multispectral absorbance imaging system effectively classified function and potency of induced pluripotent stem cell derived retinal pigment epithelial cells (iPSC-RPE) from patients with age-related macular degeneration (AMD). The finding from the system could be applied to assessing future cellular therapies, according to research presented at the 2018 ARVO annual meeting. The software, which uses convolutional neural network (CNN) deep learning algorithms, effectively evaluated release criterion for the iPSC-RPE cell-based therapy in a standard, reproducible, and cost-effective fashion. The AI-based analysis was as specific and sensitive as traditional molecular and physiological assays, without the need for human intervention. "Cells can be classified with high accuracy using nothing but absorbance images," wrote lead investigator Nathan Hotaling and colleagues from the National Institutes of Health in their poster.
Roughly 50,000 patients each year suffer from intracranial hemorrhages, and 47 percent of them die within 30 days. This means early and accurate diagnosis is crucial to patient safety. Realizing the challenges in detecting these types of hemorrhages, a team of physicians and researchers leveraged radiographic and other medical imaging data from the EHR to train a computer embedded with a machine learning algorithm to review CT scans and flag the most urgent images for priority review by radiologists. "This is not about replacing doctors with machines," said Aalpen Patel, MD, chair of Geisinger System Radiology. "This is about the smart use of machine learning technology to aid medical providers in delivering better and faster care, especially in these areas where time is critical."
The ability of neuroscientists to create, grow and use human brain tissue in the lab is moving fast. So fast in fact that the journal Nature this week published a commentary signed by 17 neuroscientists, biologists and ethicists calling for an ethical framework for this endeavour. The authors outline the three variants of such brain tissue: organoids, known as 3D "mini-brains" or "brain balls", which are structures grown in the lab from pluripotent stem cells; ex vivo brain tissues, which are those removed from humans and kept …
Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells.
Miniature human brains have grown successfully for months in mice - a breakthrough that could help people with neurological diseases such as strokes. The pin-sized brains were made from stem cells and placed inside the brain of the rodent. Around 80 per cent of the 200 mice tested survived the operation and within two weeks their brain implants were spawning new neurons. The host brains provided the mini brains with enough nutrients to keep them healthy for months. Researchers hope these tiny implants could be used as cortical repair kits that could replace parts of the brain that have failed to develop normally.