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Many drugs show promising results in laboratory research but eventually fail clinical trials. We hypothesize that one main reason for this translational gap is that current cancer models are inadequate. Most models lack the tumor-stroma interactions, which are essential for proper representation of cancer complexed biology. Therefore, we recapitulated the tumor heterogenic microenvironment by creating fibrin glioblastoma bioink consisting of patient-derived glioblastoma cells, astrocytes, and microglia. In addition, perfusable blood vessels were created using a sacrificial bioink coated with brain pericytes and endothelial cells. We observed similar growth curves, drug response, and genetic signature of glioblastoma cells grown in our 3D-bioink platform and in orthotopic cancer mouse models as opposed to 2D culture on rigid plastic plates. Our 3D-bioprinted model could be the basis for potentially replacing cell cultures and animal models as a powerful platform for rapid, reproducible, and robust target discovery; personalized therapy screening; and drug development.

Cancer is the second leading cause of death globally. It is estimated that around 30 to 40% of patients with cancer are being treated with ineffective drugs ; therefore, preclinical drug screening platforms attempt to overcome this challenge. Several approaches, such as whole-exome or RNA sequencing (RNA-seq), aim to identify druggable, known mutations or overexpressed genes that may be exploited as a therapeutic target for personalized therapy. More advanced approaches offer to assess the efficacy of a drug or combinations of drugs in patient-derived tumor xenograft models or in vitro three-dimensional (3D) organoids. Unfortunately, most of the existing methods face unmet challenges, which limit their efficacy. For instance, cells can become quiescent or acquire somatic mutations while growing many generations on plastic under the influence of static mechanical forces and in the absence of functional vasculature.

Quick vid and a reminder of the 4th conference of Lifespan.io is this weekend.


Gene editing can make stem cells invisible to the immune system, making it possible to carry out cell therapy transplants without suppressing the patients’ immune response. Scientists in the US and Germany used immune engineering to develop universal cell products that could be used in all transplant patients. The idea is to create stem cells that evade the immune system; these hypoimmune stem cells are then used to generate cells of the desired type that can be transplanted into any patient without the need for immunosuppression, since the cells won’t elicit an immune response. They used CRISPR-Cas9 to knock out two genes involved in the major histocompatibility complex, which is used for self/non-self discrimination. They also increased the expression of a protein that acts as a “don’t eat me” signal to protect cells from macrophages. Together, these changes made the stem cells look less foreign and avoid clearance by macrophages. The team then differentiated endothelial cells and cardiomyocytes from the engineered stem cells, and they used these to treat three different diseases in mice. Cell therapy treatments using the hypoimmune cells were effective in rescuing hindlimbs from vascular blockage, preventing lung damage in an engineered mouse model, and maintaining heart function following a myocardial infarction. Immunosuppression poses obvious risks to a patient, and generating custom cells for transplant therapy is often prohibitively expensive. The development of universal donor cells that can be used as therapeutics could bring the cost down significantly, making cellular therapeutics available to many more patients in a much safer way.

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What’s confusing is that some of the modifications we’re now considering could have been achieved years ago through traditional methods, so our views depend on what we think about the safety of new editing technologies, but also how desperate we are to address environmental degradation.


A process that began centuries ago with selective breeding has developed into genetic modification. We explore the consequences of these controversial tools.

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University of Advancing Technology’s Artificial Intelligence (AI) degree explores the theory and practice of engineering tools that simulate thinking, patterning, and advanced decision behaviors by software systems. With inspiration derived from biology to design, UAT’s Artificial Intelligence program teaches students to build software systems that solve complex problems. Students will work with technologies including voice recognition, simulation agents, machine learning (ML), and the internet of things (IoT).

Students pursuing this specialized computer programming degree develop applications using evolutionary and genetic algorithms, cellular automata, artificial neural networks, agent-based models, and other artificial intelligence methodologies. UAT’s degree in AI covers the fundamentals of general and applied artificial intelligence including core programming languages and platforms used in computer science.

Researchers at CU Boulder have developed a platform which can quickly identify common mutations on the SARS-CoV-2 virus that allow it to escape antibodies and infect cells.

Published today in Cell Reports, the research marks a major step toward successfully developing a universal vaccine for not only COVID-19, but also potentially for influenza, HIV and other deadly global viruses.

“We’ve developed a predictive tool that can tell you ahead of time which antibodies are going to be effective against circulating strains of virus,” said lead author Timothy Whitehead, associate professor of chemical and biological engineering. “But the implications for this technology are more profound: If you can predict what the variants will be in a given season, you could get vaccinated to match the sequence that will occur and short-circuit this seasonal variation.”


Researchers have developed a platform which can quickly identify common mutations on the SARS-CoV-2 virus that allow it to escape antibodies and infect cells, which could inform the development of more effective booster vaccines and tailored antibody treatments for patients with COVID-19.

Current tissue engineering strategies lack materials that promote angiogenesis. Here the authors develop a microfluidic in vitro model in which chemokine-guided endothelial cell sprouting into a tunable hydrogel is followed by the formation of perfusable lumens to determine the material properties that regulate angiogenesis.

Modified RNA CRISPR boosts gene knockdown in human cells.


In the latest of ongoing efforts to expand technologies for modifying genes and their expression, researchers in the lab of Neville Sanjana, PhD, at the New York Genome Center (NYGC) and New York University (NYU) have developed chemically modified guide RNAs for a CRISPR system that targets RNA instead of DNA. These chemically-modified guide RNAs significantly enhance the ability to target – trace, edit, and/or knockdown – RNA in human cells.

Longevity. Technology: In the study published in Cell Chemical Biology, the research team explores a range of different RNA modifications and details how the modified guides increase efficiencies of CRISPR activity from 2-to 5-fold over unmodified guides. They also show that the optimised chemical modifications extend CRISPR targeting activity from 48 hours to four days.

Increasing the efficiencies and “life” of CRISPR-Cas13 guides is of critical value to researchers and drug developers, allowing for better gene knockdown and more time to study how the gene influences other genes in related pathways.

DeepMind CEO and co-founder. “We believe this work represents the most significant contribution AI has made to advancing the state of scientific knowledge to date. And I think it’s a great illustration and example of the kind of benefits AI can bring to society. We’re just so excited to see what the community is going to do with this.” https://www.futuretimeline.net/images/socialmedia/


AlphaFold is an artificial intelligence (AI) program that uses deep learning to predict the 3D structure of proteins. Developed by DeepMind, a London-based subsidiary of Google, it made headlines in November 2020 when competing in the Critical Assessment of Structure Prediction (CASP). This worldwide challenge is held every two years by the scientific community and is the most well-known protein modelling benchmark. Participants must “blindly” predict the 3D structures of different proteins, and their computational methods are subsequently compared with real-world laboratory results.

The CASP challenge has been held since 1994 and uses a metric known as the Global Distance Test (GDT), ranging from 0 to 100. Winners in previous years had tended to hover around the 30 to 40 mark, with a score of 90 considered to be equivalent to an experimentally determined result. In 2018, however, the team at DeepMind achieved a median of 58.9 for the GDT and an overall score of 68.5 across all targets, by far the highest of any algorithm.