Plants are susceptible to a wide range of pathogens. For the common potato plant, one such threat is Pectobacterium atrosepticum, a bacterium that causes stems to blacken, tissues to decay, and often leads to plant death, resulting in significant agricultural losses each year.
In 2012, researchers isolated a new virus that infects and kills this bacterium—a bacteriophage named φTE (phiTE). Now, for the first time, scientists have uncovered the atomic structure of φTE, revealing a possible mechanism of infection that may be more complex than previously thought.
The study, published earlier this month in Nature Communications, is the result of a multidisciplinary collaboration between researchers from the Okinawa Institute of Science and Technology (OIST) and the University of Otago. It brings together expertise across several fields, including virology, structural biology, molecular genetics, protein engineering, biochemistry, and biophysics.
Researchers at the University of Pittsburgh have created a groundbreaking tissue engineering platform using 3D-printed collagen scaffolds called CHIPS.
By mimicking natural cellular environments, they enable cells to grow, interact, and form functional tissues — a major step beyond traditional silicone-based microfluidic models. The platform not only models diseases like diabetes but could also replace animal testing in the future. Plus, their designs are freely available to fuel broader scientific innovation.
3D bioprinting: turning science fiction into science reality.
Surgical robots can assist in medical procedures or autonomously perform surgical tasks. This Review explores the design and application of passive, interactive, teleoperated and autonomous surgical robots within the framework of computer-assisted and integrated surgical workflows.
Human cyborgs are individuals who integrate advanced technology into their bodies, enhancing their physical or cognitive abilities. This fusion of man and machine blurs the line between science fiction and reality, raising questions about the future of humanity, ethics, and the limits of human potential. From bionic limbs to brain-computer interfaces, cyborg technology is rapidly evolving, pushing us closer to a world where humans and machines become one.
If you haven’t heard of a tardigrade before, prepare to be wowed. These clumsy, eight-legged creatures, nicknamed water bears, are about half a millimeter long and can survive practically anything: freezing temperatures, near starvation, high pressure, radiation exposure, outer space and more. Researchers reporting in the journal Nano Letters took advantage of the tardigrade’s nearly indestructible nature and gave the critters tiny “tattoos” to test a microfabrication technique to build microscopic, biocompatible devices.
“Through this technology, we’re not just creating micro-tattoos on tardigrades—we’re extending this capability to various living organisms, including bacteria,” explains Ding Zhao, a co-author of the paper.
Microfabrication has revolutionized electronics and photonics, creating micro-and nanoscale devices ranging from microprocessors and solar cells to biosensors that detect food contamination or cancerous cells. But the technology could also advance medicine and biomedical engineering, if researchers can adapt microfabrication techniques to make them compatible with the biological realm.
Genome editing has advanced at a rapid pace with promising results for treating genetic conditions—but there is always room for improvement. A new paper by investigators from Mass General Brigham showcases the power of scalable protein engineering combined with machine learning to boost progress in the field of gene and cell therapy.
In their study, the authors developed a machine learning algorithm—known as PAMmla—that can predict the properties of approximately 64 million genome-editing enzymes. The work could help reduce off-target effects and improve editing safety, enhance editing efficiency, and enable researchers to predict customized enzymes for new therapeutic targets. The results are published in Nature.
“Our study is a first step in dramatically expanding our repertoire of effective and safe CRISPR-Cas9 enzymes. In our manuscript, we demonstrate the utility of these PAMmla-predicted enzymes to precisely edit disease-causing sequences in primary human cells and in mice,” said corresponding author Ben Kleinstiver, Ph.D., Kayden-Lambert MGH Research Scholar associate investigator at Massachusetts General Hospital (MGH).
Genome editing has advanced at a rapid pace with promising results for treating genetic conditions-but there is always room for improvement. A new paper by investigators from Mass General Brigham published in Nature showcases the power of scalable protein engineering combined with machine learning to boost progress in the field of gene and cell therapy. In their study, authors developed a machine learning algorithm-known as PAMmla-that can predict the properties of about 64 million genome editing enzymes. The work could help reduce off-target effects and improve editing safety, enhance editing efficiency, and enable researchers to predict customized enzymes for new therapeutic targets. Their results are published in Nature.
“Our study is a first step in dramatically expanding our repertoire of effective and safe CRISPR-Cas9 enzymes. In our manuscript we demonstrate the utility of these PAMmla-predicted enzymes to precisely edit disease-causing sequences in primary human cells and in mice,” said corresponding author Ben Kleinstiver, PhD, Kayden-Lambert MGH Research Scholar associate investigator at Massachusetts General Hospital (MGH), a founding member of the Mass General Brigham healthcare system. “Building on these findings, we are excited to have these tools utilized by the community and also apply this framework to other properties and enzymes in the genome editing repertoire.”
CRISPR-Cas9 enzymes can be used to edit genes at locations throughout the genomes, but there are limitations to this technology. Traditional CRISPR-Cas9 enzymes can have off-target effects, cleaving or otherwise modifying DNA at unintended sites in the genome. The newly published study aims to improve this by using machine learning to better predict and tailor enzymes to hit their targets with greater specificity. The approach also offers a scalable solution-other attempts at engineering enzymes have had a lower throughput and typically yielded orders of magnitude fewer enzymes.
