Innovative research into the gene-editing tool targets influenza's ability to replicate--stopping it in its tracks. As he addressed an audience of virologists from China, Australia, and Singapore at October's Pandemic Research Alliance Symposium, Wei Zhao introduced an eye-catching idea. The gene-editing technology Crispr is best known for delivering groundbreaking new therapies for rare diseases, tweaking or knocking out rogue genes in conditions ranging from sickle cell disease to hemophilia . But Zhao and his colleagues at Melbourne's Peter Doherty Institute for Infection and Immunity have envisioned a new application. They believe Crispr could be tailored to create a next-generation treatment for influenza, whether that's the seasonal strains which plague both the northern and southern hemispheres on an annual basis, or the worrisome new variants in birds and other wildlife that might trigger the next pandemic.

Try gene edited, meat-like fungi. Using CRISPR, researchers made a protein packed fungi that's easier to stomach. Breakthroughs, discoveries, and DIY tips sent every weekday. It might not seem so obvious when walking past rows of vacuum-sealed Butterball turkeys at the supermarket, but the world is on the brink of a protein shortage . Global demand for animal-based protein is expected to double by 2050 and while plant-based alternatives exist, enthusiasm around them has wavered in recent years .

Plus: California's AI safety bill has passed into law Wrinkles and gray hairs aside, it can be difficult to know how well--or poorly--someone's body is truly aging. A person who develops age-related diseases earlier in life, or has other biological changes associated with aging, might be considered "biologically older" than a similar-age person who doesn't have those changes. Some 80-year-olds will be weak and frail, while others are fit and active. Over the past decade, scientists have been uncovering new methods of looking at the hidden ways our bodies are aging. And what they've found is changing our understanding of aging itself. Can we repair the internet?

The CRISPR-Cas genome editing system has rapidly become an indispensable tool across biotechnology and medicine, enabling targeted DNA modifications with unprecedented ease. A single-guide RNA (sgRNA, or simply gRNA) directs the Cas nuclease (such as Cas9 or Cas12a) to a complementary genomic sequence, where the nuclease induces a double-strand break or nucleotide modification. The efficiency and specificity of this process are largely dictated by the gRNA sequence and its interactions with both the target DNA and the cellular environment. Designing optimal gRNAs is therefore critical for successful editing outcomes. Early gRNA design relied on empirical rules and modest machine learning models, but these approaches often struggled to capture the complex determinants of gRNA activity and off-target effects. In recent years, artificial intelligence (AI) - particularly deep learning - has been leveraged to overcome these limitations, learning predictive features from large-scale CRISPR datasets and outperforming previous rule-based methods in guide efficacy prediction[1, 2]. Deep learning models can ingest not only the gRNA and target DNA sequences but also additional contextual information (e.g.

Extinction is a part of nature. Of the five billion species that have existed on Earth, 99.9 per cent have vanished. The Triassic-Jurassic extinction, two hundred million years ago, finished off the crocodile-like phytosaur. Sixty-six million years ago, the end-Cretaceous extinction eliminated the Tyrannosaurus rex and the velociraptor; rapid climate change from an asteroid impact was the likely cause. The Neanderthals disappeared some forty thousand years ago. One day--whether from climate change, another asteroid, nuclear war, or something we can't yet imagine--humans will probably be wiped out, too.

Sam Berns was my friend. With the wisdom of a sage, he inspired me and many others about how to make the most of life. Afflicted with the rare disease called progeria, his body aged at a rapid rate, and he died of heart failure at just 17, a brave life cut much too short. My lab discovered the genetic cause of Sam's illness two decades ago: Just one DNA letter gone awry, a T that should have been a C in a critical gene called lamin A. The same misspelling is found in almost all of the 200 individuals around the world with progeria. This story is from the WIRED World in 2025, our annual trends briefing.

In 2012, the biochemist Jennifer Doudna and her colleague Emmanuelle Charpentier developed a method for using RNA-guided proteins to edit specific sections of DNA. Their innovation--for which the two won the Nobel Prize in Chemistry, in 2020--is known as the CRISPR-Cas9 gene-editing system. CRISPR has since been used to alter plants (to, for instance, produce greater yields), insects (preventing them from carrying certain diseases), and people (to treat sickle-cell disease). The technology's promise can sound as if derived from science fiction: it might help us adapt to a radically different climate, or grow organs for those in need, or reprogram a cancer patient's own cells to target tumors. But there are also worries about its possible side effects, both biological and social.

Generative artificial intelligence technologies can write poetry and computer programs or create images of teddy bears and videos of cartoon characters that look like something from a Hollywood movie. Now, new AI technology is generating blueprints for microscopic biological mechanisms that can edit your DNA, pointing to a future when scientists can battle illness and diseases with even greater precision and speed than they can today. Described in a research paper published Monday by a Berkeley, California, startup called Profluent, the technology is based on the same methods that drive ChatGPT, the online chatbot that launched the AI boom after its release in 2022.

The news: Google DeepMind has used a large language model to crack a famous unsolved problem in pure mathematics. The researchers say it is the first time a large language model has been used to discover a solution to a long-standing scientific puzzle--producing verifiable and valuable new information that did not previously exist. Why it matters: Large language models have a reputation for making things up, not for providing new facts. Google DeepMind's new tool, called FunSearch, could change that. It shows that they can indeed make discoveries--if they are coaxed just so, and if you throw out the majority of what they come up with.

Large language models (LLMs) executing tasks through instruction-based prompts often face challenges stemming from distribution differences between user instructions and training instructions. This leads to distractions and biases, especially when dealing with inconsistent dynamic labels. In this paper, we introduces a novel bias mitigation method, CRISPR, designed to alleviate instruction-label biases in LLMs. CRISPR utilizes attribution methods to identify bias neurons influencing biased outputs and employs pruning to eliminate the bias neurons. Experimental results demonstrate the method's effectiveness in mitigating biases in instruction-based prompting, enhancing language model performance on social bias benchmarks without compromising pre-existing knowledge. CRISPR proves highly practical, model-agnostic, offering flexibility in adapting to evolving social biases.