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COVID-19 Vaccine's mRNA Technology Adapted for First Antibiotic-Resistant Bacteria VaccinePosted by EditorDavid on Monday July 14, 2025 @07:34AM from the giving-it-a-shot dept.Researchers have created the world's first mRNA-based vaccine against a deadly, antibiotic-resistant bacterium — and they did it using the platform developed for COVID-19 vaccines.Medical Express publishes their announcement:CitarThe vaccine developed by the team from the Institute for Biological Research and Tel Aviv University is an mRNA-based vaccine delivered via lipid nanoparticles, similar to the COVID-19 vaccine. However, mRNA vaccines are typically effective against viruses like COVID-19 — not against bacteria like the plague... In 2023, the researchers developed a unique method for producing the bacterial protein within a human cell in a way that prompts the immune system to recognize it as a genuine bacterial protein and thus learn to defend against it.The researchers from Tel Aviv University and the Institute for Biological Research proved, for the first time, that it is possible to develop an effective mRNA vaccine against bacteria. They chose Yersinia pestis, the bacterium that causes bubonic plague — a disease responsible for deadly pandemics throughout human history. In animal models, the researchers demonstrated that it is possible to effectively vaccinate against the disease with a single dose.The team of researchers was led by Professor Dan Peer at Tel Aviv University, a global pioneer in mRNA drug development, who says the success of the current study now "paves the way for a whole world of mRNA-based vaccines against other deadly bacteria."
The vaccine developed by the team from the Institute for Biological Research and Tel Aviv University is an mRNA-based vaccine delivered via lipid nanoparticles, similar to the COVID-19 vaccine. However, mRNA vaccines are typically effective against viruses like COVID-19 — not against bacteria like the plague... In 2023, the researchers developed a unique method for producing the bacterial protein within a human cell in a way that prompts the immune system to recognize it as a genuine bacterial protein and thus learn to defend against it.The researchers from Tel Aviv University and the Institute for Biological Research proved, for the first time, that it is possible to develop an effective mRNA vaccine against bacteria. They chose Yersinia pestis, the bacterium that causes bubonic plague — a disease responsible for deadly pandemics throughout human history. In animal models, the researchers demonstrated that it is possible to effectively vaccinate against the disease with a single dose.
LLMs aren’t world modelsAugust 10th, 2025I believe that language models aren’t world models. It’s a weak claim — I’m not saying they’re useless, or that we’re done milking them. It’s also a fuzzy-sounding claim — with its trillion weights, who can prove that there’s something an LLM isn't a model of? But I hope to make my claim clear and persuasive enough with some examples.[...]
LLMs’ “simulated reasoning” abilities are a “brittle mirage,” researchers find Chain-of-thought AI "degrades significantly" when asked to generalize beyond training. In recent months, the AI industry has started moving toward so-called simulated reasoning models that use a "chain of thought" process to work through tricky problems in multiple logical steps. At the same time, recent research has cast doubt on whether those models have even a basic understanding of general logical concepts or an accurate grasp of their own "thought process." Similar research shows that these "reasoning" models can often produce incoherent, logically unsound answers when questions include irrelevant clauses or deviate even slightly from common templates found in their training data.In a recent pre-print paper, researchers from the Arizona State University summarize this existing work as "suggest[ing] that LLMs are not principled reasoners but rather sophisticated simulators of reasoning-like text." To pull on that thread, the researchers created a carefully controlled LLM environment in an attempt to measure just how well chain-of-thought reasoning works when presented with "out of domain" logical problems that don't match the specific logical patterns found in their training data.[...]