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Machine learning models have seeped into the fabric of our lives, from curating playlists to explaining hard concepts in a few seconds. Beyond convenience, state-of-the-art algorithms are finding their way into modern-day medicine as a powerful potential tool. In one such advance, published in Cell Systems, Stanford researchers are using machine learning to improve the efficacy and safety of targeted cell and gene therapies by potentially using our own proteins.
Most human diseases occur due to the malfunctioning of proteins in our bodies, either systematically or locally. Naturally, introducing a new therapeutic protein to cure the one that is malfunctioning would be ideal.
Although nearly all therapeutic protein antibodies are either fully human or engineered to look human, a similar approach has yet to make its way to other therapeutic proteins, especially those that operate in cells, such as those involved in CAR-T and CRISPR-based therapies. The latter still runs the risk of triggering immune responses. To solve this problem, researchers at the Gao Lab have now turned to machine learning models.
Resonantly tunable quantum cascade lasers (QCLs) are high-performance laser light sources for a wide range of spectroscopy applications in the mid-infrared (MIR) range. Their high brilliance enables minimal measurement times for more precise and efficient characterization processes and can be used, for example, in chemical and pharmaceutical industries, medicine or security technology. Until now, however, the production of QCL modules has been relatively complex and expensive.
The Fraunhofer Institute for Applied Solid State Physics IAF has therefore developed a semi-automated process that significantly simplifies the production of QCL modules with a MOEMS (micro-opto-electro-mechanical system) grating scanner in an external optical cavity (EC), making it more cost-efficient and attractive for industry. The MOEMS-EC-QCL technology was developed by Fraunhofer IAF in collaboration with the Fraunhofer Institute for Photonic Microsystems IPMS.
No image is infinitely sharp. For 150 years, it has been known that no matter how ingeniously you build a microscope or a camera, there are always fundamental resolution limits that cannot be exceeded in principle. The position of a particle can never be measured with infinite precision; a certain amount of blurring is unavoidable. This limit does not result from technical weaknesses, but from the physical properties of light and the transmission of information itself.
TU Wien (Vienna), the University of Glasgow and the University of Grenoble therefore posed the question: Where is the absolute limit of precision that is possible with optical methods? And how can this limit be approached as closely as possible?
And indeed, the international team succeeded in specifying a lowest limit for the theoretically achievable precision and in developing AI algorithms for neural networks that come very close to this limit after appropriate training. This strategy is now set to be employed in imaging procedures, such as those used in medicine. The study is published in the journal Nature Photonics.
Switching to camizestrant, a next-generation oral SERD, significantly prolongs progression-free survival and maintains quality of life in patients with ESR1-mutated, hormone receptor-positive advanced breast cancer. The proactive, biomarker-guided approach allows earlier intervention and may redefine standard care.