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In a study published in Advanced Materials, researchers have demonstrated that an innovative nano-vector (nanogel), which they developed, is able to deliver anti-inflammatory drugs in a targeted manner into glial cells actively involved in the evolution of spinal cord injury, a condition that leads to paraplegia or quadriplegia.

Treatments currently available to modulate the mediated by the component that controls the brain’s internal environment after acute spinal cord injury showed limited efficacy. This is also due to the lack of a therapeutic approach that can selectively act on microglial and astrocytic cells.

The nanovectors developed by Politecnico di Milano, called nanogels, consist of polymers that can bind to specific target molecules. In this case, the nanogels were designed to bind to , which are crucial in the inflammatory response following acute spinal cord injury. The collaboration between Istituto di Ricerche Farmacologiche Mario Negri IRCCS and Politecnico di Milano showed that nanogels, loaded with a drug with anti-inflammatory action (rolipram), were able to convert glial cells from a damaging to a protective state, actively contributing to the recovery of injured tissue.

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The delivery of experimental materials to individual cells with exactness and exclusivity has long been an elusive and much sought-after ability in biology. With it comes the promise of deciphering many longstanding secrets of the cell.

A research team at the Max-Planck-Zentrum für Physik und Medizin, Erlangen led by Professor Vahid Sandoghdar has now successfully shown how and single nanoparticles can be applied directly onto the surface of cells.

In the study, which was published in Nature Methods, the scientists describe their technique as a “μkiss” (microkiss)—an easy and cost-effective new method, unlocking new possibilities in single-cell science with a view to-wards next generation therapeutic applications.

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Futuristic advancements in AI and healthcare stole the limelight at the tech extravaganza Consumer Electronics Show (CES) 2024. However, battery technology is the game-changer at the heart of these innovations, enabling greater power efficiency. Importantly, electric vehicles are where this technology is being applied most intensely. Today’s EVs can travel around 700km on a single charge, while researchers are aiming for a 1,000km battery range. Researchers are fervently exploring the use of silicon, known for its high storage capacity, as the anode material in lithium-ion batteries for EVs. However, despite its potential, bringing silicon into practical use remains a puzzle that researchers are still working hard to piece together.

Enter Professor Soojin Park, PhD candidate Minjun Je, and Dr. Hye Bin Son from the Department of Chemistry at Pohang University of Science and Technology (POSTECH). They have cracked the code, developing a pocket-friendly and rock-solid next-generation high-energy-density Li-ion battery system using micro silicon particles and gel polymer electrolytes. This work was published on the online pages of Advanced Science on the 17th of January.

Employing silicon as a battery material presents challenges: It expands by more than three times during charging and then contracts back to its original size while discharging, significantly impacting battery efficiency. Utilizing nano-sized silicon (10-9m) partially addresses the issue, but the sophisticated production process is complex and astronomically expensive, making it a challenging budget proposition. By contrast, micro-sized silicon (10-6m) is superbly practical in terms of cost and energy density. Yet, the expansion issue of the larger silicon particles becomes more pronounced during battery operation, posing limitations for its use as an anode material.

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Tech giant Google has finally unveiled its much-hyped Gemini AI, a series of generative AI models it claims are its “largest and most capable” to date.

“This new era of models represents one of the biggest science and engineering efforts we’ve undertaken as a company,” said Google CEO Sundar Pichai.

Multimodal AI: Generative AIs are algorithms trained to create original content in response to user prompts. OpenAI’s first iteration of ChatGPT, for example, can understand and produce human-like text, while its DALL-E 2 system can generate images based on text prompts.

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