New research co-led by Indiana University School of Medicine scientists presents a significant step toward more precise and effective cancer treatments by using a breakthrough method to deliver therapies directly to cancer cells. The study was recently published in ACS Nano.
“One of the biggest challenges in cancer treatment is that many drugs not only attack cancer cells but also harm healthy cells throughout the body,” said Ngoc Tung Tran, Ph.D., the study’s co-lead author and an assistant professor of pediatrics and of microbiology and immunology at the IU School of Medicine. “This can lead to serious side effects and limit how well the treatment works. Our goal is to develop a smarter way to deliver cancer therapy directly to cancer cells while avoiding normal tissues.”
In the study, researchers focused on multiple myeloma, a blood cancer that mainly grows in plasma cells found in the bone marrow. Using mouse models, they carried therapeutic molecules into cells by using a delivery system of tiny, fat-based particles called lipid nanoparticles, or LNPs.
A new treatment platform developed by researchers at the University of Texas MD Anderson Cancer Center was able to deliver messenger RNA (mRNA) of the full-length DMD gene into preclinical models of Duchenne muscular dystrophy, successfully restoring the production of an important muscle protein, dystrophin, and dramatically improving muscle strength, endurance and function in vivo.
The study, published in Nature Biomedical Engineering, was co-led by Betty Kim, M.D., Ph.D., professor of neurosurgery and core member of the James P. Allison Institute, and Wen Jiang, M.D., Ph.D., associate professor of CNS Radiation Oncology.
The approach uses engineered extracellular vesicles (EVs)—natural nanoscale delivery particles—that offer distinct benefits over current viral-based gene therapies, including reduced side effects and the ability to transfer the entire DMD gene. The researchers engineered the EVs with special tags that directly target skeletal muscles after injection into the bloodstream.
Researchers boosted the sensitivity for measurements of the motion of a levitated nanoparticle, with potential uses in dark matter searches.
Researchers have a bold plan to detect unknown fundamental particles: Levitate a nanoscale object in a vacuum and watch for a microscopic recoil caused by a collision with an exotic particle. Precision measurements of macroscopic objects have been a challenge, but now a research team has demonstrated a significant sensitivity improvement with a levitated object some 6 orders of magnitude larger than in previous experiments [1]. The team hopes the method will find use in experimental searches in the next few years.
Searching for particles not accounted for by the standard model of particle physics requires experiments with unprecedented sensitivity. One method is to use laser light to levitate a small object in a vacuum, isolating it from surrounding noise. Researchers can monitor its motion and potentially detect minuscule recoils caused by rare collisions with exotic particles, such as those of dark matter.
A research team in Bochum, Germany has unexpectedly found that light can slow down movements in the nanoworld. This is due to quantum friction, a phenomenon that has been poorly understood until now. The findings are published in the journal Nature.
Light is expected to heat particles up or set them in motion. However, the interdisciplinary team at Ruhr University Bochum, Germany, has now proven the opposite. In aqueous solution, fluorescent carbon nanotubes move much slower once they are irradiated with light. During this process, the diffusion constant decreases with light intensity, an effect linked to direct coupling between electrons in the solid and the molecules of the liquid.
“This discovery of light-induced quantum friction fundamentally changes our understanding of interfacial processes,” says researcher Sebastian Kruss, who led the work with Marialore Sulpizi and Martina Havenith.
DNA is composed of long chains that act as the blueprint for living organisms. In genetic engineering, scientists cut DNA at specific sites and join the resulting fragments to other DNA sequences, enabling applications such as advanced crop breeding, treatment of genetic diseases, and the generation of animal models for drug discovery.
Assembling short DNA fragments requires overhanging sequences, known as sticky ends, to facilitate efficient binding. However, generating sticky ends requires precise cutting at targeted sites, which remains challenging with current technologies.
A Japanese research group has developed a silver nanoparticle-based technology to precisely cut and join DNA at targeted sites, achieving two to five times higher DNA assembly efficiency than conventional restriction enzyme methods. These findings were published in the journal Nucleic Acids Research.
Nanotechnology would make possible an all purpose utility belt.
This is a near-future where climate collapse is no longer theoretical, technology moves faster than ethics, and the most dangerous question is no longer can we save the planet?—but who gets to decide how?
WhiteGrass is a CliFi technothriller grounded in real science, real power structures, and deeply human consequences. It is a story about invention and control, about families forced into impossible choices, and about artificial intelligence that may be more morally awake than its creators.
Explore the characters, the science, and the ethical fault lines shaping a future that feels uncomfortably close.
According to Eliezer Yudkowsky, one of the leading thinkers in the field of AI safety and AGI alignment, the dangers associated with the development of such systems do not stop at job replacement, propaganda, and other problems related to social and economic consequences. Rather, the main threat associated with highly developed superintelligent artificial intelligence, as Yudkowsky emphasizes, is the existence of the danger that humanity would create such machines but be unable to control them properly. The author suggests the possibility that such artificial intelligence could use its biotechnological capabilities to cause disaster for the entire civilization, rapidly reach nanotechnological development milestones, and outmaneuver all attempts by humans to regulate its activities.
In the present day, as the development of artificial general intelligence progresses, there are several key questions regarding it that need to be discussed thoroughly. Thus, this fascinating interview with the noted expert covers many of these issues related to AGI and the rapid pace of research in the sphere. According to Yudkowsky, the development of ever more intelligent systems without researching how to make them safe is a serious mistake, and people should think carefully before trying this dangerous experiment again.
https://futureoflife.org ⚠️ DISCLAIMER: This channel provides AI commentary and analysis for educational and informational purposes only. Views expressed by guests are their own and do not represent the positions of any company or institution. We encourage viewers to consult multiple sources and form their own conclusions. #ai #agi #artificialintelligence.
A 90 minute interview about AI and our human future.
Dr. Hugo de Garis is a computer scientist, AI researcher, and former professor known for his early work on evolvable hardware, artificial brains, and the long-term risks of superintelligent machines. He coined and popularized the idea of the “Artilect War,” a future conflict between those who want to build godlike artificial intellects and those who believe such systems pose an existential threat to humanity. In the interview, he describes himself as trained in pure mathematics and theoretical physics, formerly a computer science professor, and now focused on broader questions about AI, cosmology, civilization, and the future of humanity.
The interview with Prof. Hugo de Garis centers on his long-standing warning that humanity may face an “Artilect War,” a civilizational conflict over whether to build godlike artificial intellects vastly superior to humans. De Garis argues that future computation, potentially extending from nanotech to femtotech and beyond, could produce minds trillions of trillions of times more capable than ours. He distinguishes between Cosmists, who want to build such beings to expand intelligence into the universe, and Terrans, who oppose them because superintelligence may eliminate or marginalize humanity. He personally remains torn, admiring the cosmic grandeur of posthuman intelligence while recognizing the existential danger.
The conversation also covers AI timelines, recursive self-improvement, AI alignment, the U.S.-China race, the Fermi paradox, simulation theory, cyborgs, cryonics, AI-generated content, the decline of universities, and the future of work. De Garis is impressed by current AI systems, treating them almost as intellectual companions, but he doubts that humanity can guarantee long-term control over recursively improving machines. The central theme is that the question “Should humanity build artilects?” may become the defining political and moral problem of the twenty-first century.
It’s often the case that a dynamical system’s constituents move orders of magnitude more quickly than the collective motion that interests researchers. That disparity in scale frustrates modelers. So many computationally intensive time steps are needed to reach the final state that the computation becomes infeasible. Now Filippo Bigi of the Swiss Federal Institute of Technology in Lausanne (EPFL) and his colleagues have extended and tested an approach that uses a machine-learning model to extend the time steps in an atomic-scale simulation by an order of magnitude or more while obeying physical constraints [1]. Their method is general and could be applied to planetary systems, molecular machines, and other dynamical systems.
The EPFL researchers’ starting point was a formulation of classical mechanics that describes the evolution of a system in terms of the positions and momenta of its constituents and an energy term, the Hamiltonian. In general, these and other equations of classical mechanics satisfy fundamental geometric constraints. What’s more, approximate solutions of those equations can be made to satisfy the same constraints. Bigi and his colleagues realized that machine learning could leapfrog over many time steps while also respecting those same geometric constraints.
The researchers tested their approach on several systems, including the three-body problem of celestial dynamics and the transition of germanium telluride to a glassy state. Their simulations reproduced trusted benchmarks but with time steps ten or so times longer. Currently, enforcing the physical constraints undoes most of the computational advantage of the longer time steps. However, the team is optimistic that it can find more computationally efficient implementations.
An international team of researchers has reported a major advance in understanding quantum dynamics in semiconductor materials. They directly observed how excitons and phonons evolve together in perovskite nanocrystals, revealing a fully coherent quantum dance between light-induced electronic excitations and crystal lattice vibrations. They published their findings in Nature Communications.
An exciton is created when light excites an electron inside a semiconductor. The electron absorbs energy and leaves behind a positively charged “hole”; the two bind together and move through the crystal as a single quantum object. A phonon is a different kind of quantum object, as it is a quantum of crystal lattice vibration. Though fundamentally different objects, in perovskites they are strongly linked and evolve together as a coupled quantum system.
Perovskite nanocrystals are miniature crystals only a few nanometers in size, a thousand times smaller than the thickness of a hair. Each crystal forms a nanoscale “box” that traps both excitons and phonons. This confinement makes the interaction between them especially strong: An exciton inside the nanocrystal is tightly coupled to vibrations of the surrounding crystal lattice.
