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Medscape
21-07-2025
- Health
- Medscape
Brain Cell Transplant Slows Rare Neurodegenerative Disease
Replacing mutant microglia with healthy donor-derived microglia has emerged as a promising treatment for adult-onset leukoencephalopathy with axonal spheroids and pigmented glia (ALSP). In a small study of eight patients with ALSP, replacing dysfunctional microglia with normal microglia using traditional bone marrow transplantation (BMT) halted the progression of ALSP and improved neurologic function. 'The critical next step is to validate these results in a larger clinical trial, which is already underway,' principal investigator Bo Peng, PhD, professor, Institute for Translational Brain Research, Fudan University in Shanghai, told Medscape Medical News . The study was published online on July 10 in Science . From Mice to Men ALSP is a rare, progressive neurological disease with an average age of onset of 43 years and an average life expectancy of only 3-5 years after symptoms begin. There is no cure and there are few treatments. ALSP is caused by mutations in the colony-stimulating factor 1 receptor gene (CSF1R), which is critical to development and survival of microglia. Mutations in this gene lead to microglial dysfunction and reduced numbers of these key brain immune cells. In earlier work, Peng and his team developed mouse models of ALSP which exhibit hallmark features of the disease including reduced numbers of microglia, myelin abnormalities, axonal swelling and motor impairments, and cognitive decline. To test whether microglial replacement could alter disease progression in mice with ALSP, the investigators tested two approaches: traditional BMT and microglia replacement by BMT (Mr BMT), a protocol that combines pharmacological depletion of microglia with traditional BMT. Both approaches effectively replaced mutant microglia with wild-type counterparts and successfully reversed myelin defects, axonal swellings, and motor and cognitive impairments in the animals. To translate this to the clinic, they performed traditional BMT in eight patients with ALSP. Although traditional BMT alone typically does not achieve efficient Mr in healthy brains, the inherent CSF1R deficiency in patients with ALSP creates a 'competitive disadvantage' for the recipient's resident microglia, allowing traditional BMT to achieve effective replacement, the researchers explained in their paper. In the 2 years following Mr, MRI and clinical evaluations indicated 'halted disease progression, preserved motor function, and stabilized cognitive abilities,' the team reported. By contrast, untreated patients with ALSP exhibited rapid worsening of brain pathology over a shorter time frame. The findings in these eight patients also provide a 'mechanistic explanation' for a prior clinical case in which an individual with ALSP, initially misdiagnosed with adult-onset metachromatic leukodystrophy, exhibited long-term stabilization after traditional BMT. 'While the path forward is active with the ongoing trial, traditional BMT becoming a widely accessible 'clinic-ready' standard treatment for ALSP is still estimated to be several years away, contingent upon successful trial outcomes, long-term safety data, and subsequent regulatory approvals,' Peng told Medscape Medical News . Beyond ALSP Peng also said Mr 'holds significant theoretical promise' for treating other neurological diseases involving microglial dysfunction, including Alzheimer's disease (AD). 'Genome-wide association studies have identified TREM2 as one of the major risk genes in sporadic AD. TREM2 mutation may cause or accelerate the progression of AD. In a 2020 paper in Cell Reports , we proposed that we can replace the TREM2 -mutated microglia with TREM2 -normal cells to treat this disease,' Peng added. In a Science perspective , Siling Du, PhD, and Jonathan Kipnis, PhD, with the Brain Immunology and Glia Center, Washington University in St Louis congratulated Peng and colleagues for demonstrating in humans that 'correcting a microglial gene defect through cell replacement can arrest disease progression.' Du and Kipnis agreed that the potential implications of this research extend beyond ALSP. For example, a recent study demonstrated that microglial replacement can also rescue pathology in a mouse model of Krabbe disease, a monogenic neurodegenerative disorder caused by mutations in the gene encoding galactosylceramidase. In addition, traditional BMT has also been shown to arrest disease progression in a mouse model of Rett syndrome — a severe neurodevelopmental condition caused by loss-of-function mutations in the gene encoding methyl-CpG binding protein 2. 'Together, these findings highlight the therapeutic potential of microglial replacement in modifying the course of monogenic neurological diseases,' Du and Kipnis said. Looking ahead, they said it will be important to establish the optimal donor cell source to achieve 'scalable, safe, and durable microglial replacement.' 'It is also not yet clear whether the systemic toxicity caused by pretransplant conditioning can be minimized without compromising engraftment. Future strategies must strike a balance between replacement efficiency, systemic toxicity, and the functional competence of engrafted cells,' they wrote. 'Moving forward, it may ultimately become possible to reprogram the brain's immune landscape from within and find the best microglial replacement approach not only for microgliopathies but for a spectrum of neurological diseases,' Du and Kipnis concluded.
Yahoo
11-05-2025
- Science
- Yahoo
Scientists clear major roadblocks in mission to build powerful AI photonic chips
When you buy through links on our articles, Future and its syndication partners may earn a commission. Electronic microchips are at the heart of the modern world. They're found in our laptops, our smartphones, our cars and our household appliances. For years, manufacturers have been making them more powerful and efficient, which increases the performance of our electronic devices. But that trend is now faltering because of the increased cost and complexity of manufacturing chips, as well as performance limits set by the laws of physics. This is happening just as there's a need for increased computing power because of the boom in artificial intelligence (AI). An alternative to the electronic microchips we currently use are photonic chips. These use light instead of electricity to achieve higher performance. However, photonic chips have not yet taken off due to a number of hurdles. Now, two papers published in Nature address some of these roadblocks, offering essential stepping stones to achieving the computing power required by complex artificial intelligence systems. By using light (photons) instead of electricity (electrons) for the transport and processing of information, photonic computing promises higher speeds and greater bandwidths with greater efficiency. This is because it does not suffer from the loss of electrical current due to a phenomenon known as resistance, as well as unwanted heat loss from electrical components. Photonic computing is also particularly suited for performing what are known as matrix multiplications — mathematical operations that are fundamental to AI. Those are some of the benefits. The challenges, however, are not trivial. In the past, the performance of photonic chips has generally been studied in isolation. But because of the dominance of electronics in modern technology, photonic hardware will need to be integrated with those electronic systems. Related: AI-designed chips are so weird that 'humans cannot really understand them' — but they perform better than anything we've created However, converting photons into electrical signals can slow down processing times since light operates at higher speeds. Photonic computing is also based around analogue operations rather than digital ones. This can reduce precision and limit the type of computing tasks that can be carried out. It's also difficult to scale them up from small prototypes because large-scale photonic circuits cannot currently be fabricated with sufficient accuracy. Photonic computing will require its own software and algorithms, compounding the challenges of integration and compatibility with other technology. The two new papers in Nature address many of these hurdles. Bo Peng, from Singapore-based company Lightelligence, and colleagues demonstrate a new type of processor for photonic computing called a Photonic Arithmetic Computing Engine (Pace). This processor has a low latency, which means that there is a minimal delay between an input or command and the corresponding response or action by the computer. The large-scale Pace processor, which has more than 16,000 photonic components, can solve difficult computing tasks, demonstrating the feasibility of the system for real world applications. The processor shows how integration of photonic and electronic hardware, accuracy, and the need for different software and algorithms can be resolved. It also demonstrates that the technology can be scaled up. This marks a significant development, despite some speed limitations of the current hardware. RELATED STORIES —New 'microcomb' chip brings us closer to super accurate, fingertip-sized atomic clocks —China achieves quantum supremacy claim with new chip 1 quadrillion times faster than the most powerful supercomputers —Light-powered computer chip can train AI much faster than components powered by electricity In a separate paper, Nicholas Harris, from California-based company Lightmatter, and colleagues describe a photonic processor that was able to run two AI systems with accuracy similar to those of conventional electronic processors. The authors demonstrated the effectiveness of their photonic processor through generating Shakespeare-like text, accurately classifying movie reviews and playing classic Atari computer games such as Pac-Man. The platform is also potentially scalable, though in this case limitations of the materials and engineering used curtailed one measure of the processor's speed and its overall computational capabilities. Both teams suggest that their photonic systems can be part of scalable next generation hardware that can support the use of AI. This would finally make photonics viable, though further refinements will be needed. These will involve the use of more effective materials or designs. This edited article is republished from The Conversation under a Creative Commons license. Read the original article.
