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Advancing human leukocyte antigen-based cancer immunotherapy: from personalized to broad-spectrum strategies for genetically heterogeneous populations

Human leukocyte antigen (HLA)-based immunotherapeutics, such as tebentafusp-tebn and afamitresgene autoleucel, have expanded the treatment options for HLA-A*02-positive patients with rare solid tumors such as uveal melanoma, synovial sarcoma, and myxoid liposarcoma. Unfortunately, many patients of European, Latino/Hispanic, African, Asian, and Native American ancestry who carry non-HLA-A*02 alleles remain largely ineligible for most current HLA-based immunotherapies. This comprehensive review introduces HLA allotype-driven cancer health disparities (HACHD) as an emerging research focus, and examines how past and current HLA-targeted immunotherapeutic strategies may have inadvertently contributed to cancer health disparities. We discuss several preclinical and clinical strategies, including the incorporation of artificial intelligence (AI), to address HACHD.

The Southern Ocean may be building up a massive burp

The ocean has helped mitigate global warming by absorbing about a quarter of anthropogenic carbon dioxide (CO2) emissions, along with more than 90% of the excess heat those emissions generate.

Many efforts, including assessments by the Intergovernmental Panel on Climate Change, have looked at how the oceans may continue to mitigate increasing emissions and . However, few have looked at the opposite: How will the oceans respond if emissions and associated atmospheric heat levels begin to decrease in response to net negative emissions?

Ivy Frenger and colleagues examined what might happen in the Southern Ocean if, after more than a century of human-induced warming, global mean temperatures were to be reduced via CO2 removal from the atmosphere. Their results are published in the journal AGU Advances.

Quasicrystals Grow Smoothly Around Obstacles

Large-scale obstacles to crystal growth can throw the whole lattice off kilter, but quasicrystals can accommodate them without losing their atomic-scale order.

When a growing crystal encounters an obstacle, the orderly array of atoms may have to adjust in ways that create lattice defects or large-scale rearrangements. But a research team has found through experiments that peculiar materials called quasicrystals can take such disruptions in stride [1] The quasicrystalline lattice, which is orderly but not periodic, can accommodate obstacles without sacrificing its order, thanks to a type of rearrangement unique to quasicrystals. The work suggests the possibility of making quasicrystalline metal alloys that are more durable than conventional alloys.

Quasicrystals, discovered in 1984, are typically compounds composed of metals such as aluminum, nickel, and manganese. X-ray diffraction seems to show that their atomic lattices have symmetries that aren’t permitted in conventional crystals, such as pentagonal or decagonal symmetry. But these symmetries can exist in small regions because quasicrystals are not conventional crystals—you can’t shift the atomic lattice in space and then superimpose it exactly on the original lattice.

New AI model for drug design brings more physics to bear in predictions

When machine learning is used to suggest new potential scientific insights or directions, algorithms sometimes offer solutions that are not physically sound.

Take, for example, AlphaFold, the AI system that predicts the complex ways in which amino acid chains will fold into 3D protein structures. The system sometimes suggests “unphysical” folds—configurations that are implausible based on the —especially when asked to predict the folds for chains that are significantly different from its .

To limit this type of unphysical result in the realm of drug design, Anima Anandkumar, Bren Professor of Computing and Mathematical Sciences at Caltech, and her colleagues have introduced a new machine learning model called NucleusDiff, which incorporates a simple physical idea into its training, greatly improving the algorithm’s performance.

Hybrid metasurface modulates light at low voltages for energy-efficient optics

Metasurfaces are two-dimensional (2D), nanoengineered surfaces that interact strongly with electromagnetic waves and can control light with remarkable precision. These ultra-thin layers can be used to develop a wide range of advanced technologies, including optical photonic, sensing and communication systems.

Active metasurfaces, whose electromagnetic response can be dynamically tuned in , are particularly promising for advanced real-world applications, particularly for the development of reconfigurable antennas, highly sensitive sensors and other adaptive systems. These metasurfaces can also serve as optical modulators, devices that adjust the intensity or phase of light and thus enable the encoding of information onto light beams.

While engineers have introduced various -based optical modulators over the past few years, most devices developed so far require electrical signals to operate. This means that to noticeably change the optical response of the metasurfaces they are based on, users need to apply a strong electrical field to them.

New Mn-rich cathode could improve sustainability and stability of high-energy Li-ion batteries

Lithium-ion batteries (LiBs) remain the most widely used rechargeable batteries worldwide, powering most portable and consumer electronics. LiBs are also used to power most electric and hybrid vehicles, which are predicted to become increasingly widespread over the next decades.

Despite their good performance and large-scale adoption, LiBs still primarily rely on based on nickel (Ni) and cobalt (Co). Yet the processes required to source both these metals are known to be destructive for , while also leaving a high carbon footprint and requiring significant water.

Moreover, most of the cobalt used worldwide originates from the Democratic Republic of the Congo (DRC), where unsafe mining conditions and child labor are still common. Over the past decades, energy researchers have been trying to identify cathode materials that can be sourced safely and sustainably, while matching the performance of Ni and Co-based cathodes.

Grid cells create multiple local maps rather than single global system for spatial navigation, study finds

Grid cells are a class of specialized neurons in a brain region called the entorhinal cortex, which is known to support spatial navigation and some memory processes. Past neuroscience studies have found that as humans and other animals move in their surroundings, these cells fire following a grid-like pattern, creating a sort of internal map of the environment.

Researchers at the Medical Faculty of Heidelberg University, the German Cancer Research Center and EBRI recently carried out a study aimed at shedding new light on the processes via which grid cells encode an animal’s position in space and contribute to spatial navigation. Their findings, published in Nature Neuroscience, suggest that rather than creating and retaining a single global map, these cells produce multiple local maps that can guide the future behavior of animals in their surrounding space.

“Soon after the discovery of grid cells by the group of May-Britt and Edvard Moser, it was proposed that these neurons might support path integration,” Kevin Allen, senior author of the paper, told Medical Xpress. “In this fundamental navigation process, an animal estimates its position by continuously integrating self-motion cues, even in the absence of external landmarks. However, most previous studies of grid cells were conducted in environments rich in external cues, making it difficult to isolate the path-integration processes.”

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