Cell Reports Physical Science最新文献

Tumor-treating fields (TTFs) are a non-invasive treatment for glioblastoma (GBM) that applies low-intensity, intermediate-frequency, alternating electric fields. Given a 5-year survival of less than 7% for GBM patients, multi-modal treatments are required to improve survival. Natural killer (NK) cells are innate lymphocytes that kill cancer cells and are thus a major target for new immunotherapy approaches. There is potential to combine TTFs with an NK cell-based therapy for GBM treatment. Here, we investigate the impact of TTFs on NK cell viability and functions. Exposure to TTFs does not affect NK cell viability or interferon (IFN)-γ production, a key NK cell function. Of significance, exposure to TTFs increases NK cell degranulation, a proxy of cell killing. These data suggest that the combination of TTFs and NK cell-based therapy may boost tumor cell killing. This provides a basis to further explore this combination, with the end goal of enhancing NK cell immunotherapy potential for patients with GBM.

When activated, aluminum reacts with water to generate hydrogen gas, heat, and aluminum oxyhydroxide, a non-toxic and valuable commodity. This process serves as an efficient and cost-effective means of producing and transporting both hydrogen and thermal energy. The study presented here focuses on recovering a gallium-indium eutectic utilized as a surface coating to induce aluminum’s reactivity in water. The findings indicate that the addition of very low concentrations (0.02 M) of imidazole to seawater leads to rapid reactions being completed in under 10 min, enabling the retrieval and reuse of over 90% of the relatively costly gallium-indium eutectic and producing 99% of the anticipated hydrogen output based on the aluminum’s mass. Additionally, conducting the reaction at elevated temperatures ensures the swift and complete reaction of aluminum in saltwater.

In recent decades, significant strides have been made in advancing biomaterials for biomedical applications. Ideal biomaterials necessitate suitable mechanical properties, excellent biocompatibility, and specific bioactivities. However, the design and preparation of materials with these essential characteristics pose formidable challenges, persisting as significant issues in the field. The development and optimization of high-performance biomaterials, along with the construction of composites and hybrids with diverse biofunctions, present promising strategies for enhancing therapeutic and diagnostic procedures. However, reliance on traditional “trial and error” methods for acquiring a substantial volume of experimental data proves to be laborious, time consuming, and unreliable. An emerging and promising approach involves the successful application of artificial intelligence (AI), specifically deep learning (DL), to investigate and optimize the preparation and manufacturing techniques for various biomaterials. DL, as an automated and intelligent tool within the AI domain, finds widespread application in devising, analyzing, and optimizing different biomaterials. Through the “experiment-AI” technique, DL predicts the potential feature information and performance of biomaterials, showcasing remarkable potential in biomaterial research and development. This review comprehensively explores the application of DL-based technologies in the biomedical field, emphasizing cutting-edge advantages and providing insights and recommendations to enhance the efficacy of such approaches in biomaterials.

As a critical element of the technological infrastructure of body sensor networks (BSNs), wearable electromagnetic vibration energy harvesters (EMVEHs) are a competitive candidate for breaking through the development bottleneck of BSNs’ sustainability, and thus facilitating their self-sustained operations with versatile functions. To this end, the prior concern of wearable EMVEHs is to enhance their adaptability to complex biomechanics of human motions for better power generation performance. Given the state-of-the-art progress of this BSN enabling technology, we provide a comprehensive and in-depth summary of recent excitation-adaptive designs of miniaturized wearable EMVEHs focusing on their insightful vibration pick-up structures here, to systematically clarify a developing roadmap of this branch of science and then offer inspirations for the underway endeavors focused on energy harvesting from human motions. In this way, we try to lift the impacts of current innovative efforts in this field and corresponding BSN achievements to a higher level.

The SARS-CoV-2 virus, responsible for the COVID-19 pandemic, has been linked to significant worldwide illness and death. Examining the ultrastructure and nanomechanical characteristics of SARS-CoV-2 viruses, from a physical standpoint, aids in categorizing their mechanical attributes, providing valuable information for novel treatment approaches and pinpointing susceptible regions that can guide precise medical interventions. This review presents the structural and mechanical characteristics of SARS-CoV-2 virus particles, focusing on their interaction with cells and their effects on nuclear pore transit and epigenetic modifications. We present the latest progress in utilizing a high-speed atomic force microscope for nanoscale observation of the SARS-CoV-2 virus and its constituents. SARS-CoV-2 viruses utilize several components to interact with the host’s nuclear transport receptors and the nucleoporins of the nuclear pore complex to influence the host’s nuclear transport and genome modality. In this review, we also provide an updated summary of how the parts of SARS-CoV-2 interact with the host’s nuclear transport system and how these interactions change the host’s chromatin.

Aryl trifluoromethyl ethers (ArOCF₃) are important structural motifs in pharmaceuticals, agrochemicals, and functional materials. However, the methods reported for the efficient synthesis of these scaffolds are extremely underdeveloped and limited. Here, we report a highly efficient mechanochemical approach for the selective transformation of aryltrimethylammonium triflates, aryldiazonium tetrafluoroborates, and aryl pinacolboranes to aryl trifluoromethyl ethers via in situ-generated OCF₃ source using triphosgene and Co(II) fluoride (CoF₂). The proposed synthetic protocol also shows potential for the selective transformation of other groups such as arylsulfonium and diaryliodonium functionalities. The present trifluoromethoxylation strategy exhibited a broad functional group tolerance and found to be superior over other existing protocols in terms of substrate scope, yields, operational simplicity, and reaction times.

To break through the Shockley-Queisser limit of single-junction photovoltaics, monolithic two-terminal (2T) perovskite/silicon tandem solar cells (TSCs) have shown promise in recent years. Self-assembled monolayers (SAMs) as interconnecting layers (ICLs) for efficient perovskite/silicon TSCs are favorable due to their negligible optical and electrical loss. However, the inhomogeneity of SAMs results in defects at the interface between SAMs and transparent conductive oxide (TCO). To solve this issue, in this work, we employ the sputtered nickel oxide (NiO_x) as the seed layer of MeO-2PACz SAMs to build hybrid ICLs in perovskite/silicon TSCs. It is found that the hybrid ICLs of NiO_x/MeO-2PACz significantly reduce current leakage and non-radiative recombination losses by avoiding direct contact between perovskites and TCO. As a result, we can fabricate reproducible and stable monolithic 2T perovskite/silicon TSCs with an efficiency of 28.47% and an impressive fill factor of 81.8%.

Flexible wearable devices require conductive hydrogels that can withstand extreme conditions. Yet, most strategies for improving environmental tolerance compromise other properties, including mechanical modulus and electromagnetic interference (EMI) shielding. Herein, we design polyvinyl alcohol/polypyrrole double-network organohydrogels with tunable EMI shielding and mechanical properties by introducing specific ions and glycerol. The synergistic effect of high-concentration “salting-in” ions and glycerol/water systems enables 3 M AlCl₃-treated organohydrogels to exhibit exceptional environmental tolerance. These gels display excellent shielding performance above 40 dB and enhanced modulus-like human skin. Glycerol restores the mechanical properties deteriorated by “salting-in” ions, and AlCl₃ promotes ion migration to improve EMI shielding. Additionally, these organohydrogels can also serve as strain sensors, monitoring human motions and maintaining stable shielding (>25 dB) even after subzero treatment or long-term use. Overall, this work offers a generalizable strategy for fabricating multifunctional organohydrogels, paving the way for advancements in gel-based flexible wearable devices.

Explaining black box predictions of machine learning (ML) models is a topical issue in artificial intelligence (AI) research. For the identification of features determining predictions, the Shapley value formalism originally developed in game theory is widely used in different fields. Typically, Shapley values quantifying feature contributions to predictions need to be approximated in machine learning. We introduce a framework for the calculation of exact Shapley values for 4 kernel functions used in support vector machine (SVM) models and analyze consistently accurate compound activity predictions based on exact Shapley values. Dramatic changes in feature contributions are detected depending on the kernel function, leading to mostly distinct explanations of predictions of the same test compounds. Very different feature contributions yield comparable predictions, which complicate numerical and graphical model explanation and decouple feature attribution and human interpretability.

Atmospheric water harvesting (AWH) has emerged as a vital technology in alleviating the significant water struggles around the globe. Advances in sorption-based AWH (SAWH) primarily focus on novel materials, often overlooking system-level considerations. We introduce the first-of-its-kind compact rapid cycling fuel-fired (CRCF) AWH device. The CRCF device packages aluminum fumarate MOF into a compact adsorbent heat exchanger (AHX), which uses fuel combustion and an ambiently cooled condenser. The CRCF prototype was designed to maximize volumetric production density ($P_v$$P_v$), defined as the daily water production per unit volume of the AHX (