Cell Reports Physical Science最新文献

Impedance spectroscopy enables the electrical properties of samples to be probed and is commonly used to characterize solids. Extending this technique to analyze fluids within microfluidic channels could enable the rapid characterization of bodily fluids such as sweat. Here, we present a low-cost microfluidic platform with integrated aerosol-jet printed electrodes for the electrical characterization of fluids via impedance spectroscopy. A novel analysis method is presented to accurately determine the concentration of several aqueous ionic chloride solutions, namely NaCl, KCl, CaCl₂, and MgCl₂. Importantly, we identify a key parameter, the turning point frequency of the capacitance-frequency graph, which is found to have a highly linear correlation with the solution concentration for each species spanning at least three orders of magnitude. This linear dependence is highly reproducible across different cationic species, making it useful for accurate fluid characterization. Applying this technique to analyze bodily fluids in real time has implications for remote health monitoring.

Transition metal dichalcogenides (TMDs) have received considerable attention in recent years because of their intriguing chemical and physical properties. However, conventional synthesis methods, including chemical vapor deposition and wet-chemical synthesis, still face many challenges in mass production. Here, we develop a dynamic salt capsulation method to massively prepare TMDs (MoS₂, WS₂) at atmospheric pressure in air with a high yield of over 95%. With the help of binary salts (KCl, KBr), TMDs can be easily obtained for a short reaction time of 1 h at a relatively low temperature (400°C). The as-synthesized MoS₂ powders show flower-like nanospheres, which exhibit a desired catalytic performance in hydrogen evolution reactions and good electrochemical performance as anode materials in lithium-ion batteries. This work provides a simple method to synthesize high-quality and large quantities of TMDs with low cost and time consumption, which has a great potential to integrate into industrial production.

Active mechanical metamaterials are an attractive proposition for soft robotics, electronic devices, and biomedical devices. However, the utilization of their uncommon physical and mechanical behaviors remains underexplored because existing fabrication processes limit the decoupling of structural frameworks from the responsive mechanisms. Here, we propose a multi-step fluidic control programming strategy by fabricating three-dimensional (3D) magnetic soft materials (MSMs) with reconfigurable mechanical metamaterial behaviors, enabling magnetic-field-driven alteration between three different geometry modes in a single structure. The MSM lattices exhibit fast 3D transitions between positive (ν_max = 3.41) and negative (ν_max = −2.64) Poisson’s ratios. We then create MSMs with reconfigurable orthotropic behaviors, which demonstrate the positive and negative Poisson’s effect in perpendicular planes. In further demonstrations, the fast and wireless response is validated by manipulating falling loads and switching the states of electrical circuits. This research provides a controllable workflow for future magnetic soft metamaterials.

We explore the potential of clamp-G nucleobase-modified peptide nucleic acids (cGPNAs) as microRNA and messenger RNA inhibitors. For proof of concept, we target miR-155, which is upregulated in diffuse large B cell lymphoma. cGPNA shows significant downregulation of miR-155 and the upregulation of its downstream targets in multiple lymphoma cell lines. Also, cGPNA treatment in vivo reduced tumor growth and improved survival in the U2932 cell-derived xenograft mouse model. To assess the broad application of cGPNA as an antisense modality, we also target transthyretin (TTR) mRNA. We establish a dose-dependent effect of antisense cGPNA on TTR mRNA levels. For in vivo studies, we conjugated cGPNA-based TTR antisense with lactobionic acid-based targeting ligand for in vivo liver delivery. We establish that cGPNA exhibits significant TTR protein knockdown compared to unmodified peptide nucleic acid (PNA) in vivo. Overall, we confirm that clamp-G-modified PNA analogs are a robust antisense therapy platform.

Transition metal oxides with small grain sizes are promising candidates for capacitive charge storage. However, the overall performance of such oxide materials is still limited by low tap density and finite conductivity. Here, we present a type of densely packed titanium dioxide (TiO₂) composite that comprises three-dimensional aligned mesoporous TiO₂ microspheres and coated ultrathin mesoporous carbon shells. The fabricated mesoporous meso-TiO₂@meso-C complex possesses a highly accessible surface area (134 m² g⁻¹), dual mesopore channels (11.8 and 21.6 nm), and a much higher tap density (1.52 g cm⁻³). As expected, this designed mesoporous composite achieves superior electrochemical performance, including both a maximized specific capacity of 255 mAh g⁻¹ and a volumetric capacity of 390 mAh cm⁻³ at 0.025 A g⁻¹. Our mesoscopic composite electrode that enables fast redox reaction reveals the importance of incorporating conductive and dense mesostructures as an alternative pathway for high-volumetric pseudocapacitive materials.

Many control algorithms for formation of robot swarms are often inspired by animal swarms. However, these algorithms require robots having sensing and computational capabilities and are not applicable to robot swarms working in extreme environments, such as at micro/nanoscale and in space. Here, we directly apply the differential adhesion hypothesis (DAH) of cell biology to the formation of robot swarms. Like cell collectives, swarms of sensor-less robots aggregate and sort in a self-organized manner. We quantitatively investigate the DAH principle in both swarms of cells and robots. We find that the sorting time is nonlinearly related to the levels of adhesion differences. This sheds light on the mechanisms of timing control in morphogenesis. Based on these findings, we program robot swarms to form functional morphologies by tuning their adhesion. This work advances swarm robotics in forming functional morphologies in a self-organized manner and enables us to investigate morphogenesis in cell collectives using robot swarms.

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.

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.