Sustaining Surface Lithiophilicity of Ultrathin Li-Alloy Coating Layers on Current Collector for Zero-Excess Li-Metal Batteries.

Zero-excess Li-metal batteries (ZE-LMBs) have emerged as the ultimate battery platform, offering an exceptionally high energy density. However, the absence of Li-hosting materials results in uncontrolled dendritic Li deposition on the Cu current collector, leading to chronic loss of Li inventory and severe electrolyte decomposition, limiting its full utilization upon cycling. This study presents the application of ultrathin (≈50 nm) coatings comprising six metallic layers (Cu, Ag, Au, Pt, W, and Fe) on Cu substrates in order to provide insights into the design of Li-depositing current collectors for stable ZE-LMB operation. In contrast to non-alloy Cu, W, and Fe coatings, Ag, Au, and Pt coatings can enhance surface lithiophilicity, effectively suppressing Li dendrite growth, thereby improving Li reversibility. Considering the distinct Li-alloying behaviors, particularly solid-solution and/or intermetallic phase formation, Pt-coated Cu current collectors maintain surface lithiophilicity over repeated Li plating/stripping cycles by preserving the original coating layer, thereby attaining better cycling performance of ZE-LMBs. This highlights the importance of selecting suitable Li-alloy metals to sustain surface lithiophilicity throughout cycling to regulate dendrite-less Li plating and improve the electrochemical stability of ZE-LMBs.

Incidence and risk factors of PICC-related thrombosis in breast cancer: a meta-analysis.

BACKGROUND: The incidence and risk factors of peripherally inserted central catheter-related thrombosis in patients with breast cancer have not been fully elucidated. METHOD: Meta-analysis was performed by searching all studies on the incidence of peripherally inserted central catheter-associated thrombosis and risk factors for its formation in breast cancer patients from the establishment of the database to May 2023, including PubMed, Embase, Web of Science, China Knowledge Network, China Biomedical Literature Service System (SinoMed) and Wanfang databases. Then the incidence of peripherally inserted central catheter-related thrombosis and risk factors for its formation were analyzed in breast cancer patients. RESULTS: A total of 15 articles were included, involving 8635 patients. The total incidence of peripherally inserted central catheter-related thrombosis in breast cancer patients was 7.0% (95% confidence interval: 4.0-13.0%) and 12.9% (95% confidence interval: 7.0-22.5%) after correction. Thirty-two risk factors were included, and eight risk factors could be combined. Among these risk factors, there were statistically significant differences (P < 0.05) in body mass index ≥ 25 (odds ratio = 6.319, 95% confidence interval: 2.733-14.613; P < 0.001), D-dimer >500 ng/ml (odds ratio = 1.436, 95% confidence interval: 1.113-1.854; P = 0.005), increased fibrinogen (odds ratio = 4.733, 95% confidence interval: 1.562-14.346; P = 0.006), elevated platelet count (odds ratio = 4.134, 95% confidence interval: 2.694-6.346; P < 0.001) and catheter malposition (odds ratio = 8.475, 95% confidence interval: 2.761-26.011; P < 0.001). CONCLUSION: The incidence rate of peripherally inserted central catheter-related thrombosis in breast cancer patients was 7.0% (95% confidence interval: 4.0-13.0%). Body mass index ≥ 25, D-dimer >500 ng/ml, elevated fibrinogen, elevated platelet count and catheter malposition were risk factors for peripherally inserted central catheter-related thrombosis in breast cancer patients.

Network dynamics-based subtyping of Alzheimer's disease with microglial genetic risk factors.

BACKGROUND: The potential of microglia as a target for Alzheimer's disease (AD) treatment is promising, yet the clinical and pathological diversity within microglia, driven by genetic factors, poses a significant challenge. Subtyping AD is imperative to enable precise and effective treatment strategies. However, existing subtyping methods fail to comprehensively address the intricate complexities of AD pathogenesis, particularly concerning genetic risk factors. To address this gap, we have employed systems biology approaches for AD subtyping and identified potential therapeutic targets. METHODS: We constructed patient-specific microglial molecular regulatory network models by utilizing existing literature and single-cell RNA sequencing data. The combination of large-scale computer simulations and dynamic network analysis enabled us to subtype AD patients according to their distinct molecular regulatory mechanisms. For each identified subtype, we suggested optimal targets for effective AD treatment. RESULTS: To investigate heterogeneity in AD and identify potential therapeutic targets, we constructed a microglia molecular regulatory network model. The network model incorporated 20 known risk factors and crucial signaling pathways associated with microglial functionality, such as inflammation, anti-inflammation, phagocytosis, and autophagy. Probabilistic simulations with patient-specific genomic data and subsequent dynamics analysis revealed nine distinct AD subtypes characterized by core feedback mechanisms involving SPI1, CASS4, and MEF2C. Moreover, we identified PICALM, MEF2C, and LAT2 as common therapeutic targets among several subtypes. Furthermore, we clarified the reasons for the previous contradictory experimental results that suggested both the activation and inhibition of AKT or INPP5D could activate AD through dynamic analysis. This highlights the multifaceted nature of microglial network regulation. CONCLUSIONS: These results offer a means to classify AD patients by their genetic risk factors, clarify inconsistent experimental findings, and advance the development of treatments tailored to individual genotypes for AD.

Superior electroadhesion force with permittivity-engineered bilayer films using electrostatic simulation and machine learning approaches.

Electroadhesive forces are crucial in various applications, including grasping devices, electro-sticky boards, electrostatic levitation, and climbing robots. However, the design of electroadhesive devices relies on speculative or empirical error approaches. Therefore, we present a theoretical model comprising predictive coplanar electrodes and protective layers for analyzing the electrostatic fields between an object and electroadhesive device. The model considers the role of protective layer and the air gap between the electrode surface and the object. To exert a higher electroadhesive force, the higher permeability of the protective layer is required. However, a high permeability of the protective layer is hard to withstand high applied voltage. To overcome this, two materials with different permeabilities were employed as protective layers-a low-permeability inner layer and a high-permeability outer layer-to maintain a high voltage and generate a large electroadhesive force. Because a low-permeability inner layer material was selected, a more permeable outer layer material was considered. A theoretical analysis revealed complex relationships between various design parameters. The impact of key design parameters and working environments on the electroadhesion behavior was further investigated. This study reveals the fundamental principles of electroadhesion and proposes prospective methods to enhance the design of electroadhesive devices for various engineering applications.

Diagnosis of Current Flow Patterns Inside Fault-Simulated Li-Ion Batteries via Non-Invasive, In Operando Magnetic Field Imaging.

With the growing popularity of Li-ion batteries in large-scale applications, building a safer battery has become a common goal of the battery community. Although the small errors inside the cells trigger catastrophic failures, tracing them and distinguishing cell failure modes without knowledge of cell anatomy can be challenging using conventional methods. In this study, a real-time, non-invasive magnetic field imaging (MFI) analysis that can signal the battery current-induced magnetic field and visualize the current flow within Li-ion cells is developed. A high-speed, spatially resolved MFI scan is used to derive the current distribution pattern from cells with different tab positions at a current load. Current maps are collected to determine possible cell failures using fault-simulated batteries that intentionally possess manufacturing faults such as lead-tab connection failures, electrode misalignment, and stacking faults (electrode folding). A modified MFI analysis exploiting the magnetic field interference with the countercurrent-carrying plate enables the direct identification of defect spots where abnormal current flow occurs within the pouch cells.

The hidden community architecture of human brain networks.

The organizational principles of the community architecture of human brain networks are still mostly unknown. Here, we found that brain networks have a moderate degree of community segregation but are specifically organized to achieve high community overlap while maintaining their segregated community structures. These properties are distinct from other real-world complex networks. Additionally, we found that human subjects with a higher degree of community overlap in their brain networks show greater dynamic reconfiguration and cognitive flexibility.

A logical network-based drug-screening platform for Alzheimer's disease representing pathological features of human brain organoids.

Developing effective drugs for Alzheimer's disease (AD), the most common cause of dementia, has been difficult because of complicated pathogenesis. Here, we report an efficient, network-based drug-screening platform developed by integrating mathematical modeling and the pathological features of AD with human iPSC-derived cerebral organoids (iCOs), including CRISPR-Cas9-edited isogenic lines. We use 1300 organoids from 11 participants to build a high-content screening (HCS) system and test blood-brain barrier-permeable FDA-approved drugs. Our study provides a strategy for precision medicine through the convergence of mathematical modeling and a miniature pathological brain model using iCOs.

The Hidden Control Architecture of Complex Brain Networks.

The brain controls various cognitive functions in a robust and efficient way. What is the control architecture of brain networks that enables such robust and optimal control? Is this brain control architecture distinct from that of other complex networks? Here, we developed a framework to delineate a control architecture of a complex network that is compatible with the behavior of the network and applied the framework to structural brain networks and other complex networks. As a result, we revealed that the brain networks have a distributed and overlapping control architecture governed by a small number of control nodes, which may be responsible for the robust and efficient brain functions. Moreover, our artificial network evolution analysis showed that the distributed and overlapping control architecture of the brain network emerges when it evolves toward increasing both robustness and efficiency.