De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning.

Architected materials design across orders of magnitude length scale intrigues exceptional mechanical responses nonexistent in their natural bulk state. However, the so-termed mechanical metamaterials, when scaling bottom down to the atomistic or microparticle level, remain largely unexplored and conventionally fall out of their coarse-resolution, ordered-pattern design space. Here, combining high-throughput molecular dynamics (MD) simulations and machine learning (ML) strategies, some intriguing atomistic families of disordered mechanical metamaterials are discovered, as fabricated by melt quenching and exemplified herein by lightweight-yet-stiff cellular materials featuring a theoretical limit of linear stiffness-density scaling, whose structural disorder-rather than order-is key to reduce the scaling exponent and is simply controlled by the bonding interactions and their directionality that enable flexible tunability experimentally. Importantly, a systematic navigation in the forcefield landscape reveals that, in-between directional and non-directional bonding such as covalent and ionic bonds, modest bond directionality is most likely to promotes disordered packing of polyhedral, stretching-dominated structures responsible for the formation of metamaterials. This work pioneers a bottom-down atomistic scheme to design mechanical metamaterials formatted disorderly, unlocking a largely untapped field in leveraging structural disorder in devising metamaterials atomistically and, potentially, generic to conventional upscaled designs.

Prognostic impact and immunotherapeutic implications of NETosis-related gene signature in gastric cancer patients.

The role of NETosis and its related molecules remains unclear in gastric cancer. The data used in this study was directly downloaded from the Cancer Genome Atlas (TCGA) database. All analysis and plots are completed in R software using diverse R packages. In our study, we collected the list of NETosis-related genes from previous publications. Based on the list and expression profile of gastric cancer patients from the TCGA database, we identified the NETosis-related genes significantly correlated with patients survival. Then, CLEC6A, BST1 and TLR7 were identified through LASSO regression and multivariate Cox regression analysis for prognosis model construction. This prognosis model showed great predictive efficiency in both training and validation cohorts. We noticed that the high-risk patients might have a worse survival performance. Next, we explored the biological enrichment difference between high- and low-risk patients and found that many carcinogenic pathways were upregulated in the high-risk patients. Meanwhile, we investigated the genomic instability, mutation burden and immune microenvironment difference between high- and low-risk patients. Moreover, we noticed that low-risk patients were more sensitive to immunotherapy (85.95% vs. 56.22%). High-risk patients were more sensitive to some small molecules compounds like camptothecin_1003, cisplatin_1005, cytarabine_1006, nutlin-3a (-)_1047, gemcitabine_1190, WZ4003_1614, selumetinib_1736 and mitoxantrone_1810. In summary, our study comprehensively explored the role of NETosis-related genes in gastric cancer, which can provide direction for relevant studies.