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Advanced Science

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Online ISSN: 2198-3844

Disciplines: General & introductory materials science

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Schematic illustration of the design principles, AM procedures, structure‐mechanism‐property relationships, and multifunctional applications of lattice metamaterials reviewed in this article. Reproduced with permission.[⁸] Copyright 2018, Elsevier. Reproduced with permission.[¹¹] Copyright 2018, Wiley. Reproduced with permission.[⁹⁶] Copyright 2021, Elsevier. Reproduced with permission.[¹⁶] Copyright 2019, Elsevier. Reproduced with permission.[¹⁷⁰] Copyright 2021, Elsevier. Reproduced with permission.[¹⁸⁴] Copyright 2022, Wiley. Reproduced with permission.[²⁰⁰] Copyright 2018, ACS. Reproduced with permission.[²⁰⁸] Copyright 2022, Elsevier. Reproduced with permission.[⁹⁵] Copyright 2020, Elsevier. Reproduced with permission.[¹³⁹] Copyright 2021, Elsevier. Reproduced with permission.[¹⁵] Copyright 2022, PNAS. Reproduced with permission.[¹⁵⁷] Copyright 2024, Springer. Reproduced with permission.[²⁰⁵] Copyright 2021, AAAS. Reproduced with permission.[²⁰⁷] Copyright 2023, Elsevier. Reproduced with permission.[²⁸⁸] Copyright 2015, Elsevier. Reproduced with permission.[³³⁷] Copyright 2018, Wiley. Reproduced with permission.[³⁸⁴] Copyright 2018, Elsevier. Reproduced with permission.[³⁹⁴] Copyright 2017, AAAS. Reproduced with permission.[⁴⁹] Copyright 2021, Wiley. Reproduced with permission.[⁵⁸] Copyright 2018, AAAS. Reproduced with permission.[⁵⁰⁵] Copyright 2021, Elsevier. Reproduced with permission.[⁵⁸¹] Copyright 2020, AAAS. Reproduced with permission.[³²³] Copyright 2023, Elsevier. Reproduced with permission.[⁵²] Copyright 2006, AAAS. Reproduced with permission.[³⁵] Copyright 2014, Springer. Reproduced with permission.[⁶⁴³] Copyright 2021, Elsevier. Reproduced with permission.[⁶³³] Copyright 2022, Elsevier. Reproduced with permission.[⁶⁰⁹] Copyright 2020, nTopology.
Design principles and AM processes of truss lattices. a) The fundamental SC, BCC, and FCC truss lattices within cubic symmetry group, and combined lattices from the three fundamental ones. Reproduced with permission.[⁸] Copyright 2018, Elsevier. b) Combined truss lattices with variable cross sections for different constituent bars. Reproduced with permission.[¹¹⁸] Copyright 2021, Elsevier. c) Truss lattices with curved‐beam struts. Reproduced with permission.[²⁹] Copyright 2020, Springer. d) Truss lattices with hollow cross sections of struts. Reproduced with permission.[⁹²] Copyright 2018, Elsevier. e) Truss lattices with different cross‐sectional radii for different constituent bars. Reproduced with permission.[⁹⁵] Copyright 2020, Elsevier. f) Additively manufactured truss lattice samples through a LPBF process. Reproduced with permission.[⁹⁰] Copyright 2020, Elsevier. g) The fabrication of composite nanolattices through DLW AM and HEA coating processes. Reproduced with permission.[¹²³] Copyright 2018, ACS. h) The fabrication of composite lattices made of SC, BCC, and FCC truss lattices and epoxy phase through LPBF AM and moulding processes. Reproduced with permission.[¹²⁴] Copyright 2020, Elsevier. i) The fabrication of composite lattices composed of variable cross‐sectional truss lattices and epoxy phase through interference lithography, carbonizing, and infiltrating. Reproduced with permission.[¹²⁵] Copyright 2012, ACS. j) Interpenetrating lattices composed of disconnected truss lattices. Reproduced with permission.[¹²⁷] Copyright 2021, Elsevier. k) Interpenetrating lattices composed of disconnected straight truss lattices and curved fibers. Reproduced with permission.[¹²⁷] Copyright 2021, Elsevier.
Design principles and AM processes of plate lattices. a) Elementary cubic plate lattices of SC, BCC, and FCC classes are assembled with appropriate volume ratios to generate combined lattices with superior isotropic elasticity. Reproduced with permission.[¹¹] Copyright 2018, Wiley. b) A family of parametric plate lattices are presented to enlarge the design space to achieve target mechanical properties. Reproduced with permission.[¹³⁴] Copyright 2023, Elsevier. c) A series of rhombic dodecahedron plate lattices are devised to achieve both isotropic elasticity and nearly isotropic yield strength. Reproduced with permission.[¹³⁶] Copyright 2024, Elsevier. d) A set of cuboidal spherical plate lattices are topologically optimized to achieve enhanced stiffness and strength. Reproduced with permission.[¹³⁷] Copyright 2024, Taylor & Francis. e) A series of half‐open‐cell plate lattices are proposed to enhance the additive manufacturability, with a moderate reduction in mechanical properties. Reproduced with permission.[¹³⁸] Copyright 2020, Elsevier. The fabrication of plate lattices through f) DLW, g) LPBF, h) SLA, and i) FDM AM processes. Reproduced with permission.[¹²] Copyright 2020, Springer. Reproduced with permission.[¹³⁸] Copyright 2020, Elsevier. Reproduced with permission.[¹³⁹] Copyright 2021, Elsevier. Reproduced with permission.[⁹⁷] Copyright 2021, Elsevier.
Design principles and AM processes of shell lattices. a) TPMS P, IWP, FRD, N, N14, and OCTO surfaces are generated within the 1/48 fundamental domain (red), 1/8 unit cell (blue), and unit cell (grey) via the open‐source Surface Evolver software, respectively. Reproduced with permission.[⁹⁶] Copyright 2021, Elsevier. b) The design of shell lattices through shape optimization of mid‐surfaces. Reproduced with permission.[¹⁴] Copyright 2019, Elsevier. Reproduced with permission.[¹⁴³] Copyright 2022, Elsevier. c) P‐surface as a TPMS that splits a cube‐like space into two sub‐volumes bounded by the blue‐ and orange‐shaded surfaces. Reproduced with permission.[¹⁴⁴] Copyright 2024, Springer. d) The design of shell lattices by combining elementary TPMS lattices with appropriate volume ratios. Reproduced with permission.[¹⁶] Copyright 2019, Elsevier. Reproduced with permission.[¹⁴⁵] Copyright 2021, Elsevier. e) The variable thickness design of shell lattices. Reproduced with permission.[⁹⁶] Copyright 2021, Elsevier. f) A parametric shell lattice model is proposed to enlarge the design space of shell lattices to obtain target mechanical properties. Reproduced with permission.[¹⁴⁸] Copyright 2022, Elsevier. The fabrication of TPMS shell lattices through g) DLW, PµSL, and h) micro‐LPBF AM processes. Reproduced with permission.[¹⁵] Copyright 2022, PNAS. Reproduced with permission.[⁹⁸] Copyright 2019, Elsevier. i) A multi‐material process that synergistically combines the advantages of FDM, FLI, and DIW AM techniques to seamlessly integrate both structural and laser‐processable functional materials into 3D engineered shell lattices with intricate geometries. Reproduced with permission.[¹⁵²] Copyright 2024, Springer.
Design principles and AM processes of hybrid lattices. a) SC, BCC, and FCC truss lattices are hybridized with SC plate lattices to enhance the specific energy absorption and broadband vibration attenuation. Reproduced with permission.[¹⁵³] Copyright 2024, Taylor & Francis. b) Elastically isotropic hybrid open‐cell lattices are devised by combining octet truss and SC plate lattices. Reproduced with permission.[¹⁵⁴] Copyright 2023, Elsevier. c) Octet truss and FCC plate lattices are appropriately hybridized to enhance the stiffness, strength, and energy absorption capabilities simultaneously. Reproduced with permission.[¹⁷] Copyright 2021, Wiley. d) MHS arrays are hybridized into the small octahedrons or tetrahedrons of WBK lattices' interior spaces to achieve exceptional energy absorption capabilities. Reproduced with permission.[¹⁸] Copyright 2011, Wiley. e) Closed‐cell and open‐cell SC plate lattices and P shell lattices are hybridized with appropriate volume fractions to achieve superior isotropic elasticity. Reproduced with permission.[¹⁶] Copyright 2019, Elsevier. f) Ultra‐stiff truss‐plate‐shell hybrid lattices with a reasonable distribution of materials are first devised through a multilayer strategy and topology optimization framework, and then fabricated via SLS in nylon (PA12) and TPU and LPBF in SS316L and AiSi10Mg. Reproduced with permission.[¹⁵⁷] Copyright 2024, Springer. g) Hybrid open‐cell lattices are first devised by combining porous BCC plate and SC, BCC, and FCC truss lattices, and then fabricated via LPBF in SS316L. Reproduced with permission.[¹⁵⁸] Copyright 2023, Elsevier. h) Tubular shell and slender cuboid truss lattices are first hybridized and then fabricated via dip‐in DLW in photosensitive polymers to enhance the elastic modulus, yield strength, and energy absorption capability. Reproduced with permission.[¹⁵⁹] Copyright 2022, Elsevier.

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Multi‐Physical Lattice Metamaterials Enabled by Additive Manufacturing: Design Principles, Interaction Mechanisms, and Multifunctional Applications

January 2025

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Aims and scope


Advanced Science, part of the prestigious Wiley Advanced portfolio, is an open access interdisciplinary science journal publishing the best-in-class fundamental and applied research in materials science, physics, chemistry, medical and life sciences, and engineering. Our mission is to give top science the maximum accessibility through open access publishing.
The Advanced portfolio from Wiley is a family of globally respected, high-impact journals that disseminates the best science from well-established and emerging researchers so they can fulfill their mission and maximize the reach of their scientific discoveries.

Recent articles


Ultra‐Tough Copper–Copper Bonding by Nano‐Oxide‐Dispersed Copper Nanomembranes
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February 2025

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Metal–metal bonding has played a pivotal role in advancing human technologies across various industrial sectors. As devices continue to miniaturize, there is an increasing need for efficient bonding techniques capable of achieving metal–metal bonds at smaller length scales. In this study, a facile but effective bonding technique is developed that enables the bonding of randomly oriented copper with copper nanomembranes under low temperatures and pressures. The fabricated copper nanomembranes, with a thickness of ≈50 nm and a width of 1 cm or above, exhibit a unique heterogeneous nanostructure, comprising copper nanocrystals along with nano‐copper‐oxide dispersions. Consequently, these copper nanomembranes display exceptional mechanical properties, including an ultra‐low elastic modulus of ≈35 GPa, a remarkable yield strength of ≈1 GPa, and excellent ductility of ≈40%, overcoming the conventional strength‐ductility trade‐off observed in various copper alloys. Most importantly, these ultra‐soft copper nanomembranes serve as metallic “glues”, promoting grain growth across the bonding interface between randomly oriented copper surfaces. This process leads to an average interfacial shear strength of up to 73 MPa at room temperature, representing an approximate 35 times increase in bonding strength compared to direct copper–copper bonding achieved under identical temperature and pressure conditions.


Central illustration of this study. Systematic analyses of the association between hemoglobin and polycystic ovary syndrome identify the potential effect of HIF‐1 pathway. Abbreviations: DEGs, differentially expressed genes; eQTL, expression quantitative trait loci; HIF, hypoxia‐inducible factor pathway; INSR, insulin receptor; NFKB, nuclear factor kappa‐light‐chain‐enhancer of activated B cells; PCOS, polycystic ovary syndrome; PRKCA, protein kinase C alpha; SD, standard deviation; SNP, single nucleotide polymorphism.
The associations between Hb levels and PCOS and its manifestations. A) Univariate and multivariate logistic regression model analysis to characterize the associations between Hb levels and PCOS, PCOS phenotypes, and separate manifestations. Model 1 was adjusted for intervals of appointment date, age, body mass index, and education; Model 2 was additionally adjusted for systolic blood pressure, endometriosis status, history of ovary surgery, LH/FSH ratio, fasting blood glucose, total cholesterol, triglycerides, and high‐ and low‐density lipoprotein levels. B) Nonlinear associations between Hb levels and PCOS, PCOS phenotypes, and separate manifestations, adjusted for confounders in Model 2. Abbreviations: PCOS, polycystic ovary syndrome; LH, luteinizing hormone; FSH, follicle‐stimulating hormone.
MR results and pathway enrichment analysis. A) Associations of Hb levels with PCOS and related traits according to the Mendelian randomization analysis. B) Significant results for KEGG pathway enrichment analysis of candidate genes from Hb IVs used in MR analysis. C) Significant tissue‐specific eQTL MR results in whole blood and ovary. D) eQTL and pQTL MR results for INSR, NFKB1, and PRKCA on testosterone levels. E) LocusCompare plots comparing genetic signals for NFKB1 levels and TT levels in colocalization analysis. Abbreviations: BT, bioavailable testosterone; TT, total testosterone; SHBG, sex hormone binding globulin; PH4, posterior probability for shared causal variants.
In vitro validation experiments in peripheral blood cells and granulosa cells. Hb levels, qPCR analyses of INSR, NFKB1 and PRKCA expression in A) peripheral blood cells and B) granulosa cells. The blue dots indicate the mRNA levels in the control group, and the red dots indicate the mRNA levels in the PCOS patients. P values, two‐tailed Student's t‐test or Mann‐Whitney test. C) Representative Western blot analysis of INSR and NF‐κB in the granulosa cells of the control and PCOS groups in three independent experiments, with the GAPDH level used as a control. D) Linear regression analysis of hemoglobin levels and INSR and NFKB1 gene expression in peripheral blood cells and granulosa cells after adjusting for BMI. E) Linear regression analysis of INSR and NFKB1 gene expression and testosterone level in peripheral blood cells before and after adjusting for BMI.
Multiomics and Systematic Analyses Reveal the Roles of Hemoglobin and the HIF‐1 Pathway in Polycystic Ovary Syndrome

February 2025

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26 Reads

Polycystic ovary syndrome (PCOS) affects reproductive and cardiometabolic health, yet its pathogenesis remains unclear. Emerging evidence links hemoglobin levels to metabolic disorders, suggesting a potential role in PCOS development. Here, we integrated a large‐scale cohort study, Mendelian randomization (A genetic tool to infer causal relationships), bioinformatics analyses, and in vitro experiments to investigate the relationship between hemoglobin levels and PCOS. In a cohort of 20 602 women, each 10 g L⁻¹ elevation in hemoglobin levels is associated with 22% higher odds of PCOS (adjusted odds ratio: 1.22, 95% confidence interval: 1.15–1.29, P < 0.001) and PCOS manifestations, particularly hyperandrogenism. Mendelian randomization analysis confirms that higher hemoglobin levels are associated with increased PCOS risk and elevated testosterone levels. The hypoxia‐inducible factor 1 (HIF‐1) pathway is enriched, identifying three testosterone‐associated genes (nuclear factor kappa B (NFKB1), insulin receptor (INSR), protein kinase C alpha. Colocalization and druggability analysis supports shared genetic regions and confirmed these genes as druggable targets. Upregulation of NFKB1 and INSR are confirmed in both blood and ovarian granulosa cells of PCOS patients. The findings demonstrate that higher‐end normal hemoglobin levels are associated with increased PCOS risk, potentially through a mechanism of elevating testosterone levels involving the HIF‐1 pathway.


Honokiol‐Magnolol‐Baicalin Possesses Synergistic Anticancer Potential and Enhances the Efficacy of Anti‐PD‐1 Immunotherapy in Colorectal Cancer by Triggering GSDME‐Dependent Pyroptosis

February 2025

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5 Reads

Significant progress is made in the treatment of metastatic colorectal cancer (mCRC) patients, however, therapeutic options remain limited for patients with mCRC. In recent years, traditional Chinese medicine (TCM) has gained significant attention. Among these, Huangqin Houpo decoction has demonstrated efficacy in mCRC treatment. Despite its promise, the active ingredients and mechanisms underlying its anticancer effects remain unclear. Using integrative pharmacological approaches, six compounds are identified as the primary active ingredients in Huangqin Houpo decoction. Among them, honokiol (H), magnolol (M), and baicalin (B) are found to exhibit a synergistic anticancer effect on CRC. The HMB combination significantly outperforms mono‐ or bi‐agent treatments in reducing tumor growth. Furthermore, the anticancer efficacy of the HMB combination surpasses that of medium‐ and high‐dose Huangqin Houpo decoction and the FOLFOX regimen. Notably, HMB is comparable in efficacy to the FOLFOIRI regimen. Most importantly, HMB is shown to enhance the sensitivity of CRC cells to anti‐PD‐1 immunotherapy in vivo. Mechanistic studies reveal that the HMB combination exerts its synergistic anticancer effects and enhances anti‐PD‐1 immunotherapy by inducing GSDME‐dependent pyroptosis. Our study will hopefully provide a potential therapeutic strategy for mCRC patients in the future.


Adrenal High‐Expressional CYP27A1 Mediates Bile Acid Increase and Functional Impairment in Adult Male Offspring by Prenatal Dexamethasone Exposure

February 2025

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Prenatal dexamethasone exposure (PDE) can impact adrenal corticosteroid synthesis in adult offspring. Furthermore, the adrenal gland can autonomously synthesize bile acids, but local bile acids accumulation has cytotoxic effects. This study found that PDE increased histone 3 lysine 27 acetylation (H3K27ac) levels in the promoter region of cholesterol 27 hydroxylase (CYP27A1) and its expression, as well as total bile acids (TBA) concentrations and enhanced endoplasmic reticulum stress (ERS) and inhibit steroid synthesis in adult male offspring rat adrenal glands. However, it is reversed in females. Tracing back to the prenatal stage and in combination with cellular experiments, it is further revealed that dexamethasone can regulate glucocorticoid receptor (GR)/SET binding protein 1 (SETBP1)/CYP27A1 signal pathway, consequently cause intracellular increase of bile acids. Finally, administration of nilvadipine (CYP27A1 inhibitor) to male offspring for 4 weeks after birth resulted in the reversal of PDE‐induced adrenal morphological and functional damage. In conclusion, PDE induces fetal adrenal corticosteroid dysfunction in adult male offspring by upregulating CYP27A1 promoter region H3K27ac levels and expression in the adrenal gland through the GR/SETBP1 signaling pathway. This effect persists beyond birth, leading to bile acids local increase and subsequent enhancement of ERS, ultimately inducing cellular dysfunction in adult adrenal glands.


Fibroblast Activation Protein Acts as a Biomarker for Monitoring ECM Remodeling During Aortic Aneurysm via Ga‐FAPI‐04 PET Imaging

February 2025

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2 Reads

Traditional imaging modalities used to monitor the diameter of aortic aneurysms (AAs) often fail to follow pathological progression. Fibroblast activation protein (FAP), a key regulator of extracellular matrix (ECM) remodeling, plays a pivotal role in aortic disease. However, its expression in the aortic wall during aneurysm progression and its potential correlation with disease severity remains unexplored. Here, utilizing histology the levels of FAP are higher in the aortic wall of patients with AA compared to healthy controls. In three distinct animal models of AA, a progressive increase in FAP expression, coincides with the advancement of ECM remodeling. Notably, the levels of ⁶⁸Ga‐FAPI‐04 uptake in a rabbit model of abdominal AA (AAA) is positively correlated with aortic dilation (r = 0.84, p < 0.01), and the histological examination further confirmed that regions of high ⁶⁸Ga‐FAPI‐04 uptake exhibited both increased FAP expression and more severe pathological changes. The ⁶⁸Ga‐FAPI‐04 imaging in AA patients showed that the radiotracer specifically accumulated in the aortic walls of persistently dilated AA. These findings suggest that ⁶⁸Ga‐FAPI‐04 positron emission tomographic (PET) imaging, by visualizing FAP localization, allows for a non‐invasive approach to potentially monitor ECM remodeling during the AA progression.


QTL mapping on eRNA transcriptome profiles across normal and tumor tissues. (A) Overview of the study and data. We integrated RNA‐seq and genotype data from GTEx and TCGA to develop a reference panel for eRNA‐QTL analysis and eRNA‐TWAS modeling. We then employed eRNA‐TWAS analysis to identify cancer susceptibility eRNA‐link genes using cancer GWAS summary statistics and eRNA‐TWAS models. The biological functions of these cancer susceptibility eRNA‐link genes were further validated by data analysis and experimental approach. (B) The number of identified eRNA‐QTLs in normal and tumor tissues, along with the corresponding number of tissue samples. The numbers of eRNA‐QTLs are transformed using log2 scale. Circles represent normal tissue, and triangles represent tumor tissue. The color coding is detailed in Table S1 (Supporting Information). (C) Replication in the Independent Dataset (PsychENCODE). The PsychENCODE dataset comprises samples from the brain cortex. The QTL mapping reveals that the overlap of significant eRNAs identified in the replication cohort (PsychENCODE) with those identified in the discovery cohort (GTEx) for the same tissue type is the highest. The overlap rates for significant eRNAs identified in other tissues are comparatively lower. (D) A scatter plot of effect sizes (beta coefficients) for eRNA‐QTLs identified in both the discovery cohort (GTEx) and the replication cohort (PsychENCODE). (E) Average fraction of eRNA variations that could be explained by eRNA‐QTLs. The y‐axis represents the proportion of eRNAs across all human normal (GTEx) tissues and tumor (TCGA) types studied. (F) BRCA1 eRNA (BRCA1e), located upstream of the BRCA1 gene, contains an independent eRNA‐QTL rs2736686. (Top panel) Genetic variants are depicted as points color‐coded according to their linkage disequilibrium (LD) with the candidate variant rs6866783 (red, ≥ 0.8; orange, 0.6‐0.8; green, 0.4‐0.6; light blue, 0.2‐0.4; grey, < 0.2). LD data is sourced from the 1,000 Genomes Project (Phase 3). The bottom panel consolidates the outcomes of applying SusieR to the eRNA‐QTL summary data, where SusieR identifies a credible set denoted by red circles, encompassing the putative causal SNP rs2736686. PIP refers to posterior inclusion probability. A higher PIP indicates a greater likelihood that the SNP is a causal SNP. (G) Pairwise eRNA‐QTL sharing by magnitude among tissues. eRNA‐QTL sharing patterns were assessed pairwise across various tissues by examining the Spearman correlation between mashR effect sizes for each tissue pair. The results are displayed in matrix format, with each cell representing the correlation value for a particular tissue pair. To identify shared eRNA‐QTLs between two tissues, the top eRNA‐QTLs that attained significance (local false sign rate < 0.05) in at least one of the two tissues were selected. The proportion of shared eRNA‐QTLs was then plotted, whereby only those with effect estimates of the same sign and within a factor of 2 in size were included. The hierarchical clustering algorithm was utilized to arrange the tissues based on their similarity in eRNA‐QTL sharing patterns. The color and shape of each dot refers to the tissue recorded in the GTEx dataset (Table S1, Supporting Information). (H) Proportion of tissues sharing lead eRNA‐QTLs/eQTLs across all 49 examined tissues. (I) The original estimates and mash estimates of eRNA‐QTL effect size for CCDC32e. The lines depict the median and the shadings of the 95% confidence intervals (CIs).
Functional annotation of eRNA‐QTLs. (A) Enrichment of eRNA‐QTLs and eQTLs for different genome annotations. Each dot in the figure represents the log‐transformed odds ratio (OR), and the lines indicate the corresponding 95% confidence interval. Enrichment was assessed using all SNPs through torus based on the eRNA‐QTL results obtained using QTL tools and the eQTL results obtained from the GTEx Portal. The peak files of histone modification markers were downloaded from the Roadmap Epigenomics Project. The bar plot on the right panel illustrates the proportion of lead eQTLs and lead eRNA‐QTLs in various annotations. The error bars represent the 95% confidence interval of the mean. (B) Relationships modeled in the mediation testing. In the complete mediation model and the partial mediation model, the effect of a SNP on a phenotype Y (eQTL) is completely or partially explained by the effect of the same SNP on a phenotype M (eRNA‐QTL). The co‐local model represents cases where the SNP independently affects without mediation between M and Y. (C) Proportion of 3,144 genes with significant eQTL mediated by eRNA‐QTLs according to each model. (D) The posterior probabilities of different models for each X‐M‐Y triplets in the test. The point size represented the ‐log10 nominal P‐value of the corresponding eRNA‐QTL. (E‐F) Examples of complete mediations for eQTL for the gene TMEM176A (E) and NENF (F) via an eRNA‐QTL, respectively. The box plots show the associations of the SNP with the eRNA expresssion and the residual expression of genes after regressing the effects of the eRNA expression. The box plots show the median in the central line, the box spans the first to the third quartiles and the whiskers extend 1.5 times the IQR from the box. Slopes (beta) and nominal P‐values are shown for each association (linear regression model). (G) eRNA‐QTLs exhibited significant enrichment within the top ten TFBSs, which were associated with the highest number of tissues showing substantial enrichment across diverse tissue types. The depth of color represents the maximum‐likelihood estimated log(OR), with darker colors indicating a higher value. In the case of eRNA‐QTLs, darker colors indicate a higher enrichment in TFBSs, while lighter colors indicate lower enrichment. The enrichment results for all transcription factor binding sites (TFBSs) can be found in Table S2 (Supporting Information). (H) The genome browser displays histone modification data, CAGE data, and CTCF binding site data within the NENF eRNA (NENFe) locus. For the histone modification data and the CTCF binding site data, the values on the y‐axis correspond to signal p‐value. For the CAGE data, the values on the y‐axis correspond to the maximum normalized TPM (Transcripts Per Million). Histone modification data were downloaded from the Roadmap Epigenomics Project, CAGE data were obtained from FANTOM5, and CTCF binding site data were acquired from Cistrome. A DNA logo is presented representing the CTCF‐binding motif based on previously reported consensus CTCF binding sites.[⁶⁴] The height of each letter in the logo indicates the relative frequency of occurrence of the corresponding nucleotide at that specific position. (I) The effects of different genotypes at rs6703982 on the expression of NENF eRNA (NENFe) and its target gene NENF. The expression of NENFe was normalized using RPM, while the expression of NENF was normalized using TPM. The A allele of rs6703982 was observed to downregulate the expression of NENFe and its target gene NENF.
Contribution of eRNA‐QTLs to cancer heritability. (A) A significant contribution to the heritability of cancers by eRNA‐QTL/eQTL. Cancer types were grouped on the x‐axis. For each cancer type, the yellow and blue colors indicate the contribution of eRNA‐QTLs and eQTLs to the heritability of cancers, respectively. (B) Number of cancer risk loci co‐localizing with eRNA‐QTL and eQTL. Cancer types are grouped on the x‐axis. For each cancer type, yellow and blue colors indicate that the colocalization events could be explained by eRNA‐QTL or eQTL, respectively. (C) Proportion of cancer risk loci co‐localized with eRNA‐QTLs. The red dashed line represents the average proportion of co‐localization with eRNA‐QTLs across all cancer risk loci. (D) Independent eRNA‐QTLs depict variants with regulation on the eRNA level but not the mRNA displayed by the significant SNP‐eRNA pair rs6866783‐CLPTM1Le. Variants are represented by points colored relative to LD with the candidate variant rs6866783 (red, ≥ 0.8; orange, 0.6–0.8; green, 0.4–0.6; light blue, 0.2–0.4; dark blue, < 0.2). LD data from 1,000 Genomes (phase 3). (E) Independent eRNA‐QTLs depict variants with regulation on the eRNA level but not the mRNA level displayed by the significant SNP‐eRNA pair rs62431527‐CNR1e. (F) Distribution of PIP for all fine‐mapped GWAS variants (95% credible set in GWAS, GWAScred) that were also fine‐mapped eRNA‐QTL variants (eRNA‐QTLcred) vs. GWAScred variants only. Mann‐Whitney P‐value is shown. (G) Elevated causal probability of cancer‐credible variants associated with eRNA‐QTLs compared to non‐eRNA‐QTLs variants, highlighted by the example of rs10941679 in the breast cancer risk locus 5p12. (H) Integration of cancer‐credible GWAS variants with credible sets from colocalizing eRNA‐QTLs increased fine‐mapping resolution. The bar plot shows the proportion of independent loci identified as candidate causal variants before and after restricting for QTL variants. The credible set sizes are binned into three groups (1, 2–10, and > 10).
Landscape of cancer susceptibility eRNAs across 23 cancer types. (A) Cancer susceptibility eRNAs and genes were identified utilizing eRNA‐TWAS and eTWAS models. The color coding denotes cancer susceptibility genes as follows: blue for those identified solely by eTWAS, yellow for those identified solely by eRNA‐TWAS, and green for genes identified by both eTWAS and eRNA‐TWAS. (B) A Manhattan plot was meticulously constructed to visually represent TWAS findings for both breast and prostate cancer. eRNAs identified via eRNA‐TWAS are marked in yellow for easy visual identification, whereas genes uncovered through eTWAS are marked in blue. Each dot signifies the negative logarithm (to base 10) of the P‐value associated with each eRNA and gene identified through eRNA‐TWAS and eTWAS, respectively, plotted along the y‐axis. (C) Enrichment of cancer susceptibility eRNA‐linked genes and cancer susceptibility genes not linked to eRNAs in cancer hallmarks. We define cancer susceptibility genes identified exclusively through eTWAS as cancer susceptibility genes not linked to eRNAs. (D) Box plots displaying data from the Sanger DepMap Project Score highlighting cancer susceptibility eRNA‐link genes contributing to cell proliferation. In this context, ‘significant eRNA‐TWAS genes’ refers to eRNA‐linked genes identified as being associated with cancer through eRNA‐TWAS. Conversely, ‘Null distribution’ refers to eRNA‐linked genes unrelated to cancer. CERES scores were used to assess the essential levels of genes considering the computational effects of copy number and depletion of gene‐targeting guide RNAs. (E) Proportion of cell lines exhibiting CERES scores < −0.5 for genes associated with eRNA‐linked breast cancer susceptibility (top panel) and the top 20 cancer susceptibility eRNA‐linked genes with the lowest CERES scores evaluated in breast cell lines (bottom panel). BRCA1, a well‐established breast cancer susceptibility gene, was used as a positive control. Red dashes represent the median CERES score cutoff value of < −0.5, indicating a crucial role in cell proliferation. (F) Proportion of cell lines exhibiting CERES scores < −0.5 for genes associated with eRNA‐linked prostate cancer susceptibility (top panel) and the top 20 cancer susceptibility eRNA‐linked genes with the lowest CERES scores evaluated in prostate cell lines (bottom panel). CCND1, a well‐known oncogene in prostate cancer, was used as a positive control.
Validation of prostate cancer novel susceptibility eRNAs and integrative network analysis of cancer susceptibility eRNA‐link genes. (A) Correlation between the absolute effect sizes (z‐scores) as assessed by eRNA‐TWAS and the PPs (PPH4) derived from colocalization analysis for significant eRNAs linked to prostate cancer susceptibility. Points highlighted in red denote eRNAs with both high PIP in colocalization analysis and substantial effect sizes in eRNA‐TWAS, underscoring the eRNAs that exhibit strong evidence of association with prostate cancer susceptibility. (B) LocusZoom plot illustrating the association of prostate cancer GWAS SNPs, eRNA‐QTLs, and eQTLs at the SNAPC1 eRNA (SNAPC1e) locus. SNPs are colored by LD (r2). Genome browser visualization highlights the landscape of histone modifications and ATAC‐seq peaks at the SNAPC1e locus in the PC3 cell line. Histone modification and ATAC‐seq data were obtained from the ENCODE Project, providing a comprehensive view of the regulatory elements influencing the SNAPC1e locus. (C) Manhattan plot depicting prostate cancer GWAS signals before (grey color) and after conditioning on SNAPC1e expression (blue color). (D‐E) Repression of SNAPC1e using Zim3‐KRAB‐dCas9 led to a significant reduction in the expression of their enhancer‐transcribed eRNAs (D) and target gene (E), as determined by quantitative PCR analysis in PC3 cells. This observation was statistically validated using a two‐sided t‐test, based on data from three independent experiments, with results presented as mean ± standard error. The term ‘sgRNA’ refers to single guide RNA. (F) LocusZoom plot illustrating the association of prostate cancer GWAS SNPs, eRNA‐QTLs, and eQTLs at the CCND1 eRNA (CCND1e) locus. Genome browser visualization highlights the landscape of histone modifications and ATAC‐seq peaks at the CCND1e locus in the PC3 cell line (bottom panels). Histone modification and ATAC‐seq data were obtained from the ENCODE Project, providing a comprehensive view of the regulatory elements influencing the CCND1e locus. (G) Manhattan plot depicting cancer GWAS signals before (grey color) and after conditioning on CCND1e expression (blue color). (H‐I) Repression of CCND1e using Zim3‐KRAB‐dCas9 led to a significant reduction in the expression of their enhancer‐transcribed eRNAs (H) and target gene (I), as determined by quantitative PCR analysis in PC3 cells. (J‐K) Quantitative reverse transcription (qRT)‐PCR measuring indicated enhancer‐transcribed eRNA (J) and target gene (K) expression upon shRNA mediated CCND1e knockdown in PC3 cells. (n = 3). (L) Cell proliferation of shRNA mediated CCND1e knockdown cells was analyzed on days 1, 3, 5, and 7 (n  =  3). (M) Integrative network analysis of cancer susceptibility eRNA‐link genes. Each node in the network corresponds to a distinct eRNA‐linked gene, with the node's color indicating the specific pathway gene set to which it belongs. The edges connecting the nodes are color‐coded: blue edges signify a negative Z‐score in eRNA‐TWAS, suggesting that reduced eRNA expression is associated with an elevated cancer risk, while yellow edges denote a positive Z‐score, indicating that increased eRNA expression is linked to a higher cancer risk.
Enhancer RNA Transcriptome‐Wide Association Study Reveals a Distinctive Class of Pan‐Cancer Susceptibility eRNAs

February 2025

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1 Read

Many cancer risk variants are located within enhancer regions and lack sufficient molecular interpretation. Here, we constructed the first comprehensive atlas of enhancer RNA (eRNA)‐mediated genetic effects from 28 033 RNA sequencing samples across 11 606 individuals, identifying 21 073 eRNA quantitative trait loci (eRNA‐QTLs) significantly associated with eRNA expression. Mechanistically, eRNA‐QTLs frequently altered binding motifs of transcription factors. In addition, 28.48% of cancer risk variants are strongly colocalized with eRNA‐QTLs. A pan‐cancer eRNA‐based transcriptome‐wide association study is conducted across 23 major cancer types, identifying 626 significant cancer susceptibility eRNAs predicted to modulate cancer risk via eRNA, from which 54.90% of the eRNA target genes are overlooked by traditional gene expression studies, and most are essential for cancer cell proliferation. As proof of principle validation, the enhancer functionality of two newly identified susceptibility eRNAs, CCND1e and SNAPC1e, is confirmed through CRISPR inhibition and shRNA‐mediated knockdown, resulting in a marked decrease in the expression of their respective target genes, consequently suppressing the proliferation of prostate cancer cells. The study underscores the essential role of eRNA in unveiling new cancer susceptibility genes and establishes a strong framework for enhancing our understanding of human cancer etiology.


Advances in Electrical Materials for Bone and Cartilage Regeneration: Developments, Challenges, and Perspectives

Severe bone and cartilage defects caused by trauma are challenging to treat, often resulting in poor outcomes. An endogenous electric field (EnEF) is crucial for bone regeneration, making electrical materials a promising therapy. This review provides a comprehensive overview of the role of bioelectric signals in bone and cartilage cells, alongside recent advancements in electrical biomaterials, with particular emphasis on nanogenerators, piezoelectric materials, triboelectric scaffolds, and zwitterionic hydrogels. It further investigates the impact of these electrical biomaterials on bone and cartilage regeneration, as well as the applications of both endogenous and exogenous electrical stimulation (ES) and the mechanisms underlying ES‐induced cellular and molecular responses. Finally, the review underscores future directions for ES systems in tissue engineering, emphasizing the critical importance of integrating structural integrity, mechanical properties, and electrical signal delivery into intelligent implantable scaffolds.


HDAC2‐Mediated METTL3 Delactylation Promotes DNA Damage Repair and Chemotherapy Resistance in Triple‐Negative Breast Cancer

February 2025

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2 Reads

The current treatment of triple‐negative breast cancer (TNBC) is still primarily based on platinum‐based chemotherapy. However, TNBC cells frequently develop resistance to platinum and experience relapse after drug withdrawal. It is crucial to specifically target and eliminate cisplatin‐tolerant cells after platinum administration. Here, it is reported that upregulated N ⁶‐methyladenosine (m⁶A) modification drives the development of resistance in TNBC cells during cisplatin treatment. Mechanistically, histone deacetylase 2 (HDAC2) mediates delactylation of methyltransferase‐like 3 (METTL3), facilitating METTL3 interaction with Wilms’‐tumor‐1‐associated protein and subsequently increasing m⁶A of transcript‐associated DNA damage repair. This ultimately promotes cell survival under cisplatin. Furthermore, pharmacological inhibition of HDAC2 using Tucidinostat can enhance the sensitivity of TNBC cells to cisplatin therapy. This study not only elucidates the biological function of lactylated METTL3 in tumor cells but also highlights its negative regulatory effect on cisplatin resistance. Additionally, it underscores the nonclassical functional mechanism of Tucidinostat as a HDAC inhibitor for improving the efficacy of cisplatin against TNBC.


Mitoxantrone‐Encapsulated ZIF‐8 Enhances Chemo‐Immunotherapy via Amplified Immunogenic Cell Death

February 2025

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7 Reads

Chemo‐immunotherapy, combining systemic chemotherapeutic drugs and immune checkpoint blockers, is a promising paradigm in cancer treatment. However, challenges such as limited induction of immune responses and systemic immune toxicity have hindered its clinical applications. Here, a zeolite imidazolate framework‐8 (ZIF‐8) that encapsulates mitoxantrone (MIT), an immune cell death (ICD)‐inducing chemotherapeutic agent (MIT@ZIF‐8), is synthesized using a one‐pot aqueous‐phase process. ZIF‐8 serves as a dual‐functional nanomaterial for chemo‐immunotherapy: a carrier to enhance tumor uptake of MIT for improved chemotherapy efficacy, and a pyroptosis inducer to amplify MIT‐induced ICD for augmented anti‐tumor immune responses. As a result, in vivo administration of MIT@ZIF‐8 markedly inhibits tumor growth in both immunologically “hot” colon cancer and immunologically “cold” prostate cancer. Moreover, MIT@ZIF‐8 treatment increases the abundance of cytotoxic CD8⁺ T cells and reduces the amount of immunosuppressive regulatory T cells in tumors, thereby enhancing anti‐tumor immunity and sensitizing prostate cancer to anti‐CTLA‐4 immunotherapy. In summary, MIT@ZIF‐8 offers a highly translational approach for chemo‐immunotherapy.


Schematic of the broadband photon upconverting materials developed. The nanostructured polymer matrix consists of a solid polymer, while the liquid nanodomains contain PdOEP and PdTPBP as sensitizers and TIPS‐Ac as emitters. The photograph was taken under simultaneous continuous‐wave excitation at 532 and 635 nm.
a) Jablonski diagram and schematic of the sTTA‐UC process at play in the dual‐sensitizer upconverting materials based on palladium(II) octaethyl porphyrin (PdOEP) and palladium(II) meso‐tetraphenyl tetrabenzoporphine (PdTPBP) as sensitizers and 9,10‐bis [(triisopropylsilyl)ethynyl] anthracene (TIPS‐Ac) as emitter/annihilator. ISC: intersystem crossing; TTET: triplet‐triplet energy transfer; GS: ground state; S1 and T1: first singlet excited and first triplet excited state of the corresponding molecule. b) Normalized absorption (dashed) and photoluminescence (PL) spectra (solid) of PdOEP (200 µm), PdTPBP (100 µm), and TIPS‐Ac (20 mm) in butyl benzoate (BuBz). c) Scanning electron microscopy (SEM) image of a multi‐wavelength upconverting nanostructured polymer sample fractured with liquid nitrogen. The image reveals a continuous polymer phase featuring nanosized pores with feature sizes of <50 nm.
a,b) Upconversion (UC) emission spectra of the nanostructured upconverting polymers containing PdOEP (2 × 10⁻⁵ m, panel a) or PdTPBP (1 × 10⁻⁵ m, panel b) and the annihilator/emitter TIPS‐Ac (2 × 10⁻³ m) as a function of the absorbed excitation intensity Iexc at λex = 532 nm (a) and λex = 635 nm (b). The top right insets show the (a) PdOEP or (b) PdTPBP phosphorescence in nanostructured polymers without/with TIPS‐Ac; the intensity drop observed in the presence of the emitter demonstrates efficient triplet‐triplet energy transfer (TTET). The top left insets are digital pictures of the samples excited with (a) a green laser at 532 nm and (b) a red laser at 635 nm. c,d) Normalized UC yield ϕuc measured as a function of Iexc at (c) λex = 532 nm and (d) λex = 635 nm. The insets show the UC emission intensity decay with time as a function of Iexc at 532 and 635 nm, respectively, fitted with multiexponential decay functions.
Optical characterization of the nanostructured upconverting polymer containing PdOEP (2 × 10⁻⁵ m), PdTPBP (1 × 10⁻⁵ m), and TIPS‐Ac (2 × 10⁻³ m). a) Absorption (solid) and transmission (dashed) spectra. b–d) UC emission spectra acquired with (b) λex = 532 nm (2.2 W cm⁻², top), (b) 635 nm (2 W cm⁻², middle), and (d) simultaneous excitation at 532 nm (2.2 W cm⁻²) and 635 nm (2 W cm⁻²), respectively. The insets report the normalized UC yield ϕuc as a function of the absorbed excitation power density Iexc. Vertical lines mark the excitation intensity thresholds Ith. e,f) Decay of the UC emission intensity under modulated excitation at (e) λex = 532 nm or (f) λex = 635 nm as a function of the excitation intensity.
a) Normalized UC emission intensity of the nanostructured upconverting polymer containing PdOEP (2 × 10⁻⁵ m), PdTPBP (1 × 10⁻⁵ m), and TIPS‐Ac (2 × 10⁻³ m) under dual‐wavelength excitation at 532 and 635 nm (blue circles) and the sum of the UC emission intensities recorded under single‐wavelength excitation at 532 and 635 nm (orange squares) as a function of the absorbed power density Iexc. The solid lines are the theoretically calculated emission intensity curves considering the probability of triplet exaction generation in each nanodroplet, the TTA yield as a function of the Iexc, sensitizer concentration, and absorption cross‐section. Inset: relative UC yield under double‐wavelength excitation conditions versus single‐wavelength excitation conditions, calculated as the ratio between the two values as a function of Iexc. b) Digital pictures of the upconverting nanostructured polymers under white lamp excitation using a 600 nm (top) long pass (LP) or 520 nm (bottom) LP optical filter at the maximum power employed. The blue upconverted emission is visible under the naked eye. c) UC emission intensity under CW broadband excitation with a white lamp using a 520 nm LP (blue circles) or 600 nm LP (orange squares) optical filter as a function of the incident intensity. Solid lines are fitted with quadratic and linear dependency on the excitation intensity, respectively. The inset reports the relative UC emission intensity (black circles).
Confinement‐Enhanced Multi‐Wavelength Photon Upconversion Based on Triplet–Triplet Annihilation in Nanostructured Glassy Polymers

February 2025

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4 Reads

Sensitized triplet–triplet annihilation photon upconversion (sTTA‐UC) allows blue‐shifting non‐coherent low‐intensity light and is potentially useful in solar‐powered devices, bioimaging, 3D printing, and other applications. For technologically viable solar energy harvesting systems, solid materials that capture a large fraction of the solar spectrum and efficiently upconvert the absorbed energy must be developed. Here, it is shown that broadband‐to‐blue UC is possible in air‐tolerant, easy‐to‐access, nanostructured polymers comprising a rigid hydrophilic matrix and liquid nanodroplets with dimensions on the order of tens of nanometers. The droplets contain 9,10‐bis[(triisopropylsilyl)ethynyl] anthracene (TIPS‐Ac) as emitter/annihilator and palladium(II) octaethyl porphyrin (PdOEP) and palladium(II) meso‐tetraphenyl tetrabenzoporphine (PdTPBP) as sensitizers. The confinement of the three dyes in the liquid domains renders the various bimolecular energy transfer processes that are pivotal for the TIPS‐Ac's triplet sensitization highly efficient, and the simultaneous use of multiple light harvesters with triplet energy levels resonant with the emitter/annihilator increases the absorption bandwidth to ca. 150 nm. The UC process at low power densities is most efficient when both sensitizers are simultaneously excited, thanks to their confinement in the nanodroplets, which leads to an increase in the triplet density, and therefore TTA rate and yield, optimizing the use of the harvested energy.


Multifunctional Boron‐based 2D Nanoplatforms Ameliorate Severe Respiratory Inflammation by Targeting Multiple Inflammatory Mediators

February 2025

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4 Reads

Effective management of serious respiratory diseases, such as asthma and recalcitrant rhinitis, remains a global challenge. Here, it is shown that induced sputum supernatants (ISS) from patients with asthma contain higher levels of cell‐free DNA (cfDNA) compared to that of healthy volunteers. Although cfDNA scavenging strategies have been developed for inflammation modulation in previous studies, this fall short in clinical settings due to the excessive neutrophil extracellular trap (NET) formation, reactive oxygen and nitrogen species (RONS) and bacterial infections in injured airway tissues. Based on this, a multifunctional boron‐based 2D nanoplatform B‐PM is designed by coating boron nanosheets (B‐NS) with polyamidoamine generation 1 (PG1) dendrimer, which can simultaneously target cfDNA, NETs, RONS, and bacteria. The effects of B‐PM in promoting mucosal repair, reducing airway inflammation, and mucus production have been demonstrated in model mice, and the therapeutic effect is superior to dexamethasone. Furthermore, flow cytometry with clustering analysis and transcriptome analysis with RNA‐sequencing are adopted to comprehensively evaluate the in vivo anti‐inflammation therapeutic effects. These findings emphasize the significance of a multi‐targeting strategy to modulate dysregulated inflammation and highlight multifunctional boron‐based 2D nanoplatforms for the amelioration of respiratory inflammatory diseases.


Burn‐Induced Gut Microbiota Dysbiosis Aggravates Skeletal Muscle Atrophy by Tryptophan‐Kynurenine Mediated AHR Pathway Activation

February 2025

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9 Reads

The hypermetabolic response associated with burns is characterized by skeletal muscle atrophy and an increased incidence of disability and death. Significant remodeling of the gut microbiota occurs after severe burn trauma. However, the specific mechanisms by which gut microbiota contribute to burn‐induced muscle atrophy remain unexplored. The results showed that the disruption of the gut microbiota exacerbated skeletal muscle atrophy. Fecal metabolite analysis revealed perturbations, primarily within the tryptophan (Trp) metabolic pathway. Animal models further demonstrated that gut microbiota disorder enhanced the expression of indoleamine 2,3‐dioxygenase 1 (IDO‐1) in the colon, ultimately resulting in Trp depletion and increased kynurenine (Kyn) levels in the serum and skeletal muscle. Excessive colonic Kyn is released into circulation, transported into skeletal muscle cells, and binds to the aryl hydrocarbon receptor (AHR), consequently triggering AHR nuclear translocation and initiating the transcription of skeletal muscle atrophy‐related genes. Notably, serum samples from patients with burns exhibited Trp depletion, and Trp supplementation alleviated skeletal muscle atrophy in rats with burns. This study, for the first time, demonstrates that gut microbiota dysbiosis upregulates colonic IDO‐1, promotes Trp‐Kyn metabolism, and exacerbates burn‐induced skeletal muscle atrophy, suggesting that Trp supplementation may be a potential therapeutic strategy.


4D Assembly of Time‐dependent Lanthanide Supramolecular Multicolor Phosphorescence for Encryption and Visual Sensing

February 2025

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6 Reads

Supramolecular dynamic room temperature phosphorescence (RTP) is the focus of current research because of its wide application in biological imaging and information anti‐counterfeiting. Herein, a time‐dependent supramolecular lanthanide phosphorescent 4D assembly material with multicolor luminescence including white, which is composed of 4‐(4‐bromophenyl)‐pyridine salt derivative (G), inorganic clay (LP)/Eu complex and pyridine dicarboxylic acid (DPA) is reported. Compared with the self‐assembled nanoparticle G, the lamellar assembly G/LP showed the double emission of fluorescence at 380 nm and phosphorescence at 516 nm over time. Within 60 min, the phosphorescence lifetime and the quantum yield increases from none to 7.4 ms and 27.53% respectively, achieving the time‐dependent phosphorescence emission, due to the limitation of progressive stacking of LP electrostatically driven “domino effect.” Furthermore, the 4D assembly of DPA and G/LP/Eu leads to a time‐resolved multicolor emission from colorless to purple to white, which is successfully applied to information multi‐level logic anti‐counterfeiting and efficiently antibiotic selective sensor.


Human Pituitary Organoids: Transcriptional Landscape Deciphered by scRNA‐Seq and Stereo‐Seq, with Insights into SOX3's Role in Pituitary Development

February 2025

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3 Reads

The 3D human pituitary organoid represents a promising laboratory model for investigating human pituitary diseases. Nonetheless, this technology is still in its nascent stage, with uncertainties regarding the cellular composition, intercellular interactions, and spatial distribution of the human pituitary organoids. To address these gaps, the culture conditions are systematically adjusted and the efficiency of induced pluripotent stem cells’ (iPSCs’) differentiation into pituitary organoids is successfully improved, achieving results comparable to or exceeding those of previous studies. Additionally, single‐cell RNA‐sequencing (scRNA‐seq) and stereomics sequencing (Stereo‐seq) are performed on the pituitary organoids for the first time, and unveil the diverse cell clusters, intricate intercellular interactions, and spatial information within the organoids. Furthermore, the SOX3 gene interference impedes the iPSCs’ differentiation into pituitary organoids, thereby highlighting the potential of pituitary organoids as an ideal experimental model. Altogether, the research provides an optimized protocol for the human pituitary organoid culture and a valuable transcriptomic dataset for future explorations, laying the foundation for subsequent research in the field of pituitary organoids or pituitary diseases.


Multi‐Omics Analysis Reveals Impacts of LincRNA Deletion on Yeast Protein Synthesis

February 2025

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11 Reads

Non‐coding RNAs (ncRNAs) are widespread across various genomic regions and play a crucial role in modulating gene expression and cellular functions, thereby increasing biological complexity. However, the relationship between ncRNAs and the production of heterologous recombinant proteins (HRPs) remains elusive. Here, a yeast library is constructed by deleting long intergenic ncRNAs (lincRNAs), and 21 lincRNAs that affect α‐amylase secretion are identified. Targeted deletions of SUT067, SUT433, and CUT782 are found to be particularly effective. Transcriptomic and metabolomic analyses of the top three strains indicate improvements in energy metabolism and cytoplasmic translation, which enhances ATP supply and protein synthesis. Moreover, a yeast strain, derived from the SUT433 deletion, that can secrete ≈4.1 g L⁻¹ of α‐amylase in fed‐batch cultivation through the modification of multiple targets, is engineered. This study highlights the significant potential of lincRNAs in modulating cellular metabolism, providing deep insights and strategies for the development of more efficient protein‐producing cell factories.


Tailoring Piezoelectric Nanogenerators and Microdevices for Cellular Excitation: Impact of Size and Morphology

February 2025

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4 Reads

The use of piezoelectric devices as wireless electrical stimulators is an emerging research topic. In this study, piezoelectric microdevices, consisting of ZnO nanosheets (NSs) functioning as piezoelectric nanogenerators (NGs) grown on top of silicon microparticles, to electrically stimulate cell are designed. The morphology of the ZnO NSs is optimized by tuning the thickness of the aluminum nitride (AlN) catalyst layer and adjusting the growth duration. ZnO NSs grown on thinner AlN layers (≤ 200 nm) and subjected to 9 h of hydrothermal growth exhibit the most suitable characteristics for cell stimulation, balancing crystal size, and electric field generation. The generation of a local electric field capable of exciting osteoblast cells is inferred from finite element simulations and intracellular calcium influx measurements. The internalization rate of silicon microdevices of varying sizes (3 × 3, 6 × 10, 12 × 18 µm²) by osteosarcoma (Saos‐2) and primary human osteoblast (hOB) cells.is assessed The results show that smaller devices have higher internalization rates, particularly in tumoral Saos‐2 cells, while primary cells exhibit minimal internalization (< 10%) across all particle sizes. This study presents an optimized piezoelectric microdevice, based on a scalable and customizable fabrication process, for minimally invasive bioelectronic applications, offering accurate electrical cell stimulation while minimizing unwanted internalization.


Representative images of an entire process of condensation frosting. A) A Teflon‐coated hydrophobic substrate was cooled from 20 °C down to − 20 °C through two steps; during these steps, dropwise condensation and dropwise freezing with halo pattern evolution were imaged using a top‐view camera (Movie S1, Supporting Information). B) Zoomed view showing a freezing drop that occurs spontaneously at 978 s. C) Zoomed view showing a freezing drop that occurs under the trigger of a contacting ice bridge at 990 s.
Dynamic evolution of the condensate halo around a freezing supercooled drop. A) Selected snapshots of the optical images (top) and simultaneously acquired thermographic images (bottom) showing the halo pattern evolution and the heat transfer landscape during the freezing of supercooled water drop at Tf ≈ 14.5 °C and χ  =  60% on the Teflon‐coated hydrophobic surface in Figure 1 (Movie S3, Supporting Information). B) Temporal evolution of the pixel intensity I and surface temperature TS at the center of the freezing drop and two nearby locations denoted in (A). C) Cartoons depicting the vapor transport in different stages of drop freezing and halo pattern evolution.
Characteristic size and duration of condensate halo formation during recalescent freezing. A) Equivalent halo radial extension ΔR∼hm$\Delta {{\tilde{R}}_{hm}}$ versus drop radius Rd. The inset is the log‐log plot of the same data. B) Selected optical images showing the growth of dendritic ice crystals in a 3.3 µL supercooled water drop at Tf = − 13.2 °C and χ  =  55% (Movie S5, Supporting Information). C) Characteristic velocity of ice propagation VP versus the supercooling temperature ΔT. D) Plot of the halo radial extension ΔR∼hm$\Delta {{\tilde{R}}_{hm}}$ measured in the experiment versus the halo radial extension ΔRhm predicted by the theoretical model.
Characterization of the halo growth and fading processes. A) Normalized volume Ω/Ω0 versus time t/(trc + tg + tfade) of five as‐formed condensate microdrops at different locations in Figure 2A during the halo growth and fading processes. B) Spatiotemporal evolution of the temperature distribution along the horizontal central line on the freezing drop in Figure 2A. The solid line denotes the farthest location of halo growth region r=Rd+ΔR∼hg$r = {{R}_d} + \Delta {{\tilde{R}}_{hg}}$ with ΔR∼hg$\Delta {{\tilde{R}}_{hg}}$ the equivalent size of the halo growth. C) Growth time of the condensate halos (tg) versus the radii (Rd) of the freezing drops that the halos surround under different experimental conditions. d) Fading times of condensate halos tfade versus the radii (Rd) of the freezing drops that the halos surround under different experimental conditions.
Frost propagation during condensation frosting. A) Sequence of images showing the ice‐bridge‐assisted frost propagation among the supercooled condensate drops at Tf = − 14.4°C and χ  =  65% (Movie S6, Supporting Information). B) Time interval Δt of two sequential freezing events without developing the ice bridge versus the normalized distance between these two drops R/(Rd+ΔR∼hm)$R/( {{{R}_d} + \Delta {{{\tilde{R}}}_{hm}}} )$ in condensation frosting occurring at Tf = − 20 °C and χ  =  60%. The dashed lines indicate the average times.
Condensate Halos in Condensation Frosting

February 2025

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32 Reads

The freezing of water drops on cold solid surfaces is ubiquitous in nature, and generally causes serious technological, engineering, and economic issues in industrial applications. Despite longstanding research efforts, existing knowledge on dropwise freezing is still limited, as this phase‐change phenomenon is always accompanied by complex heat and mass transfer processes. Herein, drop‐freezing phenomena in condensation frosting are investigated under standard laboratory conditions of humidity and pressure, highlighting their distinctions from those under some limiting conditions. Condensate halos consisting of massive tiny droplets are observed to form, grow, and eventually fade in a well‐defined region around freezing supercooled drops on sufficiently hydrophobic surfaces with low thermal conductivities. The detailed halo evolution is very different from that reported previously in ultradry and low ambient pressure environments, and it shows no identifiable effect on the long‐term frost propagation. By combining optical and thermal imaging techniques, this study scrutinizes the halo pattern evolution involving multiphase transitions on timescales from milliseconds to seconds, assesses the halo characteristics at each stage, and elucidates the underlying mechanisms. The work expands the fundamental understanding of complex dropwise freezing dynamics, and relevant findings can provide important guidance for developing anti‐icing/frosting strategies.


Single‐molecule assay for α‐syn fibril measurements. A) A schematic of the biotinylated α‐syn fibril assembly. α‐syn monomers are first assembled to form the fibril, then the biotinylated α‐syn monomers are added. Finally, the fibril is labeled with Alexa Fluor 488 NHS Ester. B) Detection of streptavidin‐Gold nanoparticles binding to the biotinylated α‐syn fibril by TEM. The red arrows indicate nanoparticles. Scale bars represent 100 nm. C) A diagram showing the single‐molecule experimental procedure (left). A single fibril tether is formed in channels 1–3 containing streptavidin‐coated microspheres, biotinylated α‐syn fibril, and buffer, respectively. The fibril was suspended by two streptavidin‐coated beads manipulated by two optical traps. Meanwhile, confocal lasers repeatedly scanned along the fibril. The fluorescence image and corresponding length of the α‐syn fibrils were held at 0, 2, 5, 10, and 50 pN, respectively (right). Scale bars, 1 µm. The force and extension are plotted as a function of time. D) Representative force–extension curves of a single α‐syn fibril during stretching (blue) and relaxation (red) within a force range of 0–50 pN. WLC fitting (gray) yields a contour length of 5.02 µm, a persistence length of 1.39 µm, and a elastic modulus of 9.3 nN. E–G. The contour length (E), persistence length (F), and the elastic modulus (G) distributions (n = 27). Error bars represent means ± SD.
Denaturing and disruption of α‐syn fibrils by axial force. A) Stretching of α‐syn fibrils by OT at high forces with simultaneous fluorescence imaging. The fibrils are stretched until the tether breaks (left, red arrow) or reaches the maximal force of the setup (right, black arrow). The dashed boxes represent the denaturation of the fibrils during stretching. Insets: zoom in on the boxed regions. The red dots correspond to the fibril's length in the fluorescent image. Scale bars,1 µm. B) A histogram displaying the contour length change of α‐syn fibrils during all identifiable deformation events (n = 195). C) A histogram illustrating the deformation force distribution for α‐syn fibrils during stretching (n = 195). D) Representative length trajectories of α‐syn fibrils under a constant force of 100 (left) or 300 pN (right). Scale bars, 10 nm. E) The distributions of the stepping lengths detected under 100 pN (n = 46) and 300 pN (n = 57). F) The percentages of disrupted and intact fibrils in stretching experiments throughout the force range from 100 to 500 pN (n = 79).
EGCG inserts into the N‐terminal polar groove and destabilizes α‐syn fibril. A) The chemical structure of EGCG and the binding affinities (KD) of EGCG to α‐syn PFFs are calculated based on the SPR association and dissociation curves. B) The topology diagram of α‐synEGCG fibril structure. The orange triangle symbol illustrates the binding sites of EGCG. C) Cartoon representations reveal the top views of three layers of the α‐synEGCG fibril. α‐Syn is colored in gray. EGCG is presented as orange sticks. The first and the last amino acid residues of the fibril core are labeled. D) The detailed top view illustrates the interactions between EGCG and the binding‐pocket residues of the α‐syn fibril (top), while the enlarged side view highlights the interactions between EGCG and the backbone amide of the α‐syn fibril (bottom). Key interaction residues and distances (Å) are labeled. E) Representative fluorescence images of α‐syn fibrils and their corresponding stretching forces as a function of length after incubation in EGCG for 2 h. Scale bars, 1 µm. The dashed boxes represent the denaturation of the fibrils during stretching. Insets: zoomed‐in of the boxed region. The red dots correspond to the fibril's length in the fluorescent image. F) The percentages of the broken and intact fibrils in the stretching experiments throughout the force range from 100 to 500 pN after the EGCG treatment (n = 99). G–H. The distribution of the length change (G) and the deformation force distribution (H) under EGCG treatment (light blue) (n = 176) compared with the ones without EGCG (pink) (n = 195).
CCA binding dramatically enhances the resistance of α‐syn fibrils to disruptive forces. A) The chemical structure of CCA and topology diagram of α‐synCCA fibril structure were reported previously.[²⁹] The green rhombus symbol illustrates the binding sites of CCA. B) Cartoon representations display top views of three layers of the α‐synCCA fibril. α‐Syn is colored in gray. CCA is highlighted in green. The first and the last amino acid residues of the fibril core are labeled. C) The binding affinities (KD) of CCA to α‐syn PFFs were calculated based on the SPR association and dissociation curves. D) Representative fluorescence images of an α‐syn fibril and the corresponding force–extension curve after incubation in CCA for 2 h. Scale bars, 1 µm. The dashed boxes represent the denaturation of the fibrils during stretching. Insets: Zoomed‐in of the boxed region. The red dots correspond to the fibril's length in the fluorescent image. E) The percentages of the broken and intact fibrils in the stretching experiments throughout the force range from 100 to 500 pN after CCA treatment (n = 27). F‐G. The distributions of the counter length change (F) and the deformation force (G) under CCA treatment (light blue) (n = 52) compared with the ones without CCA (pink) (n = 195).
Schematic representation of the mechanical properties and structures of α‐syn fibrils and their alteration by chemical compounds. A) Side view of α‐synEGCG and α‐synCCA fibril structures showing unique interactions: EGCG inserts into the polar groove at the N‐terminus, causing fibril disassembly, whereas CCA encircles the fibril core by attaching to four different interfaces (three shown). Here, α‐syn is in gray, EGCG in orange, and CCA in green. B) Topology diagrams of α‐syn, α‐synEGCG, and α‐synCCA fibril structures, highlighting the binding sites of each molecule: CCA binding sites are marked with green rhombuses, and EGCG binding sites with orange triangles. C) Correlation of the stability of α‐syn, α‐synEGCG, α‐synCCA fibril and axial force. Increase axial force is linked to decreased fibril stability. The upper section depicts α‐syn subunit's local deformations within the fibril under axial force, leading to eventual disintegration. The middle part shows EGCG's role in weakening the α‐syn fibril, as indicated by a lower breaking force. Conversely, the lower section illustrates CCA binding contributes to the stabilization of the α‐syn fibril, enhancing its resistance to greater forces.
Single‐Molecule Insight Into α‐Synuclein Fibril Structure and Mechanics Modulated by Chemical Compounds

Syn fibrils, a key pathological hallmark of Parkinson's disease, is closely associated with disease initiation and progression. Several small molecules are found to bind or dissolve α‐syn fibrils, offering potential therapeutic applications. Here, an innovative optical tweezers‐based, fluorescence‐combined approach is developed to probe the mechanical characteristics of α‐syn fibrils at the single‐molecule level. When subjected to axial stretching, local deformation within α‐syn fibrils appeared at forces above 50 pN. These structural alternations occurred stepwise and are irreversible, suggesting unfolding of individual α‐syn molecules or subdomains. Additionally, α‐syn fibrils exhibits high heterogeneity in lateral disruption, with rupture force ranging from 50 to 500 pN. The impact of different compounds on the structure and mechanical features of α‐syn fibrils is further examined. Notably, epigallocatechin gallate (EGCG) generally attenuates the rupture force of fibrils by wedging into the N‐terminal polar groove and induces fibril dissociation. Conversely, copper chlorophyllin A (CCA) attaches to four different sites wrapping around the fibril core, reinforcing the stability of the fibril against rupture forces. The work offers an effective method for characterizing single‐fibril properties and bridges compound‐induced structural alternations with mechanical response. These insights are valuable for understanding amyloid fibril mechanics and their regulation by small molecules.


Electret‐Inspired Charge‐Injected Hydrogel for Scar‐Free Healing of Bacterially Infected Burns Through Bioelectrical Stimulation and Immune Modulation

February 2025

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5 Reads

In this study, an electret‐inspired, charge‐injected hydrogel called QOSP hydrogel (QCS/OD/SDI/PANI/PS/Plasma) that promotes scar‐free healing of bacteria‐infected burns through bioelectrical stimulation and immune modulation, is presented. The hydrogel, composed of quaternized chitosan (QCS), oxidized dextran (OD), sulfadiazine (SDI), polystyrene (PS), and polyaniline nanowires (PANI), forms a conductive network capable of storing and releasing electric charges, emulating an electret‐like mechanism. This structure delivers bioelectrical signals continuously, enhancing wound healing by regulating immune responses and minimizing fibrosis. In a mouse model of second‐degree burns infected with Staphylococcus aureus (SA) and Pseudomonas aeruginosa (PA), the hydrogel accelerates wound healing by 32% and reduces bacterial load by 60%, significantly inhibited scar formation by 40% compared to controls. QOSP hydrogel modulates the Th1/Th2 immune balance toward a Th1‐dominant antifibrotic state through quaternized chitosan, thereby reducing collagen deposition by 35%. Electro‐dielectric characterization reveals a dielectric constant of 6.2, a 34% improvement in conductivity (3.33 × 10⁻⁵ S/m) and a 30 °C increase in thermal stability. Proteomic analysis highlights a 50% down‐regulation of pro‐inflammatory and pro‐fibrotic pathways, suggesting a controlled immune response conducive to scar‐free healing. This study underscores the potential of bioelectrically active hydrogels as a novel approach for treating infected wounds prone to scarring.


Brain‐Wide Neuroregenerative Gene Therapy Improves Cognition in a Mouse Model of Alzheimer's Disease

February 2025

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19 Reads

Alzheimer's disease (AD) is a progressive and irreversible brain disorder with extensive neuronal loss in the neocortex and hippocampus. Current therapeutic interventions focus on the early stage of AD but lack effective treatment for the late stage of AD, largely due to the inability to replenish the lost neurons and repair the broken neural circuits. In this study, by using engineered adeno‐associated virus vectors that efficiently cross the blood–brain‐barrier in the mouse brain, a brain‐wide neuroregenerative gene therapy is developed to directly convert endogenous astrocytes into functional neurons in a mouse model of AD. It is found that ≈500 000 new neurons are regenerated and widely distributed in the cerebral cortex and hippocampus. Importantly, it is demonstrated that the converted neurons can integrate into pre‐existing neural networks and improve various cognitive performances in AD mice. Chemogenetic inhibition of the converted neurons abolishes memory enhancement in AD mice, suggesting a pivotal role for the newly converted neurons in cognitive restoration. Together, brain‐wide neuroregenerative gene therapy may provide a viable strategy for the treatment of AD and other brain disorders associated with massive neuronal loss.


DNA Origami‐Cyanine Nanocomplex for Precision Imaging of KRAS‐Mutant Pancreatic Cancer Cells

February 2025

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6 Reads

Selective delivery of imaging agents to pancreatic cancer cells (PCCs) within the highly desmoplastic tumors of pancreatic ductal adenocarcinoma (PDAC) represents a significant advancement. This approach allows for precise labeling of PCCs while excluding cancer‐associated fibroblasts (CAFs), thereby enhancing both research and diagnostic capabilities. Additionally, it holds the potential to target and eliminate PCCs precisely without harming the surrounding stromal cells in the PDAC tumor microenvironment (TME). In this study, DNA origami‐cyanine (Do‐Cy) nanocomplexes are synthesized to image KRAS‐mutant PCCs selectively in the PDAC TME. These Do‐Cy nanocomplexes are hypothesized to be internalized preferentially to KRAS‐mutant PCCs over CAFs via elevated macropinocytosis. Several designs of Do‐Cy nanocomplexes are synthesized and characterized their cellular uptake using both engineered in vitro and xenograft pancreatic cancer models. The results are further discussed for the implication of precision delivery of therapeutic and imaging agents to KRAS‐mutant cancers.


Molecular structure and absorption spectrum of Cu4I4(py)4. a) Simplified structure of a cubane‐type metal halide cluster. Here, the symbols M, X, and L refer to metal, halide, and ligand, respectively. b) Structure of Cu4I4(py)4, with color codes: I (purple), Cu (brown), N (blue), C (gray), and H (white). c) Molar extinction coefficient and calculated oscillator strength of Cu4I4(py)4 in acetonitrile solution. The measured molar extinction coefficient is represented by the black curve, while the oscillator strengths for singlet‐singlet transitions, calculated using TD‐DFT, are depicted as red bars. d) Schematic diagram illustrating the initial state reached through 267 nm excitation and the excited states targeted for observation in this experiment.
Time‐resolved solvent‐contribution‐free difference X‐ray scattering curves of Cu4I4(py)4 in acetonitrile. a) Experimental difference scattering curves (black) are plotted together with simulated theoretical fits (red). The theoretical fits were obtained using linear combination fitting (LCF) of ΔS⟂(q, t) at each time delay. b) Difference radial distribution function, r²ΔS⟂(r, t), obtained by Fourier sine transformation of qΔS⟂(q, t) in (a).
Comprehensive analysis of decay‐associated difference scattering curves (DADSs) for Cu4I4(py)4. a) Experimental and theoretical solvent‐contribution‐free DADSs in q‐space. The experimental DADS1(q) and DADS2(q) are shown in black and blue, respectively. The theoretical DADSs, obtained through structure refinement and depicted in red, are overlaid on the experimental DADSs for comparison. The y‐axis is expressed in electron unit per molecule. b) r²ΔS(r)s obtained through Fourier sine transforms of qDADS1(q) and qDADS2(q), illustrating structural changes in r‐space. Each panel displays interatomic distances within the Cu4I4 framework as vertical bars, distinguishing positive (above) from negative (below) contributions to the signals. The bars are color‐coded by atom type and their heights are scaled according to atomic numbers to accurately reflect the contribution of the corresponding atomic pair to the scattering signal. The corresponding structures for these distances are labeled on the left side of the figure. For DADS1(r), only the changes associated with the major transition were represented using bars. c) Time‐dependent concentration profiles of molecules occupying each assigned excited state (e.g., ³(M/X)LCT and ³CC states). Solid lines represent biexponential fits using time constants derived from kinetic analysis of RSVs obtained through SVD analysis of ΔS⟂(q, t).
Refined molecular structures for GS, ³(M/X)LCT and ³CC states. a) Schematic diagram displaying the molecular structure of Cu4I4(py)4 (left), with pyridine ligands omitted for clarity (middle). On the right, a further simplified schematic diagram abstracts the representation for streamlined visualization. In this representation, atoms closer to the viewer are rendered larger and clearer, while those further away are smaller and more translucent. The images at the top and bottom each present the same structure from different perspectives for comprehensive visualization. b−d) Projections of the Cu4, I4 cores, and Cu−I3 framework in GS (left), ³(M/X)LCT (middle), and ³CC (right) states, exaggerated to highlight distinctive atomic pair distances. Here, the atomic pair distances obtained from DFT calculations are shown in parentheses, while those obtained from TRXL experiments are presented without parentheses. b,c) Structures of Cu4 (b) and I4 (c) cores for GS, ³(M/X)LCT, and ³CC states, viewed from the same direction as the top illustration in (a). For a clear description of the distortion in the Cu4 core in the ³CC state, the four Cu atoms in (b) are designated as follows: the atom in the lower left corner is labeled Cu1, with the subsequent atoms named clockwise as Cu2, Cu3, and Cu4, respectively. Likewise, the four I atoms in (c) are labeled in the same way as I1, I2, I3, and I4. In (b), the distances within 2.98 Å are depicted with solid lines, reflecting a reported Cu–Cu binding energy of −0.175 eV within that range.[³⁵] Distances beyond this threshold are indicated with dotted lines. d) Structures of the Cu−I3 framework for GS, ³(M/X)LCT, and ³CC states, viewed from the same direction as the lower illustration in (a). The illustration depicts the Cu1 atom and its adjacent three I atoms, namely I1, I2, and I4.
Proposed dynamics of Cu4I4(py)4. The 267 nm pump pulse initially excites Cu4I4(py)4 into the ¹(M/X)LCT and ¹CC states, based on the result of the TD‐DFT calculations. Then, 45% of the excited molecules rapidly decay to the ground state, releasing excess energy as heat. Of the remaining 55%, 28% populate the ³(M/X)LCT state within 100 ps, while 27% populate the ³CC state within the same period. 74% of the³(M/X)LCT state undergoes a transition to the ³CC state with a bimolecular rate constant of 1.23 × 10¹¹ m⁻¹∙s⁻¹, while the remaining 26% of the state relaxes to the GS. Subsequently, molecules in the ³CC state recover to the ground state with a time constant of 202 ns.
Excited‐State Structural Dynamics of the Cubane‐Type Metal Cluster [Cu4I4(py)4] Explored by Time‐Resolved X‐Ray Liquidography

February 2025

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6 Reads

Cubane‐type metal clusters respond uniquely to stimuli like light and electric potential, resulting in behaviors such as crystal‐to‐crystal phase transitions. While structural adaptability is known to be linked to these responses, direct experimental evidence for the associated structural changes has been missing. This study addresses this gap by examining the structural dynamics of the copper(I) iodide cubane (Cu4I4(py)4, py = pyridine) upon photoexcitation using time‐resolved X‐ray liquidography. The results reveal: 1) 100 picoseconds (ps) after excitation, two distinct excited states—the cluster‐centered triplet (³CC) state and the (metal+halide)‐to‐ligand charge transfer triplet (³(M/X)LCT) state—are present; 2) the ³(M/X)LCT state decays with an apparent time constant of 1.21 ns, primarily transitioning to the ³CC state, with a small fraction undergoing decay to the ground state (GS); and 3) the ³CC state eventually returns to the GS. The molecular structures, provided for these states serve as benchmarks for theoretical studies. Importantly, the ³CC structure exhibits significant distortion in the Cu4I4 core and reduced symmetry, findings that are unanticipated by previous models. This comprehensive investigation deepens the understanding of the structural transformations occurring upon photoexcitation, with a potential impact on future applications of these compounds as versatile components in photosensitive metal–organic frameworks.


Advanced Microarrays as Heterogeneous Force‐Remodeling Coordinator to Orchestrate Nuclear Configuration and Force‐Sensing Mechanotransduction in Stem Cells

February 2025

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5 Reads

Integrin and focal adhesion can regulate cytoskeleton distribution to govern actin‐related force remodeling and play an important role in nuclear configuration and force‐sensing mechanotransduction of stem cells. However, further exploration of the interaction between actinin complex and myosin, kinetics, and molecular mechanism of cytoskeleton structures to nucleate within the engineered stem cells is vague. An extensive comprehension of cell morphogenesis, force remodeling, and nuclear force‐sensing mechanotransduction is essential to reveal the basic physical principles of cytoskeleton polymerization and force‐related signaling delivery. Advanced microarrays are designed to determine heterogeneous cell morphology and cell adhesion behaviors in stem cells. The heterogeneity from the engineered microarrays is transferred into nuclei to regulate nuclear configuration and force‐sensing mechanotransduction by the evaluation of Lamins, YAP, and BrdU expression. Tuning the activation of adhesion proteins and cytoskeleton nucleators to adjust heterogeneous cell mechanics may be the underlying mechanism to change nuclear force‐sensing configuration in response to its physiological mechanotransduction in microarrayed stem cells.


Micropatterned viscoelastic PAAm hydrogels to capture the pathophysiology of the breast tissue microenvironment. a) Phase diagram showing the percentages of bisacrylamide (Bis) cross‐linker and acrylamide (AAm) monomer used to tune the viscoelastic properties of PAAm hydrogels in this study. Viscoelastic hydrogels have a high AAm to Bis ratio compared to elastic hydrogels. Soft E = soft elastic, Soft V = soft viscoelastic, Stiff E = stiff elastic, Stiff V = stiff viscoelastic. The dashed gray lines connect hydrogels of similar Young's modulus (E) (≈ 0.3 and ≈ 3 kPa) but different viscoelastic properties. b) Schematic representation of the strategy used to obtain elastic and viscoelastic hydrogels with the same initial E. The amount of Bis is decreased while concurrently increasing the amount of AAm to favor physical entanglements. Red dots represent chemical cross‐links, idealized by an elastic spring. Chain entanglements are idealized by a viscous dashpot. c) E of hydrogels used in this work. Each point represents a single indentation, with at least 121 indentations (121≤ n ≤ 173) from three independently prepared samples. ns p = 0.3427 (Soft group) and p = 0.1453 (Stiff group), two‐way ANOVA with Bonferroni's multiple comparisons test. Data is shown as mean ± SD. d) Average stress relaxation profiles of hydrogels used in this work. Curves were obtained by averaging at least 121 individual curves (121≤ n ≤ 151) coming from at least two independent experiments. Data is shown as mean ± SD. e) tan(δ) obtained from bulk rheology oscillatory sweeps of hydrogels used in this work (strain 1 %, Experimental Section). Data has been averaged over three independent samples. Data is shown as mean ± SD. f) Correlation between the relaxation half‐time (τ12)${{\tau }_{\frac{1}{2}}})$ obtained from nanoindentation experiments and the tan(δ) at 0.1 Hz obtained from bulk rheology experiments for the same data shown in d and e (R² = 0.9797, mean ± SD). Note that elastic hydrogels dissipated less than 50% of the original stress, so the relaxation half time was taken from the time point resulting in a stress value as close as possible to 50%. g) Representative images of homogeneous fibronectin (FN) coating on elastic and viscoelastic PAAm hydrogels. h) Representative images of micropatterned FN coating on elastic and viscoelastic PAAm hydrogels.
Viscoelasticity modulates cell spreading, FAs, and YAP nuclear import in opposite directions on soft and stiff substrates. a) Representative Actin/DNA images of typical MCF‐10A cell morphologies observed on elastic and viscoelastic PAAm matrices. b) Quantification of MCF‐10A cell spreading area on Soft E (n = 74 cells), Soft V (n = 51 cells), Stiff E (n = 100 cells), and Stiff V (n = 49 cells) hydrogels from at least two independent experiments. *p = 0.0148, ****p < 0.0001, two‐way ANOVA with Bonferroni's multiple comparisons test. c) Quantification of MCF‐10A cell circularity on Soft E (n = 74 cells), Soft V (n = 51 cells), Stiff E (n = 100 cells), and Stiff V (n = 49 cells) hydrogels from at least two independent experiments. **p = 0.0061, ****p < 0.0001, two‐way ANOVA with Bonferroni's multiple comparisons test. d) Representative FAs (Vinculin) images of MCF‐10A cells cultured on elastic and viscoelastic PAAm hydrogels. e) Distribution of individual FA area of MCF‐10A cells cultured on Soft E (n = 685 adhesions), Soft V (n = 1301 adhesions), Stiff E (n = 3055 adhesions) and Stiff V (n = 580 adhesions) hydrogels. Data was obtained from at least two independent experiments. f) Quantification of the number of FAs per cell (#FAs/cell) of MCF‐10A cells cultured on Soft E (n = 33 cells), Soft V (n = 41 cells), Stiff E (n = 58 cells) and Stiff V (n = 27 cells) hydrogels from at least two independent experiments. ns p = 0.1413, ****p < 0.0001, two‐way ANOVA with Bonferroni's multiple comparisons test. g) Plotting the #FAs/cell versus the cell spreading area reveals a linear relationship between the two variables (R² = 0.9758, mean ± SEM). h) Representative YAP images of MCF‐10A cells cultured on elastic and viscoelastic PAAm hydrogels. The cell's outline is highlighted by a dashed yellow line. Note the absence of almost any cytoplasmic YAP on Stiff E matrices compared to the other conditions. i) Quantification of the Nuclear to Cytoplasmic (Nuc/Cyto) YAP ratio of MCF‐10A cells cultured on Soft E (n = 157 cells), Soft V (n = 128 cells), Stiff E (n = 320 cells), Stiff V (n = 113 cells) hydrogels from at least two independent experiments. ns p = 0.5161, ****p < 0.0001, two‐way ANOVA with Bonferroni's multiple comparisons test. j) Nuc/Cyto YAP ratio increases linearly with cell spreading area on viscoelastic PAAm hydrogels (R² = 0.9319, mean ± SEM).
Viscoelasticity enhances migration speed and persistence on soft substrates, while impeding them on stiff substrates via actin retrograde flow and adhesions regulation. a) Representative x–y trajectories of MCF‐10A cells on elastic and viscoelastic PAAm hydrogels over 5 h (n = 48 trajectories for Soft E, n = 43 trajectories for Soft V, n = 49 trajectories for Stiff E, n = 34 trajectories for Stiff V). b) MCF‐10A cell migration speed on soft elastic (Soft E, n = 56 cells) and viscoelastic (Soft V, n = 61 cells) matrices obtained from three independent experiments. ***p = 0.0007, unpaired two‐tailed t‐test. c) Average mean square displacement (MSD) versus lag time for MCF‐10A cells on soft elastic (Soft E) and viscoelastic (Soft V) matrices. The diffusion exponent, α, is shown in the graph. Data is shown as mean ± SEM (n = 48 cells for Soft E, n = 43 cells for Soft V) from three independent experiments. d) MCF‐10A cell migration speed on stiff elastic (Stiff E, n = 55 cells) and viscoelastic (Stiff V, n = 35 cells) matrices obtained from at least two independent experiments. ***p = 0.0007, unpaired two‐tailed t‐test. ****p < 0.0001, unpaired two‐tailed t‐test. e) Average MSD versus lag time for cells on stiff elastic (Stiff E) and viscoelastic (Stiff V) matrices. The diffusion exponent, α, is shown in the graph. Data is shown as mean ± SEM (n = 49 cells for Stiff E, n = 34 cells for Stiff V) from at least two independent experiments. f) Diffusion exponent, α, plotted against average cell migration speed (R² = 0.8414, mean ± SEM) for the same number of cells and independent experiments as in b–d (for cell migration speed) and c–e (for MSD). g) Representative images of MCF‐10A tagged with live Spy555‐FastAct on elastic and viscoelastic PAAm hydrogels. Yellow line shows location where kymographs were computed, on average. Insets show representative kymographs for each condition, with yellow line indicating the slope from which the actin retrograde flow speed is computed. The spatial scale bar in the inset is 2 µm, whereas the temporal scale bar is 2 min. h) Quantification of actin retrograde flow speed for MCF‐10A cells cultured on Soft E (n = 19 kymographs), Soft V (n = 48 kymographs), Stiff E (n = 44 kymographs) and Stiff V (n = 52 kymographs) hydrogels from at least two independent experiments. ****p < 0.0001, two‐way ANOVA with Bonferroni's multiple comparisons test. i) Diffusion exponent, α, versus actin retrograde flow speed (left, R² = 0.9711); and migration speed versus actin retrograde flow speed (right, R² = 0.8322). Data is shown as mean ± SEM for the same number of cells/kymographs as in c‐e and h, respectively. j) Schematic summary of experimental findings on how matrix viscoelasticity modulates MCF‐10A cell migration speed and persistence via actin retrograde flow speed and adhesion dynamics. Green arrows inside the cells represent actin retrograde flow, whereas colorful arrows outside the cell depict schematic migration trajectories. Dashed blue box shows condition where optimal migration occurs (Soft V).
Spatial confinement blunts viscoelasticity‐mediated effects on soft matrices and reduces them on stiff matrices. a) Representative Actin/DNA images of confined MCF‐10A cells on 5 µm FN lines on elastic and viscoelastic PAAm hydrogels. FN lines are schematically represented as dashed lines for clarity. b) Quantification of MCF‐10A cell aspect ratio on 2D and 1D soft elastic (Soft E) and viscoelastic (Soft V) matrices (n = 74 cells for Soft E 2D, n = 40 cells for Soft E 1D, n = 50 cells for Soft V 2D, n = 24 cells for Soft V 1D) from at least two independent experiments. ****p < 0.0001, two‐way ANOVA with Tukey's multiple comparisons test. c) Quantification of MCF‐10A cell aspect ratio on 2D and 1D stiff elastic (Stiff E) and viscoelastic (Stiff V) matrices (n = 100 cells for Stiff E 2D, n = 59 cells for Stiff E 1D, n = 48 cells for Stiff V 2D, n = 29 cells for Stiff V 1D) from at least two independent experiments. ****p < 0.0001, ***p = 0.0006, two‐way ANOVA with Tukey's multiple comparisons test. d) Quantification of MCF‐10A cell spreading area on 2D and 1D soft elastic (Soft E) and viscoelastic (Soft V) matrices (n = 74 cells for Soft E 2D, n = 40 cells for Soft E 1D, n = 50 cells for Soft V 2D, n = 24 cells for Soft V 1D) from at least two independent experiments. ns p > 0.05, two‐way ANOVA with Tukey's multiple comparisons test. e) Quantification of MCF‐10A cell spreading area on 2D and 1D stiff elastic (Stiff E) and viscoelastic (Stiff V) matrices (n = 100 cells for Stiff E 2D, n = 59 cells for Stiff E 1D, n = 48 cells for Stiff V 2D, n = 29 cells for Stiff V 1D) from at least two independent experiments. ****p < 0.0001, *p = 0.0473, ns p = 0.0588, two‐way ANOVA with Tukey's multiple comparisons test. f) Representative FAs (Vinculin) images of confined MCF‐10A cells on 5 µm FN lines on elastic and viscoelastic PAAm hydrogels. FN lines are schematically represented as dashed lines for clarity. Scale bar in the inset is 5 µm. g) Percentage of cells forming front and rear vinculin adhesions pooled from two independent experiments (n = 13 cells for Soft E, n = 17 cells for Soft V, n = 31 cells for Stiff E, n = 16 cells for Stiff V). h) Representative temporal color‐coded time lapses of MCF‐10A migrating on 5 µm FN lines. The direction of confined migration is shown by a dashed line. i) Quantification of MCF‐10A cell migration speed on 2D and 1D soft elastic (Soft E) and viscoelastic (Soft V) matrices (n = 56 cells for Soft E 2D, n = 38 cells for Soft E 1D, n = 61 cells for Soft V 2D, n = 14 cells for Soft V 1D) from at least two independent experiments. ****p < 0.0001, ns p = 0.9911, two‐way ANOVA with Tukey's multiple comparisons test. j) Quantification of MCF‐10A cell migration speed on 2D and 1D stiff elastic (Stiff E) and viscoelastic (Stiff V) matrices (n = 55 cells for Stiff E 2D, n = 61 cells for Stiff E 1D, n = 35 cells for Stiff V 2D, n = 26 cells for Stiff V 1D) from at least two independent experiments. ****p < 0.0001, two‐way ANOVA with Tukey's multiple comparisons test. k) Schematic representation of how confinement modulates viscoelasticity sensing compared to 2D matrices. Colorful arrows show how clutch sensitivity (as indicated by the measured outputs) to ECM viscoelastic properties (here schematically represented by a standard linear solid lumped model) changes in each condition. Overall, confinement blunts ECM viscoelasticity mechanotransduction effects on soft ECMs and reduces them on stiff ECMs. Dashed blue boxes show conditions where optical migration occurs (Soft V 2D and Stiff E 1D). For all plots in this figure the central bar represents the mean, whereas the error bars represent the SD.
Epithelial Cell Mechanoresponse to Matrix Viscoelasticity and Confinement Within Micropatterned Viscoelastic Hydrogels

February 2025

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15 Reads

Extracellular matrix (ECM) viscoelasticity has emerged as a potent regulator of physiological and pathological processes, including cancer progression. Spatial confinement within the ECM is also known to influence cell behavior in these contexts. However, the interplay between matrix viscoelasticity and spatial confinement in driving epithelial cell mechanotransduction is not well understood, as it relies on experiments employing purely elastic hydrogels. This work presents an innovative approach to fabricate and micropattern viscoelastic polyacrylamide hydrogels with independently tuneable Young's modulus and stress relaxation, specifically designed to mimic the mechanical properties observed during breast tumor progression, transitioning from a soft dissipative tissue to a stiff elastic one. Using this platform, this work demonstrates that matrix viscoelasticity differentially modulates breast epithelial cell spreading, adhesion, YAP nuclear import and cell migration, depending on the initial stiffness of the matrix. Furthermore, by imposing spatial confinement through micropatterning, this work demonstrates that confinement alters cellular responses to viscoelasticity, including cell spreading, mechanotransduction and migration. These findings establish ECM viscoelasticity as a key regulator of epithelial cell mechanoresponse and highlight the critical role of spatial confinement in soft, dissipative ECMs, which was a previously unexplored aspect.


Laser‐Induced Graphene from Commercial Inks and Dyes

February 2025

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6 Reads

Laser‐induced graphene (LIG) has been so far obtained from polymer precursors and proposed for numerous applications, including various types of sensors and energy storage solutions. This study examines a radically different class of new precursors for LIG, distinct from polymers: inks and dyes. The identification of specific organic dyes present in commercial markers demonstrates that the aromatic structure, in conjunction with high thermal stability (residual weight > 20% at 800°C), are key factors for laser‐induced pyrolysis. Eosin Y is identified as an excellent LIG precursor, comparable with well‐known polyimide. The unique properties of dyes allow for dispersion in various media, such as acrylic binder. A dye concentration of 0.75 mol L⁻¹ in acrylic binder results in a conductivity of 34 ± 20 S cm⁻¹ for LIG. The composition and microstructure of LIG from dyes are thoroughly characterized, revealing peculiar features. A versatile “Paint & Scribe” methodology is introduced, enabling to integrate LIG tracks onto any wettable surface, and in particular onto printed and flexible electronics. A process for obtaining freestanding and transferrable LIG is demonstrated by dissolving acrylic paint in acetone and floating LIG in water. This advancement offers novel avenues for diverse applications that necessitate a transfer process of LIG.