Browse Articles

EpCAM silencing suppresses aggressive phenotypes and induces partial redifferentiation in anaplastic thyroid cancer cells

Anaplastic thyroid cancer (ATC) is a rare but highly aggressive malignancy with a dismal prognosis. Although recent advances in targeted therapies have modestly improved survival, the molecular mechanisms driving ATC progression remain incompletely elucidated. Epithelial cell adhesion molecule (EpCAM), a multifunctional cell-surface protein, is implicated in proliferation, migration, and stemness in various cancers. However, its role in thyroid cancer progression remains unclear. In this study, we investigated the function of EpCAM in thyroid cancer cell lines of varying differentiation status. EpCAM expression was significantly elevated in ATC cell lines compared with differentiated thyroid cancer (DTC) lines. EpCAM knockdown by siRNA suppressed proliferation, adhesion, motility, and invasion in ATC cells, but had minimal effects on DTC cells. Morphological analyses revealed that EpCAM silencing induced differentiation features, including follicle-like structure formation and increased expression of thyroid differentiation markers such as thyroglobulin and PAX8 in ATC cells. Furthermore, EpCAM inhibition decreased mesenchymal marker expression, reduced filopodia formation, and suppressed extravasation of cancer cells into the lung in an in vivo mouse model. Mechanistically, EpCAM knockdown attenuated epithelial–mesenchymal transition (EMT)-related pathways but did not affect major proliferation signaling cascades in ATC cells. These findings suggest that EpCAM promotes dedifferentiation and metastatic potential in ATC through EMT modulation. Our results provide new insights into the role of EpCAM in thyroid cancer biology and highlight its potential as a therapeutic target in ATC. Further studies are warranted to elucidate the mechanisms linking EpCAM to anaplastic transformation and to explore the therapeutic efficacy of EpCAM-targeting strategies in aggressive thyroid cancers.

DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning

Identification of prognostic genes associated with tolerogenic dendritic cells in gastric cancer based on transcriptomic data

Background Tolerogenic dendritic cells have a pivotal function in treating autoimmune illnesses, atopic diseases, and neoplasms. The precise mechanism by which Tolerogenic dendritic cells function in gastric cancer remains incompletely understood. Therefore, this research explored potential genes with prognostic value related to Tolerogenic dendritic cells in gastric cancer, to identify novel therapeutic targets that could provide valuable insights for the clinical treatment of gastric cancer. Results Five prognostic genes (CXCL1, INHBA, ASCL2, RNASE1, and GPX3) were finally obtained to construct the risk model. Immune infiltration analysis revealed that GPX3 exhibited significant positive associations with various immune cell populations, particularly regulatory T cells. While ASCL2 was weakly associated with almost all immune cells. These results suggested that there was a complex correlation between prognostic genes and immune cells. The analysis of drug sensitivity demonstrated higher IC50 values for compounds such as BIBW2992 in high-risk group relative to low-risk group. A reverse pattern was observed for GSK269962A and similar drugs, which showed significantly higher IC50 values in low-risk group than high-risk group. Conclusion The present study revealed five prognostic genes and constructed a predictive model, which provided a theoretical basis for the correlation linking Tolerogenic dendritic cells to gastric cancer, and established potential therapeutic strategies in managing gastric cancer. Single-cell analysis revealed that INHBA, ASCL2, and CD36 exhibited marked differential expression in dendritic cells.

Integrated electrochemical porous solid electrolyte reactor and packed bed reactor for efficient synthesis of nylon-6 precursor

Cognitive load and teachers’ innovative behavior in AI-enhanced English language instruction: A mediation analysis of technological adaptability

This investigation examines the complex interrelationships between teachers’ cognitive load, technological adaptability, and innovative teaching behavior in AI-enhanced educational environments. Through the integration of Cognitive Load Theory, Technology Acceptance Model, and Innovation Diffusion Theory, we develop a sophisticated theoretical framework for understanding how cognitive demands influence teaching innovation through adaptive mechanisms. Employing exploratory structural equation modeling with WLSMV estimation (N = 600), we analyze data collected through rigorously validated instruments measuring AI-assisted Teaching Cognitive Load (ATCL), technological adaptability, and innovative teaching behavior. Results reveal a significant negative relationship between cognitive load and innovative teaching behavior ( β  = −.134, p < .001), mediated by technological adaptability (indirect effect β  = −.171, p < .001). The measurement model demonstrates exceptional psychometric properties (α = .91−.93; AVE = .64−.68) and establishes measurement invariance across teacher subgroups (ΔCFI ≤ .001). These findings advance theoretical understanding of cognitive-adaptive mechanisms in technology-enhanced teaching while providing empirically validated pathways for enhancing pedagogical innovation. The study contributes methodologically through the development of the ATCL scale and analytically through sophisticated mediation analysis techniques. Implications extend to professional development strategies, institutional policy formulation, and the theoretical conceptualization of cognitive load in AI-enhanced educational environments.

Bio-inspired asymmetric Zn-N2O2 single-atom catalysts via natural skeleton for efficient N-alkylation of nitroarenes with alcohols

Abstract Although significant developments are made in non-noble metal catalysts for N-alkylation of nitroarenes with alcohols via borrowing hydrogen strategy, obtaining catalysts with superior activity, reusability and broad substrate scope under mild reaction conditions remains challenging. Single-atom catalysts (SACs) hold unique coordination/electron structures, to be the potential candidates for this reaction. In this study, we firstly and creatively fabricate bio-inspired Zn SACs with asymmetric Zn-N 2 O 2 sites by utilizing the natural skeleton of biomass chitosan (denoted as Zn/CS), and achieve the first instance of heterogeneous Zn SACs in borrowing hydrogen reaction between nitroarenes and alcohols. The results reveal that the asymmetric Zn-N 2 O 2 sites induced by natural skeleton (like ligands) and nanoporous structure of Zn/CS significantly promote the N-alkylation efficiency of nitroarenes with alcohols. Notably, the Zn/CS exhibits the highest turnover frequency (TOF) among the reported heterogeneous catalysts, as well as wide substrate scope (56 examples) and excellent reusability. Furthermore, the catalytic pathway/mechanism is investigated by combing theoretical calculations, which reveals that the asymmetric Zn-N 2 O 2 sites with electron-deficient character can facilitate the formation of Zn-H and Zn-O bonds between Zn/CS and Ph-CH 2 O − , thus easily generating the transition state Ph-CH 2 O* and driving the whole reaction.

Experimental and coupling analysis of municipal solid waste (MSW) shear strength under multiple influencing parameters

The production of municipal solid waste (MSW) in China is growing rapidly, and landfill is the primary treatment method. The shear strength of MSW is crucial to the stability and safety of landfills. However, there is limiting research available on how different environmental and compositional factors interactively influence MSW shear strength since some existing studies typically examine these variables in isolation. Through integrated testing and statistical modeling this looks into how environmental factors (moisture content 30–230%, organic matter content 15–75%, temperature 10–65°C and degradation time 15–190days) work together to change the shear strength of MSW. One hundred MSW samples were prepared according to field waste composition from Xi’an and were subjected to direct shear tests over a 190-day degradation period. The test results show that cohesion ( c ) decreased over time, to a minimum value of 0.17 kPa at 190 days, while angle of internal friction ( φ ) increased to 42.39° at 190 days. Both c and φ showed an initial increase followed by a decrease with temperature changes. The effect of organic matter on c decline was negligible, though φ increased during the early stage of degradation. Higher moisture content (80–130%) and organic matter (45–60%) along with temperatures between 10–35°C resulted in high φ values. High c values were achieved with 130% moisture content 15–30% organic matter and temperatures between 10 and 35 °C. Under the controlled conditions of the study the Mohr–Coulomb equation was modified to incorporate degradation effects, and multiple linear regression together with automatic linear modeling (ALM) were used to predict c and φ , yielding R 2 values of 0.622 and 0.468 respectively, demonstrating the models’ predictive accuracy. In practical engineering, the study’s outcomes will help to understand the effect of different parameters on the shear strength of MSW which can lead to achieve the landfill slope stability and the use of MSW as backfill material in building foundations under the controlled conditions of the study.

An H3K14ub-H3K9me3 feedback circuit governs heterochromatin spreading and inheritance in fission yeast

Abstract Heterochromatin is a bistable chromatin state essential for genome stability and gene regulation. Its spreading and inheritance have long been explained by a “read-write” cycle in which histone methyltransferases bind pre-existing tri-methylation of histone H3 lysine 9 (H3K9me3) and propagate this mark to neighboring nucleosomes. However, the weak affinity and limited catalytic stimulation provided by H3K9me3 alone challenge this model. The fission yeast H3K9 methyltransferase Clr4 functions within the CLRC complex, which also catalyzes histone H3 lysine 14 ubiquitination (H3K14ub). Here we show that H3K14ub and H3K9me3 form a feedback loop: H3K14ub strongly stimulates Clr4 activity on nucleosomes, while both H3K14ub and H3K9me3 stabilize CLRC binding to chromatin. Even subtle perturbations that disrupt this feedback, such as mutating one of the three H3 genes to prevent ubiquitination or methylation, or impairing Clr3-mediated H3K14 deacetylation, compromises heterochromatin spreading and inheritance. Conversely, counteracting activities, such as H3K14 acetylation by Mst2 and H3K9 demethylation by Epe1, synergistically constrains heterochromatin expansion. Thus, rather than relying solely on the weak H3K9me3 “read-write” cycle, heterochromatin is maintained through an integrated circuit of ubiquitination, deacetylation, and methylation, which governs spreading and inheritance.

Investigating the correlation between candidate teachers’ acceptance of generative artificial intelligence and artificial intelligence literacy across various disciplines

This study examines Generative Artificial Intelligence (GenAI) acceptance and Artificial Intelligence Literacy (AIL) levels among prospective teachers, using variables for comparative analysis and identifying influencing factors. The research uses an explanatory sequential mixed methods approach. Quantitative data were obtained from 723 prospective teachers and qualitative data from 48 prospective teachers. Data collection included an Information Form, GenAI Acceptance Scale, and AIL Scale for quantitative data, with interview forms for qualitative data. Parametric tests, independent samples t-test, ANOVA, and Pearson correlation analyzed quantitative data, while factors influencing GenAI acceptance and AIL were identified through themes using MAXQDA. Acceptance levels showed no significant differences by gender or daily internet use; however, differences emerged regarding department, grade level, AI tools used, and self-perceived proficiency. AIL showed significant differences in gender, department, grade, tool usage, and proficiency level, with higher scores among those trained in artificial intelligence. Qualitative data clarify the quantitative findings. Factors affecting GenAI acceptance include daily use, problem-solving, learning applications, mentor usage, assistance from others, proficiency, productivity, discipline-specific skills, and task efficiency. Factors influencing AIL include understanding AI importance, ethical considerations, AI support in daily life, explaining AI, understanding deep learning and machine learning relationships, big data knowledge, AI decision-making processes, knowledge of AI tools, interpretation of AI technologies, critical evaluation, data privacy importance, machine learning knowledge, and evaluation of AI applications in their discipline.

TCL1A mediates DNA methylation defects in recurrent hydatidiform mole with NLRP7 pathogenic variants

Segmented filamentous bacteria are worldwide human gut commensals

Abstract Segmented filamentous bacteria (SFB) describe morphologically similar gut commensals found in mammals, fish and birds. In mice, SFB intimately colonizes the ileal epithelium at the time of weaning and elicits a strong pleiotropic immune activation that fosters colonization resistance while augmenting disease severity in various disease models. SFB is therefore critical in both health and disease but information regarding SFB in humans remains limited. Here, we first identify and characterize a human SFB species with SFB-specific morphology, including the hook-like tip structure that mediates attachment, and unique genome features, including a starch and glycogen degradation module. This species, which we name Anisomitus miae and establish as the nomenclature type for the SFB genus, is within a SFB lineage common across Africa. We then bioinformatically identify, based on the 16S rRNA gene V3-V4 variable region sequence, four major, and two minor, human SFB lineages in forty-four countries distributed across all six inhabited continents. We provide evidence towards the co-colonization potential of the SFB lineages and their colonization dynamics, including a potent but short-lived colonization peak in children between one to five years of age. This study establishes the presence of multiple SFB species in the human population and SFB as a minor but wide-spread group of commensals in humans.

TCF21 promotes epithelial-to-mesenchymal transition and cytoskeleton reorganization in uterine development and endometriosis

Effect of stepwise nutritional intervention based on GLIM standard on children with leukemia undergoing transplantation: a retrospective study

The eco-evolutionary assembly of complex communities with multiple interaction types

Abstract Identifying the mechanisms that generate structure in complex ecological communities is fundamental for understanding their assembly. Yet a comprehensive picture of how ecology and evolution combine to generate these patterns remains limited. We use an eco-evolutionary model of community assembly that incorporates interaction-driven population dynamics and evolutionary processes, including speciation and inheritance of interactions, to unveil the mechanisms generating and maintaining biodiversity in complex species interaction networks. Importantly, our model unpicks the effects of selection of interaction types from those of inheritance by comparing evolutionary assembly with invasion-based assembly under different combinations of interaction types. We find that a cost-benefit balance in accumulating interactions separates communities into two distinct types. Weakly beneficial interactions produce sparse, competition-dominated networks, whereas strongly beneficial interactions generate highly mutualistic, more connected communities. Mutualism, driven by both selection and inheritance, facilitates the emergence of large communities with increased complexity. Comparing model results with empirical patterns from microbial communities, we identify potential drivers of ecosystem assembly and characteristic interaction structures. Our results provide a classification system of complex ecosystems based on their composition of ecological interactions, thus generating testable hypotheses on the conditions under which different community types (mutualistic vs. competitive) might emerge.

From Equity to Efficiency — Navigating Changes to the AHEAD Model

Mechanical degradation induced by the alkaline water effects of weakly cemented fine-grained sandstone

Tailoring the glassy phase in polymer semiconductors tunes their optical properties

Shunting for Idiopathic Normal-Pressure Hydrocephalus

Do pastoral and agro-pastoral perceptions align with observed climate extremes? Evidence from the Koh-e-Suleiman Range, Pakistan

Abstract This study examined the relationship between climate perceptions and observed trends among pastoralist and agro-pastoralist communities in the Koh-e-Suleiman Range, Pakistan. Household perception data were collected from 198 respondents and analyzed alongside climatic records across two time scales (1980–2022; 2013–2022). Data from the Pakistan Meteorological Department were used to compute 29 extreme climate indices, and trends were assessed using the Mann–Kendall and Sen’s slope tests. Perceptions of seven climate variables were compared with observed trends through accuracy tests, bias classification, regression, and machine-learning models. Perceptions aligned closely with observed trends for floods, rain intensity, temperature, and warm spells ( $$\ge$$ 80% accuracy), and moderately for cold spells (71.7%) and rainfall (60.6%). Perceptions of drought spells were predominantly inaccurate, with 75.3% of respondents overestimating their occurrence. Regression analyses identified education, age, and livestock ownership as associated with perception accuracy. Classification and Regression Tree (CART) machine learning analysis, in contrast, revealed non-linear effects: income, age, and livestock herd size shaped drought spell perception, while livestock numbers and age influenced rainfall perception. These findings highlight the value of integrating observed climate extremes with local perceptions to better understand perception observation alignment and inform context- sensitive climate risk communication in data-scarce pastoral regions.

Post-catalysis structures of mitochondrial complex I with ubiquinol-10 bound in the active site

Abstract Respiratory complex I is a multi-subunit energy-transducing membrane enzyme essential for mitochondrial and cellular energy metabolism. It couples NADH oxidation and ubiquinone-10 (Q 10 ) reduction to the concomitant pumping of four protons to generate the proton-motive force that powers oxidative phosphorylation. Despite recent advances in structural knowledge of complex I, many mechanistic aspects including the reactive binding poses of Q 10 , how Q 10 reduction initiates the proton transfer cascade, and how protons move through the membrane domain, remain unclear. Here, we use electron cryomicroscopy to determine structures of mammalian complex I, reconstituted into phospholipid nanodiscs containing exogenous Q 10 and reduced by NADH, to global resolutions of 2.0 to 2.6 Å. Two conformations of a reduced Q 10 H 2 molecule are observed, fully inserted into the Q-binding channel in the turnover-relevant closed state. By comparing the quinone species bound in oxidised and reduced complex I structures, paired with molecular dynamics simulations to investigate the charge states of key surrounding residues, we propose a series of substrate binding poses that Q 10 transits through for reduction. Our highly hydrated structures exhibit near-continuous proton-transfer connections along the length of the membrane domain, enabling comparisons between them to assist in identifying the proton-transfer control points that are essential to catalysis.