Browse Articles

Interplay of ECM organization, ROCK signaling, and cell polarity drives mesothelium formation and lung growth

STAT1 and m6A-mediated IL15RA upregulation promotes metastasis via ZEB1/NF-κΒ axis in ccRCC

A universal entropic pulling force caused by binding

Liver dysfunction triggers early Alzheimer’s pathology in an adult rat model of chronic liver disease

Abstract Emerging evidence links liver dysfunction to Alzheimer’s disease (AD), though few studies have investigated this connection. While cirrhosis is associated with cognitive impairment, its underlying mechanisms remain poorly understood. This study aimed to assess the presence of central nervous system (CNS) markers of Alzheimer’s disease in a rat model of chronic liver disease. Standard histochemical techniques were employed, including Congo red staining for amyloid-β (Aβ) and Gallyas silver staining for tau pathology. Immunohistochemistry was used to evaluate changes in aquaporin (Aqp1, Aqp4, Aqp9) expression and astrocytic glial fibrillary acidic protein (GFAP) levels. Furthermore, blood concentrations of neurodegeneration markers, neurofilament light chain (NfL), Aβ, phosphorylated tau (p-tau), total tau (t-tau), GFAP, and myelin oligodendrocyte glycoprotein (MOG), along with bile acids, were quantified and compared between BDL and SHAM control groups to investigate potential systemic correlates of CNS pathology. CNS analysis revealed the presence of intracellular amyloid-β (iAβ) and abnormal tau aggregates (pre-tangle/tangle stages), consistent with early AD pathology. Additionally, alterations in aquaporin water channels and astrocytic GFAP expression were observed. Peripheral blood analysis showed significant changes in neurodegeneration markers (NfL, Aβ, p-tau, t-tau, GFAP, MOG) and bile acid profiles, reinforcing the systemic nature of neuroinflammatory processes in liver disease. Our findings emphasize liver failure as a significant factor in cognitive decline and increased AD risk. The study advocates for incorporating liver function screening into the diagnostic workup of patients with dementia.

From food waste to sustainable aviation fuel: cobalt molybdenum catalysis of pretreated hydrothermal liquefaction biocrude

Behavioral TOPSIS technique based on probabilistic picture hesitant fuzzy probability splitting algorithm and novel interactive operations

Pseudouridylation of 7SK by PUS7 regulates Pol II transcription elongation

Abstract Pseudouridine (Ψ) is a widespread RNA modification in various RNA species, including rRNA, tRNA, snRNA and mRNA. Ψ plays a crucial role in RNA metabolism, where it regulates pre-mRNA splicing and affects protein translation. Whether and how Ψ may regulate transcription have not been adequately studied. Here, we report that pseudouridine synthase 7 (PUS7) can mediate pseudouridylation of 7SK small nuclear RNA (snRNA), a regulator of RNA polymerase II (Pol II) promoter-proximal pausing. PUS7 loss leads to hypo-pseudouridylation of 7SK, which promotes dissociation of the positive transcription elongation factor b (P-TEFb) complex from 7SK. The release of P-TEFb from 7SK increases serine 2 phosphorylation (Ser2P) in the RNA Pol II C-terminal domain and enhances transcription elongation. In colorectal cancer (CRC) cells, the Ψ level of 7SK can be modulated by PUS7, or by site-specifically targeted pseudouridylation through dCas13b-guided system. Hypo-pseudouridylation on 7SK upon PUS7 depletion promotes KLF6/DDIT3-mediated cell apoptosis and sensitizes CRC cells to 5-FU.

Research on the impact of explosive martial arts training on emotion regulation and attention based on questionnaire data

Daily briefing: The bowhead whale’s secret to living to 200

Reprogramming encapsulins into modular carbon-fixing nanocompartments

Continuity of carbapenem resistance determinants in carioca river and Rodrigo de Freitas Lagoon, Rio de Janeiro, Brazil, after decade

Characterizing social behavior relevant for infectious disease transmission in four low- and middle-income countries, 2021-2023

Elucidating the underlying mechanism of mechanical stress-induced impact on mRNA-LNP structure

Pt nanoparticles breathe and reversibly detach from Al2O3 in hydrogen

Regulating white blood cell activity through the novel universal receptive system

Functional specialisation of multisensory temporal integration in the mouse superior colliculus

Abstract Our perception of the world depends on the brain’s capacity to integrate information from multiple senses, with timing differences serving as crucial cues for binding or segregating cross-modal signals. The superior colliculus (SC) is a central hub for such integration, yet the contributions of its distinct regions remain poorly understood. Here we show, from recordings of over 5000 neurons in awake mice, that multisensory neurons reliably encode audiovisual delays through nonlinear integration of auditory and visual inputs. This nonlinearity enhances the precision of delay representation, with posterior-medial SC populations representing the peripheral sensory field showing superior temporal discriminability. Connectivity analyses reveal stronger coupling in the medial SC and function-specific recurrent networks, with multisensory neurons receiving about half of their local input from other multisensory neurons. Together, these results demonstrate how nonlinear integration, regional specialisation, and network architecture combine to support robust sensory binding and accurate encoding of temporal multisensory information.

Hybrid signal decomposition and deep learning framework for vehicle–vehicle crash forecasting

Electroinitiated interfacial healing for external pressure-free solid-state sodium metal batteries

Abstract Solid-state sodium metal batteries with inorganic electrolytes have long been heralded as candidates for post-lithium-ion batteries. However, challenges including interfacial instability and air sensitivity continue to impede their path to commercialization. Here, we propose an interfacial healing strategy for solid-state sodium metal batteries by utilizing an electroinitiated accelerated polymerization process facilitated by charged microdroplets to increase the polymerization rate by 21.4 times. We show that the charge-driven electrowetting enables efficient coating layers at interfaces, which impart prolonged air stability and preferentially fill voids and cracks, further constructing stable interfaces with improved compatibility and preventing dendrite-induced crack propagation. A higher critical current density of 6.8 mA cm −2 is achieved, and assembled cells exhibit prolonged cycling life at 1.0 C over 1000 cycles. In particular, the electroinitiated accelerated polymerization-assisted interfacial healing strategy enables Ah-level pouch cells to undergo stable long-term cycling without any clamping force, demonstrating the capabilities of solid-state batteries in practical applications.

The influence of diabetic retinopathy on the risk of dementia: A nationwide cohort study

Bayesian continual learning and forgetting in neural networks

Abstract Biological synapses effortlessly balance memory retention and flexibility, yet artificial neural networks still struggle with the extremes of catastrophic forgetting and catastrophic remembering. Here, we introduce Metaplasticity from Synaptic Uncertainty (MESU), a Bayesian update rule that scales each parameter’s learning by its uncertainty, enabling a principled combination of learning and forgetting without explicit task boundaries. MESU also provides epistemic uncertainty estimates for robust out-of-distribution detection; the main computational cost is weight sampling to compute predictive statistics. Across image-classification benchmarks, MESU mitigates forgetting while maintaining plasticity. On 200 sequential Permuted-MNIST tasks, it surpasses established synaptic-consolidation methods in final accuracy, ability to learn late tasks, and out-of-distribution data detection. In task-incremental CIFAR-100, MESU consistently outperforms conventional training techniques due to its boundary-free streaming formulation. Theoretically, MESU connects metaplasticity, Bayesian inference, and Hessian-based regularization. Together, these results provide a biologically inspired route to robust, perpetual learning.