Early risk stratification of severe acute liver injury (SLI) that may progress to acute liver failure (ALF), is vital for timely intervention, but no universal prognostic assessment tool covers both conditions. This study aimed to develop a simplified prognostic model for early risk assessment in SLI/ALF patients.

Epigenetic regulation is central to the development, progression, and treatment resistance of glioblastoma (GBM), a highly aggressive grade IV brain tumor whose management is hindered by the blood-brain barrier (BBB) and pronounced tumor heterogeneity. Nanomedicine—through the rational design of nanocarriers—offers a transformative strategy to overcome these challenges, enabling targeted delivery of epigenetic modulators while improving drug solubility, stability, and controlled release.  The convergence of nanotechnology and epigenetic therapy is redefining GBM treatment, providing a promising platform to penetrate the BBB, overcome drug resistance, and enhance therapeutic outcomes when combined with chemotherapy, radiotherapy, and immunotherapy.

This study successfully designed and synthesized a novel magnetic molecularly imprinted composite material, Fe₃O₄@PCN-222@MIP, which integrates Fe₃O₄ nanoparticles, zirconium-based metal-organic framework (PCN-222), and molecularly imprinted polymer. This composite exhibits excellent magnetic responsiveness, high specific surface area, and characteristic recognition, enabling efficient enrichment and specific identification of sulfamethoxazole (SMX). The molecularly imprinted cavities provide complementary spatial structures and functional sites for SMX through hydrogen bonding and π-π interactions, achieving precise recognition. Notably, the composite also exhibits inherent peroxidase (POD)-like activity. Upon binding to SMX, electrons transfer from Fe to the sulfur atom in SMX, inhibiting its catalytic activity and reducing H2O2 decomposition efficiency, thereby establishing a direct colorimetric detection mechanism. The authorsdeveloped a microfluidic chip sensor integrating magnetic solid-phase extraction (MSPE) with colorimetric detection was developed. This method achieves a broad linear range (100–4200 nmol·L^-1) and low detection limit (632.74 nmol·L^-1) for SMX. When applied to complex food samples such as milk and honey, its recovery rates reaches 90%–110% without complex elution steps, demonstrating outstanding practicality and showing high consistency with high-performance liquid chromatography (HPLC). This study not only introduces innovative functional materials but also provides solutions for integrated, simplified equipment suitable for potential field applications.

In the field of metal-based anticancer agents, gold-based drugs, including gold coordination complexes and gold clusters, have emerged as one of the most promising research directions following platinum-based drugs, owing to their unique physicochemical properties and distinct anticancer mechanisms.

High catalytic efficiency is crucial for both chemical production and laboratory research. Poly(ionic liquid)s (PILs), an emerging material capable of forming high specific surface areas through self-assembly, are considered a highly promising candidate for increasing reaction sites and reducing mass transfer limitations. PIL and its related composite materials provide valuable guidance for catalyst design and industrial production.

Researchers from the Chinese Academy of Sciences review the disconnect between academic breakthroughs and industrial mass production of Flexible Transparent Conductive Films (FTCFs), proposing an application-driven framework and Manufacturing Readiness Level (MRL) assessment to accelerate commercialization.

A new study published in Big Earth Data develops explainable Random Forest models to predict stream total nitrogen (TN) and total phosphorus (TP) concentrations using upstream catchment characteristics derived from openly accessible environmental datasets. The results show that the models provide robust predictions for TN, while reduced and spatial covariates help mitigate overfitting and improve model interpretability, demonstrating the potential of explainable machine learning for regional nutrient monitoring.