Liaocheng University

As a new type of organic porous crystalline material connected with covalent bonds, covalent organic frameworks (COFs) have shown unique advantages in many fields. However, the preparation of COFs membrane that are easy to prepare and can be applied multiple times still is a challenge. Here, 2,5-Dimethoxybenzene-1,4-dicarboxaldehyde (Dma) and 4,4',4''-(1,3,5-Triazine-2,4,6-triyl) trianiline (TAPT) was used as a reactant and Dma-TAPT fiber membranes with good crystallinity and stability were prepared through electrospinning technology and solvothermal in-situ growth. The XRD, SEM, ¹³C NMR, IR, and XPS, etc. were used to characterize the structure of obtained membrane. The prepared membranes not only showed fluorescence detection for acid but also realize the visual detection via a portable smartphone. The density functional theory calculation and experimental results verified that the methoxy group provide site for the interaction with H⁺ in acid. Additionally, because of the increase of light absorption capacity the photothermal efficiency of COFs after acidification was improved by 17.4 % for the COFs before acidification. The research provides ideas for designing new sensitive and recyclable pH sensors.

The role of bilirubin, a product of heme metabolism, has evolved from a traditionally perceived metabolic waste product to a critical molecule with diverse biological roles. This article comprehensively reviews the metabolic functions of bilirubin and advances in its application for disease prevention. Bilirubin is primarily derived from hemoglobin catabolism in senescent erythrocytes. It is subsequently metabolized and excreted by the liver through tightly regulated processes involving enzymes, nuclear receptors, hormones, and pharmaceuticals. Bilirubin exhibits diverse physiological functions, including antioxidant, anti-inflammatory, and immunomodulatory activities. Owing to its unique chemical structure, bilirubin scavenges free radicals, inhibits lipid peroxidation, and protects cells across multiple systems. By suppressing the NF-κB signaling pathway, it reduces inflammatory factor release and mitigates chronic inflammation. Additionally, it modulates immune cell activity to maintain homeostasis, offering therapeutic potential for autoimmune and infectious diseases. Bilirubin demonstrates significant potential in disease prevention. In cardiovascular diseases, it attenuates atherosclerosis and mitigates myocardial ischemia/reperfusion injury. For metabolic disorders, it improves insulin resistance, regulates blood glucose, and reduces hepatic steatosis, offering therapeutic benefits for diabetes and non-alcoholic fatty liver disease. In neurological conditions, its antioxidant and anti-inflammatory properties hold promise for preventing and managing neurodegenerative diseases and neonatal bilirubin encephalopathy. Although research on bilirubin has advanced significantly, its intracellular targets and molecular interaction networks remain incompletely understood, and numerous challenges hinder its clinical translation. Future efforts should leverage multi-omics technologies to elucidate its mechanisms, develop intelligent and personalized therapeutics, and conduct large-scale clinical trials to accelerate translational applications and improve patient outcomes.

To deploy Electroencephalogram (EEG) based Mental Workload Recognition (MWR) systems in the real world, it is crucial to develop general models that can be applied across subjects. Previous studies have utilized domain adaptation to mitigate inter-subject discrepancies in EEG data distributions. However, they have focused on reducing global domain discrepancy, while neglecting local workload-categorical domain divergence. This degrades the workload-discriminating ability of subject-invariant features. To deal with this problem, we propose a novel joint category-wise and domain-wise alignment Domain Adaptation (cdaDA) algorithm, using bi-classifier learning and domain discriminative adversarial learning. The bi-classifier learning approach is adopted to address the similarities and differences between categories, helping to align EEG data within the same mental workload categories. Additionally, the domain discriminative adversarial learning technique is adopted to consider global domain information by minimizing global domain discrepancy. By integrating both local category information and global domain information, the cdaDA model performs a coarse-to-fine alignment and achieves promising cross-subject MWR results.