ObjectiveCerebral ischemic injury triggers a complex pathological cascade characterized by excessive reactive oxygen species (ROS) accumulation, persistent oxidative stress, and sustained neuroinflammation in the injured brain microenvironment. These events collectively drive mitochondrial dysfunction, microglial overactivation, pro-inflammatory cytokine release, and progressive neuronal apoptosis, ultimately leading to severe and irreversible neurological deficits. However, conventional therapeutic strategies face critical limitations, including poor blood-brain barrier penetration, insufficient local drug concentration, uncontrolled drug release, and off-target systemic side effects. To address this pathological process, we rationally designed and fabricated an injectable ROS-responsive hydrogel loaded with polydopamine nanoparticles (PDA NPs) for spatiotemporally controlled antioxidation, anti-inflammation, and neuroprotection in the ischemic injury microenvironment. The present study aimed to systematically characterize the physicochemical properties, ROS-responsive drug release behavior, biocompatibility, and neuroprotective efficacy of this composite hydrogel system in vitro. MethodsPDA NPs were fabricated via oxidative self-polymerization. The ROS-responsive hydrogel was cross-linked using N1-(4-boronobenzyl)-N3-(4-boronophenyl)-N1,N1,N3,N3-tetramethylpropane-1, 3-diaminium (TSPBA) and polyvinyl alcohol (PVA). Morphology, particle size, Zeta potential, and structure of PDA NPs were characterized by dynamic light scattering (DLS), Zeta potential analysis, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Microstructure, rheological properties, shear-thinning behavior, and ROS-triggered release profiles of the hydrogel were examined by SEM and rheometry. Biocompatibility was evaluated using HT22 mouse hippocampal neurons with CCK-8 and live/dead staining. An oxygen-glucose deprivation/reoxygenation (OGD/R) model was established to simulate ischemic injury in vitro. ROS levels and neuronal apoptosis were detected by DHE staining and TUNEL assay. Microglial polarization and pro-inflammatory cytokine expression were analyzed using immunofluorescence and RT-qPCR in BV-2 microglia. Transwell co-culture was used to verify the indirect neuroprotection mediated by modulated microglia. ResultsCharacterization results confirmed that the as-prepared PDA NPs were monodispersed spherical nanoparticles with uniform diameter and negative surface potential, demonstrating favorable dispersibility and robust ROS-scavenging activity. The TSPBA-PVA hydrogel exhibited a highly porous interconnected network, suitable mechanical strength, and obvious shear-thinning behavior, supporting its application as an injectable implant. More importantly, the hydrogel displayed typical ROS-responsive degradation and on-demand PDA NP release in a ROS-concentration-dependent manner. In vitro cellular experiments demonstrated that the PDA NP-loaded hydrogel possessed excellent biocompatibility with HT22 cells. In the OGD/R model, the hydrogel significantly reduced intracellular ROS accumulation and markedly suppressed neuronal apoptosis. Furthermore, the composite hydrogel effectively redirected BV-2 microglia from the pro-inflammatory M1 toward the anti-inflammatory M2 phenotypes, downregulated the expression of pro-inflammatory cytokines including TNF-α, IL-1β, and IL-6, and reduced inflammatory damage. Transwell co-culture assays further validated that M2-polarized microglia mediated by the hydrogel significantly enhanced the survival of OGD/R-injured HT22 neurons and attenuated apoptosis. ConclusionIn this study, we successfully developed a novel injectable ROS-responsive hydrogel loaded with PDA NPs for synergistic antioxidative and anti-inflammatory neuroprotection. This intelligent hydrogel system enables ROS-triggered on-demand release of PDA NPs, efficiently scavenges excessive ROS, inhibits oxidative stress injury, modulates microglial polarization, and suppresses neuroinflammation, thereby exerting robust neuroprotective effects in vitro. This biomaterial platform provides a promising strategy for the targeted and controlled delivery of bioactive nanomaterials in the central nervous system diseases and establishes a solid experimental foundation for the development of in situ injectable therapies for ischemic brain injury.

To investigate the transcriptionally regulatory mechanism of the senp8 promoter in yellow cat-fish(Pelteobagrus fulvidraco);this study used P.fulvidraco as the research subject.Dual-luciferase re-porter assay and electrophoretic mobility shift assay were employed to analyze the functional activity of the promoter;coupled with in vivo experiments.The results indicated that the 2 045 bp senp8 promoter se-quence contained key transcription factor binding sites such as SP1;TATA-Box;CCAAT-Box;SREBP1;PPARα;and PPARγ.The binding sites of SREBP1(-901/-910 bp);PPARα(-1 291/-1 308 bp);and PPARγ(-1 292/-1 306 bp)in the senp8 promoter positively regulate its activity;and oleic acid or palmitic acid promote this binding.Furthermore;high-fat feeding promoted the expression of the senp8 gene and its protein in the liver of P.fulvidraco;oleic acid or palmitic acid treatment significantly en-hanced the activity of the senp8 promoter;and this enhancement could be achieved through the regulatory effects of SREBP1;PPARα;and PPARγ response elements.Additionally;high-fat feeding influenced the mRNA and protein expression levels of genes related to deSUMOylation modification in the liver of P.fulvidraco.This study provides new insights into the relationship between deSUMOylation modification and the regulation of lipid metabolism in the vertebrates.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

To investigate the transcriptionally regulatory mechanism of the senp8 promoter in yellow cat-fish(Pelteobagrus fulvidraco);this study used P.fulvidraco as the research subject.Dual-luciferase re-porter assay and electrophoretic mobility shift assay were employed to analyze the functional activity of the promoter;coupled with in vivo experiments.The results indicated that the 2 045 bp senp8 promoter se-quence contained key transcription factor binding sites such as SP1;TATA-Box;CCAAT-Box;SREBP1;PPARα;and PPARγ.The binding sites of SREBP1(-901/-910 bp);PPARα(-1 291/-1 308 bp);and PPARγ(-1 292/-1 306 bp)in the senp8 promoter positively regulate its activity;and oleic acid or palmitic acid promote this binding.Furthermore;high-fat feeding promoted the expression of the senp8 gene and its protein in the liver of P.fulvidraco;oleic acid or palmitic acid treatment significantly en-hanced the activity of the senp8 promoter;and this enhancement could be achieved through the regulatory effects of SREBP1;PPARα;and PPARγ response elements.Additionally;high-fat feeding influenced the mRNA and protein expression levels of genes related to deSUMOylation modification in the liver of P.fulvidraco.This study provides new insights into the relationship between deSUMOylation modification and the regulation of lipid metabolism in the vertebrates.

Early screening of tumors is crucial for prevention and treatment of cancer,thus identifying effective biomarkers is of great importance for early diagnosis of tumors.In recent years,tumor-secreted exosomes(Exos)have attracted widespread attention as a novel biomarker for tumor liquid biopsy.Especially,some specific proteins contained in Exos play important roles in the occurrence,development,metastasis and microenvironment regulation of tumors,indicating their enormous potential as potential diagnostic biomarkers for tumors.Compared to traditional biopsy sample testing,exosome-based protein detection methods exhibit significant advantages in liquid biopsy,including rapid sampling,easy operation,non-invasiveness,and feasibility for early detection,holding important application value for clinical diagnosis of tumors.This review aimed to comprehensively summarize and discuss various detection strategies for exosomal proteins in liquid biopsy for tumors,while comprehensively evaluating the analytical performance of these methods.Meanwhile,new perspectives and strategies for early diagnosis and treatment of tumors were discussed.Additionally,the unique advantages of exosomal proteins as a new generation of non-invasive diagnostic biomarkers and insights into their promising prospects for future clinical applications were emphasized.

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Objective To investigate the expression of myelin transcription factor 1-like(MYT1L)during amyotrophic lateral sclerosis(ALS)progression and its association with neuronal degeneration through bioinformatics analysis combined with in vivo and in vitro experiments.Methods Bioinformatics analysis of the GSE106803 dataset from the Gene Expression Omnibus(GEO)database revealed significant down-regulation of MYT1L in spinal cords of ALS transgenic mice carrying the human superoxide dismutase 1 mutant gene(hSOD1G93A)compared to the wild-type(WT)mice.hSOD1G93A transgenic mice and their WT littermates were selected to analyze MYT1L mRNA and protein changes in spinal cord tissues at different disease stages using Real-time PCR and Western blotting.Double immunofluorescent staining was used to determine the distribution and cellular localization of MYT1L in the spinal cord of mice at the middle stage of the disease.An ALS cellular model was established using hSOD1G93A mutant NSC34 cells,with hSOD1WT NSC34 cells as controls.MYT1L expression and distribution were assessed in these cells via Real-time PCR,Western blotting,and immunofluorescent staining.Based on the GSE76220 dataset from the GEO database,differentially expressed genes(DEGs)between MYT1L high-and low-expression groups in lumbar spinal motor neurons of ALS patients were identified,followed by Gene Ontology(GO)functional enrichment analysis.MYT1L overexpression was induced in the ALS cellular model to evaluate alterations in cell viability and neurite outgrowth.Results In the GSE106803 dataset,MYT1L expression was significantly down-regulated in the spinal cord of ALS mice.Animal experiments confirmed progressive reductions in MYT1L mRNA and protein levels in spinal cord tissues of ALS mice during mid-and late-disease stages.Compared to the WT group,MYT1L expression decreased in motor neurons of the lumbar spinal cord gray matter anterior horn in ALS mice,while it increased in astrocytes.In vitro,hSOD1G93Amutant NSC34 cells exhibited significantly reduced MYT1L expression than controls,with MYT1L localized to both the cytoplasm and nucleus.DEGs between MYT1L high-and low-expression groups in lumbar spinal cord motor neurons of ALS patients(GSE76220 dataset)were enriched in synaptic-related functions through GO analysis.Overexpression of MYT1L in hSOD1G93A mutant NSC34 cells enhanced cell viability and promoted neurite outgrowth.Conclusion Aberrantly low expression of MYT1L is closely associated with ALS pathogenesis.Overexpression of MYT1L promotes neurite growth and exerts protective effects on ALS motor neurons,suggesting its therapeutic potential.

This study aims to explore the pharmacodynamic material basis of Jinbei Oral Liquid(JBOL) against idiopathic pulmonary fibrosis(IPF) based on serum pharmacochemistry and network pharmacology. The ultra-high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UHPLC-Q-TOF-MS/MS) technology was employed to analyze and identify the components absorbed into rat blood after oral administration of JBOL. Combined with network pharmacology, the study explored the pharmacodynamic material basis and potential mechanism of JBOL against IPF through protein-protein interaction(PPI) network construction, "component-target-pathway" analysis, Gene Ontology(GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis. First, a total of 114 compounds were rapidly identified in JBOL extract according to the exact relative molecular mass, fragment ions, and other information of the compounds with the use of reference substances and a self-built compound database. Second, on this basis, 70 prototype components in blood were recognized by comparing blank serum with drug-containing serum samples, including 28 flavonoids, 25 organic acids, 4 saponins, 4 alkaloids, and 9 others. Finally, using these components absorbed into blood as candidates, the study obtained 212 potential targets of JBOL against IPF. The anti-IPF mechanism might involve the action of active ingredients such as glycyrrhetinic acid, cryptotanshinone, salvianolic acid B, and forsythoside A on core targets like AKT1, TNF, and ALB and thereby the regulation of multiple signaling pathways including PI3K/AKT, HIF-1, and TNF. In conclusion, JBOL exerts the anti-IPF effect through multiple components, targets, and pathways. The results would provide a reference for further study on pharmacodynamic material basis and pharmacological mechanism of JBOL.
Drugs, Chinese Herbal/pharmacokinetics* ; Animals ; Tandem Mass Spectrometry ; Network Pharmacology ; Rats ; Chromatography, High Pressure Liquid ; Rats, Sprague-Dawley ; Male ; Idiopathic Pulmonary Fibrosis/metabolism* ; Humans ; Administration, Oral ; Protein Interaction Maps/drug effects* ; Signal Transduction/drug effects*

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Lower-cost genotyping technology has promoted the generation of large genetic datasets with the evolving next-generation sequencing technology. The emergence of genome-wide association studies (GWAS) has facilitated researchers’ understanding of common complex diseases. GWAS refers to finding the sequence variations present in the human genome and screening out disease-related single nucleotide polymorphisms (SNPs). These SNPs are considered as the basis for assessing the stability of complex diseases. However, a single variation is not sufficient to assess an individual’s risk of disease. Polygenic risk score (PRS) is an emerging genetic data analysis method for quantitatively estimating an individual’s genetic risk for complex diseases by comprehensively considering multiple genetic variation sites. A single-value estimate of an individual’s genetic risk for a certain phenotype can be calculated as the cumulative impact of multiple genetic variants by building a PRS model. The finally expected risk score is weighted by the strength and direction of association of each SNP with the phenotype based on the number of alleles carried by each SNP. With the continuous development of various PRS calculation methods and the constant accumulation of genomic data, PRS has received widespread attention in the field of genetics. So far, quite a few studies at home and abroad have shown that PRS is valuable in risk prediction of different types of human traits or complex diseases, and its effectiveness has been further verified in clinical applications. At present, many studies have established PRS models based on GWAS summary statistics to quantify the genetic risk of susceptibility loci and clinical characteristics on diseases such as lung cancer, breast cancer, coronary heart disease, diabetes and Alzheimer’s disease. The disease-susceptible populations can be recognized through comparing the relative risk and absolute risk of the disease in different risk groups according to the population risk stratification results. Additionally, individual-level genotype data and omics data can also be used as data sources for PRS analysis research, especially the latter can dynamically reflect the short-term or long-term effects of environmental factors on human gene expression, and has potential application value in building early warning models to assess health risks. Since the calculation of PRS involves a large amount of genomic data analysis, there are big differences in the methods for data selection, model building and validation. Different PRS construction methods and software have different performances in disease risk prediction, and even the performance of same algorithm varies across diseases. It is worth noting that the PRS model often needs to be re-evaluated and verified for different groups of people, because PRS is affected by race and region. This review combines currently published PRS-related research and algorithms to describe the basic principles of PRS, compares their construction and verification methods, and discusses their applications and prospects. As a powerful genetic risk assessment tool, PRS has great potential in analyzing the genetic code of complex diseases and achieving precise diagnosis and personalized treatment.