Taizhou Municipal Hospital

To investigate whether age of onset (AAO) affects cerebrospinal fluid (CSF) C16 glucosylceramide (C16 GlcCer) and striatal dopamine transporter-specific binding ratios (DAT-SBRs) in Parkinson’s disease (PD), and their associations with longitudinal autonomic and cognitive outcomes, we conducted a large-scale, multicenter, prospective observational cohort study. Late-onset PD (LOPD, AAO > 50 years) patients showed higher baseline CSF C16 GlcCer and lower DAT‑SBR in some striatal regions compared to early‑onset PD (EOPD, AAO ≤ 50 years). Higher DAT-SBR was associated with a lower risk of autonomic dysfunction (AutD) in both groups and with better longitudinal cognitive performance in LOPD. Importantly, only in LOPD did the interaction between C16 GlcCer and DAT‑SBR predict more favorable autonomic progression, independent of CSF α‑synuclein. In EOPD, AutD risk was primarily related to regional DAT loss. These findings suggest that AAO modulates sphingolipid‑dopaminergic crosstalk and supports the combined use of these biomarkers for risk stratification.

Hepatocellular Carcinoma (HCC) exhibits high recurrence rates, particularly when accompanied by Microvascular Invasion (MVI). We identified MVI-related biomarkers and established a prognostic model for personalized HCC treatment. Data were downloaded from The Cancer Genome Atlas (TCGA) and HCCDB databases. Key radiomics features were identified using the support vector machine-recursive feature elimination (SVM-RFE) algorithm, and differential expression analysis was performed with DESeq2. This was followed by functional enrichment analysis using the clusterProfiler package. Through univariate and Lasso regression analyses, we constructed a robust RiskScore model to effectively stratify HCC patients into distinct risk groups based on the median RiskScore value. The model prediction performance was evaluated using ROC curves and Kaplan-Meier (KM) analysis. We used the CIBERSORT algorithm to characterize immune cell infiltration patterns and conducted GSEA to identify differentially activated pathways between the risk groups. Radiomic analysis revealed four significant features strongly associated with MVI, enabling the construction of a nomogram model with robust classification performance (AUC = 0.742). Subsequent analysis identified 241 overlapping MVI-related Differentially Expressed Genes (DEGs) enriched in critical tumor proliferation and invasion pathways. A 10-gene RiskScore model was developed, demonstrating excellent prognostic discrimination in training and validation cohorts. CIBERSORT analysis revealed significant correlations between specific immune cell infiltration and the 10 genes. GSEA analysis showed significant enrichment of cell cycle regulation pathways in the high-risk group, suggesting their important role in MVI. The RiskScore was established using MVI-related features for prognosis assessment in HCC. Our findings provided novel biomarkers and a theoretical basis for the early diagnosis and personalized treatment of HCC.

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system, closely associated with neuroinflammation, immune dysregulation, and gut microbiota imbalance. Gut microbiota-derived metabolites may modulate key targets involved in MS pathogenesis. This study integrated network pharmacology, machine learning (ML), and single-cell transcriptome analysis to identify MS-related differentially expressed genes (DEGs) and potential targets of gut microbial metabolites. Feature contributions were evaluated using the SHapley Additive exPlanations (SHAP) method, and causal relationships were validated via Mendelian randomization (MR). Single-cell analysis, molecular docking, and assessments of drug-likeness and toxicity were also performed. Caspase-3 (CASP3) was identified as a core target interacting with multiple gut microbial metabolites, including L-isoleucine, aromatic lactic acid derivatives, 3-hydroxyphenethyl alcohol, and D-xylose, potentially regulating neuroimmune responses via TNF, MAPK, IL-17, and galectin pathways. Specific microbial taxa, such as Akkermansia, Bacteroides, and Bifidobacterium, were closely associated with these metabolites. The metabolites exhibited favorable drug-likeness and low predicted toxicity, indicating potential therapeutic value. Gut microbial dysbiosis and its metabolites play a significant role in MS onset and progression, providing a theoretical basis for identifying therapeutic targets and gut-CNS axis interventions. Experimental validation is needed to confirm mechanisms and translational potential.

Nikethamide, a respiratory stimulant, is used to treat hypoxemia caused by coronavirus disease 2019 (COVID-19), but it carries a risk of convulsions. Magnesium sulfate (MgSO4), a seizure inhibitor, might serve as a rescue agent against nikethamide-induced seizures. To investigate the therapeutic effect of MgSO4 on nikethamide -induced seizures in COVID-19 patients through animal experiments, providing experimental support for the clinical application of MgSO4 in preventing and treating seizures caused by nikethamide. Forty mice were randomly divided into four groups: (1) Physiological saline; (2) Low-dose MgSO4 (50 mg/kg); (3) Medium-dose MgSO4 (100 mg/kg); and (4) High-dose MgSO4 (200 mg/kg). After 15 minutes of intraperitoneal injection of different doses of MgSO4 or an equal volume of physiological saline, the mice were injected with nikethamide (250 mg/kg). Compared to the normal saline group, all doses of MgSO4 significantly prolonged the seizure latency and reduced the severity of convulsions. However, they also extended the duration of seizures and correspondingly increased survival time (P < 0.05). The incidence of seizures and mortality rate in the MgSO4-treated groups were significantly lower than those in the normal saline group (P < 0.05). MgSO4 can prevent and treat seizures caused by nikethamide in mice. This finding has implications for the application of MgSO4 in treating and preventing seizures caused by nikethamide in COVID-19 treatment.

Vibrio alginolyticus is a zoonotic pathogenic bacterium that causes various diseases in marine animals, including Penaeus vannamei. Phage therapy, as a natural antibiotic, offers a novel approach to the prevention and control of vibriosis. In this study, we isolated, identified, and characterized a novel bacteriophage targeting V. alginolyticus, designated as PV48. PV48, a short-tailed bacteriophage isolated from the Ningbo aquaculture farm, possesses the ability to lyse V. alginolyticus strains that affect shrimp. It displays stability over a range of temperatures (30-50°C) and pH values (4-10). The latent period and burst size for phage PV48 were determined to be 20 minutes and 15.6 PFU/cell, respectively. Whole-genome sequencing revealed that PV48 has a dsDNA genome with a length of 43,398 bp and a G + C content of 49.54%. The phage genome contains 44 open reading frames (ORFs). Comparative genomic analysis suggested that PV48 might represent a new species within the genus Maculvirus. Phylogenetic analysis indicated a close evolutionary link between PV48 and other Vibrio-targeting phages, suggesting a shared viral lineage. This study lays the groundwork for the application of V. alginolyticus bacteriophage in aquaculture.