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Objective: Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma, with significant clinical heterogeneity. Recent studies suggest that the intratumoral microbiome may influence the tumor microenvironment, affecting patient prognosis and therapeutic responses. This study aims to identify microbiome-related subtypes in DLBCL and assess their impact on prognosis, immune infiltration, and therapeutic sensitivity., Methods: Transcriptomic and microbiome data from 48 DLBCL patients were obtained from public databases. Consensus clustering was used to classify patients into distinct microbiome-related subtypes. Functional enrichment analysis, immune infiltration assessments, and single-cell RNA sequencing were performed to explore the biological characteristics of these subtypes. Drug sensitivity predictions were made using the OncoPredict tool. Hub genes' expression and biological function were validated and inferred in cell lines and independent cohorts of DLBCL., Results: Two distinct microbiome-related subtypes were identified. Patients in Cluster 1 exhibited significantly better overall survival (P < 0.05), with higher immune infiltration of regulatory T cells and M0 macrophages compared to Cluster 2, which was associated with poorer outcomes. Functional enrichment analysis revealed that genes in Cluster 1 were involved in immune regulatory pathways, including cytokine-cytokine receptor interactions and chemokine signaling, suggesting enhanced anti-tumor immune responses. In contrast, genes in Cluster 2 were enriched in immunosuppressive pathways, contributing to a less favorable prognosis. Single-cell RNA sequencing analysis revealed significant heterogeneity in immune cell populations within the tumor microenvironment. B cells exhibited the most notable heterogeneity, as indicated by stemness and differentiation potential scoring. Intercellular communication analysis demonstrated that B cells played a key role in immune cell interactions, with significant differences observed in MIF signaling between B-cell subgroups. Pseudo-time analysis further revealed distinct differentiation trajectories of B cells, highlighting their potential heterogeneity across different immune environments. Metabolic pathway analysis showed significant differences in the average expression levels of metabolic pathways among B-cell subgroups, suggesting functional specialization. Furthermore, interaction analysis between core genes involved in B-cell differentiation and microbiome-driven differentially expressed genes identified nine common genes (GSTM5, LURAP1, LINC02802, MAB21L3, C2CD4D, MMEL1, TSPAN2, and CITED4), which were found to play critical roles in B-cell differentiation and were influenced by the intratumoral microbiome. DLBCL cell lines and clinical cohorts validated that MMEL1 and CITED4 with important biologically function in DLBCL cell survival and subtype classification., Conclusions: This study demonstrates the prognostic significance of the intratumoral microbiome in DLBCL, identifying distinct microbiome-related subtypes that impact immune infiltration, metabolic activity, and therapeutic responses. The findings provide insights into the immune heterogeneity within the tumor microenvironment, focusing on B cells and their differentiation dynamics. These results lay the foundation for microbiome-based prognostic biomarkers and personalized treatment approaches, ultimately aiming to enhance patient outcomes in DLBCL., Competing Interests: Declarations. Conflict of interest: The authors declare no competing interests. Ethical approval: Not applicable., (© 2025. The Author(s).)

Background: This study aimed to synthesize and quantitatively examine Health State Utility Values (HSUVs) for Type 2 Diabetes Mellitus (T2DM) and its complications, providing a robust meta-regression framework for selecting appropriate HSUV estimates., Method: We conducted a systematic review to extract HSUVs for T2DM and its complications, encompassing various influencing factors. Relevant literature was sourced from a review spanning 2000-2020, supplemented by literature from PubMed, Embase, and the Web of Science (up to March 2024). Multivariate meta-regression was performed to evaluate the impact of measurement tools, tariffs, health status, and clinical and demographic variables on HSUVs., Results: Our search yielded 118 studies, contributing 1044 HSUVs. The HSUVs for T2DM with complications varied, from 0.65 for cerebrovascular disease to 0.77 for neuropathy. The EQ-5D-3L emerged as the most frequently employed valuation method. HSUV differences across instruments were observed; 15-D had the highest (0.89), while HUI-3 had the lowest (0.70) values. Regression analysis elucidated the significant effects of instrument and tariff choice on HSUVs. Complication-related utility decrement, especially in diabetic foot, was quantified. Age <70 was linked to increased HSUVs, while longer illness duration, hypertension, overweight and obesity correlated with reduced HSUVs., Conclusion: Accurate HSUVs are vital for the optimization of T2DM management strategies. This study provided a comprehensive data pool for HSUVs selection, and quantified the influence of various factors on HSUVs, informing analysts and policymakers in understanding the utility variations associated with T2DM and its complications., (© 2024. The Author(s).)

Metal nanoclusters (NCs), composed of a metal core and protecting ligands, show promising potentials as enzyme mimics for producing fuels, pharmaceuticals, and valuable chemicals, etc. Herein, we explore the critical role of ligands in modulating the peroxidase mimic activity and stability of Au NCs. A series of Au 15 (SR) 13 NCs with various thiolate ligands [SR = N -acetyl-l-cysteine (NAC), 3-mercaptopropionic acid (MPA), or 3-mercapto-2-methylpropanoic acid (MMPA)] are utilized as model catalysts. It is found that Au 15 (NAC) 13 shows higher structural stability than Au 15 (MMPA) 13 and Au 15 (MPA) 13 against external stimuli (e.g., pH, oxidants, and temperature) because of the intramolecular hydrogen bonds. More importantly, detailed enzymatic kinetics data show that the catalytic activity of Au 15 (NAC) 13 is about 4.3 and 2.7 times higher than the catalytic activity of Au 15 (MMPA) 13 and Au 15 (MPA) 13 , respectively. Density functional theory (DFT) calculations reveal that the Au atoms on the motif of Au NCs should be the active centers, whereas the superior peroxidase mimic activity of Au 15 (NAC) 13 should originate from the emptier orbitals of Au atoms because of the electron-withdrawing effect of acetyl amino group in NAC. This work demonstrates the ligand-engineered electronic structure and functionality of atomically precise metal NCs, which afford molecular and atomic level insights for artificial enzyme design.

Au(I)-thiolate complexes are a new class of aggregation-induced emission (AIE) material. Here we demonstrate a new aggregation strategy of water-soluble Au(I)-thiolate complexes induced by cationic polymers at optimized pH values. The generated AIE shows longer wavelengths than the emission induced by other methods.