Anemia represents a common and clinically significant complication among maintenance hemodialysis (MHD) patients. Rubidium (Rb), which shares biochemical properties with potassium and enters cells through Na⁺/K⁺-ATPase, potentially influences erythrocyte function; however, its relationship with anemia in MHD populations has not been explored. This multicenter cross-sectional investigation enrolled 679 MHD patients from three hospitals in China during September to December 2021. Whole blood Rb concentrations were quantified using inductively coupled plasma mass spectrometry (ICP-MS). Anemia was defined as hemoglobin (Hb) below 11.0 g/dL. The association was evaluated through restricted cubic spline (RCS) analysis, multivariable logistic regression, and linear regression models. Anemia prevalence reached 55.23
Targeting the programmed cell death 1 (PD-1)/PD-L1 axis has revolutionized cancer therapy; however, the durability of clinical responses is frequently compromised by chronic inflammation and an immunosuppressive tumor microenvironment (TME). The Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway serves as a central intracellular node integrating cytokine signals that drive these resistance mechanisms. While physiological JAK/STAT signaling is essential for antitumor immunity, its persistent aberrant activation promotes malignant progression, upregulates PD-L1 expression, and orchestrates an immunosuppressive landscape by recruiting myeloid-derived suppressor cells (MDSCs) and polarizing tumor-associated macrophages (TAMs) toward an M2 phenotype, ultimately leading to T cell exhaustion. This review comprehensively elucidates the multifaceted role of JAK/STAT signaling in shaping the immune architecture of both hematologic and solid tumors. We examine the molecular crosstalk between JAK/STAT activation and key immune subsets within the TME and discuss the rationale for repurposing JAK inhibitors—established agents for autoimmune disorders—as adjuvants to immunotherapy. Emerging preclinical and clinical evidence suggests that combining selective JAK inhibition with PD-1 blockade can disrupt inflammatory feedback loops, reprogram the TME, and overcome resistance to immune checkpoint inhibitors. This synergistic strategy represents a promising therapeutic frontier for improving outcomes in refractory malignancies.
Diabetic foot ulcers (DFUs) complicated by multidrug-resistant (MDR) bacterial infection present a significant clinical challenge that necessitates innovative therapeutic strategies. Conventional antimicrobial treatments often fail because of limited tissue penetration, antibiotic resistance, and poor efficacy against biofilm-forming pathogens. In this study, we developed a near-infrared (NIR) light-responsive aggregation-induced emission luminogen (AIEgen) with high photothermal conversion efficiency, excellent photostability, and favorable biocompatibility. Upon NIR light activation, this AIEgen can induce localized hyperthermia capable of disrupting bacterial membranes and penetrating deep into infected tissues, thereby effectively eradicating the embedded MDR pathogens and resilient biofilms. The antibacterial efficacy of the AIEgen was validated against MDR clinical isolates, including methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem-resistant Pseudomonas aeruginosa (CRPA). In vitro and in vivo evaluations confirmed the potent bactericidal activity, efficient biofilm disruption ability, and minimal cytotoxicity of the AIEgen. Notably, in a DFU infection model, AIEgen administration and NIR light exposure significantly accelerated wound healing by eliminating pathogens, suppressing inflammation, promoting M2 macrophage polarization, enhancing collagen deposition, and stimulating angiogenesis. In conclusion, this AIEgen-based photothermal system offers a noninvasive, biocompatible, and deeply penetrating antibacterial strategy for the treatment of chronic MDR infections in DFUs and has great potential for clinical translation.
OBJECTIVE:To develop and validate a Vision Foundation Model-enhanced Multimodal Deep Learning Radiomics (VFM-MDLR) framework that integrates MR imaging with clinicopathological information for noninvasive prediction of deep myometrial invasion (DMI) in patients with endometrial cancer (EC). METHODS:This retrospective, multicenter study was conducted across seven independent centers and included 1376 EC patients. We developed a VFM-MDLR model based on two MRI sequences for the prediction of DMI. The framework was designed to first employ a General Knowledge Transfer across Heterogeneous-model (GKTH) subnetwork, which adaptively extracts general representations from vision foundation model (VFM). Building on this foundation, a Cross-Sequence Guided Attention (CSGA) module was incorporated to exploit the complementary information between CE-T1WI and T2WI, thereby achieving semantic alignment and synergistic feature representation. These features were then used to derive a deep-learning signature, termed VFM-enhanced Dual-sequence Knowledge Fusion (VFM-DKF), which was further integrated with key clinicopathological variables to construct the final VFM-MDLR predictor. Model performance was systematically evaluated in the internal validation cohort and four external cohorts, while interpretability was assessed using Grad-CAM and SHAP analyses. RESULTS:The VFM-MDLR model, which comprised age, histopathologic grade, the maximum tumor diameter (TMD), and the DLS, demonstrated the best predictive performance, with the highest AUC (0.832-0.877) across all cohorts. No significant difference was observed in performance for DMI detection between the VFM-MDLR model and experienced radiologists' readings (P = 0.915). CONCLUSION:The proposed VFM-MDLR model showed favorable accuracy in identifying DMI, potentially providing clinicians with a tool to facilitate individualized surgical treatment for patients with EC.
