Introduction: Mesenchymal stem cell-derived exosomes have garnered considerable attention in regenerative medicine due to their non-immunogenicity, low infusion toxicity, easy accessibility, straightforward preservation, and minimal ethical concerns. While ultracentrifugation is the prevailing method for high-purity exosome isolation, it is limited by low throughput and the need for specialized infrastructure. This study investigates tangential flow filtration (TFF) as a promising alternative for exosome isolation. This technique offers simpler operation, higher yields, and improved recovery rates compared to ultracentrifugation. Methods: Human umbilical cord mesenchymal stem cells (hUCMSCs) were cultured in a 3D microcarrier-bioreactor system, and exosomes were extracted from the conditioned medium using either ultracentrifugation or an automated and enclosed TFF system. Subsequently, we compared the quantity, quality and therapeutic efficacy of the exosomes isolated via both approaches, evaluating their effects in vitro and in a mouse model of diabetic wound healing. Results: Our findings demonstrate that the TFF method effectively isolates high-quality exosomes that meet the standards set by the Minimum Information for Studies of Extracellular Vesicles (MISEV) 2023 guidelines, while achieving a significantly higher extraction yield compared to the traditional ultracentrifugation. Furthermore, both TFF and ultracentrifugation-derived exosomes demonstrate comparable biological activity in vitro and similar therapeutic potential for treating diabetic wound healing, potentially through promoting M2 macrophage polarization and angiogenesis. Conclusion: The results indicate that TFF is a viable method for scalable and efficient exosome production, facilitating advancements in clinical applications for diabetic wound repair.

Bone age assessment is a critical tool for evaluating skeletal maturity in children and adolescents, with implications for growth monitoring and clinical decision-making. While traditional radiographic methods such as the Greulich-Pyle and Tanner-Whitehouse systems remain the gold standard, concerns over ionizing radiation exposure have spurred interest in ultrasound-based alternatives. This mini-review synthesizes current evidence on ultrasound bone age assessment, highlighting its advantages as a radiation-free, non-invasive modality with strong correlations to radiographic standards. Key advancements include standardized scoring systems, ossification ratios (e.g., radius/ulna/femur), and acoustic measures (e.g., speed of sound), which enhance reliability and reduce inter-operator variability. However, challenges persist, including protocol standardization, population-specific variability, and operator dependency, particularly in advanced pubertal stages. Future directions emphasize the development of large-scale, longitudinal and multi-ethnic reference databases, consensus guidelines, and AI integration to improve precision. Ultrasound bone age assessment shows promise as a viable clinical tool, but further refinements are needed to address its limitations and ensure equitable applicability across diverse populations.

Background: Tissue‐resident macrophages (TRMs) exhibit dual roles in tumor progression, yet their functional reprogramming within the tumor microenvironment (TME) remains a critical unresolved question. Methods: We integrated single‐cell and spatial transcriptomics from a pan‐cancer atlas of 1.39 million cells across five malignancies with 2,318 bulk RNA‐seq samples to investigate macrophage states. A TRM inflammatory remodeling signature (TIR‐Sig) was developed for clinical biomarker validation. Results: We identified a conserved inflammatory TRM subtype (iTRM) characterized by CXCL8/IL1B/IL6 co‐expression that correlates with poor clinical outcomes. Crucially, both TRMs and monocyte‐derived tumor‐associated macrophages (Mono‐TAMs) underwent convergent differentiation into functionally similar inflammatory phenotypes, establishing iTRM as a universal tumor‐educated state. Further integration analysis revealed an iTRM‐enriched TME subtype which featured coordinated infiltration of neutrophils and cancer‐associated fibroblasts (CAFs), forming a ‘cold tumor’ ecosystem associated with immune checkpoint blockade (ICB) resistance and poor prognosis. The derived TRM inflammatory remodeling signature (TIR‐Sig) demonstrated dual clinical utility: it predicted patient survival (HR = 19.86, p < .001) and stratified ICB responders (AUC = .706). Conclusion: This study establishes phenotypic links between tissue‐resident and recruited macrophages through inflammatory reprogramming within TME, provides a unifying framework for pan‐cancer macrophage plasticity in TME, delivers a clinically actionable biomarker suite (TIR‐Sig), and provides potential therapeutic targets for TME remodeling. Key points: Cross‐tissue single‐cell atlas of tissue‐resident macrophages (TRMs). Identification of conserved inflammatory TRM phenotype (iTRM) in pan‐cancer. Dynamic convergence of TRM and monocyte‐derived macrophage lineages. TRM inflammatory remodelling signature (TIR‐Sig) with clinical potential.

Endovascular thrombectomy (EVT) has improved outcomes in patients with acute ischemic stroke due to large vessel occlusion; however, a substantial proportion of patients still experience poor functional outcomes despite successful recanalization. Growing evidence suggests that postprocedural cerebral hemodynamics are closely associated with recovery after EVT. This study aimed to develop an interpretable machine learning model integrating transcranial Doppler (TCD)-derived hemodynamic parameters and clinical features to predict 3-month functional outcomes after EVT. This retrospective cohort study included 176 acute ischemic stroke due to large vessel occlusion patients who achieved successful recanalization (modified Thrombolysis in Cerebral Infarction ≥ 2b) and underwent TCD monitoring within 72 hours after EVT. Model development and performance assessment were conducted using a bootstrap-based internal validation framework with 1000 stratified bootstrap replicates. Within each replicate, preprocessing and least absolute shrinkage and selection operator-penalized logistic regression were performed using the in-bag data for feature selection. Five classifiers (logistic regression, support vector machine, k-nearest neighbors, random forest, and gradient boosting machine) were trained on in-bag samples and evaluated on out-of-bag samples. Performance was assessed using the area under the receiver operating characteristic curve and threshold-based metrics, with calibration and decision curve analyses. Model interpretability was examined using SHapley Additive exPlanations. Across bootstrap replicates, smoking status, peak systolic velocity (PSV)_Ratio, age, and mean flow velocity (MFV)_Index were the most frequently selected predictors. Under internal validation, all models showed stable discrimination, and the random forest demonstrated the most favorable overall performance, with good calibration and net benefit. SHapley Additive exPlanations identified MFV_Index as the most influential feature, followed by PSV_Ratio, smoking status, and age. Higher MFV_Index, higher PSV_Ratio, smoking, and older age were associated with a higher predicted risk of unfavorable outcomes. Using bootstrap-based internal validation, we developed an interpretable machine learning model integrating post-EVT TCD hemodynamics and clinical factors that showed promising internally validated performance for predicting 3-month functional outcomes after EVT. MFV_Index, PSV_Ratio, smoking status, and age were key predictors, highlighting the potential relevance of postreperfusion cerebral hemodynamics and baseline vascular risk.

Introduction: This study establishes a novel quantitative acoustic mass spectrometry (QAMS) methodology for insulin-like growth factor 1 (IGF-1) detection.Methods: Chromatographic separation utilized a Peptide C18 column (1.8 μm, 50 mm) with 0.1% formic acid/acetonitrile gradient elution, coupled to tandem mass spectrometry operated in scheduled multiple reaction monitoring (sMRM) mode.Results: The method demonstrated a lower limit of quantification (LOQ) of 10 ng/mL with linear dynamic range spanning 10-500 ng/mL. Comparative analysis of 74 paired plasma specimens revealed strong inter-matrix correlation with quantifiable bias.Discussion: These advancements position QAMS- as a robust tool for decentralized IGF-1 monitoring, particularly valuable in pediatric growth disorder studies and resource-limited settings. Longitudinal stability validation and isoform differentiation remain focal points for future optimization.

Emergency point-of-care ultrasound-guided percutaneous pericardial catheter drainage (US-GPPCD) is a life-saving intervention for cardiac tamponade, yet th