The in-situ synthesized TiC reinforced Ti6Al4V matrix composite coating (in-situ TiC/Ti6Al4V MMC coating) was fabricated on Ti6Al4V substrate via laser cladding through the reaction of Ti6Al4V and SiC mixed powders, with the aim to improve the microhardness and resistance to wear and cavitation erosion (CE). The results showed that the TiC reinforcement phases were successfully formed under laser irradiation, with fine TiC particles uniformly dispersed in the Ti6Al4V matrix. The in-situ synthesis mechanism of TiC particles was investigated from thermodynamic perspective as indicated by the lower negative Gibbs free energy change (Delta G). The coating was dense and uniform without visible cracks or pores, along with a strong metallurgical bonding with the substrate. The coating achieved significant grain refinement as the uniformly dispersed TiC reinforcement phases promoted high nucleation rate by acting as heterogeneous nucleation sites. The coating exhibited a significantly higher microhardness of 533 HV0.2 compared to the substrate with the value of 275 HV0.2. Due to the combined influence of grain refinement and TiC reinforcement phases, the coating achieved higher wear resistance with lower wear volume (2.79 & times; 107 mu m3) and specific wear rate (2.58 & times; 10-4 mm3/N & sdot;m), with the primary wear mechanism being abrasive wear accompanied by oxidative wear. The corrosion resistance of the coating was slightly higher than that of the substrate in NaCl solution as evidenced by the lower current density (Icorr) of coating (1.02 & times; 10- 7 A & sdot;cm- 2) compared to that of the substrate (1.62 & times; 10- 7 A & sdot;cm- 2). After 10-h CE test duration, the coating exhibited superior CE resistance due to the strong pinning effect of TiC particles on dislocations and the grain refinement effect.
Metabolic dysfunction-associated fatty liver disease (MAFLD) is an emerging cardiovascular risk factor; evidence-based blood pressure (BP) management strategies for this population remain limited. We aimed to evaluate the efficacy and safety of a multifaceted intervention anchored by intensive BP control (< 130/80 mmHg) in patients with MAFLD using data from the China Rural Hypertension Control Project (CRHCP) trial. This study is a post hoc analysis of the CRHCP trial, an open-label, cluster-randomized controlled trial conducted from 2018 to 2023 across 326 villages in China. Adults aged ≥ 40 years with hypertension were eligible. Participants were randomized in a 1:1 ratio to receive either intensive BP control (target < 130/80 mmHg) or usual care. MAFLD was diagnosed using the hepatic steatosis index (HSI ≥ 36) combined with metabolic criteria. The primary outcome was a composite of stroke, myocardial infarction, heart failure, or cardiovascular death; secondary outcomes included individual components of the primary outcome and all-cause death. The CRHCP trial is registered with ClinicalTrials.gov NCT03527719. A total of 29,624 participants (12,912 with MAFLD) were included in this analysis. The mean (SD) age was 63.2 (9.2) years, and 18,348 (61.9
Circular RNAs (circRNAs) have emerged as pivotal regulators of tumor immune escape, acting through multilayered mechanisms that include miRNA sequestration, RNA-binding protein scaffolding, m⁶A-dependent stabilization, metabolic rewiring, and exosomal communication. These processes collectively sustain PD-L1 expression and stability, drive CD8⁺ T-cell exhaustion, promote Treg and MDSC expansion, skew macrophages toward immunosuppressive M2 phenotypes, and reshape glycolytic and lipid metabolic pathways to generate an immune-refractory microenvironment. In parallel, exosomal circRNAs disseminate long-range immunosuppressive signals, reinforcing therapy resistance and systemic immune dysfunction. Conversely, a newly recognized subset of "immune-activating circRNAs" induces ferroptosis, immunogenic cell death, and STING-mediated innate immune activation, highlighting the dual nature of circRNA immunoregulation. Recent advances in nanomedicine-spanning lipid nanoparticles, polymeric platforms, and biomimetic membrane-coated carriers-have enabled precise silencing of oncogenic circRNAs and efficient delivery of synthetic therapeutic circRNAs, demonstrating potent synergy with immune checkpoint inhibitors, NK-cell therapy, STING agonists, and ferroptosis inducers. Although challenges remain, including delivery specificity, biosafety, biomarker standardization, and off-target effects, the convergence of circRNA biology and advanced nanotechnology presents a transformative opportunity to develop next-generation RNA-guided cancer immunotherapies. Together, these findings position circRNAs as both key mechanistic drivers of immune escape and promising therapeutic targets for nanomedicine-enabled precision immunotherapy.
Skin cancers, including melanoma, cutaneous squamous cell carcinoma (cSCC), and Merkel cell carcinoma (MCC), present a fundamental paradox: high tumor immunogenicity coexists with profound immune evasion, driven by oncogenic signaling, an immunosuppressive microenvironment, metabolic dysregulation, and fibrotic stroma. Multifunctional nanoplatforms offer a systems-level strategy to overcome this multilayered resistance. They enable coordinated reprogramming of immune checkpoints and myeloid compartments, precise induction of immunogenic cell death through organelle-targeted stress or novel death pathways like cuproptosis, and synergistic use of photo-, ultrasound-, or magnetic-energy triggers. Concurrently, these platforms remodel the tumor microenvironment by scavenging immunosuppressive metabolites, exploiting lineage-specific metabolic vulnerabilities, and degrading fibrotic barriers to restore T-cell infiltration. The integration of computational intelligence-spanning AI-driven nanocarrier design, multi-omics-based patient stratification, and real-time biomarker monitoring-further empowers adaptive therapeutic strategies. By unifying biomimetic delivery, stimuli-responsive activation, and energy-coupled immunomodulation, advanced nanocarriers actively reconfigure tumor-immune crosstalk, demonstrating synergistic antitumor efficacy in preclinical models of melanoma, cSCC, and MCC. Successful clinical translation, however, requires addressing key challenges: scalable manufacturing of complex constructs, proactive management of cytokine-driven immunotoxicity, and robust biomarker-guided patient selection. The future of nano-immunotherapy lies in adaptive platforms that leverage liquid biopsy and computational modeling to dynamically counter the spatiotemporal evolution of resistance, offering a transformative paradigm for managing aggressive skin cancers.
In the field of computational pathology, multiple instance learning (MIL) has become a critical modeling framework for analyzing gigapixel whole-slide images (WSIs). Data scarcity, particularly in survival analyses requiring long-term follow-up, is a prevalent challenge that is often mitigated through data augmentation. Although data augmentation methods for multi-instance classification have progressed, they remain insufficient for multi-instance regression in survival analysis. Compared to classification tasks, data augmentation for survival analysis faces two major challenges: i) higher quality requirements for pseudo-labels on newly generated samples, and ii) imbalanced data distribution, exhibiting the long-tail distribution. To address these challenges, this paper introduces InterMix, a novel plug-and-play multi-instance data augmentation scheme inspired by the concept of intervention. To provide high-quality pseudo-labels, sub-bag assessment rules are proposed to screen out stable and consistent sub-bags before mixing. To alleviate the long-tail problem, this paper proposes the complementary mix strategy to generate minority-augmented instances with high diversity based on the risk distribution of patients. Extensive experiments are conducted on three datasets to demonstrate the superiority of InterMix. In addition, it enables the model to focus more on key areas of concern for pathologists. Our source code has been made available at github.com/lingzhi-T/InterMIx.
