Pathological-microenvironment responsive injectable GelMA hydrogel with visualized biodegradation for pressure-assisted treatment of hypertrophic scars.

Intralesional injection of 5-fluorouracil for the clinical treatment of hypertrophic scars (HS) remains challenging due to its short half-life, as well as the absence of evidence-based dosage and frequency injection guidelines. Herein, we developed a matrix metalloproteinases (MMPs)/reactive oxygen species (ROS)-responsive injectable prodrug hydrogel (GFP) that exhibits sustained drug release and fluorescence imaging capability, aiming to facilitate the optimization of injection dosage and frequency in HS treatment. The GFP hydrogel comprises gelatin methacryloyl and pendant methacryloyl-decorated tetrapeptide (PPPK) with 5-fluorouracil acetic acid/rhodamine B at the N-terminus.

Association Between Melanoma Metastasis and Metabolic Syndrome: A Cross-Sectional Study in a Chinese Population.

Metabolic syndrome (MetS) remains a significant global public health concern. However, the relationship between MetS, its individual components and melanoma metastasis remains unexplored. We analysed the clinical data of 258 Chinese melanoma patients who had not undergo systemic therapy.

MerTK macrophages promote melanoma progression and immunotherapy resistance through AhR-ALKAL1 activation.

Despite our increasing understanding of macrophage heterogeneity, drivers of macrophage phenotypic and functional polarization in the microenvironment are not fully elucidated. Here, our single-cell RNA sequencing data identify a subpopulation of macrophages expressing high levels of the phagocytic receptor MER proto-oncogene tyrosine kinase (MerTK macrophages), which is closely associated with melanoma progression and immunotherapy resistance. Adoptive transfer of the MerTK macrophages into recipient mice notably accelerated tumor growth regardless of macrophage depletion.

Identification and validation of CCR5 linking keloid with atopic dermatitis through comprehensive bioinformatics analysis and machine learning.

There is sufficient evidence indicating that keloid is strongly associated with atopic dermatitis (AD) across ethnic groups. However, the molecular mechanism underlying the association is not fully understood. The aim of this study is to discover the underlying mechanism of the association between keloid and AD by integrating comprehensive bioinformatics techniques and machine learning methods.