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Efficient wound repair with skin appendage regeneration following severe trauma poses a challenge due to the scarcity of skin grafts and decreased drug effectiveness in protease‐rich wound microenvironments. Here, a multifunctional artificial skin (NIR‐mFAS) with photothermal‐triggered drug delivery capabilities is designed to actively and comprehensively improve the regenerative potential of full‐thickness wounds. The antibacterial chitosan/silk fibroin hydrogel matrix of artificial skin, cross‐linked by electrostatic interactions, effectively encapsulates and sustains the release of epidermal growth factor (EGF) to accelerate re‐epithelialization and neovascularization by promoting the migration and proliferation of repair cells. Subsequently, the photothermal responsive polydopamine nanoparticles (PDA‐NPs) dispersed in the matrix enable precise control over the release of BMP4 under the irradiation of 1064 nm NIR, thereby inhibiting scarring by reducing myofibroblasts during the proliferative stage. Importantly, the concurrent controlled release of CHIR99021 can modulate cell fate by inducing the conversion of myofibroblasts into dermal papilla‐like cells, leading to hair follicle and sebaceous gland regeneration. The NIR‐mFAS functions as an advanced delivery system for achieving high‐quality wound healing with appendage regeneration and offers a smart therapeutic approach that can be applied to other treatments requiring coordinated delivery of multiple pharmacological agents. [ABSTRACT FROM AUTHOR]

Background Neutrophil to lymphocyte ratio (NLR) has been indicated to be an independent predictor for all-cause mortality or adverse events in a variety of diseases. However, no consistent conclusions regarding it's relevance to patients with heart failure have been made. This meta-analysis was conducted to assess the significance of NLR in patients with heart failure. Methods Pubmed and Embase databases were searched for eligible studies that reported the association between NLR and heart failure through September 2017. The overall hazard ratio (HR) and corresponding 95% confidence interval (CI) were used to assess the associations. Results Ten studies met the eligibility criteria and a total of 5979 heart failure patients were included in the meta-analysis. The overall hazard ratio (HR) for all-cause mortality was 1.28 (95% CI 1.14–1.43) and the HR of renal dysfunction was 1.23 (95% CI 1.07–1.41) comparing the highest with the lowest category of NLR. However, the total pooled adjusted HR for in-hospital mortality (HR = 1.18, 95% CI 0.88–1.59) and rehospitalization (HR = 2.19, 95% CI 0.94–5.09) were not statistically significant. A subgroup analysis showed that sample size with moderate heterogeneity may be the origin of heterogeneity in all-cause mortality. Sensitivity analysis proved the stability of results of our meta-analysis. Conclusions The meta-analysis demonstrates that NLR is a predictor of all-cause mortality in patients with heart failure. Because the quality of the included studies varies, further well-designed studies are needed to confirm this association. [ABSTRACT FROM AUTHOR]

Deep learning models have emerged as rapid, accurate, and effective approaches for clinical decisions. Through a combination of drug screening and deep learning models, drugs that may benefit patients before and after surgery can be discovered to reduce the risk of complications or speed recovery. However, most existing drug prediction methods have high data requirements and lack interpretability, which has a limited role in adjuvant surgical treatment. To address these limitations, we propose the attention-based convolution transpositional interfusion network (ACTIN) for flexible and efficient drug discovery. ACTIN leverages the graph convolution and the transformer mechanism, utilizing drug and transcriptome data to assess the impact of chemical pharmacophores containing certain elements on gene expression. Remarkably, just with only 393 training instances, only one-tenth of the other models, ACTIN achieves state-of-the-art performance, demonstrating its effectiveness even with limited data. By incorporating chemical element embedding disparity and attention mechanism-based parameter analysis, it identifies the possible pharmacophore containing certain elements that could interfere with specific cell lines, which is particularly valuable for screening useful pharmacophores for new drugs tailored to adjuvant surgical treatment. To validate its reliability, we conducted comprehensive examinations by utilizing transcriptome data from the lung tissue of fatal COVID-19 patients as additional input for ACTIN, we generated novel lead chemicals that align with clinical evidence. In summary, ACTIN offers insights into the perturbation biases of elements within pharmacophore on gene expression, which holds the potential for guiding the development of new drugs that benefit surgical treatment., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)