Rational Design of Amorphous Carbon-Coated Laminar-Structured Wood for Integrating Repeatable Early Fire Detection and High-Temperature Affordable Flexible Pressure Sensing in One System.

High-temperature affordable flexible polymer-based pressure sensors integrated with repeatable early fire warning service are strongly desired for harsh environmental applications, yet their creation remains challenging. This work proposed an approach for preparing such advanced integrated sensors based on silver nanoparticles and an ammonium polyphosphate (APP)-modified laminar-structured bulk wood sponge (APP/Ag@WS). Such integrated sensors demonstrated excellent fire warning performance, including a short response time (minimum of 0.44 s), a long-lasting alarm time (>750 s), and reliable repeatability. Moreover, it achieved high-temperature affordable flexible pressure sensing that exhibited an almost unimpaired working range of 0-7.5 kPa and a higher sensitivity (in the low-pressure range, maximum to 226.03 kPa-1) after fire. The high stability was attributed to reliable structural elasticity, and the wood-derived amorphous carbon is capable of repeatable fire warnings. Finally, a Ag@APP/WS-based wireless fire alarm system that realized reliable house fire accident detection was demonstrated, showing great promise for smart firefighting application.

An Important Role in Novel Immune Mechanism and Diagnostic Model of Ankylosing Spondylitis: The CeRNA-ADRB2 Network.

Objective: The mechanism of ankylosing spondylitis (AS) remains unclear, and clinical diagnosis still pose challenges. This study aims to explore potential regulatory mechanisms underlying AS and develop a novel diagnostic model. Methods: Interspinous ligament (ISL) tissues were collected from control samples and ankylosing spondylitis with kyphotic deformity (AS-KD) samples during surgery, followed by high-throughput sequencing. By integrating gene expression profiles from publicly available AS peripheral blood (PB) samples, differentially expressed immune genes (DEIRGs) were identified. Through gene set enrichment analysis(GSEA), gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, the regulatory mechanisms of the immune gene family in AS were explored. A diagnostic model for AS were constructed and validated it externally. Additionally, a competing endogenous RNA(ceRNA)-protein regulatory network was built for key immune genes. Results: Adrenergic receptor beta 2 (ADRB2) was downregulated in both ISL and PB samples. It was enriched in common pathways, including natural killer cell-mediated cytotoxicity, B cell receptor signaling pathway, Th1 and Th2 cell differentiation. Using the LASSO algorithm, 12 DEIRGs were identified, including the downregulated ADRB2. Based on the DEIRGs family, a novel diagnostic model was constructed with an AUC of 0.87 for the validation set and 0.7 for the test set. The AUC for ADRB2 alone was 0.75. Subgrouping AS based on these immune genes revealed a close association with neutrophils. GSEA and KEGG analysis of ISL, PB, and subgrouping of AS showed that ADRB2 may be involved in regulating the T cell receptor signaling pathway. Immune infiltration analysis indicated a decrease in CD8+ T cell infiltration, which was positively correlated with ADRB2. ADRB2 in AS-KD was regulated by multiple ceRNA-protein (lncRNA-[hsa-miR-513a-5p]-mRNA-protein). Conclusion: The immune gene family, especially ADRB2, participates in the mechanism and contributes to the diagnosis of AS.

Clinical model of pulmonary metastasis in patients with osteosarcoma: A new multiple machine learning-based risk prediction.

BACKGROUND: Metastasis is one of the most significant prognostic factors in osteosarcoma (OS). The goal of this study was to construct a clinical prediction model for OS patients in a population cohort and to evaluate the factors influencing the occurrence of pulmonary metastasis. METHODS: We collected data from 612 patients with osteosarcoma (OS), and 103 clinical indicators were collected. After the data were filtered, the patients were randomly divided into training and validation cohorts by using random sampling. The training cohort included 191 patients with pulmonary metastasis in OS and 126 patients with non-pulmonary metastasis, and the validation cohort included 50 patients with pulmonary metastasis in OS and 57 patients with non-pulmonary metastasis. Univariate logistics regression analysis, LASSO regression analysis and multivariate logistic regression analysis were performed to identify potential risk factors for pulmonary metastasis in patients with osteosarcoma. A nomogram was developed that included risk influencing variables selected by multivariable analysis, and used the concordance index (C-index) and calibration curve to validate the model. Receiver operating characteristic curve (ROC), decision analysis curve (DCA) and clinical impact curve (CIC) were employed to assess the model. In addition, we used a predictive model on the validation cohort. RESULTS: Logistic regression analysis was used to identify independent predictors [N Stage + Alkaline phosphatase (ALP)+Thyroid stimulating hormone (TSH)+Free triiodothyronine (FT3)]. A nomogram was constructed to predict the risk of pulmonary metastasis in patients with osteosarcoma. The performance was evaluated by the concordance index (C-index) and calibration curve. The ROC curve provides the predictive power of the nomogram (AUC = 0.701 in the training cohort, AUC = 0.786 in the training cohort). Decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated the clinical value of the nomogram and higher overall net benefits. CONCLUSIONS: Our study can help clinicians effectively predict the risk of lung metastases in osteosarcoma with more readily available clinical indicators, provide more personalized diagnosis and treatment guidance, and improve the prognosis of patients. MINI ABSTRACT: A new risk model was constructed to predict the pulmonary metastasis in patients with osteosarcoma based on multiple machine learning.