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This study addresses the need for high-performance and sustainable air filters by developing a bio-based, high-efficiency particulate air (HEPA) filter. Current HEPA filters often rely on non-biodegradable materials, creating environmental burdens. In this paper, we presented a HEPA filter fabricated from natural basalt fiber (BF) and nanocellulose fiber. The developed filter featured a sandwich structure with electrospun nanocellulose fiber deposited onto a base BF layer, followed by a second BF layer and heat treatment. Various techniques were employed to characterize the obtained sample, and the results showed that the nonwoven BF fabric significantly reduced the pressure drop of the filter by up to 60 %. The nanocellulose fiber played a crucial role in achieving a remarkable filtration efficiency of 99.99 % for PM0.3. BF-based filter demonstrated exceptional fire resistance, hydrophobia, durability, and ease of cleaning, maintaining its effectiveness at temperatures up to 150 °C. Notably, it exhibited significantly better biodegradability than commercially available HEPA filters. By employing a hierarchical structure of sustainable basalt and cellulose fibers, this study paved the way for the development of next-generation hazardous particulate matter filters with exceptional performance in harsh conditions and reduced environmental impact.
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BACKGROUND: Accurate preoperative prediction of lymph node metastasis (LNM) in esophageal cancer (EC) patients is of crucial clinical significance for treatment planning and prognosis. AIM: To develop a clinical radiomics nomogram that can predict the preoperative lymph node (LN) status in EC patients. METHODS: A total of 32 EC patients confirmed by clinical pathology (who underwent surgical treatment) were included. Real-time fluorescent quantitative reverse transcription-polymerase chain reaction was used to detect the expression of B7-H3 mRNA in EC tissue obtained during preoperative gastroscopy, and its correlation with LNM was analyzed. Radiomics features were extracted from multi-modal magnetic resonance imaging of EC using Pyradiomics in Python. Feature extraction, data dimensionality reduction, and feature selection were performed using XGBoost model and leave-one-out cross-validation. Multivariable logistic regression analysis was used to establish the prediction model, which included radiomics features, LN status from computed tomography (CT) reports, and B7-H3 mRNA expression, represented by a radiomics nomogram. Receiver operating characteristic area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical application value of the model. RESULTS: The relative expression of B7-H3 mRNA in EC patients with LNM was higher than in those without metastasis, and the difference was statistically significant (P < 0.05). The AUC value in the receiver operating characteristic (ROC) curve was 0.718 (95%CI: 0.528-0.907), with a sensitivity of 0.733 and specificity of 0.706, indicating good diagnostic performance. The individualized clinical prediction nomogram included radiomics features, LN status from CT reports, and B7-H3 mRNA expression. The ROC curve demonstrated good diagnostic value, with an AUC value of 0.765 (95%CI: 0.598-0.931), sensitivity of 0.800, and specificity of 0.706. DCA indicated the practical value of the radiomics nomogram in clinical practice. CONCLUSION: This study developed a radiomics nomogram that includes radiomics features, LN status from CT reports, and B7-H3 mRNA expression, enabling convenient preoperative individualized prediction of LNM in EC patients.
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Esophageal cancer, also referred to as esophagus cancer, is a prevalent disease in the cardiothoracic field and is a leading cause of cancer-related mortality in China. Accurately determining the status of lymph nodes is crucial for developing treatment plans, defining the scope of intraoperative lymph node dissection, and ascertaining the prognosis of patients with esophageal cancer. Recent advances in diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging (MRI) have improved the effectiveness of MRI for assessing lymph node involvement, making it a beneficial tool for guiding personalized treatment plans for patients with esophageal cancer in a clinical setting. Radiomics is a recently developed imaging technique that transforms radiological image data from regions of interest into high-dimensional feature data that can be analyzed. The features, such as shape, texture, and waveform, are associated with the cancer phenotype and tumor microenvironment. When these features correlate with the clinical disease outcomes, they form the basis for specific and reliable clinical evidence. This study aimed to review the potential clinical applications of MRI-based radiomics in studying the lymph nodes affected by esophageal cancer. The combination of MRI and radiomics is a powerful tool for diagnosing and treating esophageal cancer, enabling a more personalized and effectual approach.