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Objective: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms. Methods: The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis. This was followed by employing Multilayer Perceptron (MLP), Adaptive Boosting (AB), Bagging (BAG), logistic regression (LR), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGB) algorithms to develop metastasis models. The performance of the six models was evaluated and compared, leading to the selection and visualization of the optimal model. Through integrating the prognostic factors of Cox regression analysis with the optimal models, the prognostic prediction model was constructed, validated, and assessed. Results: Logistic regression analyses identified that marital status, gender, primary site, surgery, radiation, chemotherapy, system management, and N stage were all independent risk factors for NM metastasis. MLP emerged as the optimal model among the six models (AUC = 0.932, F1 = 0.855, Accuracy = 0.856, Sensitivity = 0.878), and the corresponding network calculator (https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/) was developed. The following were examined as independent prognostic factors: MLP, age, marital status, sequence number, laterality, surgery, radiation, chemotherapy, system management, T stage, and N stage. System management and surgery emerged as protective factors (HR < 1). To predict 1-, 3-, and 5-year overall survival (OS), a nomogram was created. The validation results demonstrated that the model exhibited good discrimination and consistency, as well as high clinical usefulness. Conclusion: The developed prediction model more effectively reflects the prognosis of patients with NM and differentiates between the risk level of patients, serving as a useful supplement to the classical American Joint Committee on Cancer (AJCC) staging system and offering a reference for clinically stratified individualized treatment and prognosis prediction. Furthermore, the model enables clinicians to quantify the risk of metastasis in NM patients, assess patient survival, and administer precise treatments. [ABSTRACT FROM AUTHOR]

Hexavalent chromium (Cr(VI)) is well known for its high toxicity to humans and the environment. It is of utmost significance to develop robust and effective materials for removing this hazardous compound. Fe nanoparticles (FeNPs) play an important role in the reduction and neutralization of environmental pollutants; however, they still face considerable challenges in performance. Herein, we first propose an encapsulation-pyrolysis strategy to engineer atomically dispersed Fe–N–C sites on the surface of FeNPs (FeNPs@FeSAsNC) as synergistic active sites to increase Cr(VI) reduction. Characterization results confirmed that FeNPs@FeSAsNC was developed by both encapsulation and pyrolysis processes. The FeNPs@FeSAsNC composite demonstrated a promoted reduction of Cr(VI) to Cr(III) with an initial activity of 2036.81 mg (greductant h L)−1 and the removal capacity of 0.259 gCr greductant−1, compared with those of FeNPs/CN (26.91 mg (greductant h L)−1 and 0.024 gCr greductant−1). Spectroscopic tests and theoretical calculations indicated that both electron-rich C/N atoms and low energy barrier for electron transfer in Fe–N–C sites assist in the adsorption and reduction of Cr(VI) on the surface of FeNPs@FeSAsNC. Moreover, FeNPs@FeSAsNC exhibits superior reusability and biocompatibility in practical applications, and the encapsulation-pyrolysis strategy was effective in activating other Fe-based reductants. To summarize, this work provides an approach to engineering Fe–N–C sites on the surface of FeNPs for the remediation of Cr(VI)-contaminated water. [ABSTRACT FROM AUTHOR]

Background: Cholangiocarcinoma (CCA) is a highly malignant and easily metastatic bile duct tumor with poor prognosis. We aimed at studying the associated risk factors affecting distal metastasis of CCA and using nomogram to guide clinicians in predicting distal metastasis of CCA. Methods: Based on inclusion and exclusion criteria, 345 patients with CCA were selected from the Fifth Medical Center of Chinese PLA General Hospital and were divided into distal metastases (N = 21) and non-distal metastases (N = 324). LASSO regression models were used to screen for relevant parameters and to compare basic clinical information between the two groups of patients. Risk factors for distal metastasis were identified based on the results of univariate and multivariate logistic regression analyses. The nomogram was established based on the results of multivariate logistic regression, and we drawn the corresponding correlation heat map. The predictive accuracy of the nomogram was evaluated by receiver operating characteristic (ROC) curves and calibration plots. The utility of the model in clinical applications was illustrated by applying decision curve analysis (DCA), and overall survival(OS) analysis was performed using the method of Kaplan-meier. Results: This study identified 4 independent risk factors for distal metastasis of CCA, including CA199, cholesterol, hypertension and margin invasion, and developed the nomogram based on this. The result of validation showed that the model had significant accuracy for diagnosis with the area under ROC (AUC) of 0.882 (95% CI: 0.843-0.914). Calibration plots and DCA showed that the model had high clinical utility. Conclusions: This study established and validated a model of nomogram for predicting distal metastasis in patients with CCA. Based on this, it could guide clinicians to make better decisions and provide more accurate prognosis and treatment for patients with CCA. [ABSTRACT FROM AUTHOR]

Activated carbon has attracted much attention in the remediation of heavy metal pollution; however, it is still a challenge to explore a cheap and efficient activated carbon for the removal of chromium(VI) from water. In this study, based on the concept of "treating pollution with waste," a high adsorption performance activated carbon was prepared by in situ carbonization and activation of soybean straw residue with potassium hydroxide as a chemical activator for the purpose of enhancing Cr(VI) removal capacity. The structure and property of the generated soybean straw-based activated carbon (SSAC) was characterized by SEM, FTIR, BET analysis, and XPS. The influence factors (i.e., pH, adsorbent dosage, initial Cr(VI) concentration) on the removal of Cr(VI) by SSAC were studied by batch experiments. Results revealed that SSAC not only exhibited porosity with a huge specific surface area of 1800.28 m2 g−1 but also had abundant electron-donating groups (such as C = C, C–OH) on the surface to reduce Cr(VI) to Cr(III), which both promoted a high adsorption capability with the maximum Cr(VI) adsorption capacity of 294.12 mg g−1 at pH = 2. The adsorption process was in accordance with the Langmuir model and the pseudo second-order model corresponding to monolayer and chemical adsorption, respectively, and it was spontaneous and endothermic. As for the study of mechanism, reduction and electrostatic adsorption may dominate the removal of Cr(VI) by SSAC. All these results indicated that SSAC had the potential to efficiently remove Cr(VI) in aqueous solutions, which may be a promising adsorbent to provide a potential solution for solving the problems of heavy metal pollution and straw agricultural residue disposal. [ABSTRACT FROM AUTHOR]