Artificial intelligence-based prediction model for the elemental occurrence form of tailings and mine wastes.

With the advent of the second industrial revolution, mining and metallurgical processes generate large volumes of tailings and mine wastes (TMW), which worsens global environmental pollution. Studying the occurrence of metal and metalloid elements in TMW is an effective approach to evaluating pollution linked to TMW. However, traditional laboratory-based measurements are complicated and time-consuming; thus, an empirical method is urgently needed that can rapidly and accurately determine elemental occurrence forms. In this study, a model combining Bayesian optimization and random forest (RF) approaches was proposed to predict TMW occurrence forms. To build the RF model, a dataset of 2376 samples was obtained, with mineral composition, elemental properties, and total concentration composition used as inputs and the percentage of occurrence forms as the model output. The correlation coefficient (R), coefficient of determination, mean absolute error, root mean squared error, and root mean squared logarithmic error metrics were used for model evaluation. After Bayesian optimization, the optimal RF model achieved accurate predictive performance, with R values of 0.99 and 0.965 on the training and test sets, respectively. The feature significance was analyzed using feature importance and Shapley additive explanatory values, which revealed that the electronegativity and total concentration of the elements were the two features with the greatest influence on the model output. As the electronegativity of an element increases, its corresponding residual fraction content gradually decreases. This is because the solubility typically increases with the solvent's polarity and electronegativity. Overall, this study proposes an RF model based on the nature of TMW that can rapidly and accurately predict the percentage values of metal and metalloid element occurrence forms in TMW. This method can minimize testing time requirements and help to assess TMW pollution risks, as well as further promote safe TMW management and recycling.

Diagnosis of Pneumocystis jirovecii pneumonia by cytological examination of bronchoalveolar lavage fluid combined with metagenomic next-generation sequencing / 中国热带医学

@#Abstract：Objective To investigate the morphological features of the Pneumocystis jirovecii, in order to facilitate early detection and rapid diagnosis of this rare pathogen from a morphology point of view by laboratory technicians. By analyzing the laboratory features and application value of different pathogen detection methods in the diagnosis of Pneumocystis jirovecii pneumonia, we aim to provide the most reliable diagnostic basis for rapid diagnosis of Pneumocystis jirovecii pneumonia.Methods A retrospective analysis was conducted on the test results of bronchoalveolar lavage fluid samples from a comprehensive hospital in Zhangqiu District, Jinan City, Shandong Province, and a hospital in Changde City from April 2022 to October 2022. Five confirmed cases of Pneumocystis jirovecii pneumonia were detected. Its clinical manifestations, laboratory results, and morphological characteristics of pathogens under different stains were analyzed to discuss the advantages and disadvantages of different detection methods. Results Cytological examination of bronchoalveolar lavage fluid found the trophozoites and cysts of Pneumocystis jirovecii by Wright's-Giemsa staining in 4 cases (80%), and the cysts of Pneumocystis jirovecii by Silver hexamine staining in 4 cases (80%), while the metagenomic next-generation sequencing confirmed all the 5 positive results. All 5 patients had different degrees of reduction in the absolute count of peripheral blood lymphocytes, and the serum lactic dehydrogenase and (1-3)-β-D-Glucan were increased. Among the 5 patients in this study, 4 were treated with sulfamethoxazole combined with caspofungin, and 1 was treated with sulfamethoxazole. Three patients were cured and discharged from hospital after treatment, but two died. Conclusions The method of Wright's-Giemsa staining for the cytological examination of bronchoalveolar lavage fluid to find Pneumocystis jirovecii has the unique and irreplaceable advantages as silver staining. Metagenomic next-generation sequencing can further increase the positive detection rate of Pneumocystis jirovecii. The combination of cytological examination of bronchoalveolar lavage fluid with metagenomic nextgeneration sequencing is a powerful diagnostic method for rapid diagnosis of Pneumocystis jirovecii pneumonia, which can diagnose accurately and reduce missed diagnosis.