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Objective To construct a deep learning model based on multi -task learning to assist clinicians in differential diagnosis and staging of pneumoconiosis. Methods The digital chest radiographs of 3 600 patients from an occupational disease hospital in Sichuan Province from 2011 to 2022 were collected, and the full convolution neural network (UNet) was used to segment the lung field. Based on multi-task learning, the multi-task model was constructed using the correlation between tasks. The multi -task model was pre -trained on the ChestX -ray14 dataset, whose backbone network was DenseNetl21, and two classifiers were added behind the backbone network. Paired t-test was used to compare the differences in accuracy, precision, sensitivity, and Fl scores between single-task model and multi-task model. Results The test set results showed that the differential diagnosis and diagnostic staging performance of the single-task model was about 90% and 77%, respectively. The differential diagnosis and diagnosis staging performance of the multi-task model was about 94% and 86%, which was higher than that of the single-task model about 4% and 9%, respectively. The difference between the evaluation indexes was statistically significant (P < 0.05). Conclusion The multi-task model has more advantages than the single-task model and can effectively realize the differential diagnosis and accurate staging of pneumoconiosis and pulmonary tuberculosis. [ABSTRACT FROM AUTHOR]

Epimedium rhizomatosum is a rare endemic plant species inhabited in some high-elevation mountain area in the Sichuan province of China. In this study, we assembled the complete chloroplast genome of E. rhizomatosum from Illumina short-read sequencing data. The chloroplast genome of E. rhizomatosum was 159,151 bp in length, which comprises of a large single copy (LSC) region of 86,604 bp, a small single copy (SSC) region of 17,071 bp and two inverted repeat regions (IRa and IRb) of 27,737 bp for each. The total GC content of E. rhizomatosum chloroplast genome was 38.8%, while the corresponding values of LSC, SSC, and IR regions were 37.3%, 32.8%, and 43.0%, respectively. A total of 112 unique genes was identified from the chloroplast genome of E. rhizomatosum, including 78 protein-coding genes, four ribosomal RNA genes, and 30 tRNA genes. The phylogenetic analysis showed that E. rhizomatosum closely related to E. acuminatum. Our study will provide useful information on further clarifying the phylogenetic and evolutionary relationship in the genus Epimedium. [ABSTRACT FROM AUTHOR]