Automatic quantification and classification of microplastics in scanning electron micrographs via deep learning.

Microplastics quantification and classification are demanding jobs to monitor microplastic pollution and evaluate the potential health risks. In this paper, microplastics from daily supplies in diverse chemical compositions and shapes are imaged by scanning electron microscopy. It offers a greater depth and finer details of microplastics at a wider range of magnification than visible light microscopy or a digital camera, and permits further chemical composition analysis. However, it is labour-intensive to manually extract microplastics from micrographs, especially for small particles and thin fibres. A deep learning approach facilitates microplastics quantification and classification with a manually annotated dataset including 237 micrographs of microplastic particles (fragments or beads) in the range of 50 µm-1 mm and fibres with diameters around 10 µm. For microplastics quantification, two deep learning models (U-Net and MultiResUNet) were implemented for semantic segmentation. Both significantly outmatched conventional computer vision techniques and achieved a high average Jaccard index over 0.75. Especially, U-Net was combined with object-aware pixel embedding to perform instance segmentation on densely packed and tangled fibres for further quantification. For shape classification, a fine-tuned VGG16 neural network classifies microplastics based on their shapes with high accuracy of 98.33%. With trained models, it takes only seconds to segment and classify a new micrograph in high accuracy, which is remarkably cheaper and faster than manual labour. The growing datasets may benefit the identification and quantification of microplastics in environmental samples in future work.

Dynamic study of oral Candida infection and immune status in HIV infected patients during HAART.

OBJECTIVE: To dynamically monitor oral Candida infection, CD4+ T lymphocyte counts, CD8+ T lymphocyte counts and CD4/CD8 ratios in HIV/AIDS patients during their first year of HAART and to preliminarily explore the relationships between them. DESIGN: Forty-six patients with HIV/AIDS receiving HAART at the Infection Department of Chongqing Red Cross Hospital were followed for one year. At baseline and 3, 6, and 12 months after HAART initiation, oral rinses were collected and cultured to identify Candida species. Further, blood samples were collected to detect CD4+ T lymphocyte counts, CD8+ T lymphocyte counts and CD4/CD8 ratios. RESULTS: The prevalence of OC decreased after HAART initiation (P ＜ 0.05), and Candida albicans was the dominant species isolated from the oral cavity (66/93). At 3, 6 and 12 months after HAART initiation, CD4+ T lymphocyte counts were 327.91 ± 138.82, 329.65 ± 142.66 and 319.98 ± 97.90 cells/mm3, respectively, which were significantly higher than the level at baseline (263.39 ± 126.01 cells/mm3) (P = 0.016, P = 0.014, and P = 0.035, respectively). During the first year of HAART, CD4/CD8 ratios increased gradually, and CD8+ T lymphocyte counts decreased continually. OC was associated with low CD4+ T lymphocyte counts and a low CD4/CD8 ratio. CD4+ T lymphocyte counts <200 cells/mm3 and Candida load ≥300 CFU/mL were risk factors for OC (P ＜ 0.05), and oral Candida load was negatively correlated with CD4+ T lymphocyte counts and the CD4/CD8 ratio. CONCLUSION: OC might be a useful marker for the evaluation of immune status in patients with HIV/AIDS.

[Surgical treatment for chronic radial head dislocation].

At present, treatment for chronic radial head dislocation mainly focus on open reduction, and conservative treatment is invalid. While the surgical procedure generally applies to extend ulnar, short radial, reset radial head. Repair or reconstruction of annular ligament is the focus of study. For the dysfunction, radial head resection served as the final choice. Early diagnosis is the key to the treatment of chronic radial head dislocation. While ulnar or radial osteotomy and non-reconstruction of annular ligament is still the main trend of today.