RESUMEN
Gaseous fission products from nuclear fission reactions tend to form fission gas bubbles of various shapes and sizes inside nuclear fuel. The behavior of fission gas bubbles dictates nuclear fuel performances, such as fission gas release, grain growth, swelling, and fuel cladding mechanical interaction. Although mechanical understanding of the overall evolution behavior of fission gas bubbles is well known, lacking the quantitative data and high-level correlation between burnup/temperature and microstructure evolution blocks the development of predictive models and reduces the possibility of accelerating the qualification for new fuel forms. Historical characterization of fission gas bubbles in irradiated nuclear fuel relied on a simple threshold method working on low-resolution optical microscopy images. Advanced characterization of fission gas bubbles using scanning electron microscopic images reveals unprecedented details and extensive morphological data, which strains the effectiveness of conventional methods. This paper proposes a hybrid framework, based on digital image processing and deep learning models, to efficiently detect and classify fission gas bubbles from scanning electron microscopic images. The developed bubble annotation tool used a multitask deep learning network that integrates U-Net and ResNet to accomplish instance-level bubble segmentation. With limited annotated data, the model achieves a recall ratio of more than 90%, a leap forward compared to the threshold method. The model has the capability to identify fission gas bubbles with and without lanthanides to better understand the movement of lanthanide fission products and fuel cladding chemical interaction. Lastly, the deep learning model is versatile and applicable to the micro-structure segmentation of similar materials.
RESUMEN
This study aimed to investigate the associations between traditional Chinese medicine (TCM) syndromes and driver gene mutations as well as the clinical characteristics of patients with lung adenocarcinoma. We performed a cross-sectional study in patients with lung adenocarcinoma between June 2020 and October 2021. The patient characteristics, such as age, sex, smoking history, clinical stage, metastasis, driver gene mutations, and the type of traditional Chinese medicine syndrome/element, were collected. The associations between each TCM syndrome and sex, smoking history, clinical stage, metastasis, and driver gene mutations were analyzed. The present study included 127 patients. The most frequent TCM syndromes were Qi and Yin deficiency (39, 30.7%) and lung-spleen Qi deficiency (32, 25.2%). Eighty-one (63.8%) patients had mutations in driver genes, especially in the EGFR gene (64, 79.0%). There was a statistically significant association between a driver gene mutation and TCM syndrome (P < 0.05). Genetic mutations presented more frequently in patients with Qi and Yin deficiency (37.0%), lung-spleen Qi deficiency (30.0%), or the cold element (59.3%). Male patients were more likely to have Qi stagnation and blood stasis, whereas female patients were more likely to have lung-spleen Qi deficiency or Qi and Yin deficiency. The patients with lung-spleen Qi deficiency were usually younger than those with Qi and Yin deficiency or Qi stagnation and blood stasis (P < 0.05). Compared with the patients with other TCM syndromes, the patients with Yin and Yang deficiency were more likely to have bone metastasis. TCM syndromes were associated with driver gene mutations, sex, age, and bone metastasis in patients with lung adenocarcinoma.
RESUMEN
Hepatocellular carcinoma (HCC, accounting for 90% of primary liver cancer) was the sixth most common cancer in the world and the third leading cause of cancer death in 2020. The number of new HCC patients in China accounted for nearly half of that in the world. HCC was of occult and complex onset, with poor prognosis. Clinically, at least 15% of patients with HCC had strong side effects of interventional therapy (IT) and have poor sensitivity to chemotherapy and targeted therapy. Traditional Chinese medicine (TCM), as a multi-target adjuvant therapy, had been shown to play an active anti-tumor role in many previous studies. This review systematically summarized the role of TCM combined with clinically commonly used drugs for the treatment of HCC (including mitomycin C, cyclophosphamide, doxorubicin, 5-fluorouracil, sorafenib, etc.) in the past basic research, and summarized the efficacy of TCM combined with surgery, IT and conventional therapy (CT) in clinical research. It was found that TCM, as an adjuvant treatment, played many roles in the treatment of HCC, including enhancing the tumor inhibition, reducing toxic and side effects, improving chemosensitivity and prolonging survival time of patients. This review summarized the advantages of integrated traditional Chinese and modern medicine in the treatment of HCC and provides a theoretical basis for clinical research.