Search

Objective To investigate the inhibitory effect of cordycepin on immune function injury in lung cancer rats after radiation therapy through JAK2/STAT3 signaling pathway. Methods Fifty rats were used to establish tumor-bearing model and other 10 rats were taken as normal group. After successful modeling, the rats were randomly divided into model group, radiotherapy group, cordycepin group, agonist group and agonist+cordycepin group (10 rats in each group). We compared tumor weight, tumor volume, tumor inhibition rate, IL-6, TNF-α, spleen index and thymus index, the number of T lymphocyte subsets, JAK2, p-JAK2, STAT3 and p-STAT3 protein expression levels among all groups. Results Compared with normal group, IL-6, TNF-α, CD8+, p-JAK2 and p-STAT3 in model group were increased, while spleen index, thymus index, CD4+ and CD4+/CD8+ were decreased (P < 0.05). Compared with model group, tumor weight, tumor volume, spleen index, thymus index, CD4+ and CD4+/CD8+ were decreased in radiotherapy group, while IL-6, TNF-α, CD8+, p-JAK2 and p-STAT3 were increased (P < 0.05). Compared with radiotherapy group, tumor weight, tumor volume, IL-6, TNF-α, CD8+, p-JAK2 and p-STAT3 were decreased in cordycepin group, while tumor inhibition rate, spleen index thymus index, CD4+ and CD4+/CD8+ were increased; tumor weight, tumor volume, IL-6, TNF-α, CD8+, p-JAK2 and p-STAT3 protein expression were increased in agonist group, while tumor inhibition rate, spleen index, thymus index, CD4+ and CD4+/CD8+ were decreased (P < 0.05). Compared with agonist+cordycepin group, tumor weight, tumor volume, IL-6, TNF-α, CD8+, p-JAK2 and p-STAT3 were decreased in cordycepin group, while tumor inhibition rate, spleen index, thymus index, CD4+ and CD4+/CD8+ were increased; tumor weight, tumor volume, IL-6, TNF-α, CD8+, p-JAK2 and p-STAT3 were increased in agonist group, while tumor inhibition rate, spleen index, thymus index, CD4+ and CD4+/CD8+ were decreased (P < 0.05). Conclusion Cordycepin can effectively inhibit the immune function injury in lung cancer rats after radiation therapy, and may play a regulatory role by inhibiting the JAK2/STAT3 signal pathway.

Standardized datasets and benchmarks have spurred innovations in computer vision, natural language processing, multi-modal and tabular settings. We note that, as compared to other well researched fields fraud detection has numerous differences. The differences include a high class imbalance, diverse feature types, frequently changing fraud patterns, and adversarial nature of the problem. Due to these differences, the modeling approaches that are designed for other classification tasks may not work well for the fraud detection. We introduce Fraud Dataset Benchmark (FDB), a compilation of publicly available datasets catered to fraud detection. FDB comprises variety of fraud related tasks, ranging from identifying fraudulent card-not-present transactions, detecting bot attacks, classifying malicious URLs, predicting risk of loan to content moderation. The Python based library from FDB provides consistent API for data loading with standardized training and testing splits. For reference, we also provide baseline evaluations of different modeling approaches on FDB. Considering the increasing popularity of Automated Machine Learning (AutoML) for various research and business problems, we used AutoML frameworks for our baseline evaluations. For fraud prevention, the organizations that operate with limited resources and lack ML expertise often hire a team of investigators, use blocklists and manual rules, all of which are inefficient and do not scale well. Such organizations can benefit from AutoML solutions that are easy to deploy in production and pass the bar of fraud prevention requirements. We hope that FDB helps in the development of customized fraud detection techniques catered to different fraud modus operandi (MOs) as well as in the improvement of AutoML systems that can work well for all datasets in the benchmark.

Forest disturbance monitoring can provide scientific data for the decision making and management of nature reserves. LandTrendr algorithm has been applied to identify forest disturbances on a long-time scale through appropriate segmentation and linear fitting. In this study, 23 nature reserves were detected using LandTrendr during 1987–2020, and the vegetation loss was quantified by years and pixel numbers. The results illustrated that (1) most disturbances occurred in the 1990s and early 21st century. (2) From the spatial distribution of forest loss, the area of forest vegetation disturbance in the coastal zone was larger than the protected area in the internal Hainan Island, the area disturbed in the coastal zone protected area was 97.12 km2, and the area disturbed in the internal area of Hainan Island protected area was 63.02 km2. (3) In terms of different levels of nature reserves, the disturbed area of national nature reserves was 28.39 km2 and the total disturbed area of provincial nature reserves was 131.75 km2. (4) In terms of different types of nature reserves, forest ecological nature reserves had the largest disturbed area of 102.96 km2, followed by marine coastal nature reserves with a disturbed area of 36.99 km2, wildlife nature reserves with a disturbed area of 10.22 km2, and wild plant nature reserves with the smallest disturbed area of 9.96 km2. The results are hoped to provide scientific support and data for the management and planning of nature reserves in Hainan Island.