Lung cancer is a common malignant tumor disease with high clinical disability and death rates. Currently, lung cancer diagnosis mainly relies on manual pathology section analysis, but the low efficiency and subjective nature of manual film reading can lead to certain misdiagnoses and omissions. With the continuous development of science and technology, artificial intelligence (AI) has been gradually applied to imaging diagnosis. Although there are reports on AI-assisted lung cancer diagnosis, there are still problems such as small sample size and untimely data updates. Therefore, in this study, a large amount of recent data was included, and meta-analysis was used to evaluate the value of AI for lung cancer diagnosis. With the help of STATA16.0, the value of AI-assisted lung cancer diagnosis was assessed by specificity, sensitivity, negative likelihood ratio, positive likelihood ratio, diagnostic ratio, and plotting the working characteristic curves of subjects. Meta-regression and subgroup analysis were used to investigate the value of AI-assisted lung cancer diagnosis. The results of the meta-analysis showed that the combined sensitivity of the AI-aided diagnosis system for lung cancer diagnosis was 0.87 [95% CI (0.82, 0.90)], specificity was 0.87 [95% CI (0.82, 0.91)] (CI stands for confidence interval.), the missed diagnosis rate was 13%, the misdiagnosis rate was 13%, the positive likelihood ratio was 6.5 [95% CI (4.6, 9.3)], the negative likelihood ratio was 0.15 [95% CI (0.11, 0.21)], a diagnostic ratio of 43 [95% CI (24, 76)] and a sum of area under the combined subject operating characteristic (SROC) curve of 0.93 [95% CI (0.91, 0.95)]. Based on the results, the AI-assisted diagnostic system for CT (Computerized Tomography), imaging has considerable diagnostic accuracy for lung cancer diagnosis, which is of significant value for lung cancer diagnosis and has greater feasibility of realizing the extension application in the field of clinical diagnosis.

The fungal strain BS5 was isolated from a soil sample collected in the Tibetan Plateau, which displayed good insecticidal activity and was identified as Talaromyces purpureogenus based on morphological and molecular analysis. This study aimed to evaluate the insecticidal activity and identify the active compound of the strain BS5 against the locust Locusta migratoria manilensis. The insecticidal activity of the fermented broth of BS5 was at 100% after 7 days against locusts. We extracted the fermented broth of BS5 and then evaluated the insecticidal activity of the extracts against locusts. The ethyl acetate extract exhibited promising activity levels with an LC50 value of 1077.94 μg/mL and was separated through silica gel column chromatography. The UPLC-Q-Exactive Orbitrap/MS system was employed to analyze the active fraction Fr2.2.2 (with an LC50 value of 674.87 μg/mL), and two compounds were identified: phellamurin and rubratoxin B.

Qinghai‐Tibet Plateau is facing a serious environmental and ecological problem of Meadow degradation. Toxic weed invasion is a typical characteristic of grassland degradation. Soil microbial community composition is sensitive to environmental changes; however, the effects of poisonous weed expansion on soil bacterial communities are unclear. Here, we investigated the effects of Stellera chamaejasme L. expansion on the rhizosphere soil bacterial community structure and function using high‐throughput sequencing. The results showed that expansion of Stellera chamaejasme L. changed soil nitrogen(e.g., total nitrogen [TN, −39.02%], available nitrogen [AN, −32.95%]) and other soil nutrients. Redundancy analysis (RDA) and Variance partitioning analysis (VPA) showed that soil nutrients changed, leading to significant changes in the bacterial community structure. The expansion of Stellera chamaejasme L significantly reduced its rhizosphere bacterial alpha diversity, and the beta diversity had significant differences (p < 0.05). Principal coordinates analysis (PCoA) and analysis of similarity (ANOSIM) indicated that the expansion caused significant variations in the rhizosphere bacterial community (R = 0.7037, p < 0.01). The linear discriminant analysis (LDA) effect size (LEfSe) analysis identified 23 biomarkers, most of which were Proteobacteria, indicating that bacteria involved in soil nutrient cycling were better able to survive in the alpine grassland. The Biolog EcoPlate method was used to determine the soil microbial metabolic capacity in different S. chamaejasme expansions. The result showed that heavy expansion had higher carbon source usage ability and microbial diversity index values. Furthermore, it was also found that heavy expansion improved the usage rate of amino acid carbon sources. Tax4Fun prediction analysis further indicated that carbohydrate metabolism, amino acid metabolism, and membrane transport were central metabolic pathways of rhizosphere soil bacteria. Our study found that Stellera chamaejasme L. changed rhizosphere soil nutrient and bacterial community structure during expansion and helped it tolerate harsh conditions by enriching bacterial communities actively involved in carbon and nitrogen metabolism and promoting plant growth. These findings provided evidence to propose effective restoration measures for poisonous grassland degradation.

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