With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective:To evaluate the safety of mass vaccination of inactivated 2019-nCoV vaccine in population aged ≥3 years in Guizhou Province.Methods:The open-label study was conducted in eligible volunteers in Yanhe County of Guizhou from June 2021 to July in 2022. In the study, two doses of the inactivated 2019-nCoV vaccine were given at (0, 28) days according to the immunization schedule. The information about adverse reaction (AR) within 30 minutes and during 0-28 days after vaccination were collected, and the incidence of AR was analyzed by age, doses, and health status.Results:The overall incidence of AR was 1.51% (294/19 458), all ARs, mainly pain at injection site, occurred within 7 days after the vaccination, the AR grade was 1-2. The incidence of AR was 1.01% in age group 3- years (58/5 721), 2.44% in age group 18- years (220/9 017), and 0.34% in age group ≥60 years (16/4 720). The differences were significant ( P<0.001). The incidence of AR after the first dose vaccination (1.20%, 233/19 458) was significantly higher than that after the second dose (0.37%, 61/16 368), the difference was significant ( P<0.001). In the elderly aged ≥60 years, the incidence of AR was 0.36% (9/2 520) in healthy group and 0.32% (7/2 200) in group with underlying medical conditions, the differences were not significant ( P=0.818). Conclusion:The domestic inactivated 2019-nCoV vaccine showed good safety in mass vaccination in population aged ≥3 years, including the elderly in both healthy group and group with underlying medical conditions.

OBJECTIVETo study the effects of Astragalus polysaccharide (APS), the primary effective component of the Chinese herb medicine Astragalus membranaceus (frequently used for its anti-hepatic fibrosis effects), on nanoscale mechanical properties of liver sinusoidal endothelial cells (SECs).

METHODSUsing endothelial cell medium as the control, 5 experimental groups were established utilizing different concentrations of APS, i.e. 12.5, 25, 50, 100, and 200 μg/mL. By using atomic force microscopy along with a microcantilever modified with a silicon dioxide microsphere as powerful tools, the value of Young's modulus in each group was calculated. SAS 9.1 software was applied to analyze the values of Young's modulus at the pressed depth of 300 nm. Environmental scanning electron microscopy was performed to observe the surface microtopography of the SECs.

RESULTSThe value of Young's modulus in each APS experimental group was significantly greater than that of the control group: as APS concentration increased, the value of Young's modulus presented as an increasing trend. The difference between the low-concentration (12.5 and 25 μg/mL) and high-concentration (200 μg/mL) groups was statistically significant (P<0.05), but no significant differences were observed between moderateconcentration (50 and 100 μg/mL) groups versus low- or high-concentration groups (P>0.05). Surface topography demonstrated that APS was capable of increasing the total area of fenestrae.

CONCLUSIONSThe values of Young's modulus increased along with increasing concentrations of APS, suggesting that the stiffness of SECs increases gradually as a function of APS concentration. The observed changes in SEC mechanical properties may provide a new avenue for mechanistic research of anti-hepatic fibrosis treatments in Chinese medicine.

Animals ; Astragalus Plant ; chemistry ; Biomechanical Phenomena ; drug effects ; Elastic Modulus ; Endothelial Cells ; cytology ; ultrastructure ; Liver ; cytology ; Microscopy, Atomic Force ; Microspheres ; Nanotechnology ; Polysaccharides ; pharmacology ; Rats ; Silicon Dioxide ; chemistry ; Surface Properties

OBJECTIVETo assess the risk of local transmission of Dengue caused by introduced cases with semi-quantitative method in 2015 in Zhejiang Province.

METHODSRisk indexes of local transmission of Dengue caused by introduced cases were reviewed. The weights of indexes were computed by analytic hierarchy process and further used to generate absolute risk values by multiplying indexes. Moreover, comprehensive indexes were computed to describe relative risk by combining analytic hierarchy process and TOPSIS methods.

RESULTSFour primary indexes and 19 secondary indexes were identified for risk assessment of local transmission of Dengue. The indexes with maximum and minimum weight were the number of immigration from countries with Dengue patients (weight value: 0.0678) and density of population (weight value: 0.0371) respectively. All CR values, statistics for measuring consistency of score matrix, were less than 0.1 (minimum: 0.000, maximum: 0.0922, average: 0.0251). The absolute risk of Zhejiang Province was within the range of 0.397-0.504 (the full score was 1.0). The risk orders of 11 municipalities sorted by relative comprehensive indexes and absolute risk values methods were similar. The three highest municipalities were Hangzhou, Wenzhou and Ningbo and the ranges of absolute risk value were 0.387-0.494, 0.404-0.511 and 0.392-0.499 respectively.

CONCLUSIONThe results provides scientific basis for preventing and controlling Dengue in Zhejiang Province. The indexes and weights may be used to assess risk of Dengue in future. In addition, the semi-quantitative method constructed in this study would be a significant reference for risk assessment of public health in emergencies.

China ; epidemiology ; Dengue ; epidemiology ; transmission ; Humans ; Risk Assessment