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.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Objective To establish the energy expenditure(EE)prediction models of health Qigong Wuqinxi based on heart rate combined with accelerometer counts in different body parts,so to provide a reference for monitoring EE of Wuqinxi.Methods Seventy-four healthy college students aged 18-30 were selected as the research objects.They were divided into a skilled group of 39(21 males and 18 females)and a primary group of 35(17 males and 18 females)according to their level of practice,with 55 in the model group and 19 in the validation group.When performing a set of Wuqinxi,all subjects were recorded heart rate(HR),accelerometer counts[the average counts of X-axis,Y-axis,Z-axis and vector magnitude(VM)],and EE-related indicators by wearing the Polar heart rate moni-tor,ActiGraph-GT3X+accelerometers(9 parts:waist,both arms,both wrists,both thighs,and both ankles)and CORTEX Meta Max3B-R2 portable indirect calorimeter.Based on the accurate EE mea-sured by the gas metabolism meter,linear regression models of only HR,only accelerometer counts,or HR combined accelerometer counts were established,and their accuracy was analyzed and com-pared.Results The EE of Wuqinxi was significantly correlated with gender,height,weight,muscle mass,HR-related indicators,and accelerometer counts in different parts(X-axis counts of the waist,VM values of the left thigh and counts of the X-axis and Y-axis,VM values of the right thigh and the Y-axis,Z-axis count value,left ankle VM value and Y-axis count value,right ankle VM value and Y-axis,Z-axis count value)(P<0.05).Moreover,the adjusted R2 of the prediction model of only HR,only accelerometer counts,and the both were 0.582,0.508 and 0.678,respectively(P<0.05).The correlation between the predicted and measured values ranged between 0.706 and 0.817.Accord-ing to the Bland-Altman analysis,for each model,all except one error value fell outside the 95%confidence interval.That is,all models had an excellent fitting effect and high accuracy.Among them,the combined model was of the best prediction effect:EE(kcal)=-20.089+0.279×body weight(kg)+0.243×ΔHR(exercise HR-quiet HR,bpm)+0.001×Right thigh Y-axis count value+0.181×exer-cise HR(bpm)-4.202×gender(male=0,female=1).Conclusion The EE prediction model of Wuqinxi es-tablished on HR combined with accelerometer counts has the best effect and can calculate the EE more accurately.

Objective:To explore the repairing effect and mechanism of tangeretin in rats with spinal cord injury.Methods:The rats were divided into sham-operation group, model group and tangeretin group according to random number table, with 8 rats in each group. Except for the sham-operation group, Allen hit method was used to make rat models in the other groups. After the model was successfully established, the tangeretin group was intragastrically administered with tangeretin 50 mg/kg, and the sham-operation group and the model group were intragastrically administered with an equal volume of normal saline once a day for 14 days. On days 0, 3, 7, and 14 after modeling, the motor function recovery of rats was assessed using the Basso-Beattie-Bresnahan (BBB) score; the morphological changes of the spinal cord tissues were observed using HE staining and Nissl staining; the SOD and GSH activities and MDA, IL-1β, TNF-α, and IL-10 levels in the spinal cord tissues of rats in each group were measured using ELISA kit detection; the GFAP and Neun expressions in the spinal cord tissues were detected by immunofluorescence; the IL-1β, TNF-α, IL-10, nuclear factor E2-related factor 2 (Nrf-2), and NAD (P) H-quinone oxidoreductase 1 (NQO-1) expressions in the spinal cord tissues were detected by Western blot.Results:Compared with the model group, the BBB score increased in the tangeretin group ( P<0.05), HE staining score decreased ( P<0.05), and the number of Nissl bodies increased ( P<0.05); the level of IL-10, SOD and GSH activities increased ( P<0.05), and IL-1β, TNF-α and MDA levels decreased in the spinal cord tissue ( P<0.05); GFAP fluorescence intensity decreased ( P<0.05) and NeuN fluorescence intensity increased ( P<0.05); the relative expression of IL-1β and TNF-α decreased ( P<0.05), and the relative expressions of IL-10, Nrf-2 and NQO-1 protein increased ( P<0.05). Conclusions:Tangeretin can exert anti-inflammatory and anti-oxidative stress effects through the Nrf2/NQO1 signaling pathway and alleviate early spinal cord injury in rats. On the other hand, it may promote the recovery of spinal cord injury by reducing glial scar generation and promoting neural cellogenesis.

Objective:To investigate the levels of tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and interleukin-1beta (IL-1β) in the plasma and bronchoalveolar lavage fluid of silicosis patients with artificial quartz stone plate processing.Methods:In January 2022, 10 patients with artificial quartz stone plate processing silicosis and 20 patients with common silicosis who were hospitalized and diagnosed in a hospital at Zhejiang Province from June 2019 to December 2021 were retrospectively selected as the research objects, and 30 healthy people were selected as the control group during the same period. Plasma of all subjects and bronchoalveolar lavage fluid of all patients were collected. The levels of TNF-α, IL-6 and IL-1β in plasma and bronchoalveolar lavage fluid were detected by enzyme-linked immunosorbent assay and were analyzed.Results:The levels of TNF-α, IL-6 and IL-1β in the plasma of patients with silicosis were higher than those of the control group ( P<0.05), and the levels of TNF-α and IL-1β in the plasma of silicosis patients with artificial quartz stone plate processing were higher than those of common silicosis patients ( P<0.05). The levels of TNF-α and IL-1β in plasma of artificial quartz stone plate processing silicosis patients were higher than those of common silicosis patients at the same silicon stage ( P<0.05). The levels of IL-1β in bronchoalveolar lavage fluid of silicosis patients with artificial quartz stone plate processing was higher than that of patients with common silicosis ( P<0.05) . Conclusion:The levels of TNF-α, IL-6 and IL-1β in silicosis patients with artificial quartz stone plate processing are higher than those in patients with common silicosis, which may be related to dust components they are exposed to.

Objective To explore whether remote ischemic preconditioning(RIPC)can prolong the time of ultra-intense exercise(110%VO2max)by increasing the maximum cumulative oxygen deficit(MA-OD),and the specific ways of energy supply of the anaerobic metabolic system.Methods Twenty-four racquet athletes(22.2±2.0 years;174±9 cm;67.1±12.4 kg)completed three supramaximal intensi-ty tests on a treadmill at 110%VO2max intensity to exhaustion separated with Control,Placebo and RIPC interventions.RIPC was induced on the limbs on both sides(5×5 min alternating bilateral occlu-sion 220 and 60 mmHg for Placebo and RIPC interventions,respectively).Moreover,all groups under-went a fourth test with incremental load,and a fifth test with constant load at 40%,50%,60%,70%and 80%VO2max.Results The time to exhaustion and the MAOD of the RIPC group were both greater than those in the Placebo and Control groups(P<0.05).However,no significant differences were found in the average alternative maximal accumulated oxygen deficit(MAODALT),lactic anaerobic metabolism,alactic anaerobic metabolism and parameters of excess post-exercise oxygen consump-tion dynamic curve of the three groups(P>0.05).Meanwhile,in the RIPC group,the average MAOD was significantly higher than MAODALT(P<0.05).Pearson correlation analysis showed a significant rela-tionship between the improvement of MAOD and an increase in exhaustion time after RIPC interven-tion.Conclusion RIPC can improve supramaximal exercise performance of racquet athletes by enhanc-ing their MAOD,and the enhancement of glycolysis energy supply and lactic acid elimination is a po-tential intermediary of the improvement of sports performance.

Objective:To explore the influence of deep learning reconstruction algorithm combined with low-dose CT on image quality and bone mineral density measurement and the application value in opportunistic osteoporosis screening.Methods:A total of 119 patients (aged ≥40 years) who underwent a combined chest and upper abdominal low-dose scan were prospectively included. All the images were reconstructed using filtered back projection(FBP) alogrithm, hybrid model-based adaptive statistical iterative reconstruction (ASIR-V) 50% and three levels of deep learning reconstruction algorithm respectively. Bone mineral density (BMD) values for different reconstruction conditions were measured and compared using asynchronous quantitative CT software. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of descending aorta, liver and spleen were calculated, and the image noise was the standard deviation of anterior abdominal wall fat and the image quality was objectively evaluated by using the five-point subjective evaluation method. The objective and subjective image quality of different body parts with different reconstruction method was compared.Results:There was no statistical difference in BMD with different reconstruction method ( P > 0.05). Compared with ASIR-V 50%, the SNRs of high level deep learning image reconstruction (DLIR-H)in descending aorta, latissimus dorsi, liver and spleen were increased by 103.88%, 125.09% and 136.13% respectively, and the image noise was decreased by 55.98%. Both the CNR and subjective scores (except the ability to display lung lesions) of DLIR-H were better than those of DLIR-L and ASIR-V 50% ( χ2 =158.31-275.35, P<0.001). Conclusions:The deep learning algorithm does not affect the accuracy of bone mineral density measurement, and the image quality is better than that of ASIR-V 5%. Deep learning algorithm combined with low-dose CT can be used for opportunistic osteoporosis screening.

Objective:To investigate the levels of tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and interleukin-1beta (IL-1β) in the plasma and bronchoalveolar lavage fluid of silicosis patients with artificial quartz stone plate processing.Methods:In January 2022, 10 patients with artificial quartz stone plate processing silicosis and 20 patients with common silicosis who were hospitalized and diagnosed in a hospital at Zhejiang Province from June 2019 to December 2021 were retrospectively selected as the research objects, and 30 healthy people were selected as the control group during the same period. Plasma of all subjects and bronchoalveolar lavage fluid of all patients were collected. The levels of TNF-α, IL-6 and IL-1β in plasma and bronchoalveolar lavage fluid were detected by enzyme-linked immunosorbent assay and were analyzed.Results:The levels of TNF-α, IL-6 and IL-1β in the plasma of patients with silicosis were higher than those of the control group ( P<0.05), and the levels of TNF-α and IL-1β in the plasma of silicosis patients with artificial quartz stone plate processing were higher than those of common silicosis patients ( P<0.05). The levels of TNF-α and IL-1β in plasma of artificial quartz stone plate processing silicosis patients were higher than those of common silicosis patients at the same silicon stage ( P<0.05). The levels of IL-1β in bronchoalveolar lavage fluid of silicosis patients with artificial quartz stone plate processing was higher than that of patients with common silicosis ( P<0.05) . Conclusion:The levels of TNF-α, IL-6 and IL-1β in silicosis patients with artificial quartz stone plate processing are higher than those in patients with common silicosis, which may be related to dust components they are exposed to.

Objective To understand the pathogen composition and epidemic characteristics of viral diarrhea in Hubei province. Methods Stool samples of adults and children from sentinel monitoring of viral diarrhea in Hubei province from 2017 to 2019 were collected. Real-time RT-PCR method was used to detect the nucleic acids of rotavirus, norovirus, adenovirus, astrovirus and sapovirus. The detection results of the 5 pathogens were statistically analyzed. Results The total positive rate of 5 diarrheal viruses in 1 957 samples was 36.84%, and the number of cases of mixed infection accounted for 19.00%, among which rotavirus accounted for 9.50%, norovirus 13.29%, astrovirus 4.14%, adenovirus 14.10% and sapovirus 3.37%. In Hubei province, rotavirus and norovirus were still prevalent in autumn and winter. The epidemic seasons of adenovirus and astrovirus were similar, mainly occurring in April and May, while the infection rate of sapovirus was relatively high in November and January of the following year. The main infected population of the five diarrheal viruses was still children aged 5 years and younger, which was significantly higher than other age groups. Conclusion The main pathogens of viral diarrhea in Hubei from 2017 to 2019 are adenovirus, norovirus and rotavirus, and there are some mixed infections. It is necessary to strengthen the monitoring of major pathogens and take effective and scientific protection measures for high incidence seasons and key populations.

Objective To identify the etiology of the first local dengue infection outbreak in Hubei Province in 2019, and to determine the serotype and genotype of the virus and trace its source. Methods Serum samples were collected from dengue fever cases in the acute phase. The IgM and IgG antibodies were detected by enzyme-linked immunosorbent assay (ELISA)，and the serotype was determined by fluorescence quantitative PCR. C6/36 cells were used to isolate virus and obtain virus E gene and complete genome sequence for systematic evolution analysis to trace the possible source of infection. Results The pathogen of the outbreak was identified as dengue serotype I infection，and five virus strains were isolated. Sequence analysis showed that the virus belonged to genotype I of dengue I, and had the highest homology with the strain isolated in Guangzhou, 2019. Conclusion The first local dengue infection outbreak in Hubei Province in 2019 was caused by genotype I of the type I dengue virus.