Lung cancer is one of the malignant tumours with the highest morbidity and mortality rates worldwide today, posing a major threat to human health. Accurate diagnosis and standardised treatment play a crucial role in improving the survival rate of lung cancer patients. In recent years, the rapid rise of artificial intelligence (AI) has brought about significant changes in the medical field, providing a new diagnostic and treatment model for lung cancer, and making a series of breakthroughs in lung cancer diagnostic imaging, pathological diagnosis, surgical oncology, radiotherapy, and drug development and treatment. This article introduces the current status of AI application in the field of lung cancer diagnosis and treatment, and extensively discusses the current challenges and future prospects, hoping to provide references and suggestions for future clinical practice.

Lung cancer is one of the malignant tumours with the highest morbidity and mortality rates worldwide today, posing a major threat to human health. Accurate diagnosis and standardised treatment play a crucial role in improving the survival rate of lung cancer patients. In recent years, the rapid rise of artificial intelligence (AI) has brought about significant changes in the medical field, providing a new diagnostic and treatment model for lung cancer, and making a series of breakthroughs in lung cancer diagnostic imaging, pathological diagnosis, surgical oncology, radiotherapy, and drug development and treatment. This article introduces the current status of AI application in the field of lung cancer diagnosis and treatment, and extensively discusses the current challenges and future prospects, hoping to provide references and suggestions for future clinical practice.

Objective:To explore the interaction effects on brain activity between the smoking addiction and weight status by resting-state functional magnetic resonance imaging.Methods:Retrospective analysis of clinical data and resting-state functional magnetic resonance imaging data were analyzed from 99 recruited subjects from January 2019 to December 2021. All participants were divided into four groups: overweight smokers ( n=24), normal-weight smokers ( n=28), overweight non-smokers ( n=19), and normal weight non-smokers ( n=28). Calculate regional homogeneity (ReHo) to reflect the internal brain activity of the subjects. Two-way ANOVA was used to detect the interaction effects between smoking addiction and overweight on ReHo by SPM12 software, correcting for age, years of education and head motion. Results:The interaction effect between smoking addiction and overweight on ReHo was significant in right superior frontal gyrus(x, y, z=15, 9, 60)(GRF corrected, Pvoxel<0.005, Pcluster<0.05). The ReHo value in the right superior frontal gyrus of overweight smokers was significantly higher than that of normal weight smokers ( t=3.768, P<0.001, Bonferroni corrected). The ReHo values in the right superior frontal gyrus of overweight non-smokers were significantly lower than those of normal weight non-smokers ( t=-3.242, P=0.002, Bonferroni corrected). The ReHo values in the right superior frontal gyurs of normal-weight smokers were significantly lower than those of normal weight non-smokers( t=-3.540, P=0.001, Bonferroni corrected). The ReHo values in the right superior frontal gyrus of overweight smokers were significantly higher than those of overweight non-smokers ( t=3.392, P=0.002, Bonferroni corrected). Correlation analyses showed that the strengthen ReHo value in right superior frontal gyrus was positively associated with pack-year in smoking addicts( r=0.387, P=0.007, Bonferroni corrected). Conclusion:Smoking addiction and overweight have an antagonistic effect on brain activity in the right superior frontal gyrus, which may provide potential therapeutic targets for individuals with comorbidity of smoking addiction and overweight.

Objective:To investigate the correlation between brain gray matter volume (GMV) and interactive effects of smoking addiction and overweight.Methods:We retrospectively analyzed the clinical data and resting-state functional magnetic resonance imaging data of 99 subjects recruited from January 2019 to December 2021 through online platforms such as WeChat, including the following four groups: overweight smoking addiction group ( n=24), normal-weight smoking addiction group ( n=28), overweight non-smoking group ( n=19), and normal-weight non-smoking group ( n=28). All subjects underwent 3.0T MRI. We used voxel-based morphometry (VBM) to compare gray matter volume (GMV) among the four groups. A two-way analysis of variance was conducted with age, years of education, and head motion as covariates to explore the interaction effects between smoking addiction and weight status on GMV, the main effect of smoking addiction and main effect of overweight, which were then correlated with clinical data. Results:Significant interactive effects of smoking addiction and overweight were observed in the left dorsolateral prefrontal cortex (DLPFC) (GRF corrected, voxel-level P<0.001, cluster-level P<0.05). Correlation analysis showed a quadratic effect between body mass index and left DLPFC GMV (β=-3.846, t=-2.134, P=0.036). Additionally, the main effect of smoking addiction was primarily manifested in the bilateral orbitofrontal cortex (OFC)(GRF corrected, P voxel<0.001, P cluster<0.05). Left OFC GMV was negatively associated with the FTND (Fagerstrom Test of Nicotine Dependence) scores ( r=-0.391, P=0.006). And right OFC GMV was negatively correlated with the smoking index ( r=-0.335, P=0.019). No statistically significant main effect of overweight was found at the aforementioned threshold. Conclusion:Smoking addiction and overweight have antagonistic effects on left DLPFC GMV.

Objective:This study aims to investigate the dynamic patterns of intrinsic neural activity and their relationships with Internet Addiction Test (IAT) scores in individuals with internet gaming disorder (IGD).Methods:Resting state functional magnetic resonance imaging (fMRI) scanning was performed on 33 individuals with IGD (IGD group) who were admitted to the psychiatric department of First Affiliated Hospital of Zhengzhou University and 40 healthy volunteers (control group) recruited through the online platform. The dynamic regional homogeneity (dReHo) values were calculated. The two-sample t test was used to analyze the differences of dReHo values between two groups (Gaussian random field corrected, voxel level P<0.001, cluster level P<0.05). Spearman correlation analyses were used to analyze the correlations between IAT scores and dReHo values of brain regions with statistically significant differences between IGD group and control group. Results:Compared with the control group, the IGD group showed increased dReHo values in the right thalamus, right caudate nucleus, left putamen, bilateral medial superior frontal gyrus, bilateral supplementary motor area, and bilateral superior frontal gyrus ( t=3.84-4.94, Gaussian random field corrected, voxel level P<0.001, cluster level P<0.05). The dReHo value in the right medial superior frontal gyrus was positively correlated with IAT scores ( r=0.395, P=0.023) in IGD group. Conclusion:Individuals with IGD exhibited a wide range of abnormal dynamic patterns of intrinsic neural activity in pivotal regions associated with reward processing and cognitive control functions (the fronto-striato-thalamic circuit), providing new insights into the underlying neural mechanisms of IGD.

Objective:To investigate the correlation between brain gray matter volume (GMV) and interactive effects of smoking addiction and overweight.Methods:We retrospectively analyzed the clinical data and resting-state functional magnetic resonance imaging data of 99 subjects recruited from January 2019 to December 2021 through online platforms such as WeChat, including the following four groups: overweight smoking addiction group ( n=24), normal-weight smoking addiction group ( n=28), overweight non-smoking group ( n=19), and normal-weight non-smoking group ( n=28). All subjects underwent 3.0T MRI. We used voxel-based morphometry (VBM) to compare gray matter volume (GMV) among the four groups. A two-way analysis of variance was conducted with age, years of education, and head motion as covariates to explore the interaction effects between smoking addiction and weight status on GMV, the main effect of smoking addiction and main effect of overweight, which were then correlated with clinical data. Results:Significant interactive effects of smoking addiction and overweight were observed in the left dorsolateral prefrontal cortex (DLPFC) (GRF corrected, voxel-level P<0.001, cluster-level P<0.05). Correlation analysis showed a quadratic effect between body mass index and left DLPFC GMV (β=-3.846, t=-2.134, P=0.036). Additionally, the main effect of smoking addiction was primarily manifested in the bilateral orbitofrontal cortex (OFC)(GRF corrected, P voxel<0.001, P cluster<0.05). Left OFC GMV was negatively associated with the FTND (Fagerstrom Test of Nicotine Dependence) scores ( r=-0.391, P=0.006). And right OFC GMV was negatively correlated with the smoking index ( r=-0.335, P=0.019). No statistically significant main effect of overweight was found at the aforementioned threshold. Conclusion:Smoking addiction and overweight have antagonistic effects on left DLPFC GMV.

Objective:This study aims to investigate the dynamic patterns of intrinsic neural activity and their relationships with Internet Addiction Test (IAT) scores in individuals with internet gaming disorder (IGD).Methods:Resting state functional magnetic resonance imaging (fMRI) scanning was performed on 33 individuals with IGD (IGD group) who were admitted to the psychiatric department of First Affiliated Hospital of Zhengzhou University and 40 healthy volunteers (control group) recruited through the online platform. The dynamic regional homogeneity (dReHo) values were calculated. The two-sample t test was used to analyze the differences of dReHo values between two groups (Gaussian random field corrected, voxel level P<0.001, cluster level P<0.05). Spearman correlation analyses were used to analyze the correlations between IAT scores and dReHo values of brain regions with statistically significant differences between IGD group and control group. Results:Compared with the control group, the IGD group showed increased dReHo values in the right thalamus, right caudate nucleus, left putamen, bilateral medial superior frontal gyrus, bilateral supplementary motor area, and bilateral superior frontal gyrus ( t=3.84-4.94, Gaussian random field corrected, voxel level P<0.001, cluster level P<0.05). The dReHo value in the right medial superior frontal gyrus was positively correlated with IAT scores ( r=0.395, P=0.023) in IGD group. Conclusion:Individuals with IGD exhibited a wide range of abnormal dynamic patterns of intrinsic neural activity in pivotal regions associated with reward processing and cognitive control functions (the fronto-striato-thalamic circuit), providing new insights into the underlying neural mechanisms of IGD.

Objective:To establish a double antibody sandwich ELISA for detecting the specific antigen of Seoul virus (SEOV) L99 strain and to provide a means for antigen detection in the development, production and verification of vaccine against hemorrhagic fever with renal syndrome (HFRS).Methods:Monoclonal antibodies (McAbs) aganist L99 virus were induced in mice using four hybridoma cell lines and purified by Protein-A affinity chromatography. The purity, titer and specificity of McAbs were determined by SDS-PAGE, indirect ELISA and Western blot, respectively. Four McAbs were paired with each other and the additivity indices of paired McAbs were analyzed. After labeling McAbs with horseradish peroxidase (HRP), the concentrations of the coated and labeled antibodies were optimized by orthogonal test, and then a double antibody sandwich ELISA for virus antigen detection was established. Type Ⅱ HFRS inactivated vaccine standard was used as a quantitative standard to verify the sensitivity, linearity, specificity, accuracy and precision of the developed method. The applicability of the method was verified by testing three batches of vaccine stock solutions.Results:Four McAbs were at titers of greater than 1∶10 6 and their purity was all greater than 98%. The McAbs secreted by 1D5, 3A4 and 5B7 cells could specifically recognize the nucleocapsid protein of SEOV L99. There was cross-reaction between McAb secreted by 1D5 cells and Hantaan virus PS-6. The McAbs secreted by 3A4 and 1D5 were used as coating and labeling antibodies based on the results of antibody pairs. The working concentrations of the coating antibody and the horseradish peroxidase (HRP)-labeled antibody were 20 μg/ml and 1∶4 000, respectively. The minimum detection limit of the established method for the detection of SEOV L99 antigen was 0.078 1 μg/ml, and the linear range was 0.078 1-2.500 0 μg/ml with a R2 value of more than 0.99. There was no cross reaction with other HFRS vaccine. The virus antigen recovery rate was between 95.8% and 108.7%, and the coefficients of variation of precision was less than 10%. Three batches of Type II HFRS inactivated vaccine stocks were detected by this method and the results was dose-dependent. Conclusions:This study successfully established a double antibody sandwich ELISA method for specific detection of SEOV L99 strain antigen in the production of bivalent HFRS vaccines produced from hamster kidney cells.

Objective: To investigate the effects of interferon inducible protein 16 (IFI16), a cytosolic DNA sensor, on the expression of human T-cell leukemia virus type 1 (HTLV-1) proteins and pro-inflammatory cytokines in adult HTLV-1-positive T cells. Methods: IFI16 expression in different HTLV-1-positive T cell lines was detected by immunoblot assay. Specific siRNA targeting the IFI16 gene was constructed and the gene silencing efficiency was detected by immunoblot assay. Expression of HTLV-1 Tax protein at mRNA and protein levels was respectively detected by real-time PCR and immunoblot assay after knocking down the expression of IFI16 in HTLV-1-positive T cells with siRNA. Expression of interferon (IFN)-α, IFN-γ, tumor necrosis factor (TNF)-α, Tax and Env were detected by real-time PCR. Results: Compared with the HTLV-1-negative T cell line Jurkat, IFI16 expression was enhanced in the HTLV-1-positive T cell lines MT2, MT4 and C8166. Tax expression was increased, while that of IFN-α, IFN-γ and TNF-α was decreased in MT2 and MT4 cells after silencing the expression of IFI16 with siRNA. Conclusions IFI16 expression was increased in HTLV-1-positive MT2 and MT4 cells. Meanwhile, IFI16 promoted the production of interferon and pro-inflammatory cytokines and inhibited the expression of HTLV-1 proteins.

Objective To investigate the application value of apparent diffusion coefficient (ADC) in monitoring the response to chemotherapy of primary bone lymphoma(PBL). Methods Sixteen consecutive patients with PBL confirmed by pathology were enrolled in this retrospective study.All patients underwent a controlled MRI including a DWI sequence before chemotherapy and after the first chemotherapy session. The ADC values of PBL lesions were measured before and after the first chemotherapy session. All patients were divided into responders and non-responders groups according to the combined response assessment criteria. The difference in ADC values between the responders and non-responders groups before and after chemotherapy and the rate of change of ADC values were compared using Wilcoxon test. The paired t test was used to compare ADC values before and after chemotherapy in each group. Results Twenty-three cases of PBL lesions were diagnosed in 16 patients before the chemotherapy. After the first chemotherapy session, 10 patients (12 lesions) and 6 patients (11 lesions) were categorized into the responder and non-responder group, respectively. The mean pre-treatment ADC value of the responder group and non-responder group were 737.1(301.7,928.4)× 10-6mm2/s and 749.2(624.4, 1186.5)× 10-6mm2/s, which had no statistically significant difference (Z=1.51,P=0.132). The mean post-treatment ADC value of the responder group and non-responder group were 1843.0(721.5,2745.8)× 10-6mm2/s and 1026.3(382.8,2100.8)× 10-6mm2/s, which had no statistically significant difference (Z=-1.75,P=0.079). The post-treatment ADC value of the responder group significantly increased [156.7(60.9,227.5)%] while the post-treatment ADC value of the non-responder group slightly increased [31.7(-44.7,147.4)%]. The difference in the percentage changes of ADC values in the responder and non-responder groups before and after the treatment was statistically significant(Z=-3.66,P<0.001). Conclusion The measurement of the percentage changes of ADC values enable a good short-term response evaluation for PBL.