Background and Aims:Complex biliary stones are often accompanied by hilar adhesions,distorted anatomy,biliary variations,or biliary-enteric fistulas.Conventional laparoscopic surgery is limited in such settings,with high conversion and complication rates.Robotic surgical systems provide high-definition 3D vision and enhanced dexterity,potentially improving surgical outcomes.This study compared the clinical efficacy of robotic vs.laparoscopic surgery for complex biliary stones.Methods:A retrospective analysis was performed on 145 patients with complex biliary stones who underwent minimally invasive surgery at Fujian Provincial Hospital,Fuzhou University,from January 2023 to January 2025.Patients were divided into a robotic group(n=62)and a laparoscopic group(n=83).Intraoperative outcomes,postoperative complications,and recovery parameters were compared.Stone recurrence was assessed during follow-up.Results:Baseline characteristics were comparable between groups(all P＞0.05).The conversion rate was 0 in the robotic group vs.7.2％in the laparoscopic group(P=0.038).The postoperative complication rate was significantly lower in the robotic group(1.6％vs.10.7％,P=0.044).Time to first oral intake,first flatus,and postoperative hospital stay were all shorter in the robotic group(all P＜0.05).No stone recurrence was observed in either group during follow-up until June 2025.Conclusion:Robotic surgery is a safe and feasible approach for complex biliary stones,offering reduced conversion and complication rates and faster recovery compared with laparoscopy.It holds promise for wider clinical application in complex biliary disease.

This study aims to understand the biological characteristics of Streptococcus dysgalacti-ae of yak origin.Bacterial isolation and identification,drug susceptibility test,virulence gene test and pathogenicity test were carried out on milk samples of yaks from Naqu City to evaluate the bi-ological characteristics of the isolated strains.Meanwhile,molecular biological information such as virulence factors and drug resistance genes were analyzed by whole genome sequencing,and viru-lence genes were verified by PCR.The results showed that a strain of Streptococcus dysgalactiae was isolated from the milk of yak,and its colony morphology was pinpoint size,smooth edge and milky white.This strain is sensitive to many antibiotics(penicillin G,cephalosporin,ciprofloxacin,tetracycline,erythromycin,etc.).Virulence gene test results showed that the strain carries six key virulence genes(cyl,eno,scpB,bca,bac and napr),which may be closely related to its pathoge-nicity.In the pathogenicity test,the mice were listless and less active after infection,but no death occurred during the observation period.The pathological changes of spleen,kidney,liver and lung tissue were found,suggesting that the strain had certain pathogenic potential but not high lethali-ty.Whole genome sequencing data showed that the gene length of this strain was 4 079 280 bp,the GC content was 39.41％,3 964 coding genes were predicted,604 of which were annotated as viru-lence factors,and another 28 gene mutations may enhance its pathogenic ability.Through annota-tion of CARD database,two Pat A resistance genes and two lmrp resistance genes were found,re-vealing their potential resistance mechanism.Through whole genome sequencing technology and bioinformatics analysis method,this study revealed the genomic characteristics,drug resistance and pathogenicity mechanism of Streptococcus dysgalactiae of yak origin.The findings provide impor-tant scientific evidence for further exploration of the pathogenicity,drug resistance mechanisms,and molecular evolution of yak-derived Streptococcus agalactiae.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Objective To explore the anti-inflammatory properties of the ethyl acetate extract(ETCB)derived from Trollius chinensis Bge.using in vitro RAW264.7 cells stimulated with lipopolysaccharide and an in vivo mouse auricle model induced by xylene.Utilizing UHPLC-Q-TOF-MS(LC-MS)and network pharmacology,the components of ETCB were analyzed,and its anti-inflammatory mechanisms were preliminarily explored.Methods The anti-inflammatory activity of various solvent extracts of Trollius chinensis Bge.was assessed through the Griess assay.The impact of ETCB on the production of TNF-α and IL-6 in RAW264.7 cells induced by lipopolysaccharide was evaluated using ELISA.Real-time qPCR was conducted to determine the effect of ETCB on the expression levels of inflammatory factors such as TNF-α,IL-6,and iNOS in cells.The anti-inflammatory efficacy was further validated in a xylene-induced ear inflammation mouse model by measuring ear swelling and tissue levels of IL-6 and TNF-α.The composition of ETCB was analyzed using LC-MS.Network pharmacology was employed to screen for effective components,targets,and pathways involved in the anti-inflammatory effects of Trollius chinensis Bge.,followed by molecular docking verification between core components and targets.Results ETCB demonstrated the most potent inhibitory effect on NO production in RAW264.7 cells stimulated by lipopolysaccharide,indicating its primary role in the anti-inflammatory activity of Trollius chinensis Bge..ETCB significantly reduced TNF-α and IL-6 levels in inflammatory cells(P<0.01)and inhibited the mRNA expression of TNF-α,IL-6,and iNOS.In the xylene-induced mouse ear inflammation model,ETCB effectively alleviated ear swelling and decreased tissue levels of TNF-α and IL-6.LC-MS analysis identified 30 chemical components in ETCB,including 21 flavonoids,7 organic acids,1 polysaccharide,and 1 anthocyanin.Network pharmacology prediction and screening revealed TNF,Akt1,PTGS2,EGFR,SRC,and MMP9 as core targets,with hydroxyquercetin,lignin from fragrant leaves,zeaxanthin from willows,plantain,thistle,and sophora flavins as key anti-inflammatory active ingredients.The molecular docking analysis revealed positive interactions,characterized by favorable binding energy,between the active components and key targets.Conclusion ETCB demonstrates strong anti-inflammatory properties both inside and outside the body,functioning through various targets and pathways.This establishes a basis for deeper understanding of the anti-inflammatory mechanism of Trollius chinensis Bge.

Background and Aims:Complex biliary stones are often accompanied by hilar adhesions,distorted anatomy,biliary variations,or biliary-enteric fistulas.Conventional laparoscopic surgery is limited in such settings,with high conversion and complication rates.Robotic surgical systems provide high-definition 3D vision and enhanced dexterity,potentially improving surgical outcomes.This study compared the clinical efficacy of robotic vs.laparoscopic surgery for complex biliary stones.Methods:A retrospective analysis was performed on 145 patients with complex biliary stones who underwent minimally invasive surgery at Fujian Provincial Hospital,Fuzhou University,from January 2023 to January 2025.Patients were divided into a robotic group(n=62)and a laparoscopic group(n=83).Intraoperative outcomes,postoperative complications,and recovery parameters were compared.Stone recurrence was assessed during follow-up.Results:Baseline characteristics were comparable between groups(all P＞0.05).The conversion rate was 0 in the robotic group vs.7.2％in the laparoscopic group(P=0.038).The postoperative complication rate was significantly lower in the robotic group(1.6％vs.10.7％,P=0.044).Time to first oral intake,first flatus,and postoperative hospital stay were all shorter in the robotic group(all P＜0.05).No stone recurrence was observed in either group during follow-up until June 2025.Conclusion:Robotic surgery is a safe and feasible approach for complex biliary stones,offering reduced conversion and complication rates and faster recovery compared with laparoscopy.It holds promise for wider clinical application in complex biliary disease.

Objective To investigate the mechanisms by which astaxanthin(AST)alleviates oxaliplatin(OXA)-induced neuropathic pain through antioxidant pathways so as to provide theoretical basis for clinical intervention.Methods Animal experiments:SD rats were divided into five groups(n=6):control group,OXA(4 mg/kg)group,OXA+Oil group,OXA+AST(5 mg/kg)group,and OXA+AST(10 mg/kg)group.Mechanical and cold pain thresholds were measured at day 0,7,14,and 21.Malondialdehyde(MDA)content and superoxide dismutase(SOD)activity in the dorsal root ganglia(DRG)were detected using the thiobarbituric acid(TBA)method and WST-1 assay,respectively.Western blotting was performed to analyze the expressions of Nrf2 and HO-1.Cell experiments:neuro-2a cells were divided into control group,OXA(50 μmol/L)group,AST(10 μmol/L)group,and OXA(50 μmol/L)+AST(10 μmol/L)group.Cells were treated with nerve growth factor(NGF,50 ng/mL)to induce growth,and morphological changes were observed under an inverted microscope.Intracellular reactive oxygen species(ROS)level and mitochondrial superoxide were measured using DCFH-DA fluorescent probe and MitoSOXTM red,respectively.Mitochondrial function was assessed by JC-1 assay.Western blotting was used to detect Nrf2 and HO-1 expressions.Results Animal experiments:① Mechanical and cold pain thresholds were reduced in OXA and OXA+Oil groups(P＜0.05),while AST significantly increased these thresholds in OXA-treated rats(P＜0.05).② SOD activity decreased while MDA content increased in the DRG of OXA-treated rats(P＜0.05).AST restored SOD activity and reduced MDA level(P＜0.05,P＜0.01).③ Western blotting showed elevated Nrf2 and HO-1 expressions in OXA group(P＞0.05),which were further upregulated by AST(P＜0.05,P＜0.01).Cell experiments:① OXA reduced the number of neurite-bearing cells and shortened the average neurite length(P＜0.05).Inverted microscopic observation revealed that AST intervention increased both parameters(P＜0.01,P＜0.001).② OXA increased intracellular and mitochondrial ROS fluorescence intensity(P＜0.05),which was attenuated by AST(P＜0.01).③ JC-1 assay revealed decreased mitochondrial membrane potential in OXA group(P＜0.01),which was partially reversed by AST(P＜0.05).④ Western blotting results showed that OXA upregulated Nrf2 and HO-1 expressions(P＜0.05,P＜0.01),and AST further enhanced their levels(P＜0.01).Conclusion AST alleviates OXA-induced neuropathic pain by promoting Nrf2/HO-1 expression,enhancing SOD activity,reducing lipid peroxidation and ROS production,and improving mitochondrial function.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

This study aims to understand the biological characteristics of Streptococcus dysgalacti-ae of yak origin.Bacterial isolation and identification,drug susceptibility test,virulence gene test and pathogenicity test were carried out on milk samples of yaks from Naqu City to evaluate the bi-ological characteristics of the isolated strains.Meanwhile,molecular biological information such as virulence factors and drug resistance genes were analyzed by whole genome sequencing,and viru-lence genes were verified by PCR.The results showed that a strain of Streptococcus dysgalactiae was isolated from the milk of yak,and its colony morphology was pinpoint size,smooth edge and milky white.This strain is sensitive to many antibiotics(penicillin G,cephalosporin,ciprofloxacin,tetracycline,erythromycin,etc.).Virulence gene test results showed that the strain carries six key virulence genes(cyl,eno,scpB,bca,bac and napr),which may be closely related to its pathoge-nicity.In the pathogenicity test,the mice were listless and less active after infection,but no death occurred during the observation period.The pathological changes of spleen,kidney,liver and lung tissue were found,suggesting that the strain had certain pathogenic potential but not high lethali-ty.Whole genome sequencing data showed that the gene length of this strain was 4 079 280 bp,the GC content was 39.41％,3 964 coding genes were predicted,604 of which were annotated as viru-lence factors,and another 28 gene mutations may enhance its pathogenic ability.Through annota-tion of CARD database,two Pat A resistance genes and two lmrp resistance genes were found,re-vealing their potential resistance mechanism.Through whole genome sequencing technology and bioinformatics analysis method,this study revealed the genomic characteristics,drug resistance and pathogenicity mechanism of Streptococcus dysgalactiae of yak origin.The findings provide impor-tant scientific evidence for further exploration of the pathogenicity,drug resistance mechanisms,and molecular evolution of yak-derived Streptococcus agalactiae.

Objective:To explore the correlation between clinical and CT imaging features and epidermal growth factor receptor (EGFR) gene mutation in patients with non-small cell lung cancer (NSCLC) and screening of mutation prediction indicators.Methods:A retrospective case-control study was conducted. The clinical data of 178 NSCLC patients who were confirmed by pathology and underwent pre-treatment chest-enhanced CT scan and EGFR gene mutation testing in General Hospital of Ningxia Medical University from January 2015 to December 2019 were retrospectively analyzed. Patients were classified into EGFR mutation-positive and mutation-negative groups based on genetic testing results, and the clinical and CT imaging features were compared between the two groups; the multivariate logistic regression model was used to identify the independent influencing factors for EGFR gene mutation in NSCLC patients.Results:Among 178 NSCLC patients, 115 cases (64.6%) were EGFR gene mutation-positive and 63 cases (35.4%) were mutation-negative. Among the 115 EGFR gene mutation-positive patients, there were 61 cases (53.0%) of exon 19 deletion (19del) mutation, 45 cases (39.1%) of exon 21 L858R mutation, 8 cases (7.0%) of exon 20 mutation, and 1 case (0.9%) of exon 18 mutation. The proportions of female patients [60.0% (69/115) vs. 30.2% (19/63)] and patients with out smoking history [74.8% (86/115) vs. 36.5% (23/63)] in EGFR gene mutation-positive group were higher than those in the mutation-negative group, and the differences were statistically significant (both P < 0.001), while the proportions of patients with different pathological types and clinical stages in the two groups showed no statistically significant differences (both P > 0.05). The median maximum diameter of tumor [ M ( Q1, Q3)] detected by CT in the EGFR gene mutation-positive group was 3.70 (2.90, 4.70) cm, while in the mutation-negative group it was 5.30 (3.40, 6.80) cm, and the difference was statistically significant ( Z = -3.66, P < 0.001). The proportions of patients with air bronchogram [27.8% (32/115) vs. 7.9% (5/63)] and without emphysema [83.5% (96/115) vs. 55.6% (35/63)] in the EGFR gene mutation-positive group were higher than those in the mutation-negative group, and the differences were statistically significant (both P < 0.01). The results of multivariate logistic regression analysis showed that no smoking history (yes vs. no, OR = 0.218, 95% CI: 0.073-0.647), short maximum diameter of tumor detected by CT ( OR = 0.814, 95% CI: 0.676-0.981), air bronchogram (yes vs. no, OR = 5.354, 95% CI: 1.782-16.090), and no emphysema (yes vs. no, OR = 0.289, 95% CI: 0.128-0.653) were independent risk factors for EGFR gene mutation in NSCLC patients (all P < 0.05). Conclusions:Clinical and CT imaging features may relate to EGFR gene mutation status in NSCLC patients, and no smoking history, short maximum diameter of tumor detected by CT, air bronchogram and no emphysema may predict EGFR gene mutation.

Objective To explore the anti-inflammatory properties of the ethyl acetate extract(ETCB)derived from Trollius chinensis Bge.using in vitro RAW264.7 cells stimulated with lipopolysaccharide and an in vivo mouse auricle model induced by xylene.Utilizing UHPLC-Q-TOF-MS(LC-MS)and network pharmacology,the components of ETCB were analyzed,and its anti-inflammatory mechanisms were preliminarily explored.Methods The anti-inflammatory activity of various solvent extracts of Trollius chinensis Bge.was assessed through the Griess assay.The impact of ETCB on the production of TNF-α and IL-6 in RAW264.7 cells induced by lipopolysaccharide was evaluated using ELISA.Real-time qPCR was conducted to determine the effect of ETCB on the expression levels of inflammatory factors such as TNF-α,IL-6,and iNOS in cells.The anti-inflammatory efficacy was further validated in a xylene-induced ear inflammation mouse model by measuring ear swelling and tissue levels of IL-6 and TNF-α.The composition of ETCB was analyzed using LC-MS.Network pharmacology was employed to screen for effective components,targets,and pathways involved in the anti-inflammatory effects of Trollius chinensis Bge.,followed by molecular docking verification between core components and targets.Results ETCB demonstrated the most potent inhibitory effect on NO production in RAW264.7 cells stimulated by lipopolysaccharide,indicating its primary role in the anti-inflammatory activity of Trollius chinensis Bge..ETCB significantly reduced TNF-α and IL-6 levels in inflammatory cells(P<0.01)and inhibited the mRNA expression of TNF-α,IL-6,and iNOS.In the xylene-induced mouse ear inflammation model,ETCB effectively alleviated ear swelling and decreased tissue levels of TNF-α and IL-6.LC-MS analysis identified 30 chemical components in ETCB,including 21 flavonoids,7 organic acids,1 polysaccharide,and 1 anthocyanin.Network pharmacology prediction and screening revealed TNF,Akt1,PTGS2,EGFR,SRC,and MMP9 as core targets,with hydroxyquercetin,lignin from fragrant leaves,zeaxanthin from willows,plantain,thistle,and sophora flavins as key anti-inflammatory active ingredients.The molecular docking analysis revealed positive interactions,characterized by favorable binding energy,between the active components and key targets.Conclusion ETCB demonstrates strong anti-inflammatory properties both inside and outside the body,functioning through various targets and pathways.This establishes a basis for deeper understanding of the anti-inflammatory mechanism of Trollius chinensis Bge.