Aim To verify the mechanism of action of Carthami Flos-Lycopodii Herba in treating KOA carti-lage inflammation based on network pharmacology and in vitro and in vivo experiments.Methods The effec-tive ingredients of Carthami Flos-Lycopodii Herba were screened through the database,the core targets of"drug disease"were analyzed,and pathway enrichment analy-sis and molecular docking verification were conducted.Experimental verification:Primary chondrocytes were extracted from mice and divided into the control group,IL-1β group,treatment group,and treatment+TLR4 agonist group.CCK-8 method was used to screen the optimal intervention concentration of Carthami Flos-Ly-copodii Herba.ELISA was used to detect the content of inflammatory factors in chondrocytes.Western blot was employed to detect the protein expression related to cellular pathways.Subsequently,a KOA mouse model was constructed using the DMM method.After admin-istration,the knee joint injury of mice was evaluated u-sing safranin O-green staining.ELISA was used to de-tect the levels of inflammatory factors in serum.West-ern blot was employed to detect collagen Ⅱ,MMP13,Aggrecan,and apoptosis related protein expression in cartilage tissue.TUNEL staining was used to detect the apoptosis rate of cells.Results A total of 26 active ingredients of Carthami Flos-Lycopodii Herba were screened,as well as 123 potential targets for treating KOA.The enrichment analysis results indicated that it mainly involved mechanisms such as Toll like receptors and cell apoptosis.The experimental results showed that Carthami Flos-Lycopodii Herba alleviated the in-flammatory response of chondrocytes and affected the expression of pathway related proteins.Compared with KOA mice,safflower stretched muscle grass could im-prove cartilage damage and reduce the concentration of serum inflammatory factors,regulate the expression of collagen Ⅱ,MMP13,Aggrecan,and apoptosis related proteins in cartilage tissue,and reduce the fluorescence intensity of TUNEL staining in the tissue.Conclusions Carthami Flos-Lycopodii Herba can improve KOA cartilage inflammation,and its mechanism may be relat-ed to the TLR4/MyD88/NF-κB signaling pathway.

Objective:To explore the efficacy of deep learning-based automatic segmentation technology in the segmentation of hepatocellular carcinoma (HCC) lesions using gadoxetate disodium-enhanced MRI (EOB-MRI), and to investigate the prognostic value of radiomics analysis in predicting patient outcomes.Methods:This was a cross-sectional, retrospective study that collected data from 352 patients with solitary HCC who underwent imaging at the Harbin Medical University Cancer Hospital between June 2015 and May 2023. The patients were randomly divided into a training set ( n=213) and a validation set ( n=139) in a 3∶2 ratio using weighted random sampling. Two radiologists manually annotated the lesions. Hepatobiliary-phase EOB-MRI images were standardized, and six deep learning models,nnU-Net, nnFormer, UnetR, Swin-UnetR, UnetR++ and MedNeXt,were trained for automatic segmentation on the training set. The segmentation performance was evaluated on the validation set, and the segmentation efficacy was assessed using the Dice coefficient and 95% Hausdorff distance (HD 95), identifying of the optimal model. Radiomics features were extracted from both manual and automatic segmentation regions, and the radiomics score (Radscore) was calculated to stratify patients into high-risk and low-risk groups. Kaplan-Meier curves and log-rank tests were used to analyze the differences in relapse-free survival (RFS) and overall survival (OS) between the different stratified groups. Results:Among the automatic segmentation models, the MedNeXt model performed best in the validation set, with a Dice coefficient of 76.0%, HD 95 of 7.2, and a segmentation success rate of 90.6% (126/139). The nnFormer model was the second-best, with a Dice coefficient of 75.3%, HD 95 of 10.1, and a segmentation success rate of 89.9% (125/139). Other models showed Dice coefficients ranging from 66.3% to 74.1%. A MedNext-nnF model was established by combining the MedNeXt and nnFormer models, achieving a Dice coefficient of 78.2%, HD 95 of 5.9, and a segmentation success rate of 92.1% (128/139) in the validation group. After constructing the automatic segmentation radiomics prognostic model, patients were stratified by Radscore. Both manual and automatic segmentation models showed statistically significant differences in RFS and OS between different risk groups ( P<0.001). Conclusions:The Mednext-nnF fusion model enables efficient and automated segmentation of HCC lesions in EOB-MRI. The radiomics model constructed based on the automated segmentation demonstrates strong performance in predicting and stratifying prognostic risk.

Objective:To investigate the correlation of serum interleukin-6 (IL-6) and homocysteine (Hcy) levels, monocyte-to-lymphocyte ratio (MLR), and serum lipid levels [triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels] with ulcerative colitis.Methods:The clinical data of 98 patients with ulcerative colitis admitted to Bayannur Hospital from November 2021 to November 2023 (observation group) were retrospectively analyzed. Forty-nine healthy individuals who were selected at a 2:1 ratio during the same period were included in the control group. Serum IL-6 and Hcy levels, MLR, and lipid levels were compared between the two groups. The diagnostic efficacy of serum IL-6, Hcy, MLR, and lipid levels for ulcerative colitis was assessed using receiver operating characteristic (ROC) curves. Additionally, Pearson correlation analysis was conducted to analyze correlation of serum IL-6 and Hcy levels, MLR, and lipid levels with ulcerative colitis.Results:In the observation group, serum IL-6 and Hcy levels and MLR were (39.87 ± 12.36) pg/mL, (13.01 ± 3.52) μmol/L, and (0.38 ± 0.12), respectively, all of which were significantly higher than those in the control group [(22.3 ± 3.26) pg/mL, (10.05 ± 3.26) μmol/L, (0.29 ± 0.08), t = 9.77, 4.92, 4.78, all P < 0.05]. In the observation group, serum levels of TG, TC, LDL-C, and HDL-C levels were (1.16 ± 0.32) mmol/L, (4.12 ± 1.15) mmol/L, (2.60 ± 0.75) mmol/L, and (1.02 ± 0.17) mmol/L, respectively, all of which were significantly lower than those in the control group [(1.45 ± 0.41) mmol/L, (4.91 ± 0.99) mmol/L, (3.20 ± 0.71) mmol/L, (1.13 ± 0.16) mmol/L, t = 4.71, 4.11, 4.65, 3.77, all P < 0.05]. ROC curve analysis indicated that the areas under the curve (AUC) for diagnosing ulcerative colitis based on serum levels of IL-6, Hcy, MLR, TG, TC, LDL-C, and HDL-C were 0.957, 0.749, 0.746, 0.732, 0.678, 0.722, and 0.681, respectively. Pearson correlation analysis showed that serum levels of IL-6, Hcy, MLR, TG, TC, LDL-C, and HDL-C were all correlated with the severity of ulcerative colitis in patients ( r = 0.501, 0.615, 0.605, -0.577, -0.542, -0.548, -0.646, all P < 0.05). Additionally, serum levels of IL-6, Hcy, and MLR were negatively correlated with lipid levels ( r = -0.806, -0.801, -0.791, -0.649, -0.728, -0.671, -0.720, -0.655, -0.857, -0.877, -0.889, -0.583, all P < 0.05). Conclusions:In patients with ulcerative colitis, serum levels of IL-6, Hcy, and MLR are elevated, while lipid levels are decreased. Additionally, serum levels of IL-6, Hcy, MLR, and lipid levels are associated with the severity of the disease. There is also a correlation between serum levels of IL-6, Hcy, MLR, and lipid levels.

Objective:To explore the efficacy of deep learning-based automatic segmentation technology in the segmentation of hepatocellular carcinoma (HCC) lesions using gadoxetate disodium-enhanced MRI (EOB-MRI), and to investigate the prognostic value of radiomics analysis in predicting patient outcomes.Methods:This was a cross-sectional, retrospective study that collected data from 352 patients with solitary HCC who underwent imaging at the Harbin Medical University Cancer Hospital between June 2015 and May 2023. The patients were randomly divided into a training set ( n=213) and a validation set ( n=139) in a 3∶2 ratio using weighted random sampling. Two radiologists manually annotated the lesions. Hepatobiliary-phase EOB-MRI images were standardized, and six deep learning models,nnU-Net, nnFormer, UnetR, Swin-UnetR, UnetR++ and MedNeXt,were trained for automatic segmentation on the training set. The segmentation performance was evaluated on the validation set, and the segmentation efficacy was assessed using the Dice coefficient and 95% Hausdorff distance (HD 95), identifying of the optimal model. Radiomics features were extracted from both manual and automatic segmentation regions, and the radiomics score (Radscore) was calculated to stratify patients into high-risk and low-risk groups. Kaplan-Meier curves and log-rank tests were used to analyze the differences in relapse-free survival (RFS) and overall survival (OS) between the different stratified groups. Results:Among the automatic segmentation models, the MedNeXt model performed best in the validation set, with a Dice coefficient of 76.0%, HD 95 of 7.2, and a segmentation success rate of 90.6% (126/139). The nnFormer model was the second-best, with a Dice coefficient of 75.3%, HD 95 of 10.1, and a segmentation success rate of 89.9% (125/139). Other models showed Dice coefficients ranging from 66.3% to 74.1%. A MedNext-nnF model was established by combining the MedNeXt and nnFormer models, achieving a Dice coefficient of 78.2%, HD 95 of 5.9, and a segmentation success rate of 92.1% (128/139) in the validation group. After constructing the automatic segmentation radiomics prognostic model, patients were stratified by Radscore. Both manual and automatic segmentation models showed statistically significant differences in RFS and OS between different risk groups ( P<0.001). Conclusions:The Mednext-nnF fusion model enables efficient and automated segmentation of HCC lesions in EOB-MRI. The radiomics model constructed based on the automated segmentation demonstrates strong performance in predicting and stratifying prognostic risk.

AIM To compare total sugar,total protein,total phenol,total flavonoid,calycosin-7-O-β-D-glucoside,liquiritin,lobetyolin,quercetin,isoferulic acid,hesperidin,glycyrrhizic acid contents and their antioxidant activities,hypoglycemic activities of big honey pill,small honey pill,water pill,concentrated pill,granule,mixture and decoction of Buzhong Yiqi Recipe.METHODS Anthraquinone-sulfuric acid method,Coomassie brilliant blue method,Folin-phenol colorimetry method,sodium nitrite-aluminum nitrate method and HPLC were adopted in the content determination of total sugar,total protein,total phenol,total flavonoid and seven constituents,respectively,after which the scavenging capacities,reducing powers on DPPH·free radical,ABTS+free radical,hydroxyl free radical,and inhibition capacity on α-glucosidase activity were detected.Subsequently,correlation analysis was performed.RESULTS Total sugar,total protein,total phenol and total flavonoid contents demonstrated significant differences among different dosage forms(P＜0.05,P＜0.01).Calycosin-7-O-β-D-glucoside,glycyrrhizin,codonoside and quercetin displayed the highest contents in the decoction,while those of isoferulic acid,hesperidin and glycyrrhizin were observable in the mixture.The water pill exhibited the strongest antioxidant activity,while those of the concentrated pill and mixture were weak;the big honey pill exhibited the strongest hypoglycemic activity,while that of the decoction was the weakest.Total protein,total phenol,total flavonoid and liquiritin contents displayed significant positive correlations between antioxidant activity(P＜0.05,P＜0.01),while hesperidin content displayed significant negative correlation between the latter(P＜0.05);total protein,calycosin-7-O-β-D-glucoside,codonoside and quercetin contents displayed significant negative correlations between hypoglycemic activity(P＜0.05,P＜0.01).CONCLUSION Active constituent contents and their biological activities of Buzhong Yiqi Recipe with different dosage forms exist differences,total sugar,total protein,total flavonoids,calycosin-7-O-β-D-glucoside,licorice glycoside,hesperidin,codonoside and quercetin can be taken as quality control indices for this prescription.

Objective To compare the segmenting efficacy of automatic segmentation models for advanced gastric cancer(AGC)on CT virtual monoenergetic images(VMI),non-linear blending images(NLBI)and mixed-energy images(MEI)based on 3D-nnU-Net.Methods Totally 216 cases of AGC were retrospectively enrolled,among them 185 cases were used to construct,train and validate models and divided into training set(n=154)and test set(n=31)at the ratio of 5∶1,while the other 31 cases were used as validation set to evaluate the generalization of the models.The 70 keV energy level VMI(VMI70 keV),NLBI and MEI were reconstructed with whole-abdominal dual-energy mode venous CT,and automatic segmentation models of AGC,including VMI70 keV,NLBI and MEI models were constructed using 3D-nnU-Net,respectively.Taken manually segmented results as golden standards,the efficacy of each model for segmenting all lesions and T2 stage lesions in test set and validation set were evaluated using Dice similarity coefficient(DSC),intersection over union(IoU)and average symmetric surface distance(ASSD).Results For all lesions in test and validation sets,DSC of 3 models were all＞0.80.DSC and IoU of VMI70 keV and NLBI models were both higher,while their ASSD was lower than those of MEI model(all P＜0.05).For T2 stage AGC in both test set and validation set(each n=5),DSC of MEI model was lower than that of VMI70 keV and NLBI models(both P＜0.05),while IoU of MEI model was lower than that of VMI70 keV model(P＜0.05),and its ASSD was higher than that of NLBI model(P＜0.05).Conclusion All 3D-nnU-Net-based VMI70 keV,NLBI and MEI models could effectively segment AGC on dual-energy CT images,and the segmentation efficacy of the former two were better.

Objective To investigae microbial community structure of lower respiratory tract in severe hospital-acquired pneumonia(SHAP)patients in intensive care unit(ICU)using metagenomic next-generation sequencing(mNGS).Methods mNGS was performed on bronchoalveolar lavage fluid(BALF)of 84 patients with SHAP.Patients were grouped based on age,smoking status,underlying diseases and duration of artificial airway.The differences in α diversity,β diversity,microbial composition and community structure of airway microbiota were compared between different groups.The differential airway microbiota associated with artificial airway were screened,and microbial co-occurrence networks was constructed to observe the interaction in microorganisms.Results Results of α diversity analysis revealed that diversity and evenness of the microbial community were higher in young adults compared to those of middle-aged patients,while microbial diversity and evenness were significantly reduced in patients with comorbid stroke.In the group of artificial airway treatment,the diversity and uniformity of microorganisms decreased as the duration of artificial airway treatment increased.The diversity was the lowest when the artificial airway treatment lasted for more than 3 days.β diversity analysis confirmed that there were significant differences in the distinct microbial community structures between the>3 days support group and the non-intubated and≤3 days support cohorts.Acinetobacter baumannii was dominant in all groups.The bacterial diversity was significantly higher in the middle-aged group,the non-smoking group,the group without artificial airway therapy,the group without diabetes mellitus,the group with artificial airway therapy≥3 days and the group with chronic lung disease than those of other groups.In particular,pseudomonas aeruginosa,corynebacterium striatum and veillonella parvula were enriched in these groups.Difference analysis showed that there were significant differences in pseudomonas aeruginosa and corynebacterium striatum between the group with artificial airway treatment>3 days,the group without artificial airway therapy and the group with artificial airway treatment≤3 days.Network co-occurrence showed that there may be synergistic or antagonistic relationships between some microorganisms.Conclusion The microbial diversity of the lower respiratory tract in patients with SHAP significantly decreases in the elderly,those with concurrent stroke and those receiving artificial airway treatment.For these groups,rational use of antibiotics should be adopted to guide precise anti-infection treatment.

Objective:To investigate the correlation of serum interleukin-6 (IL-6) and homocysteine (Hcy) levels, monocyte-to-lymphocyte ratio (MLR), and serum lipid levels [triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels] with ulcerative colitis.Methods:The clinical data of 98 patients with ulcerative colitis admitted to Bayannur Hospital from November 2021 to November 2023 (observation group) were retrospectively analyzed. Forty-nine healthy individuals who were selected at a 2:1 ratio during the same period were included in the control group. Serum IL-6 and Hcy levels, MLR, and lipid levels were compared between the two groups. The diagnostic efficacy of serum IL-6, Hcy, MLR, and lipid levels for ulcerative colitis was assessed using receiver operating characteristic (ROC) curves. Additionally, Pearson correlation analysis was conducted to analyze correlation of serum IL-6 and Hcy levels, MLR, and lipid levels with ulcerative colitis.Results:In the observation group, serum IL-6 and Hcy levels and MLR were (39.87 ± 12.36) pg/mL, (13.01 ± 3.52) μmol/L, and (0.38 ± 0.12), respectively, all of which were significantly higher than those in the control group [(22.3 ± 3.26) pg/mL, (10.05 ± 3.26) μmol/L, (0.29 ± 0.08), t = 9.77, 4.92, 4.78, all P < 0.05]. In the observation group, serum levels of TG, TC, LDL-C, and HDL-C levels were (1.16 ± 0.32) mmol/L, (4.12 ± 1.15) mmol/L, (2.60 ± 0.75) mmol/L, and (1.02 ± 0.17) mmol/L, respectively, all of which were significantly lower than those in the control group [(1.45 ± 0.41) mmol/L, (4.91 ± 0.99) mmol/L, (3.20 ± 0.71) mmol/L, (1.13 ± 0.16) mmol/L, t = 4.71, 4.11, 4.65, 3.77, all P < 0.05]. ROC curve analysis indicated that the areas under the curve (AUC) for diagnosing ulcerative colitis based on serum levels of IL-6, Hcy, MLR, TG, TC, LDL-C, and HDL-C were 0.957, 0.749, 0.746, 0.732, 0.678, 0.722, and 0.681, respectively. Pearson correlation analysis showed that serum levels of IL-6, Hcy, MLR, TG, TC, LDL-C, and HDL-C were all correlated with the severity of ulcerative colitis in patients ( r = 0.501, 0.615, 0.605, -0.577, -0.542, -0.548, -0.646, all P < 0.05). Additionally, serum levels of IL-6, Hcy, and MLR were negatively correlated with lipid levels ( r = -0.806, -0.801, -0.791, -0.649, -0.728, -0.671, -0.720, -0.655, -0.857, -0.877, -0.889, -0.583, all P < 0.05). Conclusions:In patients with ulcerative colitis, serum levels of IL-6, Hcy, and MLR are elevated, while lipid levels are decreased. Additionally, serum levels of IL-6, Hcy, MLR, and lipid levels are associated with the severity of the disease. There is also a correlation between serum levels of IL-6, Hcy, MLR, and lipid levels.

Objective:To examine the near-complete genomic analysis of norovirus (NoV) subtype GⅡ.17 [P17] outbreaks in Beijing Chaoyang District from 2014 to 2024.Methods:Data and specimens related to outbreaks of the NoV aggregation in Beijing′s Chaoyang District from 2014 to 2024 were collected. The NoV was identified using real-time fluorescence reverse transcription polymerase chain reaction (RT-PCR). Specimens with positive nucleic acid were amplified by standard PCR, whole genome sequencing and evolutionary analysis. Amino acid site variations were compared.Results:In Chaoyang District, from 2014 to 2024, a total of 637 aggregated outbreaks caused by the NoV infection were reported, of which 584 were successfully typed. The epidemic caused by the GⅡ.17 [P17] subtype accounted for 8.79% (56/637), which was the dominant epidemic gene subtype in 2014-2015, sporadic in 2016-2019, reappeared in 2022, and significantly increased in 2024 (27.27%, 24/88). Outbreaks caused by the GⅡ.17 [P17] subtype occurred mainly from October to December, with the main sites of occurrence in primary schools and kindergartens. This study yielded 53 near-complete genome sequences of the GⅡ.17 [P17] subtype from 46 incidents in Chaoyang District. The GⅡ.17 [P17] subtype sequences of Chaoyang District from 2014 to 2024 were segmented into three subgroups on the evolutionary tree, with sequences from 2014 to 2019, 2022 to April 2024, and May to December 2024 clustered into the d, e, and b subgroups, respectively. In the VP1 region′s P2 area, particularly at the HBGA binding site, subgroups b and e exhibited mutations in 22 and two sites, while subgroups b and e showed mutations in four and one sites, predominantly in the RdRp region.Conclusion:The outbreak caused by the NoV GⅡ.17 [P17] subtype in Chaoyang District from 2014 to 2024 continues, with a significant increase in 2024, and it becomes the dominant gene subtype from October to December. The sequence formation of the NoV GⅡ.17 [P17] subtype in Chaoyang District from January to April 2022 and from May to December 2024 shows two different evolutions, with specific mutation sites, requiring continuous monitoring of the NoV GⅡ.17 [P17] subtype.

Objective To investigae microbial community structure of lower respiratory tract in severe hospital-acquired pneumonia(SHAP)patients in intensive care unit(ICU)using metagenomic next-generation sequencing(mNGS).Methods mNGS was performed on bronchoalveolar lavage fluid(BALF)of 84 patients with SHAP.Patients were grouped based on age,smoking status,underlying diseases and duration of artificial airway.The differences in α diversity,β diversity,microbial composition and community structure of airway microbiota were compared between different groups.The differential airway microbiota associated with artificial airway were screened,and microbial co-occurrence networks was constructed to observe the interaction in microorganisms.Results Results of α diversity analysis revealed that diversity and evenness of the microbial community were higher in young adults compared to those of middle-aged patients,while microbial diversity and evenness were significantly reduced in patients with comorbid stroke.In the group of artificial airway treatment,the diversity and uniformity of microorganisms decreased as the duration of artificial airway treatment increased.The diversity was the lowest when the artificial airway treatment lasted for more than 3 days.β diversity analysis confirmed that there were significant differences in the distinct microbial community structures between the>3 days support group and the non-intubated and≤3 days support cohorts.Acinetobacter baumannii was dominant in all groups.The bacterial diversity was significantly higher in the middle-aged group,the non-smoking group,the group without artificial airway therapy,the group without diabetes mellitus,the group with artificial airway therapy≥3 days and the group with chronic lung disease than those of other groups.In particular,pseudomonas aeruginosa,corynebacterium striatum and veillonella parvula were enriched in these groups.Difference analysis showed that there were significant differences in pseudomonas aeruginosa and corynebacterium striatum between the group with artificial airway treatment>3 days,the group without artificial airway therapy and the group with artificial airway treatment≤3 days.Network co-occurrence showed that there may be synergistic or antagonistic relationships between some microorganisms.Conclusion The microbial diversity of the lower respiratory tract in patients with SHAP significantly decreases in the elderly,those with concurrent stroke and those receiving artificial airway treatment.For these groups,rational use of antibiotics should be adopted to guide precise anti-infection treatment.