OBJECTIVE To establish the UPLC fingerprint and the method for multi-component content determination in Jingangteng capsules， and to evaluate its quality by combining chemical pattern recognition analysis. METHODS An UPLC method was established. Separation was performed on a Zorbax SB-C 18 Rapid Resolution HD column， with acetonitrile-0.1% formic acid as the mobile phase for gradient elution.Using the Similarity Evaluation System for Chromatographic Fingerprints of Traditional Chinese Medicines （2012 edition）， UPLC fi ngerprints were established for 10 batches of Jingangteng capsules， and similarity was evaluated. SPSS 22.0 and SIMCA 14.1 software were used to perform hierarchial-cluster analysis and orthogonal partial least squares discriminant analysis （OPLS-DA）， respectively. The same UPLC method was employed to determine the contents of chlorogenic acid， 3，5-dihydroxy-2-methylbenzoic acid-3- O -glucoside （M1）， caffeic acid， astilbin， oxyresveratrol， quercitrin and resveratrol in the 10 batches of samples. RESULTS A total of 17 common peaks were identified in UPLC fingerprints of the 10 batches of samples， of which 7 were identified as chlorogenic acid， M1， caffeic acid， astilbin， oxyresveratrol， quercitrin， and resveratrol. The similarities of 10 batches of samples ranged from 0.820 to 0.985. The results of hierarchial-cluster analysis showed that 10 batches of samples were grouped into four categories： S1-S4 formed one group， S5 and S6 formed another， S7， S8 and S10 formed a third， and S9 formed a fourth， consistent with the OPLS-DA results； the variable importance projection values for peaks 7， 10， 2， 16 （resveratrol）， 13 （oxyresveratrol）， 11， 6 （caffeic acid）， 5 （M1） and 15 （quercitrin） were ＞1. Quantitative analysis results showed that the contents of chlorogenic acid， M1， caffeic acid， astilbin， oxyresveratrol， quercitrin， and resveratrol were 1.650 8-4.213 7， 0.636 2-2.161 7， 0.031 0-0.086 5， 0.239 1-1.069 3， 0.211 9-1.104 0， 0.488 8-2.399 2， and 0.164 0-0.699 8 mg/g， respectively. CONCLUSIONS UPLC fingerprint and content determination methods established in this study are simple to operate， accurate， reliable and reproducible； when combined with chemical pattern recognition analysis， they can be used to evaluate the quality of Jingangteng capsules. Nine components， such as resveratrol， oxyresveratrol， caffeic acid， M1 and quercitrin， may serve as markers of quality variation.

ObjectiveTo explore the potential mechanism of Xianglian Huazhuo prescription （XLHZ） in treating chronic atrophic gastritis （CAG） by regulating cell cycle and inhibiting proliferation， using bioinformatics technology and animal experiments. MethodsDifferential expressed genes （DEGs） related to CAG were screened using GEO database and GEO2R tool. Weighted gene co-expression network analysis （WGCNA） was employed to search for hub genes of CAG. These hub genes were intersected with cell cycle proliferation based on GeneCards database. Eenrichment analysis of the intersecting genes was performed to obtain signaling pathways and biological processes related to CAG. Protein protein interaction （PPI） analysis of genes was conducted using the Protein Interaction Platform （STRING） database to search the super hub gene （hub 2.0）， and animal experiments were conducted for further validation. Fourteen of 70 male Wistar rats were randomly selected as the normal group， and the remaining 56 rats were prepared by the combined modeling method of "starvation disorder+N-methyl-N-nitro-N-nitrosoguanidine （MNNG） + sodium salicylate". The successfully modeled rats were randomly divided into the model group， XLHZ-H， XLHZ-M， and XLHZ-L groups （36， 18， 9 g·kg^-1， respectively）， and Morodan group （1.4 g·kg^-1）. Each group was given corresponding intervention for 60 days. Hematoxylin-eosin （HE） staining was used to observe the histopathological changes of gastric mucosa in rats. The ultrastructure of gastric mucosal tissue cells was observed by transmission electron microscopy. The relative expression levels of TGF-β₁， Smad2 and Smad3 proteins， S/G₂/M phase marker geminin and proliferation marker MCM2 were detected by Western blot in gastric mucosal tissue， and Spearman correlation analysis was performed. ResultsA total of 15 hub 2.0 genes were identified， including TGF-β₁， suggesting the involvement of the TGF-β₁ signaling pathway in the CAG pathogenesis. Compared with the normal group， the expressions of TGF-β₁， Smad2， geminin and MCM2 proteins in the gastric mucosa tissue of the model group were increased （P<0.05）， and the expression of Smad3 protein was decreased （P<0.05）. Compared with the model group， the expressions of TGF-β₁ and geminin in the gastric mucosa were decreased in the drug groups （P<0.05）. The XLHZ-M group， XLHZ-H group and Morodan group had significantly decreased protein expression of Smad2 and MCM2 （P<0.05）. The protein expression of Smad3 was significantly increased in XLHZ-M， XLHZ-H， and Morodan groups （P<0.05）. Spearman correlation analysis showed that Smad3 was negatively correlated with other indicators， and positively correlated with other indicators （P<0.01）. ConclusionXLHZ may inhibit TGF-β₁/Smads signaling pathway， regulate cell cycle， and inhibit proliferation in the treatment of CAG.

ObjectiveTo explore the potential mechanism of Xianglian Huazhuo prescription （XLHZ） in treating chronic atrophic gastritis （CAG） by regulating cell cycle and inhibiting proliferation， using bioinformatics technology and animal experiments. MethodsDifferential expressed genes （DEGs） related to CAG were screened using GEO database and GEO2R tool. Weighted gene co-expression network analysis （WGCNA） was employed to search for hub genes of CAG. These hub genes were intersected with cell cycle proliferation based on GeneCards database. Eenrichment analysis of the intersecting genes was performed to obtain signaling pathways and biological processes related to CAG. Protein protein interaction （PPI） analysis of genes was conducted using the Protein Interaction Platform （STRING） database to search the super hub gene （hub 2.0）， and animal experiments were conducted for further validation. Fourteen of 70 male Wistar rats were randomly selected as the normal group， and the remaining 56 rats were prepared by the combined modeling method of "starvation disorder+N-methyl-N-nitro-N-nitrosoguanidine （MNNG） + sodium salicylate". The successfully modeled rats were randomly divided into the model group， XLHZ-H， XLHZ-M， and XLHZ-L groups （36， 18， 9 g·kg^-1， respectively）， and Morodan group （1.4 g·kg^-1）. Each group was given corresponding intervention for 60 days. Hematoxylin-eosin （HE） staining was used to observe the histopathological changes of gastric mucosa in rats. The ultrastructure of gastric mucosal tissue cells was observed by transmission electron microscopy. The relative expression levels of TGF-β₁， Smad2 and Smad3 proteins， S/G₂/M phase marker geminin and proliferation marker MCM2 were detected by Western blot in gastric mucosal tissue， and Spearman correlation analysis was performed. ResultsA total of 15 hub 2.0 genes were identified， including TGF-β₁， suggesting the involvement of the TGF-β₁ signaling pathway in the CAG pathogenesis. Compared with the normal group， the expressions of TGF-β₁， Smad2， geminin and MCM2 proteins in the gastric mucosa tissue of the model group were increased （P<0.05）， and the expression of Smad3 protein was decreased （P<0.05）. Compared with the model group， the expressions of TGF-β₁ and geminin in the gastric mucosa were decreased in the drug groups （P<0.05）. The XLHZ-M group， XLHZ-H group and Morodan group had significantly decreased protein expression of Smad2 and MCM2 （P<0.05）. The protein expression of Smad3 was significantly increased in XLHZ-M， XLHZ-H， and Morodan groups （P<0.05）. Spearman correlation analysis showed that Smad3 was negatively correlated with other indicators， and positively correlated with other indicators （P<0.01）. ConclusionXLHZ may inhibit TGF-β₁/Smads signaling pathway， regulate cell cycle， and inhibit proliferation in the treatment of CAG.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveTo investigate the efficacy and safety of simplified diagnosis and treatment strategies and regimens in the treatment of patients with human immunodeficiency virus （HIV）/hepatitis C virus （HCV） co-infection. MethodsThis multicenter prospective real-world study was conducted among 198 patients with HIV/HCV co-infection who received hepatitis C treatment in 10 designated primary hospitals for hepatitis C in Yuxi， China from July 2023 to June 2024， and according to whether genotype detection was performed， they were divided into genotype detection group with 122 patients and non-genotype detection group with 76 patients. The patients were observed in terms of sustained virologic response at 12 weeks （SVR12）， safety， and liver biochemical parameters. Propensity score matching was used to match the two cohorts， using caliper matching （caliper value = 0.02） at a ratio of 1∶1. The independent-samples t test or the Wilcoxon signed-rank test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between groups. ResultsAmong the 122 patients in the genotype detection group， 63.11% （77/122） had genotype 3， while the non-genotype detection group had 76 patients； both groups achieved an SVR12 rate of 100%. From baseline to 12 weeks after treatment， both groups had a significant increase in the serum level of albumin， significant reductions in the serum levels of total bilirubin， alanine aminotransferase， aspartate aminotransferase， and alpha-fetoprotein， and a significant reduction in FibroScan value （all P<0.05）. A total of 54 patients （27.27%） experienced at least one adverse reaction， and anemia was the most common adverse reaction （38 patients， 19.19%）， with an incidence rate of 50.00% （33/66） in patients with liver cirrhosis. HIV viral load remained <50 IU/mL before and after treatment， and there was no significant change in CD4⁺ T lymphocyte count after treatment （Z=-0.969， P=0.202）. Compared with the genotype detection group， the non-genotype detection group had a significantly shorter time from positive HCV RNA testing to treatment initiation （4±2 days vs 19±4 days， t=5.321， P<0.05） and significantly lower testing costs （618±97 yuan vs 789±129 yuan， t=3.661， P<0.05）. ConclusionFor patients with HIV/HCV co-infection， the simplified diagnosis and treatment strategies can achieve a relatively high SVR12 rate， improve biochemical indicators， and effectively reduce time cost and economic burden， with a favorable safety profile.

Objective This study aimed to investigate the value of PET/CT radiomics combined with machine learning in differentia-ting primary central nervous system lymphoma(PCNSL)from brain metastases(BM).Methods Sixty-nine patients with 127 lesions(including 43 PCNSL lesions and 84 BM lesions)who attended the Department of Nuclear Medicine of Lanzhou University Second Hospi-tal from January 2019 to November 2024 were selected and divided into a training set(n=88)and a validation set(n=39)in a 7∶3 rati-o.Radiomics features of PET and CT were extracted using the 3D slicer,and feature dimensionality reduction was performed using least absolute shrinkage and selection operator(LASSO)regression combined with 5-fold cross-validation to construct three classical machine learning models-Logistic regression(LR),decision tree(DT),and support vector machine(SVM),and the diagnostic efficacy of each model was evaluated using receiver operating characteristic(ROC)curves and confusion matrices.Results The SVM model based on PET imaging performed best in the validation set[area under the curve(AUC)=0.917,sensitivity=84.6％,specificity=92.3％]and was significantly better than the CT model(AUC=0.787,sensitivity=73.1％,specificity=76.9％).Conclusion The support vector machine model constructed based on PET radiomics metabolic features demonstrated high diagnostic value in the preoperative differentia-tion between PCNSL and BM,showing potential for clinical application as a non-invasive auxiliary diagnostic tool.

Objective:To explore the relapse-related genes and their clinicopathological connections of diffuse large B cell lymphoma (DLBCL).Methods:Targeted panel sequencing was conducted on 32 eligible DLBCL samples; the patients were diagnosed, treated, and went into complete remission at the First Affiliated Hospital of Nanjing Medical University from January 2015 to December 2019, including 14 cases with recurrence (relapsed group) and 18 cases with long-term complete remission of over five years (remission group). Clinical and pathological data were further reviewed. Fisher′s exact test was employed to compare the differences in clinicopathological characteristics and mutation patterns between the two groups.Results:Among the 32 patients, there were 18 males and 14 females, with a male to female ratio of 1.3∶1.0 and a median age of 53 (45.5, 67.0) years. In the relapsed group, PIM1 (11/14), KMT2D (7/14), PRDM1 (6/14), MYD88 (6/14), DTX1 (6/14) emerged as the most frequently mutated genes. In the remission group, while recurrent PIM1, KMT2D and MYD88 mutations were also observed, the TP53 gene exhibited the highest mutation frequency (6/18). Compared to the remission group, relapsed group showed elevated mutation frequencies of PIM1 ( P=0.013) and FAT4 ( P=0.010), alongside a reduced incidence of TP53 mutations. In all 32 patients, DLBCL with CD79B, CCND3, DTX1, KMT2D and PRDM1 mutations demonstrated a propensity towards advanced clinicopathologic stage. Conclusions:Relapsed DLBCL has distinctive clinicopathological and genetic features. PIM1 and FAT4 may be served as potential biomarkers for screening relapsed DLBCL-NOS and as targets for novel therapeutic strategies.

Objective:To explore the clinical and pathological features of EBV-positive diffuse large B cell lymphoma (EBV +DLBCL) and to analyze the prognostic significance of CD30 expression in this entity. Methods:A retrospective analysis was conducted from 34 cases of EBV +DLBCL and 198 cases of EBV -DLBCL diagnosed and treated at the First Affiliated Hospital of Nanjing Medical University, from January 2017 to June 2023. Based on CD30 expression, 34 patients with EBV +DLBCL were categorized into CD30-positive and CD30-negative groups. Fisher exact test and Kaplan-Meier survival curves were employed to analyze the relationship between CD30 expression and clinicopathological parameters as well as its prognostic implications. Chi-square tests were used to compare the clinicopathological features between EBV +DLBCL and EBV -DLBCL. Results:There were 19 males and 15 females with a median age of 69.5 (15-83) years in the EBV +DLBCL group. Compared with EBV -DLBCL, EBV +DLBCL was more likely to present with clinical features such as B symptoms ( χ2=23.818, P<0.001), Ann Arbor stage Ⅲ-Ⅳ ( χ2=8.540, P=0.003), ECOG (Eastern Cooperative Oncology Group Performance Status) score 2-4 ( χ2=6.722, P=0.010), IPI score 3-5 ( χ2=9.953, P=0.002), and involvement of more than one extranodal site ( χ2=6.825, P=0.009). Additionally, EBV +DLBCL exhibited higher frequencies of elevated LDH ( χ2=4.307, P=0.038), CRP ( χ2=5.596, P=0.018), and β2-MG ( χ2=7.008, P=0.008) levels. Histopathologically, EBV +DLBCL was more commonly of the non-GCB subtype ( χ2=12.421, P<0.001), with higher frequencies of CD30-positive ( χ2=62.706, P<0.001),CD10-negative ( χ2=8.687, P=0.003),bcl-6-negative ( χ2=11.123, P<0.001), and bcl-2-negative ( χ2=22.779, P=0.003) expression. Using 20% as the positive threshold for CD30, the CD30-positive group had a higher proliferation index ( P=0.045). No significant differences were observed in overall survival between the two groups. Conclusions:EBV +DLBCL is more prevalent in the elderly and often exhibits aggressive clinical features. The expression of CD30 is not associated with the overall prognosis of EBV +DLBCL.

Objective This study aimed to investigate the value of PET/CT radiomics combined with machine learning in differentia-ting primary central nervous system lymphoma(PCNSL)from brain metastases(BM).Methods Sixty-nine patients with 127 lesions(including 43 PCNSL lesions and 84 BM lesions)who attended the Department of Nuclear Medicine of Lanzhou University Second Hospi-tal from January 2019 to November 2024 were selected and divided into a training set(n=88)and a validation set(n=39)in a 7∶3 rati-o.Radiomics features of PET and CT were extracted using the 3D slicer,and feature dimensionality reduction was performed using least absolute shrinkage and selection operator(LASSO)regression combined with 5-fold cross-validation to construct three classical machine learning models-Logistic regression(LR),decision tree(DT),and support vector machine(SVM),and the diagnostic efficacy of each model was evaluated using receiver operating characteristic(ROC)curves and confusion matrices.Results The SVM model based on PET imaging performed best in the validation set[area under the curve(AUC)=0.917,sensitivity=84.6％,specificity=92.3％]and was significantly better than the CT model(AUC=0.787,sensitivity=73.1％,specificity=76.9％).Conclusion The support vector machine model constructed based on PET radiomics metabolic features demonstrated high diagnostic value in the preoperative differentia-tion between PCNSL and BM,showing potential for clinical application as a non-invasive auxiliary diagnostic tool.