Eight compounds were isolated and purified from the ethyl acetate part of 70% acetone extract of Rehmannia glutinosa by various chromatographic techniques such as silica gel, MCI gel CHP-20, ODS, Toyopearl HW-40C, combined with TLC and semi-preparative HPLC. Their structures were elucidated by modern spectroscopy techniques (NMR, MS, UV, IR), and identified as neomartynoside A (1), osmanthuside B₆ (2), martynoside (3), isomartynoside (4), (E)-p-hydroxycinnamic acid (5), caffeic acid (6), ferulic acid (7), and methyl caffeate (8). Compound 1 is a new phenylethanol glycoside, which was identified as neomartynoside A. Compound 2 was isolated from Rehmannia glutinosa for the first time. In addition, compounds 2, 6 and 7 significantly increased relative glucose consumption, showing potential hypoglycemic activity.

With the rapid development of competitive sports, the incidence of anterior cruciate ligament (ACL) injury is on the rise. Such injuries may shorten athletes′ career and lead to other long-term adverse consequences. Although athletes generally recover well after ACL reconstruction, many still struggle to return to their pre-injury performance levels. Advances in the understanding of ACL anatomy and injury mechanisms, along with the evolution of surgical techniques and rehabilitation methods, have provided more individualized and tailored options for athletes following ACL injuries. However, there is currently no consensus in China regarding surgical and rehabilitation strategies for competitive athletes aiming to return to sports after ACL injuries. To this end, the Sports Medicine Committee of the Chinese Research Hospital Association and the Editorial Board of the Chinese Journal of Trauma jointly formulated the Expert consensus on surgical treatment and rehabilitation for competitive sports athletes returning to sports after anterior cruciate ligament injury ( version 2025), and presented 14 recommendations covering surgical indications, preoperative rehabilitation, surgical timing, surgical strategies and postoperative rehabilitation strategies, aiming to improve the surgical treatment and rehabilitation system for ACL injuries in competitive athletes and facilitate their return to high-level sports performance after injury.

Objective To explore the impact of polymorphisms in the solute carrier organic anion transporter family member 1B1(SLCO1B1)and apolipoprotein E(APOE)genes on statins selection and prognosis in patients after percutaneous coronary intervention(PCI).Methods This retrospective study enrolled 612 patients with coronary heart disease hospitalized in Tianjin Beichen Hospital from January 2021 to February 2023.Among them,411 patients who underwent PCI were assigned to PCI group,and 201 patients who did not undergo PCI were assigned to non-PCI group.According to treatment methods,PCI group was further divided into two subgroups:observation subgroup(n=305)and control subgroup(n=106).Non-PCI group was also divided into two subgroups:observation subgroup(n=147)and control subgroup(n=54).Patients in all observation subgroups underwent the detection of SLCO1B1 and APOE gene polymorphisms before medication,and an initial individualized treatment plan(including the selection of appropriate statins and dosages)was formulated based on the detection results.Patients in control subgroups were given statins at conventional dosages.Basic data before medication,post-medication lipid compliance rates[lipid intention-to-treat(ITT)compliance rate and lipid per-protocol analysis(PP)compliance rate],time to lipid compliance,liver function indicators before and after medication,and prognosis,as well as SLCO1B1 and APOE genotyping and allele frequency distribution were compared between observation subgroup and control subgroup.Conditional logistic regression was used to analyze the relationship between different genotypes and prognostic susceptibility.Multifactor dimensionality reduction(MDR)was applied to analyze the interaction between SLCO1B1 genotype and APOE genotype.Results All genotypes of SLCO1B1 and APOE genes in observation subgroups conformed to the Hardy-Weinberg genetic equilibrium test(P>0.05).Compared with control subgroups,the observation subgroups had significantly higher lipid PP compliance rates after 6 and 12 months of treatment.After 3 months of treatment,the number of completed cases and compliant cases in observation subgroups was significantly higher,and the time to lipid compliance was significantly shorter than that in control subgroups(P<0.05).Compared with before treatment,there were no significant changes in alanine aminotransferase(ALT)and creatine kinase isoenzyme(CK-MB)levels in either observation subgroups or control subgroups(P>0.05);the levels of total cholesterol(TC),triglycerides(TG),and low-density lipoprotein cholesterol(LDL-C)were significantly decreased(P<0.001),while high-density lipoprotein cholesterol(HDL-C)level was significantly increased(P<0.01).After 12 months of treatment,the aspartate aminotransferase(AST)level in observation subgroups was significantly lower than that before medication and also lower than that in the corresponding control subgroups(P<0.05).Within PCI group:there were 1 case and 5 cases of myocardial infarction,2 cases and 6 cases of target vessel revascularization,0 cases and 3 cases of stroke,0 cases and 7 cases of recurrent angina pectoris,1 case and 5 cases of readmission in observation subgroup and control subgroup,respectively.There were statistically significant differences between observation subgroup and control subgroup(P<0.05).Within non-PCI group:there were 0 cases and 3 cases of cardiogenic death,2 cases and 6 cases of myocardial infarction,2 cases and 5 cases of target vessel revascularization,0 cases and 4 cases of stroke,0 cases and 8 cases of recurrent angina pectoris,and 2 cases and 4 cases of readmission in observation subgroup and control subgroups,respectively,with statistically significant differences between the two subgroups(P<0.05).In the recessive model,SLCO1B1 and APOE genes were significantly correlated with prognosis in both PCI observation subgroup and non-PCI observation subgroup(P<0.05).MDR analysis of the interaction between SLCO1B1 genotype and APOE genotype in PCI observation subgroup and non-PCI observation subgroup showed that there was a significant interaction between the genotypes,which was closely related to prognosis(P<0.05).Conclusion Individualized statin treatment regimens based on the detection of SLCO1B1 and APOE gene polymorphisms can improve the lipid compliance rate,shorten the time to lipid compliance,and improve the prognosis of patients after PCI.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Copy number variation encompassing SNP plays an important role in IVD and precision medi-cine.As the most commonly used method,FISH could not overcome the probe cross-reactivity which is common when to detect SNP.Here we developed a quantitative and qualitative method on copy number variation encompassing SNP.In this study,the rs76711854 was used as an example to establish a quanti-tative method by advantage of probe cross-reactivity.The fragment encompassing rs76711854 and its downstream to 9 514 bp were amplified by PCR.The allelic genotypes were verified by Sanger sequen-cing.Different probes with or without cross-reactivity to be used via quantitative real-time PCR and digit-al PCR.The different clusters(2D)and fluorescence intensity layers(1D)exist by adding probe with cross-reactivity.The A/G ratio measured by digital PCR is 2︰1,which is verified by probe targeting to the SNP.The copy-number variant exists in the 9kb-long fragment upstream to the SNP of prostate cancer cell line but not in human endometrial adenocarcinoma cell line Ishikawa.The data suggest that there is a multi-copy variation at this locus in DU145 cells.The method applied here is based on one single cross-reactivity probe via digital PCR.

P53 is a key tumor suppressor gene,which is regulated in many ways.Zinc finger 148(ZNF148)and SP5,as zinc finger transcription factors(TFs),play important roles in tumor suppression and carcinogenesis.The regulatory relationship between these two TFs and p53 has not been reported.In this paper,Ishikawa and A549 cell lines with different p53 expression levels were used as research mod-els to explore the transcriptional regulation of the P53 gene by ZNF148 and SP5.The data showed that there were differences in the expression of ZNF148 and SP5 in the two cell lines.The mRNA expression of ZNF148 in Ishikawa was 1.9 times higher than that of A549,and the mRNA expression of SP5 in A549 was 802.4 times that of ZNF148.Data showed that in Ishikawa cells,the expression of P53 de-creased(81.8％)after ZNF148 knockdown,and increased(2.6 times)after SP5 overexpression.Transfection of si-SP5 and ZNF148 expression plasmids into A549 cells increased the mRNA expression of P53 by 6.6 times and 14.6 times,respectively.These results indicate that ZNF148 could activate,whereas SP5 could inhibit,P53 expression.The conserved cis-element of ZNF148 and SP5 TFs was found in the region of the P53 promoter by bioinformatics methods.The data from dual luciferase reporter gene assay showed that the luciferase activity of ZNF148 in Ishikawa and A549 cells was increased by 2.1-fold and 4.2-fold compared with the control group(P＜0.05).Compared with the control group,the normalized relative luciferase activity of transfected SP5 decreased by 77.1％and 35.7％(P＜0.05).However,when the cis-element of ZNF148 and SP5 was mutated,the effect disappeared.Further trans-fection of ZNF148 and SP5 with different ratios revealed that SP5 could reverse the transcriptional activa-tion of P53 by ZNF148.Studies have shown that ZNF148 shares a common site with SP5,and the ratio of the two TFs may influence the transcriptional activity of P53.The expression of the Wnt pathway and the cell proliferation rate after knockdown of ZNF148 and SP5 were further studied to explore the role of the two TFs.Our data show that ZNF148 and SP5 could regulate the transcriptional activity of P53,and their expression levels and interaction may be the key factors regulating P53 expression.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.