AIM To study the chemical constituents from the water fraction of rhizoma of Smilax trinervula Miq.and their biological activities.METHODS Polyamide,silica gel,Sephadex LH-20,ODS and semi-preparative HPLC were used for isolation and purification,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The antitumor activities were determined by MTT mothod,and the inhibitory activities on α-glucosidase were determined by PNPG method.RESULTS Eleven compounds were isolated and identified as tyrosine(1),uridine(2),2-(2',3',4'-trihydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(3),2-(1',2',3',4'-tetrahydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(4),2-(1',2',3',4'-tetrahydroxybutyl)-5-(2",3",4"-trihydroxybutyl)-pyrazine(5),uracil(6),2-(1',2',3',4'-tetrahydroxybutyl)-5-(1",2",3",4"-tetrahydroxybutyl)-pyrazine(7),dioscin(8),shikimic acid(9),pyrazine(10),3,4-dihydroxyphenyethyl alcohol 8-O-β-D-glycopyranoside(11).The IC50 values of compounds 8 to human breast cancer cell MCF-7 was(2.36±0.26)μg/mL,and the IC50 values of compounds 3-5 and 7 to α-glucosidase were(1.54±0.15)-(10.53±0.38)μg/mL.CONCLUSION Compounds 1-7,10 are isolated from Smilax genus for the first time,and compound 9,11 are first isolated from this plant.Compound 8 has anti-tumor activity,and compounds 3-5,7 have α-glucosidase inhibitory activities.

Objective To investigate the risk factors for pneumonia complicating infectious mononucleosis(IM)in adults and construct a nomogram prediction model.Methods A retrospective analysis was conducted on 198 IM patients admitted to the Second Affiliated Hospital of Air Force Medical University from January 2015 to December 2021.Patients were divided into pneumonia group(n=52)and non-pneumonia group(n=146)based on whether pulmonary infection occurred during hospitalization.The baseline data(age,gender,place of onset,etc.),clinical manifestations(maximum body temperature,lymph node enlargement,splenomegaly,etc.),and inflammatory indicators[white blood cell count(WBC),C-reactive protein(CRP),etc.]were compared between the two groups.Kaplan-Meier curves were plotted to analyze the key indicators affecting the hospital stay of IM patients.Multivariate logistic regression was used to analyze the independent risk factors for pneumonia complicating IM in adults and construct a nomogram prediction model based on the identified risk factors.The predictive efficacy of the model was evaluated using the receiver operating characteristic(ROC)curve and the consistency of the model was assessed using the calibration curve.The fit of the model was evaluated using the Hosmer-Lemeshow test.Additionally,the sensitivity,specificity,and accuracy of the model were assessed using confusion matrix.Results Compared with non-pneumonia group,the pneumonia group had a significantly higher proportion of patients from rural areas,with body mass index(BMI)≥24 kg/m2,smoking history,hepatomegaly,fever duration of≥7 d,as well as increased total hospitalization costs and average daily hospitalization costs,and prolonged hospital stay(P<0.05).The proportion of patients with a history of antibiotic use was lower in the pneumonia group(P<0.05).Kaplan-Meier survival analysis showed that patients from rural areas,with BMI≥24 kg/m2,smoking history,no prophylactic use of antibiotics,fever duration≥7 d,and hepatomegaly had significantly prolonged hospital stays(P<0.05).Multivariate logistic regression analysis revealed that living in a rural area(OR=4.089,P<0.05),hepatomegaly(OR=4.082,P<0.05),and elevated WBC(OR=1.205,P<0.05)were independent risk factors for pneumonia complicating IM in adults,while the prophylactic use of antibiotics(OR=0.142,P<0.05)was an independent protective factor.The area under the ROC curve of the constructed nomogram prediction model was 0.827(95%CI 0.762-0.892),and the slope of the calibration curve was close to 1,and the Hosmer-Lemeshow test showed χ2=5.299,P=0.725,indicating good consistency and fit of the prediction model.The results of the confusion matrix assessment showed that the sensitivity of the model was 0.669(0.624-0.773),the specificity was 0.827(0.724-0.930),and the accuracy was 0.732(0.665-0.793).Conclusion The nomogram prediction model based on place of onset,hepatomegaly,the prophylactic use of antibiotics and WBC has excellent fit and discrimination,providing an effective quantitative tool for prognosis assessment of IM.

AIM To study the chemical constituents from the water fraction of rhizoma of Smilax trinervula Miq.and their biological activities.METHODS Polyamide,silica gel,Sephadex LH-20,ODS and semi-preparative HPLC were used for isolation and purification,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The antitumor activities were determined by MTT mothod,and the inhibitory activities on α-glucosidase were determined by PNPG method.RESULTS Eleven compounds were isolated and identified as tyrosine(1),uridine(2),2-(2',3',4'-trihydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(3),2-(1',2',3',4'-tetrahydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(4),2-(1',2',3',4'-tetrahydroxybutyl)-5-(2",3",4"-trihydroxybutyl)-pyrazine(5),uracil(6),2-(1',2',3',4'-tetrahydroxybutyl)-5-(1",2",3",4"-tetrahydroxybutyl)-pyrazine(7),dioscin(8),shikimic acid(9),pyrazine(10),3,4-dihydroxyphenyethyl alcohol 8-O-β-D-glycopyranoside(11).The IC50 values of compounds 8 to human breast cancer cell MCF-7 was(2.36±0.26)μg/mL,and the IC50 values of compounds 3-5 and 7 to α-glucosidase were(1.54±0.15)-(10.53±0.38)μg/mL.CONCLUSION Compounds 1-7,10 are isolated from Smilax genus for the first time,and compound 9,11 are first isolated from this plant.Compound 8 has anti-tumor activity,and compounds 3-5,7 have α-glucosidase inhibitory activities.

Objective To investigate the risk factors and clinical significance of layered plaques that were detected by optical coherence tomography(OCT)in patients with angiographically intermediate coronary lesions,and relationship with prognosis.Methods This was a signal-center retrospective study focusing on patients whom underwent coronary angiography and OCT.The layered plaque group and non-layered plaque group were divided according to the presence or absence of stratified plaque.Clinical data,laboratory indicators,angiography,and OCT results were collected and compared between the two groups.Using logistic regression to analyze the relationship between stratified plaques and clinical features;Cox regression analysis was used to investigate the influencing factors of cardiovascular adverse events in patients with critical coronary artery disease.Results A total of 172 patients were enrolled,including 96 patients in non-layered plaque group and 76 patients in layered plaque group.Male(OR 2.415,95％CI 1.162-5.020,P=0.018),diabetes(OR 2.505,95％CI 1.137-5.525,P=0.023)and history of hyperlipidemia(OR 3.590,95％CI 1.478-6.333,P=0.003)were independent risk factors for stratified plaque.In OCT analysis,the proportion of thin-cap fibroatheroma(TCFA)plaque,macrophage infiltration,microvascularization,thrombosis,plaque rupture,and intimal dissection,as well as lipid plaque length,lipid plaque arc,and lipid plaque index were higher in the layered plaque group.After adjusting for other risk factors,macrophage infiltration is independently associated with stratified plaques(OR 2.106,95％CI 1.019-4.353,P=0.044).Kaplan-Meier survival analysis showed that the target lesion revascularization rate in the layered plaque group was higher than that in the non-layered plaque group(Log-rank P=0.030).Cox regression analysis shows that it has both stratified plaque and thin fibrous membrane plaque characteristics was an independent predictor of cardiovascular adverse events(HR 5.165,95％CI 1.696-15.727,P=0.004).Conclusions In patients with angiographically intermediate coronary lesions,OCT detection of stratified lesions is often accompanied by other unstable plaque features,indicating an increased risk of adverse cardiovascular events.Simultaneously possessing features of stratified plaques and TCFA is an independent predictor of adverse cardiovascular events in patients with critical coronary artery disease.

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.

Objective To investigate the risk factors and clinical significance of layered plaques that were detected by optical coherence tomography(OCT)in patients with angiographically intermediate coronary lesions,and relationship with prognosis.Methods This was a signal-center retrospective study focusing on patients whom underwent coronary angiography and OCT.The layered plaque group and non-layered plaque group were divided according to the presence or absence of stratified plaque.Clinical data,laboratory indicators,angiography,and OCT results were collected and compared between the two groups.Using logistic regression to analyze the relationship between stratified plaques and clinical features;Cox regression analysis was used to investigate the influencing factors of cardiovascular adverse events in patients with critical coronary artery disease.Results A total of 172 patients were enrolled,including 96 patients in non-layered plaque group and 76 patients in layered plaque group.Male(OR 2.415,95％CI 1.162-5.020,P=0.018),diabetes(OR 2.505,95％CI 1.137-5.525,P=0.023)and history of hyperlipidemia(OR 3.590,95％CI 1.478-6.333,P=0.003)were independent risk factors for stratified plaque.In OCT analysis,the proportion of thin-cap fibroatheroma(TCFA)plaque,macrophage infiltration,microvascularization,thrombosis,plaque rupture,and intimal dissection,as well as lipid plaque length,lipid plaque arc,and lipid plaque index were higher in the layered plaque group.After adjusting for other risk factors,macrophage infiltration is independently associated with stratified plaques(OR 2.106,95％CI 1.019-4.353,P=0.044).Kaplan-Meier survival analysis showed that the target lesion revascularization rate in the layered plaque group was higher than that in the non-layered plaque group(Log-rank P=0.030).Cox regression analysis shows that it has both stratified plaque and thin fibrous membrane plaque characteristics was an independent predictor of cardiovascular adverse events(HR 5.165,95％CI 1.696-15.727,P=0.004).Conclusions In patients with angiographically intermediate coronary lesions,OCT detection of stratified lesions is often accompanied by other unstable plaque features,indicating an increased risk of adverse cardiovascular events.Simultaneously possessing features of stratified plaques and TCFA is an independent predictor of adverse cardiovascular events in patients with critical coronary artery disease.

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.

ObjectiveThe detection of RNA single nucleotide polymorphism (SNP) is of great importance due to their association with protein expression related to various diseases and drug responses. At present, splintR ligase-assisted methods are important approaches for RNA direct detection, but its specificity will be limited when the fidelity of ligases is not ideal. The aim of this study was to create a method to improve the specificity of splintR ligase for RNA detection. MethodsIn this study, a dual-competitive-padlock-probe (DCPLP) assay without the need for additional enzymes or reactions is proposed to improve specificity of splintR ligase ligation. To verify the method, we employed dual competitive padlock probe-mediated rolling circle amplification (DCPLP-RCA) to genotype the CYP2C9 gene. ResultsThe specificity was well improved through the competition and strand displacement of dual padlock probe, with an 83.26% reduction in nonspecific signal. By detecting synthetic RNA samples, the method demonstrated a dynamic detection range of 10 pmol/L-1 nmol/L. Furthermore, clinical samples were applied to the method to evaluate its performance, and the genotyping results were consistent with those obtained using the qPCR method. ConclusionThis study has successfully established a highly specific direct RNA SNP detection method, and provided a novel avenue for accurate identification of various types of RNAs.

Glycosylation is one of the most important reactions in living organisms as it results in the formation of glycoconjugates with diverse biological functions. Sugar nucleotides are structurally composed of sugar and nucleoside diphosphate or monophosphate, which are widespread within a variety of biological cells. As glycosyl donors for the transglycosyl reactions catalyzed by Leloir-type glycosyltransferases, sugar nucleotides are essential for the synthesis of glycans and glycoconjugates. However, high costs and limited availability of nucleotide sugars prevent applications of biocatalytic cascades on an industrial scale. Therefore, attentions on synthetic strategies of sugar nucleotides have been increasing to achieve their wide applications in various fields. The 9 common sugar nucleotides in mammals have been fully studied with large-scale synthesis through chemical, enzymatic (chemo-enzymatic) and cell factory strategies. In addition to common sugar nucleotides, many rare sugar nucleotides are present in plants and bacteria. Although unnatural sugar nucleotides cannot be synthesized in organisms, they have great potential in research as substrates for glycosyltransferases in carbohydrate synthesis, as enzyme inhibitors in biochemical studies, and as components of glycoconjugate biosynthesis. Therefore, increasing attention has been paid to explore the efficient synthesis of unnatural sugar nucleotides. Currently, strategies for chemical synthesis of sugar nucleotides have been greatly improved, such as the use of effective catalysts for forming pyrophosphate bonds and the development of entirely new synthesis protocols. Multiple sugar nucleotides, especially unnatural sugar nucleotides, are synthesized chemically. However, chemical synthesis requires tedious protection and deprotection steps, resulting in complex steps, high cost and low yield. In contrast, enzymatic (chemo-enzymatic) and cell factory methods have significant advantages such as high yield, easy operation and easy process scale-up in the preparation of sugar nucleotides. Hence, they are prominent strategies for sugar nucleotide preparation. Herein, the biosynthesis and application of sugar nucleotides are reviewed, mainly focusing on the 9 sugar nucleotides common in mammals. The early strategies for enzymatic synthesis of sugar nucleotides generally used de novo synthesis pathway. With the discoveries of enzymes involved in salvage pathway of sugar nucleotide synthesis and the development of one-pot multienzyme (OPME) method, the synthesis of sugar nucleotides was greatly simplified. Cell factory method employs the microbial living cells as a “processing plant” by engineering their metabolic pathways through genetic engineering technology. The cell factory method has high yield, and has been applied for efficient synthesis of several sugar nucleotides. Moreover, the strategy of gram-scale synthesis of multiple rare sugar nucleotides by cascade reactions from common sugar nucleotides using sugar nucleotides synthases cloned from different sources was illustrated. In recent years, the synthesis cost of sugar nucleotides has been further reduced through various ways, such as regeneration of nucleotides, regeneration of organic cofactors, and application of immobilized enzyme technology. Furthermore, through the continuous improvement of sugar nucleotide purification process, the use of high concentration of multi-enzyme cascade and rapid non-chromatographic purification process, the synthesis of multiple sugar nucleotides and their derivatives from monosaccharides was achieved, which gradually broke the limitations of the existing strategy. With the efficient synthesis of sugar nucleotides, their applications in various fields have been increasingly explored, including the synthesis of glycans and glycoconjugates, biochemical characterization of glycosyltransferases and bioorthogonal labeling strategies, which are of great significance to the research of biochemistry, glycobiology and the development of related pharmaceutical products.

Objective To investigate the clinical features and prognosis of childhood acute lymphoblastic leukemia (ALL) with CREBBP gene mutation. Methods A retrospective analysis was performed for the clinical data of 14 ALL children with CREBBP gene mutation who were admitted to Children's Hospital of the First Affiliated Hospital of Zhengzhou University from January 2016 to December 2023. Results The ALL patients with CREBBP gene mutation accounted for 1.5％ (14/963) among all children diagnosed with ALL during the same period of time,among whom there were 4 boys (29％) and 10 girls (71％),with a median age of 4 years and 3.5 months. All children had an immunological type of B-cell ALL and concurrent mutations in other genes including NRAS,KRAS,ETV6,FLT3,PAX5,SH2B3,CDKN2A,and CDKN2B,and 4 children had karyotype abnormality. All 14 children received induction therapy with the VDLP regimen,with a complete remission (CR) rate of 79％ (11/14) after the first course of treatment. Three children experienced bone marrow recurrence alone,with a recurrence rate of 21％ (3/14),among whom 1 child achieved CR after blinatumomab therapy and 2 received bridging hematopoietic stem cell transplantation after chemotherapy for recurrence. Among the 14 children,1 died due to treatment discontinuation and 13 achieved disease-free survival. The 5-year overall survival rate was 92％±7％,and the event-free survival rate was 73％±13％. Conclusions ALL with CREBBP gene mutation is more common in girls and has a low induction remission rate and a high recurrence rate,and it is often accompanied by other types of gene mutations and abnormal karyotypes. Most children with recurrence can achieve long-term survival after immunotherapy or hematopoietic stem cell transplantation.