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.

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.

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.

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.

Programmed cell death 1 ligand 1 (PD-L1) is an important immunosuppressive molecule, which inhibits the function of T cells and other immune cells by binding to the receptor programmed cell death-1. The PD-L1 expression disorder plays an important role in the occurrence, development, and treatment of sepsis or other inflammatory diseases, and has become an important target for the treatment of these diseases. Mesenchymal stem cells (MSCs) are a kind of pluripotent stem cells with multiple differentiation potential. In recent years, MSCs have been found to have a strong immunosuppressive ability and are used to treat various inflammatory insults caused by hyperimmune diseases. Moreover, PD-L1 is deeply involved in the immunosuppressive events of MSCs and plays an important role in the treatment of various diseases. In this review, we will summarize the main regulatory mechanism of PD-L1 expression, and discuss various biological functions of PD-L1 in the immune regulation of MSCs.

The methanol extract of Huperzia serrata was separated and purified by ODS, AB-8 macroporous adsorption resin, dextran gel Sephadex LH-20 and silica gel column chromatography combined with the semi-pre HPLC. The chemical structures of the isolated compounds were identified by MS, IR, NMR, etc. Four compounds were isolated from Huperzia serrata and identified, named as serratinine C (1), lycobeline C (2), diphaladin A (3) and crenatine (4). Compound 1 is a new alkaloid, compounds 2-4 were isolated for the first time. In vitro biological activity experiments showed that compound 1 could significantly reduce the levels of nitric oxide and reactive oxygen species in glial cells induced by lipopolysaccharide and had significant antioxidant biological activity.

The current research status of different structures,driving modes and training modes of hand rehabilitation robots at home and abroad was introduced.The disadvantages of the existing hand rehabilitation robots were analyzed.It's pointed out hand rehabilitation robots in the future would involve in the combination of rigid and flexible wearing,new intelligent driving mode and multi-mode rehabilitation training.[Chinese Medical Equipment Journal,2024,45(11):88-96]