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 establish a rapid quantitative analysis model for saponins in Paris polyphylla var.yunnanensis(PPY)by near infrared spectroscopy.METHODS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ and there total content in PPY were determined by HPLC,while spectral data within the range of 10 000 to 4 000 cm-1 were collected.A quantitative analysis model was established by combining these data with partial least squares regression(PLSR).Multivariate scatter correction(MSC)and vector normalization(SNV)were applied prior to further preprocessing the spectra with original,first-order derivative(1stD),or second-order derivative(2ndD)treatments.Lastly,the model was optimized through non-smoothing(NS),Norris Derivative filtering(Nd),and Savitzky-Golay filtering(S-G)method.Model stability was evaluated based on correlation coefficients and variance.The predicted contents of each saponin component in the validation set samples were calculated.RESULTS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ were 0.42-17.98,0.46-10.44,0.23-3.86 mg/g,respectively.The total content ranged from 2.91 to 22.1 mg/g.The optimal parameters of three saponins were achieved when selecting the MSC+2ndD+S-G pretreatment method.The corresponding ratio of line segment length to segment gap was 13∶5,15∶5,11∶5,with correlation coefficients of 0.982,0.930,0.958,respectively.The root mean square errors of calibration(RMSEC)were 0.702,0.797,0.238,and the root mean square errors of prediction(RMSEP)were 1.120,0.835,0.304,respectively.The optimal parameters for the total content were obtained when selecting the MSC+2ndD+NS pretreatment method,with a correlation coefficient of 0.970,a RMSEC of 1.090,and a RMSEP of 1.740.CONCLUSION This accurate and rapid method can be used for detection of saponin contents in P.Polyphylla.

This study investigated the role of the nuclear factor of activated T cells c3 (NFATc3) in vascular smooth muscle cells (VSMCs) during aortic aneurysm and dissection (AAD) progression and the underlying molecular mechanisms. Cytoplasmic and nuclear NFATc3 levels were elevated in human and mouse AAD. VSMC-NFATc3 deletion reduced thoracic AAD (TAAD) and abdominal aortic aneurysm (AAA) progression in mice, contrary to VSMC-NFATc3 overexpression. VSMC-NFATc3 deletion reduced extracellular matrix (ECM) degradation and maintained the VSMC contractile phenotype. Nuclear NFATc3 targeted and transcriptionally upregulated matrix metalloproteinase 9 (MMP9) and MMP2, promoting ECM degradation and AAD development. NFATc3 promoted VSMC phenotypic switching by binding to eukaryotic elongation factor 2 (eEF2) and inhibiting its phosphorylation in the VSMC cytoplasm. Restoring eEF2 reversed the beneficial effects in VSMC-specific NFATc3-knockout mice. Cabamiquine-targets eEF2 and inhibits protein synthesis-inhibited AAD development and progression in VSMC-NFATc3-overexpressing mice. VSMC-NFATc3 promoted VSMC switch and ECM degradation while exacerbating AAD development, making it a novel potential therapeutic target for preventing and treating AAD.

OBJECTIVE: To study the clinical effects of pomalidomide-based regimen in the treatment of relapsed and refractory multiple myeloma (RRMM). METHODS: 60 patients with RRMM in hematology department of the First Affiliated Hospital of Xinxiang Medical University from November 2020 to January 2023 were selected. Among them, 15 cases were treated with PDD regimen (pomalidomide + daratumumab + dexamethasone), and 45 cases were treated with PCD regimen (pomalidomide + cyclophosphamide + dexamethasone). The clinical effects were evaluated. RESULTS: The median number of treatment cycles for the entire cohort was 5 (2-11), with an overall response rate (ORR) of 75.0%. The ORR of patients treated with PDD regimen was 73.3%, while the ORR of patients treated with PCD regimen was 75.6%. The ORR of 46 patients with non high-risk cytogenetic abnormalities (non-HRCA) was 86.9%, significantly higher than the 35.7% of 14 patients with HRCA (χ² =15.031, P < 0.05). The median PFS for all patients was 8.0(95%CI : 6.8-9.1) months and the median OS was 14.0 (95%CI : 11.3-16.7) months. Among patients treated with PDD regimen, the PFS and OS of patients with non-HRCA were significantly higher than those of patients with HRCA [PFS: 7.0(95%CI : 4.6-9.3) months vs 4.0(95%CI : 3.1-4.8) months, χ² =5.120, P < 0.05; OS: not reached vs 6.0(95%CI : 1.1-10.9) months, χ² =9.870, P < 0.05]. Among patients treated with PCD regimen, the PFS and OS of patients with non-HRCA were significantly higher than those of patients with HRCA [PFS: 9.0(95%CI : 6.2-11.8) months vs 6.0(95%CI : 5.4-6.6) months, χ²=14.396, P < 0.05; OS: not reached vs 11.0(95%CI : 6.4-15.6) months, χ² =7.471, P < 0.05]. CONCLUSION The pomalidomide-based regimen has a good clinical effect and safety in the treatment of RRMM.
Humans ; Multiple Myeloma/drug therapy* ; Thalidomide/administration & dosage* ; Dexamethasone/therapeutic use* ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Female ; Male ; Middle Aged ; Recurrence ; Aged ; Cyclophosphamide/therapeutic use* ; Treatment Outcome ; Antibodies, Monoclonal

Objective To explore the application value of MRI diaphragmatic navigation technology combined with three dimensional liver acquisition with volume acceleration-flexible(3D LAVA-FLEX)sequence in abdominal enhanced imaging of infants and young children.Methods A retrospective analysis was conducted on imaging data of 84 infants and young children who underwent abdominal enhanced MRI examination in our hospital between January 2021 and December 2023.All 84 infants and young children initially underwent conventional dynamic contrast-enhanced 3D LAVA-FLEX sequence scanning;the delayed phase images obtained were included in the dynamic enhancement group.Subsequently,diaphragmatic navigation combined with 3D LAVA-FLEX sequence examination was implemented,and the obtained images were included in the diaphragm navigation group.Subjective scoring was performed for images in both groups,while the signal to noise ratio(SNR),contrast to noise ratio(CNR),and artifact quantification(AQ)were measured and compared between the two groups.Results The respiratory motion artifacts,the clarity of liver parenchyma enhancement,the clarity of liver vascular enhancement,the clarity of spleen parenchyma enhancement and the overall image quality score in the diaphragm navigation group were higher than those in the dynamic enhancement group,and the differences were statistically significant(P<0.05).There were statistically significant differences in SNR and AQ between the two groups of images(P<0.000 1),while there was no statistically significant difference in CNR between the two groups of images(P>0.05).Conclusion Diaphragmatic navigation technology combined with 3D LAVA-FLEX sequence imaging can improve the image quality of abdominal MRI enhanced imaging in infants and young children,and provide a reference for clinical diagnosis and treatment.

Objective To explore the application value of MRI diaphragmatic navigation technology combined with three dimensional liver acquisition with volume acceleration-flexible(3D LAVA-FLEX)sequence in abdominal enhanced imaging of infants and young children.Methods A retrospective analysis was conducted on imaging data of 84 infants and young children who underwent abdominal enhanced MRI examination in our hospital between January 2021 and December 2023.All 84 infants and young children initially underwent conventional dynamic contrast-enhanced 3D LAVA-FLEX sequence scanning;the delayed phase images obtained were included in the dynamic enhancement group.Subsequently,diaphragmatic navigation combined with 3D LAVA-FLEX sequence examination was implemented,and the obtained images were included in the diaphragm navigation group.Subjective scoring was performed for images in both groups,while the signal to noise ratio(SNR),contrast to noise ratio(CNR),and artifact quantification(AQ)were measured and compared between the two groups.Results The respiratory motion artifacts,the clarity of liver parenchyma enhancement,the clarity of liver vascular enhancement,the clarity of spleen parenchyma enhancement and the overall image quality score in the diaphragm navigation group were higher than those in the dynamic enhancement group,and the differences were statistically significant(P<0.05).There were statistically significant differences in SNR and AQ between the two groups of images(P<0.000 1),while there was no statistically significant difference in CNR between the two groups of images(P>0.05).Conclusion Diaphragmatic navigation technology combined with 3D LAVA-FLEX sequence imaging can improve the image quality of abdominal MRI enhanced imaging in infants and young children,and provide a reference for clinical diagnosis and treatment.

[Purpose]To analyze the compliance and its influencing factors of lung cancer screening using low-dose computed tomography(LDCT)among high-risk population in urban districts of Chongqing from 2013 to 2021.[Methods]The lung cancer screeing of Cancer Early Diagnosis and Treatment Project was conducted among permanent residents aged 40～69 years old from 14 urban districts of Chongqing selected by cluster sampling method from 2013 to 2021.The questionnaire survey was performed to assess the risk level of lung cancer,and individuals with high risk were advised to have LDCT examination.The compliance rate of LDCT examination among high-risk populations was calculated and compared using Chi-square test among residents with different de-mographic features;the influencing factors of compliance was analyzed with generalized linear mixed models.[Results]A total of 316 066 residents completed the risk assessment questionnaire survey,52 858 people were assessed as high-risk(17.17％).Among the high-risk population,20 398 completed LDCT screening,with an overall compliance rate of 38.59％.The generalized linear mixed model showed that male participants(OR=0.871,95％CI:0.823～0.922)and smokers(light smokers:OR=0.829,95％CI:0.775～0.886;heavy smokers:OR=0.842,95％CI:0.792～0.896)had lower compliance rates;while people with higher education level(OR=1.347,95％CI:1.265～1.435),occupational exposure to harmful substances(OR=1.400,95％CI:1.340～1.463),passive smoking for 20 years or more(OR=1.472,95％CI:1.376～1.576),infrequent physical exercise(OR=1.203,95％CI:1.152～1.256),family history of lung cancer(OR=2.312,95％CI:2.201～2.429),and those having media promotion by community staff(OR=1.365,95％CI:1.223～1.524),and trained community staff(OR=1.343,95％CI:1.227～1.470)had higher compliance rates.Comorbidities were also factors influencing compliance,and there was an increasing trend of compliance rate with the increase of comorbidity numbers(P＜0.001).[Conclusion]The compli-ance rate of LDCT examination for lung cancer screening in Chongqing needs to be improved,and more precise health education should be implemented for groups with different characteristics to improve the compliance among high-risk population.

AIM To establish a rapid quantitative analysis model for saponins in Paris polyphylla var.yunnanensis(PPY)by near infrared spectroscopy.METHODS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ and there total content in PPY were determined by HPLC,while spectral data within the range of 10 000 to 4 000 cm-1 were collected.A quantitative analysis model was established by combining these data with partial least squares regression(PLSR).Multivariate scatter correction(MSC)and vector normalization(SNV)were applied prior to further preprocessing the spectra with original,first-order derivative(1stD),or second-order derivative(2ndD)treatments.Lastly,the model was optimized through non-smoothing(NS),Norris Derivative filtering(Nd),and Savitzky-Golay filtering(S-G)method.Model stability was evaluated based on correlation coefficients and variance.The predicted contents of each saponin component in the validation set samples were calculated.RESULTS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ were 0.42-17.98,0.46-10.44,0.23-3.86 mg/g,respectively.The total content ranged from 2.91 to 22.1 mg/g.The optimal parameters of three saponins were achieved when selecting the MSC+2ndD+S-G pretreatment method.The corresponding ratio of line segment length to segment gap was 13∶5,15∶5,11∶5,with correlation coefficients of 0.982,0.930,0.958,respectively.The root mean square errors of calibration(RMSEC)were 0.702,0.797,0.238,and the root mean square errors of prediction(RMSEP)were 1.120,0.835,0.304,respectively.The optimal parameters for the total content were obtained when selecting the MSC+2ndD+NS pretreatment method,with a correlation coefficient of 0.970,a RMSEC of 1.090,and a RMSEP of 1.740.CONCLUSION This accurate and rapid method can be used for detection of saponin contents in P.Polyphylla.

[Purpose]To analyze the compliance and its influencing factors of lung cancer screening using low-dose computed tomography(LDCT)among high-risk population in urban districts of Chongqing from 2013 to 2021.[Methods]The lung cancer screeing of Cancer Early Diagnosis and Treatment Project was conducted among permanent residents aged 40～69 years old from 14 urban districts of Chongqing selected by cluster sampling method from 2013 to 2021.The questionnaire survey was performed to assess the risk level of lung cancer,and individuals with high risk were advised to have LDCT examination.The compliance rate of LDCT examination among high-risk populations was calculated and compared using Chi-square test among residents with different de-mographic features;the influencing factors of compliance was analyzed with generalized linear mixed models.[Results]A total of 316 066 residents completed the risk assessment questionnaire survey,52 858 people were assessed as high-risk(17.17％).Among the high-risk population,20 398 completed LDCT screening,with an overall compliance rate of 38.59％.The generalized linear mixed model showed that male participants(OR=0.871,95％CI:0.823～0.922)and smokers(light smokers:OR=0.829,95％CI:0.775～0.886;heavy smokers:OR=0.842,95％CI:0.792～0.896)had lower compliance rates;while people with higher education level(OR=1.347,95％CI:1.265～1.435),occupational exposure to harmful substances(OR=1.400,95％CI:1.340～1.463),passive smoking for 20 years or more(OR=1.472,95％CI:1.376～1.576),infrequent physical exercise(OR=1.203,95％CI:1.152～1.256),family history of lung cancer(OR=2.312,95％CI:2.201～2.429),and those having media promotion by community staff(OR=1.365,95％CI:1.223～1.524),and trained community staff(OR=1.343,95％CI:1.227～1.470)had higher compliance rates.Comorbidities were also factors influencing compliance,and there was an increasing trend of compliance rate with the increase of comorbidity numbers(P＜0.001).[Conclusion]The compli-ance rate of LDCT examination for lung cancer screening in Chongqing needs to be improved,and more precise health education should be implemented for groups with different characteristics to improve the compliance among high-risk population.

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.