Background: and Purpose Vertebrobasilar fusiform aneurysms (VBFAs) carry substantial morbidity and mortality, but optimal management for unruptured VBFAs remains unclear. We compared the safety and efficacy of conservative management (CM), stent-assisted coiling (SAC), and flow diverters (FDs) in patients with unruptured VBFAs, focusing on long-term prognosis. Methods: This study included data from a nationwide Chinese cohort of patients with vertebrobasilar dissecting aneurysms. Inverse probability of treatment weighting (IPTW) balanced confounders across groups. The primary outcome was poor prognosis (modified Rankin Scale score >2). Secondary outcomes included aneurysm rupture, ischemic stroke, compression symptoms, and VBFA-related deaths. Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup and sensitivity analyses were performed. Results: Among 1,115 patients with unruptured VBFAs, 838 (median age, 54 years; 655 men) were included. After IPTW, baseline characteristics were balanced. Median follow-up was 54 months. FD was associated with a lower risk of poor prognosis than CM (OR, 0.48 [95% CI, 0.30 to 0.77]; p=0.002), with no difference between CM and SAC. FD also reduced aneurysm rupture (OR, 0.20 [95% CI, 0.07 to 0.60]; p=0.004) and compression symptoms (OR, 0.30 [95% CI, 0.13 to 0.68]; p=0.004) versus CM. Time-to-event analyses further revealed significant differences in vertebral artery lesions and Type I–II VBFAs, whereas no significant differences were observed in basilar or vertebrobasilar junction lesions or in Type III–IV VBFAs. Conclusions Compared with CM, FD was associated with improved long-term outcomes in unruptured VBFAs, particularly in vertebral artery lesions and Type I–II VBFAs, although residual confounding cannot be excluded.

Objective:To analyze the preferences of medical staff in TCM hospitals in Beijing regarding the achievements transformation of hospital traditional Chinese medicine preparations,and to provide a reference for formulating incentive policies.Methods:233 medical staff from five TCM hospitals in Beijing were taken as the research objects,and surveyed with a questionnaire designed based on the discrete choice experiment(DCE).Mixed logit models and latent class models were then used to analyze their transformation preferences.Results:The mixed Logit model revealed that seven key attributes significantly influenced medical staff's preferences for the achievements transformation of traditional Chinese medicine preparations(P<0.05).Latent class analysis identified three distinct preference groups among respondents:an organization-dependent group(27.0%),a pro-transformation group(61.4%),and a conservative group(11.6%).Conclusions:Medical Staff preferred transformation conditions that increased monthly income;utilized"human use+re-experimentation";involved the hospital's achievements transformation department as the entity;were funded by the hospital;offered a 70%profit share;enabled promotion three years earlier,and assigned patents to the hospital.The study recommends implementing diverse incentive measures and developing differentiated strategies tailored to the distinct Medical Staff categories to facilitate the transformation of hospital traditional Chinese medicine preparations into new drugs.

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 assess the viability of reducing radiation dose, contrast media volume, injection flow rate and contrast medium concentration (quadruple low-dose protocol) by utilizing virtual monoenergetic images (VMI) in photon-counting detector CT (PCD-CT) for aortic CT angiography (CTA), while maintaining image quality in comparison to images obtained from energy-integrating detector CT (EID-CT).Methods:From April 2024 to June 2024, a total of 40 participants who underwent aortic CTA on PCD-CT were prospectively enrolled in the experimental group (PCD-CT group), while 40 patients with similar baseline characteristics who had previously undergone aortic CTA using EID-CT were retrospectively selected for the conventional group (EID-CT group). The EID-CT group used a tube voltage of 90 kVp, a contrast media volume of 60 ml of contrast, an injection flow rate of 3 ml/s, and a contrast concentration of 350 mgI/ml; the PCD-CT group used the QuantumPlus mode, with a tube voltage of 140 kVp, a total amount of iodine in the contrast media of 140 mgI/kg, and an injection flow rate=contrast media volume/(delay time+scan time), and a contrast media concentration of 320 mgI/ml. VMIs in PCD-CT group were reconstructed in 5-keV intervals ranging from 45 to 65 keV. The effective radiation dose and contrast injection protocols were recorded and compared between two groups. Objective image quality assessment was performed for each group. CT attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured at five anatomical locations (ascending aorta, aortic arch, descending aorta, abdominal aorta, and right common iliac artery), and image noise was recorded. Subjective image quality was independently evaluated by two readers using a 5-point Likert scale in a blinded manner. Based on data normality, the one-way ANOVA or Kruskal-Wallis test was used for image quality assessment, with Bonferroni-corrected post-hoc analysis for multiple comparisons.Results:There were no significant differences in the baseline characteristics between two groups (all P0.05). The PCD-CT group demonstrated significantly lower effective radiation dose [(3.88±0.65) mSv vs. (5.97±1.15)mSv], contrast media volume [(29.25±4.56) ml vs. 60 ml], and injection rate [(2.65±0.42) ml/s vs. 3 ml/s] than the EID-CT group, with reductions of 35%, 51%, and 12%, respectively (all P0.001). For objective image quality, except for the ascending aortic CT attenuation, the CT attenuation, SNR, and CNR of other vessels in the 55 keV PCD-CT group were comparable to those in the EID-CT group. Additionally, the difference in image noise between these two groups was not statistically significant ( P0.05). Concerning subjective image quality, at 55 keV, the PCD-CT group had similar image noise scores and vessel attenuation scores (both P0.05) and better visualization of renal artery branching ( P=0.001) compared to the EID-CT group. Conclusion:In comparison to EID-CT, the use of a 55 keV image in PCD-CT for aortic CTA has demonstrated reductions in radiation dose, contrast media volume, injection flow rate and contrast medium concentration, while maintaining image quality.

Objective:To evaluate the feasibility of head and neck vascular imaging using photon-counting detector computed tomography (PCD-CT) combined with a “quadruple lows” protocol—characterized by low contrast media volume, low iodine concentration, low injection rate, and low radiation dose—and to compare the image quality with that obtained by energy-integrating detector CT (EID-CT).Methods:A total of 105 patients with suspected cerebrovascular disease were prospectively enrolled at the First Affiliated Hospital of Zhengzhou University between April and June 2024. Patients were randomly assigned to three groups ( n=35). Group A underwent conventional head and neck CTA using EID-CT. Group B underwent PCD-CT with a protocol involving ultra-low contrast media volume, low iodine concentration, and low injection rate. Group C underwent PCD-CT with the full “quadruple low” protocol. Objective image quality parameters—including CT attenuation, image noise (standard deviation, SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)—were measured at the ascending aorta, common carotid artery, internal carotid artery, vertebral artery, basilar artery, posterior cerebral artery, and middle cerebral artery. Two radiologists independently rated subjective image quality using a 5-point Likert scale. Differences among groups were analyzed using one-way ANOVA and the Kruskal-Wallis test. Results:Compared to Group A [contrast volume: (42.78±6.64)ml], contrast agent volume was significantly reduced in Groups B and C[ (26.26±4.45) ml and (26.54±3.83)ml, respectively], demonstrating reductions of 39% and 38% (both P<0.01). The iodine concentration was 320 mg/ml in Groups B and C, lower than 350 mg/ml in Group A (8.5%). The injection rate was also reduced in Groups B and C [(3.39±0.61) and (3.55±0.51)ml/s, respectively] compared to Group A [(4.28±0.66) ml/s], with reductions of 21% and 17% (both P<0.01). The effective dose (ED) was similar between Groups A and B [(1.40±0.15) vs. (1.40±0.19)mSv, P>0.05], while Group C demonstrated a significantly lower ED [(0.99±0.09) mSv], with a reduction of 30% compared to Group A and 29% compared to Group B (both P<0.01).In terms of objective image quality, significant differences in image noise (SD) were observed among the three groups at the vertebral artery, internal carotid artery, posterior cerebral artery, and middle cerebral artery (all P<0.05). Groups B and C showed significantly lower SD compared to Group A ( P<0.05), with no significant difference between B and C ( P>0.05). SNR was significantly higher in Groups B and C than in Group A at multiple vascular segments (all P<0.05). CNR differed only at the internal carotid artery, where Groups B and C demonstrated superior performance compared to Group A ( P<0.05).Subjective image quality scores showed no significant difference between Groups A and C ( P>0.05), while Group B had significantly higher scores than both A and C ( P<0.05). All images were deemed diagnostically acceptable. Conclusion:Compared with conventional EID-CT, PCD-CT combined with a “quadruple lows” protocol enables substantial reductions in contrast media and radiation dose while further improving image quality in head and neck CTA.

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 analyze and evaluate the early warning efficacy of D-dimer/albumin ratio(DAR)combined with interleukin-6(IL-6)and fibrin degradation products(FDP)in the postoperative treatment of acute Stanford type A aortic dissection(ATAAD).Methods A retrospective cohort study was conducted on 284 ATAAD patients who underwent the Sun's procedure at our hospital from July 2024 to March 2025.Patients were divided into a non-adverse outcome group(n=196)and an adverse outcome group(n=88)based on the occurrence of postop-erative complications within 30 days,including acute renal failure requiring dialysis,secondary thoracotomy for hemostasis,severe neurological complications,multiple organ failure,or all-cause mortality.Preoperative baseline data,perioperative parameters,and laboratory indicators were collected via the electronic medical record system.The Mann-Whitney U test was used to compare the differences between groups for continuous variables that did not conform to the normal distribution,and Chi-square test or Fisher's exact test was selected for statistical difference analysis according to the frequency distribution characteristics of categorical variables.On the basis of univariate analysis,multivariate logistic regression analysis was used to screen independent risk factors.Results Statistically significant differences were observed between the non-adverse and adverse outcome group in age,cardiopulmonary bypass time,lactate dehydrogenase(LDH),IL-6,D-dimer(D-D),FDP,and DAR levels(P<0.05).Multivariate analysis revealed that DAR,IL-6,D-D,FDP,and prolonged cardiopulmonary bypass time were independent risk factors for adverse postoperative outcomes(P<0.05).Combined detection analysis demonstrated that the combination of DAR,IL-6,FDP,and cardiopulmonary bypass time yielded the highest predictive efficacy,with an area under the ROC curve of 0.886(95％CI:0.846～0.927).Conclusion The combination of DAR,IL-6,FDP,and cardio-pulmonary bypass time effectively predicts adverse postoperative outcomes in ATAAD patients.This biomarker panel may serve as a robust predictive tool for postoperative risk stratification.

Objective To analyze and evaluate the early warning efficacy of D-dimer/albumin ratio(DAR)combined with interleukin-6(IL-6)and fibrin degradation products(FDP)in the postoperative treatment of acute Stanford type A aortic dissection(ATAAD).Methods A retrospective cohort study was conducted on 284 ATAAD patients who underwent the Sun's procedure at our hospital from July 2024 to March 2025.Patients were divided into a non-adverse outcome group(n=196)and an adverse outcome group(n=88)based on the occurrence of postop-erative complications within 30 days,including acute renal failure requiring dialysis,secondary thoracotomy for hemostasis,severe neurological complications,multiple organ failure,or all-cause mortality.Preoperative baseline data,perioperative parameters,and laboratory indicators were collected via the electronic medical record system.The Mann-Whitney U test was used to compare the differences between groups for continuous variables that did not conform to the normal distribution,and Chi-square test or Fisher's exact test was selected for statistical difference analysis according to the frequency distribution characteristics of categorical variables.On the basis of univariate analysis,multivariate logistic regression analysis was used to screen independent risk factors.Results Statistically significant differences were observed between the non-adverse and adverse outcome group in age,cardiopulmonary bypass time,lactate dehydrogenase(LDH),IL-6,D-dimer(D-D),FDP,and DAR levels(P<0.05).Multivariate analysis revealed that DAR,IL-6,D-D,FDP,and prolonged cardiopulmonary bypass time were independent risk factors for adverse postoperative outcomes(P<0.05).Combined detection analysis demonstrated that the combination of DAR,IL-6,FDP,and cardiopulmonary bypass time yielded the highest predictive efficacy,with an area under the ROC curve of 0.886(95％CI:0.846～0.927).Conclusion The combination of DAR,IL-6,FDP,and cardio-pulmonary bypass time effectively predicts adverse postoperative outcomes in ATAAD patients.This biomarker panel may serve as a robust predictive tool for postoperative risk stratification.

Three evaluation methods were recommended for the key properties of the intravascular catheter lubricating coating such as stability,lubricity and integrity,including insoluble particle test method,friction test procedure and appearance detection method.Fifteen batches of microcatheters produced by different manufacturers were selected for testing to clarify the three methods in test principle,step,result,characteristic.References were provided for the design,production,evaluation and regulation of intravascular catheters with lubricant coatings.[Chinese Medical Equipment Journal,2025,46(1):66-72]

Objective:To analyze the preferences of medical staff in TCM hospitals in Beijing regarding the achievements transformation of hospital traditional Chinese medicine preparations,and to provide a reference for formulating incentive policies.Methods:233 medical staff from five TCM hospitals in Beijing were taken as the research objects,and surveyed with a questionnaire designed based on the discrete choice experiment(DCE).Mixed logit models and latent class models were then used to analyze their transformation preferences.Results:The mixed Logit model revealed that seven key attributes significantly influenced medical staff's preferences for the achievements transformation of traditional Chinese medicine preparations(P<0.05).Latent class analysis identified three distinct preference groups among respondents:an organization-dependent group(27.0%),a pro-transformation group(61.4%),and a conservative group(11.6%).Conclusions:Medical Staff preferred transformation conditions that increased monthly income;utilized"human use+re-experimentation";involved the hospital's achievements transformation department as the entity;were funded by the hospital;offered a 70%profit share;enabled promotion three years earlier,and assigned patents to the hospital.The study recommends implementing diverse incentive measures and developing differentiated strategies tailored to the distinct Medical Staff categories to facilitate the transformation of hospital traditional Chinese medicine preparations into new drugs.