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 value of coronary CTA (CCTA)-based traditional features and radiomics of plaque in the identification of culprit lesions that caused acute myocardial infarction (AMI).Methods:This was a retrospective multicenter study. From July 2016 to November 2023, a total of 344 patients from the Affiliated Hospital of Qingdao University (training cohort, n=184), Shandong Provincial Hospital Affiliated to Shandong First Medical University (validation cohort, n=88) and Qilu Hospital of Shandong University (test cohort, n=72) who received percutaneous coronary intervention (PCI) due to AMI and underwent CCTA within 48 hours of AMI were enrolled. The culprit plaques and non-culprit plaques were identified using a combination of electrocardiogram, CCTA, and angiographic findings. The vessel, plaque location, plaque type, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score, high-risk plaque characteristics, plaque length, plaque volume, and burden were analyzed, and 1 904 radiomics features were extracted for each plaque. The traditional imaging model, the radiomics model, and the combined model were established by using multivariate Logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model in identifying culprit lesions. The DeLong test was used for the comparison of AUC between every two models. The net reclassification index (NRI) was used to evaluate the incremental value of the combined model to the traditional imaging model and the radiomics model. The decision curve analysis (DCA) was used to assess the clinical net benefit of these models. A correlation heatmap was used to evaluate the correlation between the radiomics score and traditional CCTA factors. The interpretable analysis of the decision process of the combined model was performed by the Shapley Additive exPlanations (SHAP). Results:In the validation cohort and the test cohort, the AUC of the traditional imaging model developed by the vessel, plaque type, positive remodeling and CAD-RADS score was 0.898 (95% CI 0.869-0.922) and 0.881 (95% CI 0.848-0.910), respectively. The radiomics model developed by six radiomics features was 0.863 (95% CI 0.831-0.891) and 0.863 (95% CI 0.827-0.864), respectively. The AUC of the combined model was 0.930 (95% CI 0.905-0.950)and 0.919 (95% CI 0.889-0.942), respectively. In the validation cohort and the test cohort, the AUC of the combined model was higher than that of the traditional imaging model ( Z=4.013, 4.272, P<0.001) and that of the radiomics model ( Z=4.819, 3.784, P<0.001), respectively. In the validation cohort, the combined model yielded an NRI of 20.43% (95% CI 10.43%-30.44%, P<0.001) and 20.21% (95% CI 9.62%-30.80%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. In the test cohort, the combined model yielded an NRI of 28.05% (95% CI 16.72%-39.38%, P<0.001) and 23.57% (95% CI 13.58%-33.56%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. DCA showed the combined model had the highest clinical net benefit. The correlation heatmap showed the radiomics score was not correlated or only weakly correlated with traditional CCTA factors. SHAP indicated the radiomics and CAD-RADS score contributed significantly to the model. Conclusion:The CCTA-based traditional features and radiomics of plaque have favorable performance for the identification of culprit plaques in patients with AMI.

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 value of coronary CTA (CCTA)-based traditional features and radiomics of plaque in the identification of culprit lesions that caused acute myocardial infarction (AMI).Methods:This was a retrospective multicenter study. From July 2016 to November 2023, a total of 344 patients from the Affiliated Hospital of Qingdao University (training cohort, n=184), Shandong Provincial Hospital Affiliated to Shandong First Medical University (validation cohort, n=88) and Qilu Hospital of Shandong University (test cohort, n=72) who received percutaneous coronary intervention (PCI) due to AMI and underwent CCTA within 48 hours of AMI were enrolled. The culprit plaques and non-culprit plaques were identified using a combination of electrocardiogram, CCTA, and angiographic findings. The vessel, plaque location, plaque type, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score, high-risk plaque characteristics, plaque length, plaque volume, and burden were analyzed, and 1 904 radiomics features were extracted for each plaque. The traditional imaging model, the radiomics model, and the combined model were established by using multivariate Logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model in identifying culprit lesions. The DeLong test was used for the comparison of AUC between every two models. The net reclassification index (NRI) was used to evaluate the incremental value of the combined model to the traditional imaging model and the radiomics model. The decision curve analysis (DCA) was used to assess the clinical net benefit of these models. A correlation heatmap was used to evaluate the correlation between the radiomics score and traditional CCTA factors. The interpretable analysis of the decision process of the combined model was performed by the Shapley Additive exPlanations (SHAP). Results:In the validation cohort and the test cohort, the AUC of the traditional imaging model developed by the vessel, plaque type, positive remodeling and CAD-RADS score was 0.898 (95% CI 0.869-0.922) and 0.881 (95% CI 0.848-0.910), respectively. The radiomics model developed by six radiomics features was 0.863 (95% CI 0.831-0.891) and 0.863 (95% CI 0.827-0.864), respectively. The AUC of the combined model was 0.930 (95% CI 0.905-0.950)and 0.919 (95% CI 0.889-0.942), respectively. In the validation cohort and the test cohort, the AUC of the combined model was higher than that of the traditional imaging model ( Z=4.013, 4.272, P<0.001) and that of the radiomics model ( Z=4.819, 3.784, P<0.001), respectively. In the validation cohort, the combined model yielded an NRI of 20.43% (95% CI 10.43%-30.44%, P<0.001) and 20.21% (95% CI 9.62%-30.80%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. In the test cohort, the combined model yielded an NRI of 28.05% (95% CI 16.72%-39.38%, P<0.001) and 23.57% (95% CI 13.58%-33.56%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. DCA showed the combined model had the highest clinical net benefit. The correlation heatmap showed the radiomics score was not correlated or only weakly correlated with traditional CCTA factors. SHAP indicated the radiomics and CAD-RADS score contributed significantly to the model. Conclusion:The CCTA-based traditional features and radiomics of plaque have favorable performance for the identification of culprit plaques in patients with AMI.

The theoretical foundations of the forward and inverse problems of two conductivity imaging methods based on magneto-acoustic coupling effects,including magneto-acoustic tomography(MAT)and magneto-acousto-electrical tomogra-phy(MAET),were introduced.The research progress of the conductivity reconstruction methods with different imaging strate-gies was reviewed.The problems of the conductivity reconstruction methods based on magnetoacoustic coupling effects were analyzed in terms of acoustic source model construction,reconstruction algorithm and imaging speed,and the future develop-ment directions were pointed out.[Chinese Medical Equipment Journal,2024,45(7):94-104]

OBJECTIVE: To retrospectively assess the advantages of the modified Uhl technique in the treatment of Colles' fracture guided by the principles of Chinese osteosynthesis (CO) concept. METHODS: A retrospective study was conducted on 358 patients with Colles' fracture treated with the modified Uhl technique of closed reduction and percutaneous pin between January 2016 and June 2021. Out of these, 120 eligible cases were selected and categorized into two groups according to different surgical methods:the closed reduction and percutaneous pin group, and the open reduction group. Sixty-eight patients in the closed reduction and percutaneous pin group were treated with the modified Uhl technique, while fifty-two patients in the open reduction group were treated with open reduction and internal fixation using plates. The modified Sarmiento imaging score, Gartland-Werley wrist score, operation time, hospital stay, and treatment costs between the two groups were compared at a 6-month postoperative follow-up. RESULTS: There were no significant differences in terms of gender, age, affected side, injure factors, time of injury to surgery, Sarmiento imaging score, and Gartland-Werley wrist joint score (P>0.05). The closed reduction and percutaneous pin group exhibited an operation time of (35.88±14.11) minutes, hospitalization stay of (9.78±2.48) days, and treatment costs of (16 074.91±1 964.48) yuan, while the open reduction group demonstrated comparatively longer operation time of (65.48±14.26) minutes, hospitalization stay of (15.88±2.00) days, and treatment costs of (20 451.27±1 760.22) yuan (P<0.01). CONCLUSION The modified Uhl technique presents notable advantages in the management of Colles' fracture, including reliable fixation, less trauma, shorter operation time, less pain, shorter hospital stay, and cost-effectiveness. This technique exhibits promising potential for broader clinical application. However, it is important to note that the pin could potentially damage tendons, and in cases of Colles' fractures with osteoporosis and comminuted fragments, additional techniques may be required for reliable fixation.
Humans ; Retrospective Studies ; Colles' Fracture/surgery* ; Fracture Fixation, Internal ; Fractures, Comminuted ; Hospitalization

Infectious bone defect is bone defect with infection or as a result of treatment of bone infection. It requires surgical intervention, and the treatment processes are complex and long, which include bone infection control,bone defect repair and even complex soft tissue reconstructions in some cases. Failure to achieve the goals in any step may lead to the failure of the overall treatment. Therefore, infectious bone defect has been a worldwide challenge in the field of orthopedics. Conventionally, sequestrectomy, bone grafting, bone transport, and systemic/local antibiotic treatment are standard therapies. Radical debridement remains one of the cornerstones for the management of bone infection. However, the scale of debridement and the timing and method of bone defect reconstruction remain controversial. With the clinical application of induced membrane technique, effective infection control and rapid bone reconstruction have been achieved in the management of infectious bone defect. The induced membrane technique has attracted more interests and attention, but the lack of understanding the basic principles of infection control and technical details may hamper the clinical outcomes of induced membrane technique and complications can possibly occur. Therefore, the Chinese Orthopedic Association organized domestic orthopedic experts to formulate An evidence-based clinical guideline for the treatment of infectious bone defect with induced membrane technique ( version 2023) according to the evidence-based method and put forward recommendations on infectious bone defect from the aspects of precise diagnosis, preoperative evaluation, operation procedure, postoperative management and rehabilitation, so as to provide useful references for the treatment of infectious bone defect with induced membrane technique.

Objective: To evaluate the efficacy of the Hodgkin lymphoma (HL)-2013 regimen in the treatment of children with HL, and to investigate the prognostic factors of childhood HL. Methods: Clinical data of 145 children (aged ≤18 years) with newly diagnosed HL, treated with HL-2013 regimen in 8 tertiary referral centers for childhood cancer from August 2011 to April 2021 were analyzed retrospectively. All the diagnosis were confirmed by histopathological morphology and immunohistochemical examination. The clinical characteristics and treatment outcomes were summarized, and the patients were divided into different groups according to clinical characteristics. Kaplan-Meier method was used for survival analysis, and the comparison of survival rates between groups was performed with Log-rank test. Results: Of the 145 cases, there were 115 males and 30 females, the age at diagnosis was 7.9 (5.8, 10.6) years. Cervical lymph node enlargement (114 cases, 78.6%) was the common symptom of the disease, and 57 patients (39.3%) were accompanied by large masses. The most common pathological classification was mixed cell type (93 cases, 64.1%). According to the Ann Arbor staging system, there were 9 cases of stage Ⅰ, 62 cases of stage Ⅱ, 45 cases of stage Ⅲ, 29 cases of stage Ⅳ. According to the risk stratification: there were 14 cases of low-risk group, 76 cases of medium-risk group and 55 cases of high-risk group. Of all patients, 68 cases (46.9%) achieved an early complete remission (CR) after 2 courses of chemotherapy, and the CR rate was 93.8% (136/145) after first-line treatment. Disease recurrence or progression occurred in 22 cases (15.2%). Of all patients, 125 cases survived, 6 cases died and 14 cases were lost to follow-up. Among the survived cases, 123 cases were continuously at CR state,and the follow-up time was 55 (40, 76) months. The 5-year overall survival (OS) and event free survival (EFS) rates were (95.3±1.9)% and (84.2±3.0)% for the entire group, respectively. 5-year OS and EFS rates for patients with stage Ⅲ-Ⅳ were both lower than those for patients with stage Ⅰ-Ⅱ (χ²=6.28 and 7.58, both P<0.05), the 5-year OS and EFS rates for patients in high-risk group were both lower than those for patients in low-risk and medium-risk group (χ²=10.93, 7.79, both P<0.05). The 5-year OS rates for the patient with early CR and without early CR were 100.0% and (90.9±3.6)% (χ²=5.77, P=0.016). EFS rates for the patient with early CR (68 cases) and without early CR (77 cases) were (93.8±3.0)% and (75.8±5.0)% (χ²=8.78, P=0.003). Conclusions: HL-2013 regimen is significantly effective in the treatment of pediatric HL. However, the patients in high-risk group and those without early CR are prone to disease recurrence or progression. Stage Ⅲ-Ⅳ and without early CR were associated with worse prognosis.
Child ; Female ; Male ; Humans ; Hodgkin Disease ; Retrospective Studies ; Neoplasm Recurrence, Local ; China ; Antineoplastic Combined Chemotherapy Protocols ; Prognosis ; Disease-Free Survival

OBJECTIVE: To investigate the therapeutic effect of Epothilone D on traumatic optic neuropathy (TON) in rats. METHODS: Forty-two SD rats were randomized to receive intraperitoneal injection of 1.0 mg/kg Epothilone D or DMSO (control) every 3 days until day 28, and rat models of TON were established on the second day after the first administration. On days 3, 7, and 28, examination of flash visual evoked potentials (FVEP), immunofluorescence staining and Western blotting were performed to examine the visual pathway features, number of retinal ganglion cells (RGCs), GAP43 expression level in damaged axons, and changes of Tau and pTau-396/404 in the retina and optic nerve. RESULTS: In Epothilone D treatment group, RGC loss rate was significantly decreased by 19.12% (P=0.032) on day 3 and by 22.67% (P=0.042) on day 28 as compared with the rats in the control group, but FVEP examination failed to show physiological improvement in the visual pathway on day 28 in terms of the relative latency of N2 wave (P=0.236) and relative amplitude attenuation of P2-N2 wave (P=0.441). The total Tau content in the retina of the treatment group was significantly increased compared with that in the control group on day 3 (P < 0.001), showing a consistent change with ptau-396/404 level. In the optic nerve axons, the total Tau level in the treatment group was significantly lower than that in the control group on day 7 (P=0.002), but the changes of the total Tau and pTau-396/404 level did not show an obvious correlation. Epothilone D induced persistent expression of GAP43 in the damaged axons, detectable even on day 28 of the experiment. CONCLUSION Epothilone D treatment can protect against TON in rats by promoting the survival of injured RGCs, enhancing Tau content in the surviving RGCs, reducing Tau accumulation in injured axons, and stimulating sustained regeneration of axons.
Animals ; Disease Models, Animal ; Epothilones ; Evoked Potentials, Visual ; Nerve Regeneration/physiology* ; Optic Nerve Injuries/metabolism* ; Rats ; Rats, Sprague-Dawley ; Retinal Ganglion Cells/physiology*

Licorice, one of the most commonly used medicinal materials in China, grows mainly in arid and semi-arid regions and has important economic and ecological values. Basic leucine zipper (bZIP) transcription factors in plants play an important role in regulating biological or abiotic stress responses, growth, and secondary metabolite synthesis. bZIP transcription factors in the published whole genome database of Glycyrrhiza uralensis were identified using bZIP sequences found in Arabidopsis thaliana genome as reference, and ABA-dependent bZIP genes were identified by using Illumina high-throughput sequencing. The physical and chemical properties, structure of the encoded proteins, and the gene expression patterns with exogenous ABA stress were analyzed. A total of 69 bZIP transcription factor genes were identified in G. uralensis, named Gubzip1-69, and they were divided into 10 subfamilies (A-I and S) according to their similarity to bZIPs of A. thaliana. By calculating the relative expression levels of the 69 GubZIPs genes under different concentrations of exogenous ABA stress, genes that may be involved in the regulation of ABA signaling pathways were identified, namely GubZIP1, GubZIP5, GubZIP8, GubZIP30, GubZIP33 and GubZIP56. The results of expression pattern analysis of these GubZIPs genes under exogenous ABA stress showed that the expression pattern of GubZIPs genes changed significantly with 50 mg·L^-1 ABA. The relative expression levels of these genes decreased 3 h after treatment, and gradually increased 6 h after treatment. Except for GubZIP8, the relative expression levels of these genes were significantly increased after 12 h. Further research on the function of bZIP transcription factors of G. uralensis and elucidating their regulatory mechanisms should be of interest and will provide a scientific basis for cultivating high-quality cultivars of G. uralensis through molecular breeding methods.