Objective To explore the application value of constructing Visually Accesable Rembrandt Images(VASARI)MRI feature model for the prediction of isocitrate dehydrogenase-1(IDH-1)mutations in high-grade glioma(HGG).Methods A total of 242 patients with HGG were randomly divided into training set and validation set according to the ratio of 7︰3,the VASARI features of the patients were extracted to evaluate the statistical difference between IDH-1 mutant and wild-type VASARI features of the HGG.The least abso-lute shrinkage and selection operator(LASSO)regression method was used to reduce the dimension of VASARI features and the logistic regres-sion(LR)machine learning model was used to construct the prediction model for single VASARI feature and combined features.The receiver operating characteristic(ROC)curve was used to evaluate the predictive performance of the model.Results A total of 11 VASARI fea-tures were associated with IDH-1 mutations in HGG,with statistically significant differences(P<0.05).Seven closely related fea-tures were finally screened out,and the area under the curve(AUC)of the prediction model established after the combination of the seven VASARI features was higher,and the AUC of training set was 0.908,and the AUC of validation set was 0.872.Conclusion The VASARI MRI feature model can better predict IDH-1 mutations of HGG with high predictive efficacy and has greater application value.

Objective To explore the application value of constructing Visually Accesable Rembrandt Images(VASARI)MRI feature model for the prediction of isocitrate dehydrogenase-1(IDH-1)mutations in high-grade glioma(HGG).Methods A total of 242 patients with HGG were randomly divided into training set and validation set according to the ratio of 7︰3,the VASARI features of the patients were extracted to evaluate the statistical difference between IDH-1 mutant and wild-type VASARI features of the HGG.The least abso-lute shrinkage and selection operator(LASSO)regression method was used to reduce the dimension of VASARI features and the logistic regres-sion(LR)machine learning model was used to construct the prediction model for single VASARI feature and combined features.The receiver operating characteristic(ROC)curve was used to evaluate the predictive performance of the model.Results A total of 11 VASARI fea-tures were associated with IDH-1 mutations in HGG,with statistically significant differences(P<0.05).Seven closely related fea-tures were finally screened out,and the area under the curve(AUC)of the prediction model established after the combination of the seven VASARI features was higher,and the AUC of training set was 0.908,and the AUC of validation set was 0.872.Conclusion The VASARI MRI feature model can better predict IDH-1 mutations of HGG with high predictive efficacy and has greater application value.

Objective:To construct a fall risk prediction model for patients with hematologic malignancies and to provide a reference for the risk assessment and accurate management of falls.Methods:The prospective study design was adopted to facilitate the selection of 510 patients with hematologic malignant in Qilu Hospital of Shandong University for investigation, and relevant data such as patient demographic characteristics, disease treatment and drugs were collected. The LASSO-Logistic regression was used to screen the risk factors of falls in patients with hematologic malignancies, to construct a nomogram risk prediction model. The receiver operating characteristic curve (ROC) and calibration curve were used to evaluate the predictive performance of the model. Bootstrap resampling were used to validate internal validation of the model.Results:Among 510 patients with hematological malignancies, there were 273 males and 237 females, aged 53.0 (41.0, 63.0) years old. A total of 6 risk factors were included in the fall risk prediction model for patients with hematological malignancies, which were disease type ( OR = 0.185, 95% CI 0.061 - 0.562), body temperature ≥38 ℃ ( OR = 2.239, 95% CI 1.128 - 4.445), pain ( OR = 15.581, 95% CI 6.592 - 36.829), anemia ( OR = 4.097, 95% CI 1.536 - 10.927), days of bone marrow suppression ( OR = 3.341, 95% CI 1.619 - 6.893), and assessment of daily self-care ability ( OR = 3.160, 95% CI 1.051 - 9.506)(all P<0.05). The ROC curve of the fall risk prediction model was 0.884 (95% CI 0.841-0.927). The optimal threshold, sensitivity, and specificity of the risk prediction model were 0.248, 87.4% and 75.6%. The internal validation C statistic was 0.873. The Calibration curve was almost coincides with the ideal curve, and the model Brier score was 0.080. Conclusions:The constructed fall risk prediction model has good predictive performance, which can efficiently and objectively quantify the risk of falls, and provide a reference for the early assessment and effective prevention of falls in patients with hematological malignancies.

Objective To observe the value of synthetic MRI(SynMRI)and diffusion tensor imaging(DTI)for evaluating grade and isocitrate dehydrogenase-1(IDH-1)status of adult gliomas.Methods Totally 115 patients with adult glioma were retrospectively enrolled and divided into adult low-grade glioma(aLGG)group(n=44)and adult high-grade glioma(aHGG)group(n=71)according to WHO classification.There were 30 cases of IDH-1 mutant type,2 cases of wild type and 12 cases of undetermined gliomas in aLGG group,whereas 26 cases of IDH-1 mutant type,24 cases of wild type and 21 cases undetermined gliomas in aHGG group,respectively.SynMRI and DTI parameters,including T1 value,T2 value,proton density(PD),apparent diffusion coefficient(ADC)and fractional anisotropy(FA)were compared between groups,and the efficacy of each parameter for distinguishing aLGG and aHGG,as well as IDH-1 mutant type and wild type was analyzed.Results Significant differences of T1 value,T2 value,ADC and FA were found between groups,also between IDH-1 mutant type and wild type gliomas within aHGG group(all P＜0.05).The area under the curve(AUC)of the above parameters for distinguishing aLGG and aHGG was 0.731,0.686,0.930 and 0.710,respectively,while for distinguishing IDH-1 mutant type and wild type in aHGG group was 0.775,0.729,0.817 and 0.705,respectively,among which ADC had the highest AUC(all P＜0.05).Conclusion SynMRI and DTI parameters such as T1 value,T2 value,ADC and FA were helpful for distinguishing aLGG and aHGG,IDH-1 mutant type and wild type,among which ADC had the highest efficacy.

Objective To evaluate the feasibility,reliability and validity of the symptom identification scale for kidney yang deficiency syndrome.Methods The symptom identification scale for kidney yang deficiency syndrome developed in previous research was administered to 200 vascular cognitive impairment patients from September 2020 to September 2022 to assess feasibility through recall rate,completion rate,and completion time;reliability was measured using retest reliability,split-half reliability,homogeneity reliability,and inter-rater reliability;and validity was evaluated based on discriminant and structural validity.Results A total of 200 scales were sent out,and all of them cooperated and were completed and retrieved within 20 min.The results of reliability analysis showed that the retest reliability of the scale was 0.828 for the dimension of yang deficiency and 0.718 for the dimension of kidney qi deficiency;the Spearman-Brown coefficient of split-half reliability was 0.784;the Cronbach coefficient of the dimension of yang deficiency was 0.799,and the Cronbach coefficient of the dimension of kidney qi deficiency in the homogeneity reliability was 0.670.The results of the analysis showed that the differences between the kidney yang deficiency syndrome group and the non-kidney yang deficiency syndrome group in the yang deficiency dimension,kidney qi deficiency dimension scores and total scale scores in the discriminant validity were statistically significant(P<0.001);the KMO value in the structural validity was 0.842,and a total of two factors with eigenvalues greater than 1 were extracted,with a cumulative variance contribution rate of 58.227%.Conclusion The symptom identification scale for kidney yang deficiency syndrome demonstrates adequate reliability and validity,potentially enhancing the prediction of kidney yang deficiency in vascular cognitive impairment.However,the validity of the scale is somewhat limited and requires further refinement for clinical application.

Objective:To investigate the value of a deep learning model based on diffusion-weighted imaging (DWI) in quick identification of the TOAST etiology classification in patients with acute ischemic stroke (AIS).Methods:In this cross-sectional study, imaging and clinical data of 504 patients with AIS admitted to the First Affiliated Hospital of Xinjiang Medical University from March 2023 to February 2024 were retrospectively reviewed. Using the TOAST etiology classification, there were 252 large artery atherosclerosis type and 252 small-artery occlusion type. The 504 cases were divided into a training set ( n=302), a validation set ( n=101) and a test set ( n=101) using stratified randomization in the ratio of 6∶2∶2. All cases had DWI data. A DenseNet network framework was used to construct DenseNet models by optimizing the model configurations of different layers. Three DenseNet models with different layers (121, 169, 201) were constructed, named DenseNet169 model, DenseNet121 model, and DenseNet201 model. The data enhancement, Adam optimizer and cross-entropy loss function methods were used to improve the convergence speed and robustness of the model, and to balance the positive and negative sample imbalance problem. Independent sample t-test or χ2 was used to compare the clinical data of patients with large artery atherosclerosis type and small-artery occlusion type AIS. Receiver operating characteristic curves and area under the curve (AUC) were performed to evaluate the efficacy of each model in identification of patients with large artery atherosclerosis type and small-artery occlusion type AIS. Results:There were statistically significant differences in age, National Institutes of Health Stroke Scale score at admission, and stenosis or occlusion of large vessels between patients with large artery atherosclerosis type and small-artery occlusion (all P<0.05). In the test set, the AUC, sensitivity, accuracy, and F1 score values of the DenseNet201 model for discriminating patients with large artery atherosclerosis type AIS and small-artery occlusion type AIS (0.826, 0.902, 0.743, 0.780, respectively) were higher than those of DenseNet121 (0.801, 0.647, 0.723, 0.702, respectively) and DenseNet169 model (0.778, 0.882, 0.733, 0.769). Conclusions:The deep learning models based DWI constructed in this study can help with the TOAST etiology classification of AIS cases. DenseNet201 model shows the best and stable performance in the deep learning-based classification.

Brain diseases in traditional Chinese medicine were complex and difficult to diagnosis and treatment， and new diagnostic and therapeutic ideas are urgently needed. The onset of the disease was the result of the struggle between healthy qi and pathogenic qi. Common types of the onset of diseases included sudden onset， slow onset， latent onset， secondary onset， and recurrent onset， reflecting the strength of the healthy qi and pathogenic qi， the pathogenic qi that reduced diseases， the site of onset， and other informatin. “Identificating the onset of diseases” was simple and easy to operate， and helped to clarify the complex development of encephalopathy. When applying it， we should first identify urgency and importance， focus on the characteristics； grasp the tendency of diseases， and know the overall situation of the disease； compare similarities and differences horizontally； and carefully observe and dynamically understand the disease. “Identificating the onset of diseases” has the characteristics of comprehensiveness and prognosis， and can lay the foundation for pattern identification and treatment and “treating disease before its onset”.

Objective:To investigate the influence of abnormal blood pressure circadian rhythms and the morning surge on aortic stiffness in elderly patients with isolated systolic hypertension(ISH).Methods:A total of 300 untreated elderly(≥60 years)patients with ISH were enrolled.24-hour ambulatory blood pressure was measured by using a non-invasive portable ambulatory sphygmomanometer with an inflatable cuff.Brachial-ankle pulse wave velocity(baPWV)and the ankle-brachial pressure index(ABI)were measured by using an automated device.Patients were divided into the dipper(n=95), no-dipper(n=177)and extreme dipper(n=28)groups according to the rate of nocturnal blood pressure reduction.They were also divided into the morning surge(n=88)and no morning surge(n=212)groups according to the morning blood pressure surge(MBPS). Differences between the groups were compared.Correlations between the parameters were calculated by partial correlation analyses.The effects on baPWV and ABI were calculated by multiple linear regression analyses.Results:baPWV was higher in the extreme dipper group than in the dipper and no dipper groups[(1 402±234)cm/s vs.(1 467±114)cm/s vs.(1 538±140)cm/s, P<0.01], while ABI values were lower in the extreme dipper group than in the dipper group(0.98±0.10 vs.1.05±0.12, P<0.01)and the no dipper group(0.98±0.10 vs.1.03±0.12, P<0.05). Moreover, baPWV[(1 508±170)cm/s vs.(1 430±163)cm/s, P<0.01]was higher while ABI values(0.98±0.13 vs.1.06±0.11, P<0.01)were lower in the morning surge group than in the no morning surge group.baPWV was positively correlated with daytime Systolic blood pressure(dSBP)( r=0.169, P<0.01), 24 hSBPCV( r=0.143, P<0.05), and MBPS( r=0.157, P<0.01), while ABI was negatively correlated with dSBP( r=-0.146, P=0.011)and MBPS( r=-0.321, P<0.01). Age( P<0.01), dSBP( P<0.05)and 24 h systolic blood pressure variation coefficient( P<0.05)were independent factors for baPWV, while dSBP and MBPS were independent factors for ABI(all P<0.01). Conclusions:Abnormal blood pressure circadian rhythms and the morning surge are associated with increased aortic stiffness in elderly patients with ISH.

Amyloid deposits are one of the hallmark pathological lesions of Alzheimer's disease (AD). They can be visualized by thioflavin-S, silver impregnation, Congo red staining, and immunohistochemical reactions. However, that amyloid deposits generate blue autofluorescence (auto-F) has been ignored. Here, we report that visible light-induced auto-F of senile plaques (SPs) was detected and validated with conventional methods. Brain slices from APP/PS1 (amyloid precursor protein/presenilin 1) transgenic mice were mounted on slides, rinsed, coverslipped and observed for details of the imaging and spectral characteristics of the auto-F of SPs. Then the slices were treated with the above classic methods for comparative validation. We found that the SP auto-F was greatest under blue-violet excitation with a specific emission spectrum, and was much easier, more sensitive, and reliable than the classic methods. Because it does not damage slices, observation of auto-F can be combined with all post-staining techniques in slices and for brain-wide imaging in AD.

Objective To establish the HPLC fingerprints of Tangbikang Granules; To scientifically evaluate and effectively control the quality of Tangbikang Granules; To ensure its production stability. Methods HPLC was performed on the column of Germany Merck RP-18 endcapped (250 mm × 4.6 mm, 5 μm) with mobile phase of acetonitrile-0.1% formic acid water; column temperature was 40 ℃; flow rate was 1.0 mL/min; detection wavelength was 240 nm; volume injection was 20 μL. Fingerprint Similarity Evaluation Software (edition 2004A) of Chinese Pharmacopoeia Commission was used to evaluate the similarity of the 10 batches of Tangbikang Granules, and to analyze the correlations of 9 ingredients in Tangbikang Granules. Results Wogonoside was used as the reference peak, and the common mode for the HPLC fingerprints was set up. The similarities of the 10 batches of Tangbikang Granules were above 0.930, and altogether 25 common peaks in the chromatograms were found, of which 18 peaks were assigned to Chinese materia medica in Tangbikang Granules. Conclusion The method has good separability and is accurate and simple, which can provide references for the quality control of Tangbikang Granules.