Objective:To investigate the local hemodynamic spatial distribution of internal carotid artery stenosis and its correlation with plaque characteristics using high-resolution vessel wall imaging (HR-VWI) combined with computational fluid dynamics.Methods:This was a cross-sectional study. A retrospective analysis was conducted on the clinical and imaging data of 70 patients with moderate to severe stenosis at the initiation of the internal carotid artery in First Teaching Hospital of Tianjin University of Traditional Chinese Medicine and Tianjin First Central Hospital from March 2018 to June 2020. All patients underwent HR-VWI and CT angiography examinations. The parameters related to plaque characteristics, such as plaque length, maximum wall thickness, plaque volume, wall volume percentage and intraplaque hemorrhage (IPH) were measured and evaluated on HR-VWI images. CT angiography images were used to construct a local hemodynamic vascular model to measure various wall shear stress (WSS) derived parameters, such as time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), and transverse wall shear stress (transWSS), at the narrowest, proximal, and distal parts of the lesion. The Friedman test was used to analyze the difference of hemodynamic parameters in different parts of the lesion. Spearman correlation analysis was performed to assess the correlation between plaque burden and local hemodynamic parameters. Univariate and multivariate logistic regression analyses were used to explore the independent risk factors for predicting IPH.Results:Among the 70 patients, 25 patients with IPH and 45 patients without IPH. The overall differences in TAWSS, OSI, RRT and transWSS at the narrowest, proximal, and distal parts of the lesion in 70 patients were statistically significant ( P<0.05). The TAWSS and transWSS at the narrowest parts were significantly higher than those at the proximal and distal parts ( P<0.05). The OSI at the distal part was significantly higher than that at the narrowest and the proximal parts ( P<0.05). The RRT at the proximal part was significantly lower than that at the narrowest and the distal parts ( P<0.05). Spearman correlation analysis showed RRT at the distal part was correlated with plaque volume ( r s=0.249, P=0.044) and wall volume percentage ( r s=0.286, P=0.016), respectively. In a multivariate logistic regression showed plaque length ( OR=1.315, 95% CI 1.073-1.612, P=0.008) and TAWSS at the narrowest part ( OR=1.631, 95% CI 1.308-1.854, P=0.008) were independent risk factors for predicting IPH. Conclusions:The spatial distribution of local hemodynamics of moderate to severe stenosis at the initiation of the internal carotid artery is different, and the WSS parameters in different parts of the lesion have different effects on plaque volume, wall volume percentage and IPH.

Objective To investigate the feasibility and accuracy of an ultrasound-based transfer learning artificial intelligence model in predicting the malignancy probability of partially cystic thyroid nodules(PCTN).Methods A retrospective analysis was conducted on 246 patients with PCTN who had definitive pathological results and were admitted to Weihai Municipal Hospital,Cheeloo College of Medicine,Shandong University from January 2021 to December 2023.Patients were randomly divided into training and test cohorts at a ratio of 7:3.Ultrasonic image features of PCTN were evaluated,and independent risk factors were identified using multivariate logistic regression analysis,with the area under the curve(AUC)subsequently calculated.Additionally,five different pre-trained models-Inception_v3,EfficientNet,VGG19,ResNet50,and DenseNet121-were selected for transfer learning after data preprocessing using the PyTorch framework in Python.The AUC values of these models were calculated and compared.Results Solid portion greater than 50%,eccentric acute angle,ill-defined margin,spiculated or microlobulated margin,rim calcification,and microcalcification exhibited statistically significant differences(P<0.05)in distinguishing between benign and malignant PCTN.The AUC value derived from these independent risk factors was 0.843.Furthermore,among the five transfer learning models evaluated,the ResNet50 model demonstrated the highest diagnostic efficiency,achieving an AUC value of 0.903 2.Conclusion The ultrasound-based transfer learning artificial intelligence model demonstrated superior performance compared to traditional ultrasound image evaluation methods,enabling accurate prediction of the nature of PCTN and thereby reducing unnecessary ultrasound-guided fine needle biopsies.

Objective:To investigate the local hemodynamic spatial distribution of internal carotid artery stenosis and its correlation with plaque characteristics using high-resolution vessel wall imaging (HR-VWI) combined with computational fluid dynamics.Methods:This was a cross-sectional study. A retrospective analysis was conducted on the clinical and imaging data of 70 patients with moderate to severe stenosis at the initiation of the internal carotid artery in First Teaching Hospital of Tianjin University of Traditional Chinese Medicine and Tianjin First Central Hospital from March 2018 to June 2020. All patients underwent HR-VWI and CT angiography examinations. The parameters related to plaque characteristics, such as plaque length, maximum wall thickness, plaque volume, wall volume percentage and intraplaque hemorrhage (IPH) were measured and evaluated on HR-VWI images. CT angiography images were used to construct a local hemodynamic vascular model to measure various wall shear stress (WSS) derived parameters, such as time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), and transverse wall shear stress (transWSS), at the narrowest, proximal, and distal parts of the lesion. The Friedman test was used to analyze the difference of hemodynamic parameters in different parts of the lesion. Spearman correlation analysis was performed to assess the correlation between plaque burden and local hemodynamic parameters. Univariate and multivariate logistic regression analyses were used to explore the independent risk factors for predicting IPH.Results:Among the 70 patients, 25 patients with IPH and 45 patients without IPH. The overall differences in TAWSS, OSI, RRT and transWSS at the narrowest, proximal, and distal parts of the lesion in 70 patients were statistically significant ( P<0.05). The TAWSS and transWSS at the narrowest parts were significantly higher than those at the proximal and distal parts ( P<0.05). The OSI at the distal part was significantly higher than that at the narrowest and the proximal parts ( P<0.05). The RRT at the proximal part was significantly lower than that at the narrowest and the distal parts ( P<0.05). Spearman correlation analysis showed RRT at the distal part was correlated with plaque volume ( r s=0.249, P=0.044) and wall volume percentage ( r s=0.286, P=0.016), respectively. In a multivariate logistic regression showed plaque length ( OR=1.315, 95% CI 1.073-1.612, P=0.008) and TAWSS at the narrowest part ( OR=1.631, 95% CI 1.308-1.854, P=0.008) were independent risk factors for predicting IPH. Conclusions:The spatial distribution of local hemodynamics of moderate to severe stenosis at the initiation of the internal carotid artery is different, and the WSS parameters in different parts of the lesion have different effects on plaque volume, wall volume percentage and IPH.

Objective To investigate the feasibility and accuracy of an ultrasound-based transfer learning artificial intelligence model in predicting the malignancy probability of partially cystic thyroid nodules(PCTN).Methods A retrospective analysis was conducted on 246 patients with PCTN who had definitive pathological results and were admitted to Weihai Municipal Hospital,Cheeloo College of Medicine,Shandong University from January 2021 to December 2023.Patients were randomly divided into training and test cohorts at a ratio of 7:3.Ultrasonic image features of PCTN were evaluated,and independent risk factors were identified using multivariate logistic regression analysis,with the area under the curve(AUC)subsequently calculated.Additionally,five different pre-trained models-Inception_v3,EfficientNet,VGG19,ResNet50,and DenseNet121-were selected for transfer learning after data preprocessing using the PyTorch framework in Python.The AUC values of these models were calculated and compared.Results Solid portion greater than 50%,eccentric acute angle,ill-defined margin,spiculated or microlobulated margin,rim calcification,and microcalcification exhibited statistically significant differences(P<0.05)in distinguishing between benign and malignant PCTN.The AUC value derived from these independent risk factors was 0.843.Furthermore,among the five transfer learning models evaluated,the ResNet50 model demonstrated the highest diagnostic efficiency,achieving an AUC value of 0.903 2.Conclusion The ultrasound-based transfer learning artificial intelligence model demonstrated superior performance compared to traditional ultrasound image evaluation methods,enabling accurate prediction of the nature of PCTN and thereby reducing unnecessary ultrasound-guided fine needle biopsies.

Cardial troponin I(cTnI)is the preferred serological marker for the diagnosis of myocardial injury.cTnI detection is based on antibody sandwich immunoassay.The epitopes of cTnI antigen targeted by detecting and capturing antibodies in different detection reagents are inconsistent,which easily leads to the heterogeneity of cTnI detection results.Endogenous interfering factors such as cTnI autoantibody,heterophile antibody,rheumatoid factor,ect,which can seriously interfere with the results of cTnI detection,and affecting the clinical diagnosis,treatment and prognosis of myocardial injury diseases.In this paper,the research progress of antibody sandwich immunoassay for cTnI and interference of endogenous factors on cTnI detection and solutions are reviewed to provide theoretical basis for differential diagnosis of abnormal cTnI detection results in clinical practice.

Hepatocellular carcinoma(HCC)is the fourth leading cause of cancer-related death,following lung cancer,colorectal cancer and gastric cancer.Chemoresistance is currently the major challenge in clinical treatment of HCC patients,and it is also the primary cause of the poor prognosis and high recurrence rate of patients.There are multiple factors and complex mechanisms in the occurrence of HCC drug resistance.Recent research has shown that non-coding RNA(ncRNA)is closely related to HCC chemoresistance.By regulating the expression of target genes and protein translation,ncRNA affects the occurrence,metastasis,and prognosis of HCC and is expected to become a therapeutic biomarker and potential drug therapeutic target for HCC.Therefore,this study reviews several common ncRNAs,including long non-coding RNAs(LncRNAs),miRNAs and transferRNA(tRNAs),in the molecular mechanisms and research progress of HCC chemoresistance,providing new ideas for solving the problem of HCC chemoresistance.

BACKGROUND:Numerous basic and clinical trials have confirmed that the low survival rate after bone marrow mesenchymal stem cell transplantation is a serious constraint on its long-term therapeutic effect.Previous studies have shown that apoptosis-related factors play an important role in the apoptosis of bone marrow mesenchymal stem cells,of which apoptosis-inducing factor may be a key factor. OBJECTIVE:Bone marrow mesenchymal stem cells,of which apoptosis-inducing factor was knocked down,were transplanted into infarcted myocardium of mice,aiming to certify the importance of apoptosis-inducing factor in the survival of bone marrow mesenchymal stem cells to further recover cardiac function after infarction. METHODS:Firstly,bone marrow mesenchymal stem cells were infected with LV-AIF-shRNA lentivirus to down-regulate the expression of apoptosis-inducing factor protein.Flow cytometry,western blot assay,and RT-qPCR were used to detect the infection efficiency of lentivirus.CCK-8 assay was used to detect the cell viability of bone marrow mesenchymal stem cells with apoptosis-inducing factor knockdown under hypoxic and ischemic conditions.Then,with the mouse model of acute myocardial infarction constructed,the normal bone marrow mesenchymal stem cells and bone marrow mesenchymal stem cells with apoptosis-inducing factor gene knockdown were transplanted into the model,respectively.The expression of apoptosis-inducing factor was examined by fluorescence immunoassay.Serum brain natriuretic peptide levels were detected by ELISA.Cardiac ultrasound was used to detect cardiac function.Myocardial fibrosis was observed by Masson staining.The expression of SRY gene was detected by RT-qPCR in apoptosis-inducing factor-knocked bone marrow mesenchymal stem cells after transplantation,reflecting cell survival. RESULTS AND CONCLUSION:(1)Bone marrow mesenchymal stem cells with apoptosis-inducing factor gene knockdown were successfully established by LV-AIF-shRNA lentivirus infection,following 97.7%of infection efficiency,and notably decline of the expression of apoptosis-inducing factor(P<0.001).(2)Under ischemia and hypoxia,the cell viability of apoptosis-inducing factor knockdown bone marrow mesenchymal stem cells was significantly increased compared with normal bone marrow mesenchymal stem cells.(3)Compared with normal bone marrow mesenchymal stem cells after transplantation,the survival number of bone marrow mesenchymal stem cells in the infarcted myocardium after apoptosis-inducing factor gene knockdown was significantly increased to 3.71 times(P<0.001),and the apoptosis-inducing factor protein expression and myocardial fibrosis degree in the infarcted area were significantly reduced.(4)Compared with normal bone marrow mesenchymal stem cells,the serum brain natriuretic peptide level of bone marrow stem cells with apoptosis-inducing factor gene knockdown after transplantation was significantly decreased(P<0.05),and left ventricular ejection fraction and left ventricular shortening fraction were significantly improved(P<0.05).(5)These findings confirm that apoptosis-inducing factor gene knockdown can reduce myocardial fibrosis and improve cardiac function after acute myocardial infarction via enhancing the bone marrow mesenchymal stem cell viability and increasing the bone marrow mesenchymal stem cell survival after transplantation in the donor.

Objective:To explore the value of the Wilcoxon-Mann-Whitney generalized dominance ratio (T max-weighted ratio) based on residual tissue time to peak (T max) delayed severity weighting in predicting the moderate to severe edema after acute anterior circulation ischemic stroke. Methods:The clinical and imaging features of patients with acute anterior circulation ischemic stroke from January 2019 to April 2022 in Yidu Central Hospital of Weifang were retrospectively analyzed. A total of 85 patients were enrolled, including 60 males and 25 females, with the age from 34 to 93 (67±11) years old. Patients underwent non-contrast CT, CT angiography of the head and neck, and CT perfusion imaging of the head, and ischemic core volume and the ratio of T max 4-6 s volume, T max 6-8 s volume, T max 8-10 s volume, and T max >10 s volume relative to the entire hypo-perfused area (T max>4 s volume) was measured, and the T max-weighted ratio was calculated, the collateral circulation were assessed. Patients were divided into mild edema group and moderate to severe edema group according to whether local swelling exceeded 1/3 of the unilateral cerebral hemisphere on non-contrast CT at 24-48 h. The indicators were compared between the two groups by independant t test, Mann-Whitney U and χ 2 test. The performance to predict moderate to severe edema was assessed using the receiver operating characteristic (ROC) curve. The univariate and multivariate logistic regression was used to analyze the risk factors for the moderate to severe edema. Differences in baseline National Institutes of Health Stroke Scale (NIHSS) score and infarct core volume were equalized by 1∶1 propensity score matching (PSM) and the differences of T max-weighted ratio between the two groups were further compared. Results:There were 52 cases in the mild edema group and 33 cases in the moderate to severe edema group. Baseline NIHSS score, T max>10 s volume, ischemic core volume, T max-weighted ratio and proportion of poor collateral circulation were higher in the moderate-severe edema group than those in the mild edema group ( P<0.001), T max 4-6 s volume was lower than in the mild edema group ( P<0.001). ROC analysis showed that the area under the curve (AUC) of T max-weighted ratio for predicting the incidence of moderate to severe edema was 0.885 (95%CI 0.798-0.944), with an optimal cut-off value of 1.17, sensitivity of 84.85% and specificity of 82.69% before PSM. The predictive ability based on T max-weighted ratio was similar to ischemic core volume( Z=0.64, P=0.520), T max 4-6 s volume ( Z=1.48, P=0.140) and superior to T max 6-8 s volume( Z=5.65, P<0.001), T max 8-10 s volume( Z=4.46, P<0.001), T max >10 s volume ( Z=2.91, P=0.004). Multivariate logistic regression analysis showed that T max-weighted ratio>1.17 was an independent predictor of the development of moderate to severe edema (OR=10.40,95%CI 2.65-40.83, P=0.001) through adjusted for baseline NIHSS score and ischemic core volume. After PSM, 14 patients in each group were included; the T max-weighted ratio was higher in the moderate-to-severe edema group than that in the mild edema group ( P<0.001), and the differences in other factors were not statistically significant (all P>0.05); ROC analysis showed that the AUC of T max-weighted ratio to predict the occurrence of moderate-to-severe edema was 0.852 (95%CI 0.667-0.957). Conclusion:The T max-weighted ratio can predict the occurrence of moderate-to-severe edema in brain tissue after acute anterior circulation ischemic stroke.

Objective:To explore the effect of dynamic changes of cerebral oxygen saturation before and after treatment on the progression of infarction in patients with acute ischemic stroke (AIS).Methods:Totally 39 patients with first onset AIS within 24 hours in Tianjin First Central Hospital and Shanghai Fourth People′s Hospital Affiliated to Tongji University from May 2018 to July 2020 were enrolled retrospectively. All patients underwent multi-modal MR at admission (baseline) and within 2 weeks after standardized treatment, including diffusion weighted imaging (DWI), susceptibility weighted imaging (SWI), and dynamic magnetic sensitive contrast-enhanced magnetic resonance perfusion imaging (DSC-PWI). The degree of asymmetrically prominent cortical vein (APCV) at admission was observed on SWI, and the venous oxygen saturation (SvO 2) of APCV on the infarcted cerebral hemisphere was calculated in all patients before and after treatment. The original DWI and DSC-PWI images obtained from two MR scans were imported into the software to obtain the delayed perfusion volume [peak time (T max)>6 s] and the infarct core volume (apparent diffusion coeffivient value<620×10 -6 mm 2/s). According to the comparison of baseline infarct core (DWI-ASPECT) score and follow-up (FUP-ASPECT) score, all patients were divided into infarct progression group (27 cases) and non-infarct progression group (12 cases). Two independent sample t-test or Mann Whitney U-test were used to compare the differences of baseline infarct core volume, baseline SvO 2, SvO 2 change, baseline hypoperfusion volume and hypoperfusion volume change between the two groups. Univariate analysis and multivariate logistic regression analysis were used to obtain independent predictors of infarct progression. Pearson correlation analysis was used to evaluate the correlation between SvO 2 change, hypoperfusion volume change and infarct change score respectively. Results:Difference in baseline infarct core volume, baseline SvO 2, hypoperfusion volume and hypoperfusion volume change between infarct progression group and non-progression group had no statistical significance ( P>0.05). There was significant difference in the change of SvO 2 between the infarct progression group and non-infarct progression group after treatment [(27±11)%, (35±6)% respectively, t=-2.56, P=0.015]. Univariate logistic regression analysis showed that the change value of SvO 2 was the influencing factor of infarction progression of AIS (OR=0.872, 95%CI 0.773-0.984, P=0.026). Multivariate logistic regression analysis showed that the baseline NIHSS score (OR=1.248, 95%CI 1.042-1.494, P=0.016) was an independent predictor of infarction progression in AIS, and the change value of SvO 2 (OR=0.814, 95%CI 0.688-0.964, P=0.017) was an independent protective factor. The change of SvO 2 was positively correlated with the score of infarct change ( r=0.425, P=0.007). Conclusions:The change of SvO 2 after AIS treatment can independently predict the progress of acute infarction. Improvement of SvO 2 after treatment is conducive to delay the progress of infarction.

Objective:To investigate the feasibility of predicting lateral cervical lymph node metastasis (LLNM) in patients with papillary thyroid carcinoma (PTC) based on the nomogram constructed by dual-energy CT data.Methods:In total 417 patients with PTC confirmed by pathology in Tianjin First Central Hospital from January 2015 to December 2018 were retrospectively analyzed as a training group. Internal validation was conducted, including 139 patients in the LLNM group and 278 patients in the non-LLNM group. A total of 169 PTC patients from January 2019 to June 2020 were included as an external validation group, including 58 patients in the LLNM group and 111 patients in the non-LLNM group. The morphological characteristics of the primary thyroid lesions on dual-energy CT iodine maps were analyzed, including tumor location, maximum diameter, calcification, and extrathyroidal extension (ETE). Iodine concentration (IC) of the PTC parenchyma and the internal carotid artery on the same level in the arterial and venous phases were measured, and normalized iodine concentration (NIC) was calculated. The independent risk factors for LLNM were obtained by univariate and multivariate logistic regression analysis. Base on the results, a prediction model was constructed and expressed in the form of a nomogram. The internal and external validation of the model was carried out using ROC curve.Results:Multivariate binary logistic regression analysis showed that the lesion location in the upper polar of the thyroid, the presence of ETE, IC in arterial phase>2.9 mg/ml, IC in the venous phase>3.2 mg/ml, and NIC in the arterial phase>0.21 were independent risk factors for LLNM prediction. The nomogram based on the above factors was constructed with an area under the ROC curve (AUC) of 0.895 (95%CI 0.862-0.923). With a cut-off value of 0.79, the sensitivity and specificity were 86.3% and 75.2%, respectively. As for the external validation group, the AUC of the model was 0.887 (95%CI 0.830-0.931), with the sensitivity of 82.8%, and the specificity of 81.1%.Conclusion:The application values of the nomogram model based on dual-energy CT data in preoperative evaluation of the possibility of LLNM of PTC patients has been verified. The model constructed in this study might be helpful with the individualized treatment in a certain degree.