ObjectiveTo investigate the role and mechanism of podoplanin （PDPN） in hepatic stellate cell （HSC） activation and liver fibrosis. MethodsLiver biopsy samples were collected from 75 patients with chronic hepatitis B who attended Department of Infectious Diseases， Putuo Hospital Affiliated to Shanghai University of Traditional Chinese Medicine， for the first time from September 2019 to June 2022， and RT-PCR and immunohistochemistry were used to measure the expression of PDPN in liver tissue of patients in different stages of liver fibrosis. A total of 12 male C57/BL6 mice were randomly divided into control group and model group. The mice in the model group were given intraperitoneal injection of 10% CCl₄， and those in the control group were injected with an equal volume of olive oil， for 6 weeks. HE staining and Sirius Red staining were used to observe liver histopathological changes； primary mouse liver cells were separated to measure the mRNA expression of PDPN in various types of cells； primary mouse HSCs were treated with PDPN protein， followed by treatment with the NF-‍κB inhibitor BAY11-708， to measure the expression of inflammatory factors in HSCs induced by PDPN. The independent-samples t test was used for comparison of normally distributed continuous data between two groups； a one-way analysis of variance was used for comparison between multiple groups， and the least significant difference t-test was used for further comparison between two groups. The Spearman correlation analysis was used to investigate data correlation. ResultsAs for the liver biopsy samples， there was a relatively low mRNA expression level of PDPN in normal liver， and there was a significant increase in the mRNA expression level of PDPN in liver tissue of stage S3 or S4 fibrosis （all P<0.001）. Immunohistochemical staining showed that PDPN was mainly expressed in the fibrous septum and the hepatic sinusoid， and the PDPN-positive area in S4 liver tissue was significantly higher than that in S0 liver tissue （t=8.892， P=0.001）. In normal mice， PDPN was mainly expressed in the hepatic sinusoid， and there was a significant increase in the expression of PDPN in CCl₄ model mice （t=0.95， P<0.001）， mainly in the fibrous septum. RT-PCR showed a significant increase in the mRNA expression of PDPN in the CCl₄ model mice （t=11.25， P=0.002）. Compared with hepatocytes， HSCs， Kupffer cells， and bile duct endothelial cells， hepatic sinusoidal endothelial cells showed a significantly high expression level of PDPN （F=20.56， P<0.001）. Compared with the control group， the primary mouse HSCs treated by PDPN protein for 15 minutes showed significant increases in the mRNA expression levels of the inflammation-related factors TNFα， CCL3， CXCL1， and CXCR1 （all P<0.05）， and there were significant reductions in the levels of these indicators after treatment with BAY11-7082 （all P<0.05）. ConclusionThere is an increase in the expression of PDPN mainly in hepatic sinusoidal endothelial cells during liver fibrosis， and PDPN regulates HSC activation and promotes the progression of liver fibrosis via the NF-κB signaling pathway.

Objective:To construct an prediction model based on magnetic resonance imaging (MRI) machine learning algorithm and radiomics and investigate its application value in predicting lymphovascular invasion (LVI) status of rectal cancer without lymph node metastasis.Methods:The retrospective cohort study was conducted. The clinicopathological data of 204 rectal cancer patients without lymph node metastasis who were admitted to Gansu Provincial Hospital from February 2016 to January 2024 were collected. There were 123 males and 81 females, aged (61±7)years. All 204 patients were randomly divided into the training dataset of 163 cases and the testing dataset of 41 cases by a ratio of 8∶2 using the electronic computer randomization method. The training dataset was used to construct the prediction model, and the testing dataset was used to validate the prediction model. The clinical prediction model, radiomics model and joint prediction model were constructed based on the selected clinical and/or imaging features. Measurement data with normal distribution were represented as Mean± SD. Count data were described as absolute numbers, and the chi-square test or Fisher exact probability were used for comparison between the groups. Comparison of ordinal data was conducted using the nonparameter rank sum test. The inter-class correlation coefficient (ICC) was used to evaluate the consistency of the radiomics features of the two doctors, and ICC >0.80 was good consistency. Univariate analysis was conducted by corres-ponding statistic methods. Multivariate analysis was conducted by Logistic stepwise regression model. The receiver operating characteristic (ROC) curve was drawn, and the area under the curve (AUC), Delong test, decision curve and clinical impact curve were used to evaluate the diagnostic efficiency and clinical utility of the model. Result:(1) Analysis of factors affecting LVI status of patients. Of the 204 rectal cancer patients without lymph node metastasis, there were 71 cases with positive of LVI and 133 cases with negative of LVI. Results of multivariate analysis showed that gender, platelet (PLT) count and carcinoembryonic antigen (CEA) were independent factors affecting LVI status of rectal cancer without lymph node metastasis in training dataset [ odds ratio=2.405, 25.062, 2.528, 95% confidence interval ( CI) as 1.093-5.291, 2.748-228.604, 1.181-5.410, P<0.05]. (2) Construction of clinical prediction model. The clinical prediction model was conducted based on the results of multivariate analysis including gender, PLT count and CEA. Results of ROC curve showed that the AUC, accuracy, sensitivity and specificity of clinical prediction model were 0.721 (95% CI as 0.637-0.805), 0.675, 0.632 and 0.698 for the training dataset, and 0.795 (95% CI as 0.644-0.946), 0.805, 1.000 and 0.429 for the testing dataset. Results of Delong test showed that there was no significant difference in the AUC of clinical prediction model between the training dataset and the testing dataset ( Z=-0.836, P>0.05). (3) Construction of radiomics model. A total of 851 radiomics features were extracted from 204 patients, and seven machine learning algorithms, including logistic regression, support vector machine, Gaussian process, logistic regression-lasso algorithm, linear discriminant analysis, naive Bayes and automatic encoder, were used to construct the prediction model. Eight radiomics features were finally selected from the optimal Gaussian process learning algorithm to construct a radiomics prediction model. Results of ROC curve showed that the AUC, accuracy, sensitivity and specificity of radiomics prediction model were 0.857 (95% CI as 0.800-0.914), 0.748, 0.947 and 0.642 for the training dataset, and 0.725 (95% CI as 0.571-0.878), 0.634, 1.000 and 0.444 for the testing dataset. Results of Delong test showed that there was no significant difference in the AUC of radiomics prediction model between the training dataset and the testing dataset ( Z=1.578, P>0.05). (4) Construction of joint prediction model. The joint prediction model was constructed based on the results of multivariate analysis and the radiomics features. Results of ROC curve showed that the AUC, accuracy, sensitivity and specificity of radiomics prediction model were 0.885 (95% CI as 0.832-0.938), 0.791, 0.912 and 0.726 for the training dataset, and 0.857 (95% CI as 0.731-0.984), 0.854, 0.714 and 0.926 for the testing dataset. Results of Delong test showed that there was no significant difference in the AUC of joint prediction model between the training dataset and the testing dataset ( Z=0.395, P>0.05). (5) Performance comparison of three prediction models. Results of the Hosmer-Lemeshow goodness-of-fit test showed that all of the clinical prediction model, radiomics prodiction model and joint prediction model having good fitting degree ( χ2=1.464, 12.763, 10.828, P>0.05). Results of Delong test showed that there was no signifi-cant difference in the AUC between the clinical prediction model and the joint prediction model or the radiomics model ( Z=1.146, 0.658, P>0.05), and there was a significant difference in the AUC between the joint prediction model and the radiomics model ( Z=2.001, P<0.05). Results of calibra-tion curve showed a good performance in the joint prediction model. Results of decision curve and clinical impact curve showed that the performance of joint prediction model in predicting LVI status of rectal cancer without lymph node metastasis was superior to the clinical prediction model and the radiomics model. Conclusions:The clinical prediction model is constructed based on gender, PLT count and CEA. The radiomics predictive model is constructed based on 8 selected radiomics features. The joint prediction model is constructed based on the clinical prediction model and the radiomics predictive model. All of the three models can predict the LVI status of rectal cancer with-out lymph node metastasis, and the joint prediction model has a superior predictive performance.

Purpose The clinical and dual-layer detector spectral CT(DLCT)features of epidermal growth factor receptor(EGFR)mutation and anaplastic lymphoma kinase(ALK)rearrangement of lung adenocarcinoma were studied by DLCT multi-parameter imaging to explore a non-invasive prediction method for clinical diagnosis of lung adenocarcinoma gene expression.Materials and Methods A total of 98 cases of lung adenocarcinoma diagnosed by pathology in Gansu Provincial Hospital were prospectively collected from August 2020 to March 2022.Clinical parameters(gender,age,lesion morphology,number,mediastinal lymph node metastasis,EGFR and ALK mutations status)and DLCT parameters including slope of the spectrum curve of the arteriovenous phase(λHUA,λHUv),the standard iodine concentration of the arteriovenous phase(NICA,NICv),the 40 keV single-energy CT value of the arteriovenous phase(CTA 40 keV,CTv 40 keV),the active atomic number of the arteriovenous phases were collected,respectively.According to the expression of EGFR and ALK,all patients were divided into three groups:EGFR mutant group[EGFR(+)],ALK rearrangement group[ALK(+)],EGFR/ALK both negative group[EGFR/ALK(-)].Clinical and DLCT parameters of each group were analyzed.Results There were statistical difference in gender between the EGFR(+)group and EGFR/ALK(-)group(x2=11.010,P＜0.05).There were statistical differences in lesion morphology among the three groups(x2=12.858,P＜0.05).The value of CTv 40 keV in the EGFR(+)group was significantly higher than that in EGFR/ALK(-)group(t=1.997,P＜0.05),and the NICv in the ALK(+)group was significantly lower than that in EGFR/ALK(-)group(t=2.155,P＜0.05).The λHUv,NICv,CTv 40 keV of EGFR(+)group were significantly higher than those of ALK(+)group(t=2.613,3.149,3.218,all P＜0.05).The sensitivity and specifiicity to identify EGFR(+)and EGFR/ALK(-)adenocarcinoma were 62.7％and 70.0％,the area under curve(AUC)was 0.634(95％CI 0.516-0.756)when the CTv 40 keV value was 141.070 Hu.The sensitivity and specificity to identify ALK(+)and EGFR/ALK(-)adenocarcinoma were 76.7％and 64.2％,the AUC was 0.706(95％CI 0.536-0.853)when NICv value was 0.287.The sensitivity to identify EGFR(+)and ALK(+)adenocarcinoma were 70.6％,64.7％,72.5％and the specificity was 76.5％,76.5％,82.4％,respectively,the AUC was 0.734(95％CI 0.606-0.829),0.751(95％CI 0.610-0.832),0.773(95％CI 0.649-0.861)when the values of λHUv,NICv and CTv 40 keV were 1.335,0.320 and 132.350,respectively.Delong test showed that the AUC of CTv 40 keV and λHUv was statistically different(Z=2.327,P＜0.05),and the AUC of CTv 40 keV was 0.773.Conclusion The gender,lesion morphology and DLCT parameters(λHUv,CTv 40 keV,NICv)of lung adenocarcinoma have certain predictive value for EGFR and ALK genetic expression,which can help clinical judgment of lung adenocarcinoma gene mutation pattern.

【Objective】 To estimate the prevalence, associated factors and patterns of multimorbidity of non-communicable diseases among adults in Shaanxi Province so as to provide evidence for the prevention and control of non-communicable diseases. 【Methods】 We used the data of adults aged 18 years and older collected in the baseline survey of Shaanxi Project in the Regional Ethnic Cohort Study in Northwest China. Multinomial logistic regression was used to explore the associated factors for multimorbidity. Exploratory factor analysis was used to extract patterns of multimorbidity. 【Results】 The prevalence of multimorbidity was 10.7% among the 44 442 participants. Age increase, being males, urban residence, and being overweight or obesity were positively associated with multimorbidity. Compared with women, men had a higher risk of multimorbidity. The OR and 95% CI was 1.25 (1.12-1.39). The risk of multimorbidity increased with age among adults. Compared with participants aged 18.0-34.9 years, the ORs and 95% CIs of those aged 35.0-44.9, 45.0-54.9, 55.0-64.9, and ≥65.0 years were 4.73 (3.47-6.46), 15.61 (11.60-21.00), 41.39 (30.76-55.70) and 90.04 (66.58-121.77), respectively. The primary multimorbidity patterns among adults in Shaanxi were cardiovascular-metabolic multimorbidity (5.4%), viscero-articular multimorbidity (1.0%), and respiratory multimorbidity (0.3%). 【Conclusion】 More than one in ten adults in Shaanxi Province had multimorbidity, and the predominant pattern of multimorbidity was cardiovascular-metabolic multimorbidity. The prevention and control of non-communicable diseases should be reinforced in middle-aged and older people, males, people living in the urban, and overweight or obese people. More attention should be paid to the prevention and control of cardiovascular-metabolic diseases.

【Objective】 To evaluate the dietary quality with the dietary balance index (DBI_16) and the association between dietary quality and bone mass among middle-aged and elderly people in Gansu Province so as to provide evidence for improving dietary quality and bone health status of Gansu population. 【Methods】 Based on the information of the type and quantity of food intake and the bone mass of middle-aged and elderly people aged 35 years and above collected by the Gansu Project in the Regional Ethnic Cohort Study in Northwest China, DBI_16 was used to evaluate the intake level of cereals, vegetables, fruits, milk, beans, fish and shrimp, eggs and other foods, and the degree of inadequate, excessive and unbalanced dietary intake of the participants. Multiple linear regression was used to evaluate the associations of three component indexes of DBI_16, high bound score (DBI_HBS), low bound score (DBI_LBS), diet quality distance (DBI_DQD), and seven single indexes of DBI_16 with bone mass. 【Results】 Analyses of the dietary and bone mass data of 11,840 participants showed that 44.8% of participants consumed excessive amounts of cereals compared to the dietary recommendation. 96.3%, 90.6%, 90.1%, 71.9%, 95.1% and 60.3% of participants’ intake of vegetables, fruits, milk, soybeans, fish and shrimp, and eggs, respectively, were inadequate. 47.7% participants consumed less than 10 types of food. 2.3% participants’ DBI_LBS levels were appropriate. 54.7% participants’ DBI_HBS levels were appropriate. Only 1.2% participants’ DBI_DQD reached a balanced level. The bone mass level in the study population was (2.5±0.6) kg [(2.8±0.5) kg for men and (2.3±0.5) kg for women]. After adjusting for sociodemographic characteristics, lifestyle, total dietary energy intake and body mass index, DBI_LBS and DBI_DQD were negatively associated with bone mass [β and 95% CI was -0.002 01 (-0.003 62--0.000 40) and -0.001 76 (-0.003 09--0.000 43), respectively]. 【Conclusion】 Dietary intake imbalance is common among middle-aged and elderly people in Gansu Province, and the more severe the dietary intake imbalance, the lower the bone mass level.

Objective:To investigated the prevalence of anxiety and depression symptoms in patients with colorectal polyps in part of Xinjiang area and to explore the associated influencing factors related to psychological status, and to provide evidence and clues to promote the diagnosis and treatment of psychosomatic diseases in digestive system.Methods:From December 2021 to June 2022, at the First Affiliated Hospital of Shihezi University, the Fourth Division Hospital of Xinjiang Production and Construction Corps, the Third Division Hospital of Xinjiang Production and Construction Corps (Kashgar Hospital), the Thirteenth Division Red Star Hospital of Xinjiang Production and Construction Corps, the Tenth Division Beitun Hospital of Xinjiang Production and Construction Corps, the Seventh Division Hospital of Xinjiang Production and Construction Corps, the Fifth Division Hospital of Xinjiang Production and Construction Corps, patients who underwent colonoscopy and colorectal polyps detected were enrolled. The generalized anxiety disorder scale-7 (GAD-7) was used to screen anxiety symptoms, and the patient health questionaire-8 (PHQ-8) was performed to screen depression symptoms, and the general situation questionnaire was used to collect clinical information of patients. The questionnairs were completed via the Questionnaire Star platform or paper questionnaire, and a database was established. The patients were divided into groups according to whether with anxiety and depression symptoms. Multivariable logistic regression models was performed to analyze the factors related to anxiety and depression.Results:A total of 516 questionnaires were distributed and collected, among which 9 questionnaires were incomplete and 507 questionnaires were valid, the effective rate of questionnaires was 98.26%. The detection rates of anxiety and depression symptoms in 507 patients with colorectal polyps were 21.50%(109/507) and 19.33%(98/507), respectively. The results of multivariable logistic analysis revealed that female ( OR=3.87, 95% confidence interval (95% CI) 2.30 to 6.51, P<0.001), maximum diameter of polyp ( OR=1.74, 95% CI 1.14 to 2.67, P=0.011), perception of polyps as cancer ( OR=13.96, 95% CI 1.48 to 132.07, P=0.022), and the occurrence of gastrointestinal symptoms after polyp detection ( OR=5.43, 95% CI 1.74 to 16.92, P=0.004) were independent risk factors of anxiety in patients with colorectal polyps. Female ( OR=2.42, 95% CI 1.47 to 4.00, P=0.001), the number of polyps ( OR=1.07, 95% CI 1.01 to 1.13, P=0.028) and the count of gastrointestinal symptoms ( OR=2.04, 95% CI 1.34 to 3.10, P=0.001) were independent risk factors of depression in patients with colorectal polyps. Conclusion:Gender, polyp size, number of polyps, disease perception, and concomitant gastrointestinal symptoms are associated with anxiety and depression in patients with colorectal polyps.

Objective:To detect the abnormal changes of myocardial blood perfusion in patients with hypertrophic cardiomyopathy(HCM) by myocardial contrast echocardiography (MCE) combined with adenosine stress test.Methods:Fifteen adult patients with HCM who were treated in Fuwai Central China Cardiovascular Hospital from May 2021 to March 2022 were prospectively selected as the HCM group, and eighteen healthy volunteers matched by gender, age and body surface area during the same period were chosen as the control group. All subjects underwent routine echocardiography, rest and adenosine stress MCE. The MCE images were analyzed by QLab software to obtain the myocardial perfusion parameters: peak signal intensity (A value), rising slope of the curve (β value) and A×β value, and the differences of above parameters between the two groups were compared.According to whether the end-diastolic wall thickness ≥12 mm, the myocardial segments in the HCM group were divided into hypertrophic segments and non-hypertrophic segments. The differences in myocardial perfusion parameters were compared among control group segments, hypertrophic segments and non-hypertrophic segments of the HCM group. The correlations of stress myocardial blood flow with maximal left ventricular wall thickness (MLVWT), left ventricular mass index (LVMI) and left atrial volume index (LAVI) in the HCM group were analyzed.Results:Compared with the control group, the A value, β value and A×β value of whole myocardium, hypertrophic segments and non-hypertrophic segments in the HCM group were significantly decreased in the rest and adenosine stress state, and the differences were statistically significant (all P<0.05). In the stress state, the A value, β value and A×β value of the hypertrophic segments were significantly lower than those in the non-hypertrophic segments in the HCM group, and the detection rate of abnormal perfusion segments in the HCM group was significantly higher than that in the rest state(all P<0.05). Compared with the control group, the myocardial blood flow reserve of whole myocardium, hypertrophic segments and non-hypertrophic segments in the HCM group were significantly decreased, and the differences were statistically significant(all P<0.05). The stress myocardial blood flow in the HCM group was negatively correlated with MLVWT, LVMI and LAVI ( r=-0.815, -0.805, -0.742; all P<0.05). Conclusions:Myocardial blood perfusion abnormalities can occur in both hypertrophic and non-hypertrophic myocardial segments in patients with HCM, and adenosine stress MCE can significantly improve the sensitivity of detecting myocardial perfusion abnormalities. The stress myocardial blood flow in patients with HCM is negatively correlated with MLVWT, LVMI and LAVI.

Objective:To construct a combined radiomics model based on preoperative enhanced computed tomography (CT) examination for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC), and provide biological explanations for the radiomics model.Methods:The retrospective cohort study was conducted. The messenger RNA (mRNA) of 424 HCC patients, the clinicopathological data of 39 HCC patients entered into the Cancer Genome Atlas database from its establishment until January 2023, and the clinicopathological data of 53 HCC patients who were admitted to the Gansu Provincial People′s Hospital from January 2020 to January 2023 were collected. The 92 HCC patients were randomly divided into a training dataset of 64 cases and a test dataset of 28 cases with a ratio of 7∶3 based on a random number table method. The CT images of patients in the arterial phase and portal venous phase as well as the corresponding clinical data were analyzed. The 3Dslicer software (version 5.0.3) was used to register the CT images in the arterial phase and portal venous phase and delineate the three-dimensional regions of interest. The original images were preprocessed and the corresponding features were extracted by the open-source software FAE (version 0.5.5). After selecting features using the Least Absolute Shrinkage and Selection Operator, the radiomics model was constructed and the radiomics score (R-score) was calculated. The nomogram was constructed by integrating clinical parameters, imaging features and R-score based on Logistic regression. The gene modules related to radiomics model were obtained and subjected to enrichment analysis by conducting weighted gene co-expression network analysis and correlation analysis. Observation indicators: (1) comparison of clinical characteristics of patients with different MVI properties; (2) establishment of MVI risk model; (3) evaluation of MVI risk model; (4) clustering of gene modules; (5) functional enrichment of feature-correlated gene modules. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the independent sample t test. Measurement data with skewed distribution were represented as M(range), and comparison between groups was conducted using the Mann-Whitney U test. Comparison of count data was conducted using the chi-square test. The intra-/inter-class correlation coefficient (ICC) was used to assess the inter-observer consistency of radiomics feature extracted by different observers. ICC >0.75 indicated a good consistency in feature extraction. The Logistic regression model was used for univariate and multivariate analyses. The receiver operating characteristic curve was drawn, and the area under curve (AUC), the decision curve and the calibration curve were used to evaluate the diagnostic efficacy and clinical practicality of the model. Results:(1) Comparison of clinical characteristics of patients with different MVI properties. Of 92 HCC patients, there were 47 cases with MVI-positive and 45 cases with MVI-negative, and there were significant differences in hepatitis, tumor diameter, peritumoral enhancement, intratumoral arteries, pseudocapsule and smoothness of tumor margin between them ( χ2=5.308, 9.977, 47.370, 32.368, 21.105, 31.711, P<0.05). (2) Establishment of MVI risk model. A total of 1 781 features were extrac-ted from arterial and portal venous phases of the intratumoral and peritumoral regions. After feature dimension reduction, 8 radiomics features were selected from arterial and portal venous phases to construct the combined model. Results of multivariate analysis showed that peritumoral enhancement, intratumoral arteries, pseudocapsule, smoothness of tumor margins, and R-score were independent risk factors for MVI in patients with HCC [ hazard ratio=0.049, 0.017, 0.017, 0.021, 2.539, 95% confidence interval ( CI) as 0.005-0.446, 0.001-0.435, 0.001-0.518, 0.001-0.473, 1.220-5.283, P<0.05]. A nomogram model was constructed incorporating peritumoral enhancement, intratumoral arteries, pseudocapsule, smoothness of tumor margins, and R-score. (3) Evaluation of the MVI risk model. The AUC of radiomics model was 0.923 (95% CI as 0.887-0.944) and 0.918 (95% CI as 0.894-0.945) in the training dataset and test dataset, respectively. The AUC of nomogram model, incorpora-ting both the R-score and radiomics features, was 0.973 (95% CI as 0.954-0.988) and 0.962 (95% CI as 0.942-0.987) in the training dataset and test dataset, respectively. Results of decision curve showed that the nomogram had better clinical utility compared to the R-score. Results of calibration curve showed good consistency between the actual observed outcomes and the nomogram or the R-score. (4) Clustering of gene module. Results of weighted gene co-expression network analysis showed that 8 gene modules were obtained. (5) Functional enrichment of feature-related gene modules. Results of correlation analysis showed 4 gene modules were significantly associated with radiomics features. The radiomics features predicting of MVI may be related to pathways such as the cell cycle, neutrophil extracellular trap formation, and PPAR signaling pathway. Conclusions:The combined radiomics model based on preoperative enhanced CT imaging can predict the MVI status of HCC. By obtaining mRNA gene expression profiles associated with radiomics features, a biological interpretation of the radiomics model is provided.

With the progress of targeted therapy and immunotherapy for lung cancer, the clinical demand for lung biopsy is increasing. An ideal biopsy specimen can be used not only for histopathological diagnosis, but also for biomarker detection. The ideal biopsy specimen should meet two requirements, including more than 60 mm2 of tumor tissue and containing more than 20% of tumor cells. In order to obtain ideal lung cancer biopsy specimens, advanced imaging techniques are needed to help. In this article, we reviewed the requirements for biopsy specimens based on biomarker detection, as well as the current status and research progress of using imaging techniques for preoperative planning and intraoperative real time guidance of lung cancer biopsy.
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Humans ; Lung Neoplasms/diagnostic imaging* ; Biopsy ; Biomarkers ; Immunotherapy

Objective:To detect the abnormal changes of myocardial blood perfusion in patients with hypertrophic cardiomyopathy (HCM) by myocardial contrast echocardiography(MCE) combined with exercise stress test.Methods:Twenty-seven patients with clinically diagnozed of asymmetric HCM in Fuwai Central China Cardiovascular Hospital from May 2020 to April 2021 were selected as the HCM group, and 29 healthy subjects during the same period were selected as the control group. All patients underwent routine echocardiography, resting and exercise stress MCE. The myocardial perfusion parameters of each segment of interventricular septum in the 2 groups were quantitatively analyzed: the peak plateau intensity (A value), ascending slope of the curve(β value) and value of A×β. According to the end-diastolic myocardial thickness, the interventricular septum of the HCM group was divided into hypertrophic and non-hypertrophic segments, and the myocardial contrast parameters of the interventricular septum of the study group were compared with those of the control group. The myocardial blood flow reserve value of the two groups were calculated, and the correlation of myocardial blood flow reserve value with left ventricular mass index (LVMI) and left ventricular remodeling index (LVRI) were analyzed.Results:No matter at rest or under stress, the A value, β value and A×β value of ventricular septal hypertrophic and non-hypertrophic segments in the hypertrophic cardiomyopathy group were lower than those in the control group, and the differences were statistically significant (all P<0.05). Under stress, the A value, β value and A×β value of interventricular septal hypertrophic segments were lower than those in non-hypertrophic segments in the HCM group, and the differences were statistically significant (all P<0.05). The myocardial blood flow reserve in the HCM group was negatively correlated with LVMI and LVRI( r=-0.899, -0.676; all P<0.001). Conclusions:In patients with HCM under resting and exercise stress, microcirculation disorders were found in both hypertrophic and non-hypertrophic segments of the ventricular wall, and the myocardial blood flow reserve was negatively correlated with LVMI and LVRI.