Left atrial appendage volume is a valuable predictor of atrial fibrillation recurrence after radiofrequency catheter ablation / 中华心血管病杂志

Objective@#To investigate the association between the left atrial appendage (LAA) volume and atrial fibrillation (AF) recurrence after radiofrequency catheter ablation.@*Methods@#We prospectively enrolled sixty-two patients with AF (40 cases with paroxysmal AF, 22 cases with persistent AF) who successfully underwent a first AF catheter ablation and had performed contrast-enhanced cardiac computed tomography (CT) prior to the procedure to measure LAA volumes in our hospital from January 2012 to August 2015. Circumferential pulmonary vein isolation was performed under the guidance of three-dimension mapping system (CARTO system). Linear ablation or ablation of complex fractioned atrial electrograms was also undertaken if necessary. All patients were followed up at the 3rd, 6th and 12th months after ablation by 24-hour ambulatory Holter monitoring, and were divided into the non-recurrence group (n=42) and the AF recurrence group (n=20). Univariate and multivariate Cox proportional hazards regression analysis were used to assess the factors related to AF recurrence. The receiver operating characteristic (ROC) curve was calculated to assess the best cut-off value of LAA volume to predict AF recurrence. Kaplan-Meier method was used to evaluate the rate of freedom from AF recurrence.@*Results@#Mean LAA volume in all patients was (9.5±3.6)ml. AF recurrence occurred in 20 patients (32%) during the follow-up period. The LAA volume was significantly larger in the AF recurrence group than in the non-recurrence group ((11.5±3.8)ml vs. (8.3±3.1)ml, P=0.002). In the univariate regression analysis, LAA volume (HR=1.36, 95%CI 1.14-1.82, P<0.001), persistent AF (HR=4.43, 95%CI 1.52-12.06, P<0.001) and hypertension (HR=1.61, 95%CI 1.13-2.04, P=0.041) were risk factors of AF recurrence. However, multivariate regression analysis revealed that LAA volume (HR=1.32, 95%CI 1.12-1.51, P<0.001) and persistent AF (HR=4.22, 95% CI 1.48-11.05, P<0.001) were independent predictors for AF recurrence after ablation. The receiver operating characteristic (ROC) curve analysis revealed that a LAA volume >8.80 ml was associated with AF recurrence after ablation (sensitivity: 94% and specificity: 66%, area under the curve=0.76). Kaplan-Meier analysis showed a lower rate free from AF recurrence in the group with LAA volume >8.80 ml (P<0.001).@*Conclusion@#Larger LAA volume is associated with AF recurrence after catheter ablation in patients with AF. A LAA volume greater than 8.80 ml could be used to predict AF recurrence after ablation.

Abietic acid attenuates allergic airway inflammation in a mouse allergic asthma model.

Abietic acid (AA), one of the terpenoids isolated from Pimenta racemosa var. grissea, has been reported to have anti-inflammatory and immunomodulatory effects. However, the anti-allergic effects of AA remain unclear. The aim of this study was to investigate the anti-allergic effects of AA in an ovalbumin (OVA)-induced asthma murine model. The model of mouse asthma was established by induction of OVA. AA (10, 20, 40mg/kg) was administered by oral gavage 1h after the OVA treatment on days 21 to 23. At 24h after the last challenge, bronchoalveolar lavage fluid (BALF) and lung tissues were collected to assess pathological changes, cytokines production, and NF-κB expression. The results showed that AA attenuated lung histopathologic changes, inflammatory cells infiltration, and bronchial hyper-responsiveness. AA also inhibited OVA-induced the nitric oxide (NO), IL-4, IL-5, IL-13, and OVA-specific IgE production, as well as NF-κB activation. In conclusion, the current study demonstrated that AA exhibited protective effects against OVA-induced allergic asthma in mice and the possible mechanism was involved in inhibiting NF-κB activation.

Scoring methods for liver tissue fibrosis based on ultrasound radio frequency time series / 生物医学工程学杂志

Trying to provide ultrasonic image-aid measures for quantitative diagnosis and dynamic monitoring of liver fibrosis, we propose two scoring methods for liver fibrosis tissue in vivo , based on ultrasound radio frequency (RF) time series in this paper. Firstly, RF echo signals of human liver were recorded in this study. Then one of the recorded frame RF data was demodulated to be B model image. After that, a region of interest (ROI) in the B model image was selected. For each point in the ROI, its all frame data were acquired so that RF time series were formed. An SMR (size measure relationship) fractal dimension and six spectral features were extracted from RF time series in the ROI. With relative deviation and Fisher's discriminant ratio, seven features were weighted and summed so that the liver tissues' scores were obtained, Score-rd and Score-fisher, respectively. Area under ROC curve (AUC) and a support vector machine (SVM) were used to evaluate whether these scoring methods would be useful in distinguishing normal and cirrhosis tissues. Experimental results are shown as follows: Score-rd's AUC was 0.843, while Score-fisher was 0.816, SVM classification accuracies were both up to 87.5%. This proved that our proposed scoring methods were effective in distinguishing normal and cirrhosis tissues. Score-rd and Score-fisher have potential for clinical applications. They can also provide quantitative references for liver fibrosis diagnosis.

A medical image semantic modeling based on hierarchical Bayesian networks / 生物医学工程学杂志

A semantic modeling approach for medical image semantic retrieval based on hierarchical Bayesian networks was proposed, in allusion to characters of medical images. It used GMM (Gaussian mixture models) to map low-level image features into object semantics with probabilities, then it captured high-level semantics through fusing these object semantics using a Bayesian network, so that it built a multi-layer medical image semantic model, aiming to enable automatic image annotation and semantic retrieval by using various keywords at different semantic levels. As for the validity of this method, we have built a multi-level semantic model from a small set of astrocytoma MRI (magnetic resonance imaging) samples, in order to extract semantics of astrocytoma in malignant degree. Experiment results show that this is a superior approach.