Collagen proportionate area correlates to hepatic venous pressure gradient in non-abstinent cirrhotic patients with alcoholic liver disease

AIM To explore the relationship between collagen proportionate area(CPA) and portal hypertension-related clinical manifestations in alcoholic liver disease(ALD).METHODS Retrospective study with chart review of patients with ALD adressed to our center between January 2012 and December 2013 for a transjugular liver biopsy(TJLB) and hepatic hemodynamic study. Patients were included if they met the following criteria:(1) Medical indication for a liver biopsy in the setting of ALD;(2) recent(< 15 d) clinical, radiological, endoscopic and biological data available; and(3) estimated follow-up of at least 6 mo. Liver tissue from cirrhotic subjects obtained from transjugular liver biopsies was stained with Picro Sirius red and computer-assisted digital image analysis to determine fibrosis density using CPA was performed. RESULTS We included 61 patients with alcoholic ALD, subdivided in 41 active alcohol drinkers and 20 durably abstinent patients. Nine healthy liver donors served as controls. Mean CPA in patients with ALD was 7.1%, with no difference between active drinkers and abstinent patients(P = 0.17). Using a fibrosis density cutoff of 5%, we observed a positive correlation between high fibrosis density and the hepatic venous pressure gradient(HVPG) only in active drinkers(P = 0.02). At 12-mo of follow-up, in the group of active alcohol drinkers, patients reaching a composite outcome showed a higher HVPG value as compared to those who did not(18.5 mm Hg vs 14.5 mm Hg P < 0.04) whereas CPA values were similar(6.9% vs 11%, P = 0.23).CONCLUSION In active alcoholic ALD, CPA correlates to portal pressure but only HVPG predicts clinical events, pointing to the role of alcohol as a modulator of portal hypertension.

Antidepressant Effects of Electroconvulsive Therapy Unrelated to the Brain＇s Functional Network Connectivity alterations at an Individual Level

Background： Electroconvulsive therapy （ECT） can alleviate the symptoms of treatment-resistant depression （TRD）. Functional network connectivity （FNC） is a newly developed method to investigate the brain＇s functional connectivity patterns. The first aim of this study was to investigate FNC alterations between TRD patients and healthy controls. The second aim was to explore the relationship between the ECT treatment response and pre-ECT treatment FNC alterations in individual TRD patients. Methods： This study included 82 TRD patients and 41 controls. Patients were screened at baseline and after 2 weeks of treatment with a combination of ECT and antidepressants. Group information guided-independent component analysis （G1G-ICA） was used to compute subject-specific functional networks （FNs）. Grassmann maniibld and step-wise forward component selection using support vector machines were adopted to perform the FNC measure and extract the functional networks＇ connectivity patterns （FCP）. Pearson＇s correlation analysis was used to calculate the correlations between the FCP and ECT response. Results： A total of 82 TRD patients in the ECT group were successfully treated. On an average, 8.50 ~ 2.00 ECT sessions were conducted. After ECT treatment, only 42 TRD patients had an improved response to ECT （the Hamilton scores reduction rate was more than 50%）, response rate 51%. 8 FNs （anterior and posterior default mode network, bilateral frontoparietal network, audio network, visual network, dorsal attention network, and sensorimotor network） were obtained using GIG-ICA. We did not found that FCPs were significantly different between TRD patients and healthy controls. Moreover, the baseline FCP was unrelated to the ECT treatment response. Conclusions： The FNC was not significantly different between the TRD patients and healthy controls, and the baseline FCP was unrelated to the ECT treatment response. These findings will necessitate that we modify the experimental scheme to explore the mechanisms underlying ECT＇s effects on depression and explore the specific predictors of the effects of ECT based on the pre-ECT treatment magnetic resonance imaging.