OBJECTIVETo investigate the dynamic activity of NF-kappaB at the early stage of injury in multiple trauma patients and the protective effects of ulinastain.

METHODSFrom January 2008 to May 2010, patients with multiple traumas admitted to our emergency department were enrolled in this study. Their age varied from 20-55 years. All enrolled patients were assigned randomly into control group (26 cases of multiple injury without ulinastain treatment), ulinastain group (25 cases of multiple injury with ulinastain treatment), and mild injury group (20 cases) for basic control. The inclusion criteria for mild injury group were AIS-2005 less than or equal to 3, single wound, previously healthy inhospital patients without the history of surgical intervention. In addition to routine treatment, patients in ulinastain group were intravenously injected 200 000 IU of ulinastain dissolved in 100 ml of normal saline within 12 hours after injury and subsequently injected at the interval of every 8 hours for 7 days. NF-kappaB activity in monocytes and the level of TNF-alpha,IL-1, IL-6 in serum on admission (day 0), day 1, 2, 3, 4, and 7 were measured. Data were compared and analyzed between different groups.

RESULTSNF-kappaB activity in monocytes and TNF-alpha,IL-1 and IL-6 of these patients reached peak levels at 24 hour after trauma, with gradual decrease to normal at 72 hour after trauma. NF-kappaB activity and levels of TNF-alpha,IL-1 and IL-6 were lower in ulinastain group than control one, without any significant difference between the two groups. The mean duration for systemic inflammatory response syndrome and multiple organ dysfunction syndrome was 7 d+/-3.1 d and 10 d+/-3.5 d in ulinastain group and control group respectively, and showed a significant difference.

CONCLUSIONSNF-kappaB activity in monocytes and the levels of inflammatory cytokines in multiply injured patients increased transiently at the early stage of trauma. Ulinastain may shorten the duration of systemic inflammatory response syndrome and multiple organ dysfunction syndrome, but does not show the ability to decrease the activity of NF-kappaB .

Cytokines ; Humans ; Interleukin-6 ; blood ; Multiple Trauma ; NF-kappa B ; Tumor Necrosis Factor-alpha

The present study clarified the molecular mechanism of curcumol against liver fibrosis based on its effects on the autopha-gy and apoptosis of hepatic stellate cells. The hepatic stellate cells were divided into a blank control group, a transforming growth factor-β1(TGF-β1)(10 ng·mL~(-1)) group, and low-(12.5 mg·L~(-1)), medium-(25 mg·L~(-1)), and high-dose(50 mg·L~(-1)) curcumol groups. The effect of curcumol on the viability of hepatic stellate cells induced by TGF-β1 was detected by the MTT assay kit. The apo-ptosis in each group was determined by flow cytometry. Real-time fluorescence-based quantitative PCR(RT-PCR) was employed for the detection of mRNA expression of α-smooth muscle actin(α-SMA), type Ⅰ collagen(collagen Ⅰ), and type Ⅲ collagen(collagen Ⅲ). Western blot was used to detect the protein expression of p62, microtubule-associated protein 1 light chain 3(LC3), beclin1, B cell lymphoma 2(Bcl-2), and Bcl-2-associated X protein(Bax). Transmission electron microscopy(TEM) was used to observe cell morphology and autophagosome formation in each group. The autophagic flux was observed after cell infection with adenovirus under double fluorescence labeling. The cell viability assay revealed that compared with the TGF-β1 group, the curcumol groups showed significantly decreased cell viability. The apoptosis assay showed that the apoptosis rates of the curcumol groups were significantly higher than that of the TGF-β1 group. RT-PCR indicated that the mRNA expression of α-SMA, collagenⅠ, and collagen Ⅲ in the curcumol groups was significantly lower than that of the TGF-β1 group. Western blot showed that the expression of p62, LC3, beclin1, Bcl-2, and Bax in the curcumol groups was significantly different from that in the TGF-β1 group. As demonstrated by TEM, compared with the TGF-β1 group, the curcumol groups showed significantly increased autophagosomes. The detection of autophagic flow by the adenovirus under double fluorescence labeling showed that autolysosomes in the curcumol groups were significantly increased compared with those in the TGF-β1 group. Curcumol can induce the autophagy and apoptosis of hepatic stellate cells, which may be one of its anti-liver fibrosis mechanisms.
Actins/metabolism* ; Apoptosis ; Autophagy ; Hepatic Stellate Cells ; Humans ; Liver/metabolism* ; Liver Cirrhosis/metabolism* ; Sesquiterpenes ; Transforming Growth Factor beta1/metabolism*

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.