Nasopharyngeal carcinoma (NPC) has a particularly high prevalence in southern China, southeastern Asia and northern Africa. Radiation resistance remains a serious obstacle to successful treatment in NPC. This study aimed to explore the metabolic feature of radiation-resistant NPC cells and identify new molecular-targeted agents to improve the therapeutic effects of radiotherapy in NPC. Methods: Radiation-responsive and radiation-resistant NPC cells were used as the model system in vitro and in vivo. Metabolomics approach was used to illustrate the global metabolic changes. 13C isotopomer tracing experiment and Seahorse XF analysis were undertaken to determine the activity of fatty acid oxidation (FAO). qRT-PCR was performed to evaluate the expression of essential FAO genes including CPT1A. NPC tumor tissue microarray was used to investigate the prognostic role of CPT1A. Either RNA interference or pharmacological blockade by Etomoxir were used to inhibit CPT1A. Radiation resistance was evaluated by colony formation assay. Mitochondrial membrane potential, apoptosis and neutral lipid content were measured by flow cytometry analysis using JC-1, Annexin V and LipidTOX Red probe respectively. Molecular markers of mitochondrial apoptosis were detected by western blot. Xenografts were treated with Etomoxir, radiation, or a combination of Etomoxir and radiation. Mitochondrial apoptosis and lipid droplets content of tumor tissues were detected by cleaved caspase 9 and Oil Red O staining respectively. Liquid chromatography coupled with tandem mass spectrometry approach was used to identify CPT1A-binding proteins. The interaction of CPT1A and Rab14 were detected by immunoprecipitation, immunofluorescence and in situ proximity ligation analysis. Fragment docking and direct coupling combined computational protein-protein interaction prediction method were used to predict the binding interface. Fatty acid trafficking was measured by pulse-chase assay using BODIPY C16 and MitoTracker Red probe. Results: FAO was active in radiation-resistant NPC cells, and the rate-limiting enzyme of FAO, carnitine palmitoyl transferase 1 A (CPT1A), was consistently up-regulated in these cells. The protein level of CPT1A was significantly associated with poor overall survival of NPC patients following radiotherapy. Inhibition of CPT1A re-sensitized NPC cells to radiation therapy by activating mitochondrial apoptosis both in vitro and in vivo. In addition, we identified Rab14 as a novel CPT1A binding protein. The CPT1A-Rab14 interaction facilitated fatty acid trafficking from lipid droplets to mitochondria, which decreased radiation-induced lipid accumulation and maximized ATP production. Knockdown of Rab14 attenuated CPT1A-mediated fatty acid trafficking and radiation resistance. Conclusion: An active FAO is a vital signature of NPC radiation resistance. Targeting CPT1A could be a beneficial regimen to improve the therapeutic effects of radiotherapy in NPC patients. Importantly, the CPT1A-Rab14 interaction plays roles in CPT1A-mediated radiation...

The objective of this study was to determine the predictive value of the model for end-stage liver disease (MELD) scoring system in patients with acute-on-chronic hepatitis B liver failure (ACLF-HBV), and to establish a new model for predicting the prognosis of ACLF-HBV. A total of 204 adult patients with ACLF-HBV were retrospectively recruited between July 1, 2002 and December 31, 2004. The MELD scores were calculated according to the widely accepted formula. The 3-month mortality was calculated. The validity of the MELD model was determined by means of the concordance (c) statistic. Clinical data and biochemical values were included in the multivariate logistic regression analysis based on which the regression model for predicting prognosis was established. The receiver-operating characteristic curves were drawn for the MELD scoring system and the new regression model and the areas under the curves (AUC) were compared by the z-test. The 3-month mortality rate was 57.8%. The mean MELD score for the patients who died was significantly greater than those who survived beyond 3 months (28.7 vs 22.4, P = 0.003). The concordance (c) statistic (equivalent to the AUC) for the MELD scoring system predicting 3-month mortality was 0.709 (SE = 0.036, P < 0.001, 95% confidence interval 0.638-0.780). The independent factors predicting prognosis were hepatorenal syndrome (P < 0.001), liver cirrhosis (P = 0.009), HBeAg (P = 0.013), albumin (P = 0.028) and prothrombin activity (P = 0.011) as identified in multivariate logistic regression analysis. The regression model that was constructed by the logistic regression analysis produced a greater prognostic value (c = 0.891) than the MELD scoring system (z = 4.333, P < 0.001). The MELD scoring system is a promising and useful predictor for 3-month mortality of ACLF-HBV patients. Hepatorenal syndrome, liver cirrhosis, HBeAg, albumin and prothrombin activity are independent factors affecting the 3-month mortality. The newly established logistic regression model appears to be superior to the MELD scoring system in predicting 3-month mortality in patients with ACLF-HBV.

The outbreak of COVID-19 has spread across the world and was characterized as a pandemic. To protect medical laboratory personnel from infection, most laboratories inactivate the clinical samples before testing. However, the effect of inactivation on the detection results remains unknown. Here, we used a digital PCR assay to determine the absolute SARS-CoV-2 RNA copy number in 63 nasopharyngeal samples and assess the effect of inactivation methods on viral RNA copy number. Viral inactivation was performed with three different methods: (1) incubation with TRIzol® LS Reagent for 10 min at room temperature, (2) heating in a waterbath at 56°C for 30 min, and (3) high-temperature treatment, including 121°C autoclaving for 20 min, 100°C boiling for 20 min, and 80°C heating for 20 min. Compared to the amount of RNA in the original sample, TRIzol treatment destroyed 47.54% of N gene and 39.85% of ORF 1ab. For samples treated at 56°C for 30 min, the copy number of N gene and ORF 1ab was reduced by 48.55% and 56.40%, respectively. Viral RNA copy number dropped by 50–66% after 80°C heating for 20 min. Nearly no viral RNA was detected after autoclaving at 121°C or boiling at 100°C for 20 min. These results indicated that inactivation reduced the quantity of detectable viral RNA and may cause false negative results especially in weakly positive cases. Thus, TRIzol is recommended for sample inactivation in comparison to heat inactivation as Trizol has the least effect on RNA copy number among the tested methods.

An artificial neural network (ANN) model was developed to predict the risks of congenital heart disease (CHD) in pregnant women.This hospital-based case-control study involved 119 CHD cases and 239 controls all recruited from birth defect surveillance hospitals in Hunan Province between July 2013 and June 2014. All subjects were interviewed face-to-face to fill in a questionnaire that covered 36 CHD-related variables. The 358 subjects were randomly divided into a training set and a testing set at the ratio of 85:15. The training set was used to identify the significant predictors of CHD by univariate logistic regression analyses and develop a standard feed-forward back-propagation neural network (BPNN) model for the prediction of CHD. The testing set was used to test and evaluate the performance of the ANN model. Univariate logistic regression analyses were performed on SPSS 18.0. The ANN models were developed on Matlab 7.1.The univariate logistic regression identified 15 predictors that were significantly associated with CHD, including education level (odds ratio  = 0.55), gravidity (1.95), parity (2.01), history of abnormal reproduction (2.49), family history of CHD (5.23), maternal chronic disease (4.19), maternal upper respiratory tract infection (2.08), environmental pollution around maternal dwelling place (3.63), maternal exposure to occupational hazards (3.53), maternal mental stress (2.48), paternal chronic disease (4.87), paternal exposure to occupational hazards (2.51), intake of vegetable/fruit (0.45), intake of fish/shrimp/meat/egg (0.59), and intake of milk/soymilk (0.55). After many trials, we selected a 3-layer BPNN model with 15, 12, and 1 neuron in the input, hidden, and output layers, respectively, as the best prediction model. The prediction model has accuracies of 0.91 and 0.86 on the training and testing sets, respectively. The sensitivity, specificity, and Yuden Index on the testing set (training set) are 0.78 (0.83), 0.90 (0.95), and 0.68 (0.78), respectively. The areas under the receiver operating curve on the testing and training sets are 0.87 and 0.97, respectively.This study suggests that the BPNN model could be used to predict the risk of CHD in individuals. This model should be further improved by large-sample-size research.