Exploring the Influence of Hepatitis B Immunoglobulin on the Immune Response to the Hepatitis B Vaccine in a Mouse Model.

Background: The extent to which maternal antibodies against the hepatitis B surface antigen (HBsAb) acquired transplacentally affect the immune responses to the hepatitis B vaccine (HBVac) in infants is still uncertain. Objective: To explore the impact of the HBsAb on the immune response to the HBVac in a mouse model. Methods: According to the doses of the HBVac (2, 5 µg) injected, 267 BALB/c mice were divided into two groups. Each group was subdivided into 3 subgroups based on the doses of the hepatitis B immunoglobulin (HBIG) (0, 25, 50 IU) administered. The HBsAb titers were detected 4 weeks after completing the HepB vaccination. Results: Among all the mice, 40 had an HBsAb titer <100 mIU/mL (non- or low-response to the HBVac). The rates of the HBsAb titer <100 mIU/mL in 0, 25 and 50 IU HBIG groups were 1.1%, 23.1%, and 20.7%, respectively. Multivariate logistic regression analysis showed that the risk factors for low- or non-response to the HBVac were injection with the HBIG, low HBVac dose, and hypodermic injection. The mean HBsAb titers (log10) reduced gradually in the 0, 25 and 50 IU HBIG groups (P<0.001). Conclusion: The HBIG administration has negative impacts on the peak level of the HBsAb and the rate of an effective immune response. This implies that the maternal HBsAb acquired transplacentally might inhibit the immune responses to the HBVac in infants.

Panaxydol attenuates ferroptosis against LPS-induced acute lung injury in mice by Keap1-Nrf2/HO-1 pathway.

BACKGROUND: Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) induces uncontrolled and self-amplified pulmonary inflammation, and has high morbidity and mortality rates in critically ill patients. In recent years, many bioactive ingredients extracted from herbs have been reported to effectively ameliorate ALI/ARDS via different mechanisms. Ferroptosis, categorized as regulated necrosis, is more immunogenic than apoptosis and contributes to the progression of ALI. In this study, we examined the impact of panaxydol (PX), isolated from the roots of Panax ginseng, on lipopolysaccharide (LPS)-induced ALI in mice. METHODS: In vivo, the role of PX on LPS-induced ALI in mice was tested by determination of LPS-induced pulmonary inflammation, pulmonary edema and ferroptosis. In vitro, BEAS-2B cells were used to investigate the molecular mechanisms by which PX functions via determination of inflammation, ferroptosis and their relationship. RESULTS: Administration of PX protected mice against LPS-induced ALI, including significantly ameliorated lung pathological changes, and decreased the extent of lung edema, inflammation, and ferroptosis. In vitro, PX inhibited LPS-induced ferroptosis and inflammation in bronchial epithelial cell line BEAS-2B cells. The relationship between ferroptosis and inflammation was investigated. The results showed that ferroptosis mediated inflammation in LPS-treated BEAS-2B cells, and PX might ameliorate LPS-induced inflammation via inhibiting ferroptosis. Meanwhile, PX could upregulate Keap1-Nrf2/HO-1 pathway, and selective inhibition of Keap1-Nrf2/HO-1 pathway significantly abolished the anti-ferroptotic and anti-inflammatory functions of PX in LPS-treated cells. CONCLUSION: PX attenuates ferroptosis against LPS-induced ALI via Keap1-Nrf2/HO-1 pathway, and is a promising novel therapeutic candidate for ALI.

Establishment of two data mining models of lung cancer screening based on three gene promoter methylations combined with telomere damage.

OBJECTIVE: To identify the significance of a support vector machine (SVM) model and a decision tree (DT) model for the diagnosis of lung cancer combined with the detection of fragile histidine triad (FHIT), RAS association domain family 1 (RASSF1A) and cyclin-dependent kinase inhibitor 2A (p16) promoter methylation levels and relative telomere length (RTL) of white blood cells from peripheral blood DNA. METHODS: The levels of p16, RASSF1A and FHIT promoter methylation and the RTL of white blood cells in peripheral blood DNA of 200 healthy individuals and 200 lung cancer patients were analyzed by SYBR Green-based quantitative methylation-specific PCR and quantitative PCR. Based on the 4 biomarkers, SVM and DT models were developed. RESULTS: The levels of FHIT, RASSF1A and p16 promoter methylation were 3.33 (1.86-6.40) and 2.85 (1.39-5.44) (p = 0.002); 27.62 (9.09-52.86) and 17.17 (3.86-50.87) (p = 0.038); and 0.59 (0.16-4.50) and 0.36 (0.06-4.00) (p = 0.008) in cases and controls, respectively. RTL was 0.93 ± 0.32 and 1.16 ± 0.57 (p<0.001). The areas under the receiver operating characteristic (ROC) curves of the Fisher discriminant analysis, SVM and DT models were 0.670 (0.569-0.761), 0.810 (0.719-0.882) and 0.810 (0.719-0.882), respectively. CONCLUSIONS: The SVM and DT models for diagnosing lung cancer were successfully developed through the combined detection of p16, RASSF1A and FHIT promoter methylation and RTL, which provided useful tools for screening lung cancer.

Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.

The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P < 0.001. The areas under the ROC curve (AUC) and 95 % CI of prediction set from Fisher discrimination analysis and BP neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

Quantitative assessment of lung cancer associated with genes methylation in the peripheral blood.

BACKGROUND: Lung cancer is the leading cause of cancer-related deaths worldwide due mainly to late diagnosis and poor prognosis. Aberrant promoter methylation is an important mechanism for silencing of tumor suppressor genes during carcinogenesis and a promising tool for the development of molecular biomarkers. METHODS: We evaluated the p16, RASSF1A, and FHIT genes promoter methylation status in peripheral blood DNA between 200 lung cancer patients and 200 normal controls by using SYBR green-based quantitative methylation-specific PCR (qMSP). RESULTS: There were statistically significant differences in the methylation status of p16, RASSF1A, and FHIT between the cancer cases and controls (p16: P = .008, RASSF1A: P = .038, FHIT: P = .002). When the subjects were categorized into quartiles based on the genes methylation status, the risk of lung cancer was found to increase as methylation status increased (p16: Ptrend = .002, RASSF1A: Ptrend = .014, FHIT: Ptrend = .001). When the median of methylation status was used as the cutoff between high and low methylation status, individuals with high methylation status were at a significantly higher risk of lung cancer than those with low methylation status (p16: adjusted odds ratio = 1.597, P = .028; RASSF1A: adjusted odds ratio = 1.551, P = .039; FHIT: adjusted odds ratio = 1.763, P = .008). In addition, there were no significant correlations between p16, RASSF1A, or FHIT methylation status and gender (P > .05), age (P > .05), smoking history (P > .05), histological type (P > .05), or clinical stage (P > .05). CONCLUSIONS: These results suggest that the high methylation statuses of p16, RASSF1A, or FHIT genes were associated with a significantly increased risk of lung cancer; the risk of lung cancer increased as the methylation status increased. Further investigation of their definitive usefulness in clinical practice is warranted.