An analytical method of on-line injection technique including on-line dilution and on-line preconcentration with inductively coupled plasma mass spectrometry ( ICP-MS ) was established for the determination of trace metals in seawater, such as Cr, Mo, Cu, Pb, Zn, Cd, Fe, Mn, Ni, V, Co. This method could automatically make standard curves and match matrix, and by the on-line dilution and preconcentration modes, 11 trace metals could be analyzed directly. With high-level automation, CASS-5 and NASS-6 seawater standard samples were analyzed accurately by this method. The recovery of metals in seawater sample addition was between 80% and 115%. The method quantitation limits (μg/L ) of theses metals in seawater were 0. 06(Cr), 0. 06(Mo), 0. 01(Cu), 0. 005(Pb), 0. 01(Zn), 0. 006(Cd), 0. 03 (Fe), 0. 02(Mn), 0. 01(Ni), 0. 01(V), and 0. 01 (Co), respectively. This method was applied to analyze seawater samples of various salinities in Zhejiang coast, and the analysis results were consistent with results of conventional atomic absorption method.

Objective To prepare melanoma antigen n(MAGEn)protein vaccine and to investigate the immune responses and anti-tumor effects of MAGE-n protein vaccine accompanied by CpG-containing oligodeoxynucleotide(CpG-ODN)adjuvant.Methods The DH5? containing the MAGE-n prokaryotic expression plasmid pGEX-MAGE-n was induced and the protein was purified as protein vaccine.The CpG-ODN was synthesized as adjuvant and the C57BL/6 mice were inoculated.The cellular and humoral immune responses were detected by ELISPOT,cytotoxicity assay and enzyme linked immunosorbent assay(ELISA).The antitumor effects were detected through tumor volume and life span.Results The MAGE-n protein accompanied by CpG-ODN could induce strong MAGE-n-specific cellular and humoral immune responses.In the MAGE-n positive B16 tumor model of C57BL/6,the growth velocity of tumor was decreased and the life span was prolonged with the treatment of vaccine.Conclusion MAGE-n protein vaccine accompanied by CpG-ODN adjuvant can induce strong immune responses and anti-tumor effects against MAGE-n positive B16 tumor,which provides a new way for tumor therapy.

China ; Humans ; Public Health ; Research

OBJECTIVETo evaluate the effectiveness of small interfering RNA (siRNA) on inhibiting severe acute respiratory syndrome (SARS)-associated coronavirus replication, and to lay bases for the future clinical application of siRNA for the treatment of viral infectious diseases.

METHODSVero-E6 cells was transfected with siRNA before SARS virus infection, and the effectiveness of siRNA interference was evaluated by observing the cytopathic effect (CPE) on Vero-E6 cells.

RESULTSFive pairs of siRNA showed ability to reduce CPE dose dependently, and two of them had the best effect.

CONCLUSIONsiRNA may be effective in inhibiting SARS-associated coronavirus replication.

Animals ; Cercopithecus aethiops ; RNA, Small Interfering ; pharmacology ; SARS Virus ; drug effects ; Transfection ; Vero Cells ; Virus Replication ; drug effects

Objective To explore the accuracy of machine learning algorithms based on SHOX2 and RASSF1A methylation levels in predicting early-stage lung adenocarcinoma pathological types. Methods A retrospective analysis was conducted on formalin-fixed paraffin-embedded (FFPE) specimens from patients who underwent lung tumor resection surgery at Affiliated Hospital of Nantong University from January 2021 to January 2023. Based on the pathological classification of the tumors, patients were divided into three groups: a benign tumor/adenocarcinoma in situ (BT/AIS) group, a minimally invasive adenocarcinoma (MIA) group, and an invasive adenocarcinoma (IA) group. The methylation levels of SHOX2 and RASSF1A in FFPE specimens were measured using the LungMe kit through methylation-specific PCR (MS-PCR). Using the methylation levels of SHOX2 and RASSF1A as predictive variables, various machine learning algorithms (including logistic regression, XGBoost, random forest, and naive Bayes) were employed to predict different lung adenocarcinoma pathological types. Results A total of 272 patients were included. The average ages of patients in the BT/AIS, MIA, and IA groups were 57.97, 61.31, and 63.84 years, respectively. The proportions of female patients were 55.38%, 61.11%, and 61.36%, respectively. In the early-stage lung adenocarcinoma prediction model established based on SHOX2 and RASSF1A methylation levels, the random forest and XGBoost models performed well in predicting each pathological type. The C-statistics of the random forest model for the BT/AIS, MIA, and IA groups were 0.71, 0.72, and 0.78, respectively. The C-statistics of the XGBoost model for the BT/AIS, MIA, and IA groups were 0.70, 0.75, and 0.77, respectively. The naive Bayes model only showed robust performance in the IA group, with a C-statistic of 0.73, indicating some predictive ability. The logistic regression model performed the worst among all groups, showing no predictive ability for any group. Through decision curve analysis, the random forest model demonstrated higher net benefit in predicting BT/AIS and MIA pathological types, indicating its potential value in clinical application. Conclusion Machine learning algorithms based on SHOX2 and RASSF1A methylation levels have high accuracy in predicting early-stage lung adenocarcinoma pathological types.