The oral and gastrointestinal opportunistic pathogen contamination was compared between tooth mugs placed upward and down-ward(n=9)for 1 4 days.Selective cultivation of the pathogens was uesd to measure the extent of contamination.The colony forming units (CFU)of colibacillus in group up and group down were 4.25 ±0.71 and 2.84 ±1 .40(P=0.046),S.mutans 89 ±0.31 and 2.84 ±1 .40 (P<0.001 ),Candida 2.28 ±1 .36 and 2.53 ±1 .92(P=0.002),fungus 2.44 ±0.99 and 0,respecitvely.Thus,tooth mug placed open-ing down is superior for health.

OBJECTIVETo investigate the effect of mTOR signal transduction pathway and down-regulating anti-oncogene PTEN on the growth of breast cancer MCF-7 cells.

METHODSMCF-7 cells were transfected with the eukaryotic expression plasmid pcDNA3.1-mTOR and non-loaded plasmid, and the expression of mTOR in the cells was detected using Western blotting. Flow cytometry was used to analyze apoptosis and cell cycle of the transfected cells, and the expression of PTEN was detected after transfection.

RESULTSThe cells transfected with pcDNA3.1-mTOR showed a increased growth rate than those transfected with the non-loaded plasmid and those without transfection. The expression of the protein PTEN decreased obviously in the cells after mTOR trasnfection.

CONCLUSIONmTOR can regulate the expression of PTEN via PI3K/AKT/PTEN pathways through a negative feedback mechanism. Increased mTOR expression promotes MCF-7 cell growth, suggesting the potential value of mTOR specific inhibitor in the treatment of breast cancer.

Apoptosis ; Breast Neoplasms ; pathology ; Cell Cycle ; Down-Regulation ; Gene Expression Regulation, Neoplastic ; Humans ; MCF-7 Cells ; PTEN Phosphohydrolase ; metabolism ; Plasmids ; Signal Transduction ; TOR Serine-Threonine Kinases ; genetics ; Transfection

Objective To investigate the effect of mTOR signal transduction pathway and down-regulating anti-oncogene PTEN on the growth of breast cancer MCF-7 cells. Methods MCF-7 cells were transfected with the eukaryotic expression plasmid pcDNA3.1-mTOR and non-loaded plasmid, and the expression of mTOR in the cells was detected using Western blotting. Flow cytometry was used to analyze apoptosis and cell cycle of the transfected cells, and the expression of PTEN was detected after transfection. Results The cells transfected with pcDNA3.1-mTOR showed a increased growth rate than those transfected with the non-loaded plasmid and those without transfection. The expression of the protein PTEN decreased obviously in the cells after mTOR trasnfection. Conclusion mTOR can regulate the expression of PTEN via PI3K/AKT/PTEN pathways through a negative feedback mechanism. Increased mTOR expression promotes MCF-7 cell growth, suggesting the potential value of mTOR specific inhibitor in the treatment of breast cancer.

Objective To investigate the effect of mTOR signal transduction pathway and down-regulating anti-oncogene PTEN on the growth of breast cancer MCF-7 cells. Methods MCF-7 cells were transfected with the eukaryotic expression plasmid pcDNA3.1-mTOR and non-loaded plasmid, and the expression of mTOR in the cells was detected using Western blotting. Flow cytometry was used to analyze apoptosis and cell cycle of the transfected cells, and the expression of PTEN was detected after transfection. Results The cells transfected with pcDNA3.1-mTOR showed a increased growth rate than those transfected with the non-loaded plasmid and those without transfection. The expression of the protein PTEN decreased obviously in the cells after mTOR trasnfection. Conclusion mTOR can regulate the expression of PTEN via PI3K/AKT/PTEN pathways through a negative feedback mechanism. Increased mTOR expression promotes MCF-7 cell growth, suggesting the potential value of mTOR specific inhibitor in the treatment of breast cancer.

Objective:To explore the risk factors of septic acute kidney injury (sAKI) in patients with sepsis, construct a predictive model for sAKI, verify the predictive value of the model, and develop a dynamic nomogram to help clinical doctors identify patients with high-risk sAKI earlier and more easily.Methods:A cross-sectional study was conducted. A total of 245 patients with sepsis admitted to intensive care unit (ICU) of the Affiliated Hospital of Jining Medical University from May 2013 to November 2023 were enrolled as the research subjects. The patients were divided into sAKI group and non-sAKI group based on whether they suffered from sAKI during ICU hospitalization. The differences of the demographic, clinical and laboratory indicators of patients between the two groups were compared. Logistic ordinal regression analysis was performed to analyze the imbalanced variables between the two groups, and to construct a sAKI predictive model. The predictive value of the sAKI predictive model was evaluated through 5-fold cross validation, calibration curve, and decision curve analysis (DCA), and to develop an online dynamic nomogram for the predictive model.Results:A total of 245 patients were enrolled in the final analysis. 110 (44.9%) patients developed sAKI during ICU hospitalization and 135 (55.1%) patients did not develop sAKI. Compared with the non-sAKI group, the patients in the sAKI group had higher ratios of female, hypertension, invasive mechanical ventilation (IMV), renal replacement therapy (RRT), vasopressin usage, and neutrophil count (NEU), aspartate aminotransferase (AST), blood urea nitrogen (BUN), serum creatinine (SCr), uric acid (UA), Na +, K +, procalcitonin (PCT), acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) score, and sequential organ failure assessment (SOFA) score. Multivariate Logistic ordinal regression analysis showed that female [odd ratio ( OR) = 2.208, 95% confidence interval (95% CI) was 1.073-4.323, P = 0.020], hypertension ( OR = 2.422, 95% CI was 1.255-5.073, P = 0.012), vasopressin usage ( OR = 2.888, 95% CI was 1.380-6.679, P = 0.002), and SCr ( OR = 1.015, 95% CI was 1.009-1.024, P < 0.001) were independent risk factors for sAKI in septic patients, and a sAKI predictive model was constructed: ln[ P/(1+ P)] = -4.665+0.792×female+0.885×hypertension+1.060×vasopressin usage+0.015×SCr. The 5-fold cross validation showed that the average area under the receiver operator characteristic curve (AUC) was 0.860, indicating the sAKI predictive model had a good performance. The calibration curve analysis showed that the calibration degree of the sAKI predictive model was good. DCA showed that the net profit of the sAKI predictive model was relatively high. A static nomogram and an online dynamic nomogram were constructed for the sAKI predictive model. Compared with the static nomogram, the dynamic nomogram allowed for manual selection of corresponding patient characteristics and viewing the corresponding sAKI risk directly. Conclusions:Female, hypertension, vasopressin usage, and SCr are the main risk factors for sAKI in patients with sepsis. The sAKI predictive model constructed based on these factors can help clinical doctors identifying high-risk patients as early as possible, and intervene in a timely manner to provide preventive effects. Compared with the common static nomogram, online dynamic nomogram can make predictive models clearer, more intuitive, and easier.