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V. parahaemolyticus is a Gram-negative bacterium that causes gastroenteritis. Within the realm of bacterial interactions with the gut, the outer membrane protein MAM7 plays a key role. However, the precise function of MAM7 in intestinal inflammation, particularly its interactions with macrophages, remains unclear. In this study, we successfully expressed and purified recombinant MAM7. After optimization of the MAM7 expression condition, it was found that the optimal concentration and temperature were 0.75 mM and 15 °C, respectively, resulting in a 27-fold increase in its yield. Furthermore, RAW264.7 cytotoxicity assay was conducted. The CCK-8 results revealed that MAM7 substantially stimulated the proliferation of RAW264.7 cells, with its optimal concentration determined to be 7.5 µg/mL. Following this, the NO concentration of MAM7 was tested, revealing a significant increase (p < 0.05) in NO levels. Additionally, the relative mRNA levels of IL-1ß, IL-6, and TNF-α in RAW264.7 cells were measured by qRT-PCR, showing a remarkable elevation (p < 0.05). Moreover, ELISA results demonstrated that MAM7 effectively stimulated the secretion of IL-6 and TNF-α by RAW264.7 cells. In summary, these findings strongly suggest that MAM7 serves as a proinflammatory adhesion factor with the capacity to modulate immune responses.
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AIMS: To explore the association between nurse managers' paternalistic leadership and nurses' perceived workplace bullying (WPB), as well as to examine the mediating role of organizational climate in this association. BACKGROUND: There is a lack of empirical evidence regarding the relationship between nurse managers' paternalistic leadership, organizational climate and nurses' perceived WPB. Clarifying this relationship is crucial to understand how paternalistic leadership influences WPB and for nursing managers to seek organizational-level solutions to prevent it. METHODS: A cross-sectional survey was performed from 4 January to 10 February 2022, in six tertiary hospitals in mainland China. Demographic information, Paternalistic Leadership Scale, Organizational Climate Scale and Negative Acts Questionnaire-Revised were used in the survey. Descriptive statistics, Spearman correlation analyses and a structural equation model were used for data analysis. RESULTS: A total of 5093 valid questionnaires were collected. Moral leadership and authoritarian leadership have both direct and indirect effects on WPB through the mediating effect of organizational climate. The former is negatively related to WPB and the latter is positively related to WPB. Benevolent leadership was only negatively associated with WPB via the mediating effect of organizational climate. CONCLUSION: The three components of paternalistic leadership have different effects on WPB through the mediating effect of organizational climate. Nurse managers are recommended to strengthen moral leadership, balance benevolent leadership, reduce authoritarian leadership and strive to create a positive organizational climate in their efforts to mitigate WPB among nurses. IMPACT: This study enhanced our comprehension of the relationship between different leadership styles and WPB. Greater emphasis should be placed on moral leadership in the promotion of nursing managers and nursing leadership training programs. Additionally, nursing managers should focus on establishing a positive organizational climate that helps to reduce WPB. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. This study did not involve patients, service users, caregivers or members of the public.
Assuntos
Bullying , Liderança , Enfermeiros Administradores , Recursos Humanos de Enfermagem Hospitalar , Cultura Organizacional , Local de Trabalho , Humanos , Bullying/psicologia , Bullying/estatística & dados numéricos , Estudos Transversais , Enfermeiros Administradores/psicologia , Masculino , Feminino , Adulto , China , Recursos Humanos de Enfermagem Hospitalar/psicologia , Pessoa de Meia-Idade , Inquéritos e Questionários , Local de Trabalho/psicologia , Paternalismo , Atitude do Pessoal de SaúdeRESUMO
Tumor subunit vaccines have great potential in personalized cancer immunotherapy. They are usually administered with adjuvant owing to their low immunogenicity. Cholera toxin (CT) is a biological adjuvant with diverse biological functions and a long history of use. Our earlier study revealed that a CT-like chimeric protein co-delivered with murine granulocyte-macrophage colony stimulating factor (mGM-CSF) and prostate cancer antigen epitope could co-stimulate dendritic cells (DCs) and enhance cross presentation of tumor epitope. To further study the molecular mechanism of CT-like chimeric protein in cross presentation, major histocompatibility complex class I (MHC I)-restricted epitope 257-264 of ovalbumin (OVAT) was used as a model antigen peptide in this study. Recombinant A subunit and pentameric B subunit of CT protein were respectively genetically constructed and purified. Then both assembled into AB5 chimeric protein in vitro. Three different chimeric biomacromolecules containing mGM-CSF and OVAT were constructed according to the different fusion sites and whether the endoplasmic reticulum (ER) retention sequence was included. It was found that A2 domain and B subunit of CT were both available for loading epitopes and retaining GM1 affinity. The binding activity of GM1 was positively correlated with antigen endocytosis. Once internalized, DCs became mature and cross-presented antigen. KDEL helped the whole molecule to be retained in the ER, and this improved the cross presentation of antigen on MHC I molecules. In conclusion, hexameric CT-like chimeric protein with dual effects of GM1 affinity and ER retention sequence were potential in improvement of cross presentation. The results laid a foundation for designing personalized tumor vaccine based on CT-like chimeric protein molecular structure.
Assuntos
Toxina da Cólera , Neoplasias , Camundongos , Animais , Humanos , Toxina da Cólera/metabolismo , Apresentação Cruzada , Gangliosídeo G(M1)/metabolismo , Gangliosídeo G(M1)/farmacologia , Proteínas Recombinantes/farmacologia , Adjuvantes Imunológicos/farmacologia , Proteínas Recombinantes de Fusão/genética , Epitopos , Apresentação de AntígenoRESUMO
BACKGROUND: Prostate cancer patients with pelvic lymph node metastasis (PLNM) have poor prognosis. Based on EAU guidelines, patients with >5% risk of PLNM by nomograms often receive pelvic lymph node dissection (PLND) during prostatectomy. However, nomograms have limited accuracy, so large numbers of false positive patients receive unnecessary surgery with potentially serious side effects. It is important to accurately identify PLNM, yet current tests, including imaging tools are inaccurate. Therefore, we intended to develop a gene expression-based algorithm for detecting PLNM. METHODS: An advanced random forest machine learning algorithm screening was conducted to develop a classifier for identifying PLNM using urine samples collected from a multi-center retrospective cohort (n = 413) as training set and validated in an independent multi-center prospective cohort (n = 243). Univariate and multivariate discriminant analyses were performed to measure the ability of the algorithm classifier to detect PLNM and compare it with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram score. RESULTS: An algorithm named 25 G PLNM-Score was developed and found to accurately distinguish PLNM and non-PLNM with AUC of 0.93 (95% CI: 0.85-1.01) and 0.93 (95% CI: 0.87-0.99) in the retrospective and prospective urine cohorts respectively. Kaplan-Meier plots showed large and significant difference in biochemical recurrence-free survival and distant metastasis-free survival in the patients stratified by the 25 G PLNM-Score (log rank P < 0.001 and P < 0.0001, respectively). It spared 96% and 80% of unnecessary PLND with only 0.51% and 1% of PLNM missing in the retrospective and prospective cohorts respectively. In contrast, the MSKCC score only spared 15% of PLND with 0% of PLNM missing. CONCLUSIONS: The novel 25 G PLNM-Score is the first highly accurate and non-invasive machine learning algorithm-based urine test to identify PLNM before PLND, with potential clinical benefits of avoiding unnecessary PLND and improving treatment decision-making.