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PurposeThe increasing diversity among workforces – as well as the increasing diversity among patient populations served – offers a variety of opportunities and potential pitfalls for healthcare organizations and leaders. To unravel this complexity, the authors aim to holistically understand how to maximize provider and patient experiences regardless of (1) the degree to which diversity is present or lacking, and (2) the type(s) of diversity under consideration.Design/methodology/approachThis conceptual paper develops a framework that combines three organizational behavior theories – emotional labor theory, similarity-attraction theory and climate theory – with evidence from the broader healthcare literature.FindingsAuthentic interactions yield positive outcomes for providers (i.e. improved job attitudes and work-related well-being) and patients (i.e. patient satisfaction) and acts as a mediator between demographic diversity and positive outcomes. Demographic similarity facilitates authentic interactions, whereas demographic diversity creates an initial barrier to engaging authentically with others. However, the presence of a positive diversity climate eliminates this barrier.Originality/valueThe authors offer a conceptual model to unlock positive outcomes – including reduced absenteeism, better morale and improved patient satisfaction – regardless of the level and types of diversity present within the workforce. In addition to deriving an agenda for future research, the authors offer practical applications regarding how diversity can be more effectively managed and promoted within healthcare organizations.

The aim of this study was to explore the facilitators and barriers in implementing a workload intensity (WI) tool.A WI tool was developed to quantify patient needs and more evenly disperse workload among nurses.A descriptive phenomenological design was used. Semistructured interviews were conducted with 16 nurses on 5 inpatient units. Questions focused on the factors that helped or hindered the change transition to workload intensity staffing (WIS).WI was perceived as a positive change. Five themes were uncovered as facilitators and barriers to the change: resistance to change, intense workloads, supportive organizational culture, resources/training, and evaluation.Understanding barriers and facilitators to change is important for successful implementation of WIS. To promote success, leaders should provide support to staff and ensure availability of adequate resources.

Purpose The purpose of this study was to evaluate four definitions of a Frailty Risk Score (FRS) derived from EHR data that includes combinations of biopsychosocial risk factors using nursing flowsheet data or International Classification of Disease, 10th revision (ICD-10) codes and blood biomarkers and its predictive properties for in-hospital mortality in adults ≥50 years admitted to medical-surgical units. Methods In this retrospective observational study and secondary analysis of an EHR dataset, survival analysis and Cox regression models were performed with sociodemographic and clinical covariates. Integrated area under the ROC curve (iAUC) across follow-up time based on Cox modeling was estimated. Results The 46,645 patients averaged 1.5 hospitalizations (SD = 1.1) over the study period and 63.3% were emergent admissions. The average age was 70.4 years (SD = 11.4), 55.3% were female, 73.0% were non-Hispanic White (73.0%), mean comorbidity score was 3.9 (SD = 2.9), 80.5% were taking 1.5 high risk medications, and 42% recorded polypharmacy. The best performing FRS-NF-26-LABS included nursing flowsheet data and blood biomarkers (Adj. HR = 1.30, 95% CI [1.28, 1.33]), with good accuracy (iAUC = .794); the reduced model with age, sex, and FRS only demonstrated similar accuracy. The poorest performance was the ICD-10 code-based FRS. Conclusion The FRS captures information about the patient that increases risk for in-hospital mortality not accounted for by other factors. Identification of frailty enables providers to enhance various aspects of care, including increased monitoring, applying more intensive, individualized resources, and initiating more informed discussions about treatments and discharge planning.

The predictive properties of four definitions of a frailty risk score (FRS) constructed using combinations of nursing flowsheet data, laboratory tests, and ICD-10 codes were examined for time to first intensive care unit (ICU) transfer in medical-surgical inpatients ≥50 years of age. Cox regression modeled time to first ICU transfer and Schemper–Henderson explained variance summarized predictive accuracy of FRS combinations. Modeling by age group and controlling for sex, all FRS measures significantly predicted time to first ICU transfer. Further multivariable modeling controlling for clinical characteristics substantially improved predictive accuracy. The effect of frailty on time to first ICU transfer depended on age, with highest risk in 50 to Frailty prevalence was highest med-surg pts 60 to

PurposeAn individual engages in a façade of conformity by attempting to appear to embrace their organization's values when, in truth, they do not. While numerous studies investigate the negative outcomes associated with facades of conformity, fewer studies consider its antecedents. Therefore, this study aims to investigate the association between diversity-related influences – including individuals' beliefs, other unit members' beliefs, unit gender diversity and unit racial diversity – and individuals' propensities to engage in a façade of conformity.Design/methodology/approachThis paper administered an online survey to 2,122 employees nested within 151 units located at a hospital site located in the southeastern United States. Hierarchical linear modeling and relative weights analyses were used to test the study hypotheses which aimed to determine how objective diversity and perceptions associated with diversity increase or diminish facades of conformity.FindingsIn this paper individuals' and other unit members' beliefs that their organization values diversity were negatively associated with facades of conformity; however, there was a positive association between unit gender diversity and facades of conformity. There were no statistically significant associations involving unit racial diversity or interactive effects. Overall, the results indicate that it is less likely that employees will engage in façades of conformity when diversity is valued within organizations.Originality/valueBy further expanding understanding of the concept of façades of conformity within the humanities and social sciences literature, this study highlight the importance of allowing and encouraging employees to “be themselves.”

This study implemented and evaluated an algorithm protocol for management of indwelling urinary catheters (IUCs) among patients treated for acute urinary retention (AUR) in an intensive care unit. The algorithm protocol (1) instituted in and out catheterization before placing an IUC for retention; (2) encouraged more consistent use of medications for AUR; and (3) provided for prompt removal of IUCs placed for AUR.An uncontrolled pre- and post-test intervention approach was used to assess the impact of the algorithm on the treatment of AUR. Bivariate and multivariate analyses assessed data collected using 2 approaches: (1) electronic extracts from the electronic medical record (EMR) vs (2) manual chart reviews.Findings suggest that the intervention decreased indwelling IUC days by 1.93 average days and increased medication prescription rates. An EMR extract identified fewer catheters per patient pre-intervention than a manual chart review, but otherwise the differences observed in the EMR extract and manual chart review were insignificant.Implications for practitioners and administrators are that the algorithm protocol may reduce CAUTI risk and - provided consistent EMR documentation - EMR extracts may represent an efficient and effective approach for monitoring data when spreading the intervention.

Summary Healthcare costs due to unplanned readmissions are high and negatively affect health and wellness of patients. Hospital readmission is an undesirable outcome for elderly patients. Here, we present readmission risk prediction using five machine learning approaches for predicting 30-day unplanned readmission for elderly patients (age ≥ 50 years). We use a comprehensive and curated set of variables that include frailty, comorbidities, high-risk medications, demographics, hospital, and insurance utilization to build these models. We conduct a large-scale study with electronic health record (her) data with over 145,000 observations from 76,000 patients. Findings indicate that the category boost (CatBoost) model outperforms other models with a mean area under the curve (AUC) of 0.79. We find that prior readmissions, discharge to a rehabilitation facility, length of stay, comorbidities, and frailty indicators were all strong predictors of 30-day readmission. We present in-depth insights using Shapley additive explanations (SHAP), the state of the art in machine learning explainability., Highlights • We report a 0.79 AUROC for 30-day readmission prediction • We use frailty, comorbidity, high-risk medications, and demographics for improved accuracy • We identify clusters of high-risk patients based on sets of patient features • Explainability is a prime focus for model predictions at different levels of granularity, The bigger picture Unplanned readmission currently costs the United States millions of dollars. Predicting whether an incoming patient is at a high risk of readmission can help target healthcare efforts better to reduce this risk. In this age of big data, we can use machine learning to analyze a cohort of variables to pinpoint the risk of readmission. Our work does exactly that. One of the hindrances for adoption of artificial intelligence in healthcare is the lack of explainability. To combat that, we provide several mechanisms for understanding reasons for the model's predictions, starting at a global level across the entire dataset and down to individual patient observations. These explanations enhance confidence in the model's decision making., Automated prediction of readmission risk has the potential to save millions of dollars in healthcare costs and can improve patient care. Our work presents machine learning models that take into account various facets of patients, such as demographics, comorbidities, and frailty parameters, to accurately estimate their risk of being readmitted within 30 days. We place high importance on explainability, thereby enhancing confidence in the automated models.

The purpose of the current retrospective study was to determine whether frailty is predictive of 30-day readmission in adults aged ≥50 years who were admitted with a psychiatric diagnosis to a behavioral health hospital from 2013 to 2017. A total of 1,063 patients were included. A 26-item frailty risk score (FRS-26-ICD) was constructed from electronic health record (EHR) data. There were 114 readmissions. Cox regression modeling for demographic characteristics, emergent admission, comorbidity, and FRS-26-ICD determined prediction of time to readmission was modest (incremental area under the receiver operating characteristic curve = 0.671). The FRS-26-ICD was a significant predictor of readmission alone and in models with demographics and emergent admission; however, only the Elixhauser Comorbidity Index was significantly related to hazard of readmission adjusting for other factors (adjusted hazard ratio = 1.26, 95% confidence interval [1.17, 1.37]; p < 0.001), whereas FRS-26-ICD became non-significant. Frailty is a relevant syndrome in behavioral health that should be further studied in risk prediction and incorporated into care planning to prevent hospital readmissions. [ Journal of Psychosocial Nursing and Mental Health Services, 59 (10), 27–39.]

Introduction The US Food and Drug Administration (FDA) has undertaken efforts to promote representation of women in clinical trials. The objectives of this research are to assess women's participation in clinical trials from a global perspective and to analyze the demographic characteristics of clinical trial participants. Methods FDA's Center for Drug Evaluation and Research-Professional Affairs and Stakeholder Engagement (CDER/PASE) and Office of Women's Health (OWH) collaborated to evaluate demographic data (race, ethnicity, gender, and age) of pivotal trials of New Molecular Entities (NMEs) approved in 2015–2016 by geographic location. One hundred fifty-four pivotal clinical trials supporting 66 NMEs were identified, and the research team analyzed demographic characteristics of 131,749 participants from 70 countries. Results U.S. sites contributed 31% of the 131,749 study participants. On the country level, the United States contributed the largest number of participants and other individual countries contributed 5% or less of the total trial population. Overall, 43% (n = 56,272) of the 131,747 clinical trial participants were women. Of the 40,833 U.S. participants, 49% were women as compared to 40% of the 90,914 non-U.S. participants. Similar levels of participation were seen after the exclusion of sex-specific drug indications, and by therapeutic area for U.S. and non-U.S. sites. Conclusions Clinical trials are becoming increasingly multi-national, and the increasing representation of women across countries is promising. FDA approval processes ensure that global data used in the drug approval process meets regulatory standards and that data can be generalized to the U.S. population.

The purpose of the current study was to investigate the predictive properties of five definitions of a frailty risk score (FRS) and three comorbidity indices using data from electronic health records (EHRs) of hospitalized adults aged ≥50 years for 3-day, 7-day, and 30-day readmission, and to identify an optimal model for a FRS and comorbidity combination. Retrospective analysis of the EHR dataset was performed, and multivariable logistic regression and area under the curve (AUC) were used to examine readmission for frailty and comorbidity. The sample ( N = 55,778) was mostly female (53%), non-Hispanic White (73%), married (53%), and on Medicare (55%). Mean FRSs ranged from 1.3 ( SD = 1.5) to 4.3 ( SD = 2.1). FRS and comorbidity were independently associated with readmission. Predictive accuracy for FRS and comorbidity combinations ranged from AUC of 0.75 to 0.77 (30-day readmission) to 0.84 to 0.85 (3-day readmission). FRS and comorbidity combinations performed similarly well, whereas comorbidity was always independently associated with readmission. FRS measures were more associated with 30-day readmission than 7-day and 3-day readmission. [ Research in Gerontological Nursing, 14 (2), 91–103.]

This article describes one heath system's creation of a new women's health hospital using an innovative model integrating patient care delivery, Lean building design, and Lean integrated project methodology. The article describes a 5-year journey detailing the innovative process that guided the planning and implementation of the new care delivery model, as well as employee and leader roles, employee behavior and engagement, and key insights and lessons learned that will benefit nurse leaders.

Healthcare costs that can be attributed to unplanned readmissions are staggeringly high and negatively impact health and wellness of patients. In the United States, hospital systems and care providers have strong financial motivations to reduce readmissions in accordance with several government guidelines. One of the critical steps to reducing readmissions is to recognize the factors that lead to readmission and correspondingly identify at-risk patients based on these factors. The availability of large volumes of electronic health care records make it possible to develop and deploy automated machine learning models that can predict unplanned readmissions and pinpoint the most important factors of readmission risk. While hospital readmission is an undesirable outcome for any patient, it is more so for medically frail patients. Here, we develop and compare four machine learning models (Random Forest, XGBoost, CatBoost, and Logistic Regression) for predicting 30-day unplanned readmission for patients deemed frail (Age ≥ 50). Variables that indicate frailty, comorbidities, high risk medication use, demographic, hospital and insurance were incorporated in the models for prediction of unplanned 30-day readmission. Our findings indicate that CatBoost outperforms the other three models (AUC 0.80) and prior work in this area. We find that constructs of frailty, certain categories of high risk medications, and comorbidity are all strong predictors of readmission for elderly patients.

Aims and ObjectiveThis article reviews a professional nurse advancement program and describes how it benefits patient care, staff engagement, and patient satisfaction.BackgroundExisting literature notes that professional nurse advancement programs can empower nurses to improve nursing care at the bedside and contribute to a safe patient care environment.MethodThe article reviews the qualitative exploration of the activities of participants in a professional nurse advancement program to determine the impact on patient care and identify benefits of the program. The article summarizes findings from thematic and narrative technique analysis of semistructured interviews with nurses, nurse leaders, and nurse administrators, and reviews of nurses’ portfolio information.ResultsImplementation of a professional nurse advancement program correlated with decreased patient falls, increased bedside clinician involvement in research, and positive outcomes in nursing quality indicators.ConclusionA professional nurse advancement program fosters continued professional growth and opportunities for advancement; promotes pride, personal responsibility, and respect for self; and prepares and enables nurses to lead change in advancing health in communities.

Background A mentally-healthy nursing workforce is vital to providing quality healthcare. While there has been research conducted internationally on depressive symptoms in nurses, most studies have lacked a theoretical framework. Purpose The purpose of this article is to describe the development of a situation-specific theory, the Systemic Assessment of Depressive Symptoms among Registered Nurses (SAD-RN). Methods The SAD-RN was developed through theory synthesis using the Roy's Adaptation Model (RAM) as a framework. Sixty-four research articles were selected to abstract relevant concepts based on a priori RAM concepts. Findings Using depressive symptoms as the anchor concept, related emergent concepts of SAD-RN include context, stressors, protective factors, negative health outcomes, and low patient care quality. Discussion SAD-RN is an evidence-based comprehensive model that offers researchers a guiding theory to be used in their future research on depressive symptoms among RNs.

Objective This study explored the types of decisions and differences in decision making that nurses made in different types of hospital units. Background The relationship between nurses' participation in decision making and the different types of hospital units where they work is not well understood. Methods Nurses' participation in decision making was explored using the Participation in Decision Activities Questionnaire. The final sample included 307 nurses in 24 nursing units in 6 hospitals. Results Nurses overall participated more in clinical than administrative decisions, and there were significant differences based on unit type. Critical care nurses had the highest and general care units the lowest levels of participation in decision making. Conclusions Nurses in critical care units participated in higher amounts and at higher levels of clinical decisions overall than either intermediate or general care units. Nurse leaders should determine barriers to decision making in general care units and explore mechanisms to increase participation by clinical nurses.

Frailty is a clinical syndrome of impaired homeostasis and decreased physiologic reserve and resilience resulting in diminished ability to recover from stressors. In the hospital setting, barriers to adoption of popular frailty assessments make them impractical for widespread use. Improving quality and costs associated with hospitalization has motivated using data from the electronic health record (EHR) to identify patients at risk for adverse outcomes such as early readmission. Patient-level factors such as frailty and comorbidity may signal high readmission risk. In this retrospective study and secondary analysis of EHR data, we investigated Frailty Risk Scores (FRS) in models that included sociodemographic, comorbidity, and laboratory data for early 3-, 7-, and 30-day unplanned readmission. Study data were collected from a health system in the Southeastern U.S. on adults >50 years with an inpatient stay of >24 hours, 2013-2017. Exclusions included planned readmission and in-hospital mortality. The FRS was constructed using ICD-10-CM codes mapped for symptoms, syndromes, and laboratory values. Cox and logistic regression were conducted to examine associations with readmission. Area under the receiver operating characteristic curve (AUC) quantified accuracy. The sample was 53% female and 73% non-Hispanic White (N=55,778). About one-third took at least 7 prescribed medications (34%) and average length of stay was 4.3 days (max=103.6). FRS was a significant predictor of readmission for almost all models, independently of three comorbidity indices (range AUC=.850-.854 for 3-day, .809-.813 for 7-day, and .757 to .768 for 30-day). Frailty and comorbidity are independently associated with early rehospitalization.

Successful system Magnet® designation requires a high level of coordination and communication. This article suggests steps in preparing for a site visit.

Cardiovascular disease (CVD) is the number 1 killer of women in the United States, yet few younger women are aware of this fact. CVD campaigns focus little attention on physicians and their roles in assessing risk.In 2014, the Women's Heart Alliance (WHA) conducted a nationwide survey to determine barriers and opportunities for women and physicians with regard to CVD.From September 18 to 26, 2014, a total of 1,011 U.S. women (age 25 to 60 years) were interviewed using the GfK ("Gesellschaft für Konsumforschung" Knowledge Panel). From May 6 to 12, 2014, the e-Rewards Inc. Physician and Healthcare Professional Panel surveyed 200 primary care physicians (PCPs) and 100 cardiologists.Overall, 45% of women were unaware that CVD is the number 1 killer of women; only 11% knew a woman who died from CVD. Overall, 45% of women reported it was common to cancel or postpone a physician appointment until losing weight. CVD was rated as the top concern by only 39% of PCPs, after weight and breast health. Only 22% of PCPs and 42% of cardiologists (p = 0.0477) felt extremely well prepared to assess CVD risk in women, while 42% and 40% felt well-prepared (p = NS), respectively. Few comprehensively implemented guidelines.CVD was rated as the top concern less frequently than weight issues by both women and physicians. Social stigma particularly regarding body weight appeared to be a barrier. Physicians reported limited training and use of guideline assessment, whereas most supported a campaign and improved physician education. Campaigns should make CVD "real" to U.S. women, countering stereotypes with facts and validated assessments. Both community women and physicians endorsed investment in women's CVD research and physician education.

The dogmas of the quiet past are inadequate to the stormy present. The occasion is piled high with difficulty, and we must rise with the occasion. As our case is new, so we must think anew and act anew. —Abraham Lincoln In a world challenged by healthcare reform, critical elements create an environment conducive to withstand the volatility and vast array of redesign. The nurse executive of today and tomorrow must recognize essential criteria to build an effective foundation. 1

Achieving Magnet designation as a system requires careful planning and strategy on the part of nursing leaders. This article delineates system structures to support success.

While Magnet® is a commitment to excellence and thus recognition, the accountable care organization (ACO) focus is to quality and reimbursement. Each can be built on a model of shared characteristics that in the end will prove a source of success for the creation of an organization that is both an ACO and Magnet. In an effort to align strategic initiatives as synergistic processes, not competing priorities, 10 shared characteristics of Magnet organizations and ACOs are presented.

When leaders of a system decide to pursue Magnet® designation, a required high-stake decision is whether to pursue this designation as a system or individual entities in the system. To assist leaders to make the best decision, the authors evaluate the pros and cons of system versus single-entity pursuit of Magnet status to assist a system chief nursing officer to determine if the risk is worth the gain.

Mindfulness has received increased attention in organizational research and practice, with the majority of work focusing on the beneficial, buffering effects of trait mindfulness and mindfulness in...

Ensuring a quality nursing workforce is a key challenge to the health care industry. This study empirically tests the impact of nurse managers' servant leadership orientation on nurse job satisfact...

Galectins are family of galactose-binding proteins known to play critical roles in inflammation and neoplastic progression. Galectins facilitate the growth and survival of neoplastic cells by regulating their cross-talk with the extracellular microenvironment and hampering anti-neoplastic immunity. Here, we review the role of galectins in the biology of hematological malignancies and their promise as potential therapeutic agents in these diseases.

A major breakthrough in the field of medical oncology has been the discovery of galectins and their role in cancer development, progression and metastasis. In this review article we have condensed the results of a number of studies published over the past decade in an effort to shed some light on the unique role played by the galectin family of proteins in neoplasia, and how this knowledge may alter the approach to cancer diagnosis as well as therapy in the future. In this review we have also emphasized the potential use of galectin inhibitors or modulators in the treatment of cancer and how this novel treatment modality may affect patient outcomes in the future. Based on current pre-clinical models we believe the use of galectin inhibitors/modulators will play a significant role in cancer treatment in the future. Early clinical studies are underway to evaluate the utility of these promising agents in cancer patients.

Osteoporosis is caused by an imbalance in bone remodeling, a process involving bone-building osteoblasts and bone-resorptive osteoclasts. Excessive reactive oxygen species and inflammatory responses have been shown to stimulate differentiation and function of osteoclasts while inducing osteoblast apoptosis and suppressing osteoblastic proliferation and differentiation via extracellular signal-regulated kinases (ERK), ERK-dependent nuclear factor-κB and Wnt/β-catenin signaling pathways. The anti-oxidant and anti-inflammatory green tea catechins (GTC) have been shown to promote osteoblastogenesis, suppress osteoclastogenesis and stimulate the differentiation of mesenchymal stem cells into osteoblasts rather than adipocytes by modulating the signaling pathways. This paper reviews the pharmacokinetics and metabolism of GTC, their bone-protective activities evidenced in in vitro and in vivo studies, and the limited clinical studies supporting these preclinical findings. In light of the physical, economical, and social burdens due to osteoporosis, easily accessible and affordable preventive measures such as GTC deserves further clinical studies prior to its clinical application.

Cardiovascular disease is the leading killer of women, yet few take risk assessment action. In November 2014, the Women's Heart Alliance conducted a nationwide survey to determine risk assessment barriers and opportunities. From September 18-26, 2014, 1,011 U.S. women ages 25-60 were interviewed

Background Ensuring a quality nursing workforce for the future in a time of increasing labor shortage and declining nurse satisfaction is a key challenge to the health care industry. Understanding what impacts job satisfaction is vital to solving the problem of nurse attrition. Purposes We suggest that the approach to supporting staff in the care giving role requires additional expectations of managers who supervise inpatient nursing staff. This study empirically tested the impact of nurse managers' servant leadership orientation on nurse job satisfaction. Methodology/approach Nurses providing direct bedside patient care within inpatient departments of a five-hospital system were asked to respond to four questionnaires. Seventeen departments participated. There were 346 available nurses across the departments. The average response rate was 73% across all of the units surveyed. Hypotheses were tested using multivariate regression analysis of the nurse-nurse manager dyad. Findings Statistical findings of this study provided evidence that behaviors and attitudes of the nurse manager do impact employee job satisfaction. Departments where staff perceived that managers had higher servant leadership orientation demonstrated significant positive impact on individual employee job satisfaction. Practice implications Nursing is a unique occupation in that it requires both competence in professional service and compassion in patient caregiving. Hospitals are not factories dealing with inanimate objects or data. The results of this research suggested that the management approach in a health care environment might be enhanced by a more servant-oriented management approach. Specific policy changes that may be implied on the basis of findings of this research include key areas of management selection, management development, and management reward/evaluation.

Despite recent findings for the treatment of multiple myeloma (MM) it still remains a deadly disease. In the effort to develop new and more effective therapies for human tumors, murine animal models have been proven critical for a variety of neoplasias. Unfortunately, although recent studies have described different animal models of human MM, none is available for tracking the tumor progression with a specific biomarker employing human MM cells. This lack hampers the development of new approaches for treating MM. Here we describe a human MM model in NOD/SCID mice that allows for highly sensitive and specific monitoring of disease progression through the endogenously-expressed tumor specific cancer/testis antigen AKAP-4. The human MM cells U266 were subcutaneously injected in NOD/SCID animals. AKAP-4 expression resulted to be a reliable and sensitive antigen to monitor tumor growth and spread by different and independent techniques, namely flow-cytometry, E.L.I.S.A., Western blot and RT-PCR. The relevance of our findings stems from the suitability of this innovative model for a thorough pre-clinical evaluation of experimental pharmacological strategies for the treatment of human MM.

Sperm protein (Sp17) is an attractive target for cancer vaccines because of its over-expression in primary and metastatic ovarian cancer (OC). Our studies suggest that a Sp17-based vaccine can induce an enduring defense against OC development in C57BL/6 mice with ID8 OC cells, following prophylactic and therapeutic treatments. Our results show that a SP17 protein based vaccine combined with CpG induced an effective CD8 mediated immune response against Sp17 protein. This is the first time that a mouse counterpart of a cancer testis antigen (Sp17) was shown to be expressed in an OC mouse model. and that vaccination against this antigen significantly controlled tumor growth . The CpG-adjuvanted vaccine are activating Th17 cells while inhibiting immunosuppressive T-reg cells, suggesting a reprogramming of active cellular-mediated response against Sp17-expressing tumor cells. Taken together, these results indicate that prophylactic and therapeutic vaccinations can induce long-standing protection against OC and delay tumor growth, suggesting that this strategy may provide additional treatments of human OC and the prevention of disease onset in women with a family history of OC.