Brady (Norway)

Nurse retention remains a pivotal issue, necessitating urgent strategies aimed at the recruitment and retention of highly qualified nurses. However, there is limited knowledge regarding the influence of nurses' personal and professional values, along with work environment, on predicting intent to leave an organization. Utilizing a secondary analysis approach, this study employed logistic regression to predict intent to leave among registered nurses (n=671) using results from the Short Schwartz's Value Survey, the Nurses Professional Values Scale-3, and the Practice Environment Scale of the Nursing Work Index. Regression analysis indicates that the work environment including "Participation in Hospital Affairs" (B=-.665, odds ratio=.514), "Nurse Manager Ability, Leadership, and Support" (B=-.448, odds ratio=.639), and "Staffing and Resource Adequacy" (B=-.589, odds ratio=.555) are significant predictors of nurses' intentions to leave. Establishing and maintaining supportive work environments is essential for retention, highlighting the importance of effective leadership.

The overall prevalence of hypertension in childhood is 2% to 5%, and the leading type of childhood hypertension is primary hypertension, especially in adolescence. As in adults, the leading risk factors for children with primary hypertension are excess adiposity and suboptimal lifestyles; however, environmental stress, low birth weight, and genetic factors may also be important. Hypertensive children are highly likely to become hypertensive adults and to have measurable target organ injury, particularly left ventricular hypertrophy and vascular stiffening. Ambulatory and home blood pressure monitoring may facilitate diagnosis. Primordial prevention of hypertension through public health implementation of healthier diet and increased physical activity will reduce the prevalence of primary hypertension, and evidence-based treatment guidelines should be implemented when hypertension is diagnosed. Further research to optimize recognition and diagnosis and clinical trials to better define outcomes of treatment are needed.

Linear bandits have a wide variety of applications including recommendation systems yet they make one strong assumption: the algorithms must know an upper bound $S$ on the norm of the unknown parameter $\theta^*$ that governs the reward generation. Such an assumption forces the practitioner to guess $S$ involved in the confidence bound, leaving no choice but to wish that $\|\theta^*\|\le S$ is true to guarantee that the regret will be low. In this paper, we propose novel algorithms that do not require such knowledge for the first time. Specifically, we propose two algorithms and analyze their regret bounds: one for the changing arm set setting and the other for the fixed arm set setting. Our regret bound for the former shows that the price of not knowing $S$ does not affect the leading term in the regret bound and inflates only the lower order term. For the latter, we do not pay any price in the regret for now knowing $S$. Our numerical experiments show standard algorithms assuming knowledge of $S$ can fail catastrophically when $\|\theta^*\|\le S$ is not true whereas our algorithms enjoy low regret.

Introduction/Hypothesis: To develop and validate a deep learning model to automatically segment and measure left atrial (LA) volumes from routine noncontrast chest CTs and evaluate for outcomes prognosis. Methods: A prototype AI algorithm, with a conditional variational auto-encoder architecture, was initially developed and trained using training data from 1313 patients (paired CTA and non-contrasted exams; “training group”). Left atrium contours from contrasted cases were used to train the model on corresponding non-contrasted dataset. We then retrospectively evaluated 274 patients (mean age 67.9 years, 54.6% male, 57.2% caucasian) who underwent a routine non ECG-gated, noncontrast chest CT for coronary artery calcium or lung cancer screening for validation of the AI model (“validation group”). Outcomes included heart failure (HF) hospitalization and new-onset atrial fibrillation within 5 years. Simple logistic regressions with appropriate univariate statistics were used for modelling outcomes. Results: In the validation group, there was excellent correlation between AI and expert results with a LA volume index (LAVi) intraclass correlation coefficient of 0.950 (0.936-0.960). Bland-Altman plot demonstrated that the AI underestimated LAVi by a mean 5.86 mL/m2. AI-LAVi independently predicted new-onset atrial fibrillation (AUC = 0.855, OR = 1.12, 95% CI 1.08 - 1.18; p<0.0001). AI-LAVi was independently associated with HF hospitalization (AUC = 0.903, OR = 1.08, 95% CI 1.04 - 1.13, p = 0.0009). Conclusion: In this large validation cohort, AI for automated measurement of LA volume on routine noncontrast chest CTs had excellent agreement with manual quantification. AI-LAVi is independently predictive for new-onset atrial fibrillation and HF hospitalization within 5 years.

The 2018 Coffey-Holden Prostate Cancer Academy (CHPCA) Meeting, "Tumor Cell Heterogeneity and Resistance," was held in Los Angeles, California from June 21 to 24, 2018. The CHPCA Meeting is a unique, discussion-oriented scientific conference convened annually by the Prostate Cancer Foundation (PCF), which focuses on the most critical topics in need of further study to advance the treatment of lethal prostate cancer. The 6th Annual CHPCA Meeting was attended by 70 investigators and concentrated on prostate cancer heterogeneity and treatment resistance. The meeting focused on topics including: recognition of tumor heterogeneity, molecular drivers of heterogeneity, the role of the tumor microenvironment, the role of heterogeneity in disease progression, metastasis and treatment resistance, clinical trials designed to target resistance and tumor heterogeneity, and immunotherapeutic approaches to target and overcome tumor heterogeneity. This review article summarizes the presentations and discussions from the 2018 CHPCA Meeting in order to share this knowledge with the scientific community and encourage new studies that will lead to improved treatments and outcomes for men with prostate cancer.

Abstract Introduction: The prostate-specific antigen (PSA) test has improved the early detection of PCa and also recurrence after radical prostatectomy (RP). However, it is estimated that 35% of patients submitted to RP that progress with detectable PSA levels will never present metastatic disease. Thus, there is a need to distinguish localized curable disease from a subset of patients at risk for progression. Currently, the D’Amico Stratification System defines high risk PCa as any combination of the following factors: PSA >20ng/ml, a Gleason score (GS) of 8-10, or clinical stage T2c or greater. As some aggressive PCa did not fit these criteria, a more accurate test to classify high risk patients is crucial. Objective: The objective of this study is to identify a cancer/testis antigen (CTA)-based biomarker to discern PCa patients with potentially aggressive disease. The CTAs are a unique group of genes whose expression is normally confined to germ cells in normal testis and placenta, but aberrantly expressed in several types of cancers. Methods and results: Using Nanostring multiplex approach, we screened the expression of 22 CTA genes in 20 localized (LPCa) and 20 metastatic prostate cancer (MPCa) samples, obtained from University of Washington. Using ROC curve analysis we ranked the CTAs capable of discriminating LPCa and MPCa. We found one under-expressed CTA (PAGE4) and seven over-expressed (CEP55, NUF2, PBK, RQCD1, SPAG4, SSX2 and TTK) in MPCa, considering AUC>0.70. qRT-PCR was used to validate Nanostring results in the same set of samples and to identify the candidate CTA biomarkers. The eight CTAs selected showed significant expression changes and were also capable of separating LPCa and MPCa cases (AUC>0.70). A multivariate logistic regression model (MLR) showed that combined expression patterns of CEP55, NUF2, PBK and TTK was capable of discriminating MPCa from LPCa with AUC = 0.95 (pr = 0.15), sensitivity = 80%, specificity = 90%. Using this model 85% of the PCa cases were correctly classified. We then evaluated, by q-RT-PCR, if CTA expression profile is associated with GS using the same 20 LPCa samples and a new set of 25 PCa samples. The PCa cases were separated in two groups according to GS: (1) 3+3/3+4 and (2) other GS. The expression pattern of the CTA panel CEP55, NUF2 and RQCD1 correctly separated 83% of the samples according to the GS with AUC = 0.89 (pr = 0.05), sensitivity = 88%, specificity 75%. Conclusion: The results of the study are clinically and biologically relevant, since little is known about the role of CTAs in PCa. The CTA expression pattern was shown to be different between two extreme pathologic phenotypes and also among samples of different GS. The CTA profile may be useful to identify high risk LPCa patients and to stratify patients according to GS, which may guide treatment strategies for the different groups of patients. Citation Format: Luciane T. Kagohara, Prakash Kulkarni, Takumi Shiraishi, Guangjing Zhu, Robert Vessella, Robert Veltri. Cancer/testis antigen expression pattern is a potential biomarker for prostate cancer aggressiveness. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4826. doi:10.1158/1538-7445.AM2015-4826

Introduction: Left ventricular (LV) regional contractile information is currently limited to subjective interpretation of low-resolution echocardiography. In order to establish a more specific and standardized form of imaging, this study compared strain patterns in normal patients to patients with coronary artery disease (CAD) using high-resolution contractile data from cardiac MRI. Methods: Sixty normal test subjects underwent cardiac MRI with multiple 3D strain parameters calculated from tissue tag-plane displacement data. 15,300 LV grid points contributed to a normal human strain database. Sixty patients with known CAD underwent cardiac MRI. Normalized z-scores for 3 strain parameters at each of the LV grid-points were calculated by comparing raw strain values to the normal database. The z-scores at each point were then averaged to generate 15,300 normalized strain z-scores. Clinical evidence for prior ischemic events was sought in each patient’s medical record. Results: Multi-parametric strain information was successfully obtained in all 60 CAD patients with post-processing display of “quantified” regional ischemic injury over patient-specific LV geometry. We displayed “normalized” micro-regional contractile function by intuitive z-score color-coding of regions (<1 standard deviation=blue; 1-2=yellow; >2=red) over patient-specific 3D LV geometry (Figure). Clinical evidence for prior ischemic events obtained from echo, viability studies, ECG, and left heart catherization correlated well with regional injury patterns. Conclusions: The quantitative “normalization” of raw patient-specific values by z-score calculation from a normal human strain database can effectively create vivid displays of prior ischemic events. These high-resolution images provide detailed contractile information that may shape a new paradigm in preoperative surgical evaluation prior to revascularization.

Prostate cancer progression is concomitant with quantifiable nuclear structure and texture changes as compared to non-cancer tissue. Malignant progression is associated with an epithelial-mesenchymal transition (EMT) program whereby epithelial cancer cells take on a mesenchymal phenotype and dissociate from a tumor mass, invade, and disseminate to distant metastatic sites. The objective of this study was to determine if epithelial and mesenchymal prostate cancer cells have different nuclear morphology. Murine tibia injections of epithelial PC3 (PC3-Epi) and mesenchymal PC3 (PC3-EMT) prostate cancer cells were processed and stained with H&E. Cancer cell nuclear image data was obtained using commercially available image-processing software. Univariate and multivariate statistical analysis were used to compare the two phenotypes. Several non-parametric classifiers were constructed and permutation-tested at various training set fractions to ensure robustness of classification between PC3-Epi and PC3-EMT cells in vivo. PC3-Epi and PC3-EMT prostate cancer cells were separable at the single cell level in murine tibia injections on the basis of nuclear structure and texture remodeling associated with an EMT. Support vector machine and multinomial logistic regression models based on nuclear architecture features yielded AUC-ROC curves of 0.95 and 0.96, respectively, in separating PC3-Epi and PC3-EMT prostate cancer cells in vivo. Prostate cancer cells that have undergone an EMT demonstrated an altered nuclear structure. The association of nuclear changes and a mesenchymal phenotype demonstrates quantitative morphometric image analysis may be used to detect cancer cells that have undergone EMT. This morphometric measurement could provide valuable prognostic information in patients regarding the likelihood of [future] metastatic disease.