Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion

Cervical cancer, a common chronic disease, is one of the most prevalent and curable cancers among women. Pap smear images are a popular technique for screening cervical cancer. This study proposes a computer-aided diagnosis for cervical cancer utilizing the novel Cervical Net deep learning (DL) structures and feature fusion with Shuffle Net structural features. Image acquisition and enhancement, feature extraction and selection, as well as classification are the main steps in our cervical cancer screening system. Automated features are extracted using pre-trained convolutional neural networks (CNN) fused with a novel Cervical Net structure in which 544 resultant features are obtained. To minimize dimensionality and select the most important features, principal component analysis (PCA) is used as well as canonical correlation analysis (CCA) to obtain the best discriminant features for five classes of Pap smear images. Here, five different machine learning (ML) algorithms are fed into these features. The proposed strategy achieved the best accuracy ever obtained using a support vector machine (SVM), in which fused features between Cervical Net and Shuffle Net is 99.1% for all classes

The use of echocardiographic and clinical data recorded on admission to simplify decision making for elective percutaneous coronary intervention: a prospective cohort study

Abstract Background Coronary artery disease (CAD), a leading cause of mortality, affects patient health-related quality of life (HRQoL). Elective percutaneous coronary interventions (ePCIs) are usually performed to improve HRQoL of CAD patients. The aim of this study was to design models using admission data to predict the outcomes of the ePCI treatments on the patients’ HRQoL. Methods This prospective cohort study was conducted with CAD patients who underwent ePCIs at the King Abdullah University Hospital in Jordan from January 2014 through May 2015. Six months after their ePCI procedures, the participants completed the improved MacNew (QLMI-2) questionnaire, which was used for evaluating three domains (physical, emotional and social) of HRQoL. Multivariate linear regression was used to design models to predict the three domains of HRQoL from echocardiographic findings and clinical data that are routinely measured on admission. Results The study included 239 patients who underwent ePCIs and responded to the QLMI-2 questionnaire. The mean age (± standard deviation) of the participants was 55.74 ± 11.84 years, 54.58 ± 11.37 years for males (n = 174) and 59.11 ± 12.49 years for females (n = 65). The average scores for physical, emotional and social HRQoL were 4.38 ± 1.27, 4.4 ± 1.11, and 4.37 ± 1.32, respectively. Out of the 42 factors inputted to the models to predict HRQoL scores, 10, 9, and 9 factors were found to be significant determinants for physical, emotional and social domains, respectively, with adjusted coefficients of determination of 0.630, 0.604 and 0.534, respectively. Basophil levels on admission showed a significant positive correlation with the three domains of HRQoL, while aortic root diameter showed a negative correlation. Scores for the three domains were significantly lower in women than in men. Hypertensive and diabetic patients had significantly lower HRQoL scores than patients without hypertension and diabetes. Conclusion The prediction of HRQoL scores 6 months after an ePCI is possible based on data acquired on admission. The models developed here can be used as decision-making tools to guide physicians in identifying the efficacy of ePCIs for individual patients, hence decreasing the rate of inappropriate ePCIs and reducing costs and complications

Validity and Reliability of an Arabic-language Version of the Postpartum Specific Anxiety Scale Research Short-Form in Jordan

Objective: The English-language Postpartum Specific Anxiety Scale (PSAS) is a valid, reliable measure for postpartum anxiety (PPA), but its 51-item length is a limitation. Consequently, the PSAS Working Group developed the PSAS Research Short-Form (PSAS-RSF), a statistically robust 16-item tool that effectively assesses PPA. This study aimed to assess and validate the reliability of an Arabic-language version of the PSAS-RSF in Jordan (PSAS-JO-RSF). Methods: Using a cross-sectional methodological design, a sample of Arabic-speaking mothers (N = 391) with infants aged up to 6 months were recruited via convenience sampling from a prominent tertiary hospital in northern Jordan. Factor analysis, composite reliability (CR), average variance extracted (AVE), McDonald's ω, and inter-item correlation measures were all examined. Results: Explanatory factor analysis revealed a four-factor model consistent with the English-language version of the PSAS-RSF, explaining a cumulative variance of 61.5%. Confirmatory factor analysis confirmed the good fit of the PSAS-JO-RSF (χ 2/df = 1.48, CFI = 0.974, TLI = 0.968, RMSEA = 0.039, SRMR = 0.019, p &lt; 0.001). The four factors demonstrated acceptable to good reliability, with McDonald's ω ranging from 0.778 to 0.805, with 0.702 for the overall scale. The CR and AVE results supported the validity and reliability of the PSAS-JO-RSF. Conclusion: This study establishes an Arabic-language version of the PSAS-JO-RSF as a valid and reliable scale for screening postpartum anxieties in Jordan.</p

Integrating the Principles of Evidence Based Medicine and Evidence Based Public Health: Impact on the Quality of Patient Care and Hospital Readmission Rates in Jordan

Introduction: Hospital readmissions impose not only an extra burden on health care systems but impact patient health outcomes. Identifying modifiable behavioural risk factors that are possible causes of potentially avoidable readmissions can lower readmission rates and healthcare costs. Methods: Using the core principles of evidence based medicine and public health, the purpose of this study was to develop a heuristic guide that could identify what behavioural risk factors influence hospital readmissions through adopting various methods of analysis including regression models, t-tests, data mining, and logistic regression. This study was a retrospective cohort review of internal medicine patients admitted between December 1, 2012 and December 31, 2013 at King Abdullah University Hospital, in Jordan. Results: 29% of all hospitalized patients were readmitted during the study period. Among all readmissions, 44% were identified as potentially avoidable. Behavioural factors including smoking, unclear follow-up and discharge planning, and being non-compliant with treatment regimen as well as discharge against medical advice were all associated with increased risk of avoidable readmissions. Conclusion: Implementing evidence based health programs that focus on modifiable behavioural risk factors for both patients and clinicians would yield a higher response in terms of reducing potentially avoidable readmissions, and could reduce direct medical costs

Validation of near infrared spectroscopic (NIRS) imaging using programmable phantoms

For much of the past decade, we have developed most of the essential hardware and software components needed for practical implementation of dynamic NIRS imaging. Until recently, however, these efforts have been hampered by the lack of calibrating phantoms whose dynamics substantially mimic those seen in tissue. Here we present findings that document the performance of a dynamic phantom based on use of twisted nematic liquid crystal (LC) technology. Programmable time courses of applied voltage cause the opacity of the LC devices, which are embedded in a background matrix consisting of polysiloxane (silicone) admixed with scattering and absorbing materials, to vary in a manner that mimics the spatiotemporal hemodynamic pattern of interest. Methods for producing phantoms with selected absorption and scattering, internal heterogeneity, external geometry, hardness, and number and locations of embedded LCs are described. Also described is a method for overcoming the apparent limitation that arises from LCs being mainly independent of the illumination wavelength. The results presented demonstrate that: the opacity vs. voltage response of LCs are highly stable and repeatable; the dynamic phantom can be driven at physiologically relevant speeds, and will produce time-varying absorption that follows the programmed behavior with high fidelity; image time series recovered from measurements on the phantom have high temporal and spatial location accuracy. Thus the dynamic phantom can fill the need for test media that practitioners may use to confirm the accuracy of computed imaging results, assure th

Salivary cortisol, perceived stress and coping strategies: A comparative study of working and nonworking women

AimsThis study investigated stress levels and coping strategies among working and nonworking women in the United Arab Emirates.BackgroundStress levels in working and nonworking women have previously been studied, but few studies used cortisol to measure stress or examined how coping strategies affect stress levels.MethodsWe employed a cross-sectional design with a convenience sample of women aged 20–65 years. Information on women’s sociodemographic characteristics, perceived stress (using the Perceived Stress Scale) and coping strategies (using the Brief-COPE) was collected. Participants’ morning (07:00–08:00) and evening (19:00–20:00) cortisol levels were measured using unstimulated saliva samples.ResultsIn total, 417 working and 403 nonworking women participated in this study. More nonworking women reported high stress levels than working women (14.1% vs. 4.1%, p = .001). Working women reported more use of informational support and venting to cope with stress compared with nonworking women (94.0% vs. 88.1%, p = .001). More nonworking women had impaired morning (0.359 mg/dl) cortisol compared with working women (58.1% vs. 28.5% and 41.7% vs. 18.0%, respectively). Compared with working women, nonworking women had 3.25 (95%CI: 2.38, 4.47) and 3.78 (95%CI: 2.65, 5.43) times the odds of impaired morning and evening cortisol, respectively.ConclusionNonworking women exhibited higher levels of stress than working women. There is an urgent need to support nonworking women to manage stress through appropriate awareness campaigns and public health policies.Implications for ManagementPolicymakers and community leaders should consider the mental health of nonworking women as a priority in planning public health policies and programmes. Nurse managers must have a voice in reforming public health policy to support early assessment and management of stress among nonworking women.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/175502/1/jonm13697_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/175502/2/jonm13697.pd