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.

Objective: This study aimed to evaluate the association between perceived social support during pregnancy and levels of anxiety among postpartum women using an anxiety-specific screening instrument. Methods: Using a prospective cohort design, a two-stage methodology was conducted to collect data from women seeking maternal care at the King Abdullah University Hospital in northern Jordan. In the first stage, perceived social support was assessed among pregnant women using the Medical Outcomes Study Social Support Survey. During the first six months after childbirth, postpartum women were contacted to complete the second stage, wherein their perceptions of infant-focused anxieties were assessed using the Postpartum Specific Anxiety Scale. In our study, two types of infant-focused anxieties were investigated among a final sample of 419 mothers: infant safety and welfare anxieties and practical infant care anxieties. Results: The results of multivariate linear regression analysis indicated that providing pregnant women with high levels of emotional support from close social networks (β= − 0.08, p= 0.01) and perceiving informational support from health care providers (β= − 0.71, p< 0.01) were protective factors for reducing the levels of postpartum anxiety concerning infant safety and welfare. Our findings also demonstrated that pregnant mothers who perceived high informational support from health care providers had a lower level of postpartum anxiety about practical infant care (β= − 0.20, p< 0.01). In contrast, mothers who reported receiving high tangible support from close social networks during pregnancy had a significantly higher level of perceived anxiety concerning practical infant care after delivery (β= 0.13, p= 0.02). Conclusion: Our study suggests that postpartum anxiety would be reduced if effective informational support were readily available for pregnant women. There is a clear need for building bridges between women, their families, and providers to distinguish the specific type and amount of support that should be provided to mothers during pregnancy.

Background: Elevated potassium level is a common and reversible peri-arrest condition. Diagnosis and management of hyperkalemia in a short time is critical, where electrocardiogram (ECG) alterations might be helpful. We aimed to investigate the role of clinical features and ECGs in early diagnosing and treating hyperkalemia. Methods: Prospectively, adult patients who presented to the emergency department (ED) from July 2019 to March 2020 with hyperkalemia (serum potassium ≥5.5mmol/L) were included. History was obtained, and laboratory investigations and ECGs were performed at the presentation and before initiating hyperkalemia therapy. Hyperkalemia severity was divided into mild (5.5–5.9mmol/L), moderate (6.0–6.4mmol/L), and severe (≥6.5mmol/L). A cardiologist and emergency physician blinded to laboratory values, study design, and patients’ diagnoses interpreted ECGs and presenting symptoms independently to predict hyperkalemia. Results: Sixty-seven hyperkalemic patients with a mean (±SD) serum potassium level of 6.5±0.7mmol/L were included in this study. The mean age was 63.9±15.1, and 58.2% were females. Hyperkalemia was mild in 10.4%, moderate in 40.3%, and severe in 49.3%. Almost two thirds of patients (71.6%) had hypertension, 67.2% diabetes, and 64.2% chronic kidney disease. About one-quarter of patients (22.4%) were asymptomatic, while fatigue (46.3%), dyspnea (28.4%), and nausea/vomiting (20.9%) were the most common presenting symptoms. Normal ECGs were observed in 25.4% of patients, while alterations in 74.6%. Atrial fibrillation (13.4%), peaked T wave (11.9%), widened QRS (11.9%), prolonged PR interval (10.5%), and flattening P wave (10.5%) were the most common. Peaked T wave was significantly more common in severe hyperkalemia (87.5%) than in mild and moderate hyperkalemia (12.5%, 0.0%, respectively) (p=0.041). The physicians’ sensitivities for predicting hyperkalemia were 35.8% and 28.4%, improved to 51.5% and 42.4%, respectively, when limiting the analyses to severe hyperkalemia. The mean (±SD) time to initial hyperkalemia treatment was 63.8±31.5 min. Potassium levels were positively correlated with PR interval (r=0.283, p=0.038), QRS duration (r=0.361, p=0.003), peaked T wave (r=0.242, p=0.041), and serum levels of creatinine (r=0.347, p=0.004), BUN (r=0.312, p=0.008), and CK (r=0.373, p=0.039). Conclusions: The physicians’ abilities to predict hyperkalemia based on ECG and symptoms were poor. ECG could not be solely relied on, and serum potassium tests should be conducted for accurate diagnosis.

Objective: Frontline healthcare workers experienced high levels of psychological distress and emotional turmoil due to the changes necessitated by the COVID-19 public health emergency. Given that workplace stress can negatively influence both quality of work, and job performance, this study sought to explore the lived experiences of work-related stress among female healthcare workers in United Arab of Emirates during the COVID-19 pandemic. Methods: A qualitative phenomenological investigation was performed using a purposive sampling approach. A total of 26 semi-structured interviews were conducted with female healthcare workers who worked directly with COVID-19 patients. Using Giorgi’s descriptive method, the data were analyzed to identify the main themes. Results: Three main themes emerged from the data analysis, with nine categories under these themes. The first theme was “sources of work-related stress.” The participants reported a high workload, fear of being infected and transmitting the virus to others, and uncertainty and lack of knowledge regarding COVID-19 to be the main sources of their work-related stress. The second theme was “challenges of working during the pandemic.” The participants related being challenged by changes in the organization of care, the need to use personal protective equipment, a work-life imbalance, and witnessing patients’ suffering. The third theme was “coping strategies.” The participants perceived having sources of social support and using self-adjustment skills to be helpful strategies in terms of coping with the stressful situations they experienced. Conclusion: The findings suggest a number of strategies and interventions that could be used at the individual and institutional levels to promote the preparedness and efficacy of healthcare workers during future crises and public health emergencies.