To reduce the spread of COVID-19, Jordan enforced 10 weeks of home quarantine in the spring of 2020. A cross-sectional study was designed to assess this extended quarantine's effect on smartphone addiction levels among undergraduates. A random sample of 6,157 undergraduates completed an online questionnaire (mean age 19.79 ± 1.67 years; males 28.7%). The questionnaire contains different sections to collect socio-demographic, socio-economic, academic, quarantine-related information, and smartphone usage. The smartphone addiction scale-short version was used to assess the degree of addiction during the quarantine. The mean addiction score across the whole sample was 35.66 ± 12.08, while the prevalence of addiction among participants was 62.4% (63.5% in males and 61.9% in females). The majority of the participants (85%) reported that their smartphone usage during the quarantine increased or greatly increased (27.6 and 57.2%, respectively), with some 42% using their smartphones for more than 6 h a day. Nevertheless, three-quarters of the students wished to reduce their smartphone usage. Several demographic and quarantine factors have been assessed, and students' gender, the field of study, parental education, household income in addition to the location of quarantine (urban, rural) and the house specifications (apartment, independent house, with/without a garden) showed statistically significant associations with smartphone addiction during the quarantine. Female students, students studying scientific- and medical-related majors compared to those studying humanity majors, those with higher incomes, those who had been quarantined in an apartment without a garden, and those who lived in urban areas showed significantly higher addiction scores.

The scope of this paper is focused on the multidimensional poverty problem in Jordan. Household expenditure and income surveys provide data that are used for identifying and measuring the poverty status of Jordanian households. However, carrying out such surveys is hard, time consuming, and expensive. Machine learning could revolutionize this process. The contribution of this work is the proposal of an original machine learning approach to assess and monitor the poverty status of Jordanian households. This approach takes into account all the household expenditure and income surveys that took place since the early beginning of the new millennium. This approach is accurate, inexpensive, and makes poverty identification cheaper and much closer to real-time. Data preprocessing and handling imbalanced data are major parts of this work. Various machine learning classification models are applied. The LightGBM algorithm has achieved the best performance with 81% F1-Score. The final machine learning classification model could transform efforts to track and target poverty across the country. This work demonstrates how powerful and versatile machine learning can be, and hence, it promotes for adoption across many domains in both the private sector and government.

Objectives: This study was designed to assess the effect of COVID-19 home quarantine and its lifestyle challenges on the sleep quality and mental health of a large sample of undergraduate University students in Jordan. It is the first study applied to the Jordanian population. The aim was to investigate how quarantine for several weeks changed the students' habits and affected their mental health.Methods: A cross-sectional study was conducted using a random representative sample of 6,157 undergraduate students (mean age 19.79 ± 1.67 years, males 28.7%) from the University of Jordan through voluntarily filling an online questionnaire. The Pittsburgh Sleep Quality Index (PSQI) and the Center for Epidemiologic Studies-Depression Scale (CES-D) were used to assess sleep quality and depressive symptoms, respectively.Results: The PSQI mean score for the study participants was 8.1 ± 3.6. The sleep quality of three-quarters of the participants was negatively affected by the extended quarantine. Nearly half of the participants reported poor sleep quality. The prevalence of poor sleep quality among participants was 76% (males: 71.5% and females: 77.8%). Similarly, the prevalence of the depressive symptoms was 71% (34% for moderate and 37% for high depressive symptoms), with females showing higher prevalence than males. The overall mean CES-D score for the group with low depressive symptoms is 9.3, for the moderate group is 19.8, while it is 34.3 for the high depressive symptoms group. More than half of the students (62.5%) reported that the quarantine had a negative effect on their mental health. Finally, females, smokers, and students with decreased income levels during the extended quarantine were the common exposures that are significantly associated with a higher risk of developing sleep disturbances and depressive symptoms.Conclusions: Mass and extended quarantine succeeded in controlling the spread of the COVID-19 virus; however, it comes with a high cost of potential psychological impacts. Most of the students reported that they suffer from sleeping disorders and had a degree of depressive symptoms. Officials should provide psychological support and clear guidance to help the general public to reduce these potential effects and overcome the quarantine period with minimum negative impacts.

<!-- *** Custom HTML *** --> The role of Ki-67 index in determining the prognosis and management of gastroenteropancreatic neuroendocrine tumors (GEP-NETs) has become more important yet presents a challenging assessment dilemma. Although the precise method of Ki-67 index evaluation has not been standardized, several methods have been proposed, and each has its pros and cons. Our study proposes an imaging semiautomated informatics framework [semiautomated counting (SAC)] using the popular biomedical imaging tool “ImageJ” to quantify Ki-67 index of the GEP-NETs using camera-captured images of tumor hotspots. It aims to assist pathologists in achieving an accurate and rapid interpretation of Ki-67 index and better reproducibility of the results with minimal human interaction and calibration. Twenty cases of resected GEP-NETs with Ki-67 staining that had been done for diagnostic purposes have been randomly selected from the pathology archive. All of these cases were reviewed in a multidisciplinary cancer center between 2012 and 2019. For each case, the Ki-67 immunostained slide was evaluated and five camera-captured images at 40 × magnification were taken. Prints of images were used by three pathologists to manually count the tumor cells. The digital versions of the images were used for the semiautomated cell counting using ImageJ. Statistical analysis of the Ki-67 index correlation between the proposed method and the MC revealed strong agreement on all the cases evaluates (n = 20), with an intraclass correlation coefficient of 0.993, “95% CI: 0.984 to 0.997.” The results obtained from the SAC are promising and demonstrate the capability of this methodology for the development of reproducible and accurate semiautomated quantitative pathological assessments. ImageJ features are investigated carefully and accurately fine-tuned to obtain the optimal sequence of steps that will accurately calculate Ki-67 index. SAC is able to accurately grade all the cases evaluated perfectly mating histopathologists’ manual grading, providing reliable and efficient solution for Ki-67 index assessment.

COVID-19 is a global pandemic that affected the everyday life activities of billions around the world. It is an unprecedented crisis that the modern world had never experienced before. It mainly affected the economic state and the health care system. The rapid and increasing number of infected patients overwhelmed the healthcare infrastructure, which causes high demand and, thus, shortage in the required staff members and medical resources. This shortage necessitates practical and ethical suggestions to guide clinicians and medical centers when allocating and reallocating scarce resources for and between COVID-19 patients. Many studies proposed a set of ethical principles that should be applied and implemented to address this problem. In this study, five different ethical principles based on the most commonly recommended principles and aligned with WHO guidelines and state-of-the-art practices proposed in the literature were identified, and recommendations for their applications were discussed. Furthermore, a recent study highlighted physicians' propensity to apply a combination of more than one ethical principle while prioritizing the medical resource allocation. Based on that, an ethical framework that is based on Fuzzy inference systems was proposed. The proposed framework's input is the identified ethical principles, and the output is a weighted value (per patient). This value can be used as a rank or a priority factor given to the patients based on their condition and other relevant information, like the severity of their disease status. The main idea of implementing fuzzy logic in the framework is to combine more than one principle when calculating the weighted value, hence mimicking what some physicians apply in practice. Moreover, the framework's rules are aligned with the identified ethical principles. This framework can help clinicians and guide them while making critical decisions to allocate/reallocate the limited medical resources during the current COVID-19 crisis and future similar pandemics.