bluuebunny/crossref_metadata_2025_split huggingface dataset:
Therapeutic abortion in Iran: an epidemiologic study of legal abortion in 2 years
Authors: Mahdavi Seyed Amirhosein , Jafari Asieh , Azimi Khadijeh , Dehghanizadeh Nikoo , Bazegar Abdolrazagh 
Published: April 2020

Abstract
Objectives
Unsafe abortion is one of the most important causes of death and disability among mothers in countries where abortion is illegal. These conditions have changed since then. The present study has investigated the cases who were referred to the legal medicine organization to receive abortion permission. This country level cross sectional retrospective study, investigated all the cases who were referred to the legal medicine centers of Iran for abortion permission during 2015 to 2017. The results were analyzed using SPSS software version 21.
Results
From 21477 applicants, 15617 (72.71%) received permission including 14367 (91.99%) for fetal abnormalities and 1250 (8.01%) for maternal diseases. The most common fetal abnormalities/disorders were nervous system malformations (26.4%), chromosomal abnormalities (18.4%) and of maternal diseases were circulatory system diseases (43.9%), neoplasms (13.4%) and genitourinary system diseases (9.9%). The most common reasons for not permission were lack of supplementary documents to prove (38.8%), not competency with the criteria (33.9%), and gestational age of more than 19 weeks (25.8%).
Lifetime Co-Occurring Psychiatric Disorders in Newly Diagnosed Adults with Attention Deficit Hyperactivity Disorder (ADHD) or/and Autism Spectrum Disorder (ASD)
Authors: Pehlivanidis Artemios , Papanikolaou Katerina , Mantas Vasilios , Kalantzi Eva , Korobili Kalliopi , Xenaki Lida- Alkistis , Vassiliou Georgia , Papageorgiou Charalambos 
Published: April 2020

Abstract
Background Co-occurring psychiatric disorders in adults with Attention Deficit Hyperactivity Disorder (ADHD) and/or Autism Spectrum Disorder (ASD) contribute to the burden of the healthcare and possibly to the delay of diagnosis. Aim of the study was to clinically assess the prevalence and compare lifetime co-occurring psychopathology in a sample of newly diagnosed ADHD and/or ASD adults and discuss the diagnostic challenges they pose.Methods The lifetime prevalence rates of ten of the most frequently co-occurring psychiatric diagnoses was registered in 336 adults of normal intelligence who underwent a thorough clinical evaluation for the diagnosis of ADHD and/or ASD for the first time in their lives. Four study groups were formed: the ADHD (n = 151), the ASD (n = 58), the ADHD + ASD (n = 28) and the nonADHD/nonASD (NN) (n = 88) group.Results At least one co-occurring psychopathology was found in 72.8% of the ADHD group, in 50% of the ASD group, in 72.4% of the ADHD + ASD group and in 76.1% of the NN group (p = 0.004). In all groups the most frequent psychiatric disorder was depressive disorder. The only significant difference regarding the patterns of psychiatric co-occurrence between the ADHD, the ASD and NN groups was found for substance use disorder (SUD) (p = 0.001). Also, the proportion of subjects with Bipolar Disorder was significantly greater in the NN group as compared to those with ASD (p = 0.025).Conclusions Our results support the high prevalence of co-occurring psychiatric disorders in adults with ADHD and/or ASD with the ASD group presenting the lowest rate. The most marked difference between the ADHD and the nonADHD groups was found for SUD. Moreover, our findings highlight the need for a thorough clinical assessment of all referred patients both in the presence and absence of ADHD and/or ASD.
Magnitude of Nurses’ Intention to Leave Their Jobs and Its Associated Factors of Nurses Working in Tigray Regional State, North Ethiopia: Cross sectional study
Authors: Negarandeh Reza , Kahsay Woldegebriel Gebregziabher , Pashaeypoor Shahzad , Navab Elham 
Published: April 2020

Abstract
Introduction A significant number of nurses is leaving the health care sector. High rates of turnovers contribute to labor shortages of nursing and adversely affect patient outputs and nursing costs.Objectives To determine the magnitude of nurses’ intention to leave a job and its associated factors.Methods A cross-sectional, analytical descriptive study design among 634 nurses working in Tigray Regional State general hospitals from March 2018 to 30 June 2019 was conducted. Clustered random sample nurses completed self-administered three-item Michigan Organizational Assessment Questionnaire during data collection. Descriptive and multiple logistic regression analyses were done using SPSS version 20, 95% confidence interval and p-value < 0.05 was considered.Findings Response rate was 95.9%. Nurses were with a mean of 33.60 ± 9.71, 10.07 ± 9.54, and 2.10 ± 1.455 in years for age, work experience, and service in hospital respectively. About 56.4% were females, 95.4% Orthodox, and 56.7% were Bachelors of Science in Nursing. The magnitude of nurses’ intention to leave their jobs was 43.9%. The male nurses were 1.5 times more likely (AOR = 1.565, 95% CI = 1.095–2.237) had the intention to leave. Those nurses working in medical ward were also 2 times more likely (AOR = 1.886, 95% CI = 1.113–3.193) than working in the Out-Patient Department of the hospitals had the intention to leave.Conclusions Nurses in hospitals intend to leave their jobs. Gender and working in medical ward are the predictors for their intentions. Health care policymakers are recommended to develop nurses’ retention strategies and establish continuing education to socialize male nurses and set economic incentive strategies to encourage male nurses to stay in hospitals fulfilling their families’ income to earn. Managers are recommended to make regular rotation of nurses within hospitals to minimize a possible burden in medical wards. For the quality nursing care, nursing managers should create a favorable environment in medical wards. Educators are recommended to increase the intake of the nursing students. This study did not show the cause and effect of variables. Therefore, other researchers are recommended to conducting longitudinal studies.
Clinicopathological and Prognostic Significance of Platelet-Lymphocyte Ratio (PLR) in Gastric Cancer: An Updated Meta-Analysis
Authors: Zhang Xunlei , Zhao Wenjing , Yu Yang , Qi Xue , Song Li , Zhang Chenfei , Li Guoxing , Yang Lei 
Published: April 2020

Abstract
Background
Systemic inflammatory parameters, such as the elevator PLR (platelet-lymphocyte ratio), have been found to be associated with the prognosis in gastric cancer (GC); however, the results remain controversial. So we aimed to evaluate the prognostic role of the PLR in gastric cancer by conducting this meta-analysis.
Methods
We performed a systematic literature search in PubMed, Embase and the Cochrane Library. The hazard ratio (HR) /Odds Ratio (OR) and its 95% confidence (CI) of survival outcomes and clinicopathological parameters were calculated.
Results
A total of 49 studies (51 cohorts) with 28,929 GC patients were included in the final meta-analysis. The pooled results showed that elevated PLR was significantly associated with poor overall survival (OS) (HR: 1.37, 95% CI: 1.26–1.49, p < 0.001; I2 = 79.90%, Ph < 0.001) and disease-free survival (DFS) (HR 1.52, 95%CI 1.22–1.90, P < 0.001, I2 = 88.6%, Ph< 0.001) of GC patients. Furthermore, patients with elevated PLR had a higher risk of lymph node metastasis (OR = 1.17, 95% CI: 1.02–1.33, p = 0.023), serosal invasion (T3 + T4) (OR = 1.34, 95% CI: 1.10–1.64, p = 0.003) and increased advanced stage (III + IV) (OR = 1.20, 95% CI: 1.06–1.37, p = 0.004).
Conclusions
This meta-analysis demonstrated that elevated PLR was a prognostic factor for poor OS and DFS, and associated with clinicopathological parameters in patients with GC.
ILC2-derived IL-9 inhibit s colorectal cancer progression by activating CD8+ T cells
Authors: Wan Jie , Huang Lan , Wu Yinqiu , Ji Xiaoyun , Yao Shun , Abdelaziz Mohamed Hamed , Cai Wei , Wang Huixuan , Cheng Jianjun , kumar Kesavan Dinesh , Aparna Vasudevan , Sun Caixia , Su Zhaoliang , Wang Shengjun , xu huaxi 
Published: April 2020

Abstract
Background
Type 2 innate lymphoid cells (ILC2s), characterized by secreting type 2 cytokines, regulate multiple immune responses. ILC2s are found in different tumor tissues and ILC2-derived interleukin (IL)-4, IL-5, and IL-13 act on the cells in tumor microenvironment to participate in tumor progression. ILC2s are abundant in colorectal cancer (CRC) tissue, but the role of ILC2s in CRC remains unclear.
Methods
ILC2s were sorted from the spleen using microbeads combined with flow cytometry and tumor infiltrating CD8+ T cells were isolated from tumor tissue by microbeads. Flow cytometry and immunofluorescence were used to detect the percentage of ILC2s and CD8+ T cells in the spleen and CRC tissue. Effects of IL-9 and IL-9-stimulated CD8+ T cells on CT26 cells were measured by proliferation, apoptosis, and migration assays in vitro. GEPIA was used to detect the ILC2s chemokines in CRC tissue and adjacent normal tissue.
Results
We found that ILC2s were increased in CRC tissue compared with the adjacent normal tissue. In vitro experiments showed that IL-9 could activate CD8+ T cells to promote the death of CT26 cells. ILC2s were the main IL-9-secreting cells in CRC tissue as shown by flow cytometry analysis. In vivo experiments showed that neutralizing ILC2s promoted the tumor growth, while tumor inhibition occurred by intravenous injection of IL-9.
Conclusions
Our results demonstrated that ILC2-derived IL-9 activated CD8+ T cells to promote anti-tumor effects in CRC.
Geospatial Approach For Assessment Of Vulnerability To Flood In Local Self Governments
Authors: S Deepak , Rajan Gopika , G Jairaj P 
Published: April 2020

Abstract
Recent years have shown a significant increase in the occurrence of flood globally, with impact on habitation and different sectors of the economy. This, in turn, necessitates the use of different flood mitigation strategies, wherein flood vulnerability assessment plays a significant role. The proposed work presents a methodology which combines vulnerability under physiographic and socio-economic domains to assess the overall flood vulnerability at the local self-government level. The methodology was illustrated to the case of Aluva municipality, located on the banks of River Periyar, in Kerala state, India. The spatial variation of hazard inducing factors and population statistics were analysed using GIS tools. The machine learning algorithm, Random Forest, which uses hazard inducing factors as input was implemented for the evaluation of physiographic vulnerability. The social vulnerability of the region was analysed using GIS Multi-criteria decision analysis approach, with criteria weights to incorporate the interests of different stakeholders. The critical combinations of the two domains of vulnerability in the assessment of the vulnerability to flood, to have efficient flood management in local self-government was demonstrated in this study and can be made use of for any flood event.
Association of CT Findings with clinical severity in Patients with COVID-19, a multicenter Cohort observational study
Authors: Liu Min , Zhang Hongxia , Yu Nan , Hou Qing , Zhu Li , Li Honglun , Wang Jianguo , Zhang Zhi , Gao Liu , Wang Yimin , Dai Rufen , Guo Xiaojuan , Guo Youmin 
Published: April 2020

Abstract
Background

2019 Novel Coronavirus disease (COVID-19) may cause critical illness including severe pneumonia and acute respiratory distress syndrome. Our purpose is to was to analyze the radiological features of COVID-19 pneumonia and its association with clinical severity.
Methods

This retrospective study included 212 patients (122 males, Mean age, 45.6 ± 12.8 years) from 10 hospitals. Chest CT, chest X-ray (CXR), clinical and laboratory data at admission and follow-up CT were collected. Chest CT and CXR were reviewed and CT score of the involved lung was calculated.
Results

94.3% patients had pneumonia on the baseline CT at admission. The most CT findings were as follows: GGO (140/200), GGO with consolidation (38/200) and consolidation (16/200) most involving the lower lobes with a predilection for the peripheral aspects. The CT score negatively correlated with Lymphocyte count while it positively correlated with C-reactive protein. ROC curve showed an optimal cutoff value of the CT score of 15 had a sensitivity of 70% and a specificity of 96.5% for the prediction of severe status. Series CT showed GGO or consolidation gradually reduced in 52 patients while 6 patients had reticular opacities. 14 patients showed the normal CXR while GGO were found on CT.
Conclusion

COVID-19 pneumonia manifests as focal, multifocal ground-glass opacities with/without consolidations. Higher CT score correlated severe clinical status. CXR is yet insufficient for evaluation of COVID-19 pneumonia.
Temperature, Air Pollution and Fatal Unintentional Injuries in Jiangsu Province China, 2015-2017
Authors: Robertson Leon , Zhou Lian , Chen Kai 
Published: April 2020

Abstract
Background The correlation of unintentional injury mortality to rising temperatures found in several studies could result from changes in behavior that increases exposure to hazards or risk when exposed. An increase of .82 degrees (C) daily maximum temperature in Jiangsu Province, China from 2015 through 2017 provides the opportunity to study further this phenomenon including a potential effect of air pollution. Air pollutants may cause symptoms and distractions that increase risk and avoidance behavior that reduces risk. This study examines data that allows estimates of the effects of temperature, precipitation and five air pollutants on injury mortality risk, each corrected statistically for the potential effects of the others.Methods Daily data on unintentional injury deaths and exposures to temperature, precipitation and pollutants from 9 cities in Jiangsu Province, China during 2015-2017 were analyzed using logistic regression. Application of the regression equations to 2016 and 2017 data substituting 2015 values for each of the predictor variables respectively was used to estimate changes in deaths related to changes in the risk factors.Results Unintentional injury death risk was unrelated to temperature when temperatures were below 25 degrees (C) but increased substantially in relation to higher temperatures. The correlations mainly occurred for non-transport injuries. Fatal injury risk was higher in relation to higher concentrations of PM 10 , NO 2 , and SO 2 in certain temperature ranges but lower in relation to higher concentrations of CO in the lowest and highest temperature ranges. Lower risk was associated with O 3 at low temperatures but higher risk at high temperatures. Risk was lower on weekends but higher on holidays when temperatures were moderate.Conclusions The increase in fatal unintentional injuries from 2015 through 2017 was substantially related to the rise in average maximum daily temperature. Negative associations of certain pollutants, particularly O 3, with injury risk suggests the possibility that changes in behavior to avoid those pollutants also results in lower injury risk. Most pollutants, however, are related to increased risk.
Aid for Trade, Export Product Diversification and Foreign Direct Investment
Authors: GNANGNON Sèna Kimm 
Published: April 2020

Abstract
The few existing studies on the relationship between Aid for Trade (AfT) flows and Foreign Direct Investment (FDI) inflows tend to report a positive effect of total AfT flows, in particular of Aid flows for building economic infrastructure, on FDI inflows. The present article aims to complement these works by investigating whether the effect of AfT flows on inward FDI stock depends on recipient-countries' level of export product concentration. The empirical analysis has shown that AfT flows exert a strong positive effect on inward FDI stock in countries that experience a high level of export product concentration. These findings are relevant for developing countries in light of the concentration of their export products on primary commodities, and given the strong role of FDI flows for employment generation, economic growth and development in these countries.
Introduction of a Radiologic Severity Index for the 2019 Novel Corona Virus (COVID-19)
Authors: Lotfi Mehrzad , Sefidbakht Sepideh , Moghadami Mohsen , Iranpour Pouya , Emami Yasaman , Jafari Seyed Hamed , Mirahmadizadeh Alireza , Ranjbar Keivan , Emami Mehrdad , Erfani Amirhossein , Shahriarirad Reza 
Published: April 2020

Abstract
Background: Given the limited number of beds in intensive care units, establishing a system that can predict the outcome in COVID19 positive patients based on imaging plays an important role in using resources efficiently. Therefor this study was conducted to design an optimal scoring system related to the severity of COVID19 cases for distinguishing severe from non-severe patients. Materials and Methods: In this cross-sectional retrospective study, 82 patients with a definite diagnosis of COVID-19 infection, who had at least one chest CT scan in hospital course were enrolled.To assess the severity of pulmonary parenchymal involvement, we semi-quantitatively evaluated the extent and nature of abnormalities. The area of lung involvement was scored in three levels based on a 0-4 grading scale. Also, we established a 4-point scoring system for defining the nature of lung abnormalities. The two scores were multiplied by each other. A final radiologic severity score was determined after adding together the scores of all levels.Result: Of all cases, fifty-three (64.6%) were male with an average age of age 53.75. Among the patients in our study, 7 (8.5%) had severe disease and the mortality rate was 7.2%. The mean (±standard deviation) of the radiologic severity score was 34.3(±18.4) in the severe group and 11.3(±11.4) in the non-sever group. (P-value <0.05). Also, we found a significant reverse relationship between our severity score and O2 saturation (P-value <0.05).Conclusion: The radiologic severity score demonstrated a significant correlation with the patients' mortality and severity of illness in COVID-19 patients.
Deep learning convolutional neural network in rainfall–runoff modelling
Authors: Van Song Pham , Le Hoang Minh , Thanh Dat Vi , Dang Thanh Duc , Loc Ho Huu , Anh Duong Tran 
Published: April 2020

Abstract
Rainfall–runoff modelling is complicated due to numerous complex interactions and feedback in the water cycle among precipitation and evapotranspiration processes, and also geophysical characteristics. Consequently, the lack of geophysical characteristics such as soil properties leads to difficulties in developing physical and analytical models when traditional statistical methods cannot simulate rainfall–runoff accurately. Machine learning techniques with data-driven methods, which can capture the nonlinear relationship between prediction and predictors, have been rapidly developed in the last decades and have many applications in the field of water resources. This study attempts to develop a novel 1D convolutional neural network (CNN), a deep learning technique, with a ReLU activation function for rainfall–runoff modelling. The modelling paradigm includes applying two convolutional filters in parallel to separate time series, which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series. The developed modelling framework is evaluated with measured data at Chau Doc and Can Tho hydro-meteorological stations in the Vietnamese Mekong Delta. The proposed model results are compared with simulations of long short-term memory (LSTM) and traditional models. Both CNN and LSTM have better performance than the traditional models, and the statistical performance of the CNN model is slightly better than the LSTM results. We demonstrate that the convolutional network is suitable for regression-type problems and can effectively learn dependencies in and between the series without the need for a long historical time series, is a time-efficient and easy to implement alternative to recurrent-type networks and tends to outperform linear and recurrent models.
Discharge coefficients for ogee weirs including the effects of a sloping upstream face
Authors: Salmasi Farzin , Abraham John 
Published: April 2020

Abstract
Discharge coefficients (C0) for ogee weirs are essential factors for predicting the discharge-head relationship. The present study investigates three influences on the C0: effect of approach depth, weir upstream face slope, and the actual head, which may differ from the design head. This study uses experimental data with multiple non-linear regression techniques and Gene Expression Programming (GEP) models that are applied to introduce practical equations that can be used for design. Results show that the GEP method is superior to the regression analysis for predicting the discharge coefficient. Performance criteria for GEP are R2 = 0.995, RMSE = 0.021 and MAE = 0.015. Design examples are presented that show that the proposed GEP equation correlates well with the data and eliminates linear interpolation using existing graphs.
Engagement and Clinical Improvement Among Older Adult Primary Care Patients Using a Mobile Intervention for Depression and Anxiety: Case Studies
Authors: Orr L Casey , Graham Andrea K , Mohr David C , Greene Carolyn J 
Published: April 2020

Background
Technology-based mental health interventions are an increasingly attractive option for expanding access to mental health services within the primary care system. Older adults are among the groups that could potentially benefit from the growing ubiquity of technology-based mental health interventions; however, older adults are perceived to be averse to using technology and have reported barriers to use.

Objective
The aim of this paper is to present a case study of 3 participants from a clinical trial evaluating IntelliCare, an evidence-based mobile intervention for depression and anxiety, among adults recruited from primary care clinics. Our report of these 3 participants, who were aged 60 years or older, focuses on their engagement with the IntelliCare service (ie, app use, coach communication) and clinical changes in depression or anxiety symptoms over the intervention period.

Methods
The 3 case study participants were offered IntelliCare with coaching for 8 weeks. The intervention consisted of 5 treatment intervention apps that support a variety of psychological skills, a Hub app that contained psychoeducational content and administered weekly assessments, and coaching for encouragement, accountability, and technical assistance as needed. The 3 case study participants were selected to reflect the overall demographics of participants within the trial and because their interactions with IntelliCare provided a good illustration of varied experiences regarding engagement with the intervention.

Results
The 3 participants’ unique experiences with the intervention are described. Despite potential barriers and experiencing some technical glitches, the participants showed proficient ability to use the apps, high levels of participation through frequent app use and coach interaction, and decreased depression and anxiety scores. At the end of the 8-week intervention, each of these 3 participants expressed great enthusiasm for the benefit of this program through feedback to their coach, and they each identified a number of ways they had seen improvements in themselves.

Conclusions
These 3 cases provide examples of older individuals who engaged with and benefitted from the IntelliCare service. Although the results from these 3 cases may not generalize to others, they provide an important, informed perspective of the experiences that can contribute to our understanding of how older adults use and overcome barriers to mental health technologies. The findings also contribute toward the ultimate goal of ensuring that the IntelliCare intervention is appropriate for individuals of all ages.