bluuebunny/crossref_metadata_2025_split huggingface dataset:
Latent Profile Analysis of moral identity of nursing students: a cross-sectional study
Authors: jianghu Hu , Mei Yongxia , Zhao Zhixin , Wang Xiaoxuan , Wang Wenna , Lin Beilei , Zhang Zhenxiang 
Published: April 2023

Abstract
Background The cornerstone of the nursing profession is moral identity, and the establishment of professional identity goes hand in hand with moral growth. The existing state of affairs and the elements affecting nursing students' moral identity are not well understood from a multifaceted perspective.Objectives To define moral identity subgroups and research the variables affecting profile membership.Methods A convenient sampling. was used to conduct a cross-sectional study among nursing students from November 16th, 2022, to January 17th, 2023. The Moral Identity Measure and the General Demographic Information Questionnaire were provided to the participants. Students studying nursing were divided into several moral identity subgroups using a latent profile analysis (LPA). To determine the variables connected to the putative moral identity categories, we also conducted ordinal and multinomial logistic regression analyses.Results A total of 10583 nursing students responded to legitimate surveys. The LPA results validated the presence of three unique moral identity profiles: low (4.2%), moderate (41.9%), and high (53.9%), and demonstrated that the three-profile model was the most appropriate. The three profiles stood out from one another greatly. Profiles were predicted by gender, grade, political stance, rural background, leadership experience, and junior college degree.Conclusion In this study, three profiles were found, and the majority of nursing students had a strong moral identity. Regarding variables determining profile membership and moral identity subgroups, nursing students should get specialized support.
Electrospun PAN Membranes Strengthened in Situ Grown TiO 2 Particles for High-Performance Lithium-Ion Batteries
Authors: Tang Liping , Wu Yankang , Lei Zhiqiang , He Yu , Chen Jian 
Published: April 2023

Abstract
The electrostatic spinning method with polyacrylonitrile as the spinning precursor was used to investigate the performance of battery separator prepared from tetrabutyl titanate and isopropyl titanate as two titanium source precursors hydrolyzed under the inhibition of acrylic acid to produce different contents of TiO2 for lithium-ion batteries. The experimental results showed that when both titanium sources were prepared at 3 wt%, the comprehensive performance of the separator manufcatured with tetrabutyl titanate as the titanium source (PAN/TBT-3) and isopropyl titanate as the titanium source (PAN/TPT-3) was finest than that of the Celgard 2400 and the composite membrane with direct TiO2 addition (PAN/TiO2-3). The tensile strength of PAN/TPT-3 and PAN/TBT-3 membranes were 7.82 MPa and 4.03 MPa higher than that of Celgard 2400, and 13.29 MPa and MPa higher than that of PAN/TiO2-3 separator, respectively. PAN/TPT-3 and PAN/TBT-3 membranes exhibited a discharge capacity of 107.72 mAh/g and 115.79 mAh/g at 2 C, both the capacity retention rate was above 99.5% higher than 80.5% for Celgard 2400.
What is the required quantity of COVID-19 vaccines for outbreak prevention? A global analysis based on growth curve modeling
Authors: Le-Van Truong , Vu-Thi-Thu Trang 
Published: April 2023

Abstract
Introduction
The SARS-CoV-2 virus, which has the ability to rapidly spread, has caused multiple waves of deaths, resulting in nearly 7 million deaths in the past 3 years. During the early phase, most governments focused on implementing strict measures to cut off the transmission vector. However, the introduction of COVID-19 vaccines has changed the course of the fight against the COVID-19 pandemic.
Methods
A joinpoint regression analysis was used to identify mortality waves in 224 countries from February 22nd, 2020, to March 1st, 2023. Only countries with at least 2 waves were included in the analysis using Superimposition by Translation And Rotation (SITAR) to determine the growth curve of daily deaths and the impact of COVID-19 vaccine doses per population (CVDP), Cumulative incidence of COVID-19 (CIC), Rate of active cases per hospital bed (RAPHB), Active cases with diabetes (ACD), and Stringency index (SI).
Results
The analysis included over 3 million COVID-19 deaths from 82 countries to construct the growth curve. The increase in CVDP was associated with a decrease in wave size, intensity, and duration. However, an increase in CIC, ACD, RAHB, and SI was related to an increase in wave intensity and duration. The results suggest that maintaining CVDP at 120% (equivalent to 60% full doses) was associated with a decrease of 94.4% in COVID-19 deaths.
Conclusion
This research offers evidence for governments to enhance COVID-19 vaccination efforts in order to maintain herd immunity at 60% of the population and consider avoiding strict control measures.
Haemorrhoidal Disease in Pregnancy: Results From a Self-assessment Questionnaire Administered by Means of a Social Network
Authors: D’Alfonso Angela , De Carolis Francesca , Serva Alessandro , Valiyeva Sayali , Guido Maurizio , Pietroletti Renato 
Published: April 2023

Abstract

Background: The anal symptoms occurring pregnancy and post-partum, mainly related to Haemorrhoidal Disease (HD), have been reported with a wide range of incidence in the Literature. Although in many cases the course of the disease is mild and self-limiting, sometimes it is severe enough to give a bad quality of life.
Methods: Our study has been conducted by means of a questionnaire administered via of a social media with the aim of obtaining an epidemiologic data on incidence of symptoms of HD in an unselected population of pregnant women. In addition, we investigated for the presence of those factors notoriously predisposing or associated to haemorrhoidal disease (constipation, straining on the toilet, low dietary fibers and fluid intake).
Results: Out of 133 patients 51% reported Symptoms of HD during pregnancy, mainly in the second and third trimester. Constipation, straining on the toilet, low dietary fiber and fluid intake were not significantly related to incidence of haemorrhoids. Only a previous history of haemorrhoidal disease correlated to onset of hemorrhoids in pregnancy and reached a statistical significance (odds ratio = 5.2, p<0.001).
Conclusion: Although with the limitations posed by the nature of our retrospective study via a self-assessment interview, our results suggest that the occurrence of HD in pregnancy seems not sustained by the classical risk factors observed in the general population. At the moment, specific therapeutic measuresare lacking and treatment relies on empiric suggestions concerning diet, fluid intake, bowel care, local ointment. Further studies are needed in order to identify a targeted etiologic treatment, taking in mind however, that surgery can be an option in selected cases.
Job preference of preventive medicine students during the COVID-19 pandemic: a discrete choice experiment survey in Shandong Province, China
Authors: Tian Zhuang , Zhai Min , Li Hongmin 
Published: April 2023

Abstract
Background:
Public health workers are a crucial part of the health workforce, particularly during the coronavirus disease (COVID-19) pandemic. They play an important role in achieving universal health coverage and sustainable development goals. Human resources in public health in China are in short supply, their distribution is unequal, and their turnover rate is high. A discrete choice experiment (DCE) was applied to investigate preventive medicine students’ preferred job choice criteria and trends in trade-offs by calculating the marginal rate of substitution between these criteria. This study identified the properties of jobs primarily selected by preventive medicine students and estimated the monetary value of each attribute.
Methods:
Based on discussions and in-depth interviews with preventive medicine students and a literature review, we developed a DCE that assessed how students’ stated preferences for a certain choice were influenced by several job attributes, including location, salary, bianzhi, career development opportunities, working environment, and workload. We applied this DCE to preventive medicine students in Shandong Province,China,using a brief, structured questionnaire. Conditional logit models were used to estimate the utility of each job’s attributes. Willingness to pay (WTP) was estimated as the ratio of the value of the coefficient of interest to the negative value of the cost attribute.
Results:
A total of 307 respondents completed the questionnaire, and 261 passed the internal consistency test. All the attributes were statistically significant. Career development opportunities and work locations were the most important factors for the respondents. Preference heterogeneity existed among respondents, e.g., 3-year medical education college students placed a higher value on jobs with bianzhi compared to 5-year medical education college students. Furthermore, rural students’ WTP was much lower than that of urban students.
Conclusions:
The heterogeneity of attributes indicates the complexity of job preferences. Monetary and nonmonetary job characteristics significantly influenced the job preferences of preventive medicine students in China. A more effective policy intervention to attract graduates to work in rural areas should consider both job incentives and the backgrounds of preventive medicine graduates.
Response to furmonertinib in a patient with non-Small cell lung cancer harboring HER2 exon 21 insertion mutation : a case report
Authors: Xu Jiaju , Ni Chunxiao , Zhang Ling , Yu Xin , Pang Yu 
Published: April 2023

Abstract
Background This is the first case report describing a patient with non-small cell lung cancer(NSCLC) harboring two rare human epidermal growth factor receptor 2 (HER2) exon 21 insertion mutations, who responded to furmonertinib treatment. Furmonertinib maybe one effective and economical treatment for NSCLC patients harboring HER2 mutations with minor side effects.Case presentation: We present a case report of a 49-year-old female diagnosed with stage IV lung adenocarcinoma. Firstly, We describe the patient's treatment history, including failed third-line combination treatments of systemic chemotherapy with bevacizumab or carrelizumab or anlotinib, primary lung tumor recurrence, bilateral lung metastases progression, and new brain metastatic lesion detection. Next, we detail the patient's fourth-line treatment with radiotherapy for brain metastases and two cycles of bevacizumab plus Abraxane and cisplatin, however, the disease progressed and relapsed. After that, comprehensive genomic profiling revealed two HER2 exon 21 insertion mutations. Finally, the patient received furmonertinib 80 mg a day and achieved a near-complete response after one month of treatment. The patient is still receiving treatment, without disease progression 8 months after starting treatment with furmonertinib.Conclusion Furmonertinib may be effective for NSCLC patients with HER2 T8962A and L869R mutations and further studies are needed to confirm these results in prospective clinical trials.
Auto-STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items from in vivo publications
Authors: Zürrer Wolfgang Emanuel , Cannon Amelia Elaine , Ewing Ewoud , Brüschweiler David , Bugajska Julia , Hild Bernard Friedrich , Rosso Marianna , Reich Daniel S. , Ineichen Benjamin Victor 
Published: April 2023

Abstract
Background: Systematic reviews, i.e., research summaries that address focused questions in a structured and reproducible manner, are a cornerstone of evidence-based medicine and research. However, certain systematic review steps such as data extraction are labour-intensive which hampers their applicability, not least with the rapidly expanding body of biomedical literature. To bridge this gap, we aimed at developing a data mining tool in the R programming environment to automate data extraction from neuroscience in vivo publications. The function was trained on a literature corpus (n=45 publications) of animal motor neuron disease studies and tested in two validation corpora (motor neuron diseases, n=31 publications; multiple sclerosis, n=244 publications).
Results: Our data mining tool Auto-STEED (Automated and STructured Extraction of Experimental Data) was able to extract key experimental parameters such as animal models and species as well as risk of bias items such as randomization or blinding from in vivo studies. Sensitivity and specificity were over 85 and 80%, respectively, for most items in both validation corpora. Accuracy and F-scores were above 90% and 0.9 for most items in the validation corpora. Time savings were above 99%.
Conclusions: Our developed text mining tool Auto-STEED that can extract key experimental parameters and risk of bias items from the neuroscience in vivoliterature. With this, the tool can be deployed to probe a field in a research improvement context or to replace one human reader during data extraction resulting in substantial time-savings and contribute towards automation of syste99matic reviews. The function is available on Github.
Development and validation of Digi-MEE Instrument measuringonline learning environments in medical education
Authors: Naeem Noor-i-Kiran , Hadie Siti Nurma Hanim , Ismail Irwan Mahazir , Waheed Khadija , Yusoff Muhamad Saiful Bahri 
Published: April 2023

Abstract
Background Despite the widely acknowledged uses of technology-enhanced learning in medical education, the evaluation approach for these online learning environments specific to medical education are limited. Identifying online learning environment key components for medical education may provide an opportunity to address this gap. This study aimed to develop and validate Digital Medical Education Environment (Digi-MEE) Instrument that measures the online learning environment in medical education.Methods Questionnaire development and validation studies were conducted involving 726 participants in total. Components and items of online learning environments were generated and identified through a scoping review and a modified e-Delphi study. A panel of selected content experts carried out content validation leading to calculation of content validity index (CVI). A group of medical students performed face validation leading to determination of face validity index (FVI).This validation process was followed by exploratory factor analysis, confirmatory factor analysis and assessment of reliability to determine Digi-MEE’s factorial structure and internal consistency.Results A total of 15 experts reviewed nine components with 73 items of Digi-MEE to appraise its content validity. As a result, 26 items were removed, and 17 items were revised that led to Digi-MEE 2.0 with 47 items. The S-CVI/Ave was 0.98. Following response process validation, the number of items were reduced to 46, S-FVI/Ave of the Digi-MEE 3.0 was 0.87. Exploratory factor analysis yielded 46 items within nine components and depicted 57.18% variance. After confirmatory factor analysis, the final version of Digi-MEE contained 28 items within nine components. All goodness of fit indices achieved acceptable level. The overall Cronbach alpha of the final Digi-MEE was more than 0.90, while for the nine constructs ranged in between 0.62 and 0.76.Conclusion This study proposed a promising valid and reliable instrument, Digi-MEE, to evaluate online education environment in medical education. The content, response process, factorial structure and internal consistency evidence support the validity of Digi-MEE. Medical schools can use Digi-MEE as an evaluation tool for the continuous quality improvement of online learning environments.
Crop-Net: A Novel Deep Learning Framework for Crop Classification using Time-series Sentinel-1 Imagery by Google Earth Engine
Authors: Seydi Seyd Teymoor , Arefi Hossein , Hasanlou Mahdi 
Published: April 2023

Abstract
Agricultural land management relies heavily on accurate and timely estimation of uncultivated land. Geographical heterogeneity limits the ability of the model to map crops at large scales. This is because the spectral profile of a crop varies spatially. In addition, the generation of robust deep features from remotely sensed SAR data sets is limited by the conventional deep learning models (lacks a mechanism for informative representation). To address these issues, this study proposes a novel dual-stream framework by combining convolutional neural network (CNN) and nested hierarchical transformer (NesT). Based on a hierarchical transformer structure and convolutional layers with spatial/spectral attention modules, the proposed deep learning framework, called Crop-Net, was designed. Time-series Sentinel-1 SAR data were used to evaluate the performance of the proposed model. Sample datasets were also collected by field survey in ten classes including non-crop classes (i.e. water, built-up and barren) and agricultural crop classes (i.e. arboretum, alfalfa, agricultural-vegetable, broad-bean, barley, canola and wheat). The effectiveness of the Crop-Net model was compared with other advanced machine learning and deep learning frameworks. The proposed Crop-Net model is shown to outperform other models through numerical analysis and visual interpretation of crop classification results. It provides accuracy of more than 98.6 (%) and 0.983 in terms of overall accuracy and kappa coefficient, respectively.
QFS-RPL: RPL Based Energy and Mobility Aware Multi Path Routing Protocol for the Internet of Mobile Things Data Transfer Infrastructures
Authors: Alilou Mahmoud , Sangar Amin Babazadeh , Majidzadeh Kambiz , Masdari Mohammad 
Published: April 2023

Abstract
The Internet of Things (IoT) is a network of various interconnected objects capable of collecting and exchanging data without human interaction. These objects have limited processing power, storage space, memory, bandwidth and energy. Therefore, due to these limitations, data transmission and routing are challenging issues where data collection and analysis methods are essential. The Routing Protocol for Low-power and Lossy Networks (RPL) is one of the best alternatives to ensure routing in LoWPAN6 networks. However, RPL lacks scalability and basically designed for non-dynamic devices. Another drawback of the RPL protocol is the lack of load balancing support, leading to unfair distribution of traffic in the network that may decrease network efficiency. This study proposes a novel RPL-based routing protocol, QFS-RPL, using Q-learning algorithm policy and ideation from the Fisheye State Routing (FSR) protocol. The proposed QFS-RPL is as lightweight and agile as the standardized RPL and partially outperforms the mRPL protocol on mobile networks. This method supports multi-path routing, and at any given time in the network lifetime, all possible paths for sending data from any node to the sink are available. Therefore, QFS-RPL provides high resilience against errors, failures, and sudden network changes. To evaluate the performance of the proposed method, the Contiki operating system and Cooja simulator have been used in scenarios with mobile and stationary nodes and random network topologies. The results have been compared with RPL and mRPL. We have developed an algorithm for ease of data transfer in the IoT, which provides better performance than existing protocols, especially when dealing with a mobile network. The performance evaluation criteria considered for simulation are load balancing, energy consumption, number of table entries, Packet Delivery Ratio (PDR), End-to-End (E2E) latency, network throughput, convergence speed, and control packet overhead.
Valvar Bypass Operation to Cure Stubborn Lower Limb Edema Caused by Post- thrombotic Syndrome: Case Report and A Literature Review
Authors: Wang Yanyang , Zhao Liang , Zhang Jie , Du Yali , Chen Jianfeng , Wu Yingfeng 
Published: April 2023

Abstract
Obstruction and/or reflux compromise venous emptying, make for the different pathophysiology of chronic venous insufficiency (CVI). We present a patient, presented with stubborn lower limb edema of CVI caused by post-thrombotic syndrome (PTS), succeeded in responding to the therapy of femoral vein valve by axillary vein bypass after ineffectual valvuloplasty and led a normal life. During 3 months observation, the bridging vessel may completely restore the original anatomical structure. Through the literature study, there is no similar operation reported. It shows that this operation may be feasible for some selected patients.
Gender-gap in Internet Literacy in India: a state-level Analysis
Authors: Gupta Tanushree , Maiti Susobhan , Y Meenakshi , Jana Anindita 
Published: April 2023

Abstract
Internet literacy is the knowledge of using internet facility, deals with intellectual ability of men and women. Internet plays very important role in our daily life to live, learn, work in a society to access information, knowledge, entertainment and improve our skills and internet literacy is one of the basic literacy whereas the gender gap is difference between men and Women in social, political, intellectual, cultural, economic attainments’ or attitudes. The present study analyses gender gap in internet literacy in India using National Family Health Survey (NFHS) 5 data employing descriptive statistics, and t test. Study analyses gender gap in internet literacy in all the states of India shows clearly there is considerable level of gender gap among men and women. The finding indicates that just 63.06% of males and 40.65% of females have ever used the internet. Assam has one of the lowest rates of male Internet literacy in India, whereas Goa has one of the highest (82.90). Bihar has the lowest percentage of Internet-literate women, while Sikkim has the most. The gender disparity is most in the state of Telengana, with a score of 30.9, followed by Chhattisgarh (29.6), and it is at its smallest in the state of Sikkim, with a score of 1.5 and the gender difference seems to be more than 20 in the majority of the states.