bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling the Effect of Organizational Justice on Employee's Well-Being, Organizational Citizenship Behavior and Turnover Intentions through Employee Engagement
Authors: Aggarwal Arun , Mittal Amit 
Published: October 2021

Introduction:The last two decades had witnessed an increased interest in employee engagement by the academician and the practitioner. The reason for such interest is employee engagement potential to influence the individual and organizational level consequences.Methods:Hence, the current study's objective was to identify the key antecedents and consequences of employee engagement and establish their inter-relationship. Apart from this, the study also validates the different scales to measure different antecedents and consequences of employee engagement. The data were collected from 656 employees working in the FMCD industry in India to achieve this objective.Results:Results of the structural equation modeling analysis show that perceptions of organizational justice positively impact employee engagement. Further, employee engagement positively impacts satisfaction with life, positive affect, and organizational citizenship behavior.Conclusion:However, employee engagement showed a negative relationship with negative affect and employee turnover intentions. In the end, the practical and theoretical implications of the study were discussed.
The Energy Consumption of Passenger Vehicles in a Transformed Mobility System with Autonomous, Shared and Fit-For-Purpose Electric Vehicles in the Netherlands
Authors: Hogeveen Peter , Steinbuch Maarten , Verbong Geert , Hoekstra Auke 
Published: October 2021

Aims:
This article explores the tank-to-wheel energy consumption of passenger transport at full adoption of fit-for-purpose shared and autonomous electric vehicles.

Background:
The energy consumption of passenger transport is increasing every year. Electrification of vehicles reduces their energy consumption significantly but is not the only disruptive trend in mobility. Shared fleets and autonomous driving are also expected to have large impacts and lead to fleets with one-person fit-for-purpose vehicles. The energy consumption of passenger transport in such scenarios is rarely discussed and we have not yet seen attempts to quantify it.

Objective:

The objective of this study is to quantify the tank-to-wheel energy consumption of passenger transport when the vehicle fleet is comprised of shared autonomous and electric fit-for-purpose vehicles and where cheap and accessible mobility leads to significantly increased mobility demand.

Methodology:
The approach consists of four steps. First, describing the key characteristics of a future mobility system with fit-for-purpose shared autonomous electric vehicles. Second, estimating the vehicle miles traveled in such a scenario. Third, estimating the energy use of the fit-for-purpose vehicles. And last, multiplying the mileages and energy consumptions of the vehicles and scaling the results with the population of the Netherlands.

Results:
Our findings show that the daily tank-to-wheel energy consumption from Dutch passenger transport in full adoption scenarios of shared autonomous electric vehicles ranges from 700 Wh to 2200 Wh per capita. This implies a reduction of 90% to 70% compared to the current situation.

Conclusion:
Full adoption of shared autonomous electric vehicles could increase the vehicle-miles-travelled and thus energy use of passenger transport by 30% to 150%. Electrification of vehicles reduces energy consumption by 75%. Autonomous driving has the potential of reducing the energy consumption by up to 40% and implementing one-person fit-for-purpose vehicles by another 50% to 60%. For our case study of the Netherlands, this means that the current 600 TJ/day that is consumed by passenger vehicles will be reduced to about 50 to 150 TJ/day at full adoption of SAEVs.
Cytotoxic and Antitumoral Activity of N-(9H-purin-6-yl) Benzamide Derivatives
and Related Water-soluble Prodrugs
Authors: Cros-Perrial Emeline , Saulnier Steve , Raza Muhammad Zawwad , Charmelot Rémi , Egron David , Dumontet Charles , Chaloin Laurent , Peyrottes Suzanne , Jordheim Lars Petter 
Published: October 2021

Background:
The development of small molecules as cancer treatments is still of both
interest and importance.

Objective:
Having synthesized and identified the initial cytotoxic activity of a series of chemically
related N-(9H-purin-6-yl) benzamide derivatives, we continued their evaluation on cancer cell models.
We also synthesized water-soluble prodrugs of the main compound and performed in vivo experiments.

Method:
We used organic chemistry to obtain compounds of interest and prodrugs. The biological
evaluation included MTT assays, synergy experiments, proliferation assays by CFSE, cell cycle distribution
and in vivo antitumoral activity

Results:
Our results show activities on cancer cell lines ranging from 3-39 μM for the best compounds,
with both induction of apoptosis and decrease in cell proliferation. Two compounds evaluated
in vivo showed weak antitumoral activity. In addition, the lead compound and its prodrug had
a synergistic activity with the nucleoside analogue fludarabine in vitro and in vivo.

Conclusion:
Our work allowed us to gain better knowledge on the activity of N-(9H-purin-6-yl)
benzamide derivatives and showed new examples of water-soluble prodrugs. More research is warranted
to decipher the molecular mechanisms of the molecules.
The Alleviating Effect of Herniarin Against Ionizing Radiation-Induced
Genotoxicity and Cytotoxicity in Human Peripheral Blood Lymphocytes
Authors: Molazadeh Mikaeil , Al Fares Elham , Sanikidze Tamar , Kalmakhelidze Sophio , Topuria David , Mansi Luigi , Kitson Sean 
Published: October 2021

Background and Objective:
Herniarin is a simple coumarin that is found naturally in
some plant species. In this study, we aimed to evaluate the protective effect of herniarin against ionizing
radiation-induced genotoxicity and cytotoxicity in human peripheral blood lymphocytes.

Methods:
Herniarin was added to human lymphocytes before irradiation with a dose of 2 Gy of Xrays.
The antagonistic potential of herniarin against radiation was measured by MTT [3-(4,5-
dimethyl-2-thiazolyl)-2,5-diphenyl-2H tetrazolium bromide] assay, micronucleus assay, flow cytometry,
and reactive oxygen species (ROS) level analysis.

Results:
The maximum survival of lymphocytes against radiation was observed at a concentration
of 50 μM of herniarin and a treatment time of 1 h. Pretreatment with 50 μM herniarin significantly
decreased the micronuclei frequency, the percentage of apoptotic lymphocytes, and the ROS level
in irradiated human lymphocytes. Moreover, 50 μM herniarin significantly increased the cytokinesis
blocked proliferation index in irradiated lymphocytes.

Conclusion:
Herniarin could reduce radiation-induced cytotoxicity and genotoxicity in human lymphocytes.
To complete the results of this study, it is suggested that in the future, more preclinical
studies with larger samples or animal models be performed on herniarin.
Perceptions and Experiences of Participating in PMTCT Option B Plus: An Explorative Study on HIV-positive Pregnant Women in Eswatini
Authors: Dlamini Nompilo , Ntuli Busisiwe , Madiba Sphiwe 
Published: October 2021

Background:Eswatini has adopted the PMTCT Option B+ approach as a strategy for the prevention of mother-to-child transmission of HIV.Objective:This study aimed to explore how pregnant Swazi women perceived and experienced PMTCT Option B+ and examined challenges they faced in disclosing their HIV status to their male partners.Methods:We interviewed 15 HIV-positive pregnant women selected using purposeful sampling from the PMTCT programme in Manzini Region, Eswatini. The data were analysed thematically.Results:The women had to deal with the pregnancy, the HIV-positive test results, the immediacy of the antiretroviral treatment (ART), and disclosure issues, all in one visit. They perceived the mandatory HIV testing and the same-day ART initiation as coercive. Regardless, they perceived PMTCT in a positive manner and as a gateway to early treatment for them. The drive to enroll in and remain in PMTCT was motivated by the belief in the efficacy of ART and the desire to protect their unborn babies from HIV infection. Their anticipation of rejection and violence from their partners led to their delaying disclosure and initiation of ART. Following disclosure, some of them were stigmatised, blamed for the infection, and abandoned by their partners.Conclusion:As Eswatini continues to roll out Option B+, there is a need to consider providing individualised counselling sessions to meet the individual needs of women.
Association between Patient Satisfaction and the Willingness to Return for Rehabilitation- A Pilot Study
Authors: Thao Nguyen Thi Thu , Xuan Trinh Thanh , Tran Nguyen Bao , Huong Bui Thi , Anh Nguyen Tuan , Duc Nguyen Quang , Thuy Nguyen Thi Ngoc , Tam Nguyen Thi Thanh , Ngan Nguyen Thi Hong , Van Khai Nguyen , Ngoc Nguyen Thi Minh 
Published: October 2021

Background:
Patient satisfaction has played an important role in the development of health systems. However, the information on patient satisfaction and the returning of the patients for rehabilitation is limited. This study aims to explore the role of different domains in patient satisfaction with the willingness to return for further rehabilitation.

Methods:
The cross-sectional study was conducted among all 163 patients who were treated at different departments but needed a combination of treatment with rehabilitation at Viettiep hospital, Haiphong city. The Patient Satisfaction Index (PSI) Questionnaire was applied to record the average score of satisfaction. Multivariate logistic regression was used to determine the association between each domain of the PSI and the return for rehabilitation treatment.

Results:
Among the PSI scale, the strongest correlation was observed between the attitude of medical staff and the equipment and infrastructure of the hospital (r=0.305). The linear regression model indicated that patient satisfaction was associated significantly with the willingness to return for further rehabilitation (OR= 58.442). There were significant associations between the accessibility, equipment and infrastructure, quality of care and treatment, and cost of treatment with the returning for treatment of the patients.

Conclusion:
The patient satisfaction regarding individual domains should be considered in more detail in order to enhance the rehabilitation treatment of patients after discharging them from the hospital.
Identifying the Lowest Effective Initial Dose of Prednisolone for the Treatment of Subacute Granulomatous Thyroiditis: A Systematic Review and Meta-Analysis
Authors: Hadavi Marzieh , Soltani Akbar , Nourani Fatemeh , Roudsari Sahar Behnam , Jouybari Leila , Fathi Mobina , Haghighat Somayeh , Aletaha Azadeh 
Published: October 2021

Background:Subacute granulomatous thyroiditis (SAGT) is an inflammatory disease due to viral infections. Glucocorticoids, especially prednisolone (PSL), are one of the first approaches in the treatment of patients with SAGT. To date, no study has determined the lowest effective dose of prednisolone with the lowest recurrence rate in the treatment of SAGT. This study aimed to use meta-analysis methods to identify the appropriate dosage of prednisolone with the lowest recurrence rate in the treatment of patients with SAGT.Methods:This study was conducted according to the PRISMA checklist in February 2021. Two independent researchers performed a search for relevant literature published before March 2021 in English databases, including Scopus, MEDLINE (via PubMed), Web of Science, Cochrane Library, Google Scholar, EMBASE, and also Persian electronic databases including SID, Iran medex, Magiran, and Irandoc. The search algorithm was initially developed by using a combination of MeSH terms, keywords, and also Boolean operators (“AND”; “OR”; “NOT”): Subacute thyroiditis, De Quervain Thyroiditis, Glucocorticoids, Prednisolone, Recurrence, and Meta-Analysis. All statistical analyses were performed using STATA 15.0 (StataCorp LLC, College Station, TX, USA) and SPSS 17.0. A random-effects model based on Metaprop was applied for the Meta-analysis. To assess heterogeneity between studies, the chi-squared test and I2 index were used, and for evaluating publication bias, funnel plots and Egger tests were performed.Results:The overall recurrence rate was 14.72% [95% CI: 9.63- 20.58] and there was a significant heterogeneity among the studies [I2 = 69.56%; P=0.000]. To evaluate the lowest effective dose of prednisolone, we divided the studies into two groups based on the mean initial dose of prednisolone: less than or equal to 20 mg/day (group one) and greater than 20 mg/day (group two). The recurrence rate in group 1 was 11% [95% CI: 5.7- 16.2] and in group 2 was 23.6% [95% CI: 11.5- 35.6]. Significant correlations were observed between the initial mean dose of PSL and recurrence rate (r= 0.71; P= 0.013). Begg’s funnel plot had no evidence of publication bias in these studies (p=0.160).Conclusion:According to the results of this meta-analysis, 15 to 20 mg/day of prednisolone is the most effective dosage with the lowest recurrence rate in the treatment of subacute Granulomatous thyroiditis.
Deep Learning–Assisted Burn Wound Diagnosis: Diagnostic Model Development Study
Authors: Chang Che Wei , Lai Feipei , Christian Mesakh , Chen Yu Chun , Hsu Ching , Chen Yo Shen , Chang Dun Hao , Roan Tyng Luen , Yu Yen Che 
Published: October 2021

Background
Accurate assessment of the percentage total body surface area (%TBSA) of burn wounds is crucial in the management of burn patients. The resuscitation fluid and nutritional needs of burn patients, their need for intensive unit care, and probability of mortality are all directly related to %TBSA. It is difficult to estimate a burn area of irregular shape by inspection. Many articles have reported discrepancies in estimating %TBSA by different doctors.

Objective
We propose a method, based on deep learning, for burn wound detection, segmentation, and calculation of %TBSA on a pixel-to-pixel basis.

Methods
A 2-step procedure was used to convert burn wound diagnosis into %TBSA. In the first step, images of burn wounds were collected from medical records and labeled by burn surgeons, and the data set was then input into 2 deep learning architectures, U-Net and Mask R-CNN, each configured with 2 different backbones, to segment the burn wounds. In the second step, we collected and labeled images of hands to create another data set, which was also input into U-Net and Mask R-CNN to segment the hands. The %TBSA of burn wounds was then calculated by comparing the pixels of mask areas on images of the burn wound and hand of the same patient according to the rule of hand, which states that one’s hand accounts for 0.8% of TBSA.

Results
A total of 2591 images of burn wounds were collected and labeled to form the burn wound data set. The data set was randomly split into training, validation, and testing sets in a ratio of 8:1:1. Four hundred images of volar hands were collected and labeled to form the hand data set, which was also split into 3 sets using the same method. For the images of burn wounds, Mask R-CNN with ResNet101 had the best segmentation result with a Dice coefficient (DC) of 0.9496, while U-Net with ResNet101 had a DC of 0.8545. For the hand images, U-Net and Mask R-CNN had similar performance with DC values of 0.9920 and 0.9910, respectively. Lastly, we conducted a test diagnosis in a burn patient. Mask R-CNN with ResNet101 had on average less deviation (0.115% TBSA) from the ground truth than burn surgeons.

Conclusions
This is one of the first studies to diagnose all depths of burn wounds and convert the segmentation results into %TBSA using different deep learning models. We aimed to assist medical staff in estimating burn size more accurately, thereby helping to provide precise care to burn victims.
Impact of Medical Blog Reading and Information Presentation on Readers’ Preventative Health Intentions: Mixed Methods, Multistudy Investigation
Authors: Taylor Kimberly A , Humphrey Jr William F 
Published: October 2021

Background
Medical blogs have become valuable information sources for patients and caregivers. Most research has focused on patients’ creation of blogs as therapy. But we know less about how these blogs affect their readers and what format of information influences readers to take preventative health actions.

Objective
This study aimed to identify how reading patient medical blogs influences readers’ perceived health risk and their intentions to engage in preventative health actions. Further, we aimed to examine the format of the medical blog and the reader’s response.

Methods
We surveyed 99 university participants and a general-population, online panel of 167 participants. Both studies randomly assigned participants to conditions and measured blog evaluation, intentions for preventative health action, and evaluation of health risk and beliefs, and allowed open-ended comments. The second study used a different sample and added a control condition. A third study used a convenience sample of blog readers to evaluate the link between reading medical blogs and taking preventative health action.

Results
Across 3 studies, participants indicated a desire to take future preventative health action after reading patient blogs. Studies 1 and 2 used experimental scenario-based designs, while Study 3 employed a qualitative design with real blog readers. The 2 experimental studies showed that the type of blog impacted intentions to engage in future preventative health actions (Study 1: F2,96=6.08, P=.003; Study 2: F3,166=2.59, P=.06), with a statistical blog being most effective in both studies and a personal narrative blog showing similar effectiveness in Study 2, contrary to some prior research. The readers’ perceptions of their own health risk did not impact the relationship between the blog type and health intentions. In contrast, in one study, participants’ judgments about the barriers they might face to accessing care improved the fit of the model (F2,95=13.57, P<.001). In Study 3’s sample of medical blog readers, 53% (24/45) reported taking preventative health action after reading a health blog, including performing a self-check, asking a doctor about their health risk, or requesting a screening test. Additionally, these readers expressed that they read the blogs to follow the author (patient) and to learn general health information. All studies demonstrated the blogs were somewhat sad and emotional but also informative and well-written. They noted that the blogs made them appreciate life more and motivated them to consider taking some action regarding their health.

Conclusions
Reading patient blogs influences intentions to take future health actions. However, blog formats show different efficacy, and the readers’ disease risk perceptions do not. Physicians, medical practitioners, and health organizations may find it useful to curate or promote selected medical blogs to influence patient behavior.
Using Social Media to Understand Web-Based Social Factors Concerning Obesity: Systematic Review
Authors: Li Chuqin , Ademiluyi Adesoji , Ge Yaorong , Park Albert 
Published: October 2021

Background
Evidence in the literature surrounding obesity suggests that social factors play a substantial role in the spread of obesity. Although social ties with a friend who is obese increase the probability of becoming obese, the role of social media in this dynamic remains underexplored in obesity research. Given the rapid proliferation of social media in recent years, individuals socialize through social media and share their health-related daily routines, including dieting and exercising. Thus, it is timely and imperative to review previous studies focused on social factors in social media and obesity.

Objective
This study aims to examine web-based social factors in relation to obesity research.

Methods
We conducted a systematic review. We searched PubMed, Association for Computing Machinery, and ScienceDirect for articles published by July 5, 2019. Web-based social factors that are related to obesity behaviors were studied and analyzed.

Results
In total, 1608 studies were identified from the selected databases. Of these 1608 studies, 50 (3.11%) studies met the eligibility criteria. In total, 10 types of web-based social factors were identified, and a socioecological model was adopted to explain their potential impact on an individual from varying levels of web-based social structure to social media users’ connection to the real world.

Conclusions
We found 4 levels of interaction in social media. Gender was the only factor found at the individual level, and it affects user’s web-based obesity-related behaviors. Social support was the predominant factor identified, which benefits users in their weight loss journey at the interpersonal level. Some factors, such as stigma were also found to be associated with a healthy web-based social environment. Understanding the effectiveness of these factors is essential to help users create and maintain a healthy lifestyle.
Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study
Authors: Pumplun Luisa , Fecho Mariska , Wahl Nihal , Peters Felix , Buxmann Peter 
Published: October 2021

Background
Recently, machine learning (ML) has been transforming our daily lives by enabling intelligent voice assistants, personalized support for purchase decisions, and efficient credit card fraud detection. In addition to its everyday applications, ML holds the potential to improve medicine as well, especially with regard to diagnostics in clinics. In a world characterized by population growth, demographic change, and the global COVID-19 pandemic, ML systems offer the opportunity to make diagnostics more effective and efficient, leading to a high interest of clinics in such systems. However, despite the high potential of ML, only a few ML systems have been deployed in clinics yet, as their adoption process differs significantly from the integration of prior health information technologies given the specific characteristics of ML.

Objective
This study aims to explore the factors that influence the adoption process of ML systems for medical diagnostics in clinics to foster the adoption of these systems in clinics. Furthermore, this study provides insight into how these factors can be used to determine the ML maturity score of clinics, which can be applied by practitioners to measure the clinic status quo in the adoption process of ML systems.

Methods
To gain more insight into the adoption process of ML systems for medical diagnostics in clinics, we conducted a qualitative study by interviewing 22 selected medical experts from clinics and their suppliers with profound knowledge in the field of ML. We used a semistructured interview guideline, asked open-ended questions, and transcribed the interviews verbatim. To analyze the transcripts, we first used a content analysis approach based on the health care–specific framework of nonadoption, abandonment, scale-up, spread, and sustainability. Then, we drew on the results of the content analysis to create a maturity model for ML adoption in clinics according to an established development process.

Results
With the help of the interviews, we were able to identify 13 ML-specific factors that influence the adoption process of ML systems in clinics. We categorized these factors according to 7 domains that form a holistic ML adoption framework for clinics. In addition, we created an applicable maturity model that could help practitioners assess their current state in the ML adoption process.

Conclusions
Many clinics still face major problems in adopting ML systems for medical diagnostics; thus, they do not benefit from the potential of these systems. Therefore, both the ML adoption framework and the maturity model for ML systems in clinics can not only guide future research that seeks to explore the promises and challenges associated with ML systems in a medical setting but also be a practical reference point for clinicians.