bluuebunny/crossref_metadata_2025_split huggingface dataset:
Picture fuzzy WASPAS method for selecting last-mile delivery mode: a case study of Belgrade
Authors: Simić Vladimir , Lazarević Dragan , Dobrodolac Momčilo 
Published: July 2021

Abstract
Background
Last-mile delivery (LMD) is becoming more and more demanding due to an increasing number of users and traffic problems in cities. Besides, medical crises (like the COVID-19 outbreak) and air pollution represent additional motives for the transition from traditional to socially and environmentally sustainable LMD mode. An emerging problem for companies in the postal and logistics industry is how to determine the best LMD mode in a multi-criteria setting under uncertainty.

Method
For the first time, an extension of the Weighted Aggregated Sum Product ASsessment (WASPAS) method under the picture fuzzy environment is presented to solve the LMD mode selection problem. The introduced picture fuzzy set (PFS) based multi-criteria decision-making (MCDM) method can be highly beneficial to managers who are in charge of LMD since it can take into account the neutral/refusal information and efficiently deal with high levels of imprecise, vague, and uncertain information. The comparative analysis with the existing state-of-the-art PFS-based MCDM methods approved the high reliability of the proposed picture fuzzy WASPAS method. Its high robustness and consistency are also confirmed. The presented method can be used to improve LMD in urban areas worldwide. Besides, it can be applied to solve other emerging MCDM problems in an uncertain environment.

Findings
A real-life case study of Belgrade is presented to fully illustrate the potentials and applicability of the picture fuzzy WASPAS method. The results show that postomates are the best mode for LMD in Belgrade, followed by cargo bicycles, drones, traditional delivery, autonomous vehicles, and tube transport.
Case report: the etiology of anterior inferior cerebellar artery infarction: what does basi-parallel anatomic scanning magnetic resonance imaging tell us?
Authors: Zhang Zhi-yong , Zhou Zhi , Zhang Hai-bo , Jiao Jin-song 
Published: July 2021

Abstract
Background
The precise etiology of anterior inferior cerebellar artery (AICA) infarction is difficult to identify because of the high anatomic variability of vertebrobasilar arteries and the limitations of conventional vascular examinations. Basi-parallel anatomic scanning magnetic resonance imaging (BPAS-MRI) can reveal the outer contour of the intracranial vertebrobasilar arteries, which may be helpful to distinguish the arteriosclerosis from congenital dysplasia and dissection.

Case presentation
In this study, we reported 3 cases of AICA infarction and discussed the diagnostic value of BPAS-MRI in the evaluation of vascular etiology.

Conclusions
The BPAS-MRI could be considered as an important supplementary in the diagnosis of vascular etiology of infarction in AICA territory.
NR1D1 suppressed the growth of ovarian cancer by abrogating the JAK/STAT3 signaling pathway
Authors: Wang Huailin , Fu Yan 
Published: July 2021

Abstract
Background
Nuclear receptor subfamily 1 group D member 1 (NR1D1), a nuclear receptor associated with a variety of physiological processes, has a low level in ovarian cancer tissues compared with adjacent normal tissues. However, its role in ovarian cancer remains unclear.

Methods
The level of NR1D1 in ovarian cancer cells was determined by quantitative real-time PCR. Its role in ovarian cancer was explored through gain-of-function and lose-of-function. Cell growth was evaluated by CCK8 assay, immunofluorescence and flow cytometry. Western blot was conducted to assess the activation of JAK/STAT3 signaling pathway. A xenograft model of ovarian cancer was established to explore the role of NR1D1 in vivo.

Results
Up-regulation of NR1D1 repressed the ovarian cancer cell proliferation and induced cell cycle arrest and apoptosis, while silencing NR1D1 promoted their proliferation and G1/S transition. In addition, the JAK/STAT3 signaling pathway, an intracellular signal transduction closely associated with cancer progression, was inhibited by NR1D1. Consistently, xenografts with NR1D1 over-expression grew more slowly in vivo than the controls. Furthermore, NR1D1 up-regulated the expression of suppressor of cytokine signaling 3 (SOCS3), an inhibitor of the JAK/STAT3 signaling pathway. Whereas, SOCS3 silencing abolished the function of NR1D1 over-expression on ovarian cancer growth and JAK/STAT3 signaling pathway.

Conclusions
NR1D1 up-regulated the expression of SOCS3, resulting in suppression of the JAK/STAT3 signaling pathway, thus retarding the growth of ovarian cancer cells. This study highlights a profound role of NR1D1 in the treatment of ovarian cancer.
Higher psychological distress in patients seeking care for a knee disorder is associated with diagnostic discordance between health care providers: a secondary analysis of a diagnostic concordance study
Authors: Lowry Véronique , Bass Alec , Vukobrat Tatiana , Décary Simon , Bélisle Patrick , Sylvestre Marie-Pierre , Desmeules François 
Published: July 2021

Abstract
Background
Knee disorders are highly prevalent and may be a disabling condition. An accurate diagnosis is necessary to guide toward a rapid and efficient management of knee disorders. However, the ability to make a valid diagnosis is often complex for clinicians and evidence is mainly focused on clinician cognitive biases or errors produced during clinical reasoning. The aim of this secondary exploratory analysis is to identify patient-specific characteristics associated with diagnostic discordance between health care providers in making a diagnosis for a new knee disorder.

Methods
We performed a secondary analysis of a diagnostic study comparing the diagnostic ability of a physiotherapist to medical musculoskeletal specialists. Patients’ socio-demographic, psychosocial and clinical characteristics were compared between the concordant and discordant diagnostic groups. Psychosocial symptoms were evaluated using the validated Kessler 6 (K6) questionnaire. We performed multivariable logistic regressions using the Bayesian Information Criterion to identify the most probable model including patients’ characteristics associated with diagnostic discordance. Overall probability of identified variables to explain diagnostic discordance and associated odd ratios (OR) with 95% credibility intervals (95% CrI) were calculated.

Results
Overall, 279 participants were evaluated by a physiotherapist and medical musculoskeletal specialists. The mean age of the participants was 49.1 ± 15.8 years and 57.7% were female. The most common disorder was osteoarthritis (n = 117, 18.8% of cases were discordant). The most probable model explaining diagnostic discordance (11.13%) included having depressive symptoms, which was associated with an increased probability of diagnostic discordance (OR: 3.9; 95% CrI: 1.9 – 8.0) and having a higher number of comorbidities, which was associated with a decreased probability of diagnostic discordance (OR: 0.6; 95% CrI: 0.5 – 0.9). The depression item of the K6 questionnaire had a 99.4% chance to be included in a model explaining diagnostic discordance. Other variables taken separately had less than 50% chance to be included in a model explaining diagnostic discordance and cannot be considered significant.

Conclusion
Our results suggest that depressive symptoms may increase the risk of knee diagnostic discordance. Clinicians may be more likely to make diagnostic errors and should be more cautious when evaluating patients with knee disorders suffering from psychological distress.
Advance care planning conversations in primary care: a quality improvement project using the Serious Illness Care Program
Authors: Hafid Abe , Howard Michelle , Guenter Dale , Elston Dawn , Fikree Shireen , Gallagher Erin , Winemaker Samantha , Waters Heather 
Published: July 2021

Abstract
Background
Advance care planning (ACP) conversations are associated with improved end-of-life healthcare outcomes and patients want to engage in ACP with their healthcare providers. Despite this, ACP conversations rarely occur in primary care settings. The objective of this study was to implement ACP through adapted Serious Illness Care Program (SICP) training sessions, and to understand primary care provider (PCP) perceptions of implementing ACP into practice.

Methods
We conducted a quality improvement project guided by the Normalization Process Theory (NPT), in an interprofessional academic family medicine group in Hamilton, Ontario, Canada. NPT is an explanatory model that delineates the processes by which organizations implement and integrate new work. PCPs (physicians, family medicine residents, and allied health care providers), completed pre- and post-SICP self-assessments evaluating training effectiveness, a survey evaluating program implementability and sustainability, and semi-structured qualitative interviews to elaborate on barriers, facilitators, and suggestions for successful implementation. Descriptive statistics and pre-post differences (Wilcoxon Sign-Rank test) were used to analyze surveys and thematic analysis was used to analyze qualitative interviews.

Results
30 PCPs participated in SICP training and completed self-assessments, 14 completed NoMAD surveys, and 7 were interviewed. There were reported improvements in ACP confidence and skills. NoMAD surveys reported mixed opinions towards ACP implementation, specifically concerning colleagues’ abilities to conduct ACP and patients’ abilities to participate in ACP. Physicians discussed busy clinical schedules, lack of patient preparedness, and continued discomfort or lack of confidence in having ACP conversations. Allied health professionals discussed difficulty sharing patient prognosis and identification of appropriate patients as barriers.

Conclusions
Training in ACP conversations improved PCPs’ individual perceived abilities, but discomfort and other barriers were identified. Future iterations will require a more systematic process to support the implementation of ACP into regular practice, in addition to addressing knowledge and skill gaps.
Investigation of patients’ access to EHR data via smart apps in Chinese Hospitals
Authors: Shu Ting , Xu Fan , Li Hongxia , Zhao Wei 
Published: July 2021

Abstract
Background
Given that China has encouraged EHR usage in hospitals for more than a decade, patients’ access to their own EHR data is still not as widely utilized as expected.

Methods
We cultivated a survey with four categories and field interviews of measures to identify whether hospitals have already released EHR data to patients, inpatients or outpatients, the top EHR release contents and the most popular release software.

Results
Of the 1344 responding hospitals from 30 provinces nationwide, 41.37% of hospitals have already released their EHR data to patients, of which 97.12% are through smart apps. More than 91% of hospitals use WeChat, and 32.37% of hospitals developed their own standalone apps or use vendors’ apps. A total of 54.63% were released to both outpatients and inpatients, and the top release contents were all objective. A rough estimation is made that releasing EHR data to patients via smart apps may save the hospital 15.9 million RMB per year and patients 9.4 million RMB altogether.

Conclusions
EHR data release is believed to bring both patient and hospital cost savings and efficiency gains but is still considered spontaneous and requires legal support and government regulation.
A deep semantic segmentation correction network for multi-model tiny lesion areas detection
Authors: Liu Yue , Li Xiang , Li Tianyang , Li Bin , Wang Zhensong , Gan Jie , Wei Benzheng 
Published: July 2021

Abstract
Background
Semantic segmentation of white matter hyperintensities related to focal cerebral ischemia (FCI) and lacunar infarction (LACI) is of significant importance for the automatic screening of tiny cerebral lesions and early prevention of LACI. However, existing studies on brain magnetic resonance imaging lesion segmentation focus on large lesions with obvious features, such as glioma and acute cerebral infarction. Owing to the multi-model tiny lesion areas of FCI and LACI, reliable and precise segmentation and/or detection of these lesion areas is still a significant challenge task.

Methods
We propose a novel segmentation correction algorithm for estimating the lesion areas via segmentation and correction processes, in which we design two sub-models simultaneously: a segmentation network and a correction network. The segmentation network was first used to extract and segment diseased areas on T2 fluid-attenuated inversion recovery (FLAIR) images. Consequently, the correction network was used to classify these areas at the corresponding locations on T1 FLAIR images to distinguish between FCI and LACI. Finally, the results of the correction network were used to correct the segmentation results and achieve segmentation and recognition of the lesion areas.

Results
In our experiment on magnetic resonance images of 113 clinical patients, our method achieved a precision of 91.76% for detection and 92.89% for classification, indicating a powerful method to distinguish between small lesions, such as FCI and LACI.

Conclusions
Overall, we developed a complete method for segmentation and detection of WMHs related to FCI and LACI. The experimental results show that it has potential clinical application potential. In the future, we will collect more clinical data and test more types of tiny lesions at the same time.
Early warning of citric acid overdose and timely adjustment of regional citrate anticoagulation based on machine learning methods
Authors: Chen Huan , Ma Yingying , Hong Na , Wang Hao , Su Longxiang , Liu Chun , He Jie , Jiang Huizhen , Long Yun , Zhu Weiguo 
Published: July 2021

AbstractBackgroundRegional citrate anticoagulation (RCA) is an important local anticoagulation method during bedside continuous renal replacement therapy. To improve patient safety and achieve computer assisted dose monitoring and control, we took intensive care units patients into cohort and aiming at developing a data-driven machine learning model to give early warning of citric acid overdose and provide adjustment suggestions on citrate pumping rate and 10% calcium gluconate input rate for RCA treatment.MethodsPatient age, gender, pumped citric acid dose value, 5% NaHCO3solvent, replacement fluid solvent, body temperature value, and replacement fluid PH value as clinical features, models attempted to classify patients who received regional citrate anticoagulation into correct outcome category. Four models, Adaboost, XGBoost, support vector machine (SVM) and shallow neural network, were compared on the performance of predicting outcomes. Prediction results were evaluated using accuracy, precision, recall and F1-score.ResultsFor classifying patients at the early stages of citric acid treatment, the accuracy of neutral networks model is higher than Adaboost, XGBoost and SVM, the F1-score of shallow neutral networks (90.77%) is overall outperformed than other models (88.40%, 82.17% and 88.96% for Adaboost, XGBoost and SVM). Extended experiment and validation were further conducted using the MIMIC-III database, the F1-scores for shallow neutral networks, Adaboost, XGBoost and SVM are 80.00%, 80.46%, 80.37% and 78.90%, the AUCs are 0.8638, 0.8086, 0.8466 and 0.7919 respectively.ConclusionThe results of this study demonstrated the feasibility and performance of machine learning methods for monitoring and adjusting local regional citrate anticoagulation, and further provide decision-making recommendations to clinicians point-of-care.
Study on structured method of Chinese MRI report of nasopharyngeal carcinoma
Authors: Huang Xin , Chen Hui , Yan Jing-Dong 
Published: July 2021

AbstractBackgroundImage text is an important text data in the medical field at it can assist clinicians in making a diagnosis. However, due to the diversity of languages, most descriptions in the image text are unstructured data. The same medical phenomenon may also be described in various ways, such that it remains challenging to conduct text structure analysis. The aim of this research is to develop a feasible approach that can automatically convert nasopharyngeal cancer reports into structured text and build a knowledge network.MethodsIn this work, we compare commonly used named entity recognition (NER) models, choose the optimal model as our triplet extraction model, and present a Chinese structuring algorithm. Finally, we visualize the results of the algorithm in the form of a knowledge network of nasopharyngeal cancer.ResultsIn NER, both accuracy and recall of the BERT-CRF model reached 99%. The structured extraction rate is 84.74%, and the accuracy is 89.39%. The architecture based on recurrent neural network does not rely on medical dictionaries or word segmentation tools and can realize triplet recognition.ConclusionsThe BERT-CRF model has high performance in NER, and the triplet can reflect the content of the image report. This work can provide technical support for the construction of a nasopharyngeal cancer database.
A novel deep learning approach to extract Chinese clinical entities for lung cancer screening and staging
Authors: Zhang Huanyao , Hu Danqing , Duan Huilong , Li Shaolei , Wu Nan , Lu Xudong 
Published: July 2021

AbstractBackgroundComputed tomography (CT) reports record a large volume of valuable information about patients’ conditions and the interpretations of radiology images from radiologists, which can be used for clinical decision-making and further academic study. However, the free-text nature of clinical reports is a critical barrier to use this data more effectively. In this study, we investigate a novel deep learning method to extract entities from Chinese CT reports for lung cancer screening and TNM staging.MethodsThe proposed approach presents a new named entity recognition algorithm, namely the BERT-based-BiLSTM-Transformer network (BERT-BTN) with pre-training, to extract clinical entities for lung cancer screening and staging. Specifically, instead of traditional word embedding methods, BERT is applied to learn the deep semantic representations of characters. Following the long short-term memory layer, a Transformer layer is added to capture the global dependencies between characters. Besides, pre-training technique is employed to alleviate the problem of insufficient labeled data.ResultsWe verify the effectiveness of the proposed approach on a clinical dataset containing 359 CT reports collected from the Department of Thoracic Surgery II of Peking University Cancer Hospital. The experimental results show that the proposed approach achieves an 85.96% macro-F1 score under exact match scheme, which improves the performance by 1.38%, 1.84%, 3.81%,4.29%,5.12%,5.29% and 8.84% compared to BERT-BTN, BERT-LSTM, BERT-fine-tune, BERT-Transformer, FastText-BTN, FastText-BiLSTM and FastText-Transformer, respectively.ConclusionsIn this study, we developed a novel deep learning method, i.e., BERT-BTN with pre-training, to extract the clinical entities from Chinese CT reports. The experimental results indicate that the proposed approach can efficiently recognize various clinical entities about lung cancer screening and staging, which shows the potential for further clinical decision-making and academic research.
A pilot study of patient satisfaction with a self-completed tablet-based digital questionnaire for collecting the patient’s medical history in an emergency department
Authors: Melms Leander , Schaefer Juergen R. , Jerrentrup Andreas , Mueller Tobias 
Published: July 2021

Abstract
Background
The increasing popularity and availability of tablet computers raises questions regarding clinical scenarios. This pilot study examined the patient’s satisfaction when using a tablet-based digital questionnaire as a tool for obtaining medical history in an emergency department and to what extent gender, age, technical competence and mother tongue influence the user satisfaction. Patients were asked to complete three consecutive questionnaires: The first questionnaire collected basic epidemiological data to measure past digital usage behaviour, the second questionnaire collected the patient’s medical history, and the third questionnaire assessed the overall perceived user satisfaction when using the tablet-based survey application for medical anamnesis.

Results
Of 111 consenting patients, 86 completed all three questionnaires. In summary, the user evaluation was positive with 97.7% (n = 84) of the patients stating that they had no major difficulties using the digital questionnaire. Only 8.1% (n = 7) of patients reported a preference to fill out a paper-and-pen version on the next visit instead, while 98.8% (n = 85) stated that they would feel confident filling out a digital questionnaire on the next visit. The variables gender, age, mother tongue and/or technical competence did not exert a statistically significant influence towards the defined scales usability, content and overall impression.

Conclusion
In conclusion, self-administered tablet-based questionnaires are widely accepted tools for collecting medical information in the emergency room across all ages and genders, regardless of technical competence.
The ESCRT-0 subcomplex component Hrs/Hgs is a master regulator of myogenesis via modulation of signaling and degradation pathways
Authors: Coudert L. , Osseni A. , Gangloff Y. G. , Schaeffer L. , Leblanc P. 
Published: July 2021

Abstract
Background
Myogenesis is a highly regulated process ending with the formation of myotubes, the precursors of skeletal muscle fibers. Differentiation of myoblasts into myotubes is controlled by myogenic regulatory factors (MRFs) that act as terminal effectors of signaling cascades involved in the temporal and spatial regulation of muscle development. Such signaling cascades converge and are controlled at the level of intracellular trafficking, but the mechanisms by which myogenesis is regulated by the endosomal machinery and trafficking is largely unexplored. The Endosomal Sorting Complex Required for Transport (ESCRT) machinery composed of four complexes ESCRT-0 to ESCRT-III regulates the biogenesis and trafficking of endosomes as well as the associated signaling and degradation pathways. Here, we investigate its role in regulating myogenesis.

Results
We uncovered a new function of the ESCRT-0 hepatocyte growth factor-regulated tyrosine kinase substrate Hrs/Hgs component in the regulation of myogenesis. Hrs depletion strongly impairs the differentiation of murine and human myoblasts. In the C2C12 murine myogenic cell line, inhibition of differentiation was attributed to impaired MRF in the early steps of differentiation. This alteration is associated with an upregulation of the MEK/ERK signaling pathway and a downregulation of the Akt2 signaling both leading to the inhibition of differentiation. The myogenic repressors FOXO1 as well as GSK3β were also found to be both activated when Hrs was absent. Inhibition of the MEK/ERK pathway or of GSK3β by the U0126 or azakenpaullone compounds respectively significantly restores the impaired differentiation observed in Hrs-depleted cells. In addition, functional autophagy that is required for myogenesis was also found to be strongly inhibited.

Conclusions
We show for the first time that Hrs/Hgs is a master regulator that modulates myogenesis at different levels through the control of trafficking, signaling, and degradation pathways.