bluuebunny/crossref_metadata_2025_split huggingface dataset:
Usefulness of the d-dimer to albumin ratio for risk assessment in patients with acute variceal bleeding at the emergency department: retrospective observational study
Authors: Seo Jun Seok , Kim Yongwon , Lee Yoonsuk , Chung Ho Young , Kim Tae Youn 
Published: July 2022

AbstractBackgroundAcute variceal bleeding (AVB) is a severe complication of portal hypertension that is caused by rupture of the esophageal or gastric varix. Scoring system for risk stratification of AVB is difficult to use because various variables must be entered, and it is difficult to apply early in the emergency department (ED). We compared and analyzed the usefulness of the D-dimer to albumin ratio (DAR) for risk stratification of AVB.MethodsIn this retrospective observational study, medical records of patients with AVB Between January 2019 and December 2020 were assessed. The primary endpoint was to evaluate whether DAR was a predictor of clinical outcomes for AVB. Receiver operating characteristic (ROC) curves were constructed using cut-off values determined by the Youden Index. Univariate and multivariate logistic regression analyses were performed to assess the factors contributing to the development of outcomes.ResultsOverall, 67 patients required intensive care. The cut-off value of DAR for patients requiring intensive care was 400. A DAR > 400 (adjusted HR: 5.636 [95% CI: 2.216–14.332]) independently predicted the need for ICU admission in these patients. Overall, 13 patients required long-term hospitalization. The cut-off value of DAR for patients requiring long-term hospitalization was 403. A DAR > 403 (adjusted HR: 9.899 [95% CI: 2.012–48.694]) independently predicted the need for long-term hospitalization. Overall, 95 patients required transfusion. The cut-off value of DAR for patients requiring transfusion was 121. A DAR > 121 (adjusted HR: 4.680 [95% CI: 1.703–12.862]) independently predicted the need for transfusion. Overall, 11 patients died during study period. The cut-off value of DAR for mortality was 450. A DAR > 450 (adjusted HR: 26.261 [95% CI: 3.054–225.827]) independently predicted mortality.ConclusionsThe DAR can be used for outcome assessment in patients with AVB with various scoring systems, but its explanatory power is not high.
Evolutionary game model of health care and social care collaborative services for the elderly population in China
Authors: Sun Yin , Zhang Xudong , Han Yuehong , Yu Bo , Liu Haidan 
Published: July 2022

Abstract
Introduction
The synergy of health care and elderly social care organizations has become the focus of the research on integrated health care and social care. This study aims to propose a collaborative strategy among health care and elderly social care service providers.

Methods
An evolutionary game model is applied for performance analysis and optimization of the cooperation between health care and elderly social care organizations. The behavioural strategies and the impact of key parameters on promoting the cooperation of the players are presented in detail.

Results
Simulation experiments and sensitivity analysis results indicate that (1) the behavioural evolution of health care organizations and elderly social care organizations forms three types of integrated health care and social care services, namely, the bilateral cooperation type, health care organization-led type and elderly social care organization-led type. (2) Increasing the additional benefits for cooperation and reducing the additional costs for cooperation can promote the willingness to synergize to provide integrated health care and elderly social care services. At the early stage of evolution, increasing the costs that elderly social care organizations pay to purchase health care services or pay for negotiation in the bilateral cooperation type can provide incentives for health care organizations to cooperate while reducing the cooperation preferences of elderly social care organizations. However, the long-term impact of the costs on the behavioural strategies for cooperation of the two players cannot be determined.

Conclusion
The behavioural decisions on cooperation between health care and elderly social care organizations influence each other; commitment to integration and effective collaboration can be achieved by increasing the additional benefits and reducing the marginal costs. The findings suggest that the political-economic context and government policies have a greater influence on promoting cooperation, thus yielding positive or negative results for integrated care practice.
Extended middle meatal antrostomy via antidromic extended medial wall for the treatment of fungal maxillary sinusitis
Authors: Ding Lijun , Na-guo  , Lou Zhengcai 
Published: July 2022

Abstract
Objective
The objective of this study was to compare the long-term results of extended middle meatal antrostomy (MMA) and MMA combined with inferior meatal antrostomy (IMA, combined approach) for the treatment of fungal maxillary sinusitis (FMS).

Methods and materials
A retrospective analysis including 90 patients with non-invasive FMS was treated with endoscopic extended MMA via antidromic extended medial wall (extended MMA group), or with both MMA and IMA (combined approach group). The recurrence rate, operation time, and complications were evaluated at postoperative 12 and 36 months.

Results
Of the 90 patients, 52 patients were in the extended MMA group and 38 patients in the combined approach group. CT revealed the thin medial wall or bone defect in 63.33% (57/90) patients. The mean operation time in the extended MMA group was significantly shorter than that of combined approach group (42.5 ± 6.5 vs 57.4 ± 4.9, P < 0.01). At postoperative 12 months postoperatively, the recurrence rate was 3.85% (2/52) in the extended MMA group and 0.0% (0/38) in the combined approach group, the difference wasn’t significant (X2 = 0.618, P > 0.05). The recurrence rate wasn’t increased during the follow-up period over time in both groups.13.5% (7/52) patients complained of cheek numbness in the extended MMA group, 60.5% (23/38) patients complained of cheek numbness and epiphora in 5.3% (2/38) patients in the combined approach group, the difference was significant (X2 test, P < 0.01). However, no major complications were observed in both groups. In addition, IMA closure was observed in 4 (10.5%) in the combined approach group at 12 months postoperatively and in 9 (23.6%) at 36 months postoperatively.

Conclusions
Extended MMA via antidromic extended medial wall may effectively prevent the recurrence and reduce the complications of FMS, IMA wasn’t necessary for the treatment of FMS in most cases.
Individual and community-level determinants of cervical cancer screening in Zimbabwe: a multi-level analyses of a nationwide survey
Authors: Isabirye Alone , Elwange Bob Charlestine , Singh Kavita , De Allegri Manuela 
Published: July 2022

Abstract
Background
Despite the benefits of cervical cancer (CC) screening to reduce the disease burden, uptake remains limited in developing countries. This study aims to assess the individual and community-level determinants of cervical cancer screening among women of reproductive age in Zimbabwe.

Methods
We analyzed data collected from 400 communities from the 2015 Zimbabwe Demographic and Health Survey with a sample size of 9955 women aged 15–49 years. The descriptive statistics and multi-level regression models adjusted for potential covariates were performed to examine the association between individual, household and community-level factors and the uptake of cervical cancer screening in women.

Results
The mean (SD) age of women in Zimbabwe using cervical cancer screening was 27.9 (9.9) years. A relatively small proportion of women, i.e., only 13.4% had ever screened for cervical cancer, with higher screening rates observed in the following sub-groups: middle aged women 31–49 years (odds ratio (OR) = 2.01; 95% confidence intervals (CI) 1.72–2.34), and currently working (OR = 1.35; 95% CI 1.17–1.55), those with health insurance (OR = 1.95; 95% CI 1.63–2.34), used modern contraceptives (OR = 1.51; 95% CI 1.22–1.86), exposed to multiple media (OR = 1.27; 95% CI 1.03–1.58), those living in communities that had a high predominance of women with favorable attitude towards Intimate Partner Violence (IPV) against women (OR = 1.21; 95% CI 1.04–1.41) and a non-poor wealth index (OR = 1.54; 95% CI 1.14–2.05).

Conclusions
Our data shows a significantly low prevalence of cervical cancer screening among reproductive age women in Zimbabwe. To increase the uptake of cervical cancer screening, there is an urgent need both to implement behavioral interventions targeted at women from low socio-economic groups and to advocate for universal health coverage that includes financial risk protection to help all women realize their right to health.
Network pharmacology-based analysis and experimental in vitro validation on the mechanism of Paeonia lactiflora Pall. in the treatment for type I allergy
Authors: Zhao Yang , Li Hui , Li Xiangsheng , Sun Yizhao , Shao Yuxin , Zhang Yanfen , Liu Zhongcheng 
Published: July 2022

Abstract
Background
The incidence of allergic reaction is increasing year by year, but the specific mechanism is still unclear. Paeonia lactiflora Pall.(PLP) is a traditional Chinese medicine with various pharmacological effects such as anti-tumor, anti-inflammatory, and immune regulation. Previous studies have shown that PLP has potential anti-allergic activity. However, there is still no comprehensive analysis of the targeted effects and exact molecular mechanisms of the anti-allergic components of PLP. This study aimed to reveal the mechanism of PLP. in the treatment of type I allergy by combining network pharmacological methods and experimental verification.

Methods
First, we used the traditional Chinese medicine systems pharmacology (TCMSP) database and analysis platform to screen the main components and targets of PLP, and then used databases such as GeneCards to retrieve target information related to ‘allergy’. Protein–protein interaction (PPI) analysis obtained the core target genes in the intersection target, and then imported the intersection target into the David database for gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis. Furthermore, the therapeutic effect of paeoniflorin, the main component of PLP, on IgE-induced type I allergy was evaluated in vitro.

Results
GO analysis obtained the main biological processes, cell components and molecular functions involved in the target genes. KEGG analysis screened out MAPK1, MAPK10, MAPK14 and TNF that have a strong correlation with PLP anti-type I allergy, and showed that PLP may pass through signal pathways such as IgE/FcεR I, PI3K/Akt and MAPK to regulate type I allergy. RT-qPCR and Western Blot results confirmed that paeoniflorin can inhibit the expression of key genes and down-regulate the phosphorylation level of proteins in these signal pathways. It further proved the reliability of the results of network pharmacology research.

Conclusion
The results of this study will provide a basis for revealing the multi-dimensional regulatory mechanism of PLP for the treatment of type I allergy and the development of new drugs.
A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients
Authors: Pishgar M. , Harford S. , Theis J. , Galanter W. , Rodríguez-Fernández J. M. , Chaisson L. H , Zhang Y. , Trotter A. , Kochendorfer K. M. , Boppana A. , Darabi H. 
Published: July 2022

Abstract
Background
Various machine learning and artificial intelligence methods have been used to predict outcomes of hospitalized COVID-19 patients. However, process mining has not yet been used for COVID-19 prediction. We developed a process mining/deep learning approach to predict mortality among COVID-19 patients and updated the prediction in 6-h intervals during the first 72 h after hospital admission.

Methods
The process mining/deep learning model produced temporal information related to the variables and incorporated demographic and clinical data to predict mortality. The mortality prediction was updated in 6-h intervals during the first 72 h after hospital admission. Moreover, the performance of the model was compared with published and self-developed traditional machine learning models that did not use time as a variable. The performance was compared using the Area Under the Receiver Operator Curve (AUROC), accuracy, sensitivity, and specificity.

Results
The proposed process mining/deep learning model outperformed the comparison models in almost all time intervals with a robust AUROC above 80% on a dataset that was imbalanced.

Conclusions
Our proposed process mining/deep learning model performed significantly better than commonly used machine learning approaches that ignore time information. Thus, time information should be incorporated in models to predict outcomes more accurately.
Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths
Authors: Wilstrup Casper , Cave Chris 
Published: July 2022

Abstract
Background
Heart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about the appropriate application of devices, transplantation, medications, and palliative care. In this study, we demonstrate that combining symbolic regression with the Cox proportional hazards model improves the ability to predict death due to heart failure compared to using the Cox proportional hazards model alone.

Methods
We used a newly invented symbolic regression method called the QLattice to analyse a data set of medical records for 299 Pakistani patients diagnosed with heart failure. The QLattice identified non-linear mathematical transformations of the available covariates, which we then used in a Cox model to predict survival.

Results
An exponential function of age, the inverse of ejection fraction, and the inverse of serum creatinine were identified as the best risk factors for predicting heart failure deaths. A Cox model fitted on these transformed covariates had improved predictive performance compared with a Cox model on the same covariates without mathematical transformations.

Conclusion
Symbolic regression is a way to find transformations of covariates from patients’ medical records which can improve the performance of survival regression models. At the same time, these simple functions are intuitive and easy to apply in clinical settings. The direct interpretability of the simple forms may help researchers gain new insights into the actual causal pathways leading to deaths.
Factors influencing the professional identity of nursing interns: a cross-sectional study
Authors: Zeng Lihong , Chen Qirong , Fan Sisi , Yi Qifeng , An Wenhong , Liu Huan , Hua Wei , Huang Rong , Huang Hui 
Published: July 2022

AbstractBackgroundImproving the professional identity of nursing intern is significant for enhancing the number of new registered nurses and easing the shortage of nursing personnel. The clinical internship is a key period for the formulation of professional identity. However, we know little about the factors influencing the nursing interns’ professional identity during clinical internship. Therefore, this study explore the influencing factors of nursing interns’ professional identity during clinical internship. This study will provide evidence and suggestions for generating effective strategies contributing to professional identity improvement of nursing interns.MethodsThis was a cross-sectional study. The convenience sampling was used to recruit 398 nursing interns from a teaching hospital in Hunan, China. The demographic characteristics information was collected by a self-developed questionnaire. The nursing interns’ professional identity and potential influencing factors (e.g., work atmosphere, teacher capacity) were measured by questionnaires with good psychometric properties. The appropriate indicators were used for descriptive statistics, andt test, analysis of variance, Pearson’s correlation analysis and multiple linear regression were used to analyse the influencing factors.ResultsIn this study, the influencing factors of nursing interns' professional identity are education level, first choice of major, residential status, work atmosphere, and teacher capacity. The results showed that: (1) the nursing interns with a higher education level reported a lower level of professional identity; (2) the nursing interns whose first choice of major was not nursing discipline reported a lower level of professional identity; (3) the nursing interns live in rural areas (compared to urban areas) reported a higher level of professional identity; (4) the nursing interns in better work atmosphere reported a higher level of professional identity; (5) the nursing interns under the guidance of the teachers equipped with better teaching capacity reported a higher level of professional identity.ConclusionThe education level, first choice of major and residential status are influence factors of nursing interns’ professional identity. The nursing educators need to pay attention to nursing interns whose first choice is not nursing, and in a bachelor program, who may have a lower level of professional identity. It is crucial to enhance the nursing interns’ professional identity by improve the work atmosphere and clinical teachers’ capacity, to promote nursing interns to choose nursing as a profession and reduce the shortage of nursing workforce.
Analysis and prognostic significance of tumour immune infiltrates and immune microenvironment of m6A-related lncRNAs in patients with gastric cancer
Authors: Huang Jiarong , Song Jinxuan , Li xiangyu , Liu Shuangfei , Huang Wentao , Shen Ziyi , Cheng Yan , Kou Shien , Gao Zhenguo , Tian Yunhong , Hu Jiani 
Published: July 2022

Abstract
Background
Studies have shown that long noncoding RNAs and N6-methyladenosine play important roles in gastric cancer. The purpose of this study was to determine the correlation and prognostic value of m6A-related lncRNAs and immune infiltration in gastric cancer.

Methods
We downloaded the clinically related information and RNA-Seq transcriptome data of gastric cancer patients from the TCGA database. Univariate Cox regression analysis and Pearson analysis were used to screen out m6A-related lncRNAs. Consensus cluster analysis was used to divide the sample into two clusters, and LASSO analysis and Cox regression analysis were used to construct a risk scoring model.

Results
A total of 25 lncRNA expression profiles were screened, and gastric cancer patients were divided into different subtypes. Cluster 2 had a better prognosis, but its stromal score, ESTIMATE score and immune score were low. Cluster 1 was rich in resting memory CD4 T cells, regulatory T cells, monocytes, and resting mast cells, and Cluster 2 was rich in activated memory CD4 T cells and follicular helper T cells. Thirteen lncRNAs were selected to construct a risk model, and the prognosis of gastric cancer patients in the high-risk group was poor. The expression of PD-L1 in tumours is significantly higher than that in normal tissues. Univariate and multivariate Cox regression analysis results showed that the overall survival rate was significantly related to stage and the risk score, which can be used as an independent prognostic factor. The results of the heatmap and scatter plot showed that clusters (P = 0.0045) and grade (G1–2, G3, P = 0.0037) were significantly related to prognosis. The relationship between the risk score and immune cell infiltration showed that memory B cells, resting dendritic cells, M0 macrophages, and M2 macrophages were positively correlated with the risk score, while resting mast cells, monocytes, activated NK cells, and follicular helper T cells were negatively correlated with the risk score.

Conclusion
The results of this study indicate that m6A-related lncRNAs may play an important role in the prognosis of gastric cancer patients and the tumour immune microenvironment and may provide help for the treatment of gastric cancer patients.
Feasibility research of enhanced recovery after surgery implemented in esophageal cancer patients who underwent neoadjuvant chemotherapy
Authors: Tang Zhanpeng , Zhu Xirui , Li Yanzhi , Qu Chenghao , Li Lin , Li Shuhai , Qi Lei , Lu Ming , Cheng Chuanle , Tian Hui 
Published: July 2022

Abstract
Background
Enhanced recovery after surgery (ERAS) is a perioperative management protocol to accelerate patient recovery. This study aimed to evaluate the feasibility of ERAS protocols implemented in patients who underwent neoadjuvant chemotherapy (NACT) before minimally invasive McKeown esophagectomy.

Methods
This retrospective study compared the short-term clinical outcomes in esophagectomy patients from June 2018 to June 2021. Subjects were divided into two categories: those who underwent NACT (NACT group) and the non-NACT group.

Results
There was no significant difference in total postoperative complication morbidity between the NACT and non-NACT groups (21.2% vs. 20.7%, P=0.936). In addition, the hospital length of stay post-surgery (7.90 vs. 7.71 days, P=0.424) was not significantly longer when compared to the non-NACT group. The time to chest tube removal (5.37 vs. 5.13 days, P=0.238) and first bowel movement (2.92 vs. 3.01 days, P=0.560) was also similar between the two groups.

Conclusions
There was no significant difference in postoperative complications rate, postoperative hospital length of stay, and readmission rate between the two group. This study proved that ERAS protocols seemed to be safe and feasible for patients who received NACT before esophagectomy.
Deep learning for detecting herbicide weed control spectrum in turfgrass
Authors: Jin Xiaojun , Bagavathiannan Muthukumar , Maity Aniruddha , Chen Yong , Yu Jialin 
Published: July 2022

Abstract
Background
Precision spraying of postemergence herbicides according to the herbicide weed control spectrum can substantially reduce herbicide input. The objective of this research was to evaluate the effectiveness of using deep convolutional neural networks (DCNNs) for detecting and discriminating weeds growing in turfgrass based on their susceptibility to ACCase-inhibiting and synthetic auxin herbicides.

Results
GoogLeNet, MobileNet-v3, ShuffleNet-v2, and VGGNet were trained to discriminate the vegetation into three categories based on the herbicide weed control spectrum: weeds susceptible to ACCase-inhibiting herbicides, weeds susceptible to synthetic auxin herbicides, and turfgrass without weed infestation (no herbicide). ShuffleNet-v2 and VGGNet showed high overall accuracy (≥ 0.999) and F1 scores (≥ 0.998) in the validation and testing datasets to detect and discriminate weeds susceptible to ACCase-inhibiting and synthetic auxin herbicides. The inference time of ShuffleNet-v2 was similar to MobileNet-v3, but noticeably faster than GoogLeNet and VGGNet. ShuffleNet-v2 was the most efficient and reliable model among the neural networks evaluated.

Conclusion
These results demonstrated that the DCNNs trained based on the herbicide weed control spectrum could detect and discriminate weeds based on their susceptibility to selective herbicides, allowing the precision spraying of particular herbicides to susceptible weeds and thereby saving more herbicides. The proposed method can be used in a machine vision-based autonomous spot-spraying system of smart sprayers.