bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of a deep learning-based automated algorithm for labeling coronary arteries in computed tomography angiography images
Authors: Ren Pengling , He Yi , Luo Nan , Li Fang , Wang Zhenchang , Yang Zhenghan , Guo Ning 
Published: August 2022

Abstract
Objective
To evaluate the performance of a fully automatic algorithm for labeling coronary arteries in CCTA images using deep learning based on the two 3-dimensional (3D) U-Net architectures for myocardium structure extraction.
Methods
In total, 157 patients who underwent CCTA scanning were retrospectively included. An automatic coronary artery labeling algorithm based on the distance transformation algorithm was proposed to identify the anatomical segments of the centerlines extracted from CCTA images. Sixteen segments were identified and labeled. The results obtained via the algorithm were recorded and reviewed by three experts. The performance of segment detection and labeling of each segment was evaluated, and the proportion of agreement between the two experts on the manually labeled segments was also calculated.
Results
Compared with the labels of the experts, 117 labels (5.4%) (2180 segments) from the algorithm needed to be changed or removed. The overall accuracy of label presence was 96.2%. The average overlap between the expert reference and algorithm labels was 94.0%. The average agreement between the two experts was 95.0%.
Conclusions
The proposed deep learning algorithm provided a high accuracy of the automatic labeling with respect to the labels from the clinical experts. This method is promising for labeling coronary arteries automatically and alleviating the workload of radiologists in the near future.
Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy
Authors: Arezzo Francesca , Cormio Gennaro , Mongelli Michele , Cazzato Gerardo , Silvestris Erica , Kardashi Anila , Cazzolla Ambrogio , Lombardi Claudio , Venerito Vincenzo , Loizzi Vera 
Published: August 2022

Abstract
Purpose: Concurrent cisplatin-based chemotherapy and radiotherapy (CCRT) plus brachytherapy is the standard treatment for locally advanced cervical cancer (LACC). Platinum-based neoadjuvant chemotherapy (NACT) followed by radical hysterectomy is an alternative for patients with stage IB2-IIB disease. Therefore the correct pre-treatment staging is essential to the proper management of this disease. Pelvic magnetic resonance imaging (MRI) is the gold standard examination but studies about MRI accuracy in the detection of lymph node metastasis (LNM) in LACC patients show conflicting data. Machine learning (ML) is emerging as a promising tool for unraveling complex non-linear relationships between patient attributes that cannot be solved by traditional statistical methods. Here we investigated whether ML might improve the accuracy of MRI in the detection of LNM in LACC patients.Methods: We analyzed retrospectively LACC patients who underwent NACT and radical hysterectomy from 2015 to 2020. Demographic, clinical and MRI characteristics before and after NACT were collected, as well as information about post-surgery histopathology. Random features elimination wrapper was used to determine an attribute core set. A ML algorithm, namely Extreme Gradient Boosting (XGBoost) was trained and validated with 10-fold cross-validation. The performances of the algorithm were assessed.Results: Our analysis included n.92 patients. FIGO stage was IB2 in n.4/92 (4.3%), IB3 in n.42/92 (45%), IIA1 in n.1/92 (1.1%), IIA2 in n.16/92 (17.4%) and IIB in n.29/92 (31.5%). Despite detected neither at pre-treatment and post-treatment MRI in any patients, LNM occurred in n.16/92 (17%) patients. The attribute core set used to train ML algorithms included grading, histotypes, age, parity, largest diameter of lesion at either pre and post-treatment MRI, presence/absence of fornix infiltration at pre-treatment MRI and FIGO stage. XGBoost showed a good performance (accuracy 89%, precision 83%, recall 78%, AUROC 0.79).Conclusions: We developed an accurate model to predict LNM in LACC patients in NACT, based on a ML algorithm requiring few easy-to-collect attributes.
A multi-source behavioral and physiological recording system for cognitive assessment
Authors: Wang Zi-yang , Liu Li , Liu Yu 
Published: August 2022

Abstract
Cognitive assessment has a broad application prospect, including estimate of childhood neuro development and maturation, diagnosis of neurodegenerative diseases, and selection for special profession. With the development of computer technique and behavioral recording sensors, the method of cognitive assessment has been replaced from paper scale test to human-computer interaction. We can not only obtain the results of tasks, but also make it possible to acquire multiple behavioral and physiological data during the task. However, there is still a strong challenge of recording multi-source data synchronously during multi-dimensional cognitive assessments. Therefore, we built a multi-source cognitive assessment system can record multi-pattern behavioral and physiological data and feedback at different spatiotemporal levels. Under this system, we developed a multi-source diagnostic toolset for cognitive assessment, including eye tracking, hand movement, EEG and human-computer interaction data during the cognitive task. 238 participants with different mental disorders were assessed using this system. The results showed that our diagnostic toolset can be used to study the behavioral abnormalities of patients with mental disorders through the characteristics of multi-source data. Furthermore, this system can provide some objective diagnostic criteria such as behavioral characters and EEG features for diagnosis of mental disorders.
Physical Activity Virtual Intervention for Improving Mental Health among University Students During COVID-19 Pandemic: A Co-Creation Process and Evaluation Using the Behavior Change Wheel
Authors: Sofro Zaenal Muttaqien , Wibowo Rakhmat Ari , Wasityastuti Widya , Agustiningsih Denny , Utomo Prattama Santoso , Kusumadewi Andrian Fajar , Ekawati Fitriana Murriya , Putri Rayhani Erika , Aldrin Enrique , Fatmawati Jihan Santika , Chang Trisha Cheeren , Pratista Muhammad Ivan 
Published: August 2022

Abstract
Background: During the COVID-19 pandemic, there were reductions in university students' physical activity, which further increased their mental distress, calling for technology-based physical activity interventions to address the challenges in delivering in-person interventions. This study aimed to develop a technology-based physical activity intervention and pilot test it.Methods: We developed a virtually-delivered team-based physical activity challenge using the Behavior Change Wheel and Co-creation Framework based on Self-determination Theory. A pilot study was conducted in the evaluation phase to measure the recruitment rate, dropout rate, change in physical activity and mental distress, while identifying problems and collecting participants' opinions regarding the challenge. Wilcoxon signed-rank tests were conducted to assess the change in physical activity and mental distress. Qualitative data were analyzed using thematic analysis.Results: A three-week physical activity challenge comprising five identified intervention functions was held with 480 participants. The recruitment rate was 84.8% resulting from 407 virtual challenge participants who were conveniently joined as research participants. The dropout rate for the pilot study was 10.96% resulting from the incompatibility problems with the application. Among sample participants who lacked physical activity, participation in this challenge improved their physical activity by 52.5 minutes of moderate-intensity physical activity per week and reduced their mental distress by 3 points of self-reporting questionnaire-20 score. Issues regarding the virtual application, the influence of participation in the challenge on basic psychological needs, and lack of time were identified as the main barriers to physical activity from participants' opinions.Conclusion: A co-created physical activity intervention developed using the Behavioral Change Wheel Framework inspired high interest from university students and may increase their physical activity and improve their mental health. Several suggestions were discussed to address the identified problems and improve the internal and external validity of the evaluation phase.Trial registration: TCTR20220720004 (retrospectively registered on 19/07/2022)
N6-methyladenosine-mediated SH3BP5-AS1 upregulation promotes GEM chemoresistance in pancreatic cancer by activating the Wnt signaling pathway
Authors: Lin Chengjie , Wang Yan , Dong Yihong , Lai Shihui , Wang Liang , Weng Shangeng , Zhang Xiang 
Published: August 2022

AbstractBackground:Pancreatic cancer (PC) is highly malignant. Chemotherapy is the main treatment strategy, especially for patients with advanced PC. However, chemoresistance has always been a frequently encountered bottleneck. Hence, there is an urgent need to enhance the sensitivity of PC to gemcitabine (GEM).Results:We demonstrated that SH3BP5-AS1 was significantly upregulated in GEM-resistant PC and predicted a poorer prognosis. SH3BP5-AS1 stability was regulated by ALKBH5/IGF2BP1-mediated m6A modification. Loss of SH3BP5-AS1 reduced PC cell migration and invasion and enhanced the sensitivity of PC to GEM, as confirmed by gain- and loss-of-function assays in vitro and in vivo. Bioinformatics analysis revealed that SH3BP5-AS1 acted as a ceRNA against miR-139-5p and directly targeted CTBP1, affecting the biological behavior of PC cells. The mechanistic studies revealed that the upregulation of SH3BP5-AS1 increased CTBP1 expression by directly activating the Wnt signaling pathway, promoting GEM resistance.Conclusions:This study revealed that SH3BP5-AS1 activated Wnt signaling pathway by sponging miR-139-5p, upregulating CTBP1 expression, and contributing to the sensitivity of PC cells to GEM. SH3BP5-AS1 might be a potential target for PC therapy.
Qubit and Bit-Based Quantum Hybrid Secret Key Generation
Authors: Sihare Shyam 
Published: August 2022

Abstract
For the generation of a secret key, hardly a quantum algorithms integrating states and bits have yet developed. Integrating random states and bits is difficult for a combiner component. The underlying problems of the study are the design of a quantum circuit, an algorithm, state polarization setup, and the concatenation of bits and states. By combining either rectilinear, orthogonal (superposition), or both states with bits, we have investigated three different possibilities for the quantum hybrid protocol. We investigated errors in each case and compared them with regard to decoherence and other quantum mechanics properties by taking into consideration the effectiveness of states during transmission time across an untrusted channel. Furthermore, we observed that key size, state errors, design complexity, and security are all addressed in a reasonable manner for identifying solutions while comparing our results to earlier proposed quantum protocols. Because of this, the suggested key protocol's effectiveness is greater than that of earlier proposed protocols.
Epidemiology and outcomes of traumatic chest injuries in children: a nationwide study in the Netherlands
Authors: Sweet Arthur A.R. , de Bruin Ivar G.J. , Peek Jesse , IJpma Frank F.A. , Baal Mark C.P.M. van , Leenen Luke P.H. , Houwert Roderick M. 
Published: August 2022

Abstract
Purpose
Thoracic injuries are infrequent among children, but still represent one of the leading causes of pediatric mortality. Studies on pediatric chest trauma are dated and little is known of outcomes in different age categories. This study aims to provide an overview of the incidence, injury patterns, and in-hospital outcomes of children with chest injuries.
Methods
A nationwide retrospective cohort study was performed on children with chest injuries, using data from the Dutch Trauma Registry. All patients admitted to a Dutch hospital between January 2015 and December 2019, with an abbreviated injury scale score of the thorax between 2 and 6, or at least one rib fracture, were included. Incidence rates of chest injuries were calculated with demographic data from the Dutch Population Register. Injury patterns and in-hospital outcomes were assessed in children in four different age groups.
Results
A total of 66,751 children were admitted to a hospital in the Netherlands after a trauma between January 2015 and December 2019, of whom 733 (1.1%) sustained chest injuries accounting for an incidence rate of 4.9 per 100,000 person-years. The median age was 10.9 (interquartile range [IQR] 5.7–14.2) years and 62.6% were male. In a quarter of all children the mechanisms were not further specified or unknown. Most prevalent injuries were lung contusions (40.5%) and rib fractures (27.6%). The median hospital length of stay was 3 (IQR 2–8) days, with 43.4% being admitted to the intensive care unit. The 30-day mortality rate was 6.8%.
Conclusions
Pediatric chest trauma still results in substantial adverse outcomes, such as disability and mortality. Lung contusions may be inflicted without fracturing the ribs. This contrasting injury pattern compared to adults underlines the importance of evaluating children with chest injuries with additional caution.
Mortality risk model to predict bacterial infection in Pediatric Intensive Care Unit
Authors: Hou Ming-Hui , Li Ya-Ting , Dai Zhen-Yuan , Yang Ke-xin , Qu Jing-Xin , Liu Zi-Feng , Chen Zhuang-Gui 
Published: August 2022

Abstract
BackgroundBacterial infections have long been a significant cause of child mortality worldwide; however, models for predicting the risk of death in children with bacterial infections that combine predictive ability and interpretability are scarce. Here we try to explore a new method to complete the task. MethodsWe use Paediatric Intensive Care Database (PIC) to carry out the research. Data from hospitalized children with positive bacterial culture results was extracted and categorized into three groups: the positive culture group, the gram-positive group, and the gram-negative group, which were divided into 80% training and 20% test sets. Then we extracted the demographic information, vital signs, and laboratory data within 24 hours of admission of the datasets. We use the XGBoost algorithm to select the features and rank their importance，and the Logistic Regression (LR) algorithm for model development based on various numbers of feature. All the models were evaluated by Receiver Operating Characteristic curve (ROC), area under the ROC curve (ROC-AUC), Precision-Recall curve (PR), the area under the PR curve (PR-AUC), and compared with Paediatric Mortality Risk Score III (PRISM III), the paediatric logistic organ dysfunction score-2 (PELOD-2), and the paediatric multiple organ dysfunction scores (P-MODS).ResultIn total, 3695 children with bacterial infection were included, with an average age of 20.17 ± 36.74 months, average paediatric intensive care unit stay of 18.51 ± 28.84 days, and overall mortality rate of 8.39%. The following predictors appeared in the 64 most important predictors of the three datasets: average white blood cell count, maximum value of white blood cell technology, average value of anion gap, minimum value of anion gap, maximum value of type B natriuretic peptide, and maximum value of thrombocytocrit. Finally, we established a LR model for bacterial infected children with 4 features and a LR model for gram-negative bacterial infected children with 10 features, which achieve a 0.7244 and a 0.7848 ROC-AUC score respectively. The ROC-AUC scores of the two models were better than PRISM III、PELOD-2 and P-MODS. ConclusionThis study developed models to predict the risk of death in children with bacterial infections. The final models use fewer features and achieve better mortality prediction performance than traditional scoring models, and the models are easier for paediatricians to understand.
Salivary microbiome and metabolome analysis of severe early childhood caries
Authors: Li kai , Wang Jinmei , Du Ning , Sun Yanjie , Sun Qi , Yin Weiwei , Li Huiying , Meng Lingqiang , Liu Xuecong 
Published: August 2022

Abstract
BACKGROUND: Severe early childhood caries (SECC) is a bacterial inflammatory disease with complex pathology. Although changes in the oral microbiota and metabolic profile of patients with ECC have been identified, the salivary metabolites and the relationship of host-bacterial interactions with biochemical metabolism remain unclear. We aimed to analyse alterations in the salivary microbiome and metabolome of children with SECC as well as their correlations. Accordingly, we aimed to explore potential salivary biomarkers in order to gain further insight into the pathophysiology of dental caries. Methods: We collected 120 saliva samples from 30 children with SECC and 30 children without caries. The microbial community was identified through 16S ribosomal RNA (rRNA) gene high-throughput sequencing. Additionally, we conducted non-targeted metabolomic analysis through ultra-high-performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry to determine the relative metabolite levels and their correlation with the clinical caries status.RESULTS: There was a significant between-group difference in 8 phyla and 32 genera in the microbiome. Further, metabolomic and enrichment analyses revealed significantly altered 32 salivary metabolites in children with dental caries, which involved pathways such as amino acid metabolism, pyrimidine metabolism, purine metabolism, ATP-binding cassette transporters, and cyclic adenosine monophosphate signalling pathway. Moreover, four in vivo differential metabolites (2-benzylmalate, epinephrine, 2-formaminobenzoylacetate, and 3-Indoleacrylic acid) might be jointly applied as biomarkers (area under the curve = 0.734). Furthermore, the caries status was correlated with microorganisms and metabolites. Additionally, Spearman's correlation analysis of differential microorganisms and metabolites revealed that Veillonella, Staphylococcus, Neisseria, and Porphyromonas were closely associated with differential metabolites.Conclusion: This study identified different microbial communities and metabolic profiles in saliva, which may be closely related to caries status. Our findings could inform future strategies for personalized caries prevention, detection, and treatment.
Metagenomic Analysis of the Interaction Between Gut Microbiota and Colorectal Cancer: A Paired-Sample Study Based on GMrepo Database
Authors: Chen Han , Jiao Jianhua , Wei Min , Jiang Xingzhou , Yang Ruoyun , Yu Xin , Zhang Guoxin , Zhou Xiaoying 
Published: August 2022

Abstract
Background
Based on previous evidence, the gut microbiota participates in the development and progression of colorectal cancer (CRC). This study aims to provide quantitative analysis and visualization of the interaction between gut microbiota and CRC and to establish a more precise microbiota panel for CRC diagnosis.
Method
A paired-sample study was designed by retrieving the original metagenomic data from the data repository for Gut Microbiota (GMrepo) database. The distribution difference was analyzed in gut microbiota at the species level between CRCs and controls. The co-occurrence network was established, and the microbial interaction with environmental factors was analyzed. The functional prediction was performed using PICRUSt2. Linear discriminant analysis effect size (LEfSe) and random forest model were used to determine significant biomarkers for differentiating CRC and control samples.
Results
A total of 709 metagenomic samples from 6 projects were identified. After matching, 86 CRC patients with 86 matched healthy controls from six countries were enrolled. A total of 484 microbial species and 166 related genera were analyzed. Besides the previously-recognized species of Fusobacterium nucleatum and species belonging to Peptostreptococcus, Porphyromonas, and Prevotella genera with CRC, we found significant associations with novel species of Parvimonas micra and Collinsella tanakaei. In CRC patients, Bacteroides uniformis was positively correlated with age whereas Dorea longicatena, Adlercreutzia equolifaciens, and Eubacterium hallii had positive associations with the body weight index (BMI). Finally, a random forest model was established by integrating different numbers of species with the highest model-building importance and lowest inner subcategory bias. The mean value of area under the receiver-operating curve (AUC) was 0.858 in the training cohort and 0.813 in the validation sets, respectively.
Conclusions
Our study provides a novel bioinformatics approach for investigating the interaction between gut microbiota and CRC patients using the online free database. Identification of key species and their associated genes should be further emphasized to disclose the relative causality of microbial organisms and CRC development.
A lower extremity rehabilitation system for ground training at 16 weeks after total hip arthroplasty: a randomized controlled trial
Authors: Yuan Bo , Long Tao , Wang Zheng-Hao , Feng Yu-Ning , Li Bo , Jia Zi-Shan , li kainan 
Published: August 2022

Abstract
Objective To observe the effect of the lower limb rehabilitation system on the function of the lower limbs of patients after total hip replacement, and to explore the advantages and clinical application value of the lower limb rehabilitation system. Methods Sixty patients admitted to the orthopaedic ward of the Affiliated Hospital of Chengdu University from January 2021 to June 2021 who underwent total hip arthroplasty for different etiologies were analyzed.The patients were divided into a conventional rehabilitation group (Group A) and a lower limb rehabilitation system rehabilitation group (Group B) using a random number table, with 30 cases in each group.Fugl-Meyer scores were collected at the follow-up visits after 8, 12 and 16 weeks of rehabilitation treatment respectively. Holden Functional Ambulation Classification (FAC) data were collected at the follow-up visits after 16 weeks of rehabilitation treatment for evaluation and analysis. Peak moment values, single foot support time, step frequency and 10m maximum walking speed were collected at week 16 using the lower limb rehabilitation system. Results There was no statistical difference between the general data of the two groups and they were comparable. After 16 weeks of training, the Fugl-Meyer scores of limb function increased in both groups as the training time increased; the Fugl-Meyer scores of limb function were higher in Group B than in Group A for the same training time (P < 0.05); there was a statistical difference in the comparison of unipedal support time, peak hip torque, stride frequency and maximum walking speed between the two groups (P < 0.05). Lower limb function was better in Group B than in Group A. However, there was no statistical difference between the two groups in Holden's Functional Ambulation Classification (FAC) (p > 0.05). Conclusion The Lower Limb Rehabilitation System combined with conventional rehabilitation training can significantly improve the lower limb motor function, balance function and walking ability of patients after total hip arthroplasty in the early postoperative period, enabling patients to recover better and faster, and has greater clinical application value.