bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mechanical properties of high-performance concrete with a hole under triaxial compression
Authors: Zhang Yanbin , Zhang Shoufeng , Wang Zhe 
Published: November 2023

Abstract
Holes are often reserved in concrete for holding cables and other industrial facilities. In order to ensure the safe application of high-performance concrete (HPC) members in a state of three-dimensional compression, it is of great significance to research the effect of holes on the mechanical properties of HPC under triaxial compression. However, there have been no previous studies on the triaxial compressive mechanical properties of HPC with holes. In this paper, HPC with a hole are experimentally studied under triaxial compression. The stress-strain curves, the strength, and the failure mechanisms of HPC are obtained. The experiment results show that both the confining pressure and the hole size jointly influence on the mechanical properties of HPC. Moreover, the Power-law failure criterion is modified to eludidate the relationship between the triaxial compressive strength of HPC and the hole size. In addition, the stress averaging method is firstly introduced to predict the sidewall failure of the hole in concrete, and the experimental results are used to modify the stress averaging method. The results show that the modified method can accurately predict the sidewall failure of the hole, and both the decrease of the confining pressure and the increase of the hole size are conducive to failure of the hole. This study makes up for the blank of the research of HPC with holes under triaxial compression, and presents an effective method to predict the failure of the hole.
Complications and Management of Inadequate Aortic Remodeling Following Elephant Trunk Surgery in Stanford Type A Aortic Dissection: A Case Report
Authors: Tang Chuanbin , Zou Yanqiang , Song Yu , Zhang Chao , Wu Long 
Published: November 2023

Abstract
Background: This case report describes a rare complication encountered in a 37-year-old male patient with subacute Type A aortic dissection.
Case presentations: The patient underwent a Bentall procedure, total arch replacement, descending thoracic aorta elephant trunk stent implantation, mitral valve repair, and tricuspid valve repair. Postoperatively, the patient exhibited poor blood flow in the lower extremities and kidneys. Digital Subtraction Angiography (DSA) revealed an angulation and stenosis at the proximal end of the elephant trunk stent, leading to inadequate blood supply. Balloon angioplasty and stent graft implantation were performed using a Thoracic Endovascular Aortic Repair (TEVAR) approach to restore blood flow.
Conclusions: The case highlights the importance of prompt diagnosis and the effectiveness of TEVAR in addressing inadequate aortic remodeling.
The efficacy and safety of continuous intravenous infusion of rh-endostatin combined with platinum-based doublet chemotherapy for advanced non-small-cell lung cancer
Authors: Liu Xinyi , Guo Zihan , Zuo Anli , Ji Xiang , Gao Min , Jiang Yunxiu , Yang Shuran , Lu Degan 
Published: November 2023

Abstract

Background
Platinum-based doublet chemotherapy is the current standard first-line treatment for patients with driver-gene-negative advanced non-small-cell lung cancer (NSCLC). However, its recurrence rate is relatively high. Accumulating evidence suggests that the addition of antiangiogenic agents to platinum-based chemotherapy can improve the survival outcomes for NSCLC patients. However, the optimal administration protocol for intravenous recombinant human endostatin (rh-endostatin), an antiangiogenic agent, remains uncertain at present.
Aim
This study aims to investigate the efficacy and safety of 5-d continuous intravenous infusion of rh-endostatin in combination with chemotherapy for patients with advanced NSCLC. The predictive biomarkers for this treatment regimen were further probed.
Methods
This prospective, single-arm multicenter study enrolled a total of 48 patients with advanced NSCLC who were histologically or cytologically confirmed but had not received any prior treatment from February 2021 to July 2022. Prior to chemotherapy, the patients received a continuous intravenous infusion of rh-endostatin (210mg) over a period of 120 hours, using an infusion pump. The chemotherapy regimen consisted of platinum in combination with either pemetrexed or paclitaxel, administered in cycles of 21 days. The primary endpoint of the study was progression-free survival (PFS), and the secondary endpoints included objective response rate (ORR), disease control rate (DCR), overall survival (OS), and assessment of adverse events (AEs).
Results
The median PFS (mPFS) was 6.7 months, the median OS (mOS) was 12.1 months, the ORR was 52.1%, and the DCR was 79.2%. The most common adverse effects were leukopenia (52.1%), anemia (33.3%) and thrombocytopenia (20.8%). These toxicities were deemed acceptable and manageable. In addition, we observed a correlation between higher serum carcinoembryonic antigen (CEA) levels and shorter PFS and OS.
Conclusions
The combination of a 5-day continuous intravenous infusion of rh-endostatin with chemotherapy demonstrated both safety and efficacy for the treatment of advanced NSCLC. Additionally, we found that baseline serum levels of CEA may serve as a predictor of the efficacy of rh-endostatin plus chemotherapy in NSCLC patients.
Open data for energy networks: Introducing DAVE - a data fusion tool for automated network generation
Authors: Banze Tobias , Kneiske Tanja 
Published: November 2023

Abstract
Developing a sustainable energy system for the future requires new ways of planning and operating energy infrastructure. A large part of this involves suitable network models. Real network data is not available for research without restrictions since energy networks are part of the critical infrastructure. Using open datasets and expert rules to generate non-restricted models is one solution to this. This paper introduces open data for energy networks generated by the open-source software “DAVE”. The Python-based data fusion tool DAVE can automatically generate customised energy network models quickly and on demand. The software collects data from various databases and uses appropriate methods to fuse them. The current version of the tool can create GIS-based power networks and gas transportation networks, with output that is compatible with common network simulation software. Further developments are planned for creating thermal and gas distribution networks, as these are important for local heat power transition. Implementing a quality description for fused data sets will also be included in future development.
Desloratadine-Loaded Flexible Orally Dissolvable Tablets Made of Electrospun Fiber Mats
Authors: Azizoğlu Gülçin Arslan , Dönder Buse , İnal Nergis , Azizoğlu Erkan 
Published: November 2023

Abstract
Patient compliance and personalized dose adjustments are important factors, especially in pediatric care. Orodispersible tablets (ODTs) are gaining importance as they increase patient compliance compared to other solid oral dosage forms. ODTs are solid dosage forms that disintegrate within seconds upon contact with a small amount of liquid. However, the main drawback of the ODTs is their brittle nature which requires special packaging and careful handling and use. In this study, flexible ODTs were fabricated with the electrospinning method. The formulation composed of Kollidon 90F and ethanol was selected for desloratadine-loaded fiber fabrication due to its ease of production and reduced electrospinning time. The single-dose fiber mat fabrication time was adjusted to 2 and 4 minutes to obtain a pediatric dose of 2.5 mg and a 5 mg adult dose, respectively. A 3D-printed tablet compression tool was successfully used to transform the electrospun fibers into ODTs. Fabricated ODTs were compared with commercial desloratadine ODTs and the results showed that they both have similar disintegration times. However, ODTs fabricated from electrospun mats demonstrated a distinct advantage in terms of flexibility, as they did not fracture under applied pressure by a texture profile analyzer.
The manufacturing process, along with the utilization of 3D-printed tools, streamlined the production of flexible ODTs and simplified dose adjustments. These advancements hold the potential to facilitate customized therapies for individual patients, thereby enhancing patient compliance, particularly among pediatric populations.
Abortion Timing, Access, and Trajectory Study (ATAS): Study Protocol
Authors: Wallays Anna , de Velde Sarah Van 
Published: November 2023

Abstract
Background
This study protocol outlines the phased construction of a questionnaire, data collection, and a strategy for analysis, within the framework of the ATAS project. The study has two primary objectives. First, it allows us to map the duration of the various phases of the abortion trajectory for women presenting for abortion in Flanders, Belgium. Second, it identifies barriers that cause delays within these phases.
Methods
The questionnaire is presented to all women seeking abortion care in one of the Flemish abortion centers; specifically, the LUNA centers located in Ostend, Ghent, Antwerp, and Hasselt, as well as the VUB-Dilemma center in Brussels during the fall of 2023. Ethical clearance for the described research was obtained from the University of Antwerp's Ethics Committee for the Social Sciences and Humanities (reference numbers: SHW_2023_48_1 and SHW_2023_48_2).
Discussion
The collected data will provide a dataset on abortion trajectories of women presenting for abortion in Flanders. This study protocol outlines the ATAS project’s rationale, the phased development, and implementation of the questionnaire, as well as the upcoming data analyses. To our knowledge, this is the first study within the Flemish context to investigate abortion timing, access, and trajectories. Furthermore, this study protocol provides a phased and systematic approach to adapt validated research instruments to fit within diverse legal and cultural contexts.
Multimodal Biomedical Data Fusion Using Sparse Canonical Correlation Analysis and Cooperative Learning: A Cohort Study on COVID-19
Authors: Er Ahmet Gorkem , Ding Daisy Yi , Er Berrin , Uzun Mertcan , Cakmak Mehmet , Sadée Christoph , Durhan Gamze , Ozmen Mustafa Nasuh , Tanriover Mine Durusu , Topeli Arzu , Son Yesim Aydin , Tibshirani Robert , Unal Serhat , Gevaert Olivier 
Published: November 2023

Abstract

Through technological innovations, patient cohorts can be examined from multiple views with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict outcomes. Here, we aim to present our approach for analyzing multimodal data using unsupervised and supervised sparse linear methods in a COVID-19 patient cohort. This prospective cohort study of 149 adult patients was conducted in a tertiary care academic center. First, we used sparse canonical correlation analysis (CCA) to identify and quantify relationships across different data modalities, including viral genome sequencing, imaging, clinical data, and laboratory results. Then, we used cooperative learning to predict the clinical outcome of COVID-19 patients. We show that serum biomarkers representing severe disease and acute phase response correlate with original and wavelet radiomics features in the LLL frequency channel (𝑐𝑜𝑟𝑟(𝑋u𝟏, Zv𝟏) = 0.596, p-value < 0.001). Among radiomics features, histogram-based first-order features reporting the skewness, kurtosis, and uniformity have the lowest negative, whereas entropy-related features have the highest positive coefficients. Moreover, unsupervised analysis of clinical data and laboratory results gives insights into distinct clinical phenotypes. Leveraging the availability of global viral genome databases, we demonstrate that the Word2Vec natural language processing model can be used for viral genome encoding. It not only separates major SARS-CoV-2 variants but also allows the preservation of phylogenetic relationships among them. Our quadruple model using Word2Vec encoding achieves better prediction results in the supervised task. The model yields area under the curve (AUC) and accuracy values of 0.87 and 0.77, respectively. Our study illustrates that sparse CCA analysis and cooperative learning are powerful techniques for handling high-dimensional, multimodal data to investigate multivariate associations in unsupervised and supervised tasks.
A Review of the Parallelization Strategies for Iterative Algorithms
Authors: Zhou Xingxing , Ling Ming , Tang Shidi , Zhu Yanxiang 
Published: November 2023

Abstract
Iteration-based algorithms have been widely used and achieved excellent results in many fields. However, in the big data era, data that needs to be processed is enormous in terms of both depth (the dimensionality of data) and breadth (the volume of data). Due to the slowdown of Moore's Law, the computing power of single-core CPUs is becoming saturated. The increase in the computational complexity and the bottleneck of the single-core processor’s speed exacerbate the time-consuming problem of iterative algorithms. With the rise of multi-core computers and distributed computing systems, parallelizing and deploying iterative algorithms on such systems can make full use of computing resources and accelerate iterative computation, providing a new idea for solving the aforementioned problems. However, due to the logical dependency between two consecutive iterations in an iterative algorithm, it is difficult to directly implement the concurrent computation of such algorithms. To this end, many studies have been conducted on the parallelization of iterative algorithms in both academia and industry. This paper aims to conduct an in-depth research and analysis of these parallelization strategies. Firstly, the abstract description and classification of iterative algorithms are given. Then four concurrency strategies for iterative algorithms are summarized, including logical units that can be intrinsically concurrently computed, multi-initial state parallel search strategy, data parallelism, and task parallelism. Finally, the paper detailed the convergence of parallel iterative algorithms, focusing on building the mathematical model of asynchronous iterative algorithms, and summarizing the convergence conditions of asynchronous iterative algorithms.
Application of ultrahigh frequency transcutaneous electrical nerve stimulation for alleviation of neuropathic pain and neuroinflammation modulation in rat sciatic nerve chronic constriction injury
Authors: Chen Szu-Han , Lin Yu-Wen , Tseng Wan-Ling , Lin Wei-Tso , Lin Sheng-Che , Hsueh Yuan Yu 
Published: November 2023

Abstract
A challenging complication in patients with peripheral compressive neuropathy is neuropathic pain. Excessive neuroinflammation and neuropeptide buildup at the injury site worsen neuropathic pain and impair function. Currently, non-invasive modulation like transcutaneous electrical nerve stimulation (TENS) showed therapeutic promise with positive results. However, underlying regulatory molecular mechanism for peripheral neuropathic pain remains complex and unexplored. This study aimed to validate the therapeutic effect of ultrahigh frequency (UHF)-TENS in chronic constriction injury of rat sciatic nerve. The efficacy and safety of UHF-TENS were examined including mechanistical exploration. Alleviation of mechanical allodynia was achieved through the application of UHF-TENS, which lasted for 3 days for a one-session therapy, without additional damage on the myelinated axon structure. Significant reduction of pain-related neuropeptides, MEK, c-Myc, c-FOS, COX2, and substance P, were observed in the injured DRG neurons. RNA sequencing of differential gene expression of the sensory neurons revealed a significant downregulation in Cables, Pik3r1, Vps4b, Tlr7, and Ezh2 after nerve injury, while upregulation was observed in Nfkbie and Cln3. UHF-TENS effectively and safely relieved neuropathic pain without causing further nerve damage. The decreased production of pain-related neuropeptides within the DRG neurons provided the therapeutic benefit. Possible molecular mechanisms by UHF-TENS might result from the modulation of the NF-κB complex, toll-like receptor-7, and phosphoinositide 3-kinase/Akt signaling in sensory neurons. This results suggest the neuromodulatory effects of UHF-TENS in rat sciatic nerve chronic constriction injury, in terms of alleviation of neuropathic pain, amelioration of pain-related neuropeptides, and regulation of neuroinflammatory gene expression. In combination with related molecular medication, UHF-TENS would be a new modality to potentiate the treatment of neuropathic pain in the future.
Evaluation of the frequency of congenital heart disease in children with recurrent or persistent pneumonia
Authors: Kaya Mustafa Mesut , Akın Alper , Balık Hasan , Uç Duygu , Türe Mehmet , Şen Velat 
Published: November 2023

Abstract
Aim
Pneumonia is one of the leading causes of death in young children and infants, especially in developing countries. Many pathologies are involved in the etiology of recurrent and persistent pneumonia. This study aims to determine the underlying congenital heart disease in recurrent or persistent pneumonia and to present the data from our region to the literature.
Material and Method:
Our study included 102 patients with recurrent or persistent pneumonia between June 2019 and February 2021. The number of pneumonia episodes, echocardiographic evaluations, physical examination, imaging, and laboratory findings were evaluated.
Results
Of the 102 patients in our study, 69 (67.6%) were male, and the mean age was 24.36 ± 32 (3-180) months. All patients met the criteria for recurrent pneumonia, and 55 patients (54%) met the criteria for persistent pneumonia. Congenital heart disease was found in 7.8% of patients with recurrent pneumonia and 23.6% with persistent pneumonia. Congenital heart disease was detected in 22 (21.5%) patients. The most common congenital heart disease was isolated Atrial Septal Defect (7.8%) and isolated Ventricular Septal Defect (4.9%). 59.1% of patients with congenital heart disease had a murmur on cardiac auscultation compared to 1.3% without CHD (p:<0.001). Body weight Z score was significantly lower in the group with congenital heart disease compared to the group without congenital heart disease (p:0.04). The incidence of congenital heart disease was significantly higher in girls with recurrent pneumonia than in boys with recurrent pneumonia (p:0.05).
Conclusion
Congenital heart disease is highly prevalent among the etiologies that can be identified in both recurrent pneumonia and persistent pneumonia. The presence of a pathological murmur on cardiac auscultation is a significant finding in favor of CHD. The frequency of congenital heart disease is higher in patients who fulfill the criteria for persistent pneumonia and in girls. Body weight is significantly affected in children with congenital heart disease associated with pneumonia.
Navigating protein landscapes with a machine-learned transferable coarse-grained model
Authors: Clementi Cecilia , Charron Nicholas , Musil Felix , Guljas Andrea , Chen Yaoyi , Bonneau Klara , Pasos-Trejo Aldo , Venturin Jacopo , Gusew Daria , Zaporozhets Iryna , Krämer Andreas , Templeton Clark , Kelkar Atharva , Durumeric Aleksander , Olsson Simon , Pérez Adrià , Majewski Maciej , Husic Brooke , Patel Ankit , De Fabritiis Gianni , Noé Frank 
Published: November 2023

Abstract
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been a long-standing challenge.
By combining recent deep learning methods with a large and diverse training set of all-atom protein simulations, we here develop a bottom-up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences not used during model parametrization. We demonstrate that the model successfully predicts folded structures, intermediates, metastable folded and unfolded basins, and the fluctuations of intrinsically disordered proteins while it is several orders of magnitude faster than an all-atom model.
This showcases the feasibility of a universal and computationally efficient machine-learned CG model for proteins.
Identification of 4 autophagy-related genes in ischemic heart failure by bioinformatics analysis and machine learning
Authors: Deng Xiwei , Yang Ziqi , Li Tongzheng , Wang Yang , Yang Qinchuan , An Rui , Xu Jian 
Published: November 2023

Abstract
Background
Autophagy refers to the process of breaking down and recycling damaged or unnecessary components within a cell to maintain cellular homeostasis. Heart failure (HF) is a severe medical condition that poses a serious threat to the patient's life. Autophagy is known to play a pivotal role in the pathogenesis of HF. However, our understanding of the specific mechanisms involved remains incomplete. Here, we identify autophagy-related genes (ARGs) associated with HF, which we believe will contribute to further comprehending the pathogenesis of HF.
Methods
By searching the GEO (Gene Expression Omnibus) database, we found the GSE57338 dataset, which was related to ischemic HF. ARGs were obtained from the HADb and HAMdb databases. Annotation of GO and enrichment analysis of KEGG pathway were carried out on the differentially expressed ARGs (AR-DEGs). We employed machine learning algorithms to conduct a thorough screening of significant genes and validated these genes by analyzing external dataset GSE76701 and conducting mouse models experimentation. At last, immune infiltration analysis was conducted, target drugs were screened and a TF regulatory network was constructed.
Results
Through processing the dataset with R language, we obtained a total of 442 DEGs. Additionally, we retrieved 803 ARGs from the database. The intersection of these two sets resulted in 15 AR-DEGs. Upon performing functional enrichment analysis, it was discovered that these genes exhibited significant enrichment in domains related to "regulation of cell growth", "icosatetraenoic acid binding", and "IL-17 signaling pathway". After screening and verification, we ultimately identified 4 key genes. Finally, an analysis of immune infiltration illustrated significant discrepancies in 16 distinct types of immune cells between the HF and control group and up to 194 potential drugs and 16 TFs were identified based on the key genes.
Conclusion
In this study, TPCN1, MAP2K1, S100A9, and CD38 were considered as key autophagy-related genes in HF. With these relevant data, further exploration of the molecular mechanisms of autophagy in HF can be carried out.
Nocardiosis with suspected multiple metastases of lung cancer: a case report
Authors: Lu Huiying , Yao Mengxian , Gan Lei , chen zhigung 
Published: November 2023

Abstract
Background
Nocardiosis is a rare opportunistic infection susceptible to immunocompromised patients. The condition frequently manifests as pulmonary infection and may have imaging characteristics which resemble to lung cancer, such as the nodules and the lymph nodes enlargement, leading to confusion in diagnosis. In addition, Nocardia can also spread through the blood, causing cutaneous and cerebral infections.
Case presentation:
We report a female patient with long-term oral methylprednisolone and ciclosporin, who had uncommon multiple systemic infections. CT scan showed the patient may be lung cancer with intrapulmonary, subcutaneous and musculature metastases, while the cranial enhanced MRI scan showed brain metastases. Finally, Nocardia was cultured through subcutaneous puncture fluid. After treatment, her multiple organ lesions became significantly smaller or subsided.
Conclusions
Doctors should consider nocardiosis as a potential infectious disease in patients with compromised immune systems to prevent incorrect diagnosis and treatment.