bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Early Prediction of Neonatal Necrotizing Enterocolitis in High-Risk Newborns- Based on Two Medical Center Clinical Databases
Authors: Mou Yanling , Li Jinhao , Wang Jianjun , Yu Daiyue , Yang Huirong , Zhang Xi , Tan Rongying , Mahamat Djibril Adam , Yang Liucheng , Wu Kai 
Published: July 2024

Abstract

Background: To improve the prognosis of necrotizing enterocolitis (NEC) in newborns, early identification and timely preventive interventions play an essential role. Based on the current situation, establishing a novel and simple prediction model is of great clinical significance.
Methods: The clinical data of NEC neonates in Zhujiang Hospital of Southern Medical University from October 2010 to October 2022 were collected, and 429 non-NEC patients in the same period were selected as the control group by random sampling method. After that, all participants were randomly divided into training group (70%) and testing group (30%). Combining relevant clinical features and laboratory results, five machine learning (ML) algorithms and classical logistic regression models were established. To evaluate the performance of each model, the area under the receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity of various models were compared. 10-folds cross-validation was used to find the best hyperparameters for each model. Decision curve analysis (DCA) was further used to evaluate the performance of the established models for clinical applications, and create a column-line graph, ranking the feature importance in model by SHapely Additive exPlanation (SHAP). The column plots were calibrated using calibration curves. In addition, the established model was validated in time series analysis as well as in another medical center.
Results: Six important features were finally included for modeling, including the Day (OR=1.15; 95% CI: 1.07-1.23; P=0.001), Gestational age (OR=0.77; 95% CI: 0.62-0.95; P=0.016), Eosinophil (EOS) (OR=3.76; 95% CI: 1.76-8.02; P=0.001), Hemoglobin (HB) (OR=0.98; 95% CI: 0.97-1.00; P=0.011), Platelet distribution width (PDW) (OR=1.21; 95% CI: 1.08-1.35; P=0.001) and High-sensitivity C-reactive protein (HSCRP) (OR=1.03; 95% CI: 1.01-1.06; P=0.007). While the logistic regression model achieved an AUC of 0.919, accuracy of 0.897, sensitivity of 0.832, F1-score of 0.778, and a Brier score of 0.0878 in the training group, the AUCs for the five machine learning models ranged from 0.774 to 0.972. Among these models, the LightGBM model performed the best, with an AUC of 0.960, accuracy of 0.894, sensitivity of 0.901, F1-score of 0.813, and a Brier score of 0.072.
Conclusion: The LightGBM machine learning model can effectively identify neonatal patients at higher risk of NEC based on Day age, Gestational age, EOS, HB, PDW, and HSCRP levels. This model is useful for assisting in clinical decision-making.
Gene Analysis of LBX1 Mutation, Tetralogy of Fallot,and Hypertrophic Cardiomyopathy Patient
Authors: An Yong , Cheng peiwen , Wang Guozhen , Song Yingzi 
Published: July 2024

Abstract

Background: Tetralogy of Fallot (TOF) and hypertrophic cardiomyopathy (HCM) are common types of congenital heart disease with unique pathophysiologic features. Mutations in LBX1, a key regulator of muscle precursor cell migration, may disrupt these critical developmental processes, resulting in severe developmental abnormalities.
Case Presentation: This case reports on a 4-year-old girl diagnosed with both TOF and HCM. Genetic analysis revealed iUPI-F (uniparental disomy) on the entire chromosome 10, with a c.808G>A (p. Glu270Lys) pure mutation in the LBX1.
Conclusion: This patient exhibited both TOF and HCM with mutations in the LBX1 gene, suggesting a potentially novel genetic link. This case study emphasizes the need for further studies on the function of the LBX1 gene in cardiac development and its potential impact on TOF and HCM. e LBX1 gene in cardiac development and its potential impact on TOF and HCM.
Value of an 18F-FDG-based peritoneal cancer index in predicting tumor grade, tumor burden, and completeness of cytoreductive surgery in epithelial ovarian cancer
Authors: Zhang Li , Li Bing , Tong GuanSheng , Wen Zhe , Yang MinFu 
Published: July 2024

Abstract

Background
The challenge of treating epithelial ovarian cancer (EOC) is significantly heightened by peritoneal metastasis. 18F-FDG PET/CT is employed as a preoperative assessment method for evaluating the extent of peritoneal spread in EOC, while peritoneal cancer index (PCI) serves as a vital tool in assessing peritoneal dissemination during surgery. We sought to investigate the value of a PCI derived from 18F-FDG PET/CT (PET-PCI) in predicting tumor pathological grade, tumor burden, and completeness of cytoreductive surgery (CRS) in patients with EOC.
Methods
We conducted a retrospective analysis of 64 patients with the International Federation of Gynecology and Obstetrics (FIGO) stages III–IV or recurrent EOC with peritoneal metastasis who underwent 18F-FDG PET/CT before therapy. PET-PCI was calculated by summing the 18F-FDG uptake scores across 13 abdominopelvic regions. Among them, 23 patients underwent CRS within 2 months after 18F-PET/CT. The relationship between PET-PCI, histological type (I or II), and surgical PCI was analyzed, as was the ability of PET-PCI to predict the completeness of CRS.
Results
Pathological analysis revealed 14 patients with type I and 50 patients with type II tumors. Compared to patients with type I tumors, those with type II tumors exhibited higher PET-PCI values (19.0 ± 11.1 vs. 12.4 ± 11.5 points, p = 0.022). Setting a cutoff of 15 points for PET-PCI to identify type II EOC resulted in a sensitivity of 56.0%, a specificity of 78.6%, and an AUC of 0.701 (p = 0.023). PET-PCI exhibited a positive correlation with surgical PCI (r = 0.885, p < 0.001). PET-PCI was a significant predictor of CRS completeness, with an AUC of 0.967 (p = 0.004). The cutoff value of 16 for PET-PCI facilitated the identification of CRS completeness in EOC patients, providing a sensitivity of 84.2% and a specificity of 100%.
Conclusions
This study demonstrated that PET-PCI is a valuable parameter in predicting tumor grade and burden in patients with advanced EOC. Moreover, PET-PCI may serve as a tool for predicting CRS completeness.
Enhanced Multifaceted Model for Plasmon-Driven Schottky Solar Cells with Integrated Thermal Effects
Authors: Aïssa Brahim , Baloch Ahmer A. , Ali Adnan , Mitra Anirban 
Published: July 2024

Abstract

This paper explores the development of an opto-thermal-electrical model for plasmonic Schottky solar cells (PSSCs) using a comprehensive multiphysics approach. We simulated the optical properties and energy conversion efficiencies of PSSCs with varying nanoparticle (NP) configurations and sizes. Our spectral analysis focused on the absorption characteristics of these solar cells, examining systems sized 3x3, 5x5, and 7x7, with NP radii ranging from 10 nm to 150 nm. Our study addresses a significant gap in solar cell research by presenting a novel multi-physics model for PSSCs decorated with gold nanoparticles (Au-NPs) on thin silicon absorbers. This framework uniquely couples optical, electrical, and thermal responses. The total spectral heat absorption was evaluated over a range of 300 nm to 1200 nm. This spectral heating was further deconvoluted into nanoparticle heating and thermalization heating in silicon absorber. The findings demonstrate that a 5x5 NP array with a 70 nm radius optimizes electrical output, achieving a short circuit current (Jsc) of 11.54 mA/cm², representing a 47% increase over traditional bare silicon Schottky cells. Optimal NP coverage, about 34.9% of the Si absorber's frontal area, is particularly effective for silicon layers as thin as 2 µm, enhancing light absorption and carrier generation. However, this electrical enhancement is countered by significant thermal gains in NPs, reaching up to 182.5%, highlighting the importance of balancing thermal management. Enhanced energy yield maps confirm our model's predictions, showing improved outputs globally, especially in sunny regions with potential annual energy yield gains up to 60 kWh/m².
Association Between Metabolic Dysfunction-Associated Steatotic Liver Disease and Increased Risk of Severe Bacterial Infections: A Meta-Analysis
Authors: Mantovani Alessandro , Fiorio Veronica , Morandin Riccardo , Lando Maria Giovanna , Gaviraghi Alberto , Motta Leonardo , Gobbi Federico , Tilg Herbert , Byrne Christoper , Targher Giovanni 
Published: July 2024

Abstract

Background
Previous studies have reported an association between metabolic dysfunction-associated steatotic liver disease (MASLD) and risk of severe bacterial infections. However, the magnitude of the risk and whether this risk varies with the severity of MASLD remains uncertain. We performed a meta-analysis of observational studies to quantify the association between MASLD and severe bacterial infections requiring hospital admission.
Methods
We systematically searched PubMed, Scopus, Web of Science and Embase from database inception to April 1, 2024, using predefined keywords to identify studies examining the risk of severe bacterial infections amongst individuals with and without MASLD. MASLD was diagnosed using biopsy, imaging, or International Classification of Diseases codes. Meta-analysis was performed using random-effects modelling.
Results
We identified six cross-sectional and two prospective studies with aggregate data on ~ 26.6 million individuals. MASLD was significantly associated with higher odds of severe bacterial infections (pooled random-effects odds ratio 1.93, 95%CI 1.44–2.58; I2 = 93%). Meta-analysis of data from prospective studies showed that MAFLD was associated with an increased risk of developing incident severe infections (pooled random-effects hazard ratio 1.80, 95%CI 1.62-2.0; I2 = 89%). This risk significantly increased across the severity of MASLD, especially the severity of fibrosis (pooled random-effects hazard ratio 2.42, 95%CI 1.89–2.29; I2 = 92%). These results remained significant after adjusting for age, sex, obesity, diabetes, and other potential confounders. Sensitivity analyses did not modify these findings. The funnel plot did not reveal any significant publication bias.
Conclusions
This meta-analysis shows a significant association between MASLD and increased risk of severe bacterial infections requiring hospital admission.
Neurofilament light chain (Nf-L) in cerebrospinal fluid and serum as a potential biomarker in the differential diagnosis of neurological diseases in cattle
Authors: Muro Giorgia Di , Tessarolo Carlotta , Cagnotti Giulia , Favole Alessandra , Ferrini Sara , Ala Ugo , Bellino Claudio , Borriello Giuliano , Gallo Marina , Iamone Giulia , Iulini Barbara , Pezzolato Marzia , Casalone Cristina , Caramelli Maria , Capucci Lorenzo , Cavadini Patrizia , Corona Cristiano , D’Angelo Antonio 
Published: July 2024

Abstract

Neurofilament light chain (Nf-L) is a biomarker for axonal damage in human neurology but is understudied in cattle. With this study we wanted to determine Nf-L stability at two different storage temperatures and Nf-L levels in healthy cattle and the relationship with age, evaluate whether Nf-L holds diagnostic potential for neurological disorders, and whether an association exists between Nf-L in serum and in cerebrospinal fluid (CSF). To do this, we measured Nf-L levels in CSF and serum samples from 49 healthy and 75 sick cattle. Storage at -80°C or -20°C had no impact on Nf-L concentration. Physiological median Nf-L levels were 6.3 pg/mL (serum) and 414 pg/mL (CSF) in calves and 5.5 pg/mL (serum) and 828 pg/mL (CSF) in adult cattle. There was no association between Nf-L levels in CSF and calf age (r2 0.07, p = 0.13), while a weak association was found for Nf-L in serum (r2 0.26, p = 0.01), and a significant association in adult cattle (CSF, r2 0.69, p = 0.0001; serum, r2 0.68, p = 0.0003). CSF Nf-L levels were higher in samples from animals with degenerative (median Nf-L 49971 pg/mL) and infectious central nervous system (CNS) disorders (median Nf-L, age < 2 months 8863 pg/mL; age 2–12 months 17474 pg/mL; age 1–6 years 3546 pg/mL), CNS anomalies and metabolic/toxic disorders. There was a significant association between CSF Nf-L and serum Nf-L in cattle with neurological disorders (r2 0.2, p = 0.009). Taken together, these findings suggest the potential of Nf-L as a diagnostic tool in cattle neurology.
Impact of WWP1-Encoded Extrachromosomal DNA on Chemoresistance in Ovarian Cancer
Authors: LU Chenyang , GUO Xiaojuan , DU Ruijuan , BIAN Hua , GUO Kelei , Li Tongle , Tu Yunfei , HAN Li , LI Ruifang 
Published: July 2024

Abstract

Background Ovarian cancer is one of the most lethal gynecological malignancies, primarily due to chemoresistance, which complicates effective treatment. Extrachromosomal circular DNA (eccDNA) has recently been implicated in cancer progression and drug resistance, yet its specific contributions remain underexplored. This study investigates the role of WWP1-encoded eccDNA (WWP1-eccDNA) in fostering drug resistance in ovarian cancer cells.Objective To elucidate the impact of WWP1-eccDNA on the chemoresistance of ovarian cancer cells and assess the efficacy of digital PCR (ddPCR) compared to quantitative PCR (qPCR) in detecting this eccDNA under various treatment conditions.Methods We utilized human ovarian cancer SKOV3 cells and their cisplatin-resistant variant, SKOV3/DDP. Following eccDNA extraction, BALB/cA-nu female mice were treated with saline, cisplatin, or hydroxyurea. The expression levels of WWP1-eccDNA were quantitatively analyzed using ddPCR and qPCR to compare the methodologies' sensitivity and accuracy.Results ddPCR demonstrated superior sensitivity and accuracy in detecting WWP1-eccDNA compared to qPCR. Treatment with cisplatin significantly increased the levels of WWP1-eccDNA in SKOV3 cells, whereas hydroxyurea treatment reduced these levels in SKOV3/DDP cells, highlighting the potential modulatory effects of these treatments on eccDNA abundance.Conclusion WWP1-eccDNA contributes to the chemoresistance observed in ovarian cancer cells. The enhanced sensitivity of ddPCR suggests its utility in eccDNA research and potential for clinical applications in diagnosing and monitoring resistance patterns. These insights are foundational to developing targeted therapies that could disrupt eccDNA-mediated resistance mechanisms.
Pirfenidone inhibits TGF-β1-induced fibrosis via downregulation of Smad and ERK pathway in MDCK cells
Authors: Im Chae-Yoon , Kim Se-Hoon , Song Ki-Hoon , Ryu Min-Ok , Youn Hwa-Young , Seo Kyoung-Won 
Published: July 2024

Abstract

The prevalence of chronic kidney disease (CKD) in dogs increases with age, and renal fibrosis is an important pathophysiological mechanism in this process. However, only a few drugs that can effectively inhibit fibrosis in the kidneys of dogs are currently available. In this study, we aimed to determine whether pirfenidone, a drug that has shown antifibrotic effects in various clinical studies, also exerts antifibrotic effects on canine renal tubular epithelial cells (MDCK). To this end, we treated MDCK cells with various concentrations of pirfenidone, followed by transforming growth factor-beta1 (TGF-β1) to stimulate fibrotic conditions. A cell viability assay was performed to determine the effect of pirfenidone on cell survival. Fibrosis-related markers and TGF-β1 fibrotic pathway-related markers were assessed using qPCR, Western blot analysis and immunocytochemistry. A one-way analysis of variance (ANOVA) was performed, followed by Tukey’s post-hoc test for multiple comparisons. Pirfenidone treatment significantly reduced the expression of profibrotic markers such as α-smooth muscle actin, fibronectin, and collagen. Additionally, it upregulated the expression of E-cadherin, an epithelial marker. Furthermore, pirfenidone effectively inhibited the phosphorylation of key factors involved in the TGF-β1 signaling pathway, including Smad2/3 and ERK1/2. These results demonstrate that pirfenidone suppresses TGF-β1-induced fibrosis in MDCK cells by attenuating epithelial-mesenchymal transition and the relevant signaling pathways.
Utilization of Tree-Based Machine Learning Models for Predicting Low Birth Weight Cases
Authors: Morais Flávio Leandro , Rocha Élisson Silva , Masson Gabriel , Gomes Ariel Nunes Braz , Filho Dimas Cassimiro Nascimento , Mendes Kátia Maria , Neto Waldemar Brandão , Endo Patricia Takako 
Published: July 2024

Abstract

Background: The monitoring of medical teams is crucial for assessing maternal and fetal health and early identification of complications such as Low Birth Weight (LBW), affecting over 20 million cases globally annually. This issue is associated with various risks, negatively impacting the development and quality of life of newborns. In Brazil, data from the Information System on Live Births (SINASC) indicate that 8% to 9% of newborns are diagnosed with LBW annually, emphasizing the need for comprehensive health policies for pregnant women. This study proposes the application of machine learning models to predict pregnant women at risk of having LBW newborns, aiming to contribute to prevention and adequate care.
Methods: The methodology employed five models in four different scenarios, considering variations in attribute selection and experimenting with the removal or retention of duplicate data after preprocessing.
Results: The results indicate that the models can identify newborns with Normal Birth Weight (NBW) but have limitations in detecting LBW cases, facing challenges in generalization, especially when tested with more recent data. The analysis of attribute importance highlights sociodemographic characteristics and gestational history as influential in the models’ learning.
Conclusions: The study provides a comprehensive view of the capabilities and limitations of machine learning models in predicting LBW, with Adaboost and Gradient Boosting standing out as more robust in dynamic data scenarios.
A generalized matching method for geographic entities based on fine-grained feature segments
Authors: Yan YuHan , Wu PengDa , Yin Yong , Guo PeiPei 
Published: July 2024

Abstract

Geographic entity matching is an important means for multi-source spatial data fusion and information association and sharing. Corresponding matching methods have been designed by existing studies for different types of entity data characteristics, such as line and area. However, these approaches are often limited in the generalization ability for matching heterogeneous data from multiple sources and the accuracy for complex pattern matching. To resolve these problems, a generalized matching method for geographic entities based on fine-grained feature segments is proposed. First, the entire entity is segmented based on shape features, and the partitioned feature segments are extracted as matching primitives; Second, feature segments are grouped into patterns, encompassing three major categories and 14 subcategories; Following this, pattern matching is performed based on spatial similarity metric such as maximum projection distance, etc.; Finally, the spatial matches are detected and refined through semantic similarity calculation. The proposed method is tested using two datasets from a region in southeast China. The experimental results demonstrate that our method can be effectively applied to both area and line entity matching. Specifically, 9 different strategies for matching area entities and 6 for line entities are utilized, and the precision and recall are almost above 90%.
Research on the Application of Tree Drawing Projection Tests in the Auxiliary Diagnosis and Condition Assessment of Depression
Authors: Liu Guorui , Zhang Yanfei , Li Ziyang , Liu Yige , Jin Hui , Shi Peiqi , Wen Jing , Wang Yihao , Bai Yonghai , Liu Wei 
Published: July 2024

Abstract

Objective
This study aims to extract and quantitatively analyze the tree drawing projection indices from groups of patients with depression, patients in remission, and a normal control group, to explore characteristic indicators of tree drawing projections in individuals with depression and provide a basis for auxiliary diagnosis and condition assessment of depression.
Methods
Tree drawing tests were administered to 59 patients with depression, 57 patients in remission, and 59 normal controls. Computer image recognition and data collection were used for quantitative analysis of the tree projections, and statistical analysis was conducted on the results from the three groups.
Results
ANOVA tests revealed significant statistical differences between the depression patients, remission patients, and normal controls in the following quantitative indices: canopy area, canopy height, canopy width, trunk area, trunk width, total area, and the ratio of canopy width to trunk width (P values: 0.000, 0.000, 0.000, 0.003, 0.000, 0.004, 0.000). No significant differences were found in trunk height, root width, root height, root area, total height, the ratio of canopy height to trunk height, and the ratio of canopy area to trunk area. Further LSD-t tests showed that compared to the depression group, the remission group exhibited significant differences in canopy area, canopy height, canopy width, trunk area, trunk width, the ratio of canopy height to trunk width, and total area (P values: 0.001, 0.000, 0.009, 0.002, 0.000, 0.046, 0.007, 0.000). No significant differences were found in the other six indices; also, no significant differences were found between the remission group and the normal control group across all 14 indices.
Conclusion
There are seven quantitative indices where significant statistical differences exist among the tree drawings from the depression group, the remission group, and the normal control group, and seven indices where no significant differences were found. The quantitative indices of tree drawing projections have potential value for the auxiliary diagnosis and condition assessment of depression.
Design and experiment of mechanical dustpan type-hole wheel corn seeder
Authors: Wang Shihao , Pan Shuai , Ren Dinglin , Chen Yongxin , Li Zhaodong 
Published: July 2024

Abstract

Using variable capacity seed filling technology, a dustpan type-hole wheel type corn seed discharging device with stopping plate was developed in order to enhance the mechanical hole wheel type corn seeder's seed discharging performance. By combining discrete element numerical simulation and methods that combine dynamics and kinematics theories. The structural parameters of the key components of the seeder were determined. Simultaneous corn hole sowing performance tests were done. The experimental findings indicated that the use of the dustpan type-hole resulted in qualified sowing rates of 97.2% for corn. The hole spacing rates for minimum qualification was 98.8%. Within the design parameters, changes in sowing operating parameters had a minor impact on the performance of the seeder. The bench performance test results demonstrate that the seeder's overall performance satisfies the criteria for agricultural planting.