bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neurological Presentations in Pediatric Patients with Inborn Errors of Metabolism: A single-center study
Authors: Abdelmoneim Zahraa , Hussein Mohamed Abdelghafar , Eltaher Heba 
Published: November 2024

Abstract

Background
Inborn errors of metabolism (IEMs) represent a diverse group of genetic disorders characterized by enzyme deficiencies in metabolic pathways, with a collective incidence exceeding 1 in 1,000 live births. Although many patients may appear asymptomatic at birth, they often present with nonspecific neurological symptoms later, complicating diagnosis and treatment. This study aims to identify various neurological manifestations in pediatric patients with IEMs and emphasize the importance of early recognition and management for improved outcomes.
Methods
This single-center study recruited pediatric patients (ages 0–18) diagnosed with IEMs presenting with neurological symptoms from the Mansoura University Children’s Hospital. Clinical data were collected, including demographic information, seizure characteristics, perinatal events, and family history. Laboratory investigations encompassed metabolic panels, tandem mass spectrometry, and urine organic acid analysis. Neuroimaging, including MRI and EEG, was also performed. Descriptive statistics were used to summarize the prevalence and types of neurological presentations in different IEM disorders.
Results
The study included 31 patients (15 males, 16 females), predominantly from consanguineous backgrounds (83.9%). The most common diagnosis was organic acidemia (51.6%), followed by mitochondrial disorders (22.6%). Seizures were the most frequent neurological presentation (71%), with generalized tonic-clonic seizures being the most prevalent type. EEG findings varied, with generalized epileptogenic dysfunction observed in 22.6% of cases. MRI results showed white matter involvement in 54.8% of patients, with basal ganglia and thalamic changes noted in organic acidemia cases.
Conclusion
Neurological manifestations of IEMs are varied, with seizures being a common feature. The study highlights the challenges in diagnosing IEMs due to their rarity and the necessity of a high index of suspicion, particularly in patients with acute encephalopathy. Timely diagnosis and intervention are crucial to prevent irreversible neurological damage. This study underscores the need for further research into IEMs in pediatric populations to enhance clinical understanding and management strategies.
Integration of machine learning and risk modeling to analyze and validate the role of senescent genes in lupus nephritis with monkeypox virus
Authors: Wang Yaojun , Li Qiang 
Published: November 2024

Abstract

Objective
Monkeypox virus is now spreading rapidly around the world, but the mechanism of interaction between it and lupus nephritis is not yet clear.The purpose of this study is to explore the role and mechanism of cell aging in lupus nephritis combined with monkeypox virus infection.
Method
The data comes from GEO and GeneCards.Through Limma and WGCNA analysis, differential expression genes (DEGs) and module genes were identified, and KEGG and GO enrichment analysis was carried out.In addition, a protein-protein interaction (PPI) network was constructed and LASSO regression was used to screen genes related to aging. The diagnostic effectiveness was evaluated by Norma diagram and ROC curve, and verified on GSE99967.Immune infiltration and gene set enrichment analysis (GSEA) Were also included in the study.In the end, miRNet was used to construct a miRNA-mRNA-TF network and screen targeted drugs through DGIdb.
Results
5707 DEGs were identified in the lupus nephritis data set and 737 in the monkeypox data. The two have a total of 113 genes, which are related to immune cell function and aging.The three central genes screened showed an AUC of 0.71 to 0.88 in the column chart, indicating good diagnostic potential.Immune infiltration analysis showed immune cell disorders and related pathway activation.The miRNA-mRNA-TF network covers 516 miRNAs and 15 transcription factors, and enrichment analysis shows that it plays an important role in aging and inflammation.Potential Target Drugs Screened Include Guttiferone K And Silicon Phthalocyanine 4.
Conclusion
This study confirmed the key genes (STAT1, ORC1, GTF2B) related to cell aging and immunity, and developed a line chart for the diagnosis of monkeypox virus infection combined with lupus nephritis, and at the same time screened drug candidates with targeted potential.
Prevalence and determinants of anaemia among pregnant women who use biomass for cooking in rural parts of Tamil Nadu
Authors: Aravindalochanan Vigneswari , Sinharoy Sheela , Thangavel Gurusamy , Puttaswamy Naveen , Garg Sarada , Sambandam Sankar , Mukhopadhyay Krishnendu , Natesan Durairaj , Ramasamy Rengaraj , Rajamani Karthikeyan , Natarajan Priyakumar , Gopalkrishna Meenakshi Sundarem , Jaacks Lindsay M. , Balakrishnan Kalpana , Ramakrishnan Usha , Jabbarzadeh Shirin , DB Barr , Clasen Thomas , Checkley William , Peel Jennifer L. 
Published: November 2024

Abstract

Background
Anaemia during pregnancy is a major public health concern in India. Biomass fuel use is common in a few rural areas of Tamil Nadu state and there is growing evidence that its use is associated with anaemia. The aim of this study was to estimate the prevalence of anaemia and to identify the coexisting determinants of anaemia among exclusive biomass-using rural pregnant women.
Methods
We used baseline data collected from 799 rural pregnant women with a gestational age between 9 and 20 weeks from two districts of Tamil Nadu, India who were enrolled in a multi-country randomised controlled trial of a liquefied petroleum gas intervention. Haemoglobin (Hb) was measured in capillary blood using the Haemocue-201 point of case device. Hb was categorized as normal (Hb ≥ 11g/dL), mild anaemia (Hb 10.0-10.9g/dL), moderate anaemia (Hb 7.0-9.9g/dL), or severe anaemia (Hb < 7.0g/dL). Multinomial logistic regression was used to identify factors associated with anaemia.
Results
Overall prevalence of any anaemia was 66.7% (95% confidence interval: 63.4–70%) and that of mild anaemia was 31.9% (28.7–35.1%), moderate anaemia 33.7% (30.4–36.9%), and severe anaemia was 1.1% (0.6–2.2%) Wealth index, body mass index classified as underweight (< 18.5 kg/m2), being a multigravida, and hand wash area not observed were associated with a significantly higher odds of anaemia.
Conclusion
This exclusive biomass using pregnant women had quite higher rates of anaemia and underweight than that of rural estimates from recent NFHS reports for Tamil Nadu state indicating the need to improve their overall anthropometric status and anaemia together emphasizing on healthy dietary habits.
Flow diverter alone versus flow diverter combine with coiling for larger intracranial aneurysm treatment: a retrospective， propensity score matched study with a focus on midterm outcomes
Authors: Ma Wulin , An Mengsi , Deng Xin , Liu Shuo , Guo Xinbin 
Published: November 2024

Abstract

Purpose
Flow diverter devices (FDs) have been widely used in the treatment of intracranial aneurysms, particularly the large and giant aneurysms, and have gained considerable clinical experience. The objective of this study was to assess the occlusion rate and adverse event of the large and giant aneurysms in the medium term by means of a combined treatment strategy involving Flow Diverter and coils.
Materials and Methods
This is a single-center, retrospective study. The large and giant aneurysms were categorized according to the treatment received: flow-diverter devices combined with coil embolization (FDC group) and flow-diverter devices alone (FD group). After propensity score matching for both groups, the patients’ clinical and imaging data were analyzed and compared.
Results
A total of 103 aneurysms were included, the complete occlusion rate of aneurysms in the FDC group was 59.2% (32/71) at follow up. One unruptured aneurysm underwent delayed rupture. After PSM, 28 aneurysms were included in each group. There was a statistically significant difference in occlusion grade between the two groups at 0–6 months (p=0.03; p=0.004). There was no statistically significant difference between the two groups in terms of occlusion changes, mRS scores, and complications.
Conclusion
The flow diverter combined with coil embolization for the large and giant aneurysms is effective in improving complete occlusion rates and reducing neck remnants at mid-term. Patients are rarely experiencing delayed rupture and retreated. The staged treatment approach, in conjunction with the selection of an appropriate coil pack density, can minimize the incidence of delayed rupture and rebleeding.
Study on risk factors of preoperative deep vein thrombosis in patients with lower limb fractures and construction and validation of risk prediction nomogram model
Authors: Zheng Fan , Chen Xiaobin , Huang Jianqiang , Lin Chen 
Published: November 2024

Abstract

Background
To explore the correlation between the levels of D-dimer (D-D), fibrinogen (FIB), fibrinogen degradation products (FDP) and platelets (PLT) in peripheral blood of patients with lower limb fractures and the formation of deep vein thrombosis in lower limbs, and to establish a new thrombosis prediction model for patients with lower limb fractures.
Methods
The patients were divided into DVT group and non DVT group according to whether there was deep vein thrombosis of the lower extremity. The differences in the levels of D-D, FIB, FDP and platelets between the two groups were analyzed and compared. ROC curve was used to evaluate the levels of D-D, FIB, FDP and PLT in the peripheral blood of patients with lower extremity fracture to predict the formation of deep vein thrombosis of the lower extremity. Logistic regression analysis was used to analyze the related risk factors of deep vein thrombosis, and the corresponding nomogram risk prediction model of lower limb deep vein thrombosis in patients with lower limb fractures was drawn according to the regression coefficient, which was verified by calibration curve and consistency curve.
Results
The levels of D-D, FIB, FDP, and PLT in the DVT group were higher than those in the non DVT group, with statistical significance (P < 0.05); Moreover, FIB is superior to D-D, FDP, and PLT in predicting the risk of fractures and thrombosis, while PLT has the weakest predictive power. Multivariate logistic analysis showed that platelet, D-D, FIB and FDP were independent risk factors for deep vein thrombosis in patients with lower limb fractures (P < 0.05); Based on the independent risk factors mentioned above, the complex logistic regression formula was transformed into a visual column chart (with a C-index of 0.962), which has good discrimination and consistency.
Conclusions
The levels of D-D, FIB, FDP and PLT in peripheral blood of patients with lower limb fracture and DVT were significantly increased. Early monitoring of D-D, FIB, FDP and PLT levels in patients with lower limb fracture can effectively screen for lower limb deep vein thrombosis.
The Association Between Hand Grip Strength and Chronic Kidney Disease Progression: Insights from SMP-CKD Studies
Authors: Huang Qiong , Chen Linyi , Ouyang Wenwei , Jie Xi-na , Fu Li-zhe , Tang Fang , Wang Jing , Wu Yifan , Liu Xusheng 
Published: November 2024

Abstract

Purpose
This study aims to investigate the relationship between handgrip strength (HGS) and the progression of chronic kidney disease (CKD) in non-dialysis patients in China, as part of the Self-Management Program for Patients with CKD Cohort (SMP-CKD).
Methods
In the SMP-CKD cohort, we utilized Cox regression and Kaplan-Meier survival analysis to explore the association between HGS and CKD progression. Data were stratified by sex-specific HGS quartiles, sarcopenia status, and HGS thresholds. The HGS thresholds were determined through curve analysis of HGS against composite renal outcomes. Group differences were compared to assess the impact of HGS on CKD outcomes.
Results
A total of 441 participants (mean age 57.0 ± 17 years, 56.0% male) with CKD stages 3–5 from the SMP-CKD cohort who underwent grip strength evaluation between April 2019 and June 2024 were included in the analysis. The findings revealed that participants in the highest bilateral HGS quartile had a significantly lower risk of renal endpoints, with a hazard ratio (HR) of 0.109 (95% CI: 0.044–0.272) compared to those in the lowest quartile. Patients with sarcopenia exhibited more than twice the risk of increased serum creatinine or acute CKD exacerbations (HR 2.429, 95% CI: 1.218–4.846), as well as a markedly higher risk of severe renal endpoints (HR 4.237, 95% CI: 1.595–11.256). Gender-specific cutoffs identified through ROC analysis were 64.35 kg for men and 39.35 kg for women. Participants with bilateral HGS above these thresholds demonstrated better renal outcomes, underscoring the protective effect of higher HGS against CKD progression.
Conclusion
The study provides strong evidence that HGS is a crucial factor in reducing the risk of CKD progression. Higher levels of HGS are significantly associated with a lower occurrence of renal endpoint events.
Study on response of PM2.5 to urban form in Urumqi, China
Authors: Xi Mingyue , Chen Xuegang , Liu Hongyu , Zhao Gang 
Published: November 2024

Abstract

Urban form significantly affects air quality, which in turn affects public health. In this study, the effects of urban form on PM2.5 concentration levels in the central city of Urumqi in 2000, 2010 and 2020 were analyzed by using high-resolution remote sensing data through a geographically weighted regression (GWR) model that integrates four urban form indicators and four control variables. The study shows that the PM2.5 concentration in the central urban area of Urumqi has not only declined in the last two decades, but also the distribution range is gradually narrowing, and the relative high values are mainly concentrated in Midong District, New Urban District, and Toutunhe District; Agglomeration Index (AI), Largest Patch Index (LPI), and Road Density (RD) have significant effects on PM2.5 concentration, (Average Minimum Neighborhood Distance) ENN_MN, Population density, GDP and precipitation have all changed from negative to positive correlation with PM2.5 concentration in the last two decades, while temperature is always positively correlated with PM2.5 concentration. It is shown that reducing the fragmentation of the urban landscape and the complexity of the urban shape in Urumqi as well as lowering the density of the road network can help to mitigate the concentration of PM2.5. The results of this study are of great significance for better understanding the relationship between urban form and PM2.5 concentration, and for more scientific urban spatial planning.
Unsupervised local learning based on voltage-dependent synaptic plasticity for resistive and ferroelectric synapses
Authors: Alibart Fabien , Garg Nikhil , Balafrej Ismael , Palhares Joao , Begon-Lours Laura , Florini Davide , Falcone Donato , Stecconi Tommaso , Bragaglia Valeria , Offrein Bert , Portal Jean-Michel , Querlioz Damien , Beilliard Yann , Drouin Dominique 
Published: November 2024

Abstract

In this study, we introduce voltage-dependent synaptic plasticity (VDSP) as an efficient approach for unsupervised and local learning in memristive synapses based on Hebbian principles. This method enables online learning without requiring complex pulse-shaping circuits typically necessary for spike-timing-dependent plasticity (STDP). We show how VDSP can be advantageously adapted to three types of memristive devices (TiO2, HfO2-based metal-oxide filamentary synapses, and HfZrO4-based ferroelectric tunnel junctions (FTJ)) with disctinctive switching characteristics. System-level simulations of spiking neural networks incorporating these devices were conducted to validate unsupervised learning on MNIST-based pattern recognition tasks, achieving state-of-the-art performance. The results demonstrated over 83% accuracy across all devices using 200 neurons. Additionally, we assessed the impact of device variability, such as switching thresholds and HRS/LRS levels, and proposed mitigation strategies to enhance robustness.
Factors associated with e-cigarette initiation and conversion to conventional cigarette among adolescents in Malaysia, a scoping review.
Authors: Mohdkhairi Muzzammil , Mulenga David 
Published: November 2024

Abstract

Introduction
E-cigarettes are a contentious disruptor in the global fight against tobacco, with debates surrounding their role as smoking cessation tools versus the risk of renormalizing smoking, particularly among adolescents. While factors influencing e-cigarette initiation and conversion to conventional smoking have been extensively studied in high-income Western countries, there is limited research on this issue in an upper-middle-income country such as Malaysia. This scoping review aims to explore factors associated with e-cigarette initiation and conversion to cigarette smoking in Malaysian adolescents.
Methods
In December 2022, a search was conducted in Scopus, Ovid Medline, and Web of Sciences databases for studies focused on e-cigarettes and adolescents in Malaysia. Additional sources, such as gray literature from Google Scholar, government reports, and non-governmental organizations, were also reviewed. Studies focusing on populations outside Malaysia or including age groups beyond adolescents were excluded.
Results
Eleven studies were included, all focusing specifically on Malaysian adolescents. Similar to findings from high-income countries, factors associated with e-cigarette initiation include male gender, appealing flavors, media influence, and legal loopholes with poor enforcement. However, data on conversion to conventional smoking, especially among never-smokers, remains limited.
Discussion
This review underscores key research gaps, particularly the limited understanding of the risk of conversion to cigarette smoking. Further research tailored to Malaysia’s unique social and legal context is necessary to address these gaps.
Development and Validation of Nomograms for Preoperative Differentiation of Benign and Malignant Breast Tumors Using Ultrasound and MRI
Authors: Ma dianpei , Liu Chun , Hao Xiaohan , Zhu Yun , Luo Changfan , Gao Zhizhen , Wang Changliang , Du Huiyu , Chen Lin , Gao Frankliu , Huang Yu , Qiu Ben sheng 
Published: November 2024

Abstract

Purpose: To develop nomograms based on magnetic resonance imaging (MRI) and ultrasound (US) features for accurate preoperative classification of benign and malignant breast tumors.
Material and Methods: This retrospective study included 185 patients with breast tumors, divided into training and test sets in a 7:3 ratio. A least absolute shrinkage and selection operator (LASSO) and logistic regression were employed to identify independent risk factors. Two models were developed based on these risk factors: Model 1 (US morphology + MRI border + lymph node metastasis) and Model 2 (US morphology + MRI border + lymph node metastasis + density). These models were evaluated using calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis, and clinical impact curves.
Results: Multivariate logistic analysis of the training set identified four independent risk factors: US morphology (OR: 0.075, P<0.05), density (OR: 1.581, P=0.608), MRI border (OR: 0.12, P<0.04), and lymph node metastasis (OR: 208.980, P=0.001). These factors were incorporated into the diagnostic nomogram for breast tumors. The nomograms demonstrated good predictive performance in distinguishing benign from malignant breast tumors. The area under the curve (AUC) for Model 1 was 0.960 (training set) and 0.922 (test set), while for Model 2, the AUC was 0.964 (training set) and 0.940 (test set).
Conclusion: This study highlights the effectiveness of MRI and US features in differentiating between benign and malignant breast tumors. Constructing nomogram models based on these features can enhance the accuracy of breast tumor diagnosis, facilitating personalized diagnosis and treatment for patients.
miR-518a-3p Inhibits Triple-Negative Breast Cancer Cell Proliferation Through Targeting to LncRNA-NONHSAT192404.1
Authors: Yang Huachao , Gan Lin , Zheng Yu , Min Jie , Xiong Zhifeng , Wang Ting , Hu Shisi , Lv Gang 
Published: November 2024

Abstract

Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer with limited treatment options and poor patient outcomes. We demonstrate that LncRNA-NONHSAT192404.1 acts as a molecular sponge for the tumor-suppressive miR-518a-3p, modulating the PI3K/AKT signaling pathway, which is critical for cell survival, proliferation, and invasion. Overexpression of LncRNA-NONHSAT192404.1 inhibits TNBC cell proliferation, migration, and invasion, while its knockdown enhances these oncogenic behaviors. By binding miR-518a-3p, LncRNA-NONHSAT192404.1 reduces its availability for targeting mRNAs. These findings suggest that targeting LncRNA-NONHSAT192404.1 could be a promising therapeutic strategy to improve outcomes in TNBC.
Unveiling Trends in Forecasting Models for Crop Pest and Disease Outbreaks: A Systematic and Scientometric Analysis
Authors: Goyal Abha , Singh Abhishek , Raghuraman Mahadevan , Ghosh Pritha , Jadhav Aaditya 
Published: November 2024

Abstract

The exacerbation of global warming has significantly heightened the occurrence of crop pest and disease outbreaks, resulting in substantial agricultural losses worldwide. Weather-driven forecasting models play a pivotal role in predicting these outbreaks, facilitating timely and effective intervention strategies. This paper undertakes a comprehensive systematic review of the existing literature, to evaluate the comparative strengths, limitations, and relative efficacy of statistical versus machine learning forecasting models. Additionally, a bibliometric analysis encompassing 1,215 scientific studies sourced from the Web of Science Core Collection database (2000–2023), uncovering a sharp increase in research activity, particularly post-2019, across key disciplines like entomology, agronomy, and plant science. The continents leading in publication volume are Asia, Europe, and North America, with China, the United States, and India ranking as the top publishing countries. Chinese researchers rank highest among the top ten most productive authors in the field. Key journals with the highest citation rates include Plos One, Insects, and Computers and Electronics in Agriculture. The major research themes identified include deep learning, convolutional neural networks, artificial neural networks, and forecasting models, with keywords such as regression, prediction, insects, population dynamics etc. frequently occurring in the literature. Current research increasingly focuses on leveraging statistical as well as advanced machine learning methodologies, including hybrid and ensemble techniques, aimed at enhancing the accuracy and efficiency of forecasting pest and disease outbreaks. This study not only provides valuable insights into the current landscape of crop pest and disease forecasting but also offers a foundational framework for future research endeavors.