bluuebunny/crossref_metadata_2025_split huggingface dataset:
Semaglutide and major adverse cardiovascular events in patients with and without DM: a systematic review and meta-analysis
Authors: Li Yanhua , Lv Chunmei , Cao Lianlian , Lihua Sun , Ma Yanqu 
Published: December 2024

Abstract

Cardiovascular disease remains a leading cause of morbidity and mortality worldwide, particularly in patients with type 2 diabetes mellitus (T2DM) and obesity. Semaglutide, a glucagon-like peptide-1 receptor agonist, has shown promising effects on cardiovascular outcomes. This systematic review and meta-analysis aimed to evaluate semaglutide's effect on major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke, in patients with and without T2DM. A comprehensive search of MEDLINE, Embase, Cochrane CENTRAL, and Web of Science databases followed PRISMA guidelines. Randomized controlled trials (RCTs) published up to January 2025 were included. Data from 11 high-quality RCTs, involving over 25,000 participants, were analyzed using a random-effects model. The pooled odds ratio (OR) for MACE was 0.68 (95% CI: 0.52–0.91, p < 0.01), indicating a significant 32% reduction in cardiovascular risk with semaglutide compared to placebo or active comparators. Sensitivity analyses confirmed the robustness of these findings, with ORs ranging between 0.630 and 0.761 across sequential exclusions. Moderator analysis revealed significant positive associations for age (slope = 0.025, p = 0.000), HbA1c (slope = 0.155, p = 0.002), and blood pressure, while body weight (slope = -0.126, p = 0.005), LDL, and total cholesterol were inversely associated. As confirmed by Egger’s regression and fail-safe N analyses, minimal publication bias was observed. These findings highlight semaglutide’s multifactorial benefits in reducing cardiovascular risk, which are driven by weight loss, blood pressure reduction, and metabolic improvements. Semaglutide represents a powerful intervention for cardiovascular risk management in patients with and without diabetes.
Interleukin-37 modulates microglial phenotype and inhibits inflammatory response via the MyD88/NF-κB pathway in Lipopolysaccharide-induced neuroinflammation
Authors: Zhang Jiangwen , Hayat Muhammad , Si Yu , Guo Tao , Ni Yinying , Wang Qian , Hong Yancheng , Cao Yudie , He Sijia , Weng Zijuan , Li Fengmei , Zuo Hao , Sun Xin , Chen Bo , Hu Jiabo 
Published: December 2024

Abstract

Objective
Interleukin-37 (IL-37), an anti-inflammatory cytokine within the interleukin-1 (IL-1) family, exhibits immunomodulatory properties. Here we evaluate the effects of IL-37 on microglia in neuroinflammation and its potential mechanisms.
Methods
C57BL/6 mice were injected intraperitoneally with 1 μg of recombinant human IL-37 protein (rhIL-37), and 24 h later with lipopolysaccharide (LPS) (5 mg/kg) to induce neuroinflammation. After 2-hour pretreatment of BV2 cells with rhIL-37 (100 ng/mL), an in vitro model was established by treating with LPS (100 ng/mL). Mice were assessed for behavioral tests, and neuronal damage was evaluated by Nissl staining and hematoxylin and eosin staining. The expression of Iba1, CD86, CD206, and NF-κB were detected by immunofluorescence staining, and inflammatory mediators and pathway proteins were evaluated by ELISA, qRT-PCR, and Western blot.
Results
IL-37 significantly ameliorated LPS-induced behavioral deficits and protected mice from inflammatory injury. In vitro experiments suggested that IL-37 modulates polarization of microglia from M1 to M2 phenotype, along with reducing pro-inflammatory cytokine production. Moreover, IL-37 attenuated the production of NF-κB and MyD88.
Conclusions
IL-37 regulates microglia against neuroinflammatory responses by blocking the MyD88/NF-κB pathway and shows for the first time how IL-37 influences the phenotype of microglia, suggesting a potential therapeutic target for neuroinflammation.
Use of analgesics and psychotropic medication: Findings from the Lolland- Falster Health Study
Authors: Bruun-Rasmussen Neda Esmailzadeh , Lophaven Søren Nymand , Rasmussen Mads Reinhold , Lynge Elsebeth 
Published: December 2024

Abstract

Aim
To descripe the use of analgesics and psychotropic medication, common medical diseases, and alcohol consumption in adult persons living in a rural-provincial area of Denmark.
Methods
Using cross-sectional data from the Lolland-Falster Health Study (LOFUS), we examined a total of 16,084 individuals aged 18 years and above. Data on use of analgesics and psychotropic medication, alcohol consumption, and somatic and psychiatric diseases were obtained from self-administered questionnaires.
Results
At the time of the survey, 23.4% of the subjects reported daily/almost daily use of analgesics and/or psychotropic medication; women more than men, relative risk (RR) 1.62 (95% confidence interval (CI) 1.53–1.72). The use of analgesics was reported by 19.2%, antidepressants by 4.7%, tranquilizers/sedative by 3.5%, and sleeping medication by 2.4%. In addition, 7.3% reported frequent intake of 5 + units of alcohol; men more than women, RR 3.38 (95% CI 2.97–3.85). In total, 70.7% of participants reported having or having had at least one somatic disease, and 10.0% a psychiatric disease; together 71.8%. Out of the participants, 29% reported suffering from osteoarthritis, but only one third of them reported use of analgesics; and out of the 7% reported suffering from depressions, only half of them reported use of antidepressants.
Conclusions
One fourth of the adult population in a rural-provincial area of Denmark reported daily/almost daily intake of analgesics/psychotropic medication, and 7% had a high alcohol consumption. However, suffering from or having suffered from at least one somatic and/or psychiatric disease was reported by almost three-fourths of the participants, so there was a considerable gap between the level of intake of the pain and relief agents, and the reported level of diseases. The results may indicate that several somatic and psychiatric conditions remain untreated or inadequately managed in the general population of Lolland-Falster.
Manipulating growth of hyperfine CsPbBr 3 particles by confinement in ZIF-8 and resultant stability of perovskite: selective trace-level Hg 2+ detection
Authors: Jiang Lingyu , Xiang Li , Tang Jianshe 
Published: December 2024

Abstract

Inorganic metal halide perovskite has been widely utilized in multi-disciplines such as sensors, solar cells, photodetectors, solar light-emitting diodes, and so on due to its remarkable unique properties, narrow emission, and tunable wavelength. The predominant problem, like long-term stability under ambient conditions, hindered its practical application in sensing. A novel water-dispersed CsPbBr3 nanocrystal with solid fluorescence and admirable water stability was generated in situ within a confined space of a ZIF-8 metal-organic framework. The synthesized composite material was noted as CsPbBr3@ZIF-8, which was developed in the ultrafine CsPbBr3 perovskite (with a mean radius of 5 nm) particles. Through the method of confinement growth, the stable and ultrafine CsPbBr3 could be obtained without being encapsulated by organic ligand or polymer, which could effectively avoid the hindered interaction between targets and the perovskite quantum dots due to the introduction of surface capping stabilizer. With such remarkable optical properties and water stability of CsPbBr3@ZIF-8, a novel, selective, and sensitive fluorescent quenching-based sensing platform for Hg2+ in aqueous solutions has been established via direct chemical reactions with a low limit of detection and a broad linear range of quantify at 8.25 nM and 10 ~ 5000 nM, respectively. The mechanism of CsPbBr3@ZIF-8 fluorescence quenching by Hg2+ was elucidated as photoinduced electron transfer by the comparison of the difference in the fluorescence lifetime of CsPbBr3@ZIF-8 before and after the introduction of Hg2+. It is envisioned that a highly water-stable and stable inorganic metal halide perovskite formed through the confined space will have promising applications in the sensing of heavy metal ions.
Executive phase of flood water control plan (case study: Kangavar city, Kermanshah province in middle of the western region of Iran)
Authors: Askari Kaveh Ostad Ali , Kianmehr Peiman 
Published: December 2024

Abstract

The importance of watershed management can be specified in its goals vista. Watershed management can be defined in managing the watershed basins in order to maintain, revival and principled utilization of them. The floodwater control plan of Kangavar urban area was being studied by experts in watershed management study office of construction organization of Kermanshah province due to its special priority. Briefly, this plan was performed in two primary parts including base studies and preparing executive plan. Base studies were done by using topography map, statistics, necessary information and field surveys and the executive plan was being created by using general conclusion of base studies, aerial photos and field control. The considerable tip here which is necessary to mention is the close contribution of related offices such as: natural resources, municipality, government, agriculture and environment office to the construction organization in different executive steps of plans and the most important of all is to maintain them.
Part B: Innovative Data Analysis Approach to Boost Machine Learning for Seakeeping Purposes; Computational Efficiency
Authors: Majidiyan Hamed , Enshaei Hossein , Howe Damon , Gubesch Eric 
Published: December 2024

Abstract

The increasing influence of AI across various scientific domains has prompted engineering to embark on new explorations for complex challenges. However, recent studies often overlook the foundational aspects of the maritime field, leading to over-optimistic or oversimplified outputs for real-world application purposes. To address these concerns, in previous works, we highlighted the sensitivity of trained models to noise, computational efficiency, and the necessity for feature engineering/compactness in seakeeping given the stochastic nature of the wave. In response, a novel data analysis framework was introduced with two purposes to augment data for machine learning (ML) models in seakeeping: transferring features from high-fidelity to low-fidelity modelling which resulted in improving feature accuracy for simulation and increasing computational efficiency through feature engineering; the current issue addresses the second objectives. To this end, the experimental data of a spherical model in a wave basin excited by different waves has been employed. The response time series of the model underwent a continuous wavelet transform to extract fine spectral-temporal features. After feature reorganization based on coefficient intensity in a new feature map, additional endogenous features are attached to the arrays to improve feature variability and entropy. Different ML models were trained, where the new data analysis framework substantially reduced training costs while maintaining fair accuracy, with training times slashed from hours to seconds. The significance of the current study extends beyond the maritime context and can be utilised for ML applications in intrinsically stochastic data.
Enhancing Real-Time Intrusion Detection with Variational Autoencoders-Based Dimensionality Reduction and Attention-Driven MLP Classification
Authors: Immastephy Anto Jenisha , PUNITHA K 
Published: December 2024

Abstract

Real-time intrusion detection is crucial for safeguarding modern networks against evolving cyber threats. Traditional detection systems often face challenges such as high dimensionality, leading to increased computational complexity and reduced detection accuracy. In this paper, we propose an enhanced real-time intrusion detection framework that integrates Variational Autoencoders (VAEs) for dimensionality reduction and an Attention Network in conjunction with a Multi-Layer Perceptron (MLP) for robust classification. The VAE-based dimensionality reduction technique effectively compresses high-dimensional data while preserving key features essential for accurate threat detection. By employing an Attention Network, the model selectively focuses on the most relevant features, improving the classification of both known and unknown intrusion patterns. The MLP serves as the final classifier, utilizing the reduced and attention-refined feature set to provide fast and accurate intrusion detection. Extensive experiments were conducted on benchmark intrusion detection datasets, demonstrating that our proposed model outperforms traditional methods in terms of detection accuracy, computational efficiency, and real-time performance. The combination of VAE for dimensionality reduction and attention-based feature selection with MLP classification presents a powerful approach for enhancing intrusion detection systems, making them more resilient to sophisticated and zero-day attacks.
Ellipse-Fit Alignment for Robust 2D Shape Recognition
Authors: Fidan Mehmet , Ergin Semih , Koç Mehmet , Gülmezoğlu Mehmet Bilginer , Gerek Ömer Nezih , Barkana Atalay 
Published: December 2024

Abstract

This study proposes a novel approach for robust 2D shape recognition through ellipse-fit alignment. The method aligns shapes using an ellipse fitting algorithm, addressing issues of rotation, translation, and scaling commonly observed in shape databases. Following alignment, shapes are classified using various similarity measures. Experimental results on the Kimia and TARI databases demonstrate the effectiveness of the proposed method. In the Kimia database, metrics such as Threat Score, Accuracy, F1 Score, Matthews Correlation Coefficient, Fowlkes-Mallows index, and Vote achieved perfect recognition rates. The code and databases can be accessed via https://doi.org/10.5281/zenodo.14423922. While the TARI database yielded slightly lower recognition rates, with Accuracy and Markedness contributing to an 85.5% recognition rate, the results highlight the potential and limitations of the approach. The study introduces a significant advancement in 2D shape recognition by combining ellipse fitting, affine transformation, and similarity measurement, offering a robust solution for applications in computer vision and image retrieval.
Impact of Anthropogenic Landscape Transformation on Soil Fertility and Diversity of Carabid Beetles and Butterflies in Poland
Authors: Szyszko-Podgórska Katarzyna , Ukalska Joanna , Ukalski Krzystof , Kondras Marek 
Published: December 2024

Abstract

Anthropogenic transformation of the landscape, including urbanization and intensive agriculture, significantly affects the structure and functioning of ecosystems. The result of these changes is disruption of species population dynamics and a decline in biodiversity. Understanding the impact of soil characteristics on biodiversity is crucial to landscape ecology, particularly in the context of indicator species such as butterflies and Carabid beetles. The present study aimed to investigate the influence of soil variables on the occurrence and diversity of butterflies and Carabid beetles in three regions of Poland. In addition, the study sought to determine how the history of land use affects the current soil properties and community structure of the organisms in different types of ecosystems. The results showed significant differences in soil variables depending on land use form. High HSI values in post-agricultural soils indicate the influence of historical agricultural use on soil fertility. High butterfly diversity was associated with rich open land soils, while beetle diversity was less dependent on soil fertility and more resistant to environmental changes. The RDA showed that K, Ca, Mg and N are key soil variables affecting butterfly diversity, while in beetles K, Mg and HSI. The study underscores the need to consider land use history in soil management to promote biodiversity. The results suggest that a diverse approach to environmental management, tailored to local conditions, can increase the effectiveness of biodiversity conservation. Management strategies should focus on maintaining habitat diversity, which is key to maintaining healthy ecosystems.
Smart Disaster Prediction: A Unified Machine Learning Approach for Earthquakes and Floods
Authors: Bhole Chitra , Shetty Pratik , Tripathi Sachin , Varma Shubham 
Published: December 2024

Abstract

Natural disaster prediction is vital for the minimization of effects of these calamities on human life and infrastructure. The aim of this research work is to find an application of machine learning algorithms in earthquake magnitude and flood event prediction. This is achieved using historical seismic data, provided by the Indian Meteorological Department and United States Geological Survey. This paper was trained by optimizing the Random Forest model using RandomizedSearchCV. The model had a MAE of 14.5%, RMSE of 19.8%, and an R-squared value of 0.75, thus it proved to be efficient in the prediction of earthquake magnitudes. For flood prediction, a Voting Classifier ensemble model was used that comprised XGBoost, Random Forest, KNN and Decision Tree and, for the prediction of flood events with environmental and socioeconomic factors. The ensemble model presented significant results with an accuracy of 93.64%, precision of 89%, recall of 85%, and an F1-Score of 87%, which shows how this model is robust in flood-non-flood classification. Results therefore indicate the potentiality that machine learning models have for improving disaster prediction by indicating the strength of the ensemble methods in improving early preparedness, resource allocation, and early warning systems.
Effect of hormonal Imbalance on Semen Quality in male infertility : A Cross-sectional Study in Puntland, Somalia
Authors: Ibrahim Mohammad Sulieman 
Published: December 2024

Abstract

Introduction:
This cross-sectional study investigated the relationship between hormonal imbalances and semen quality parameters in 48 male patients seeking fertility treatment in Puntland, Somalia. Semen analysis and hormonal assays were conducted to examine correlations between reproductive hormones (FSH, LH, Prolactin, and Testosterone) and a composite Semen Quality Index (SQi).
Results
Statistical analysis revealed a significant positive correlation between testosterone and SQi (r = 0.28, p = 0.042) and a negative correlation between FSH and SQi (r = -0.31, p = 0.024). Multiple regression analysis demonstrated that hormonal factors accounted for 32.4% of the variance in semen quality (R² = 0.324, p < 0.001). FSH showed a significant negative association (β = -0.286, p = 0.003) while testosterone demonstrated a significant positive relationship (β = 0.245, p = 0.012) with semen quality.
Conclusion
These findings suggest that hormonal imbalances, particularly in FSH and testosterone levels, significantly influence male fertility parameters. The study provides valuable insights for understanding male infertility in the Somali context, though larger longitudinal studies are needed to establish causality and explore additional contributing factors.
Implementing Open Science Principles in Ukrainian Legislation: European Experiences and National Specificities
Authors: Luchenko Dmytro , Tsuvina Tetiana , Karnaukh Bohdan , Filatova-Belous Nataliia , Yevkov Andrii , Shumilo Inesa 
Published: December 2024

In the context of Ukraine's European integration and ongoing scientific sector reform, implementing Open Science principles has become increasingly relevant for the national academic community. This paper analyzes EU and Ukrainian legislation regarding open access to scientific and technical information and research infrastructure, while also proposing ways to improve the quality assessment system for scientific and technical activities. The authors employ a comprehensive methodology, combining general scientific methods (analysis, synthesis, comparison, and historical analysis) with specialized legal research methods (doctrinal analysis, forecasting, and modeling) to conduct a multifaceted study and support their conclusions on the need for legislative updates. The first section examines the legal framework supporting open access to scientific and technical information, arguing for the introduction of new legislative concepts including «open access to scientific and technical information», «public funding of scientific research», «research data,», «metadata». The second section addresses copyright protection within the open science context, proposing amendments to Ukrainian legislation regarding open access to scientific publications, in-depth text and data analysis, regulation of orphan works, and the right of secondary publication. The third section evaluates the alignment of Ukrainian legislation with open science principles in research assessment and incentivization, recommending a new certification methodology for scientific institutions that balances quantitative metrics with substantive qualitative assessment. The fourth section examines EU policy and legislation on open access to research infrastructure, proposing updates to Ukrainian legislative terminology by introducing concepts such as «open access to research infrastructure» and «research infrastructure register». The final section analyzes the academic integrity draft law from an Open Science policy perspective, offering recommendations for improving specific legal provisions.
Development of a Novel Mitochondrial Dysfunction-Related Alzheimer's Disease Diagnostic Model Using Bioinformatics and Machine Learning
Authors: Zhang Kuo , Yang Kai , Yu Gongchang , Shi Bin 
Published: December 2024

Introduction:
Alzheimer's disease (AD) represents the most common neurodegenerative
disorder, characterized by progressive cognitive decline and memory loss. Despite the recognition
of mitochondrial dysfunction as a critical factor in the pathogenesis of AD, the specific molecular
mechanisms remain largely undefined.

Method:
This study aimed to identify novel biomarkers and therapeutic strategies associated with
mitochondrial dysfunction in AD by employing bioinformatics combined with machine learning
methodologies. We performed Weighted Gene Co-expression Network Analysis (WGCNA) utilizing
gene expression data from the NCBI Gene Expression Omnibus (GEO) database and isolated
mitochondria-related genes through the MitoCarta3.0 database. By intersecting WGCNA-derived
module genes with identified mitochondrial genes, we compiled a list of 60 mitochondrial dysfunction-
related genes (MRGs) significantly enriched in pathways pertinent to mitochondrial function,
such as the citrate cycle and oxidative phosphorylation.

Results:
Employing machine learning techniques, including random forest and LASSO, along with
the CytoHubba algorithm, we identified key genes with strong diagnostic potential, such as ACO2,
CS, MRPS27, SDHA, SLC25A20, and SYNJ2BP, verified through ROC analysis. Furthermore, an
interaction network involving miRNA-MRGs-transcription factors and a protein-drug interaction
network revealed potential therapeutic compounds such as Congo red and kynurenic acid that target
MRGs.

Conclusion:
These findings delineate the intricate role of mitochondrial dysfunction in AD and
highlight promising avenues for further exploration of biomarkers and therapeutic interventions in
this devastating disease.