bluuebunny/crossref_metadata_2025_split huggingface dataset:
Maternal and Fetal Factors Affecting the Effectiveness of Vaginal Dinoprostone in Labor Induction
Authors: Atlihan Ufuk , Yavuz Onur , Ata Can , Avsar Huseyin Aytug 
Published: September 2024

Abstract

Objective
Induction of labor (IOL) is a common obstetric intervention used to encourage the onset of labor. The aim of study was to evaluate the maternal and fetal factors that play a role in the efficacy of vaginal dinoprostone in labor induction.
Materials and Methods
Our study included 780 patients who underwent pregnancy follow-up and delivery in our hospital between March 2018 and 2024. All data including body mass index (BMI), age, parity, induction criteria, obstetric history, bishop score, time of delivery, newborn weight and Apgar score were entered by the delivery room physician. Data from 620 patients who had a successful delivery with vaginal dinoprostone application and 120 patients who could not have a vaginal delivery were evaluated retrospectively.
Results
The BMI value of women in Group 2 at admission to the hospital was found to be significantly higher than that of women in Group 1 (p < 0.001). The Bishop score of women in Group 1 at admission to the hospital was found to be significantly higher than that of women in Group 2 (p < 0.001). The nulliparity rate of women in Group 2 was found to be significantly higher than those in Group 1 (p < 0.001). The uterocervical angle measurement of women in Group 2 was found to be significantly lower than those in Group 1 (p < 0.001). The rate of newborns with 1st and 5th minute Apgar scores ≥ 8 in Group 1 was found to be significantly higher than those in Group 2 (p = 0.006, p = 0.04, respectively).
Conclusion
It was determined that parity was one of the important determinants in achieving vaginal delivery in pregnancies where dinoprostone vaginal insert was applied. It was determined that multiparous women benefited more from induction with dinoprostone. However, according to the results of our study, more research is needed to evaluate nulliparous pregnancies requiring induction.
The performance of LaAlO x gate dielectric films prepared by sol-gel method at different temperatures of annealing
Authors: Huang Zhenhua , Tuokedaerhan Kamale , Yang Linyu , Guo Chaozhong , Cai Zhengang , Ibraimov Margulan , Sailanbek Serikbek 
Published: September 2024

Abstract

In recent years, there has been growing interest in the exploration of rare earth oxides for their potential as high-k gate dielectrics. Lanthanum oxide (La2O3) has emerged as a particularly noteworthy candidate due to its impressive dielectric properties, wide bandgap, strong thermodynamic stability, and remarkable compatibility with silicon substrates. However, La2O3 has a significant problem for MOS device applications due to its hygroscopicity and low crystallization temperature, making it unsuitable for high-temperature treatment during fabrication. To improve its oxide quality, it can be doped. Al-doped La2O3 (LaAlOx) thin films annealed at different temperatures were prepared on n-type Si substrates by sol-gel method and the effect of temperature variation on LaAlOx thin films as gate dielectrics were investigated in terms of root-mean-square, interfacial properties, and electrical properties. The findings indicate that the introduction of Al doping raises the crystallization temperature of La2O3. Additionally, it was observed that the root-mean-square roughness of the LaAlOx films decreases from 0.919 nm to 0.320 nm with an increase in annealing temperature from 500 to 700°C. XPS has the capability to assess the interfacial characteristics of LaAlOx. It is evident that elevated temperatures promote the formation of M-O bonds, diminish film defects, and enhance the overall interfacial quality of the film. In addition, analyzed the electrical properties of the Al/ LaAlOx/Si/Al metal-oxide-semiconductor capacitors The findings indicate that the samples annealed at 700°C exhibit favorable electrical characteristics, demonstrating a dielectric constant of 20.91 under a gate voltage of 1 V and a leakage current density of 3.54×10− 3A/cm2.
Models-based study of radiative Hybrid nanofluid flow over a vertical porous stretching cylinder
Authors: Abbas Nadeem , Shatanawi Wasfi , Hasan Fady , Shatnawi Taqi A. M. 
Published: September 2024

Abstract

This research aims to develop and analyze different models, such as the Yamada-Ota, Xue, and Tiwari-Das models. The study examines how thermal radiation affects the flow of a mixture of single-walled and multi-walled carbon nanotubes (SWCNT-MWCNT) in water over a vertical porous stretching cylinder with heat generation. The goal is to understand the impact of these models on thermal performance, boundary layer behavior, and heat transfer enhancement. The study offers valuable insights for optimizing industrial applications that involve advanced nanofluid cooling and heating systems. The governing momentum and energy conservation equations are formulated to include thermal radiation and heat generation terms. These equations are transformed using similarity transformations and solved numerically using the bvp4c method. A comparative analysis evaluates model performance, focusing on temperature distribution, velocity profiles, and heat transfer rates. The results are validated against existing literature to ensure the accuracy and reliability of the proposed models. Thermal radiation improves heat transfer; the Tiwari-Das model exhibits the highest thermal conductivity enhancement. Velocity profiles show a thicker boundary layer for the Yamada-Ota model, while the Xue model predicts higher heat generation effects.
DomHMM – Analysis of Membrane Domains
Authors: Tunç Eren , Trollmann Marius , Böckmann Rainer 
Published: September 2024

Abstract

Lateral nano- or microdomains are a major hallmark of biological membranes. A key challenge in analyzing membrane domains from molecular dynamics (MD) simulations lies in accurately assigning lipids to more ordered (Lo) or disordered (Ld) membrane domains, particularly when these domains exhibit similar chemical compositions but differ in structural characteristics. To address this issue, we present the implementation of DomHMM, a novel application previously developed for the unsupervised classification of Lo and Ld domains based on coarse-grained or all-atom MD simulations. DomHMM employs a Gaussian-based Hidden MarkovModel (HMM) for initial lipid state assignment based on specific lipid characteristics, followed by state reweighing through local spatial correlations andsubsequent clustering. This approach significantly improves the efficiency and accuracy of membrane domain analysis, establishing DomHMM as a robust tool for investigating membrane domain behavior.
Antimicrobial strategies of Lower Respiratory Tract Infection in Immunocompromised Patients based on Metagenomic Next-Generation Sequencing：a retrospective study
Authors: Meng Beibei , Liu Haichao , Wu Qinliang , Qu Lei , Mao Congzheng , Yang Fang , Lan Tianzhou , Fang Juan , Hu Zhenhong , Fang Yao 
Published: September 2024

Abstract

Background
Immunocompromised patients with Lower Respiratory Tract Infection(LRTI) frequently encounter a diverse range of pathogenic infections, characterized by rapid disease progression and significant mortality rates due to reckless or excessive utilization of antibiotics. Therefore, it is crucial to promptly and accurately identify the causative microorganisms for pathogen diagnosis and clinical decision-making. The objective of this study is to evaluate the clinical applicability of metagenomic next-generation sequencing (mNGS) in the diagnosis and management of LRTI, as well as its impact on empirical antibacterial therapy for patients with varying immune statuses.
Methods
We conducted a comparative analysis of positivity rate, detection accuracy, pathogen spectrum, duration of treatment (DOT), and antibiotic management in a cohort of 283 patients diagnosed with lower respiratory tract infections.
Results
The positive detection rate was higher in mNGS compared to conventional culture in both immunocompetent group (89.92% vs. 28.57%, P < 0.001) and immunocompromised group (84.44% vs. 33.33%, P < 0.001). The antibiotic escalation in the immunocompromised group was more frequent than that in the immunocompetent group (49.00% vs. 31.00%, P = 0.018), but no difference was observed for antibiotic de-escalation (20.00% vs. 15.00%, P = 0.458).
Conclusions
The application of mNGS can significantly enhance the pathogen detection rate and optimize antimicrobial drug management in immunocompromised patients with LRTI.
Pioneering Precision in Lumbar Spine MRI Segmentation with Advanced Deep Learning and Data Enhancement
Authors: Ahmed Istiak , Hossain Md. Tanzim , Nahid Md. Zahirul Islam , Sanjid Kazi Shahriar , Junayed Md. Shakib Shahariar , Uddin M. Monir , Khan Mohammad Monirujjaman 
Published: September 2024

Abstract

This study presents an advanced approach to lumbar spine segmentation using deep learning techniques, focusing on addressing key challenges such as class imbalance and data preprocessing. Magnetic resonance imaging (MRI) scans of patients with low back pain are meticulously preprocessed to accurately represent three critical classes: vertebrae, spinal canal, and intervertebral discs (IVDs). By rectifying class inconsistencies in the data preprocessing stage, the fidelity of the training data is ensured. The modified U-Net model incorporates innovative architectural enhancements, including an upsample block with leaky Rectified Linear Units (ReLU) and Glorot uniform initializer, to mitigate common issues such as the dying ReLU problem and improve stability during training. Introducing a custom combined loss function effectively tackles class imbalance, significantly improving segmentation accuracy. Evaluation using a comprehensive suite of metrics showcases the superior performance of this approach, outperforming existing methods and advancing the current techniques in lumbar spine segmentation. These findings hold significant advancements for enhanced lumbar spine MRI and segmentation diagnostic accuracy.
A rapid and accurate method for determining titers of baculovirus for protein expression
Authors: Ding Feng , Wang Jinyang , Shi Hui , Peng Jinrong 
Published: September 2024

Abstract

The Baculovirus Expression Vector System (BEVS) is extensively used for expressing target proteins, necessitating accurate baculovirus titration to ensure the output. Traditional plaque assay assisted titration, while reliable, is labor-intensive and time consuming. Here, we introduce an improved quantitative real-time polymerase chain reaction (qPCR)-based method for determining baculovirus titers. Briefly, target-gene specific primers are designed to produce a 180-bp PCR product using viral DNA as the template. The PCR products are purified and the mass weight of the PCR products are quantified based on 260-nm absorbance. Based on the molecular weight of the 180bp PCR product, the mass weight is then converted to the molar concentration which is finally converted to molecule number by applying the Avogadro constant. Such PCR product is serially diluted and used as the template for qPCR. The qPCR Ct values between serial dilutions are used to generate the standard curve. Finally, the Avogadro’s number (i.e. titer) of the test viral DNA is inferred from its qPCR Ct value by comparing with the standard curve. Our method eliminates the need for a pre-titrated viral stock, reducing variability caused by storage conditions. This advancement is particularly beneficial for large-scale production of the target protein using the BEVS.
Discriminating activating, deactivating and resistance variants in protein kinases
Authors: Singh Gurdeep , Schmenger Torsten , Gonzalez-Sanchez Juan Carlos , Kutkina Anastasiia , Bremec Nina , Diwan Gaurav , Mozas Pablo , López Cristina , Siebert Reiner , Sotillo Rocio , Russell Robert B 
Published: September 2024

Abstract

We present a data-driven approach to predict the functional consequence of genetic changes in protein kinases. We first created a large curated dataset of 375 activating/gain-of-function, 1028 deactivating/loss, 98 resistance and 1004 neutral protein variants in 441 human kinases by scouring the literature and various databases. For any variant, we defined a vector of 7 types of sequence, evolutionary and structural features. We used these vectors to train machine learning predictors of kinase variant classes that obtain excellent performance (Mean AUC = 0.941), which we then applied to uncharacterized variants found in somatic cancer samples, hereditary diseases and genomes from healthy individuals. Encouragingly we predicted a greater tendency of activating variants in cancers, deactivating in hereditary diseases and few of both in healthy individuals. Using this method on clinical data can identify potential functional variants. In cancer samples we experimentally assessed the impact of several such mutations, including potential activating variants p.Ser97Asn in PIM1, where phosphorylation analysis suggests an increase in activity, and p.Ala84Thr in MAP2K3, where gene expression and mitochondrial staining shows a reduction in mitochondrial function when contrasting mutant to wild type, the opposite having been observed previously during deletion experiments. We provide an online application to study any variant in the kinase domain that provides prediction scores in addition to a detailed list of what is known across all kinases near the position of interest. Besides supporting the interpretation of genomic variants of unknown significance, knowledge of kinase activation can lead to immediate therapeutic suggestions, we thus believe our approach will be a key component in the repertoire of tools for personalised medicine.
Zinc-mediated regulation of TMEM106B function in the endolysosomal pathway revealed through the systematic identification of TRIM2/3 ubiquitination substrates
Authors: Perez-Borrajero Cecilia , Stein Frank , Schweimer Kristian , Kotova Iuliia , Fernandez-Novel Nadine , Rettel Mandy , Schwarz Jennifer , Haberkant Per , Lapouge Karine , Loedige Inga , Gayk Jesse , Stachera Weronika , Trovato Matteo , Hoffmann Thomas , Murciano Brice , Bhogaraju Sagar , Savitski Mikhail , Mahamid Julia , Noh Kyung-Min , Hennig Janosch 
Published: September 2024

Abstract

The understanding of ubiquitin E3 ligase function hinges on thoroughly identifying their cellular targets, but the transient nature of signaling complexes leading to ubiquitination poses a significant challenge for detailed mechanistic studies. TRIM2 and TRIM3 are paralogous mammalian E3 ligases with particularly high expression in the brain, where they contribute to neuronal development and homeostasis. Here, we tailored recently developed ubiquitin-specific proximity labelling tools to identify substrates of TRIM2 and TRIM3 activity. We show that despite their high amino acid sequence identity, the ligases have distinct intracellular dynamics, binding partners, and ubiquitination substrates. Using biochemical and structural studies, we show that TRIM2 ubiquitinates the lysosomal protein TMEM106B at lysine residues located in the cytosolic N-terminal region. Substrate recognition involves a direct interaction between TRIM2 and a newly identified zinc-coordination motif in TMEM106B that mediates homodimerization and is required for lysosomal size regulation. We found that in addition to catalysis, the tripartite motif is involved in substrate recruitment, and we provide insights into the assembly of the ubiquitination complex. Deletion of TRIM2/TRIM3 in mouse embryonic stem-cell derived neurons impacted the extracellular matrix composition, likely through acting on the endolysosomal pathway. Our study thus contributes a catalogue of TRIM2 and TRIM3-associated effectors and supports a key role at the interface of vesicle trafficking and the cytoskeleton.
Experimental confirmation of secondary flows in granular media
Authors: Einav Itai , Escobar Andres , Baker James , Guillard Francois , Faug Thierry 
Published: September 2024

Abstract

As collections of grains flow, their free surface rarely remains flat. We can observe this first-hand during laboratory flow experiments or infer from undulating deposits left after landslides on Earth or Mars. The free-surface disturbances that develop suggest the presence of secondary flows, smaller in magnitude than the primary motion but driving complex three-dimensional structures inside the flow. While one can infer such behaviour from the boundaries or simulations, until now we have not been able to directly observe secondary flows experimentally. In this paper we present the first experimental confirmation of secondary kinematics in granular media using dynamic x-ray radiography, without needing to stop the motion for tomography. Specifically, we create a bulldozing mechanism by driving granular material with a conveyor belt. This sets up a unidirectional primary flow, but also generates a non-uniform, indented free-surface, hinting that secondary mechanisms are at play. Modelling of the system using discrete element method simulations is consistent with this secondary-flow explanation. We then probe further experimentally using two perpendicular pairs of x-ray sources and detectors to measure the velocity field inside the bulk. This indeed unveils a complex three-dimensional flow pattern that must include vortices and convection rolls. This advancement is pertinent for many industrial and natural scenarios where grains impact obstacles, and has even broader relevance for secondary rheology of other amorphous soft materials such as dense emulsions, pastes and colloidal suspensions.
AI vs. Humans: A Comparative Analysis of Time, Cost, and Performance on a Clinical Code Conversion Task
Authors: Hudson Carly , Randall Marcus , Bowman Candice , Joy Anu , Goldsworthy Adrian 
Published: September 2024

Abstract

Background: Healthcare services generate and store large quantities of data which historically required significant resources to manually analyse and gain meaningful insights to assist in informing healthcare policy and system design. Recent advancements in automation tools, such as generative artificial intelligence (GAI), provides new opportunities to disencumber human labour and thinking for difficult tasks. This study explores the potential utilisation of GAI for a healthcare data analysis task, specifically, the conversion of clinical data from one diagnostic classification system to another (i.e., SNOMED-CT-AU to the ICD-10-CM). Additionally, it examines the time and cost benefit of performing this using GAI when compared to a human rater.
Methods: Conversions were completed on SNOMED-CT-AU codes to the ICD-10-CM using three methods: 1) manual conversion using the National Library of Medicine’s I-MAGIC tool, 2) conversion using ChatGPT 4o, and 3) conversion using Claude Sonnet 3.5. The accuracy of the GAI tools was mapped against the manually extracted codes, and examined in terms of a perfect, partial, or incorrect match. Researchers’ time was recorded and extrapolated to calculate and compare the cost associated with each method.
Results: When compared to the manually extracted codes, Claude Sonnet 3.5 yielded the highest level of agreement over ChatGPT 4o, whilst also being the most time and cost effective.
Conclusion: GAI tools have greater utility than they have currently been given credit for. The automation of big data healthcare analytics, whilst still the domain of humans is increasingly capable of being undertaken using automation tools with low barriers to entry. The further development of the capability of GAI alongside the capability of the healthcare system to use it appropriately has the potential to result in significant resource savings.
Advanced Blockchain-Enabled Deep Quantum Computing Model for Secured Machine-to-Machine Communication
Authors: Arora Rajeev Kumar , Tiwari Aniruddh , Mohd.Muqeem  
Published: September 2024

Abstract

Machine-to-machine (M2M) communication has become an integral part of various industries, facilitating efficient data exchange and enabling autonomous decision-making processes. However, the proliferation of interconnected devices has brought about unprecedented security challenges. Conventional cryptographic methods may not be sufficient to protect sensitive data in the face of rapidly evolving cyber threats. To address this issue, a novel approach combining blockchain technology and deep quantum computing has been proposed to enhance the security and privacy of M2M communication. This paper presents an advanced blockchain-enabled deep quantum computing model designed to safeguard machine-to-machine communication from potential cyber threats. At the core of the model is a quantum key distribution (QKD) protocol, which enables the secure establishment of cryptographic keys between communicating devices. To ensure the scalability and immutability of the M2M communication network, a blockchain framework is integrated into the system. Additionally, the model incorporates deep learning techniques to bolster the security infrastructure further. In this paper we introduce an innovative solution to address the security challenges in machine-to-machine communication. By integrating advanced quantum computing, blockchain technology, and deep learning, the proposed model establishes a robust and secure framework that ensures the confidentiality, integrity, and authenticity of M2M data exchanges.
Meta-analytic Review of Bacteria and Parasites in Elasmobranchs Provides Insight into Research Gaps
Authors: Modzelesky Alexis 
Published: September 2024

Abstract

Elasmobranchs have conservational and commercial importance. There is a need for a more complete understanding of their health, due to their ability to shape trophic webs and their money-making potential with fishers and ecotourists. Sharks have been known to influence the strength of food webs, including reef ecosystems where many fish species are harvested for food. The investigation of pathogenic agents and diseases in elasmobranchs has been biased to favor those natural enemies that inhabit the digestive system and epidermal surfaces. Certain groups of parasites such as cestodes, copepods, and monogeneans, were most often recorded in this review. The bacterial microbiota of elasmobranchs is currently being researched, but much information is lacking in the field, with the exception of certain well-known strains, such as members of the genus Vibrio. In addition, information on the 22 species of sharks included in this study is more in depth than that which can be found for the 11 ray species and three skates. A quantitative study was undertaken to investigate relationships between taxonomic Order and Family, as well as the relationship between diet and number of natural enemies with the use of the statistical software R. It was found that number of natural enemies present in an elasmobranch species correlates with the number of sources available. This first attempt at an exhaustive review of elasmobranch natural enemies from Florida waters includes over 400 pathogens as well as indicates the 14 most under-studied elasmobranch species.