bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of inoculation of inactivated COVID-19 vaccine on the outcomes of frozen-thawed embryo transfer
Authors: Qiu Yu , Chen Fu-rui , Leng Mei , Deng Xi , Liang Mei-yu , Wei Jia-jing , Chen Shi-qi , Hei Jing , Ma Rong-ning , Li-Ling Jesse , Gong Yan 
Published: December 2024

Abstract

To assess the impact of inoculation of inactivated corona virus disease 2019 (COVID-19) vaccine on the outcome of frozen-thawed embryo transfer (FET). From January 2022 to November 2023, patients aged 20 ~ 45 years old undergoing FET at the Reproductive Medicine Center of a tertiary teaching hospital were prospectively enrolled. The patients were divided into vaccinated group (n = 458) and unvaccinated group (n = 530) based on the inoculation of inactivated COVID-19 vaccine before FET. Vaccinated group was further divided into three subgroups based on the dose (single dose, n = 55; double dose, n = 292; triple dose, n = 111) or interval from the first inoculation to FET (< 3 months, n = 51; 3 ~ 6 months, n = 101; > 6 months, n = 306). The primary outcome was live birth rate (LBR). The LBR (43.87% vs. 40.57%) was not significantly different between the vaccinated and unvaccinated groups, and so were embryo implantation rate (IR), clinical pregnancy rate (CPR), the gestational age at delivery and birth weight (P > 0.05). IR was significantly decreased with the shorter interval of vaccination (28.57% vs. 32.02% vs.45.24%, P = 0.007), while LBR and CPR were not significantly different (P > 0.05). For the dose subgroups of vaccination, IR, LBR and CPR were not significantly different (P > 0.05). Inoculation of inactivated COVID-19 vaccine did not affect the outcome of FET. Clinical Trial Registration Number: ChiCTR2200055597 (Chinese Clinical Trial Registry), January 14, 2022. (https://www.chictr.org.cn/bin/project/edit?pid=148312)
A Qualitative Research on the Control Status and Needs of Hypertension Patients in Guangxi Region
Authors: ZHONG Yingqi , LIU Zhaopeng , DING Xifeng , ZHENG Jinghui 
Published: December 2024

Abstract

Objective
Understanding the current status and needs of hypertension patients in the Guangxi region to provide a reference for clinical diagnosis and treatment by medical staff.
Methods
Using purposeful sampling, 16 hypertension patients treated at a Class III Grade A integrated traditional Chinese and Western medicine hospital in Nanning City, Guangxi Zhuang Autonomous Region from March to July 2024 were selected as research subjects and semi-structured interviews were conducted. Data was analyzed using Colaizzi's 7-step analysis method in phenomenological research.
Results
The control status and needs of hypertension patients in the Guangxi region can be summarized into four themes：Psychological and social adaptation ( disease understanding and acceptance, treatment expectations and concerns, future planning and uncertainty ) ; medical needs and access ( hypertension-related knowledge of popular science, convenient medical needs, high-quality medical services needs, regular treatment needs, doctor-patient communication needs ) ; support system and resources ( family support, psycho-social support ) ; self-management and control ( lifestyle changes, treatment options, standardized monitoring and treatment ).
Conclusion Society should pay attention to the control status and needs of hypertension patients, provide support to patients from multiple aspects and dimensions, reduce the incidence of adverse events, and improve the quality of life for patients.
Ligands from Propolis as Antioxidants through Inhibition of Xanthine Oxidase (XOD): An In Silico Exploration.
Authors: Adeyanju Muinat Moronke , Omirin Emmanuel Sunday , Adelokiki Ibukunoluwa Folasade , Aribisala Precious Oluwasanmi , Odubela Olukayode Olusola 
Published: December 2024

Abstract

Excessive reactive oxygen species (ROS) contribute to various chronic diseases, and xanthine oxidase (XOD) is a significant source of ROS in human metabolism. Propolis, known for its antioxidant properties, contains bioactive compounds that may inhibit XOD and reduce ROS production. This study aimed to investigate the inhibitory potential of propolis-derived phytochemicals on XOD through in silico methods. We conducted molecular docking to assess binding affinities and interaction profiles of 48 propolis compounds with XOD’s active site. Key XOD interacting residues including Glu802, Arg880, Thr1010, and Glu1261 played a role in stabilizing the binding of lead compounds through hydrogen bonds and hydrophobic contacts. Compounds luteolin, apigenin, and acacetin exhibited high docking scores of -11.650, -11.470, and − 11.284 kcal/mol, respectively, indicating strong binding affinity than the common standards, febuxostat (-7.922 kcal/mol) and allopurinol (-5.707 kcal/mol). MMGBSA calculations supported these findings, with catechin, myricetin, and hesperetin showing favorable binding free energies of -53.48, -48.69, and − 48.40 kcal/mol. QSAR modeling further confirmed the inhibitory potential, with low IC50 values for the most active compounds. ADME-T analysis indicated high gastrointestinal absorption and favorable drug-likeness for several compounds, suggesting promising pharmacokinetic and pharmacodynamic properties. In conclusion, propolis compounds, particularly luteolin, apigenin, and acacetin, show significant potential as natural XOD inhibitors. These findings support further experimental validation of these compounds as antioxidant therapies for managing ROS-related diseases.
Identifying delayed neurological sequelae during the acute phase of carbon monoxide poisoning based on diffusion-weighted imaging and clinical features
Authors: Chen Siying , Yang Shijun , Tan Minghui , Lu Heying , Li Huan , Li Jinlan , Yang Xiuqiong , Huang Yanzhi , Tian Senbiao , Liu Qunhui 
Published: December 2024

Abstract

Background
Delayed neurological sequelae (DNS) represents a critical and potentially fatal complication. Therefore, the timely recognition of individuals at risk of developing DNS in early phase holds significant clinical value. This study aims to identify diffusion-weighted imaging (DWI) characteristics related to developing DNS, and construct a predictive model encompassing DWI characteristics and clinical variables to early prediction of DNS during the acute phase of carbon monoxide (CO) poisoning.
Methods
We retrospectively include 120 poisoned patients with newly diagnosed with CO poisoning. The subjects were divided into non-DNS group (n = 75) and DNS group (n = 45) after at least 60-day follow-up. The fractional anisotropy value and mean diffusivity value were measured in the regions of interest placed on the amygdala, caudate, hippocampus, pallidus, putamen, thalamus and ventricle. A support vector machine (SVM) model integrated both DWI and clinical features was developed and evaluated. And mean impact value was used to rank the features that had impacts on classification.
Results
A total of 24 clinical features and 28 DWI features were included. 8 clinical features and 23 DWIs were included in the SVM model. Three SVM models were established based solely on clinical features or DWI features, and combined clinical and DWI features, with prediction accuracy of 0.76, 0.94, and 0.97, respectively. The precision, sensitivity, F1 score, macroscopic mean, weighted mean, and AUC of the combined model is 1.00, 0.92, 0.96, 0.98, 0.97, 0.97, respectively, and the result of 10-flod cross validation was 0.98. The mean fractional anisotropy of left caudate had the highest impact on the SVM model.
Conclusions
The fractional anisotropy and mean diffusivity of DWI may be a potential biomarker in identifying patients at risk of developing DNS. Our comprehensive SVM model with multimodal features had excellent accuracy and clinical practicability in identifying DNS.
Development of a quantitative PMA-16S rRNA gene sequencing workflow for absolute abundance measurements of seawater microbial communities
Authors: Thomas Marie C. , Waugh Gretel , Damjanovic Katarina , Vanwonterghem Inka , Webster Nicole S. , Negri Andrew P. , Luter Heidi M. 
Published: December 2024

Abstract

Background
Ecological risk assessments rarely consider the impacts of environmental stress on microbial communities. The incorporation of microbial community responses into these evaluations requires establishing sensitivity thresholds based on the absolute abundance of viable taxa. While essential for describing microbial community dynamics, sequencing-based analyses are generally limited to assessing relative proportions and fail to reveal the magnitude or directionality of abundance shifts. To address this, we developed and validated a workflow combining propidium monoazide (PMA) treatment, 16S rRNA gene amplicon sequencing, and quantitative microbiome profiling (QMP) to determine the absolute abundance of viable taxa in seawater microbiomes.
Results
Using natural seawater, microbial load estimates from droplet digital PCR (ddPCR) and flow cytometry (FC) correlated strongly for total and intact cell counts, confirming the suitability of both methods for normalising 16S rRNA gene amplicon sequencing data. Additionally, we demonstrated that PMA at concentrations of 2.5–15 µM effectively inhibited PCR amplification of DNA from membrane-compromised cells, reducing 16S RNA gene copies by 24–44% relative to untreated samples. Samples with known proportions of intact cells were generated by mixing heat-killed and natural seawater, enabling abundance assessments by normalising 16S rRNA gene amplicon sequencing data to intact cell loads estimated via ddPCR and FC. This approach facilitated detailed comparisons of the effects of QMP versus relative microbiome profiling (RMP) on alpha and beta diversity metrics and on relative and absolute amplicon sequence variant (ASV) abundance profiles. Unlike RMP, QMP captured significant shifts in the microbial community composition across samples with decreasing proportions of intact cells. Furthermore, RMP failed to detect abundance changes at ASV-level, while QMP revealed consistent abundance declines.
Conclusion
The PMA-16S rRNA sequencing workflow, coupled with QMP, enhanced the accuracy in representing microbial community dynamics by addressing key limitations of RMP such as the inclusion of damaged cells or extracellular DNA and the misleading proportions of identified taxa. This workflow is particularly suited for quantifying the magnitude and direction of changes in taxa abundance following stress exposure, making it directly applicable to stress-response modelling and supporting the integration of microbial sensitivity thresholds into future regulatory guidelines.
Identification of the ferroptosis-related genes in Sepsis- Associated Encephalopathy by multiple transcriptional datasets analysis
Authors: Xie Zhefan , Zhang Wei , Li Lingfeng , Luo Ming , Chen Jialong , Gong Liya , Cao Jingjing , Liu Ye , Liu Jiean , Fu Chunlai 
Published: December 2024

Abstract

Sepsis associated encephalopathy (SAE), a severe sepsis complication, remains challenging to treat due to limited effective options. Current therapies focus on symptomatic relief and managing complications, without addressing the underlying causes, highlighting the need for further research. The condition's complex pathophysiology involves inflammation, blood-brain barrier disruption, and neuronal damage, necessitating a comprehensive understanding of its mechanisms through multi-dataset research. We used RNA sequencing, Mendelian randomization, and bioinformatics to study gene changes in SAE. Our analysis identified 1,259 differentially expressed genes related to inflammatory and bacterial responses. Mendelian randomization revealed 20 key genes, including ITPA and IL18R1, associated with sepsis risk. GSVA and GSEA analyses validated their roles in signaling pathways. Single-cell analysis showed gene expression across cell types and co-expression with ferroptosis-related genes. We also constructed regulatory and miRNA networks, identifying several transcription factors and miRNAs involved in gene regulation. Our multi-omics approach offers new insights into the molecular mechanisms of SAE and identifies potential therapeutic targets for this condition.
Determination of Natural Radionuclide Activity Concentrations and Radiological Hazards Assessment in the Bukit Karang Putih Mine Area of PT Semen Padang
Authors: Aj Aulia Fitri , Fardela Ramacos , Iskandar Dadong , Ladira Maulani Ruby , Annisa Emilia 
Published: December 2024

Abstract

Abstrak A study was conducted to determine the activity concentration of radionuclides 226Ra (Radium), 232Th (Thorium), and 40K (Potassium) and the radiological hazard index in the form of the rate of absorbed radiation dose and annual effective dose for workers in the Bukit Karang Putih mining area of PT Semen Padang using a gamma spectrometer. The samples measured were soil and water. The results of the study revealed that the quantities of radionuclides 226Ra, 232Th, and 40K were exclusively detected in soil samples, while the values obtained in water samples were below the MDC. Soil samples with the Soil 3 code obtained also contained radionuclides 232Th and 40K that were below the MDC. The average activity concentration of radionuclide 226Ra is 93,67 ± 13,52 Bq/kg, while that of 232Th is 221,63 ± 31,05 Bq/kg. The average activity concentration of 40K is 527,25 ± 75,13 Bq/kg. The activity concentration of radionuclides obtained is still below the intervention value established by PERKA BAPETEN No. 9 of 2009. However, the value in soil samples for 226Ra and 232Th is higher, and the value for 40K is lower, in comparison to the world average reported by UNSCEAR in 2000. The radiological hazard index value in soil samples for the absorbed radiation dose rate was obtained with an average of 200,573 nGy/hour, while for the annual effective dose the average value obtained was 0,2460 mSv/year. The value of the absorbed radiation dose rate and the annual effective dose in the air samples cannot be calculated because the activity concentration values obtained in the air samples have values below the MDC. The annual effective dose obtained is less than the limit established by ICRP and PERKA BAPETEN No. 4 of 2013, which is 1 mSv/year.
Surface profile estimation in milling through vibration analysis and long short-term memory networks
Authors: Wu Tian-Yau , Lin Cheng-Yi 
Published: December 2024

Abstract

The objective of this research is to investigate the feasibility of utilizing tooling vibration signals and cutting parameters to predict workpiece surface profiles in the milling process, enabling a comprehensive evaluation of surface quality. Information-contained (IC) signals were synthesized from milling vibration measurements using principal component analysis (PCA) and empirical mode decomposition (EMD). Statistical features and sweeping-frequency features were then extracted from individual intrinsic mode functions (IMFs) of the IC signals in both time and frequency domains. Subsequently, independent long short-term memory (LSTM) networks were used to estimate the individual IMFs of the surface profiles, incorporating the milling parameters and selected signal features as inputs. Results demonstrated that mid-low frequency components of surface profiles could be estimated accurately with an average mean absolute percentage error (MAPE) of 14.72%, while low-frequency components achieved an average MAPE of 7.79%.
Holistic Interference Management for Wireless Networks in the Era of Artificial Intelligence
Authors: Husen Arif , Nisar Shafaq , Chaudary Muhammad Hasanain , Khan Zuhaib Ashfaq 
Published: December 2024

Abstract

Future networks are expected to exhibit intense use of artificial intelligence due to the increasing use of intelligent devices in domestic and industrial life. The intelligent devices will communicate with networks and exchange information about expected performance, available cost packages, and availability of network resources along the destination. Therefore, networks need intelligent techniques to learn the state of various network functions and resources and adjust their configurations in an automated way. Machine learning techniques allow the networks to realize such learning and automate the optimization of the network functions and resources. Several techniques have been discussed in the literature to optimize and manage interference in radio networks. However, the existing approaches generally optimize one or a few aspects in a stand-alone fashion. Recently introduced global learning and deep holistic learning techniques can optimize the network function considering all known aspects. This article proposes a novel holistic learning and optimization technique for interference management in wireless networks. It uses a novel objective functions-based feature engineering process to capture the effects of various parameters and actions related to interference management. Transfer learning reduces computational complexity, and ensemble learning aggregates knowledge from base learners corresponding to each objective function. The experimental network is constructed using the NS3 LENA module, and standard Python libraries are used to implement the base learners and proposed model. It uses several base learners that learn the information from possible interference variables and determine the optimal actions across the cells. The experimental results show that the holistic learning-based approach efficiently manages the interference, improves the system capacity, and reduces the interference caused by user arrivals twofold compared to the state-of-the-art techniques.
Improved Human Activity Recognition Through Controllable GAN-Generated Synthetic Data and Large Language Models for Classification
Authors: Djemaa Mohamed Hedi , Jemili Farah , Megdiche Imen , Thabet Rafika , Lamine Elyes , Korbaa Ouajdi 
Published: December 2024

Abstract

Human Activity Recognition (HAR) is crucial in healthcare monitoring and smart home systems, tracking patient movements, de- tecting falls, and monitoring daily activities. Despite its importance, HAR faces significant challenges due to the scarcity of large-scale, diverse datasets and the lack of data representing abnormal activities, essential for detecting rare but critical health events. This paper addresses these challenges through advanced synthetic data generation and state-of-the- art classification techniques. We introduce a Generative Adversarial Net- work (GAN) designed for time series data to generate synthetic samples, significantly expanding the WISDM dataset. Our method includes gener- ating an ’abnormal’ activity class, enhancing the dataset’s diversity and real-world applicability. The synthetic data quality is rigorously evalu- ated using Dynamic Time Warping (DTW) to ensure fidelity to original data distributions. For classification, we leverage transformer-based mod- els to interpret and classify human activities from accelerometer data. This approach demonstrates the adaptability of advanced language mod- els to numeric, time-series data, opening new avenues in HAR research. Transformers, known for their success in natural language processing, show promise in capturing complex patterns in human activity data, leading to improved accuracy and robustness. Our methodology aims to expand the dataset, particularly for rare and abnormal activities, and enhance classification accuracy. This work contributes to HAR by pro- viding a framework for dataset enhancement and classification, paving the way for more robust and versatile activity recognition systems, espe- cially where data collection is challenging. The implications for health- care are significant, potentially leading to better patient outcomes and more efficient healthcare delivery.
Quasi-Static Analysis of Stress and Strain Distribution in Vocal Folds Under Constant Loading Conditions
Authors: Ávila Carlos Arturo Bohórquez , Forero-Casallas John Alejandro , Rosas Victor Elberto Ruiz 
Published: December 2024

Abstract

This study presents a detailed biomechanical analysis of vocal cords using the M5 model, focusing on their viscoelastic properties and dynamic behavior during phonation. The research investigates the interactions between airflow, tissue deformation, and acoustic conditions to understand the complex mechanisms underlying sound production. The primary components analyzed are the lamina propria, ligament, and body, each displaying distinct responses to applied pressures ranging from 1 to 3 kPa.
Key results reveal that the lamina propria is the most stress-concentrated region, particularly under higher frequencies, highlighting its critical role in absorbing mechanical loads and maintaining structural integrity. Displacement analysis shows anisotropic behavior, with higher flexibility in the X-direction (up to 3.5 mm) and alternating positive and negative values in the Y-direction, with maximum magnitudes of approximately -1 mm in the ligament. Nonlinear stress-deformation behavior was observed, with stresses rising sharply to 9 kPa when deformations exceeded 3 mm under a 3 kPa load. Additionally, natural vibration frequencies significantly affect stress distribution, potentially causing resonances that may lead to pathologies such as dysphonia.
The conclusions emphasize the importance of the viscoelastic properties of the lamina propria in enabling efficient sound modulation and stress absorption while preventing structural damage.
Low-Power Tunable Differentiator using Voltage Difference Transconductance Amplifier
Authors: Krishna Motkuri , Nagar Bal Chand 
Published: December 2024

Abstract

This paper demonstrates an innovative design of a voltage–mode differentiator circuit using the active element called voltage difference transconductance amplifier (VDTA). The proposed circuit utilizes one VDTA element, a single resistor, and a capacitor. The proposed circuit is designed in a gpdk 180nm CMOS processing a cadence Virtuoso tool. The designed circuit works with ± 0.9V supply voltage and uses a bias current of order 150µA, and also its amplitude is electronically tunable with bias current. Moreover, the proposed circuit provides 5% total harmonic distortion and 257µW power dissipation.