bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gender-specific responses in gene expression of Nile tilapia (Oreochromis niloticus) to heavy metal pollution in different aquatic habitats
Authors: Awad Simone T. , Hemeda Shabaan A. , El Nahas Abeer F. , Abbas Eman M. , Abdel-Razek Mohamed A. S. , Ismail Mohamed , Mamoon Ahmed , Ali Fawzia S. 
Published: June 2024

AbstractMonitoring heavy metal accumulation is essential for assessing the viability of aquatic ecosystems. Our methodology involved integrating analysis of immunological, stress, inflammatory, and growth-related gene expression in male and female Nile tilapia with on-site recordings of physicochemical parameters. Additionally, we assessed the effect of different physicochemical parameters on heavy metal bioavailability and residual concentration in fish and water. Samples of fish and water were gathered from three different localities: Lake Brullus, a brackish lake sited in northern Egypt; Lake Nasser, an artificial freshwater reservoir located in southern Egypt; and El-Qanater El-Khayria, a middle-freshwater location belonging to the Rashid branch of the river Nile. The assessment of heavy metal residues (Fe, Cu, Zn, Mn, and Ni) revealed that their concentrations were higher in fish specimens compared to their counterparts in water (except for Ni). In addition, Lake Brullus emerges as the most polluted area, exhibiting elevated levels of heavy metals concentrations in water and fish specimens. In contrast, Lake Nasser showed the least degree of heavy metals pollution. Gene expression analysis revealed gender-specific responses to heavy metal exposure at the three investigated water bodies. The expression of hepatic antioxidant genes (GST and MT) and inflammatory-related genes (CC-chemokine and TNFα) increased in males compared to females. In females, the immune and pro-inflammatory-related genes (IgM and CXC2-chemokine) transcripts were upregulated. Additionally, growth-related genes were downregulated in both Lake Brullus and El-Qanater; on the contrary, fish samples from Lake Nasser exhibited a normal expression pattern of growth-related genes. Stress-related genes (HSP70 and HSP27) showed significant downregulation in gills of both genders from Lake Brullus. The minimal presence of heavy metal contaminants in Lake Nasser seems to endorse the normal patterns of gene expression across all gene categories. A potential gender-specific gene expression response towards pollution was noticed in genes associated with inflammation and antioxidant activities. This highlights the importance of considering gender-related responses in future environmental assessments.
Comparison of specialized stent versus generic stent and bare stent combination for transjugular intrahepatic portosystemic shunt creation
Authors: Bai Yaowei , Liu Jiacheng , Wang Chaoyang , Yao Wei , Ju Shuguang , Wang Yingliang , Zhou Chen , Dong Xiangjun , Zheng Chuansheng 
Published: June 2024

AbstractTransjugular intrahepatic portosystemic shunt (TIPS) creation using the Viatorr stent remains relatively uncommon in underdeveloped and high-burden disease regions in Asia–Pacific, and there is a lack of comparative studies regarding its prognostic effects compared with the generic stent-graft/bare stent combination. The purpose of this retrospective study is to compare the prognostic endpoints of these two treatments in patients who underwent TIPS creation. Clinical data from 145 patients were collected, including 82 in the combination group and 63 in the Viatorr group. Differences in prognostic endpoints (shunt dysfunction, death, overt hepatic encephalopathy [OHE], rebleeding) between the two groups were analyzed using Kaplan–Meier curves. The Cox proportional hazards model was used to identify independent risk factors for post-TIPS shunt dysfunction. The TIPS procedure was successful in all patients. After TIPS creation, both groups showed a significant decrease in porto-caval pressure gradient compared to that before TIPS creation. The stent patency rates at 6, 12, and 18 months were high in both the combination and Viatorr groups (93.7%, 88.5%, and 88.5% vs. 96.7%, 93.4%, and 93.4%, respectively). The stent patency rates was higher in the combination group than in the Viatorr group, although not statistically significant (HR = 2.105, 95% CI 0.640–6.922, Log-rank P = 0.259). There were no significant differences in other prognostic endpoints (death, OHE, rebleeding) between the two groups. The Cox model identified portal vein diameter (HR = 0.807, 95% CI 0.658–0.990, P = 0.040) and portal vein thrombosis (HR = 13.617, 95% CI 1.475–125.678, P = 0.021) as independent risk factors for post-TIPS shunt dysfunction. The shunt patency rates between the Viatorr stent and the generic stent-graft/bare stent combination showed no significant difference and the generic stent-graft/bare stent combination may be a viable alternative in areas where the Viatorr stent is not yet available.
Resynthesizing Brassica napus with race specific resistance genes and race non-specific QTLs to multiple races of Plasmodiophora brassicae
Authors: Masud Karim Md. , Yu Fengqun 
Published: June 2024

AbstractClubroot disease in canola (Brassica napus) continues to spread across the Canadian prairies. Growing resistant cultivars is considered the most economical means of controlling the disease. However, sources of resistance to clubroot in B. napus are very limited. In this study, we conducted interspecific crosses using a B. rapa line (T19) carrying race-specific resistance genes and two B. oleracea lines, ECD11 and JL04, carrying race non-specific QTLs. Employing embryo rescue and conventional breeding methods, we successfully resynthesized a total of eight B. napus lines, with four derived from T19 × ECD11 and four from T19 × JL04. Additionally, four semi-resynthesized lines were developed through crosses with a canola line (DH16516). Testing for resistance to eight significant races of Plasmodiophora brassicae was conducted on seven resynthesized lines and four semi-resynthesized lines. All lines exhibited high resistance to the strains. Confirmation of the presence of clubroot resistance genes/QTLs was performed in the resynthesized lines using SNP markers linked to race-specific genes in T19 and race non-specific QTLs in ECD11. The developed B. napus germplasms containing clubroot resistance are highly valuable for the development of canola cultivars resistant to clubroot.
The surface modification of the silica-coated magnetic nanoparticles and their application in molecular diagnostics of virus infection
Authors: Zeleňáková A. , Zeleňák V. , Beňová E. , Kočíková B. , Király N. , Hrubovčák P. , Szűcsová J. , Nagy Ľ. , Klementová M. , Mačák J. , Závišová V. , Bednarčík J. , Kupčík J. , Jacková A. , Volavka D. , Košuth J. , Vilček Š. 
Published: June 2024

AbstractThe study presents a series of examples of magnetic nanoparticle systems designed for the diagnosis of viral diseases. In this interdisciplinary work, we describe one of the most comprehensive synthetic approaches for the preparation and functionalization of smart nanoparticle systems for rapid and effective RT-PCR diagnostics and isolation of viral RNA. Twelve different organic ligands and inorganic porous silica were used for surface functionalization of the Fe3O4 magnetic core to increase the number of active centres for efficient RNA binding from human swab samples. Different nanoparticle systems with common beads were characterized by HRTEM, SEM, FT-IR, XRD, XPS and magnetic measurements. We demonstrate the application of the fundamental models modified to fit the experimental zero-field cooling magnetization data. We discuss the influence of the nanoparticle shell parameters (morphology, thickness, ligands) on the overall magnetic performance of the systems. The prepared nanoparticles were tested for the isolation of viral RNA from tissue samples infected with hepatitis E virus—HEV and from biofluid samples of SARS-CoV-2 positive patients. The efficiency of RNA isolation was quantified by RT-qPCR method.
A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method
Authors: Nobel S. M. Nuruzzaman , Swapno S. M. Masfequier Rahman , Islam Md. Rajibul , Safran Mejdl , Alfarhood Sultan , Mridha M. F. 
Published: June 2024

Abstract
In the healthcare domain, the essential task is to understand and classify diseases affecting the vocal folds (VFs). The accurate identification of VF disease is the key issue in this domain. Integrating VF segmentation and disease classification into a single system is challenging but important for precise diagnostics. Our study addresses this challenge by combining VF illness categorization and VF segmentation into a single integrated system. We utilized two effective ensemble machine learning methods: ensemble EfficientNetV2L-LGBM and ensemble UNet-BiGRU. We utilized the EfficientNetV2L-LGBM model for classification, achieving a training accuracy of 98.88%, validation accuracy of 97.73%, and test accuracy of 97.88%. These exceptional outcomes highlight the system’s ability to classify different VF illnesses precisely. In addition, we utilized the UNet-BiGRU model for segmentation, which attained a training accuracy of 92.55%, a validation accuracy of 89.87%, and a significant test accuracy of 91.47%. In the segmentation task, we examined some methods to improve our ability to divide data into segments, resulting in a testing accuracy score of 91.99% and an Intersection over Union (IOU) of 87.46%. These measures demonstrate skill of the model in accurately defining and separating VF. Our system’s classification and segmentation results confirm its capacity to effectively identify and segment VF disorders, representing a significant advancement in enhancing diagnostic accuracy and healthcare in this specialized field. This study emphasizes the potential of machine learning to transform the medical field’s capacity to categorize VF and segment VF, providing clinicians with a vital instrument to mitigate the profound impact of the condition. Implementing this innovative approach is expected to enhance medical procedures and provide a sense of optimism to those globally affected by VF disease.
Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference
Authors: Checcucci Curzio , Wicinski Bridget , Mazzamuto Giacomo , Scardigli Marina , Ramazzotti Josephine , Brady Niamh , Pavone Francesco S. , Hof Patrick R. , Costantini Irene , Frasconi Paolo 
Published: June 2024

Abstract3D reconstruction of human brain volumes at high resolution is now possible thanks to advancements in tissue clearing methods and fluorescence microscopy techniques. Analyzing the massive data produced with these approaches requires automatic methods able to perform fast and accurate cell counting and localization. Recent advances in deep learning have enabled the development of various tools for cell segmentation. However, accurate quantification of neurons in the human brain presents specific challenges, such as high pixel intensity variability, autofluorescence, non-specific fluorescence and very large size of data. In this paper, we provide a thorough empirical evaluation of three techniques based on deep learning (StarDist, CellPose and BCFind-v2, an updated version of BCFind) using a recently introduced three-dimensional stereological design as a reference for large-scale insights. As a representative problem in human brain analysis, we focus on a $$4~\text {-cm}^3$$

4

-cm
3

portion of the Broca’s area. We aim at helping users in selecting appropriate techniques depending on their research objectives. To this end, we compare methods along various dimensions of analysis, including correctness of the predicted density and localization, computational efficiency, and human annotation effort. Our results suggest that deep learning approaches are very effective, have a high throughput providing each cell 3D location, and obtain results comparable to the estimates of the adopted stereological design.
Prevalence and associated factors of metabolic syndrome among pregnant Ethiopian women: a hospital-based cross-sectional study
Authors: Fentie Alemie , Fetene Getnet , Kassahun Zeleke , Ambachew Sintayehu 
Published: June 2024

AbstractMetabolic syndrome (MetS) poses a significant public health challenge globally, including in Ethiopia, with risks for both mothers and children. Unfortunately, there is limited data on MetS in pregnant Ethiopian women. This study aims to evaluate the prevalence and factors associated with MetS in this population. A cross-sectional study was conducted using a systematic random sampling technique. Data were collected through face-to-face interviews using a structured questionnaire adapted from the World Health Organization Steps Survey Tool for Non-communicable Diseases. About five ml of fasting peripheral blood samples were collected from each participant. The Beckman Coulter DXC 700 AU clinical chemistry analyzer was employed for lipid profile and glucose analysis. Subsequently, data were inputted into Epi Data and later exported to SPSS Version 20 for further analysis. Bivariable and multivariable binary logistic regression analyses were carried out, with a predefined level of statistical significance at p < 0.05. A total of 318 pregnant women were included in this study. The prevalence of MetS was 13.2% (95% CI: 9.7, 17.0) based on the American Heart Association/National Heart Lung and Blood Institute definition. The most prevalent components of MetS were elevated triglyceride levels, reduced high-density lipoprotein levels, and elevated blood pressure. Unhealthy sleep duration (AOR = 5.6, 95% CI (2.4, 13.1), p < 0.001), high daily salt intake (AOR = 4.2, 95% CI (1.8, 9.5), p = 0.001), and alcohol consumption [AOR = 4.2, 95% CI (1.6, 10.9), p = 0.003] were significantly associated with MetS. The study reported a high prevalence of MetS in pregnant Ethiopian women. Factors including alcohol, high salt intake, and sleep disturbances were associated with MetS. Policymakers might utilize this data to create targeted interventions and public health policies for MetS among pregnant women, focusing on nutrition, sleep, and alcohol consumption during pregnancy to safeguard maternal and fetal health.
Assessing the relationship between lipoprotein(a) levels and blood pressure among hypertensive patients beyond conventional measures. An observational study
Authors: Vazquez-Agra Nestor , Cruces-Sande Anton , Barbosa-Gouveia Sofia , Lopez-Paz Jose-Enrique , Camafort Miguel , Casariego-Vales Emilio , Pose-Reino Antonio , Hermida-Ameijeiras Alvaro 
Published: June 2024

AbstractHigh lipoprotein(a) (Lp(a)) levels are associated with an increased risk of arterial hypertension (AHT) and atherosclerotic cardiovascular disease. However, little is known about the detailed profile of AHT based on Lp(a) levels. This observational study focused on elucidating the relationship between Lp(a) concentrations and specific indices obtained from 24-h ambulatory blood pressure (BP) monitoring in hypertensive patients over 18 years of age. We gathered and analyzed data on BP indices along with demographic, epidemiological, clinical, and laboratory variables from 227 hypertensive patients, median age 56 years, including 127 women (56%). After comparing hypertensive patients with Lp(a) levels above and below 125 nmol/L, we found that a 10 mmHg increase in nocturnal systolic BP and all pulse pressure indices (24-h, daytime, and night-time) was associated with an increased risk of high Lp(a) levels by more than 20% and 40%, respectively. Similarly, each 10% increase in the area under the function over time of nocturnal diastolic BP dipping was associated with more than a 30% decrease in the odds of belonging to the elevated Lp(a) levels category. Additionally, Lp(a) levels above 125 nmol/L were associated with higher 24-h, daytime, and night-time systolic BP and pulse pressure load. The relationship between Lp(a) and AHT appears to extend beyond conventional BP measurements, which may be relevant given the prognostic implications of nocturnal BP and pulse pressure indices.
Postpartum development of metabolic dysfunction-associated steatotic liver disease in a lean mouse model of gestational diabetes mellitus
Authors: Hribar K. , Eichhorn D. , Bongiovanni L. , Koster M. H. , Kloosterhuis N. J. , de Bruin A. , Oosterveer M. H. , Kruit J. K. , van der Beek E. M. 
Published: June 2024

AbstractGestational diabetes mellitus (GDM) is associated with increased postpartum risk for metabolic dysfunction-associated steatotic liver disease (MASLD). GDM-related MASLD predisposes to advanced liver disease, necessitating a better understanding of its development in GDM. This preclinical study evaluated the MASLD development in a lean GDM mouse model with impaired insulin secretion capacity. Lean GDM was induced by short-term 60% high-fat diet and low-dose streptozotocin injections (60 mg/kg for 3 days) before mating in C57BL/6N mice. The control dams received only high-fat diet or low-fat diet. Glucose homeostasis was assessed during pregnancy and postpartum, whereas MASLD was assessed on postpartum day 30 (PP30). GDM dams exhibited a transient hyperglycemic phenotype during pregnancy, with hyperglycaemia reappearing after lactation. Lower insulin levels and impaired glucose-induced insulin response were observed in GDM mice during pregnancy and postpartum. At PP30, GDM dams displayed higher hepatic triglyceride content compared controls, along with increased MAS (MASLD) activity scores, indicating lipid accumulation, inflammation, and cell turnover indices. Additionally, at PP30, GDM dams showed elevated plasma liver injury markers. Given the absence of obesity in this double-hit GDM model, the results clearly indicate that impaired insulin secretion driven pregnancy hyperglycaemia has a distinct contribution to the development of postpartum MASLD.
Evaluation of agricultural non-point source pollution using an in-situ and automated photochemical flow analysis system
Authors: Chen Yongqi , Awais Muhammad , Wu Junfeng , Li Zhenfeng , Abbas Naqvi Syed Muhammad Zaigham , Abdulraheem Mukhtar Iderawumi , Zhang Hao , Wang Ling , Zhang Wei , Raghavan Vijaya , Hu Jiandong 
Published: June 2024

AbstractOff-line leachate collection from agricultural landscapes cannot guarantee precise evaluation of agricultural non-point source (ANPS) due to geospatial variations, time, and transportation from the field to the laboratory. Implementing an in-situ nitrogen and phosphorous monitoring system with a robust photochemical flow analysis is imperative for precision agriculture, enabling real-time intervention to minimize non-point source pollution and overcome the limitations posed by conventional analysis in laboratory. A reliable, robust and in-situ approach was proposed to monitor nitrogen and phosphorous for determining ANPS pollution. In this study, a home-made porous ceramic probe and the frequency domain reflectometer (FDR) based water content sensors were strategically placed at different soil depths to facilitate the collection of leachates. These solutions were subsequently analyzed by in-situ photochemical flow analysis monitoring system built across the field to estimate the concentrations of phosphorus and nitrogen. After applying both natural and artificial irrigation to the agricultural landscape, at least 10 mL of soil leachates was consistently collected using the porous ceramic probe within 20 min, regardless of the depth of the soil layers when the volumetric soil water contents are greater than 19%. The experimental results showed that under different weather conditions and irrigation conditions, the soil water content of 50 cm and 90 cm below the soil surface was 19.58% and 26.08%, respectively. The average concentrations of NH4+-N, NO3−-N, PO43− are 0.584 mg/L, 15.7 mg/L, 0.844 mg/L, and 0.562 mg/L, 16.828 mg/L and 0.878 mg/L at depths of 50 cm and 90 cm below the soil surface, respectively. Moreover, the comparison with conventional laboratory spectroscopic analysis confirmed R2 values of 0.9951, 0.9943, 0.9947 average concentration ranges of NH4+-N, NO3−-N, and PO43−, showcasing the accuracy and reliability of robust photochemical flow analysis in-situ monitoring system. The suggested monitoring system can be helpful in the assessment of soil nutrition for precision agriculture.
Stability, enrichment, and quantification of total and HPV16-specific IgG present in first-void urine
Authors: Téblick Laura , Lipovac Marijana , Bell Margo , De Smet Annemie , De Meester Ingrid , Delputte Peter , Vorsters Alex 
Published: June 2024

AbstractFirst-void urine (FVU) samples, containing human papillomavirus (HPV)-specific IgG from female genital tract secretions, provide a non-invasive option for disease monitoring and vaccine impact assessment. This study explores the utility of FVU for IgG quantification, exploring stability and compatibility with DNA preservation methods, alongside various IgG enrichment methods. Healthy female volunteers provided FVU and serum samples. FVU was collected with or without urine conservation medium (UCM) and stored under different conditions before freezing at −80 °C. Four IgG enrichment methods were tested on FVU samples. All samples were analyzed using three total human IgG quantification assays and an in-house HPV16-specific IgG assay. Samples stored with UCM buffer had higher total and HPV16-specific IgG concentrations (p ≤ 0.01) and IgG remained stable for at least 14 days at room temperature. Among IgG enrichment methods, Amicon filtration (AM) and AM combined with Melon Gel purification (AM-MG) provided similar HPV16-IgG concentrations, correlating strongly with serum levels. Protein G magnetic beads methods were incompatible with time-resolved fluorescence-based assays. This study highlights FVU as a reliable and convenient sample for IgG quantification, demonstrating stability for at least 14 days at room temperature and compatibility with UCM DNA preservation. It emphasizes the need to select appropriate IgG enrichment methods and confirms the suitability of both AM and AM-MG methods, with a slightly better performance for AM-MG.
Image-based facial emotion recognition using convolutional neural network on emognition dataset
Authors: Agung Erlangga Satrio , Rifai Achmad Pratama , Wijayanto Titis 
Published: June 2024

AbstractDetecting emotions from facial images is difficult because facial expressions can vary significantly. Previous research on using deep learning models to classify emotions from facial images has been carried out on various datasets that contain a limited range of expressions. This study expands the use of deep learning for facial emotion recognition (FER) based on Emognition dataset that includes ten target emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust, fear, sadness, and neutral. A series of data preprocessing was carried out to convert video data into images and augment the data. This study proposes Convolutional Neural Network (CNN) models built through two approaches, which are transfer learning (fine-tuned) with pre-trained models of Inception-V3 and MobileNet-V2 and building from scratch using the Taguchi method to find robust combination of hyperparameters setting. The proposed model demonstrated favorable performance over a series of experimental processes with an accuracy and an average F1-score of 96% and 0.95, respectively, on the test data.