bluuebunny/crossref_metadata_2025_split huggingface dataset:
Challenges and prospects in integrating abortion care with HIV/Family planning services as a one-stop maternal health service
Authors: Adane Haile Bekele , Lizeth Roets 
Published: January 2024

Abstract
Introduction: Delivering a standalone health service not only reduces the accessibility of comprehensive healthcare at a given time but also diminishes service quality, raises service costs, increases facility visits, and amplifies stigma and discrimination especially given the religious, moral, cultural, and political implications associated with abortion care. Therefore, integrating abortion care with HIV-FP services can enhance the utilization of maternal health services and increase client satisfaction.
Method: A facility-based cross-sectional mixed-method study was conducted, collecting quantitative data through face-to-face interviews with abortion care users and self-administered questionnaires for healthcare providers. The collected data underwent verification, cleaning, and entry into EpiData version 3.1 software, followed by exportation and analysis using SPSS version 25.
Result: The study included 413 women receiving abortion care and 306 healthcare providers from 58 public health facilities. Healthcare providers identified factors such as enhancing regulatory documents, providing training, improving infrastructure, ensuring a consistent supply of medical resources, program monitoring and evaluation, and fostering interaction between healthcare facility units as motivating factors for implementing integrated health services. Abortion care users emphasized that making health facilities available and equipping them with necessary materials, equipment, and supplies can enhance integrated abortion care services.
Conclusion: Successful implementation of integrated maternal health services requires a strategic action plan addressing challenges and engaging with motivational factors.
Morphological, biological, and molecular characterization of Type I granuloviruses of Spodoptera frugiperda
Authors: Ordóñez-García Magali , Bustillos-Rodríguez Juan Carlos , Ornelas-Paz José de Jesús , Acosta-Muñiz Carlos Horacio , Salas-Marina Miguel Ángel , Cambero-Campos Octavio Jhonathan , Estrada-Virgen Mario Orlando , Morales-Ovando Mario Alberto , Velasco Claudio Rios 
Published: January 2024

Abstract

The granuloviruses or GVs (Betabaculovirus) associated with the fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), especially those of Type I, have scarcely been studied but they might represent an alternative for the biocontrol of this insect. In this study, the native granuloviruses SfGV-CH13 and SfGV-CH28 isolated from FAW larvae were characterized for morphology, molecular traits, and insecticidal activity. The elapsed time between symptomatic infection of larvae and stop feeding as well as the weight of larvae before death or prior to pupation were also evaluated. Both granuloviruses isolates showed ovoid shape with a length of 0.4 µm. They showed the same DNA restriction profiles and their genome sizes were about 126 kb. The symptomatic infection with tested GVs mainly caused flaccidity of larva body and discoloration of integument. The integument lysis was only observed in 8% of infected larvae. Infected larvae gradually stopped feeding. Overall, these symptoms are characteristic of infections caused by Type I granuloviruses, which are known as monoorganotropic or slow-killing. The median lethal doses (LD50) values for SfGV-CH13 and SfGV-CH28 isolates were 5.4 × 102 and 1.1 × 103 OBs/larva, respectively. The median lethal time (LT50) ranged from 17 to 24 d. LT50 values decreased as the viral dose was increased. The elapsed time since symptomatic infection until pupation (LD50) and body weight of larvae (third instar) were higher with SfGV-CH28 than SfGV-CH13. Both granulovirus isolates were able to kill the FAW larvae from the 12th day.
CovidSafe: A Deep Learning Framework for Covid Detection Using Multi-Modal Approach
Authors: Panigrahi Srikanth , Behera Chandan kumar , Routhu Srinivasa Rao 
Published: January 2024

Abstract
This work introduces CovidSafe, a framework designed to classify COVID patients as positive or negative based on CXR images. With the global impact of COVID-19, there is a need for effective tools to aid in patient diagnosis. CovidSafe utilizes a multi-model deep feature extractor, combining features from the ResNet family and an autoencoder. The ResNet 18, ResNet 34, and ResNet 50 architectures are employed, and model weights are fine-tuned for optimal performance. The framework leverages the ResNet family models and theautoencoder to extract relevant features. The features from intermediate layers in Resnet family are extracted to capture increasingly complex and high-level information. Similarly, the bottleneck features from Autoencoder are extracted to capture compressed, abstract representation and important features of the image for the classification. These features are then utilized for analysis and classification within the CovidSafe framework. To ensure accessibility for online users, the proposed model is integrated into a web application. Through experimental analysis, the performance of the proposed model is evaluated with various metrics. Results indicate that CovidSafe outperforms baseline models, achieving a precision of 98.67%, recall of 98.62%, and F1-score of 98.64%. These promising results highlight the efficacy of CovidSafe in accurately classifying COVID patients based on CXR images, offering a valuable tool in the fight against the ongoing pandemic.
Substrate promiscuity of key resistance P450s confers clothianidin resistance whilst increasing chlorfenapyr potency in malaria vectors
Authors: Tchouakui Magellan , Ibrahim Sulaiman , Mangoua Mersimine , Thiomela Ricardo , ASSATSE Tatiane , Ngongang-Yipmo Sonia , Muhammad Abdullahi , Mugenzi Leon J. M. , Menze Benjamin , Mzilahowa Themba , Wondji Charles 
Published: January 2024

Abstract
Novel insecticides were recently introduced to counter pyrethroid resistance threats in African malaria vectors. To prolong their effectiveness, potential cross-resistance from promiscuous pyrethroids metabolic resistance mechanisms must be elucidated. Here, we demonstrate that the duplicated P450s, CYP6P9a/b, proficient pyrethroid metabolizers, are reducing neonicotinoid efficacy in Anopheles funestus while exacerbating the potency of Chlorfenapyr. Clothianidin resistance was strongly linked to CYP6P9a/-b genotypes with homozygote resistant individuals significantly surviving more than other genotypes. An additive effect of both genes was observed as double homozygote resistant genotypes were more resistant to clothianidin than other genotype combinations. However, homozygote resistant mosquitoes were more susceptible to chlorfenapyr than homozygote susceptible using laboratory and field tests. Transgenic expression of CYP6P9a/-b in Drosophila confirmed that flies expressing both genes were significantly more resistant to neonicotinoids than controls whereas the contrasting pattern was observed for chlorfenapyr. This phenotype was also confirmed by RNAi knock-down experiments. In vitro expression of recombinant CYP6P9a and metabolism assays established that it significantly depletes both clothianidin and chlorfenapyr, with metabolism of chlorfenapyr producing the insecticidally active intermediate, metabolite tralopyril. This study highlights the risk of cross-resistance between pyrethroid and neonicotinoid and reveals that chlorfenapyr-based control interventions such as Interceptor G2 could remain efficient against some P450-based resistant mosquitoes.
AI Based Model for Prediction of Heavy Metals Using Physio-Chemical Characterization of Agricultural Waste Ashes
Authors: Abbass Wasim , Ahmed Muneeb , Ahmed Ali , Aslam Fahid , Aziz Iram , Mohamed Abdullah 
Published: January 2024

Abstract
The escalating volume of waste materials generated as byproducts is a growing concern in the context of recycling. These waste materials encompass a variety of heavy metals (HMs) that pose significant environmental hazards to plants, animals, and ecosystems. To address that HMs, there was a need to develop an artificial intelligence-based model capable of predicting the presence and quantity of HMs based on the chemical composition of the discards as AWAs. This study delved into a comprehensive analysis of the diverse origins of AWAs, exploring their multifaceted characteristics across different sources. In this research, a total of thirty-two types of SCBA and RHA were accumulated from various sources. The properties and attributes of residual ashes were assessed utilizing various methods of analysis, including X-ray fluorescence (XRF), Fourier-Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscope (SEM), Energy dispersive X-Ray (EDX), X-ray Diffraction Analysis (XRD), Thermogravimetric Analysis / Differential Scanning calorimetry (TGA/DSC), and Atomic Absorption Spectroscopy (AAS). The results were presented in the light of existing literature and standards. The results accordingly revealed that AWAs can be categorized in three fractions based on loss on ignition. At the end some, recommendations for the utilization of SCBA and RHA based on the characterization results were also made for utilization as supplementary material in construction industry. Moreover, the machine learning model was constructed using input variables such as the physio-chemical properties of SCBA and RHA, element properties, and total HMs concentrations to predict the HM fractions. The application of machine learning tool to procured SCBA and RHA revealed that the model utilizing deep neural networks demonstrated performance robustly, possessing strong generalization capabilities (R2 = 0.99 on the testing set), enabling the rapid and accurate prediction of HMs fractions. The element properties were found to be the primary determinant of the HMs fractions. This study adds value to the creation of sustainable approaches for managing waste and provides a framework for the characterization of waste ashes for potential utilize as a primary substance in construction materials.
Brusatol hinders the advancement of bladder cancer by Chac1/Nrf2/SLC7A11 pathway
Authors: Yu Xi , HE ZIQI , WANG QINGHUA , KE SHUAI , WANG HUAXIN , WANG ZHONG , LI SHENGLAN 
Published: January 2024

Abstract
Background
Bladder cancer, also known as BCa, is a common tumor that impacts the urinary system. It is marked by a significant fatality rate and an unfavorable outlook. Promising antineoplastic properties are exhibited by brusatol, which is obtained from the dried ripe fruit of Brucea javanica. The present study aimed to evaluate the influence of brusatol on the advancement of BCa and uncover the molecular mechanism involved.
Materials and methods
We used Cell Counting Kit-8, colony formation and EdU assays to detect cell activity and degree of apoptosis. We used transwell migration assay to detect cell migration and invasion ability. The mechanism of brusatol inhibition of BCa proliferation was studied by flow cytometry, western blotting.
Results
It was revealed that brusatol could reduce the viability and proliferation of BCa cells. A transwell migration assay revealed that brusatol was able to attenuate the invasion and migration of BCa cells. In addition, treatment with RSL3 or ferrostatin-1 enhanced or reversed the brusatol-induced inhibition of BCa cells. While testing for indicators related to iron death it was determined that treatment with brusatol increased the levels of reactive oxygen species, malondialdehyde and Fe2+ in BCa cells. Mechanistically, brusatol induced ferroptosis by upregulating the expression of ChaC glutathione-gpecific gamma-glutamylcyclotransferase (Chac1) in BCa cells. Moreover, treatment with brusatol significantly suppressed the tumor growth in nude mice.
Conclusions
To summarize, the findings of this research demonstrated that brusatol hindered the growth of BCa and triggered ferroptosis by increasing the expression of Chac1. Brusatol has the potential to be considered as a promising candidate for treating BCa.
The Expression and Prognostic Significance of P3H1 in Bladder Cancer: A Bioinformatics Analysis and In Vitro Experiments
Authors: Meng Shuang , Li Zhaowei 
Published: January 2024

Abstract
Background
Bladder cancer (BLCA) is associated with poor prognosis and the identification of new prognostic markers is needed. This study aimed to investigate the significance of Prolyl 3-hydroxylase 1 (P3H1) as a potential prognostic biomarker for BLCA.
Methods
The expression levels of P3H1 in BLCA and normal bladder tissues were analyzed using TCGA database, and P3H1's prognostic value was determined using Kaplan-Meier survival analysis. GSEA analysis was performed to investigate its biological functions. Assessment of the tumor microenvironment was conducted using the CIBERSORT algorithm, and the results were verified through RT-PCR and western blotting. Functional analysis of P3H1 was carried out using Colony-formation, CCK-8, wound healing, transwell, and flow cytometry assays.
Results
The study found that P3H1 was upregulated in BLCA tissues, and elevated expression was linked to an unfavorable prognosis. GSEA analysis revealed putative involvement of P3H1 in the JAK-STAT and WNT signaling pathways. P3H1 expression was also strongly associated with the infiltration of diverse immune cells and immune checkpoints.
Conclusion
Finally, knocking down P3H1 expression effectively suppressed the ability of BLCA cells to proliferate and migrate, while promoting apoptosis. In conclusion, P3H1 has the potential to serve as a prognostic biomarker for BLCA.
Organic Matter Additions for Improved Revegetation of Arsenic-Rich Waste Rock with Planted Boreal Conifers: A Three-Year In Situ Monitoring Study
Authors: Taurines Simon , Guittonny Marie , Séguin Armand 
Published: January 2024

Abstract
Mining waste creates challenging soil conditions that hinder tree establishment for boreal forest restoration. This study investigated the effects of adding topsoil or ramial chipped wood (RCW) on the physicochemical properties of waste rock and the growth and survival of planted native tree species. An randomized-block setup with four treatments and repetitions was established on a gold mine site in western Quebec, Canada in 2018, and planted with Pinus banksiana and Abies balsamea. Results demonstrated that topsoil addition significantly improved height and diameter growth, aerial and root biomasses, survival, and nutrient uptake (N, P, and S) in conifer seedlings, concomitantly to increased water content, decreased pH, and elevated nutrient concentrations in the substrate. However, multivariate analysis revealed that these improved soil conditions alone did not determine the survival and growth of conifer seedlings. In contrast, the application of RCW-based treatments had no discernible impact on the growth and survival of the planted trees. Additionally, topsoil addition effectively reduced the concentration of potentially phytotoxic elements in soil and needles, particularly arsenic. The total arsenic concentration in the mineral substrate (84.1 to 507 µg.g− 1) emerged as a growth-limiting factor for both conifer species. The total concentration of arsenic in the waste rock correlated positively with arsenic accumulation in the tree needles, indicating potential root uptake of this element. This study emphasizes the significance of addressing arsenic availability during reclamation efforts at mine sites. Nonetheless, further research is required to determine the phytotoxic thresholds of arsenic on conifers and its potential metabolic effects.
Evaluation of thalamic volume in patients diagnosed with ESES using the MRI-Cloud method
Authors: Güngör Olcay , Güngör Gülay , Kaban Yunus Emre , Orhan Onur , Aygün Danış , Yüzbaşı Beste Kipçak , Sağtaş Ergin , Egemen Emrah 
Published: January 2024

Abstract
Purpose
Benign epilepsy with centrotemporal spikes (BECTS) is one of the most common epilepsy syndromes in childhood. Accordingly, the main aim of this study was to investigate brain anatomical differences in patients with BECTS using MRI-Cloud, a more objective and quantitative evaluation method for the first time in the literature.
Methods
We included 27 patients diagnosed with BECTS and 27 healthy subjects in the study. We analysed magnetic resonance imaging (MRI) scans of patients diagnosed with BECTS for the first time via the "MRI-Cloud" method.
Results
The mean age of the 54 patients in our study was 8.0 (4–14) years for patients with BECTS, 7.75 (5–14) years for patients with ESES and 8.0 (4–14) years for the control group. Total relative thalamic volume was significantly lower in patients with ESES than in healthy controls (2045.57 ± 343.84 vs. 8775.85 ± 3687.58, p < 0.001), and the total relative thalamic volume was significantly lower in patients with ESES than in patients with BECTS without ESES (2045.57 ± 343.84 vs. 7829.60 ± 3152.85, p < 0.001). The thalamus volumes in patients with ESES were significantly lower than those in patients with BECTS without ESES (right thalamus: 1028 ± 260 vs. 3844 ± 1572, p < 0.001; left thalamus: 1017 ± 150 vs. 3985 ± 2135, p < 0.001).
Conclusion
In this study, we emphasize the contribution of volume analysis and the MRI-Cloud method for detecting microstructural changes at the thalamic level in ESES patients even when conventional MRI findings are normal.
Prediction method for residual stress evolution caused by rolling/sliding contact in ultrasonic vibration-assisted grinding
Authors: Ye Shi Jie , Jun Wen , Tang Jin Yuan , Zhou Wei Hua 
Published: January 2024

Abstract
Ultrasonic vibration-assisted grinding (UVAG) technology has advantages in improving part surface integrity. Currently, the calculation of residual stresses in UVAG typically relies on commercial finite element software. However, because commercial finite element software is not specialized for residual stress calculations, the computational efficiency could be higher, and the accuracy could be better. This paper proposes a semi-analytical residual stress prediction model that considers the Influence of ultrasonic vibration. This paper introduces a semi-analytical residual stress prediction model that considers the impact of ultrasonic vibration. The model calculates the grinding mechanical and thermal stress fields separately using contact mechanics and finite difference methods. Based on Hertz's contact theory and Timoshenko's thermoelastic theory, a correlation model is provided for the association between mechanical and thermal loads with the internal stresses in the workpiece. Subsequently, the residual stress field is solved by considering the thermal-mechanical coupling effects in UVAG. Ultrasonic-assisted grinding experiments were conducted using 12Cr2Ni4A alloy steel as the object, and the maximum error between the predicted residual stresses by the model and the measured residual stresses was 10.5%.In addition, the detailed discussion on the Influence of grinding parameters on residual stress reveals that reducing the grinding wheel speed can significantly increase surface compressive residual stress. The paper's research shows that the residual stress distribution obtained by the new residual stress calculation model is correct, and the calculation efficiency is high, providing a new method for predicting residual stress in UVAG.
Extracellular vesicles of patients on peritoneal dialysis inhibit the TGF-β and PDGF-B mediated fibrotic processes
Authors: Szebeni Beáta , Veres-Székely Apor , Pap Domonkos , Bokrossy Péter , Varga Zoltán , Gaál Aniko , Mihály Judith , Pállinger Éva , Takács István M , Pajtók Csenge , Bernáth Mária , Reusz György S. , Szabó Attila J. , Vannay Ádám 
Published: January 2024

Abstract
Background: Peritoneal fibrosis develops in 50%-80%, and life-threatening encapsulating peritoneal sclerosis (EPS) in 0.5-4.4% of the patients on peritoneal dialysis (PD). Here we investigated the role of extracellular vesicles (EVs) on the transforming growth factor (TGF)-β and platelet derived growth factor (PDGF)-B driven processes of peritoneal fibrosis.
EVs were isolated from the peritoneal dialysis effluent (PDE) of children receiving continuous ambulatory peritoneal dialysis (PD). The impact of PDE-EVs on the epithelial-mesenchymal transition (EMT) and collagen production of the peritoneal mesothelial cells and fibroblasts were investigated in vitro, and in vivo in the chlorhexidine digluconate (CG) induced mice model of peritoneal fibrosis.
Results: PDE-EVs showed spherical morphology in the 100 nm size range, their spectral features, CD63, and annexin positivity was characteristic to EVs. PDE-EVs penetrated into the peritoneal mesothelial cells and fibroblasts and reduced their PDE or PDGF-B induced proliferation. Furthermore, PDE-EVs inhibited the PDE or TGF-β induced EMT and collagen production of the investigated cell types. PDE-EVs contributed to the mesothelial layer integrity and decreased submesothelial thickening of CG-treated mice.
Conclusions: We demonstrated that PDE-EVs significantly inhibit the PDGF-B or TGF-β induced fibrotic processes in vitroand in vivo, suggesting that EVs may contribute to new therapeutic strategies to treat peritoneal fibrosis and other fibroproliferative diseases.
Dynamic Bayesian Network Structure Learning Based on an Improved Bacterial Foraging Optimization Algorithm
Authors: Meng Guanglei , Cong Zelin , Li Tingting , Wang Chenguang , Zhou Mingzhe , Wang Biao 
Published: January 2024

Abstract
With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications.