bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep-learning-based extraction of circle of Willis topology with anatomical priors
Authors: Alblas Dieuwertje , Vos Iris N. , Lipplaa Micha M. , Brune Christoph , van der Schaaf Irene C. , Velthuis Mireille R. E. , Velthuis Birgitta K. , Kuijf Hugo J. , Ruigrok Ynte M. , Wolterink Jelmer M. 
Published: December 2024

AbstractThe circle of Willis (CoW) is a circular arrangement of arteries in the human brain, exhibiting significant anatomical variability. The CoW is extensively studied in relation to neurovascular pathologies, with certain anatomical variants previously linked to ischemic stroke and intracranial aneurysms. In an individual CoW, arteries might be absent (aplasia) or underdeveloped (hypoplasia, diameter < 1 mm). As the assessment of such variations is time-consuming and susceptible to subjectivity, robust automatic extraction of personalized CoW topology from time-of-flight magnetic resonance angiography (TOF-MRA) images would highly benefit large-scale clinical investigations. Previous work has sought to extract CoW topology from voxel-based semantic segmentation masks. However, hypoplastic arteries are challenging to recover in voxel-based segmentation. Instead, we propose using a complete CoW as an anatomical prior for extracting all possible CoW arteries as shortest paths between automatically identified anatomical landmarks, guided by automatically determined artery orientation vector fields. These fields are obtained using a scale-invariant and rotation-equivariant mesh-CNN-based model (SIRE). For a 3D TOF-MRA volume, a potentially overcomplete graph of the CoW is thus extracted in which each edge represents an artery. Subsequently, a binary Random Forest classifier labels each artery as normal or hypo-/aplastic. The model was optimized and validated using a data set of 351 3D TOF-MRA scans in a cross-validation setup. We showed that using a shortest path algorithm with a cost function based on local artery orientations results in continuous artery paths, even in hypoplastic cases. We tracked the correct path in the posterior communicating arteries in 70–74% of the cases, an artery that is known to pose challenges in voxel-based segmentation models. Our downstream artery path classifier obtained an average F1 score of 0.91, demonstrating the potential of our proposed framework to extract personalized CoW topology automatically.
Role of P2X7 receptor in the progression and clinicopathological characteristics of gastric cancer
Authors: Zhang Wen-jun , Pi Xiong-wei , Le Yi-guan , Li Teng-zheng 
Published: December 2024

AbstractP2X7 receptor (P2X7R) plays a role in regulating tumor progression, but it is unclear whether P2X7R affects the pathological characteristics of patients with gastric cancer and the activity of gastric cancer cells. Therefore, this study preliminarily investigated the relationship between P2X7R and clinicopathological features of patients with gastric cancer, and further explored the effect of P2X7R on the proliferation, migration and invasion of gastric cancer cells through functional experiments. The results showed that P2X7R was highly expressed in gastric cancer tissues and gastric cancer cells. High expression of P2X7R was closely related to lymphatic metastasis, vascular invasion and Tumor-Node-Metastasis (TNM) stage in patients with gastric cancer. High expression of P2X7R predicted poor overall survival in patients. Moreover, the activation of P2X7R by ATP and its analogue BzATP increased the calcium current of gastric cancer cells, enhanced YF actin stress and cell viability, and promoted the proliferation, migration and invasion of gastric cancer cells. While P2X7R antagonists (A438079 and AZD9056) inhibited the proliferation, migration and invasion of gastric cancer cells induced by ATP. Therefore, the data obtained in this experiment suggest that P2X7R may be another potential molecular target for the prevention and treatment of gastric cancer.
Estimation of the size, density, and demographic distribution of the UK pet dog population in 2019
Authors: McMillan Kirsten M. , Harrison Xavier A. , Wong David C. , Upjohn Melissa M. , Christley Robert M. , Casey Rachel A. 
Published: December 2024

AbstractThere is limited knowledge about the size of the UK dog population. This makes it difficult to reliably monitor population dynamics and management. A repeatable method of measuring the UK dog population, including owned and unowned dogs i.e., those housed long term by animal welfare charities, would help inform interventions to reduce risks to dog welfare such as poor breeding practices and the illegal import of young or pregnant dogs. However, estimating the total dog population in the UK is not straightforward. Although several estimates of dog numbers have been previously suggested, differing methodologies and limited public access to data sources makes comparison and replication difficult. In this study, we compiled a comprehensive dataset of UK pet dogs in 2019, from multiple sources, including a breed registry, veterinary corporations, pet insurance companies, animal welfare charities and an academic institution. Using a hierarchical Bayesian N-mixture model, we estimate 12.64 million (mean, 95% CI 8.54–15.16 million; median, 13.03 million, 95% CI 8.51–15.24 million) owned and unowned pet dogs within the UK in 2019. Estimates were modelled at the postcode area scale, allowing for aggregation to region or country level, as well as providing associated metrics of uncertainty. Furthermore, we provide spatial demographic estimates, regarding age, breed, cephalic index, and body size. Establishing a population baseline offers significant analytical benefits to welfare, veterinary, epidemiological, and business stakeholders alike: as it provides the spatial data required to underpin robust canine welfare strategies and campaigns. We plan to repeat the described process, in order to provide ongoing estimates: allowing for the interrogation of changes to the UK pet dog population over space and time.
A systematic review of circulating IP-10/CXCL10 in patients with Plasmodium infections in relation to disease severity
Authors: Kotepui Manas , Mahittikorn Aongart , Masangkay Frederick Ramirez , Kotepui Kwuntida Uthaisar 
Published: December 2024

AbstractInterferon γ-induced protein 10 kDa (IP-10) or C–X–C motif chemokine 10 (CXCL10) is produced and secreted from specific leukocytes such as neutrophils, eosinophils, and monocytes, which play key roles in the immune response to Plasmodium infections. This systematic review aimed to collate and critically appraise the current evidence on IP-10 levels in malaria patients. It provided insights into its role in malaria pathogenesis and potential as a biomarker for Plasmodium infections and disease severity. The protocol for this systematic review was registered in PROSPERO (number CRD42024556087). A comprehensive literature search was conducted across multiple databases, including Embase, PubMed, Scopus, Ovid, ProQuest, and MEDLINE, to identify relevant studies examining the role of IP-10 in patients with Plasmodium infections. A narrative synthesis was applied to summarize key findings and to provide an overview of the relationship between IP-10/CXCL10 levels and Plasmodium infection and disease severity. A total of 1933 records were identified, and 26 studies were included in the synthesis. The studies collectively indicated that IP-10 levels are elevated in patients with Plasmodium infections compared to healthy or non-malarial controls. Most studies reported that increased IP-10 levels were associated with increased disease severity. However, a few studies found no significant difference or decreased levels in patients with severe Plasmodium infections compared to those with uncomplicated or mild malaria. Additionally, several studies indicated that IP-10 levels were elevated in cerebral malaria. The systematic review suggests that IP-10 is elevated in patients with Plasmodium infections. However, the variability in findings across different studies regarding the association between IP-10 and severe malaria, particularly cerebral malaria, highlights the need for further comprehensive studies. Addressing confounding factors will be crucial in future research to better understand the role of IP-10 in Plasmodium infections and the pathogenesis of severe disease.
Biocompatible ionized air alleviates rat osteoarthritis by modulating polarization from M1 to M2 macrophages
Authors: Yu Haoran , Ding Chengbiao , Hu Zhongyao , Liu Qi , Gu Xuesong , He Junyan , Yan Yiqun , Yu Shenrui , Gao Lin , Cheng Wendan , Wu Zhengwei , Jing Juehua 
Published: December 2024

AbstractThe imbalance in the proportion of M1/M2 macrophage polarization is a crucial contributor to the persistent progression of osteoarthritis (OA). This study aimed to evaluate the effects of low-dose biocompatible ionized air (BIA) on macrophage polarization and its subsequent chondroprotective effects, thereby validating the potential of BIA in slowing the progression of OA. In vitro experiments demonstrated that BIA modulates the polarization of M1 macrophages toward the M2 phenotype via the ROS-mediated STAT6 pathway. This shift reduces the expression of pro-inflammatory mediators while increasing the expression of anti-inflammatory mediators and pro-chondrogenic factors, leading to an improved microenvironment surrounding chondrocytes. The direct benefits of this improved microenvironment include enhanced chondrocyte viability, inhibition of apoptosis, and reduced degradation of the extracellular matrix. In vivo studies in rats showed that BIA inhibited M1 macrophage infiltration in the synovium, upregulated the proportion of M2 macrophages, alleviated cartilage degeneration, and delayed OA progression. This gas-based regulatory strategy may open new avenues for the treatment of OA.
Effects of exercise on vascular remodelling and fetal growth in uncomplicated and abortion-prone mouse pregnancies
Authors: Lovell Evangeline A. K. , Hosking Shanna L. , Groome Holly M. , Moldenhauer Lachlan M. , Robertson Sarah A. , Gatford Kathryn L. , Care Alison S. 
Published: December 2024

AbstractStudies in humans and rodents show exercise in pregnancy can modulate maternal blood pressure, vascular volume, and placental efficiency, but whether exercise affects early uteroplacental vascular adaptations is unknown. To investigate this, CBA/J female mice mated with BALB/c males to generate healthy uncomplicated pregnancies (BALB/c-mated) or mated with DBA/2J males to generate abortion-prone pregnancies (DBA/2J-mated), were subjected to treadmill exercise (5 days/week, 10 m/min, 30 min/day for 6 weeks before and throughout pregnancy), or remained sedentary. In uncomplicated pregnancies, exercise caused symmetric fetal growth restriction in fetuses evidenced by reductions in fetal weight, crown-to-rump length, abdominal girth and biparietal diameter. Placental insufficiency was indicated by reduced fetal: placental weight ratio and increased glycogen cell content in the junctional zone of placentas of exercised BALB/c-mated mice on gestational day (GD)18.5. In abortion-prone pregnancy, exercise increased placental efficiency, but the number of late-pregnancy resorptions were elevated. Effects of paternal genotype independent of exercise were evidenced by a greater number of resorptions, poorer spiral artery remodelling, and larger placentas in the DBA/2J-mated compared to BALB/c-mated mice. Effects of exercise independent of paternal genotype included increased implantation sites at both mid and late pregnancy, accompanied by decreased junctional zone areas of placentas. Our findings show that exercise before and during pregnancy in mice can have different effects on fetal outcomes, depending on the paternal and/or fetal genotype. This suggests that the underlying mechanisms are responsive to fetal cues.
Symmetry breaking organizes the brain’s resting state manifold
Authors: Fousek Jan , Rabuffo Giovanni , Gudibanda Kashyap , Sheheitli Hiba , Petkoski Spase , Jirsa Viktor 
Published: December 2024

AbstractSpontaneously fluctuating brain activity patterns that emerge at rest have been linked to the brain’s health and cognition. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a mechanistic description of the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major data features across scales and imaging modalities. These include spontaneous high-amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability, and characteristic functional connectivity dynamics. When aggregated across cortical hierarchies, these match the profiles from empirical data. The understanding of the brain’s resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function. In addition, it shifts the focus from the single recordings towards the brain’s capacity to generate certain dynamics characteristic of health and pathology.
Research on floating object classification algorithm based on convolutional neural network
Authors: Yang Jikai , Li Zihan , Gu Ziyan , Li Wei 
Published: December 2024

AbstractWith the advancement of artificial intelligence technology, unmanned boats utilizing deep learning models have shown significant potential in water surface garbage classification. This study employs Convolutional Neural Network (CNN) to extract features of water surface floating objects and constructs the VGG16-15 model based on the VGG-16 architecture, capable of identifying 15 common types of water surface floatables. A garbage classification dataset was curated to obtain 5707 images belonging to 15 categories, which were then split into training and validation sets in a 4:1 ratio. Customized improvements were made on the base VGG-16 model, including adjusting the neural network structure to suit 15 floating object categories, applying learning rate decay and early stopping strategies for model optimization, and using data augmentation to enhance model generalization. By tweaking certain parameters, the study analyzed the impact of the number of epochs and batch sizes on the model’s classification effectiveness. The results show that the model achieves the best performance with 20 epochs and a batch size of 64, reaching a recognition accuracy of 93.86%. This is a 10.09% improvement over the traditional VGG-16 model and a 4.91% increase compared to the model without data augmentation, demonstrating the effectiveness of model improvements and data augmentation in enhancing image recognition capabilities. Additionally, the few-shot test demonstrates the fine-tuned model’s improved generalization capability. This research illustrates the applicability of transfer learning in the task of water surface garbage classification and provides technical support for the application of unmanned boats in environmental protection.
A machine learning-based clinical predictive tool to identify patients at high risk of medication errors
Authors: Abdo Ammar , Gallay Lyse , Vallecillo Thibault , Clarenne Justine , Quillet Pauline , Vuiblet Vincent , Merieux Rudy 
Published: December 2024

AbstractA medication error is an inadvertent failure in the drug therapy process that can cause serious harm to patients by increasing morbidity and mortality and are associated with significant economic costs to the healthcare system. Medication reconciliation is the most cost-effective intervention and can result in a 66% reduction in medication errors. To improve patient safety, we developed a machine learning-based tool that prioritizes patients at risk of medication errors upon admission to the hospital to ensure that they undergo medication reconciliation by clinical pharmacists. The data were collected from the electronic health records of patients admitted to Reims University Hospital who underwent medication reconciliation between 2017 and 2023. The data from 7200 patients were used to train four machine learning-based models based on 52 variables in the development dataset. These models were used to prioritize admitted patients according to their likelihood of being exposed to a medication error. Our models, particularly the voting classifier model, demonstrated good performance (a recall of 0.75, precision of 0.65, F1 score of 0.70, AUROC of 0.74 and AUCPR of 0.75). In a retrospective evaluation simulating real-life use, the voting classifier model successfully identified 45% of the total patients selected who were found to have at least one unintended discrepancy compared to 21% when using the existing tool. The positive experimental results of this tool showed a superior improvement of 113% over the existing tool by targeting patients at risk of medication errors upon admission to Reims University Hospital.
Exploring salinity induced adaptations in marine diatoms using advanced photonic techniques
Authors: Cvjetinovic Julijana , Bedoshvili Yekaterina D. , Davidovich Nickolai A. , Maksimov Eugene G. , Prikhozhdenko Ekaterina S. , Todorenko Daria A. , Bodunova Daria V. , Davidovich Olga I. , Sergeev Igor S. , Gorin Dmitry A. 
Published: December 2024

AbstractPhotonic-based methods are crucial in biology and medicine due to their non-invasive nature, allowing remote measurements without affecting biological specimens. The study of diatoms using advanced photonic methods remains a relatively underexplored area, presenting significant opportunities for pioneering discoveries. This research provides a comprehensive analysis of marine diatoms, specifically Nitzschia sp., across varying salinity levels, integrating fluorescence lifetime imaging microscopy (FLIM), combined photoacoustic and fluorescence tomographies (PAFT), and ultrastructural examinations using transmission electron microscopy. Key findings include a systematic shift in the mean fluorescence lifetime from 570 ps at 20‰ to 940 ps at 80‰, indicating functional adaptations in chlorophyll molecules within light-harvesting complexes. At 60‰ salinity, anomalies are observed in the development of silica valves and polysaccharide layers, suggesting abnormalities in valve morphogenesis. Lipid droplets within the cells display a minimum diameter at 40‰, indicating metabolic adjustments to osmotic stress. The intensity of both fluorescence and photoacoustic signals increases with increasing salinity levels. These insights enhance understanding of the ecological implications of salinity stress on diatom communities and pave the way for future research on leveraging the unique adaptive mechanisms of microalgae for environmental monitoring and sustainable biotechnological applications.
Exploratory testing of functional blood oxygenation level dependent-MRI to image the renoprotective effect of Remote Ischaemic PreConditioning during partial nephrectomy
Authors: Heuzeroth Frederick , Wetterauer Christian , Boll Daniel , Westhoff Timm H. , Dreher Maeve , Seifert Helge , Rentsch Cyrill , Ebbing Jan 
Published: December 2024

AbstractUrinary biomarker studies in cardiothoracic and kidney-sparing surgery have demonstrated renal protection by Remote Ischaemic PreConditioning (RIPC). RIPC intervention generates cycles of ischaemia and reperfusion of the limbs before the actual ischaemia of the target organ (e.g. kidney) is initiated. This explorative trial aims to investigate whether Blood Oxygenation Level Dependent-MRI (BOLD-MRI) can be a suitable technique to image and quantify the renoprotective effect of RIPC on ischaemia/reperfusion injury (IRI) after partial nephrectomy (PN). Overall, 15 patients were enrolled in this randomized controlled trial. Randomization was 1:1, with RIPC in the intervention arm. Urinary neutrophil gelatinase-associated lipocalin (NGAL), a sensitive biomarker for renal tubular damage was measured preoperatively and for the first 5 days after surgery. Functional BOLD-MRI was successfully performed preoperatively and 48 h after PN in 11 patients. BOLD-MRI uses ∆R2* to express acute tubular damage induced by IRI. The more the ∆R2* values have decreased postoperatively, the more damage the renal tubuli have taken. The cumulative urinary concentration of NGAL in the first 5 postoperative days was significantly lower in the RIPC group (p = 0.02) as compared to the control arm, indicating that the RIPC maneuver performed was effective. The highest difference was seen 6 h after surgery with NGAL being 65% lower in the RIPC arm. IRI of the operated kidney expressed by ∆R2* in BOLD-MRI was 2.1 times less pronounced in the RIPC group as compared to the noRIPC group (∆R2* in % preop/postop RIPC: 14.73/12.57 vs. noRIPC 16.33/11.82, p = 0.36). We were able to demonstrate the potential of BOLD-MRI in measuring IRI. For the first time, it was shown that the renoprotective effects of RIPC can be visualized and measured using BOLD-MRI. Larger studies are required to validate these initial findings.
Temporal evolution of insecticide resistance and bionomics in Anopheles funestus, a key malaria vector in Uganda
Authors: Oruni Ambrose , Tchouakui Magellan , Tagne Carlos S. Djoko , Hearn Jack , Kayondo Jonathan , Wondji Charles S. 
Published: December 2024

Abstract
Insecticide resistance escalation is decreasing the efficacy of vector control tools. Monitoring vector resistance is paramount in order to understand its evolution and devise effective counter-solutions. In this study, we monitored insecticide resistance patterns, vector population bionomics and genetic variants associated with resistance over 3 years from 2021 to 2023 in Uganda. Anopheles funestus s.s was the predominant species in Mayuge but with evidence of hybridization with other species of the An. funestus group. Sporozoite infection rates were relatively very high with a peak of 20.41% in March 2022. Intense pyrethroid resistance was seen against pyrethroids up to 10-times the diagnostic concentration but partial recovery of susceptibility in PBO synergistic assays. Among bednets, only PBO-based nets (PermaNet 3.0 Top and Olyset Plus) and chlorfenapyr-based net (Interceptor G2) had high mortality rates. Mosquitoes were fully susceptible to chlorfenapyr and organophosphates, moderately resistant to clothianidin and resistant to carbamates. The allele frequency of key P450, CYP9K1, resistance marker was constantly very high but that for CYP6P9A/b were very low. Interestingly, we report the first detection of resistance alleles for Ace1 gene (RS =  ~ 13%) and Rdl gene (RS =  ~ 21%, RR =  ~ 4%) in Uganda. The qRT-PCR revealed that Cytochrome P450s CYP9K1, CYP6P9A, CYP6P9b, CYP6P5 and CYP6M7 were consistently upregulated while a glutathione-S-transferase gene (GSTE2) showed low expression. Our study shows the complexity of insecticide resistance patterns and underlying mechanisms, hence constant and consistent spatial and temporal monitoring is crucial to rapidly detect changing resistance profiles which is key in informing deployment of counter interventions.