bluuebunny/crossref_metadata_2025_split huggingface dataset:
Early-stage effect of HIBD on neuro-motor function and organic composition of neurovascular units in neonatal rats
Authors: Mo Yanjun , Zeng Ying , Huo Luyao , Liu Gang , Tao Jingwei , Jiang Yu , Zhao Tuo , Zhou Zhuoluo , Mu Xiaohong 
Published: November 2023

ObjectiveThis study aimed to investigate the effects of neonatal hypoxic–ischemic brain damage (HIBD) on early-stage neuro-motor function, cerebral blood flow, and the neurovascular unit.MethodsTwenty-four Sprague–Dawley newborn rats aged 7 days were obtained and randomly assigned to either the sham or the model group using a random number table. The HIBD model was established using the Rice-Vannucci method. After the induction of HIBD, the body weight of the rats was measured and their neuro-motor function was assessed. Further, cerebral blood flow perfusion was evaluated using laser speckle flow imaging, and immunofluorescent staining techniques were employed for examining the activation of specific markers and their morphological changes in different cell populations, which included vascular endothelial cells, neurons, astrocytes, and microglia within the motor cortex.ResultsAfter HIBD, the model group exhibited impaired neuro-motor function and growth. Cerebral blood flow perfusion decreased in both the hemispheres on day 1 and in the ipsilateral brain on day 4. However, no significant difference was observed between the two groups on day 7. Moreover, the CD31 and NeuN showed a sharp decline on day 1, which was followed by a gradual increase in the expression levels. The activated microglia and astrocytes formed clusters in the injured cortex. Notably, the regions with positive staining for Arg-1, Iba-1, CD68, and GFAP consistently displayed higher values in the model group as compared to that in the sham group. The total number of branch endpoints and microglia branches was higher in the model group than in the sham group. Immunofluorescent co-localization analysis revealed no co-staining between Iba-1 and Arg-1; however, the Pearson’s R-value for the co-localization of Iba-1 and CD68 was higher in the model group, which indicated an increasing trend of co-staining in the model group.ConclusionEarly-stage neuro-motor function, cerebral blood flow, microvasculature, and neurons in neonatal rats exhibited a trend of gradual recovery over time. The activation and upregulation of neuroglial cells continued persistently after HIBD. Furthermore, the impact of HIBD on early-stage neuro-motor function in newborn rats did not synchronize with the activation of neuroglial cells. The recovery of neuro-motor function, microvasculature, and neurons occurred earlier than that of neuroglial cells.
MFA-UNet: a vessel segmentation method based on multi-scale feature fusion and attention module
Authors: Cao Juan , Chen Jiaran , Gu Yuanyuan , Liu Jinjia 
Published: November 2023

IntroductionThe accurate segmentation of retinal vessels is of utmost importance in the diagnosis of retinal diseases. However, the complex vessel structure often leads to poor segmentation performance, particularly in the case of microvessels.MethodsTo address this issue, we propose a vessel segmentation method composed of preprocessing and a multi-scale feature attention network (MFA-UNet). The preprocessing stage involves the application of gamma correction and contrast-limited adaptive histogram equalization to enhance image intensity and vessel contrast. The MFA-UNet incorporates the Multi-scale Fusion Self-Attention Module(MSAM) that adjusts multi-scale features and establishes global dependencies, enabling the network to better preserve microvascular structures. Furthermore, the multi-branch decoding module based on deep supervision (MBDM) replaces the original output layer to achieve targeted segmentation of macrovessels and microvessels. Additionally, a parallel attention mechanism is embedded into the decoder to better exploit multi-scale features in skip paths.ResultsThe proposed MFA-UNet yields competitive performance, with dice scores of 82.79/83.51/84.17/78.60/81.75/84.04 and accuracies of 95.71/96.4/96.71/96.81/96.32/97.10 on the DRIVE, STARE, CHASEDB1, HRF, IOSTAR and FIVES datasets, respectively.DiscussionIt is expected to provide reliable segmentation results in clinical diagnosis.
Attention-based multi-semantic dynamical graph convolutional network for eeg-based fatigue detection
Authors: Liu Haojie , Liu Quan , Cai Mincheng , Chen Kun , Ma Li , Meng Wei , Zhou Zude , Ai Qingsong 
Published: November 2023

IntroductionEstablishing a driving fatigue monitoring system is of utmost importance as severe fatigue may lead to unimaginable consequences. Fatigue detection methods based on physiological information have the advantages of reliable and accurate. Among various physiological signals, EEG signals are considered to be the most direct and promising ones. However, most traditional methods overlook the functional connectivity of the brain and fail to meet real-time requirements.MethodsTo this end, we propose a novel detection model called Attention-Based Multi-Semantic Dynamical Graph Convolutional Network (AMD-GCN). AMD-GCN consists of a channel attention mechanism based on average pooling and max pooling (AM-CAM), a multi-semantic dynamical graph convolution (MD-GC), and a spatial attention mechanism based on average pooling and max pooling (AM-SAM). AM-CAM allocates weights to the input features, helping the model focus on the important information relevant to fatigue detection. MD-GC can construct intrinsic topological graphs under multi-semantic patterns, allowing GCN to better capture the dependency between physically connected or non-physically connected nodes. AM-SAM can remove redundant spatial node information from the output of MD-GC, thereby reducing interference in fatigue detection. Moreover, we concatenate the DE features extracted from 5 frequency bands and 25 frequency bands as the input of AMD-GCN.ResultsFinally, we conduct experiments on the public dataset SEED-VIG, and the accuracy of AMD-GCN model reached 89.94%, surpassing existing algorithms.DiscussionThe findings indicate that our proposed strategy performs more effectively for EEG-based driving fatigue detection.
Topological properties and connectivity patterns in brain networks of patients with refractory epilepsy combined with intracranial electrical stimulation
Authors: Sun Yulei , Shi Qi , Ye Min , Miao Ailiang 
Published: November 2023

ObjectiveAlthough intracranial electrical stimulation has emerged as a treatment option for various diseases, its impact on the properties of brain networks remains challenging due to its invasive nature. The combination of intracranial electrical stimulation and whole-brain functional magnetic resonance imaging (fMRI) in patients with refractory epilepsy (RE) makes it possible to study the network properties associated with electrical stimulation. Thus, our study aimed to investigate the brain network characteristics of RE patients with concurrent electrical stimulation and obtain possible clinical biomarkers.MethodsOur study used the GRETNA toolbox, a graph theoretical network analysis toolbox for imaging connectomics, to calculate and analyze the network topological attributes including global measures (small-world parameters and network efficiency) and nodal characteristics. The resting-state fMRI (rs-fMRI) and the fMRI concurrent electrical stimulation (es-fMRI) of RE patients were utilized to make group comparisons with healthy controls to identify the differences in network topology properties. Network properties comparisons before and after electrode implantation in the same patient were used to further analyze stimulus-related changes in network properties. Modular analysis was used to examine connectivity and distribution characteristics in the brain networks of all participants in study.ResultsCompared to healthy controls, the rs-fMRI and the es-fMRI of RE patients exhibited impaired small-world property and reduced network efficiency. Nodal properties, such as nodal clustering coefficient (NCp), betweenness centrality (Bc), and degree centrality (Dc), exhibited differences between RE patients (including rs-fMRI and es-fMRI) and healthy controls. The network connectivity of RE patients (including rs-fMRI and es-fMRI) showed reduced intra-modular connections in subcortical areas and the occipital lobe, as well as decreased inter-modular connections between frontal and subcortical regions, and parieto-occipital regions compared to healthy controls. The brain networks of es-fMRI showed a relatively weaker small-world structure compared to rs-fMRI.ConclusionThe brain networks of RE patients exhibited a reduced small-world property, with a tendency toward random networks. The network connectivity patterns in RE patients exhibited reduced connections between cortical and subcortical regions and enhanced connections among parieto-occipital regions. Electrical stimulation can modulate brain network activity, leading to changes in network connectivity patterns and properties.
Autism spectrum disorder-specific changes in white matter connectome edge density based on functionally defined nodes
Authors: Weber Clara F. , Lake Evelyn M. R. , Haider Stefan P. , Mozayan Ali , Bobba Pratheek S. , Mukherjee Pratik , Scheinost Dustin , Constable Robert T. , Ment Laura , Payabvash Seyedmehdi 
Published: November 2023

IntroductionAutism spectrum disorder (ASD) is associated with both functional and microstructural connectome disruptions. We deployed a novel methodology using functionally defined nodes to guide white matter (WM) tractography and identify ASD-related microstructural connectome changes across the lifespan.MethodsWe used diffusion tensor imaging and clinical data from four studies in the national database for autism research (NDAR) including 155 infants, 102 toddlers, 230 adolescents, and 96 young adults – of whom 264 (45%) were diagnosed with ASD. We applied cortical nodes from a prior fMRI study identifying regions related to symptom severity scores and used these seeds to construct WM fiber tracts as connectome Edge Density (ED) maps. Resulting ED maps were assessed for between-group differences using voxel-wise and tract-based analysis. We then examined the association of ASD diagnosis with ED driven from functional nodes generated from different sensitivity thresholds.ResultsIn ED derived from functionally guided tractography, we identified ASD-related changes in infants (pFDR≤ 0.001–0.483). Overall, more wide-spread ASD-related differences were detectable in ED based on functional nodes with positive symptom correlation than negative correlation to ASD, and stricter thresholds for functional nodes resulted in stronger correlation with ASD among infants (z= −6.413 to 6.666,pFDR≤ 0.001–0.968). Voxel-wise analysis revealed wide-spread ED reductions in central WM tracts of toddlers, adolescents, and adults.DiscussionWe detected early changes of aberrant WM development in infants developing ASD when generating microstructural connectome ED map with cortical nodes defined by functional imaging. These were not evident when applying structurally defined nodes, suggesting that functionally guided DTI-based tractography can help identify early ASD-related WM disruptions between cortical regions exhibiting abnormal connectivity patterns later in life. Furthermore, our results suggest a benefit of involving functionally informed nodes in diffusion imaging-based probabilistic tractography, and underline that different age cohorts can benefit from age- and brain development-adapted image processing protocols.
Identification and discovery of imaging genetic patterns using fusion self-expressive network in major depressive disorder
Authors: Pang Mengqian , Liu Xiaoyun , Hao Xiaoke , Wang Meiling , Xie Chunming , Zhang Li , Yuan Yonggui 
Published: November 2023

IntroductionMajor depressive disorder (MDD) is a prevalent mental illness, with severe symptoms that can significantly impair daily routines, social interactions, and professional pursuits. Recently, imaging genetics has received considerable attention for understanding the pathogenesis of human brain disorders. However, identifying and discovering the imaging genetic patterns between genetic variations, such as single nucleotide polymorphisms (SNPs), and brain imaging data still present an arduous challenge. Most of the existing MDD research focuses on single-modality brain imaging data and neglects the complex structure of brain imaging data.MethodsIn this study, we present a novel association analysis model based on a self-expressive network to identify and discover imaging genetics patterns between SNPs and multi-modality imaging data. Specifically, we first build the multi-modality phenotype network, which comprises voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. Then, we apply intra-class similarity information to construct self-expressive networks of multi-modality phenotype features via sparse representation. Subsequently, we design a fusion method guided by diagnosis information, which iteratively fuses the self-expressive networks of multi-modality phenotype features into a single new network. Finally, we propose an association analysis between MDD risk SNPs and the multi-modality phenotype network based on a fusion self-expressive network.ResultsExperimental results show that our method not only enhances the association between MDD risk SNP rs1799913 and the multi-modality phenotype network but also identifies some consistent and stable regions of interest (ROIs) multi-modality biological markers to guide the interpretation of MDD pathogenesis. Moreover, 15 new potential risk SNPs highly associated with MDD are discovered, which can further help interpret the MDD genetic mechanism.DiscussionIn this study, we discussed the discriminant and convergence performance of the fusion self-expressive network, parameters, and atlas selection.
Generating Shigella that internalize into glioblastoma cells
Authors: Shipley Austin , Frampton Gabriel , Davies Bryan W. , Umlauf Benjamin J. 
Published: November 2023

IntroductionThe use of microorganisms as drug delivery systems to treat cancer has expanded recently, including FDA approval of certain viruses as oncolytics. Microorganisms have several unique benefits compared to traditional pharmacologic agents including dose independence, the ability to produce therapeutic proteins locally within the tumor, and simplicity of administration. However, current microbial delivery systems such as AAV9 and herpes virus have limited cassette sizes, minimal cancer cell selectivity, and low innate cytotoxicity. To address these issues, we sought to generate a strain of Shigella flexneri to selectively internalize into glioblastoma (GBM) brain tumor cells as an initial step to generating a bacterial-based drug delivery system.MethodsWe generated S. flexneri that selectively internalize into GBM cells using iterative co-cultured assays.ResultsAfter 50 rounds of co-culture, the new strain infected 95 percent of GBM cells in 2 hours. GBM-infecting Shigella demonstrate a 124-fold preference for internalizing in nine different GBM cell lines compared to Normal Astrocytes (NA) controls. Additionally, we developed an in-cell western to identify GBM-infecting Shigella clones that preferentially internalize in patient samples without iterative co-culture. Finally, we demonstrate internalization into GBM cells is mediated via a factor modified by myristoylation.DiscussionIn conclusion, here we present a novel bacterial platform that preferentially internalizes in brain tumor cells. This system provides numerous potential benefits over current interventions and other microbial strategies for treating brain tumors.
Case Report: Renal artery stenosis in children: ultrasound as a decisive diagnostic and therapy-accompanying technique!
Authors: Brasseler Maire , Finkelberg Ilja , Müntjes Carsten , Cetiner Metin 
Published: November 2023

IntroductionRenal artery stenosis in children is rare, and the recommended diagnostic algorithm, including techniques such as catheter-based angiography, CT angiography, magnetic resonance angiography, and ultrasound, is controversial in pediatric cohorts.Case presentationWe report a case of an 11-year-old girl with renal artery stenosis in whom ultrasonography played a decisive role in confirming the diagnosis and accompanying therapeutic percutaneous transluminal renal artery angioplasty.ConclusionImproved ultrasound techniques and the examiner’s experience contribute to improving renal artery stenosis diagnosis in children. In particular, localized sensitive blood flow velocity analysis indicates the advantages of ultrasound compared to other imaging modalities in renal artery stenosis. Therefore, ultrasound should be a focus of future study designs addressing the search for the best diagnostic algorithm.SummaryThe advantages of ultrasound techniques in pediatric patients with renal artery stenosis compared to other imaging modalities are highlighted.
Performance of deamidated gliadin peptide antibodies as first screening for celiac disease in the general pediatric population
Authors: Al-Hussaini Abdulrahman , Al-Jurayyan Abdullah , Alharbi Sahar , Salman Bashir Muhammed , Troncone Riccardo 
Published: November 2023

BackgroundCeliac serology has evolved, with the identification of newer antibodies against deamidated gliadin peptides (DGP) [e.g., anti-DGP, immunoglobulin A (IgA), and immunoglobulin G (IgG) types] with sensitivity and specificity in detecting celiac disease (CeD) that are equivalent to anti-tissue transglutaminase [anti-tissue transglutaminase (TTG) IgA]-based tests, particularly in populations with high pretest probability of CeD (prevalence of CeD &gt; 50% of the population under study). This opens the possibility that anti-DGP assays can be used to identify CeD in the general population where the prevalence of CeD is very low (≈1%).ObjectiveThis study aimed (1) to determine the diagnostic performance of DGP antibodies-based serologic assays in identifying CeD during the screening of the general population and (2) to compare the levels of anti-DGP antibodies among CeD patients with mild and severe degrees of enteropathy.MethodsSerology tests for DGP antibodies (DGP-IgA, DGP-IgG, and conjugate TTG/DGP antibodies) were performed on 104 serum samples of positive TTG-IgA (100 confirmed and four potential celiac patients) and a randomly selected 1,000 negative TTG-IgA serum samples collected during mass screening of children (aged 6–15 years) in 2014–2015.ResultsSera from 32 of the 1,000 TTG-IgA negative serum specimens (3.2%) tested positive for one or more of the three anti-DGP serology tests. A total of 13 of the 32 anti-DGP seropositive patients had persistent positive results on follow-up samples in 2020 (1.3%). Eight of the 13 underwent endoscopy with biopsies, and only two had confirmed CeD (both DGP-IgG positive) (0.2%). The sensitivity and specificity of the serology assays were as follows: DGP-IgA (62.7%, 40%), DGP-IgG (80.4%, 100%), and conjugate TTG/DGP (96%, 10%). Based on receiver operating characteristic curves, the area under the curve for DGP-IgG (0.919; 95% CI −0.00406 to 0.114) was comparable to TTG-IgA (0.974; 95% CI 0.924–0.995) (P = 0.0679). Titers of antibodies to DGPs were significantly higher in children with severe intestinal damage than in those in children with mild lesions (P &lt; 0.001).ConclusionThe TTG-IgA assay remains the most reliable screening serology test for CeD in mass screening studies. The performance of TTG-IgA has improved marginally by adding DGP-IgG to the mass screening protocol. In CeD patients detected by mass screening, the anti-DGP antibody titer was significantly higher among patients with a severe degree of enteropathy as compared to the group with mild enteropathy.
Prediction of trough concentration and ALK occupancy in plasma and cerebrospinal fluid using physiologically based pharmacokinetic modeling of crizotinib, alectinib, and lorlatinib
Authors: Li Bole , Liu Shan , Feng Honglei , Du Chunshuang , Wei Liman , Zhang Jie , Jia Guangwei , Wu Chunnuan 
Published: November 2023

Backgrounds: Brain metastases occur in approximately 30% of patients with non-small-cell lung cancer (NSCLC). Therefore, the free drug concentration in cerebrospinal fluid (CSF) is strongly associated with the clinical efficacy.Purpose: The present study aimed to develop physiologically based pharmacokinetic (PBPK) models that can predict the steady-state trough concentration (Ctrough) in plasma and CSF, as well as anaplastic lymphoma kinase (ALK) occupancy (AO), for three inhibitors: crizotinib (CRI), alectinib (ALE), and lorlatinib (LOR).Methods: To achieve this, population PBPK models were successfully developed and validated using multiple clinical pharmacokinetics (PK) and drug–drug interaction (DDI) studies, both in healthy subjects and patients.Results: The prediction-to-observation ratios for plasma AUC, Cmax, and Ctrough in heathy subjects and patients ranged between 0.5 and 2.0. In addition, PK profiles of CRI, ALE, and LOR in CSF aligned well with observed data. Moreover, the AUC and Cmax ratios of the three inhibitors when co-administered with CYP3A4 inhibitors/inducers also matched with clinically observed values. Utilizing PK thresholds for effective plasma Ctrough and AO values on wild-type and four ALK mutations in plasma and CSF, PBPK models were then combined with the mean and 95% confidence interval to predict optimal dosing regimens.Conclusions: Overall, these PBPK models provide valuable insights into determining appropriate dosing regimens for the three ALK inhibitors, understanding their effectiveness in brain metastasis therapy, and analyzing the underlying mechanisms of on-target resistance.