bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and evaluation of the recombinant BP26 protein-based C-ELISA for human brucellosis diagnosis
Authors: Xie Yujia , Lin Shaoqing , Guo Liping , Qi Xinru , Zhao Shiqi , Pei Qichuan , Chen Yixiao , Wu Qi , Wang Yun , Yao Meixue , Yin Dehui 
Published: January 2025

IntroductionTimely and accurate diagnosis is crucial for the effective treatment and prevention of brucellosis. Current serological diagnostics, primarily based on lipopolysaccharide (LPS), suffer from cross-reactivity with other Gram-negative bacteria, which limits their specificity. Periplasmic protein 26 (BP26), a highly immunogenic antigen found in Brucella, has emerged as a promising alternative for enhancing diagnostic specificity. This study aimed to develop and evaluate a competitive enzyme-linked immunosorbent assay (C-ELISA) utilizing monoclonal antibodies against BP26 for the diagnosis of human brucellosis, thereby providing a more accurate and specific diagnostic approach.MethodsThe study produced monoclonal antibody (mAb) against the BP26 protein through traditional mouse hybridoma technology and developed the C-ELISA method, and compared with a C-ELISA method based on LPS mAb. The detection performance was validated through the analysis of 190 human serum samples, which included 95 brucellosis serum samples and 95 negative serum samples collected by the Xuzhou Center for Disease Control and Prevention, and a comparative analysis was conducted on the diagnostic efficacy of indirect ELISA for brucellosis using both BP26 and LPS-based methods.ResultsThe BP26 mAb based C-ELISA achieved 100% sensitivity and specificity in detecting human brucellosis, significantly outperforming the C-ELISA based LPS mAb. Furthermore, the accuracy of the indirect enzyme-linked immunosorbent assay (I-ELISA) using BP26 protein was 98.95%, compared to an accuracy of LPS diagnosis was 99.47%. These results indicated that the BP26 mAb can effectively and accurately detected human brucellosis infections.ConclusionThis study successfully developed and evaluated a BP26 protein-based C-ELISA method for diagnosing human brucellosis, establishing a foundation for identifying alternative diagnostic antigens for brucellosis.
Unveiling and validating biomarkers related to the IL-10 family in chronic sinusitis with nasal polyps: insights from transcriptomics and single-cell RNA sequencing analysis
Authors: Liu Xinghong , Peng Yi , Guo Ling , Xiong Weilan , Liao Weijiang , Fan Jiangang 
Published: January 2025

IntroductionExtensive efforts have been made to explore members of the IL-10 family as potential therapeutic strategies for various diseases; however, their biological role in chronic rhinosinusitis with nasal polyps (CRSwNP) remains underexplored.MethodsGene expression datasets GSE136825, GSE179265, and GSE196169 were retrieved from the Gene Expression Omnibus (GEO) for analysis. Candidate genes were identified by intersecting differentially expressed genes (DEGs) between the CRSwNP and control groups (DEGsall) with those between the high- and low-score groups within the CRSwNP cohort (DEGsNP). Biomarker selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and the Boruta algorithm. Further refinement of biomarkers was carried out using receiver operating characteristic (ROC) analysis, with genes demonstrating an area under the curve (AUC) greater than 0.7 being considered significant. Genes exhibiting consistent expression trends and significant differences across both GSE136825 and GSE179265 were selected as potential biomarkers. Cell-type annotation was performed on GSE196169, and the expression profiles of the biomarkers across various cell types were analyzed. A competing endogenous RNA (ceRNA) network and a biomarker-drug interaction network were also established. Additionally, the mRNALocater database was utilized to determine the cellular localization of the identified biomarkers.ResultsThe intersection of 1817 DEGsall and 24 DEGsNP yielded 15 candidate genes. Further filtering through LASSO, SVM-RFE, and Boruta led to the identification of seven candidate biomarkers: PRB3, KRT16, MUC6, SPAG4, FGFBP1, NR4A1, and GSTA2. Six of these genes demonstrated strong diagnostic performance in GSE179265, while four biomarkers, showing both significant differences and consistent expression trends, were validated in both GSE179265 and GSE136825. Single-cell sequencing analysis of GSE196169 revealed seven distinct cell types, including endothelial cells, with the biomarkers predominantly expressed in epithelial cells. The ceRNA network comprised nine nodes and eleven edges, with only FGFBP1 exhibiting a complete lncRNA-miRNA-mRNA interaction.DiscussionThis study identifies several novel biomarkers and their associated drugs for CRSwNP therapy, as well as potential therapeutic targets, such as spiperone and arnenous acid, identified through molecular docking. Ultimately, this work underscores the identification of four IL-10 family-related biomarkers, providing a theoretical foundation for future clinical research in CRSwNP.
Sleep structure of short-term insomnia disorder with mild cognitive impairment in older adults and their correlation with cognitive function: a case-control study
Authors: Zeng Jinkun , Wei Jia , Qi Ruobing 
Published: January 2025

ObjectivesThis study seeks to delineate the sleep architecture characteristics in older adults with short-term insomnia and mild cognitive impairment (MCI) and to explore their association with cognitive performance.MethodsNinety elderly individuals with short-term insomnia were enrolled and stratified into two cohorts based on their Montreal Cognitive Assessment (MoCA) scores: the Short-Term Insomnia Group (STID) comprising 35 participants and the Short-Term Insomnia with Cognitive Impairment Group (STID-MCI) with 55 participants. Demographic data, Pittsburgh Sleep Quality Index (PSQI), MoCA, Hamilton Depression Rating Scale (HAMD-17), Hamilton Anxiety Rating Scale (HAMA), and polysomnography (PSG) parameters were compared between groups. Correlations between MoCA scores and PSG metrics were also analyzed.ResultsNo significant disparities were noted between groups in terms of HAMD-17, HAMA, and PSQI scores (p &gt; 0.05). However, marked differences were identified in MoCA scores and its subdomains (p &lt; 0.05). Significant variations were also observed in the duration and proportion of slow-wave sleep (N3) between groups (p &lt; 0.05). In STID-MCI patients, memory scores correlated positively with N3 duration and percentage (p &lt; 0.05), while verbal functions and attention were positively associated with rapid eye movement (REM) sleep duration.ConclusionThis study highlights the potential of PSG in the clinical assessment of cognitive function and underscores the need for targeted interventions to improve sleep quality in this vulnerable population.
Architectural planning robot driven by unsupervised learning for space optimization
Authors: Zhang Zhe , Zheng Yuchun 
Published: January 2025

IntroductionSpace optimization in architectural planning is a crucial task for maximizing functionality and improving user experience in built environments. Traditional approaches often rely on manual planning or supervised learning techniques, which can be limited by the availability of labeled data and may not adapt well to complex spatial requirements.MethodsTo address these limitations, this paper presents a novel architectural planning robot driven by unsupervised learning for automatic space optimization. The proposed framework integrates spatial attention, clustering, and state refinement mechanisms to autonomously learn and optimize spatial configurations without the need for labeled training data. The spatial attention mechanism focuses the model on key areas within the architectural space, clustering identifies functional zones, and state refinement iteratively improves the spatial layout by adjusting based on learned patterns. Experiments conducted on multiple 3D datasets demonstrate the effectiveness of the proposed approach in achieving optimized space layouts with reduced computational requirements.Results and discussionThe results show significant improvements in layout efficiency and processing time compared to traditional methods, indicating the potential for real-world applications in automated architectural planning and dynamic space management. This work contributes to the field by providing a scalable solution for architectural space optimization that adapts to diverse spatial requirements through unsupervised learning.
Cycloastragenol promotes dorsal column axon regeneration in mice
Authors: Zihan Tao , Wenwen Teng , Yanxia Ma , Saijilafu  
Published: January 2025

IntroductionCycloastragenol (CAG) has a wide range of pharmacological effects, including anti-inflammatory, antiaging, antioxidative, and antitumorigenic properties. In addition, our previous study showed that CAG administration can promote axonal regeneration in peripheral neurons. However, whether CAG can activate axon regeneration central nervous system (CNS) remains unknown.MethodsHere, we established a novel mouse model for visualizing spinal cord dorsal column axon regeneration involving the injection of AAV2/9-Cre into the lumbar 4/5 dorsal root ganglion (DRG) of Rosa-tdTomato reporter mice. We then treated mice by intraperitoneal administration of CAG.ResultsOur results showed that intraperitoneal CAG injections significantly promoted the growth of vitro-cultured DRG axons as well as the growth of dorsal column axons over the injury site in spinal cord injury (SCI) mice. Our results further indicate that CAG administration can promote the recovery of sensory and urinary function in SCI mice.ConclusionTogether, our findings highlight the therapeutic potential of CAG in spinal cord injury repair.
A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults
Authors: Abe Daisu , Inaji Motoki , Hase Takeshi , Suehiro Eiichi , Shiomi Naoto , Yatsushige Hiroshi , Hirota Shin , Hasegawa Shu , Karibe Hiroshi , Miyata Akihiro , Kawakita Kenya , Haji Kohei , Aihara Hideo , Yokobori Shoji , Maeda Takeshi , Onuki Takahiro , Oshio Kotaro , Komoribayashi Nobukazu , Suzuki Michiyasu , Maehara Taketoshi 
Published: January 2025

ObjectiveNeurological deterioration after mild traumatic brain injury (TBI) has been recognized as a poor prognostic factor. Early detection of neurological deterioration would allow appropriate monitoring and timely therapeutic interventions to improve patient outcomes. In this study, we developed a machine learning model to predict the occurrence of neurological deterioration after mild TBI using information obtained on admission.MethodsThis was a retrospective cohort study of data from the Think FAST registry, a multicenter prospective observational study of elderly TBI patients in Japan. Patients with an admission Glasgow Coma Scale (GCS) score of 12 or below or who underwent surgical treatment immediately upon admission were excluded. Neurological deterioration was defined as a decrease of 2 or more points from a GCS score of 13 or more within 24 h of hospital admission. The model predictive accuracy was judged with the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC), and the Youden index was used to determine the cutoff value.ResultsA total of 421 of 721 patients registered in the Think FAST registry between December 2019 and May 2021 were included in our study, among whom 25 demonstrated neurological deterioration. Among several machine learning algorithms, eXtreme Gradient Boosting (XGBoost) demonstrated the highest predictive accuracy in cross-validation, with an AUROC of 0.81 (±0.07) and an AUPRC of 0.33 (±0.08). Through SHapley Additive exPlanations (SHAP) analysis, five important features (D-dimer, fibrinogen, acute subdural hematoma thickness, cerebral contusion size, and systolic blood pressure) were identified and used to construct a better performing model (cross-validation AUROC of 0.84 and AUPRC of 0.34; testing data AUROC of 0.77 and AUPRC of 0.19). At the cutoff value from the Youden index, the model showed a sensitivity, specificity, and positive predictive value of 60, 96, and 38%, respectively. When neurosurgeons attempted to predict neurological deterioration using the same testing data, their values were 20, 94, and 19%, respectively.ConclusionIn this study, our predictive model showed an acceptable performance in detecting neurological deterioration after mild TBI. Further validation through prospective studies is necessary to confirm these results.
Oxidative imbalance as a co-player in jaw functional limitations and biopsychosocial profile in patients with temporomandibular disorder—myofascial pain with referral
Authors: Kuć Joanna , Szarejko Krzysztof Dariusz , Maciejczyk Mateusz , Dymicka-Piekarska Violetta , Żendzian-Piotrowska Małgorzata , Zalewska Anna 
Published: January 2025

IntroductionTemporomandibular disorders have a multifactorial etiology including biological, biomechanical, neuromuscular, and biopsychosocial factors. Current research on temporomandibular disorders focuses on identifying clinically relevant biomarkers thus creating a new way of thinking about this dysfunction. The aim of the study was to determine the relationship between salivary/blood concentrations of oxidative/nitrosative stress biomarkers and biopsychosocial findings in patients with temporomandibular disorder—myofascial pain with referral.MethodsThe sample enrolled a total of 26 individuals with temporomandibular myofascial pain with referral (twenty women, six men). The procedure included clinical examination according to the Diagnostic Criteria for Temporomandibular Disorders, saliva and blood collection. Biochemical analysis concerned, among others, the content of reduced glutathione, uric acid, total antioxidant capacity, advanced glycation end products, malondialdehyde, total lipid hydroperoxides, kynurenine, N-formylkynurenine, and peroxynitrite. All determinations were considered with respect to the Patient Health Questionnaire-4 (PHQ-4), Patient Health Questionnaire-9 (PHQ-9), Patient Health Questionnaire-15 (PHQ-15), Generalized Anxiety Disorder-7 (GAD-7), Jaw Functional Limitation Scale-20 (JFLS-20), Perceived Stress Scale-10 (PSS-10), and Beck Depression Inventory (BDI).Results and discussionThe average age of participants was 24.2 ± 1.23. High content of kynurenine and N-formylkynurenine in plasma was related to intensified psychological distress (PHQ-4) and anxiety (GAD-7). Low concentration of plasma malondialdehyde and total lipid hydroperoxides was linked with severe somatization (PHQ-15) and stress (PSS-10), respectively. Reduced levels of non-enzymatic antioxidants were associated with greater jaw functional mobility restrictions as well as limited mastication and communication factor with respect to JFLS-20. These findings indicate that oxidative stress biomarkers are significantly related to the biopsychosocial profile in patients with temporomandibular disorder.
Leveraging deep learning for robust EEG analysis in mental health monitoring
Authors: Liu Zixiang , Zhao Juan 
Published: January 2025

IntroductionMental health monitoring utilizing EEG analysis has garnered notable interest due to the non-invasive characteristics and rich temporal information encoded in EEG signals, which are indicative of cognitive and emotional conditions. Conventional methods for EEG-based mental health evaluation often depend on manually crafted features or basic machine learning approaches, like support vector classifiers or superficial neural networks. Despite the potential of these approaches, they often fall short in capturing the intricate spatiotemporal relationships within EEG data, leading to lower classification accuracy and poor adaptability across various populations and mental health scenarios.MethodsTo overcome these limitations, we introduce the EEG Mind-Transformer, an innovative deep learning architecture composed of a Dynamic Temporal Graph Attention Mechanism (DT-GAM), a Hierarchical Graph Representation and Analysis (HGRA) module, and a Spatial-Temporal Fusion Module (STFM). The DT-GAM is designed to dynamically extract temporal dependencies within EEG data, while the HGRA models the brain's hierarchical structure to capture both localized and global interactions among different brain regions. The STFM synthesizes spatial and temporal elements, generating a comprehensive representation of EEG signals.Results and discussionOur empirical results confirm that the EEG Mind-Transformer significantly surpasses conventional approaches, achieving an accuracy of 92.5%, a recall of 91.3%, an F1-score of 90.8%, and an AUC of 94.2% across several datasets. These findings underline the model's robustness and its generalizability to diverse mental health conditions. Moreover, the EEG Mind-Transformer not only pushes the boundaries of state-of-the-art EEG-based mental health monitoring but also offers meaningful insights into the underlying brain functions associated with mental disorders, solidifying its value for both research and clinical settings.
Postoperative evaluation of visual and cognitive functions following cataract surgery in patients with age-related cataracts: a prospective longitudinal study
Authors: Zhao Chenzhu , Li Xuan , Deng Bolin , Shui Bingyue , Zhou Lin , Wu Zhengzheng 
Published: January 2025

IntroductionCataracts are associated with a decline in both cognitive and visual functions. This study examines postoperative changes in cognitive and visual functions in patients with age-related cataracts, focusing on the differential effects of unilateral and bilateral cataract surgeries on these functions. Additionally, the study evaluates changes in cognitive function following cataract surgery in individuals with mild cognitive impairment (MCI).MethodsA cohort of patients (n = 35, 59 eyes) aged 60 years and older (69.9 ± 7.0 years) with age-related cataracts who underwent unilateral or bilateral cataract surgery between May and June 2024 was selected. Cognitive and visual functions were evaluated preoperatively and at 1 week, 1 month, and 3 months postoperatively. Cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA). Visual function was assessed using a binocular visual function testing system based on virtual reality (VR) technology, which evaluated low spatial frequency suppression, simultaneous vision, stereopsis, and perceptual eye position under 3D viewing conditions without glasses. Based on preoperative MoCA scores, patients were classified into cognitively normal and mild cognitive impairment (MCI) groups.ResultsPatients with age-related cataracts demonstrated significant improvements in both cognitive and visual functions at 1 week, 1 month, and 3 months postoperatively, compared to preoperative assessments (p &lt; 0.05). Specifically, both the bilateral surgery group and the MCI group exhibited substantial improvements in cognitive function at these time points (p &lt; 0.05). Additionally, the bilateral surgery group outperformed the unilateral surgery group in cognitive function throughout the follow-up period (p &lt; 0.05). In terms of visual function, the bilateral surgery group showed significant improvements in low spatial frequency suppression, simultaneous vision, and stereopsis at 1 week, 1 month, and 3 months postoperatively, compared to preoperative measurements (p &lt; 0.05).ConclusionBoth cognitive and visual functions significantly improved after cataract surgery. Bilateral cataract surgery is more effective in increasing the cognitive functions than unilateral surgery. Additionally, cataract surgery plays a critical role in facilitating cognitive recovery in patients with mild cognitive impairment (MCI).
Parental intentions and requests to provide pain care for their infants in neonatal intensive care units
Authors: Shimizu Aya , Arimitsu Takeshi , Harada Kana , Ozawa Mio 
Published: January 2025

PurposeThe Japan Association of Neonatal Nursing evaluated the pain care provided by parents to their infants admitted to the neonatal intensive care unit (NICU). However, further collaborations with families based on family-centered care are necessary to clarify the parental intentions and requests regarding pain care for their infants. This study aimed to describe the experiences and content of nonpharmacological pain care provided by parents to their infants, the intentions and requests of parents regarding each type of recommended pain care (irrespective of whether they had provided pain care at the NICU), and the reasons for their hesitation to implement specific pain management methods.MethodsA total of 108 parents with NICU-hospitalized infants, including 66 (65.6%) infants with a birth weight of &lt;1,000 g, voluntarily responded to an anonymous self-administered online electronic survey. Sociodemographic and clinical data were quantitatively analyzed.ResultsIn our study population, 30.6% (N = 33) had provided pain care to their infants, 56.5% (N = 61) hoped to provide pain care in the future, and 40.7% (N = 44) expected advice for pain care options from healthcare professionals (HCPs). Swaddling, facilitated tucking, and skin-to-skin contact were the most popular options (≥60%). By contrast, the use of sucrose and breastfeeding (both 13.0%), skin-to-skin contact (7%), and use of expressed breast milk and non-nutritive sucking (both 3.7%) were less frequently used due to indifference or doubts, lack of knowledge about pain care, differences between recommended pain care methods and parental values, and pain care methods being inappropriate for the child's condition.ConclusionsThis survey demonstrated that when parents provide pain care for their children in the NICU, they are required to make choices based on the advice and knowledge offered by HCPs, taking into account the diverse values of parents as well as the overall condition of their infant and their breastfeeding status. Therefore, we suggest that HCPs support parents in choosing not only the recommended care but also the most appropriate pain care for the condition of their infant.