bluuebunny/crossref_metadata_2025_split huggingface dataset:
Altered Self‐Referential‐Related Brain Regions in Depersonalization‐Derealization Disorder
Authors: Jia Yuan , Song Nan , Ning Yanzhe , Zhu Hong , Dong Linrui , Feng Sitong , Jia Hongxiao , Song Mingkang , Zheng Sisi 
Published: February 2025

ABSTRACTObjectiveWe aimed to explore the alteration in topology and network properties in self‐referential‐related brain regions of individuals with depersonalization–derealization disorders (DPD), using evidence from resting‐state functional magnetic resonance imaging (rs‐fMRI).MethodsWe first determined the regions of interest (ROIs) using Neurosynth, based on which we conducted an ROI‐wise functional connectivity search to create a self‐referential‐related network and performed a topographical analysis. We then compared the analyzed properties from the rs‐fMRI of disordered individuals to those of healthy controls to generate differential properties, based on which we conducted a machine learning‐based disease diagnostic model.ResultsThe study found significant changes in connectivity between brain regions associated with self‐referential processing in individuals with DPD compared to healthy controls. Correlation analysis showed negative correlations between “unreality of surroundings” and connectivity between the left inferior frontal gyrus (IFG) pars orbitalis and left insula and between “perceptual alterations” and connectivity between the left pregenual and subgenual anterior cingulate cortex (ACC). Graph theoretical analysis revealed increased local and global efficiency but decreased characteristic path length. The accuracy of the classification model was 0.885, and the area under the curve was 0.928.ConclusionsIndividuals with DPD showed alterations in brain topography and changes in network properties within self‐referential‐related brain regions; specifically, the changes in cortical midline structures and insula could be related to the underlying mechanism of DPD, highlighting potential targets for future research and therapeutic strategies.
Determining the Diagnostic Value of Venous Sinus Density Indices in Non‐Contrast Brain CT Scan for Early Diagnosis of Cerebral Venous Sinus Thrombosis
Authors: Maghbooli Mehdi , Kermani Mohammad , Sany Seyed Nariman Tavakoli , Arfaei Melina 
Published: February 2025

ABSTRACTBackground and AimA non‐contrast brain CT Non‐contrast computed tomography (NCCT) scan is a valuable and cost‐effective way to detect cerebral venous sinus thrombosis (CVST) during its acute phase. The goal of this study was to evaluate how effective this diagnostic approach is, including its various density indices, to enable a more precise and timely diagnosis of this debilitating condition.MethodThis retrospective case‐control study was conducted on 88 patients with suspected acute CVST. We analyzed NCCT scans of patients with suspected CVST using a Bayesian regression model with a 95% confidence level. We also conducted ROC analysis in R4.1.0 to determine the optimal cut‐off point.ResultWe discovered a significant variance in the average sinus attenuation when comparing patients with acute CVST (p < 0.001). By utilizing an optimal cutoff of 61 HU (Hounsfield unit), we achieved sensitivities of 77.1% and specificities of 92.5% for average sinus attenuation. In addition, the optimal cutoff for standardized parameters included the ratios of HU/H (Hematocrit), HU/ICA (internal carotid artery), HU/BA (basilar artery), HU/FRONTAL lobe, HU/TEMPORAL lobe, and HU–BA, which were 1.41, 1.52, 1.63, 1.6, 1.6, and 23, respectively (p < 0.001). Area under the ROC curve for absolute venous Hounsfield was 0.88.ConclusionNCCT is a reliable diagnostic approach for acute cerebral venous sinus thrombosis in emergency scenarios. Standardized parameters of absolute Hounsfield unit venous sinus thrombosis increase diagnosis accuracy. It is suggested to use these parameters as a complement to each other for more accurate diagnosis.
Effects of Repetitive Transcranial Magnetic Stimulation on Tumor Necrosis Factor Alpha in Neuropsychological Disorders: A Systematic Review and Meta‐Analysis
Authors: Asadizeidabadi Arya , Hosseini Seyedmohammadamin , Pyatkov Artem 
Published: February 2025

ABSTRACTIntroduction:Repetitive transcranial magnetic stimulation (rTMS) shows promise in treating neuropsychological disorders. This systematic review and meta‐analysis investigated the effects of rTMS on inflammatory cytokines and brain‐derived neurotrophic factor (BDNF) under these conditions.Method:We searched five electronic databases for relevant studies. Twelve studies (six randomized controlled trials [RCTs] and six cohort studies) were included in the systematic review, with six RCTs included in the meta‐analysis. The primary outcome was tumor necrosis factor‐alpha (TNF‐α) levels, with secondary outcomes including interleukin‐6 (IL‐6), interleukin‐1 beta (IL‐1β), high‐sensitivity C‐reactive protein (hs‐CRP), interferon‐gamma (IFN‐γ), and BDNF.Results:Meta‐analysis revealed a significant decrease in TNF‐α levels after rTMS (weighted mean difference [WMD]: −6.65, 95% confidence interval [CI]: −10.47–2.83, p < 0.05), with greater effects associated with longer interventions. No significant change was found in the IL‐6 levels. BDNF levels increased significantly (WMD: 7.97, 95% CI: 2.8–13.15, p < 0.05). Qualitative synthesis indicated consistent reductions in IL‐1β. High heterogeneity was observed in some analyses.Conclusion:rTMS may exert therapeutic effects on neuropsychological disorders partly through modulating neuroinflammation and promoting neuroplasticity. However, high heterogeneity and study limitations necessitate larger, more standardized clinical trials to confirm these effects and explore their clinical significance.
Ethical Concerns of AI in Neurosurgery: A Systematic Review
Authors: khan Muhammad Mohsin , Scalia Gianluca , Shah Noman , Umana Giuseppe Emmanuele , Chavda Vishal , Chaurasia Bipin 
Published: February 2025

ABSTRACTBackgroundThe relentless integration of Artificial Intelligence (AI) into neurosurgery necessitates a meticulous exploration of the associated ethical concerns. This systematic review focuses on synthesizing empirical studies, reviews, and opinion pieces from the past decade, offering a nuanced understanding of the evolving intersection between AI and neurosurgical ethics.Materials and MethodsFollowing PRISMA guidelines, a systematic review was conducted to identify studies addressing AI in neurosurgery, emphasizing ethical dimensions. The search strategy employed keywords related to AI, neurosurgery, and ethics. Inclusion criteria encompassed empirical studies, reviews, and ethical analyses published in the last decade, with English language restriction. Quality assessment using Joanna Briggs Institute tools ensured methodological rigor.ResultsEight key studies were identified, each contributing unique insights to the ethical considerations associated with AI in neurosurgery. Findings highlighted limitations of AI technologies, challenges in data bias, transparency, and legal responsibilities. The studies emphasized the need for responsible AI systems, regulatory oversight, and transparent decision‐making in neurosurgical practices.ConclusionsThe synthesis of findings underscores the complexity of ethical considerations in the integration of AI in neurosurgery. Transparent and responsible AI use, regulatory oversight, and mitigation of biases emerged as recurring themes. The review calls for the establishment of comprehensive ethical guidelines to ensure safe and equitable AI integration into neurosurgical practices. Ongoing research, educational initiatives, and a culture of responsible innovation are crucial for navigating the evolving landscape of AI‐driven advancements in neurosurgery.
Determinants of Generative AI in Promoting Green Purchasing Behavior: A Hybrid Partial Least Squares–Artificial Neural Network Approach
Authors: Foroughi Behzad , Naghmeh‐Abbaspour Bita , Wen Jun , Ghobakhloo Morteza , Al‐Emran Mostafa , Al‐Sharafi Mohammed A. 
Published: February 2025

ABSTRACTIn the era of rapid technological advancement, generative artificial intelligence (AI) has emerged as a transformative force in various sectors, including environmental sustainability. This research investigates the factors and consequences of using generative AI to access environmental information and influence green purchasing behavior. It integrates theories such as the information adoption model, value–belief–norm theory, elaboration likelihood model, and cognitive dissonance theory to pinpoint and prioritize determinants of generative AI usage for environmental information and green purchasing behavior. Data from 467 participants were analyzed using a hybrid methodology that blends partial least squares (PLS) with artificial neural networks (ANN). The PLS outcomes indicate that interactivity, responsiveness, knowledge acquisition and application, environmental concern, and ascription of responsibility are key predictors of generative AI use for environmental information. Furthermore, environmental concerns, green values, personal norms, ascription of responsibility, individual impact, and generative AI use emerge as predictors of green purchasing behavior. The ANN analysis offers a unique perspective and discloses variations in the hierarchy of these predictors. This research provides valuable insights for stakeholders on harnessing generative AI to promote sustainable consumer behaviors and environmental sustainability.
Urticalean Rosids Clade (Rosales): Deciphering the Riddle From a Chemical Point of View
Authors: Maciel Jéssica da Silva , de Sousa Adriana Lima , Ramos Carolina Chaves , Feitoza Rodrigo Barbosa Braga , de Oliveira Rodrigo Rodrigues 
Published: February 2025

ABSTRACTCannabaceae, Moraceae, Ulmaceae and Urticaceae are members of the Urticalean rosids clade (Rosales). Their infrafamilial and infrageneric relationships remain poorly understood. The aim of this study was to assess chemical similarities and differences between families of the Urticalean rosids clade based on aromatic phenolic compounds. Analysis of the structural variability of these compounds may help to understand the taxonomic relationships of the clade. Data on the occurrence and type of aromatic phenolic compounds were obtained from the SciFinder database. Parameters of phenolic protection, oxidation and skeletal specialisation were calculated. Multivariate statistical analyses were performed. Flavones and flavonols were found to be chemosystematic markers of the clade. Moraceae showed aromatic phenolic compounds with more specialised skeletons, while Cannabaceae, Ulmaceae and Urticaceae showed regular skeletons. Phenolic protection mechanisms are more specialised in Moraceae (O‐prenylation and O‐methylation), while O‐glycosylation predominated in the other families. Distinct evolutionary trends in structural types and oxidation patterns distinguish Moraceae, suggesting metabolic differences within the clade.
Essential Oil from Magnolia braianensis (Gagnep.) Figlar Leaves Growing Wild in Vietnam: Chemical Analysis, Antimicrobial, and Antioxidant Activities
Authors: Binh Hoang Thi , Linh Le Truc , Van Ngoc Nguyen 
Published: February 2025

ABSTRACTMagnolia braianensis (Gagnep.) Figlar is an endemic species native to Vietnam. To date, its essential oil's (EO's) chemical composition and biological activity have not been studied. This study aimed to investigate the chemical composition and biological activities of the leaf EO from M. braianensis. The chemical composition was analyzed using gas chromatography‐mass spectrometry, while antimicrobial activity was evaluated through a microdilution broth assay. Additionally, the 2,2'‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulfonic acid) (ABTS⁺) and 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) scavenging assays were conducted to assess the EO's antioxidant potential. The results show that twenty‐four compounds were identified in the EO, with oxygenated sesquiterpenes being dominant at 53.6%. The major compounds were α‐selinene (17.9%), flourensadiol (13.7%), germacrene D (10.0%), globulol (9.7%), β‐elemene (8.4%), carotol (5.7%), and pogostol (5.7%). The EO inhibited only Bacillus subtilis and Staphylococcus aureus (IC₅₀: 61.28 ± 2.7 and 169.5 ± 7.98 µg/mL, respectively), showing minimal effect on other strains. The EO demonstrated significant DPPH radical scavenging activity, with an SC₅₀ value of 98.81 µg/mL. However, it exhibited poor antioxidant activity in the ABTS assay, as indicated by an SC₅₀ value exceeding 100 µg/mL. For the first time, this report explores the chemical composition, antimicrobial, and antioxidant potential of EOs extracted from M. braianensis leaves.
Homobifunctional Photocaged 2‐Butene‐1,4‐dials (bis‐PBDAs) as Amino‐Reactive Photo‐Crosslinkers for Biomaterial Fabrication and Cell Fixation
Authors: Li Jiahui , Wang Qi , Yin Mengyuan , Shan Xiajing , Guan Wenli , Lu Kuan , Fan Chaochao , Zhou Yifei , Li Yanjun , Guo Weiwei , Chen Quan , Zhou Chuanzheng 
Published: February 2025

AbstractCrosslinking amine‐rich biomolecules with aldehydes, such as paraformaldehyde (PFA) and glutaraldehyde (GA), is a widely used method for biomaterial fabrication and cell fixation. In this study, we introduce homobifunctional photocaged 2‐butene‐1,4‐dials (bis‐PBDAs) as novel amine‐reactive photo‐crosslinkers. Compared to PFA and GA, bis‐PBDAs are inherently less toxic. Upon photo‐deprotection, bis‐PBDAs efficiently react with the amino groups of biomolecules, such as carboxymethyl chitosan and histone H3, forming stable, well‐defined crosslinks. Even at low concentrations (~100 μM), bis‐PBDAs facilitate effective cell fixation without significantly altering cellular morphology or interfering with biochemical analyses, including protein/mRNA extraction and immunofluorescence imaging. Therefore, bis‐PBDAs are promising reagents for crosslinking amine‐rich biopolymers and hold great potential in biomaterial fabrication and the fixation of living cells and tissues.
Ultrasound‐Guided Percutaneous Sclerotherapy With Bleomycin for Management of Infantile Subcutaneous Hemangioma: A Case Report
Authors: Kiani Iman , Khalili Mitra , Abdollahimajd Fahimeh , Eshghi Peyman , Khameneh Bagheri Arash 
Published: February 2025

ABSTRACTPercutaneous sclerotherapy with bleomycin is an effective, minimally invasive treatment for aggressive pediatric hemangiomas, especially when traditional therapies fail. This approach can improve clinical outcomes, including thrombocytopenia resolution and tumor size reduction, with minimal systemic side effects.
Use of Wax and Resin Patterns in Global Fixed Prosthetic Rehabilitation
Authors: Lefrançois Etienne , Aubault Ludovic , Provost Salomé 
Published: February 2025

ABSTRACTIn clinical situations of complete rehabilitation with fixed prostheses, mastering each parameter of all restorations (shape, marginal adaptation, proximal and occlusal contacts) proves to be a challenge. The large number of restorations involves many potential adjustments that are time‐consuming and lead to final ceramic properties degradation. This clinical report highlights the benefits of using castable patterns in complete fixed prosthetic rehabilitation. Minimal preparations of the teeth were carried out using the conventional mock‐up technique. The impressions were then digitized to produce wax and resin patterns (Castable Wax Resin; Formlabs) with a stereolithography 3D printer (Form 2; Formlabs). During oral try‐in of patterns, adjustments can be carried out to correct any defects of shape, marginal adaptation, proximal, and occlusal contacts. The definitive restorations were directly pressed from the patterns into lithium disilicate glass‐reinforced ceramic (IPS e.max Press; Ivoclar Vivadent) using the lost‐wax casting process. Finally, the restorations were placed permanently in the mouth with complete patient satisfaction after 4 years of follow‐up. This procedure is intended to reduce ceramic adjustments on definitive restorations. It is especially relevant in complete fixed rehabilitation and in situations where the thickness of ceramic restorations does not allow for try‐in before bonding.
A Stable Polyoxovanadate‐based Metal−organic Framework Containing Dual Catalytic Sites for Efficient Synthesis of p–benzoquinones
Authors: Dang Tian‐Yi , Tian Hong‐Rui , Lu Ying , Li Run‐Han , Liu Shu‐Xia 
Published: February 2025

AbstractA novel polyoxovanadate‐based metal−organic framework (POV‐MOF), [Cu(bix)]{V2O6} (V–Cu‐MOF, bix=1,4‐bis(imidazol‐1‐ylmethyl)benzene), has been successfully synthesized and characterized. The V–Cu‐MOF demonstrates exceptional performance in the selective oxidation of 2,3,6‐trimethylphenol to 2,3,5‐trimethyl‐1,4‐benzoquinone within just 10 minutes under mild conditions using hydrogen peroxide as the oxidant. The V–Cu‐MOF exhibits remarkable versatility, efficiently oxidizing various substituted phenols while maintaining high catalytic activity. Additionally, it shows excellent recyclability, with conversion and selectivity consistently exceeding 97 % after multiple cycles. Mechanistic studies reveal that the reaction benefits from dual‐active centers {V2O6}2− clusters and binuclear copper units. The weak substrate adsorption by binuclear copper prevents catalyst poisoning, ensuring sustained activity throughout the process.
BMI Association With Treatment Outcomes in Head and Neck Cancer Patients Receiving Immunotherapy: A Comprehensive Review and Meta‐Analysis
Authors: Saowapa Sakditad , Polpichai Natchaya , Siladech Pharit , Wannaphut Chalothorn , Tanariyakul Manasawee , Wattanachayakul Phuuwadith , Bernal Diego Olavarria , Pleitez Hector Garcia , Tijani Lukman 
Published: February 2025

ABSTRACTBackgroundIn recent years, immunotherapy using immune checkpoint inhibitors (ICIs) has revolutionized the treatment of advanced malignancies. As such, numerous ICIs are establishing themselves as prospective therapy alternatives for individuals with head and neck cancer (HNC). Evidence suggests a potential correlation between body mass index (BMI) and the efficacy of ICIs in cancer patients. However, this association in HNC patients subjected to immunotherapy is still unclear.AimsTo investigate the effect of BMI on the survival outcomes of HNC patients treated with immunotherapy.MethodsPubMed, Web of Science, and Google Scholar databases were searched extensively for records published until January 2024. Full‐text articles aligned with the research objective were included, while records published in English, case reports, reviews, editorials, and studies reporting immunotherapy combined with other cancer therapies were excluded. The data required for review and analysis was abstracted in Excel files by two independent reviewers. Additionally, data synthesis was carried out using the Review Manager program, and evaluation of methodological quality was done with the Newcastle Ottawa scale. The statistical analyses were stratified according to the BMI values, of which patients were categorized as follows: Obese (BMI ≥ 27.5), non‐obese (BMI < 27.5), overweight (BMI: 23.5–27.5), underweight (BMI < 18.5), normal (BMI: 18.5–23.5), low (BMI < 20), and high (BMI ≥ 20).ResultsOnly six studies were reviewed and analyzed. A subgroup analysis of data from these studies showed that obese HNC patients on immunotherapy had significantly better overall survival (OS) rates than non‐obese patients (HR: 0.51; 95% CI: 0.29–0.93; p = 0.03). However, the progression‐free survival (PFS) was statistically similar between obese and non‐obese patients (HR: 0.72; 95% CI: 0.39–1.33; p = 0.30). In addition, when BMI was stratified as either low or high, no significant difference was observed in the OS and PFS of HNC patients (HR: 0.99; 95% CI: 0.59–1.66; p = 0.97 and HR: 0.93; 95% CI: 0.61–1.41; p = 0.42, respectively). Similarly, the statistical analyses showed that overweight patients have similar OS and PFS as patients with normal BMI (HR: 0.53; 95% CI: 0.15–1.92; p = 0.33 and HR: 0.55; 95% CI: 0.20–1.52; p = 0.25, respectively). In contrast, underweight patients demonstrated poor OS and PFS (HR: 2.56; 95% CI: 1.29–5.12; p = 0.008 and HR: 2.76; 95% CI: 1.17–6.52; p = 0.02, respectively).Discussion and ConclusionObese HNC patients on immunotherapy tend to have improved OS than non‐obese patients, while underweight patients have worse clinical prognoses than those with normal or above BMI.
Optimizing Resource Estimation for Scientific Workflows in HPC Environments: A Layered‐Bucket Heuristic Approach
Authors: Alvarenga Luis C. R. , Frota Yuri , de Oliveira Daniel , Coutinho Rafaelli 
Published: February 2025

ABSTRACTAs computational simulations become complex and the amount of processed data grows, executing scientific workflows in High‐Performance Computing (HPC) environments is increasingly essential. However, accurately estimating the required computational resources for such executions presents a significant challenge, requiring a thorough examination of the workflow structure and the characteristics of the computational environment. This manuscript introduces the GraspCC‐LB heuristic, based on the Greedy Randomized Adaptive Search Procedure (GRASP), for estimating the necessary resources for executing scientific workflows in HPC environments. Unlike existing methods, GraspCC‐LB incorporates the layered structure of workflows into its estimation process. The proposed approach was evaluated using real traces of workflows from the fields of bioinformatics and astronomy. The resource estimations produced by GraspCC‐LB were compared against the actual resource usage in a real‐world HPC environment to evaluate its effectiveness. The results demonstrate the effectiveness of GraspCC‐LB as a robust approach for resource optimization in the context of large‐scale scientific workflows that require HPC capabilities.
Cold Start Prediction and Provisioning Optimization in Serverless Computing Using Deep Learning
Authors: Saravana Kumar N. , Selvakumara Samy S. 
Published: February 2025

ABSTRACTServerless computing has emerged as a significant framework for application development, offering benefits such as simplified deployment and enhanced developer productivity. Serverless designs accelerate app development, but user experience and performance are put at risk through the delay during a cold start. In this paper, an optimized concurrent provisioning methodology for the AWS Lambda environment has been proposed along with a cold start prediction technique based on deep learning. Employing historical data and real‐time features like timestamp, invoke frequency, cold start indicator, duration, previous cold starts, event type, historical cold starts, time since the last cold start, and consecutive cold starts, an attention‐based bi‐directional gated recurrent unit (ABiGRU) is used to predict the cold start occurrences with high precision. In the AWS Lambda environment, the proposed DL model was able to predict the cold start likelihood for incoming Lambda invocations very accurately. In addition, the performance of the ABiGRU model is enhanced by hyperparameter tuning using the RMSProp optimizer. The findings of the experiments establish the proposed DL model to perform in the reduction of cold starts compared to the existing approach. Further, the ODL‐CSP technique achieves an accuracy of 90.36%, a precision of 91.87%, a recall of 90.42%, an F1_score of 90.28%, and an MCC of 82.28% when applied to the testing dataset. Additionally, the proposed paradigm optimizes Lambdas using provisioned concurrency, similar to a function warmer. The proposed DL paradigm will eliminate cold start times by early deployment of Lambdas so that the ice age of the serverless architecture is eliminated.