bluuebunny/crossref_metadata_2025 huggingface dataset:
Comprehensive Analysis of Predictors and Outcomes in Breast Cancer Screening in Romania: Insights from Demographic, Clinical, and Lifestyle Factors
Authors: Burciu Oana Maria , Sas Ioan , Merce Adrian-Grigore , Cerbu Simona , Moatar Aurica Elisabeta , Merce Adrian-Petru , Cobec Ionut Marcel
Published: February 2025

Background/Objectives: The primary purpose of this study is to provide a more in-depth insight into various demographic, clinical, and lifestyle factors in relation to breast cancer and to predict the extent to which certain variables described as “predictors” might lead to further investigation. By analyzing a large cohort, we are able to provide valuable and up-to-date information on breast cancer screening, support breast specialists, and further enhance international screening guidelines. Methods: We screened for breast cancer in a population of women aged 50 to 69 years by using the standardized breast cancer imaging screening method (breast mammography) and ultrasonography as a complementary imagistic tool, and we compared the results with the gold standard, breast biopsy. For this, 58,760 women with no known history of breast cancer coming from 4 major regions of Romania (North-East, North-West, South-East, and West) were first evaluated through mammography. Out of these, 3197 women with positive mammograms subsequently underwent a breast ultrasound examination. The remaining 688 patients with positive breast ultrasound were further referred for a breast biopsy. Results: The statistical analysis revealed several predictors such as the body mass index (BMI), positive family medical history of breast cancer, age at first birth, and age at menopause that influenced the progression from mammography (first stage of the screening program) towards echography (additional imaging modality). Furthermore, we established that age, age at first birth, and BMI are significant predictors of progression from echography towards biopsy (the last stage of the screening program). Furthermore, by analyzing the number of positive biopsies (688) out of the total number of patients in the study (58,760), we calculated a total breast cancer detection rate of 8 per 1000 patients. Lastly, by studying the patient demographics in the context of breast cancer (BC) screening, we observed that participants coming from an urban environment presented a higher rate of positive mammographic results as compared to ones of rural provenience. Conclusions: Our study analyzed a large cohort of patients and offers real world data which shows that multiple factors were positively associated with an increased risk of BC. Older age, older age at first birth, and an older menopausal age are all estrogen-dependent risk factors that were linked with an increased breast cancer risk in our study. Furthermore, our findings concerning the rural/urban disparities and regional differences highlight the need for region-specific interventions to address lifestyle risk factors, improve healthcare access, and enhance breast cancer screening and follow-up protocols, particularly in underserved areas like the North-East and South-East regions.
Comparison of Gestational Age Assessment Methods in the Second and Third Trimesters: Evaluating Alternative Approaches Against Ultrasound in Urban Burkina Faso
Authors: Ouattara Cheick Ahmed , Compaoré Anderson , Ouédraogo Lionel Olivier , Ouédraogo Moctar , Ouattara Hermann , Coulibaly Moussa , Deng Lishi , Nikiéma Zakari , Hanley-Cook Giles T. , Argaw Alemayehu , Huybregts Lieven , Hadush Kokeb Tesfamariam , Lachat Carl , Toe Laeticia Celine , Dailey-Chwalibóg Trenton
Published: February 2025

Background: Accurate determination of gestational age by way of ultrasound is challenging in resource-limited settings like Burkina Faso, leading to the use of alternative methods, though their accuracy and agreement remain poorly established. This practice leads to inadequate risk assessment during pregnancy and failure to identify preterm birth, potentially contributing to high neonatal mortality rates. The purpose of this study was to determine the agreement among alternative methods for gestational age estimation and the Alliance for Maternal and Newborn Health Improvement (AMANHI) method in Burkina Faso. Methods: Data were obtained from a prospective cohort study involving pregnant women in the second or third trimester in Bobo-Dioulasso to evaluate the agreement of last menstrual period (LMP), symphysis-fundal height (SFH), Dubowitz, Hadlock, and AMAHNI methods to estimate gestational age. The degree of agreement was assessed using the Bland–Altman method and intraclass correlation coefficients. The AMANHI method, validated for late pregnancy, was used as the reference standard. Results: A total of 768 pregnant women were included in the analysis. Plots showed a lack of agreement between the AMANHI method and all other methods, with 95% limits of agreement ranging from −7.6 to +9.8 weeks. Additionally, the incidence of preterm birth was consistently higher when assessed using the alternative methods compared with the AMANHI method. Conclusions: The clinical methods (SFH, LMP, Dubowitz) disagree with the ultrasound methods (AMANHI, Hadlock), but the ultrasound methods produce more similar results. The routine application of other methods is likely to result in an overestimation of preterm birth incidence compared with AMANHI. These findings highlight the urgent need to improve access to obstetric ultrasound and to provide comprehensive training in the application of the AMANHI method for accurate late-term gestational age estimation in Burkina Faso.
Multiparametric Outcome Assessment After Transcatheter Aortic Valve Implantation—A Systematic Review
Authors: Świątoniowska-Lonc Natalia , Klausa Filip , Ściborski Krzysztof , Wysokińska-Kordybach Agnieszka , Banasiak Waldemar , Doroszko Adrian
Published: February 2025

Backround/Objectives: Aortic stenosis (AS) is the most commonly acquired valvular disorder. Patient risk stratification and the development of an accurate and reliable tool are crucial in identifying suitable candidates for TAVI. The present review summarized the current state of knowledge on the influence of selected factors on the outcomes and course of patients with AS undergoing transcatheter aortic valve implantation (TAVI). Methods: The inclusion criteria for the present systematic review were as follows: (1) studies indexed in the medical databases PubMed, MEDLINE, EMBASE, CINAHL, Web of Science, and Scopus; (2) full-text articles available in English; (3) papers published between 2013 and 2023; and (4) addressing the topic of assessing the impact of factors on the outcomes of patients with aortic stenosis undergoing TAVI. This review used PRISMA 2020 reporting guidelines for systematic reviews and meta-analyses. Results: One hundred and thirty-two studies were eligible for this review. The available studies showed an association of psychosocial and socioeconomic factors, valve parameters, comorbidities, clinical factors, treatment-related factors, biomarkers, and treatment methods with the outcomes of patients with AS undergoing TAVI. Conclusions: Given the conflicting results obtained regarding the impact of right ventricular dysfunction, paravalvular leaks, and treatment method on the mortality of patients undergoing aortic valve implantation, further research in these areas is needed. In view of the researchers’ differing views on some of the factors affecting patient outcomes after TAVI, further analysis is needed to develop a new tool for assessing predictive outcomes in AS patients. This study is registered at PROSPERO (CRD42024612752).
Prediction of Postoperative Pain and Side Effects of Patient-Controlled Analgesia in Pediatric Orthopedic Patients Using Machine Learning: A Retrospective Study
Authors: Joe Young-Eun , Ha Nayoung , Lee Woojoo , Byon Hyo-Jin
Published: February 2025

Background/Objectives: Appropriate postoperative management, especially in pediatric patients, can be challenging for anesthesiologists. This retrospective study used machine learning to investigate the effects and complications of patient-controlled analgesia (PCA) in children undergoing orthopedic surgery. Methods: The medical records of children who underwent orthopedic surgery in a single tertiary hospital and received intravenous and epidural PCA were analyzed. Predictive models were developed using machine learning, and various demographic, anesthetic, and surgical factors were investigated to predict postoperative pain and complications associated with PCA. Results: Data from 1968 children were analyzed. Extreme gradient boosting effectively predicted moderate postoperative pain for the 6–24-h (area under curve (AUC): 0.85, accuracy (ACC): 0.79) and 24–48-h (AUC: 0.89, ACC: 0.87) periods after surgery. The factors that predicted moderate postoperative pain included the pain score immediately before the measurement period, the total amount of opioid infused, and age. For predicting side effects during the 6–24-h period after surgery, a least absolute shrinkage and selection operator model (AUC: 0.75, ACC: 0.64) was selected, while a random forest model (AUC: 0.91, ACC: 0.87) was chosen for the 24–48-h period post-surgery. The factors that predicted complications included the occurrence of side effects immediately before the measurement period, the total amount of opioid infused before the measurement period, and age. Conclusions: This retrospective study introduces machine-learning-based models and factors aimed at forecasting moderate postoperative pain and complications of PCA in children undergoing orthopedic surgery. This research has the potential to enhance postoperative pain management strategies for children.
Joint Probability Distribution of Wind–Wave Actions Based on Vine Copula Function
Authors: Wu Yongtuo , Feng Yudong , Zhao Yuliang , Yu Saiyu
Published: February 2025

During its service life, a deep-sea floating structure is likely to encounter extreme marine disasters. The combined action of wind and wave loads poses a threat to its structural safety. In this study, elliptical copula, Archimedean copula, and vine copula models are employed to depict the intricate dependence structure between wind and waves in a specific sea area of the Shandong Peninsula. Moreover, hourly significant wave height, spectral peak period, and 10 m average wind speed hindcast data from 2004 to 2023 are utilized to explore the joint distribution of multidimensional parameters and environmental design values. The results indicate the following: (1) There exists a significant correlation between wind speed and wave parameters. Among them, the C-vine copula model represents the optimal trivariate joint distribution, followed by the Gaussian copula, while the Frank copula exhibits the poorest fit. (2) Compared with the high-dimensional symmetric copula models, the vine copula model has distinct advantages in describing the dependence structure among several variables. The wave height and period demonstrate upper tail dependence characteristics and follow the Gumbel copula distribution. The optimal joint distribution of wave height and wind speed is the t copula distribution. (3) The identification of extreme environmental parameters based on the joint probability distribution derived from environmental contour lines is more in line with the actual sea conditions. Compared with the design values of independent variables with target return periods, it can significantly reduce engineering costs. In conclusion, the vine copula model can accurately identify the complex dependency characteristics among marine variables, offering scientific support for the reliability-based design of floating structures.
Firm Complexity and the Accuracy of Auditors’ Going Concern Opinions in Emerging Markets: Does Auditor Work Stress Matter?
Authors: Saleh Safaa , Diab Ahmed , Abouelela Osama
Published: February 2025

This study examines the direct and indirect effects of firm complexity on the accuracy of auditors’ going concern opinion (GCAO), and whether and how auditors’ work stress (AWS) can serve as a mediating variable in such a relationship. We analyzed a sample of 705 firm-year observations from 105 non-financial firms listed on the Egyptian Stock Exchange between 2017 and 2023. Binary logistic regression, OLS regression, and path analysis were employed to test the study hypotheses. The results suggested that firm complexity is negatively associated with GCAO accuracy but positively associated with AWS. Furthermore, a negative relationship was observed between AWS and GCAO accuracy. Finally, the analysis revealed that AWS mediates the relationship between firm complexity and GCAO accuracy. The findings remained robust across various sensitivity tests. Policymakers, audit firms, and investors can benefit from the findings, which emphasize the necessity of AWS mitigation techniques to improve GCAO accuracy and ultimately contribute to transparent financial reporting. This study provides unique evidence from a developing country on how firm complexity can indirectly impact the quality of auditors’ judgments.
Optimizing Provider Test Ordering and Patient Outcomes Through Best Practice Alerts and Doctorate in Clinical Laboratory Sciences (DCLS) Consultation for Urine Cultures
Authors: Fountain Amy , Williams-Bouyer Natalie , Ren Ping , Carman Carol , Salazar Jose H. , Rajendran Rajkumar
Published: February 2025

Recent initiatives have discouraged the treatment of asymptomatic bacteriuria in specific patient populations due to its lack of clinical benefit, no improvement in morbidity or mortality, and its contribution to antibiotic overuse. This study aimed to evaluate whether an intervention at order entry, combined with DCLS laboratory consultation for urine cultures and urinalyses, could reduce unnecessary lab tests and inappropriate antibiotic use, thereby improving patient outcomes. Our research design was a quasi-experimental study with a retrospective and prospective chart review on non-pregnant adult patients 18 years of age and older from July 2021 to September 2022. Data collected for both reviews included patient demographics, provider demographics, patient signs and symptoms, laboratory test results, test order type, test order utilization and antibiotic prescriptions. Our study included 6372 patients, with 3408 in the retrospective review and 2964 in the prospective review. Before the intervention, 60% (n = 2053) of test orders were inappropriate, which decreased to 20% (n = 591) post-intervention. In asymptomatic patients, reflexed urine cultures decreased from 51% to 13% post-intervention. Lastly, in asymptomatic patients, antibiotic therapy at discharge dropped from 54% to 25% after the intervention. Post-intervention ordering practices improved, decreasing the number of inappropriate orders across all patient and provider types. Overall, this initiative showed a significant reduction in the treatment of asymptomatic bacteriuria, which has been linked to the overuse of antibiotic therapy.
Analysis of LULC Change Dynamics That Have Occurred in Tuscany (Italy) Since 2007
Authors: Arcidiaco Lorenzo , Corongiu Manuela
Published: February 2025

The dynamics of Land Use/Land Cover changes are crucial to environmental sustainability, socio-economic development, and spatial planning. These changes stem from complex interactions between human activities, natural processes, and policies. In recent decades, LULC transformations have been linked to global challenges such as biodiversity loss, climate change, and resource degradation. Key drivers include urban sprawl, agricultural expansion and abandonment, and deforestation, emphasizing the need for effective frameworks to monitor and assess their impacts. This study investigates Land Use/Land Cover (LULC) changes in Tuscany (Italy) over the period from 2007 to 2019. To achieve this, statistical analyses were conducted to quantify variations in LULC across different classes and administrative territories represented by provincial local authorities. Specifically, data spanning five temporal intervals (2007, 2010, 2013, 2016, and 2019) enabled a comprehensive comparative analysis of spatial persistence in LULC patterns. Changes were assessed using a statistical approach based on Odds Ratios (OR). Additionally, Generalized Linear Models (GLMs) at the provincial level were employed to facilitate one-to-many provincial comparisons and to evaluate the statistical significance of observed LULC changes. The analysis revealed that certain classes exhibit a greater susceptibility to changes compared to others. Specifically, the classes categorized under ’Artificial Surfaces’ (LC_100) were, on average, 6.7 times more likely to undergo changes than those classified as ’Agricultural Areas’ (LC_200) and 11 times more likely than those under ’Forest and Semi-natural Areas’ (LC_300). Over time, the areas classified as artificial territories have exhibited a progressively decreasing probability of change. Notably, during the first update period (2007–2010), these areas were 3.5 times more susceptible to change compared to the most recent update period (2016–2019). An additional significant finding emerged from the statistical comparison of LULC changes across administrative regions governed by different authorities (Provinces). These findings underscore the potential of using administrative indicators and morphological parameters to analyze LULC change trends. The proposed approach provides a robust framework for interpreting territorial resilience and informing spatial planning strategies effectively.
Investigating the Sources of Silver in 17th- and 18th-Century Silver Coins from the Rooswijk Shipwreck by Compositional Studies
Authors: Gherardi Francesca , Pelsdonk Jan
Published: February 2025

The colonisation of the Americas and the discovery of its rich ores had a great impact on the world economies, making them quickly become the main suppliers of precious metals in Europe. The compositional studies of several coins (ducatons, eight reales cob8, four reales cob4, eight reales pillar dollar, four reales half pillar dollars, rijderschellings and silver rijders) recovered from the 18th-century Dutch East India Company Rooswijk wreck by micro X-ray fluorescence (µXRF) spectroscopy revealed further knowledge about the silver trade and the silver sources used to produce coins in mints in the Low Countries over a wide timeframe (1618–1739). The results provided trace elemental ‘fingerprints’ of coins minted with silver from known mines, and matching against them revealed the silver sources used in coins, whose mint location could not be identified due to their poor state of preservation. This study proved that, despite the decrease in silver production in European mines in the 17th century and the huge influx of American silver into Europe, in the 18th century, the mints in the Dutch Republic and, to a lesser extent, in the Spanish Netherlands still highly relied on the recycling of older coins and on the import of silver from central European mines.
Production of Nd and Nd–Fe Alloys from NdCl3 by Calciothermic Reduction
Authors: Yu Joo-Won , Chung Yeon-Jun , Wang Jei-Pil
Published: February 2025

This study presents a metallothermic reduction mechanism for fabricating Nd and Nd–Fe alloys at 850–1050 °C using anhydrous NdCl3 and Ca, which have relatively low melting points. Our method decreased the process temperature while improving the recovery rate of Nd using the thermodynamic parameters of the CaCl2–KCl–NaCl and Nd–Fe liquid solutions. To reduce the activity of the product (CaCl2), the optimal composition of the CaCl2–KCl–NaCl molten salt was XCaCl2=0.4 (XKCl:XNaCl=6:4). The molten metal bath (Nd or Nd–Fe) that formed at the bottom of the reaction zone during Nd and Nd–Fe alloy production absorbed metal particles generated in the molten salt during the reaction, thereby facilitating ingot formation. In Nd produced at 1050 °C using 1.2× the stoichiometric amount (by mass) of Ca, the Nd recovery rate was 97.0%. Moreover, in the Nd–Fe alloys produced at 1050 °C targeting eutectic compositions, the Nd recovery rate was 96.3%. Increased Fe contents in the Nd–Fe liquid solution reduced the Nd recovery rates, and the Nd–Fe alloy (Nd recovery rate: 89.8%) was produced at 850 °C, suggesting the possibility of increasing the energy efficiency of the Nd production process. The Nd–Fe alloy produced through this proposed process could be used as a raw material in the NdFeB strip casting process.
A Resonance-Identification-Guided Autogram for the Fault Diagnosis of Rolling Element Bearings
Authors: Liu Mingxuan , Shen Yiping , Xu Yuandong
Published: February 2025

Rolling element bearings are key components for reducing friction and supporting rotors. Harsh working conditions contribute to the wear of bearings and consequent breakdown of machines, which leads to economic losses and even catastrophic accidents. Faulty impulses from bearings can excite resonance behavior in a system and produce modulation phenomena. Fault characteristics in modulated signals can be extracted using demodulation analysis methods, significantly improving the reliability and effectiveness of the fault diagnosis of rolling bearings. Optimal demodulation frequency band selection is a primary step for the demodulation-analysis-based fault diagnosis of bearing faults. To exploit the resonant modulation mechanism in the fault diagnosis of rolling element bearings, resonant frequencies identified through stochastic subspace identification are employed to guide the impulsive sparsity measures of an Autogram for bearing fault diagnosis, which combines physical modulation dynamics and data characteristics. The frequency band that not only matches the natural frequencies but also shows highly sparse impulsive characteristics is selected as the optimal demodulation frequency band for bearing fault diagnosis. The results of simulations and experimental data validate the advantages of the proposed method, which exploits physics-guided data processing for optimal demodulation frequency band determination.
Temporal Graph Attention Network for Spatio-Temporal Feature Extraction in Research Topic Trend Prediction
Authors: Guo Zhan , Lu Mingxin , Han Jin
Published: February 2025

Comprehensively extracting spatio-temporal features is essential to research topic trend prediction. This necessity arises from the fact that research topics exhibit both temporal trend features and spatial correlation features. This study proposes a Temporal Graph Attention Network (T-GAT) to extract the spatio-temporal features of research topics and predict their trends. In this model, a temporal convolutional layer is employed to extract temporal trend features from multivariate topic time series. Additionally, a multi-head graph attention layer is introduced to capture spatial correlation features among research topics. This layer learns attention scores from the data by using scaled dot product operations and updates edge weights between topics accordingly, thereby mitigating the issue of over-smoothing. Furthermore, we introduce WFtopic-econ and WFtopic-polit, two domain-specific datasets for Chinese research topics constructed from the Wanfang Academic Database. Extensive experiments demonstrate that T-GAT outperforms baseline models in prediction accuracy, with RMSE and MAE being reduced by 4.8% to 7.1% and 14.5% to 18.4%, respectively, while R2 improved by 4.8% to 7.9% across varying observation time steps on the WFtopic-econ dataset. Moreover, on the WFtopic-polit dataset, RMSE and MAE were reduced by 4.0% to 5.3% and 10.0% to 10.7%, respectively, and R2 improved by 7.6% to 14.4%. These results validate the effectiveness of integrating graph attention with temporal convolution to model the spatio-temporal evolution of research topics, providing a robust tool for scholarly trend analysis and decision making.
Parametric Study on Mechanical Behavior of Beam–Column Joint with L-Shaped Components
Authors: Xia Yuanyuan , Wang Suipeng , Li Shen , Yang Haizeng
Published: February 2025

This paper introduces a beam–column joint with an L-shaped component formed by combining two traditional semi-rigid connection methods, namely top and bottom angle steel connections and end-plate connections. Test studies and finite element simulations are performed on two designed specimens (LJD-1 and LJD-2). The reliability of the finite element model is confirmed, and the hysteresis curves are found to be relatively full, indicating excellent energy dissipation capacity. Moreover, it is revealed that the larger the joint length, the better the improvement in load-bearing capacity and ductility. Based on this, a finite element simulation is conducted considering the joint configuration, thickness of the vertical and horizontal plates, length of the horizontal plate, thickness of the end-plate, and the number of haunches. The optimal ranges for each parameter are determined.
Advancing the Understanding of Sediment Contamination Dynamics in the Iron Quadrangle (Brazil): A Comparative Analysis of Pollution Indices for PTE Assessment
Authors: Vicq Raphael , Leite Mariangela G. P. , Leão Lucas P. , Nallini Júnior Hermínio A. , Gomes Patricia , Fonseca Rita , Valente Teresa
Published: February 2025

The assessment of sediment contamination is a critical component in understanding the dynamics of potentially toxic elements (PTEs) in aquatic ecosystems, particularly in regions with intensive mining activities. This study focuses on the Rio das Velhas basin, located in the Iron Quadrangle (IQ), one of the most important mining provinces in the world, characterized by extensive anthropogenic pressures and rich geological diversity. A comprehensive evaluation of sediment contamination in this region was conducted, applying multiple univariate and multielement indices, including the contamination factor (CF), enrichment factor (EF), modified contamination degree (mCd), pollution index (PI), modified pollution index (MPI), and ecological risk index (RI). A high sampling density (1 sample per 15 km2) enabled the creation of geochemical maps and the identification of contamination hotspots. The results revealed that As and Cd are the most concerning elements, with concentrations exceeding regional background levels. While EF provided a more sensitive and comprehensive spatial distribution of contamination, MPI emerged as a robust index for capturing geochemical trends in complex environments. The study also highlighted that over 20% of the samples exceeded guideline values for sediment quality, posing ecological risks. Elevated concentrations of PTEs, particularly As and Cd, raise concerns about their potential mobilization and bioaccumulation, threatening aquatic ecosystems. These findings underscore the urgent need for enhanced monitoring and targeted management strategies in mining-impacted basins. This work not only advances the understanding of sediment contamination dynamics in the IQ but also establishes a methodological framework for evaluating sediment quality in heavily impacted mining regions worldwide.
A Randomized, Double-Blind, Controlled Trial Protocol for Therapeutic Neuroscience Education in Chronic Migraine Patients: A Clinical–Neurophysiological Combined Study Design
Authors: Castaldo Matteo , Atzori Tiziana , Comanducci Angela , Querzola Giacomo , Derchi Chiara-Camilla , Lovattini Daniele , Manzoni Carlo , Lovati Carlo , Baglio Francesca , Tiberio Paola , De Sanctis Rita , Sarasso Simone , Viganò Alessandro
Published: February 2025

Chronic migraine (CM) is a highly disabling condition, affecting about 2% of the global population. Non-pharmacological treatments can be optimal for their non-invasive nature. This prospective, randomized, double-blind, controlled trial aimed to test the efficacy of therapeutic neuroscience education (TNE) in CM. Early response biomarkers were also evaluated. A total of 80 CM patients were consecutively enrolled and randomly allocated to TNE or a general education program. Treatment effectiveness was evaluated at baseline (T1) and 2 months after the end of treatment (T4). We collected the responses to disability and comorbidity questionnaires at the start (T1) and end of treatment (T3, 10 weeks after start). Early response biomarkers were evaluated at screening (T0) and mid-way through the process (T2, 5 weeks after start). We expected that TNE would provide a greater benefit than the general education program, which served as the primary outcome of this study. We also expected that a change in clinical and neurophysiological measures could potentially occur, reflecting plasticity-induced reorganization and predicting clinical response. This is the first study selectively exploring the effect of TNE as a standalone treatment for CM. A new, effective treatment regime without interactions with other medication could be of great interest as an addition to migraine therapeutic strategies.
Longitudinal Multi-Channel Focused Vortex and Vector Beams Generation by Quarter-Wave Plate Meta-Atom Metasurfaces
Authors: Ma Teng , Zhao Kaixin , Gu Manna , Zhou Haoyan , Liu Chunxiang , Cheng Chuanfu , Dong Qingrui , Ma Li
Published: February 2025

Metasurface-based longitudinal modulation introduces the propagation distance as a new degree of freedom, extending the light modulation with metasurfaces from 2D to 3D space. However, relevant longitudinal studies have been constrained to designing the metasurface of half-wave plate (HWP) meta-atoms and generating either non-focused or two-channel vortex and vector beams. In this study, we propose a metasurface composed of quarter-wave plate (QWP) meta-atoms to generate the longitudinal multi-channel focused vortex and vector beams. The metasurface consists of two interleaved sub-metasurfaces of QWP meta-atoms. For each sub-metasurface, the helical and hyperbolic phase profiles are designed independently in the propagation and geometric phases to generate focused co- and cross-polarized vortices with corresponding topological charges. Under the illumination of x-linearly polarized light, the metasurface generates two circularly polarized vortices, two linearly polarized vortices, and one vector beam on five focal planes. Theoretical analysis and simulation results demonstrate the feasibility of the proposed QWP metasurface. Our study presents a significant advancement in the development of integrated and multifunctional optical devices and systems, with significant potential applications in light–matter interaction, laser processing, and optical communication.
Nanomedicine: Pioneering Advances in Neural Disease, Stroke and Spinal Cord Injury Treatment
Authors: Loushambam Borish , Yanglem Sangeeta , Krishnaswami Venkateswaran , Kumar Munish , Vijayaraghavalu Sivakumar
Published: February 2025

Background: Neurological disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), stroke, and spinal cord injury (SCI) are significant global health challenges due to their complex pathology and limited therapeutic options. Conventional treatments often fail to efficiently cross the blood–brain barrier (BBB), leading to poor bioavailability and systemic toxicity. This narrative review explores the potential of nanomedicine in addressing these limitations and advancing targeted therapies for neural disorders. Methods: This review examines recent studies on the use of engineered nanoparticles (NPs), including liposomes, dendrimers, micelles, and nanogels, for targeted drug delivery and multifunctional theranostics in neural diseases. It evaluates their role in promoting axon regeneration, reducing neuroinflammation, and repairing neural damage. Additionally, innovative applications in gene therapy and RNA-based treatments, such as CRISPR-Cas9 and RNA interference (RNAi), are discussed. Challenges related to toxicity, scalability, affordability, and regulatory barriers are highlighted, along with potential strategies to address these issues. Results: Nanoparticles have shown significant promise in crossing the BBB, delivering therapeutic agents to neural tissues, and minimizing off-target effects. Emerging applications in gene and RNA-based therapies demonstrate their versatility in addressing disease-specific challenges. However, unresolved issues such as long-term safety, manufacturing scalability, and cost continue to pose challenges. Conclusions: Nanomedicine offers a promising approach to overcoming current limitations in the treatment of neural disorders. This review emphasizes the need for continued interdisciplinary efforts to address translational barriers and highlights the potential for nanomedicine to improve the outcomes and quality of life for patients with neural disorders, stroke, and SCI.
Percutaneous Endoscopic Gastrostomy in the 21st Century—An Overview of 1415 Consecutive Dysphagic Adult Patients
Authors: Mendes Ivo , Vara-Luiz Francisco , Palma Carolina , Nunes Gonçalo , Lima Maria João , Oliveira Cátia , Brito Marta , Santos Ana Paula , Santos Carla Adriana , Meira Tânia , Mascarenhas Paulo , Fonseca Jorge
Published: February 2025

Background/Objectives: Percutaneous endoscopic gastrostomy (PEG) is recommended for long-term enteral nutrition in dysphagic patients. This study aims to characterize conditions motivating PEG, assess nutritional status on the gastrostomy day, evaluate survival and search for survival predictors. Methods: Retrospective study of adult patients who underwent PEG in a tertiary hospital from 2001 to 2023. Data collected included demographics, underlying disorders, nutritional status (anthropometry/laboratory evaluation) on the day of PEG and survival recorded until death or December 2023. Multivariable analysis was performed with Cox regression to search for survival predictors. Results: A total of 1415 patients were included (61.8% males, mean age 66.9 years); 66.4% presented a neurological disorder and 31.3% head and neck or esophageal cancers (HNC/EC). The mean BMI was 20.9 kg/m2, with 49.8% underweight. Albumin, transferrin and total cholesterol were low at 43.2%, 62.2% and 50%, respectively. Median overall survival was 11.1 months; 14.1% of deaths occurred within 4 weeks. HNC/EC patients showed lower survival than neurological patients. Potentially regressive neurological conditions presented longer survival than progressive ones. Predictors of increased survival included female gender, younger age, higher albumin and higher BMI. The protective effect of BMI and albumin was more pronounced in males than in females. Conclusions: Neurological disorders were the most frequent underlying conditions. Nearly half of the patients displayed malnutrition before PEG feeding. Although PEG-fed patients displayed a considerable median survival time, some died early without benefit from PEG. Patients with potentially regressive neurological conditions presented better outcomes. Female gender, younger age, higher albumin and higher BMI were associated with longer survival.
Effects of Live and Peptide-Based Antimicrobiota Vaccines on Ixodes ricinus Fitness, Microbiota, and Acquisition of Tick-Borne Pathogens
Authors: Maitre Apolline , Mateos-Hernandez Lourdes , Kratou Myriam , Egri Natalia , Maye Jennifer , Juan Manel , Hodžić Adnan , Obregón Dasiel , Abuin-Denis Lianet , Piloto-Sardinas Elianne , Fogaça Andrea C. , Cabezas-Cruz Alejandro
Published: February 2025

This study explored the effects of antimicrobiota vaccines on the acquisition of Borrelia and Rickettsia, and on the microbiota composition of Ixodes ricinus ticks. Using a murine model, we investigated the immunological responses to live Staphylococcus epidermidis and multi-antigenic peptide (MAP) vaccines. Immunized mice were infected with either Borrelia afzelii or Rickettsia helvetica, and subsequently infested with pathogen-free I. ricinus nymphs. We monitored the tick feeding behavior, survival rates, and infection levels. Additionally, we employed comprehensive microbiota analyses, including the alpha and beta diversity assessments and microbial co-occurrence network construction. Our results indicate that both live S. epidermidis and MAP vaccines elicited significant antibody responses in mice, with notable bactericidal effects against S. epidermidis. The vaccination altered the feeding patterns and fitness of the ticks, with the Live vaccine group showing a higher weight and faster feeding time. Microbiota analysis revealed significant shifts in the beta diversity between vaccine groups, with distinct microbial networks and taxa abundances observed. Notably, the MAP vaccine group exhibited a more robust and complex network structure, while the Live vaccine group demonstrated resilience to microbial perturbations. However, the effects of antimicrobiota vaccination on Borrelia acquisition appeared taxon-dependent, as inferred from our results and previous findings on microbiota-driven pathogen refractoriness. Staphylococcus-based vaccines altered the microbiota composition but had no effect on B. afzelii infection, and yielded inconclusive results for R. helvetica. In contrast, previous studies suggest that E. coli-based microbiota modulation can induce a pathogen-refractory state, highlighting the importance of both bacterial species and peptide selection in shaping microbiota-driven pathogen susceptibility. However, a direct comparison under identical experimental conditions across multiple taxa is required to confirm this taxon-specific effect. These findings suggest that antimicrobiota vaccination influences tick fitness and microbiota assembly, but its effects on pathogen transmission depend on the bacterial taxon targeted and the selected peptide epitopes. This research provides insights into the need for strategic bacterial taxon selection to enhance vaccine efficacy in controlling tick-borne diseases.
Emerging Role of Mesenchymal Stromal Cell and Exosome Therapies in Treating Cognitive Impairment
Authors: Tew Vick Key , Barathan Muttiah , Nordin Fazlina , Law Jia Xian , Ng Min Hwei
Published: February 2025

Cognitive aging, characterized by the gradual decline in cognitive functions such as memory, attention, and problem-solving, significantly impacts daily life. This decline is often accelerated by neurodegenerative diseases, particularly Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). AD is marked by the accumulation of amyloid-beta plaques and tau tangles, whereas PD involves the degeneration of dopaminergic neurons. Both conditions lead to severe cognitive impairment, greatly diminishing the quality of life for affected individuals. Recent advancements in regenerative medicine have highlighted mesenchymal stromal cells (MSCs) and their derived exosomes as promising therapeutic options. MSCs possess regenerative, neuroprotective, and immunomodulatory properties, which can promote neurogenesis, reduce inflammation, and support neuronal health. Exosomes, nanosized vesicles derived from MSCs, provide an efficient means for delivering bioactive molecules across the blood–brain barrier, targeting the underlying pathologies of AD and PD. While these therapies hold great promise, challenges such as variability in MSC sources, optimal dosing, and effective delivery methods need to be addressed for clinical application. The development of robust protocols, along with rigorous clinical trials, is crucial for validating the safety and efficacy of MSC and exosome therapies. Future research should focus on overcoming these barriers, optimizing treatment strategies, and exploring the integration of MSC and exosome therapies with lifestyle interventions. By addressing these challenges, MSC- and exosome-based therapies could offer transformative solutions for improving outcomes and enhancing the quality of life for individuals affected by cognitive aging and neurodegenerative diseases.
Effect of Surface Treatments and Thermal Aging on Bond Strength Between Veneering Resin and CAD/CAM Provisional Materials
Authors: Robaian Ali , Alshehri Abdullah Mohammed , Alqhtani Nasser Raqe , Almudahi Abdulellah , Alanazi Khalid K. , Abuelqomsan Mohammed A. S. , Raffat Eman Mohamed , Elkaffas Ali , Hashem Qamar , Soliman Tarek Ahmed
Published: February 2025

The oral environment significantly influences the esthetic appearance of CAD/CAM provisional restorative materials. Therefore, a veneering layer is required. Bonding veneering resin composites to these materials presents challenges, particularly under conditions of thermal aging. This study evaluated the impact of various surface treatments and thermal aging on the bond strength between veneering resin and CAD/CAM provisional materials. Fifty disk-shaped specimens of each CAD/CAM material (CAD-Temp, Everest C-Temp, and PEEK), measuring 10 mm in diameter and 3 mm in height, were fabricated. After being ultrasonically cleaned, specimens were embedded in acrylic resin blocks, leaving one surface exposed for surface treatments. Specimens were assigned to five groups at random. Group C: no surface treatments applied; DB: mechanically roughened with a diamond bur; DB + TC: DB group subjected to 5000 cycles of thermocycling; SB: treated with aluminum oxide airborne abrasion; SB + TC: SB group subjected to 5000 cycles of thermocycling. After the surface treatments, the primer and resin veneering composite were applied to the specimens. The shear bond strength (SBS) was calculated using a universal testing machine and the mode of failure was evaluated with an optical stereomicroscope with 40× magnification. Scanning electron microscopy evaluation was conducted to examine the surface topography of the materials’ surfaces after surface treatments. C-Temp in the SB group exhibited the highest SBS values (20.38 ± 1.04 MPa), while CAD-Temp in the C group showed the lowest values (4.60 ± 0.54 MPa). PEEK recorded significantly higher SBS values in DB + TC and SB + TC groups (9.26 ± 1.07 and 12.92 ± 0.97 MPa, respectively) compared to CAD-Temp in DB + TC and SB + TC groups (6.04 ± 0.76 and 8.82 ± 0.86 MPa, respectively). C-Temp exhibited higher SBS without surface treatment (13.11± 0.55 MPa), whereas PEEK showed higher SBS after diamond bur roughening and air particle abrasion (10.87 ± 1.02 MPa, and 14.37 ± 0.98 MPa, respectively). The thermocycling significantly reduced SBS values for C-Temp in the DB + TC and SB + TC groups (11.18 ± 0.92, 15.56 ± 0.87 MPa, respectively) and CAD-Temp in the DB + TC and SB + TC (6.04 ± 0.76 MPa and 8.82 ± 0.86 MPa, respectively). Conversely, the thermocycling had no significant effect on SBS values for PEEK material in the air particle abrasion group (12.92 ± 0.97 MPa).
Carbon Emission Modeling for High-Performance Computing-Based AI in New Power Systems with Large-Scale Renewable Energy Integration
Authors: Liu Haoyang , Zhai Jiangtao
Published: February 2025

Under the global impetus toward carbon peak and carbon neutrality, large-scale renewable energy integration has become a key driver in transforming traditional power grids into new power systems. Meanwhile, the growing adoption of advanced artificial intelligence (AI) approaches, especially large-scale models, heavily relies on high-performance computing (HPC) resources, which pose significant sustainability challenges due to their energy consumption and carbon emissions. This study introduces a newly developed carbon emission model (CEM) that accounts for both embodied and operational emissions in HPC systems. The CEM integrates parameters such as energy intensity coefficients, workload distribution patterns, and renewable deficiency rates, providing a lifecycle perspective of emissions in HPC-based AI applications for power systems. Results reveal that operational emissions dominate, constituting 87% of the total lifecycle footprint. Different regions exhibit varying carbon emissions, and on average, increasing the renewable energy share from 20% to 50% reduces total emissions by 43%, while a full transition to renewable energy achieves a 92% reduction. Circular economy practices, including hardware recycling and sustainable design, are also highlighted to mitigate embodied emissions. This study offers quantitative evidence and actionable insights for power industry stakeholders, enabling the balance between high-performance AI computations and ambitious carbon neutrality goals in renewable-integrated systems.
Assessing the Effect of Organic, Inorganic, and Hybrid Phase Change Materials on Thermal Regulation and Energy Efficiency in Asphalt Pavements—A Review
Authors: Rashid Farhan Lafta , Al-Obaidi Mudhar A. , Hatem Wadhah Amer , Almuhanna Raid R. A. , Abdul Redha Zeina Ali , Al Maimuri Najah M. L. , Dulaimi Anmar
Published: February 2025

Harnessing the power of phase change materials (PCMs) in asphalt pavements proposes a sustainable solution for addressing temperature-related issues, affording more robust and energy-efficient infrastructure. PCMs hold enormous potential for reforming various industries due to their ability to store and release large amounts of thermal energy, offering noteworthy benefits in energy efficiency, thermal management, and sustainability. The integration of PCMs within pavements presents an increasingly exciting field of research. PCMs have the ability to efficiently manage the changes in and distribution of temperature in asphalt pavements via the release and absorption of latent heat that occurs during the phase shifts of PCMs. Asphalt pavements experience less severe temperatures and a slower rate of temperature fluctuation as a result of this, which in turn reduces the amount of stress caused by temperature. In addition, the function of temperature adjustment that PCMs provide is natural, intelligent, and in line with the direction in which the development of smart pavements is heading in the future. This study aims to explore the impact of organic, inorganic, and mixed organic–inorganic PCMs on diverse surface characteristics of asphalt. In addition, this review addresses current challenges associated with using PCMs in asphalt and explores potential advantages that could facilitate future research in addition to broadening the implementation of PCMs in construction.
Deep Learning-Based Mapping of Textile Stretch Sensors to Surface Electromyography Signals: Multilayer Perceptron, Convolutional Neural Network, and Residual Network Models
Authors: Lee Gyubin , Kim Sangun , Kim Ji-seon , Kim Jooyong
Published: February 2025

This study evaluates the mapping accuracy between textile stretch sensor data and surface electromyography (sEMG) signals using Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Residual Network (ResNet) models. Data from the forearm, biceps brachii, and triceps brachii were analyzed using Root Mean Square Error (RMSE) and R2 as performance metrics. ResNet achieved the lowest RMSE (e.g., 0.1285 for biceps brachii) and highest R2 (0.8372), outperforming CNN (RMSE: 0.1455; R2: 0.7639) and MLP (RMSE: 0.1789; R2: 0.6722). The residual learning framework of ResNet effectively handles nonlinear patterns and noise, enabling more accurate predictions even for low-variability datasets like the triceps brachii. CNN showed moderate improvement over MLP by learning temporal features but struggled with low-variability datasets. MLP, as the baseline model, demonstrated the highest RMSE and lowest R2, highlighting its limitations in capturing complex relationships. These results suggest the potential reliability of ResNet for mapping textile stretch sensor data to sEMG signals, showing promising performance within the scope of this study. Future research could explore broader applications across different sensor configurations and activities to further validate these findings.