bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Biomarker Levels in Nasopharyngeal Swabs, Serum, and Saliva Across Different Health Conditions
Authors: Pencheva Mina , Manchorova-Veleva Neshka , Baruh David , Rusinov Georgi , Vangelov Lyubomir
Published: February 2025

Background: Coronavirus disease 2019 (COVID-19) is associated with a wide variety of clinical manifestations. Aim: This study aims to evaluate the levels of angiotensin-converting enzyme 2 (ACE2), metalloprotease 17 (ADAM17), Interleukin-17A (IL-17A), transmembrane serine protease 2 (TMPRSS2), apelin (AP), and vitamin D (VD) biomarkers in nasopharyngeal swab (NPS), serum, and saliva, as well as the change in their values depending on the health status of individuals. Material and methods: The analysis was performed by using enzyme-linked immunosorbent assay (ELISA) methods. Results: Comparing the levels of the investigated markers in saliva, we found significantly elevated ACE2 values in vaccinated patients, followed by those with severe COVID-19, compared to healthy, previously infected, and mild COVID-19 groups. For TMPRSS2, IL-17A, ADAM-17, and AP, values were significantly higher in all non-healthy groups (previously infected, mild, and severe COVID-19) compared to healthy individuals. Serum levels of VD were consistently low across all five studied groups, suggesting values below normal ranges. Analysis of marker data in saliva, NPS, and serum revealed a positive correlation between NPS and serum and saliva and serum, as well as between saliva and NPS for all studied markers. Conclusions: In summary, monitoring changes in biomarkers present in Saliva holds promise as a predictive tool for various diseases. This approach enables the early implementation of preventive measures and protective strategies, potentially improving overall health outcomes.
Interfacial Characteristics and Mechanical Properties of TiAl4822/Ti6Al4V Metal–Intermetallic Laminate Composite Prepared Through Vacuum Hot Pressing
Authors: Qin Jianwen , Zhang Shouyin , Ma Zhijian , Lu Baiping
Published: February 2025

In this work, the TiAl4822/Ti6Al4V metal–intermetallic laminate (MIL) composite was fabricated using vacuum hot pressing (VHP). The interfacial morphologies and mechanical properties of the composites were investigated. No discernible defect was observed in the well-bonded interface region. This interface region comprised two distinct areas: the Ti2Al (6 μm) region near the TiAl layer and the Ti3Al (4 μm) region near the Ti6Al4V layer. Electron backscatter diffraction analysis revealed that dynamic recrystallization (DRX) took place at the interface during the hot pressing process. The ductile brittle nature of Ti6Al4V and TiAl4822 layers and the formation of fine grains within the interface are conducive to enhancing toughness and tensile strength. Room temperature tensile testing exhibited that the tensile strength of TiAl4822/Ti6Al4V MIL composite was 636.9 MPa, approximately 225 MPa higher than single TiAl4822 alloy. The Ti6Al4V layer, as well as the formation of fine grain interface, effectively inhibited further propagation of the main crack through crack passivation, crack deflection, and load transformation. The bending strength of the TiAl4822/Ti6Al4V MIL composite was 1114.1 MPa. The fracture toughness of the TiAl4822/Ti6Al4V MIL composite reached 33.15 MPam1/2, which increased by 78.2% compared with single TiAl4822 alloy.
Study on Mechanical Properties and Impact Energy Release Characteristics of Skeleton-Structured Al/Ni Reactive Materials
Authors: Sun Zhichao , Yang Yansong , Zhang Rui , Guo Lei , He Yuan , Liu Enliang , Wang Chuanting , He Yong
Published: February 2025

Reactive materials can be employed to realize the integration of damage element kinetic energy and chemical energy damage, as well as strengthen the destruction ability of warheads. Among them, Al/Ni material has become a research hotspot because of its simple structure, easy process, and high reaction heat. In this paper, a skeleton-structured Al/Ni reactive material was successfully prepared. Additionally, both static and dynamic mechanical performance tests were conducted, along with impact experiments in a quasi-sealed chamber. Furthermore, numerical simulations of the mechanical properties of the materials were performed. The results show that the prepared reactive material has a compressive strength of 150 MPa and a tensile strength of 51 MPa, and the numerical simulation results are in good agreement with the experimental data. The impact experiments and product recovery analysis show that the material has a certain energy release ability, and the overpressure can attain 0.081 MPa at a velocity of 1370 m/s in an air atmosphere. However, the overpressure in all experiments under an argon atmosphere is less than 0.02 MPa, which proves that the main reaction under the impact condition is an oxidation reaction rather than a metal intermetallic reaction. The results of this paper provide theoretical support and a data basis for the design of three-dimensional skeletons of reactive materials and the structural optimization and improvement in mechanical properties and have certain guiding significance in the application of Al/Ni reactive materials.
Save Forests Through Sustainable Papermaking: Repurposing Herbal Waste and Maple Leaves as Alternative Fibers
Authors: Kolya Haradhan , Kang Chun-Won
Published: February 2025

This study explores a sustainable papermaking approach to contribute to forest conservation by repurposing delignified herbal waste and maple leaves as alternative cellulose sources. By reducing reliance on traditional wood-based materials, this method supports forest conservation while promoting environmental sustainability and creating economic opportunities from agricultural byproducts. Controlled experiments were conducted to extract cellulose and form paper using four fiber compositions: 100% leaf (P1), 100% herbal waste (P2), 75% leaf + 25% herbal waste (P3), and 75% leaf + 25% wood pulp (P4). Both treated and untreated herbal waste and leaves were characterized using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR) and X-ray Diffraction (XRD) to analyze chemical functionality and structural changes. The Kürschner cellulose content (22.4% in herbal waste and 15.2% in maple leaves) was determined through concentrated nitric acid and ethanol treatments, confirming high cellulose levels suitable for papermaking. Papers produced from these compositions exhibited enhanced mechanical properties, with the P2 sample (100% herbal waste) demonstrating the highest tensile strength (with P2 exhibiting a tensile strength of 1.84 kN/m) due to its elevated cellulose content. This innovative recycling approach contributes to deforestation reduction by valorizing agricultural waste materials, highlighting the feasibility of integrating alternative fibers into paper manufacturing. These findings present a promising pathway toward an eco-friendly, forest-saving paper industry while adding economic value to agro-waste resources.
Experimental Investigation of the Drying Shrinkage Performance of a Modified Ceramsite Geopolymer Concrete
Authors: Deng Peng , Wang Xuening , Guo Jian , Liu Yan , Zheng Qi
Published: February 2025

The experiments were divided into two groups to establish a drying shrinkage model suitable for modified ceramsite geopolymer concrete (MCGC). In the first experimental group, via comparison with dry ceramsite (untreated), a method for modifying the ceramsite surface with a 6% silicone resin was proposed which could reduce its water absorption, enhance the compressive strength and slump of the corresponding concrete, and decrease the drying shrinkage. The second group systematically explored the influences of control factors on MCGC prepared from modified ceramsite. Different water/binder (w/b) ratios, [Na2O]/b ratios, and metakaolin content (MK/b) ratios were used in the experiment. Compressive strength and drying shrinkage tests were performed for 90 d. High w/b and Na2O/b ratios could enhance drying shrinkage. Moreover, 8% Na2O/b enhanced the compressive strength. Low compressive strength was obtained using 10% Na2O/b. A high MK/b ratio reduced drying shrinkage. However, high w/b and MK/b ratios hindered strength development. Finally, a model predicting drying shrinkage for MCGC with a high prediction accuracy was proposed by considering three control factors. Due to the variety of ceramsite pretreatment methods and the considered factor limitations, the model had potential for additional enhancements.
Unmanned Aerial Vehicle Path Planning in Complex Dynamic Environments Based on Deep Reinforcement Learning
Authors: Liu Jiandong , Luo Wei , Zhang Guoqing , Li Ruihao
Published: February 2025

In this paper, an enhanced deep reinforcement learning approach is presented for unmanned aerial vehicles (UAVs) operating in dynamic and potentially hazardous environments. Initially, the capability to discern obstacles from visual data is achieved through the application of the Yolov8-StrongSort technique. Concurrently, a novel data storage system for deep Q-networks (DQN), named dynamic data memory (DDM), is introduced to hasten the learning process and convergence for UAVs. Furthermore, addressing the issue of UAVs’ paths veering too close to obstacles, a novel strategy employing an artificial potential field to adjust the reward function is introduced, which effectively guides the UAVs away from proximate obstacles. Rigorous simulation tests in an AirSim-based environment confirm the effectiveness of these methods. Compared to DQN, dueling DQN, M-DQN, improved Q-learning, DDM-DQN, EPF (enhanced potential field), APF-DQN, and L1-MBRL, our algorithm achieves the highest success rate of 77.67%, while also having the lowest average number of moving steps. Additionally, we conducted obstacle avoidance experiments with UAVs with different densities of obstacles. These tests highlight fast learning convergence and real-time obstacle detection and avoidance, ensuring successful achievement of the target.
Method of Dynamic Modeling and Robust Optimization for Chain Transmission Mechanism with Time-Varying Load Uncertainty
Authors: Liu Taisu , Liu Yuan , Liu Peitong , Du Xiaofei
Published: February 2025

Time-varying driving loads and uncertain structural parameters affect the transmission accuracy of chain transmission mechanisms. To enhance the transmission accuracy and placement consistency of these mechanisms, a robust optimization design method based on Karhunen–Loeve expansion and Polynomial Chaos Expansion (KL-PCE) is proposed. First, a dynamic model of the chain transmission mechanism, considering multiple contact modes, is established, and the model’s accuracy is verified through experiments. Then, based on the KL-PCE method, a mapping relationship between uncertain input parameters and output responses is established. A robust optimization design model for the chain transmission process is formulated, with transmission accuracy and consistency as objectives. Finally, case studies are used to verify the effectiveness of the proposed method. Thus, the transmission accuracy of the chain transmission mechanism is improved, providing a theoretical foundation for the design of chain transmission mechanisms under time-varying load uncertainties and for improving the accuracy of other complex mechanisms.
Optimizing Automated Negotiation: Integrating Opponent Modeling with Reinforcement Learning for Strategy Enhancement
Authors: Zhang Ya , Wu Jinghua , Cao Ruiyang
Published: February 2025

Agent-based automated negotiation aims to enhance decision-making processes by predefining negotiation rules, strategies, and objectives to achieve mutually acceptable agreements. However, most existing research primarily focuses on modeling the formal negotiation phase, while neglecting the critical role of opponent analysis during the pre-negotiation stage. Additionally, the impact of opponent selection and classification on strategy formulation is often overlooked. To address these gaps, we propose a novel automated negotiation framework that enables the agent to use reinforcement learning, enhanced by opponent modeling, for strategy optimization during the negotiation stage. Firstly, we analyze the node and network topology characteristics within an agent-based relational network to uncover the potential strength and types of relationships between negotiating parties. Then, these analysis results are used to inform strategy adjustments through reinforcement learning, where different negotiation strategies are selected based on the opponent’s profile. Specifically, agents’ expectations are adjusted according to relationship strength, ensuring that the expectations of negotiating parties are accurately represented across varying levels of relationship strength. Meanwhile, the relationship classification results are used to adjust the discount factor within a Q-learning negotiation algorithm. Finally, we conducted a series of experiments, and comparative analysis demonstrates that our proposed model outperforms existing negotiation frameworks in terms of negotiation efficiency, utility, and fairness.
Smoothing the Subjective Financial Risk Tolerance: Volatility and Market Implications
Authors: Heo Wookjae , Kim Eunchan
Published: February 2025

This study explores smoothing techniques to refine financial risk tolerance (FRT) data for the improved prediction of financial market indicators, including the Volatility Index and S&P 500 ETF. Raw FRT data often contain noise and volatility, obscuring their relationship with market dynamics. Seven smoothing methods were applied to derive smoothed mean and standard deviation values, including exponential smoothing, ARIMA, and Kalman filter. Machine learning models, including support vector machines and neural networks, were used to assess predictive performance. The results demonstrate that smoothed FRT data significantly enhance prediction accuracy, with the smoothed standard deviation offering a more explicit representation of investor risk tolerance fluctuations. These findings highlight the value of smoothing techniques in behavioral finance, providing more reliable insights into market volatility and investor behavior. Smoothed FRT data hold potential for portfolio optimization, risk assessment, and financial decision-making, paving the way for more robust applications in financial modeling.
Evaluation of Respiratory Intensive Care Experiences and Relationships with Nutritional Status Among Patients Admitted to the Intensive Care Unit
Authors: Yildiz Murat , Celik Deniz , Ozdemir Tarkan , Ensarioglu Kerem , Cakir Melek , Savur Tugce Dondu , Mentes Oral , Cirik Mustafa Ozgur
Published: February 2025

Background and Objectives: This study aimed to evaluate patients’ experiences in the intensive care unit (ICU) setting and investigate whether there was a correlation between these experiences and their nutritional status. Materials and Methods: This study included patients admitted to the respiratory ICU between 1 January 2023 and 31 December 2023. Only patients aged 18 years or older were eligible for inclusion. Written and verbal consent was obtained from all participants, while those unable to provide nonverbal communication were excluded from the study. The Intensive Care Experience Questionnaire (ICEQ), developed by Rattray et al. in 2004, was utilized to assess the overall experiences of ICU patients. Results: The ICEQ results were analyzed across four categories: awareness of surroundings, recall of experiences, frightening experiences, and satisfaction with care. A total score was generated by summing the scores of these four categories. While the initial parameters were analyzed as ordinal data, the results for the four subcategories and the total score followed a parametric distribution and were thus analyzed accordingly. Conclusions: These findings reinforce the hypothesis that nutritional support requirements play a critical role in shaping patients’ experiences in the ICU, regardless of their preadmission status. Adequate nutritional support was shown to have a positive effect on ICU experience.
Dielectrophoretic Microfluidic Designs for Precision Cell Enrichments and Highly Viable Label-Free Bacteria Recovery from Blood
Authors: Thomas Dean E. , Kinskie Kyle S. , Brown Kyle M. , Flanagan Lisa A. , Davalos Rafael V. , Hyler Alexandra R.
Published: February 2025

Conducting detailed cellular analysis of complex biological samples poses challenges in cell sorting and recovery for downstream analysis. Label-free microfluidics provide a promising solution for these complex applications. In this work, we investigate particle manipulation on two label-free microdevice designs using cDEP to enrich E. coli from whole human blood to mimic infection workflows. E. coli is still a growing source of bacteremia, sepsis, and other infections in modern countries, affecting millions of patients globally. The two microfluidic designs were evaluated for throughput, scaling, precision targeting, and high-viability recovery. While CytoChip D had the potential for higher throughput, given its continuous method of DEP-based sorting to accommodate larger clinical samples like a 10 mL blood draw, it could not effectively recover the bacteria. CytoChip B achieved a high-purity recovery of over 98% of bacteria from whole human blood, even in concentrations on the order of <100 CFU/mL, demonstrating the feasibility of processing and recovering ultra-low concentrations of bacteria for downstream analysis, culture, and drug testing. Future work will aim to scale CytoChip B for larger volume throughput while still achieving high bacteria recovery.
Programmable Photonic Logic Array Based on Micro-Ring Resonators and All-Optical Modulation
Authors: Liu Jia , Zhou Shenghang , Sui Xiubao
Published: February 2025

All-optical computing is an emerging information processing technology. As a cutting-edge technology in the field of photonics, it effectively leverages the unique advantages of photons to achieve rapid computation. However, the lack of a fully functional and programmable design has slowed the progress of this type of optical computing system, especially in optical logic computing. In this paper, we design and propose a programmable photonic logic array based on all-optical computing methods. By efficiently combining on-chip photonic devices such as micro-ring resonators, we have realized a complete set of reconfigurable all-optical logic computation functions, including basic logic such as IS&NOT, AND, and OR, as well as combined logic, such as XOR and XNOR. To the best of our knowledge, the proposed architecture not only introduces three structurally similar standard logic units but also allows for their multiple-level cascading to form a large-scale photonic logic array, enabling multifunctional logic computation. Furthermore, using two independent wavelengths to represent the high and low levels of logic can effectively reduce cross-talk and overlap between signals, decreasing the dependence on the strength of the optical signal and the decision threshold. Simulation results by Photonic Integrated Circuit Simulator (INTERCONNECT) demonstrate the effectiveness and feasibility of the proposed programmable photonic logic array.
Mode Optimization of Microelectromechanical-System Traveling-Wave Ultrasonic Motor Based on Kirigami
Authors: Li Rong , Ran Longqi , Wang Cong , He Jiangbo , Zhou Wu
Published: February 2025

High-quality traveling waves in stators are critical for traveling-wave ultrasonic motors (TUSMs) to achieve good stability and efficiency. However, the modal splitting and shape distortion that is induced by the anisotropic elasticity induce severe traveling wave distortion. In this study, mode optimization based on kirigami is proposed to suppress modal splitting and shape distortion. Initially, the kirigami pattern on the inner boundary of the stator was built by linear interpolation. Subsequently, the optimization model for the orthogonal modes with even and odd nodal diameters was established. An extended Nelder–Mead Simplex Algorithm with the advantages of derivative-free and bound constraints was employed to search the solution. After optimization, the mode shape of the orthogonal modes with odd nodal diameters was much closer to the sine-style. For instance, the distortion of the B13 mode was significantly reduced to 0.003. Meanwhile, the intrinsic frequency matching was still retained after the optimization. In contrast, the optimization suppressed both the frequency splitting and shape distortion of the orthogonal modes, with even nodal diameters. For instance, the frequency splitting relating to the B14 mode was significantly reduced from 380 Hz to 1 Hz, and the shape distortion was as low as 0.004.
Leveraging Microorganisms to Combat Skin Cancer
Authors: Oyler Hayden J. , Callister Austen W. , Kutch Makenzi N. , Wakefield Mark R. , Fang Yujiang
Published: February 2025

Skin cancer, including melanoma and non-melanoma types, presents a significant and growing global health challenge due to its increasing incidence and mortality rates. While conventional treatments such as surgical excision, immunotherapy, and targeted therapies are well-established, microorganism-based approaches represent an innovative and promising alternative. These therapies employ live, genetically engineered, or commensal bacteria, viral vectors, or bacterial components to achieve various therapeutic mechanisms, including tumor targeting, immune system modulation, vascular disruption, competitive exclusion, drug delivery, and direct oncolysis. Despite their potential, these approaches require further investigation to address safety concerns, optimize treatment protocols, and gain a comprehensive understanding of their long-term outcomes.
Current Approaches on Nurse-Performed Interventions to Prevent Healthcare-Acquired Infections: An Umbrella Review
Authors: Teixeira Joana , Reis Neuza , Chawłowska Ewelina , Rocha Paula , Czech-Szczapa Barbara , Godinho Ana Catarina , Bączyk Grażyna , Agrelos João , Jaracz Krystyna , Fontoura Carlos , Lucas Pedro , Pinto M. Rosário
Published: February 2025

To analyze nursing interventions for preventing healthcare-associated infections (HAIs), major complications in acute care impacting length of stay, costs, morbidity, and mortality, an umbrella review was conducted between 1 February and 26 February 2024, following the Joanna Briggs Institute methodology and PRISMA reporting guidelines, resulting in the inclusion of 22 articles. The 22 final articles obtained addressed the following Centers for Disease Control and Prevention (CDC) categories: surgical site infections (e.g., skin antisepsis, antibiotic prophylaxis), catheter-related bloodstream infections (e.g., taurolidine lock solutions), ventilator-associated pneumonia (e.g., oral hygiene, semi-recumbent positioning), and catheter-associated urinary tract infections (e.g., catheter duration management). Using Neuman’s holistic framework, the review emphasized patient–environment interactions. Further primary research is needed to refine these interventions and enhance interprofessional care. The protocol was registered in PROSPERO (CRD42024506801).
Influence of Tectonic Setting and Sedimentary Environment on Organic-Rich Shale in the Wufeng Formation, Fenggang Area
Authors: Zhang Qian , Zhang Haiquan , Men Yupeng , Yu Qian , Cao Junfeng , Zhou Yexin , Feng Xintao , Zhao Ankun , Zhou Daorong
Published: February 2025

Drawing on extensive geological surveys, as well as systematic mineralogical, petrological, and geochemical analyses, the study evaluates the provenance, tectonic setting, paleo-redox conditions, and paleoclimate characteristics of the Wufeng Formation black shale in the Fenggang area. The analysis of mineral components reveals that quartz content is notably the highest, suggesting that the shale is primarily siliceous. The TOC content was highest in the YI Outcrop, ranging from 2.19 to 5.56, with an average of 3.23, followed by the SX Outcrop. Redox-sensitive indices including MoEF, UEF, V/Cr, and U/Th exhibit considerable variability, indicating significant heterogeneity of the redox conditions, which is primarily characterized as a restricted marine shelf setting. The bottom water of YI Outcrop has the strongest reducing property, and mainly deposited in an anoxic environment. Organic-rich siliceous mudstone is widely distributed across the region. The provenance analysis indicates that the study area is predominantly sourced from felsic igneous rocks. Additionally, paleoclimate and paleo-weathering analyses suggest that the region underwent intense chemical weathering under warm climatic conditions. We found that the sedimentary environment exhibits pronounced spatial variability. In the northern part of the study area, water conditions were deeper, anoxic, and reducing. Toward the east, water depth gradually decreased, transitioning to weakly oxidizing and suboxic conditions. Furthermore, significant tectonic activity in the region led to the formation of multiple underwater highs, indicative of an active continental margin likely associated with the rapid uplift of paleo-uplifts. The formation of organic-rich shale was primarily influenced by two factors: favorable preservation conditions in reducing water bodies and high primary productivity driven by biological activity in weakly oxic environments.
Advanced Nanomedicine Delivery Systems for Cardiovascular Diseases: Viral and Non-Viral Strategies in Targeted Therapy
Authors: Chen Qian , Yu Tong , Gong Jingyi , Shan Hongli
Published: February 2025

Cardiovascular diseases (CVDs) represent a leading global health crisis, significantly impairing patients’ quality of life and posing substantial risks to their survival. Conventional therapies for CVDs often grapple with challenges such as inadequate targeting precision, suboptimal therapeutic efficacy, and potential adverse side effects. To address these shortcomings, researchers are intensively developing advanced drug delivery systems characterized by high specificity and selectivity, excellent biodegradability, superior biocompatibility, and minimal toxicity. These innovative systems enable the precise delivery of pharmaceuticals with high drug-loading capacities, minimal leakage, and expansive specific surface areas, thereby enhancing therapeutic outcomes. In this review, we summarize and classify various drug delivery materials targeting CVDs and application values. We also evaluate the feasibility and efficacy of viral and non-viral vectors for the treatment of CVDs, the existing limitations and application prospects are also discussed. We hope that this review will provide new perspectives for the future development of drug delivery systems for the treatment of CVDs, ultimately contributing to improved patient care and outcomes.
Gold Nanoparticle-Enhanced Production of Reactive Oxygen Species for Radiotherapy and Phototherapy
Authors: Nguyen Viet-Khang , Tsai Shiao-Wen , Cho I-Chun , Chao Tsi-Chian , Hsiao Ing-Tsung , Huang Hsiao-Chieh , Liaw Jiunn-Woei
Published: February 2025

Gold nanoparticles (GNPs) have gained significant attention as multifunctional agents in biomedical applications, particularly for enhancing radiotherapy. Their advantages, including low toxicity, high biocompatibility, and excellent conductivity, make them promising candidates for improving treatment outcomes across various radiation sources, such as femtosecond lasers, X-rays, Cs-137, and proton beams. However, a deeper understanding of their precise mechanisms in radiotherapy is essential for maximizing their therapeutic potential. This review explores the role of GNPs in enhancing reactive oxygen species (ROS) generation through plasmon-induced hot electrons or radiation-induced secondary electrons, leading to cellular damage in organelles such as mitochondria and the cytoskeleton. This additional pathway enhances radiotherapy efficacy, offering new therapeutic possibilities. Furthermore, we discuss emerging trends and future perspectives, highlighting innovative strategies for integrating GNPs into radiotherapy. This comprehensive review provides insights into the mechanisms, applications, and potential clinical impact of GNPs in cancer treatment.
Gestational Weight Gain as a Modifiable Risk Factor in Women with Extreme Pregestational BMI
Authors: Lubrano Chiara , Locati Federica , Parisi Francesca , Anelli Gaia , Ossola Manuela , Cetin Irene
Published: February 2025

The global rise in obesity presents serious concerns, particularly due to its association with pregnancy complications such as gestational diabetes, preeclampsia, cesarean delivery, and fetal macrosomia. Maternal obesity also contributes to intergenerational health risks, increasing the likelihood of long-term issues in offspring. Preconception counseling is an essential preventive measure to reduce complications; however, many women miss this opportunity due to unplanned pregnancies. This study explores the impact of pregestational body mass index (BMI) and gestational weight gain (GWG) on pregnancy outcomes, underscoring the importance of routine monitoring of these parameters. Existing studies identify both BMI and GWG as independent risk factors for adverse maternal and neonatal outcomes, with elevated BMI combined with excessive GWG posing an even greater risk. Specifically, a BMI > 30 kg/m2 doubles the risk of complications such as gestational diabetes, hypertension, and cesarean delivery. Additionally, a review of national and international guidelines highlights a lack of consensus on managing gestational weight gain in women with obesity, particularly regarding antepartum surveillance and timing of delivery. Similarly, no specific guidelines have been established for underweight pregnant women. Additionally, few studies have thoroughly assessed the maternal and fetal risks associated with underweight during pregnancy. Despite this, numerous studies have highlighted an increased risk of preterm birth (PTB) and small-for-gestational-age (SGA) infants. This narrative review emphasizes the need for further research to develop tailored guidelines for managing pregnant women based on pregestational BMI, ultimately improving maternal and neonatal health outcomes.
Knowledge Assessment of Hospital Nursing Staff in Saudi Arabia Regarding Clostridioides difficile Infection: A Descriptive Cross-Sectional Study
Authors: Albougami Abdulrhman , Banawas Saeed S.
Published: February 2025

Clostridioides difficile (C. difficile), a gram-positive, spore-forming bacillus, has emerged as a leading cause of healthcare-associated infections, significantly contributing to infectious diarrhea and increasing healthcare costs. This descriptive, cross-sectional study was conducted among Saudi Arabian nursing staff from July to December 2023 to assess their knowledge and practices related to the diagnosis and management of C. difficile infection (CDI). Data were collected using a modified questionnaire. Overall, 358 nurses were surveyed, and 66% reported knowledge of C. difficile procedures. However, only 30.4% of the respondents correctly classified C. difficile as an anaerobic bacillus, while 42.2% were aware of the organism’s common occurrence in healthy adult volunteers. Additionally, 55.6% of respondents were aware of risk factors and 48.9% could name typical medicines that might cause illness. Only 24.0% acknowledged the cytotoxin test as the gold standard for detection, 26.8% identified hand washing with water and soap as an effective method to prevent the transmission of CDI, and 36.3% identified oral metronidazole as the first-line treatment for CDI. In summary, this study revealed a significant lack of awareness among nurses in Saudi Arabia regarding various aspects of CDI, emphasizing the need for improved education and training to address the knowledge gaps and quality of patient care.
Contrasting Effect of Curcumin on Hepatitis B Virus Replication According to the Hepatoma Cell Line
Authors: Elizalde María Mercedes , Fuentes Pedro , Chiappetta Diego , Flichman Diego Martín
Published: February 2025

In recent decades, considerable advances have been achieved in the treatment of chronic hepatitis B. However, the currently available drugs have shortcomings. In this context, several natural compounds have been proposed as potential agents to improve either the outcome of antiviral treatment or the progression of chronic infection, with curcumin being one of the most evaluated compounds due to its pleiotropic antiviral activity. The aim of this study was to characterize the effect and mechanism of curcumin on hepatitis B virus (HBV) replication in two different experimental models. Treatment of HepG22.15 and HBV-transfected Huh7 cells with curcumin revealed that the phytochemical differentially modulated HBV replication in both cell lines. In HepG22.15 cells, the addition of curcumin had no effect on viral DNA, pregenomic RNA (pgRNA), and e antigen (HBeAg) levels, while it decreased Precore RNA and s antigen (HBsAg) levels. Conversely, in Huh-7 cells, curcumin significantly increased viral progeny more than tenfold, as well as HBV RNAs and viral antigens. Furthermore, the analysis of the cellular mechanisms associated with the modulation of viral replication revealed that in Huh-7 cells, curcumin-induced cell cycle arrest in the G2/M phase and the modulation of genes involved in proliferation, cell cycle progression, and apoptosis, whereas no changes in cell cycle progression and gene expression were observed in HepG22.15 cells. In conclusion, curcumin elicits a differential cellular response in two hepatoma cell lines, which, in the case of Huh-7 cells, would provide an optimal cellular setting that enhances HBV replication. Therefore, the antiviral effect of this phytochemical remains controversial.
A Pilot Study Evaluating the Impact of an Algorithm-Driven Protocol on Guideline-Concordant Antibiotic Prescribing in a Rural Primary Care Setting
Authors: Nkemdirim Okere Arinze , Pinto Anthony Ryan , Suther Sandra , Ten Eyck Patrick
Published: February 2025

Approximately 2.8 million cases of bacterial antimicrobial resistance (AMR) infections result in over 35,000 deaths annually in the U.S. AMR is driven largely by inappropriate prescribing of antibiotics, especially in clinics serving rural communities or underserved populations. Antibiotic Stewardship Programs (ASPs) improve prescribing practices, but many rural clinics lack fully functional ASPs. This pilot study evaluated the impact of an algorithm-driven protocol on antibiotic prescribing in a rural primary care setting. We conducted a pre–post quasi-experimental study at a Federally Qualified Health Center (FQHC), focusing on upper respiratory infections, urinary tract infections, and sexually transmitted infections. Eligible patients were enrolled in the study during their primary care visits. The primary outcome was the frequency of guideline-concordant treatment, analyzed using descriptive statistics and Chi-square tests. Among 201 patients (101 pre-intervention, 100 post-intervention), the pre-intervention group consisted of 77% females and 47% African Americans, while the post-intervention group consisted of 72% females and 46% African Americans. The intervention was associated with a 12.6% decrease in the number of antibiotic prescriptions discordant with clinical guidelines (37.6% to 25%) from the pre- to post-intervention periods. This corresponded to an odds ratio of 0.55 (95% CI: 0.30–1.01, p = 0.054). Although not statistically significant at α = 0.05, this numerical decrease suggests potential benefits of algorithm-driven protocols in improving antibiotic stewardship in resource-limited settings. Longer study periods may further elucidate these benefits.
Carbon Molecular Sieve Membranes from Acenaphthenequinone–Biphenyl Polymer; Synthesis, Characterization, and Effect on Gas Separation and Transport Properties
Authors: Ortiz-Espinoza Jesús , Hernández-Cruz Olivia , Zolotukhin Mikhail , Ruiz-Treviño F. Alberto , Loría-Bastarrachea María Isabel , Aguilar-Vega Manuel
Published: February 2025

A rigid, high temperature-resistant aromatic polymer, poly(1,1′-biphenyl)-6,8a-dihydroacenaphthylene-1(2H)-one (BDA) comprising acenaphthenequinone and biphenyl was successfully synthesized by superacid catalyzed polymerization. BDA has a high decomposition temperature (Td = 520 °C) that renders it a viable candidate for carbon molecular sieve membranes (CMSM) formation. BDA precursor pyrolysis at 600 °C (BDA-P600) leads to a carbon turbostratic structure formation with graphene-like amorphous strands in a matrix with micropores and ultramicropores, resulting in a carbon structure with higher diffusion and higher selectivity than dense BDA. When the BDA pyrolysis temperature is raised to 700 °C (BDA-P700), the average stacking number of carbon layers N increases, along with an increase in the crystallite thickness stacking Lc, and layer plane size La, leading to a more compact structure. Pure gas permeability coefficients P are between 3 and 5 times larger for BDA-P600 compared to the BDA precursors. On the other hand, there is a P decrease between 10 and 50% for O2 and CO2 between CMSM BDA-P600 and BDA-P700, while the large kinetic diameter gases N2 and CH4 show a large decrease in permeability of 44 and 67%, respectively. It was found that the BDA-P700 WAXD results show the emergence of a new peak at 2θ = 43.6° (2.1 Å), which effectively hinders the diffusion of gases such O2, N2, and CH4. This behavior has been attributed to the formation of new micropores that become increasingly compact at higher pyrolysis temperatures. As a result, the CMSM derived from BDA precursors pyrolyzed at 700 °C (BDA-P700) show exceptional O2/N2 gas separation performance, significantly surpassing baseline trade-off limits.
Toward Intelligent Materials with the Promise of Self-Healing Hydrogels in Flexible Devices
Authors: Song Han-Seop , Rumon Md. Mahamudul Hasan , Rahman Khan Mohammad Mizanur , Jeong Jae-Ho
Published: February 2025

Flexible sensors are revolutionizing wearable and implantable devices, with conductive hydrogels emerging as key materials due to their biomimetic structure, biocompatibility, tunable transparency, and stimuli-responsive electrical properties. However, their fragility and limited durability pose significant challenges for broader applications. Drawing inspiration from the self-healing capabilities of natural organisms like mussels, researchers are embedding self-repair mechanisms into hydrogels to improve their reliability and lifespan. This review highlights recent advances in self-healing (SH) conductive hydrogels, focusing on synthesis methods, healing mechanisms, and strategies to enhance multifunctionality. It also explores their wide-ranging applications, including in vivo signal monitoring, wearable biochemical sensors, supercapacitors, flexible displays, triboelectric nanogenerators, and implantable bioelectronics. While progress has been made, challenges remain in balancing self-healing efficiency, mechanical strength, and sensing performance. This review offers insights into overcoming these obstacles and discusses future research directions for advancing SH hydrogel-based bioelectronics, aiming to pave the way for durable, high-performance devices in next-generation wearable and implantable technologies.
Ultrashort Pulse Laser Fabrication and Evaluation of Innovative Resorbable Barbed Sutures
Authors: Nambi Gowri Karuna , Al Asad Walid , Majumder Shubha , Zhao Xin , King Martin William
Published: February 2025

Laser micro-machining is a rapidly growing technique to create, manufacture and fabricate microstructures on different materials ranging from metals and ceramics to polymers. Micro- and nano-machining on different materials has been helpful and useful for various biomedical applications. This study focuses on the micro-machining of innovative barbed sutures using an ultrashort pulse laser, specifically a femtosecond (fs) laser system. Two bioresorbable polymeric materials, namely, catgut and poly (4-hydroxybutyrate) (P4HB), were studied and micro-machined using the femtosecond (fs) laser system. The optimized laser parameter was used to fabricate two different barb geometries, namely, straight and curved barbs. The mechanical properties were evaluated via tensile testing, and the anchoring performance was studied by means of a suture–tissue pull-out protocol using porcine dermis tissue which was harvested from the medial dorsal site. Along with the evaluation of the mechanical and anchoring properties, the thermal characteristics and degradation profiles were assessed and compared against mechanically cut barbed sutures using a flat blade. The mechanical properties of laser-fabricated barbed sutures were significantly improved when compared to the mechanical properties of the traditionally/mechanically cut barbed sutures, while there was not any significant difference in the anchoring properties of the barbed sutures fabricated through either of the fabrication techniques. Based on the differential scanning calorimetry (DSC) results for thermal transitions, there was no major impact on the inherent material properties due to the laser treatment. This was also observed in the degradation results, where both the mechanically cut and laser-fabricated barbed sutures exhibited similar profiles throughout the evaluation time period. It was concluded that switching the fabrication technique from mechanical cutting to laser fabrication would be beneficial in producing a more reproducible and consistent barb geometry with more precision and accuracy.
A Lightweight Drone Detection Method Integrated into a Linear Attention Mechanism Based on Improved YOLOv11
Authors: Zhou Sicheng , Yang Lei , Liu Huiting , Zhou Chongqing , Liu Jiacheng , Zhao Shuai , Wang Keyi
Published: February 2025

The timely and accurate detection of unidentified drones is vital for public safety. However, the unique characteristics of drones in complex environments and the varied postures they may adopt during approach present significant challenges. Additionally, deep learning algorithms often require large models and substantial computational resources, limiting their use on low-capacity platforms. To address these challenges, we propose LAMS-YOLO, a lightweight drone detection method based on linear attention mechanisms and adaptive downsampling. The model’s lightweight design, inspired by CPU optimization, reduces parameters using depthwise separable convolutions and efficient activation functions. A novel linear attention mechanism, incorporating an LSTM-like gating system, enhances semantic extraction efficiency, improving detection performance in complex scenarios. Building on insights from dynamic convolution and multi-scale fusion, a new adaptive downsampling module is developed. This module efficiently compresses features while retaining critical information. Additionally, an improved bounding box loss function is introduced to enhance localization accuracy. Experimental results demonstrate that LAMS-YOLO outperforms YOLOv11n, achieving a 3.89% increase in mAP and a 9.35% reduction in parameters. The model also exhibits strong cross-dataset generalization, striking a balance between accuracy and efficiency. These advancements provide robust technical support for real-time drone monitoring.
High-Resolution Reconstruction of Total Organic Carbon Content in Lake Sediments Using Hyperspectral Imaging
Authors: Lin Xuening , Zhou Xin , Zhao Hongfei , Zhang Guangcheng , Chen Yiyan , Jiang Shiwei , Zhan Tao , Tu Luyao
Published: February 2025

The total organic carbon (TOC) content in lake sediments is an effective archive indicating past climate changes. However, the resolution of the TOC record has generally been limited by factors such as subsampling intervals, hampering further comprehension of past climate change. Recently, hyperspectral imaging technology has been increasingly employed to scan lake sediment cores, presenting new opportunities to reconstruct high-resolution sequences, but the reconstruction of long-term high-resolution TOC records using hyperspectral imaging and the climate implications have not been well studied. In this study, we scanned sedimentary cores from Wudalianchi Crater Lake in northeast China with a spatial resolution of 400 × 400 μm, utilizing visible and near-infrared (VNIR) hyperspectral imaging technology. Then, a partial least-squares regression (PLSR) model was constructed by comparing eight different preprocessing methods and optimally selecting the best spectral subset combined with a genetic algorithm (GA). Our analysis demonstrates that the PLSR model, constructed using 62 relevant bands selected by the Savitzky–Golay second derivative (D2) preprocessing method and GA, was the most reliable, with the validation set’s R-value reaching a high of 0.91 and RMSE as low as 1.18%. Notably, the spectral range of 656–669 nm showed a strong positive correlation with measured TOC, indicating its sensitivity for TOC estimation. Given this advantage, we reconstructed the TOC records of sediments from the Wudalianchi Crater Lake during the 38–13 ka BP period, which exhibited significant millennial-scale fluctuation events. These corresponded well with the millennial-scale events in pollen and TOC from Lake Sihailongwan, δ18O records of Greenland ice cores, and δ18O records from Asian stalagmites. Thus, the combination of hyperspectral imaging and the PLSR model is effective in reconstructing high-resolution TOC changes in lake sediments, which is essential for understanding climate change as well as carbon burial in lakes.
A Weakly Supervised and Self-Supervised Learning Approach for Semantic Segmentation of Land Cover in Satellite Images with National Forest Inventory Data
Authors: Moraes Daniel , Campagnolo Manuel L. , Caetano Mário
Published: February 2025

National Forest Inventories (NFIs) provide valuable land cover (LC) information but often lack spatial continuity and an adequate update frequency. Satellite-based remote sensing offers a viable alternative, employing machine learning to extract thematic data. State-of-the-art methods such as convolutional neural networks rely on fully pixel-level annotated images, which are difficult to obtain. Although reference LC datasets have been widely used to derive annotations, NFIs consist of point-based data, providing only sparse annotations. Weakly supervised and self-supervised learning approaches help address this issue by reducing dependence on fully annotated images and leveraging unlabeled data. However, their potential for large-scale LC mapping needs further investigation. This study explored the use of NFI data with deep learning and weakly supervised and self-supervised methods. Using Sentinel-2 images and the Portuguese NFI, which covers other LC types beyond forest, as sparse labels, we performed weakly supervised semantic segmentation with a convolutional neural network to create an updated and spatially continuous national LC map. Additionally, we investigated the potential of self-supervised learning by pretraining a masked autoencoder on 65,000 Sentinel-2 image chips and then fine-tuning the model with NFI-derived sparse labels. The weakly supervised baseline achieved a validation accuracy of 69.60%, surpassing Random Forest (67.90%). The self-supervised model achieved 71.29%, performing on par with the baseline using half the training data. The results demonstrated that integrating both learning approaches enabled successful countrywide LC mapping with limited training data.