bluuebunny/crossref_metadata_2025_split huggingface dataset:
Electrospun Nanofiber Platforms for Photodynamic Therapy: Role and Efficacy in Cancer, Antimicrobial, and Wound Healing Applications
Authors: Eldem Aslı , Tekintaş Yamaç , Ucuncu Muhammed , Horzum Nesrin 
Published: February 2025

AbstractElectrospinning offers a versatile platform for developing nanofibrous scaffolds capable of enhancing the therapeutic potential of photodynamic therapy (PDT). Photosensitizer (PS) loaded fibers exhibit a high surface area‐to‐volume ratio, promoting efficient drug delivery, prolonged retention at the target site, and controlled release profiles. Inducing reactive oxygen species (ROS) generation through light activation offers a targeted therapeutic approach, selectively generating cytotoxic effects in cancerous or pathogenic cells while minimizing damage to healthy tissue. This selective cytotoxicity arises because the ROS are produced only in illuminated areas where PS releases and accumulates, limiting their harmful effects to desired regions. Additionally, PS‐loaded fibers are highly effective in wound healing applications, promoting cell proliferation and tissue regeneration while providing a barrier against microbial infections. This review highlights recent advances in the design and biomedical application of PS‐loaded nanofibers, emphasizing their influence on cell viability and effectiveness in microbial inhibition, thereby setting the stage for future innovations in cancer therapy, wound healing, and infection control.
Comparison techniques for detection of nonoscillation in half‐linear differential equations of the second order
Authors: Jaroš Jaroslav 
Published: February 2025

AbstractWe present a new comparison method which improves the classical Hille–Wintner theorem and its generalization to a pair of half‐linear differential equations of the second order by incorporating a solution of the Riccati‐type equation associated with the majorant nonoscillatory differential equation into the comparison inequality. Our results are formulated in terms of the weighted primitives of the coefficient functions of compared half‐linear differential equations.
Dynamic Properties of a Prey–Predator Food Chain Chemostat Model With Ornstein–Uhlenbeck Process
Authors: Chen Xiao , Gao Miaomiao , Jiang Yanhui , Jiang Daqing 
Published: February 2025

ABSTRACTThe food chain in an ecosystem is a complex, interconnected system of organisms that depend on each other and their environment. Chemostat model can be used to evaluate the stability and resilience of the food chain, as well as the response capacity of the system in the face of different disturbances and environmental changes. In this paper, we construct a prey–predator food chain chemostat model with Ornstein–Uhlenbeck processes and consider the dynamics of this stochastic model. Firstly, we prove the existence and uniqueness of the global solution. Secondly, we deduce the extinction in two cases: One is the extinction of prey and predator, and the other is the extinction of predator and the survival of prey. In addition, by constructing appropriate Lyapunov functions, we obtain the sufficient condition for the existence of stationary distribution, which means that prey and predator can coexist over a long period of time. Then, on this basis, we give the concrete expression of the density function of the distribution around the positive equilibrium point of corresponding deterministic system. Finally, numerical simulations prove the correctness of the theoretical results and show how the speed of reversion and intensity of volatility affect the food chain behavior.
Perceived Learning Needs of Hospitalised Cancer Patients and Nurses
Authors: Zhang Xi , Wu Meiliyang , Chen Ye , Su Yisui , Zeng Tieying 
Published: February 2025

ABSTRACTAimTo understand the differences in perceived learning needs between cancer patients and nurses.DesignA cross‐sectional descriptive design.MethodsData were collected from 1147 cancer patients and 1000 nurses using a convenience sampling method from February 2022 to June 2023. Sociodemographic data of patients and nurses were collected through a self‐designed general information questionnaire. Patients and nurses completed the Revised Chinese version of the Patient Learning Needs Scale to identify their perceptions of the learning needs and the difference between patient and nurse knowledge of needs.ResultsAccording to cancer patients and nurses, the top learning needs were information on complications and symptoms. In contrast, patients perceived the lowest priority learning needs to be disease‐related issues, while nurses perceived the lowest priority learning needs to be daily treatment and activities.ConclusionsPatients and nurses have different views on the essential contents to be learned, highlighting the importance of considering these two aspects in formulating health education programmes.Patient or Public ContributionParticipating cancer patients and nurses took the time to complete the questionnaire during the data collection phase and answered the questions sincerely.
Bankrolling the Belgrade Bandits? Civil Society, NGOs, and Foreign Aid Localization in Serbia
Authors: Herrold Catherine E. 
Published: February 2025

ABSTRACTThis article examines how foreign aid professionalized Serbia's civil society sector and analyzes how Serbian NGOs navigate upward accountability to donors and downward accountability to grassroots communities. Drawing upon 11 months of ethnographic fieldwork in Serbia, I demonstrate how the administrative bureaucracies of foreign aid constructed a sector of managerial NGOs that are relatively removed from local communities. Nonetheless, I suggest that the staff members of NGOs are mission‐driven and mission‐oriented, and I unveil the strategies they use to support social change at grassroots levels. Arguing that NGOs are neither the schools of democracy envisioned by neo‐Tocquevillian theories of civil society, nor the lucrative industries portrayed by critical theories of civil society, I suggest that NGOs are best understood as bridges between foreign donors and grassroots communities. I reflect on the implications of these arguments for donors' aid localization strategies.
A deep‐learning model for predicting tyrosine kinase inhibitor response from histology in gastrointestinal stromal tumor
Authors: Kong Xue , Shi Jun , Sun Dongdong , Cheng Lanqing , Wu Can , Jiang Zhiguo , Zheng Yushan , Wang Wei , Wu Haibo 
Published: February 2025

AbstractOver 90% of gastrointestinal stromal tumors (GISTs) harbor mutations in KIT or PDGFRA that can predict response to tyrosine kinase inhibitor (TKI) therapies, as recommended by NCCN (National Comprehensive Cancer Network) guidelines. However, gene sequencing for mutation testing is expensive and time‐consuming and is susceptible to a variety of preanalytical factors. To overcome the challenges associated with genetic screening by sequencing, in the current study we developed an artificial intelligence‐based deep‐learning （DL） model that uses convolutional neural networks (CNN) to analyze digitized hematoxylin and eosin staining in tumor histological sections to predict potential response to imatinib or avapritinib treatment in GIST patients. Assessment with an independent testing set showed that our DL model could predict imatinib sensitivity with an area under the curve (AUC) of 0.902 in case‐wise analysis and 0.807 in slide‐wise analysis. Case‐level AUCs for predicting imatinib‐dose‐adjustment cases, avapritinib‐sensitive cases, and wildtype GISTs were 0.920, 0.958, and 0.776, respectively, while slide‐level AUCs for these respective groups were 0.714, 0.922, and 0.886, respectively. Our model showed comparable or better prediction of actual response to TKI than sequencing‐based screening (accuracy 0.9286 versus 0.8929; DL model versus sequencing), while predictions of nonresponse to imatinib/avapritinib showed markedly higher accuracy than sequencing (0.7143 versus 0.4286). These results demonstrate the potential of a DL model to improve predictions of treatment response to TKI therapy from histology in GIST patients. © 2025 The Pathological Society of Great Britain and Ireland.
Cover Image
Authors: Yoshida Yuki , Furushima Yoshitomo , Okino Shunnosuke , Mizaikoff Boris 
Published: February 2025

The cover image is based on the article Cross‐referenceable microscopic and micro‐spectroscopic analysis of fiber reinforced thermoplastics by Yuki Yoshida et al., https://doi.org/10.1002/pc.29114.
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Impact of animal‐assisted intervention on therapy engagement in hospitalized children with traumatic brain injuries
Authors: Yelvington Miranda , Spray Beverly , Bregy Frank , Ramick Amy , Lovenstein Austin , Walden Marlene 
Published: February 2025

AbstractBackgroundIndividuals who sustained traumatic brain injuries in childhood may experience global challenges including disorders of consciousness, motor dysfunctions, and sensory impairments. Occupational and physical therapy are important parts of recovery from an acute brain injury, addressing deficits that directly affect functional recovery and return to age‐appropriate activities of daily living. Difficulty with active engagement in therapy tasks can limit the effectiveness of therapeutic interventions. Therapists search for methods to increase patient engagement to bolster patient progress.ObjectiveTo examine the effect of animal‐assisted interventions on patient engagement and therapy participation in children with traumatic brain injuries.DesignProspective, crossover design observational study with each patient serving as their own control.SettingThis study was conducted in a pediatric acute rehabilitation unit.ParticipantsNine participants completed all study sessions.InterventionStudy participants completed four visits, two with animal‐assisted intervention and two without animal‐assisted intervention.Outcomes and MeasuresThe Pediatric Rehabilitation Intervention Measure of Engagement, length of session, breaks needed.ResultsThe use of animal‐assisted intervention resulted in significantly greater mean engagement scores and significantly greater affective and behavioral engagement scores. No significant differences were found with cognitive engagement score, length of session tolerated, or number of breaks required. No participants had decreased engagement when animal‐assisted intervention was used.ConclusionsAnimal‐assisted intervention has the potential to increase therapy engagement in acute rehabilitation with children recovering from traumatic brain injuries.
Progression‐Free Survival Prediction Performance of ChatGPT: Analysis With Real Life Data in Early and Locally Advanced Prostate Cancer
Authors: Kavak Engin Eren , Dilli İsmail 
Published: February 2025

ABSTRACTObjectiveTo evaluate the progression‐free survival (PFS) time in patients with early‐stage and locally advanced prostate cancer and to compare the estimates provided by ChatGPT with actual survival data.MethodsA retrospective analysis was conducted on patients diagnosed with early‐stage/locally advanced prostate cancer. Each patient's estimated PFS times were calculated using an artificial intelligence chatbot. These estimates were generated by considering several factors, including the patient's clinical characteristics, tumor stage, treatment modalities, and biochemical parameters. A statistical comparison was conducted between the predicted PFS and actual PFS times.ResultsThe AI chatbot tended to overestimate the overall PFS times. A statistically significant discrepancy was observed between the predicted and actual survival times (p < 0.05). A discrepancy of 9.19 months was observed between the PFS predictions made by ChatGPT and the actual PFS. The bias value was 48.57, yet this discrepancy had a negligible impact on clinical practice (Cohen's d = 0.189).DiscussionArtificial intelligence‐based models have the potential to play an important role in the prediction of progression in cancers such as prostate cancer, where 5–10‐year survival rates can reach 100%. However, this study's findings indicate that the AI model's predictions are not aligned with the actual clinical data. The reliability of the integration of artificial intelligence into clinical decision support systems can be enhanced through the undertaking of comprehensive future studies.ConclusionThe use of artificial intelligence in predictive modeling may prove an effective approach for forecasting the PFS of prostate cancer. It has the potential to supplant the nomograms that are currently in use. Nevertheless, further studies are required to substantiate the accuracy and reliability of these systems.
Improving Cycle Life of Ni‐Rich Li‐Ion Battery Cathodes by Using Compartmentalized Anode and Cathode Electrolytes
Authors: Sun Jianqi , Wen Bo , Li Yaogang , Wang Hongzhi , De Volder Michael 
Published: February 2025

AbstractAs new, ever more energy‐dense battery materials are being developed, it is becoming increasingly challenging for electrolytes to cater for both requirements set by the anode and cathode. Ethylene carbonate plays a key role in forming a stable solid‐electrolyte‐interphase on the graphite anodes, but in combination with nickel‐rich cathodes, such as LiNi0.8Mn0.1Co0.1O2 (NMC811), it leads to excessive cathode oxygen loss at high voltage. In this work, the study proposes a cell design, where different electrolytes are compartmentalized in the anode and the cathode. An ionically conductive polymer electrolyte membrane is used to prevent two electrolytes from mixing. NMC811 versus graphite full cells using this electrolyte achieved a capacity retention of 85.1% over 520 cycles compared to 61.7% for control systems using a standard carbonate electrolyte under the same conditions. In addition, it is shown that cells using the compartmentalized electrolyte show less transition metal cross‐over from the cathode to the anode and less impedance build‐up during cycling. Overall, this cell design proposed in this work allows to independently optimize the anode and cathode electrolyte and holds the promise to better support the diverging electrolyte requirements of next‐generation anodes and cathodes.
Advances in the Catalytic Mechanism of Metal Oxides for Lithium–Sulfur Batteries
Authors: Min Yi , Zou Xiaohong , Lu Qian , Cai Wei , Bu Yunfei 
Published: February 2025

AbstractLithium–sulfur (Li–S) batteries are one of the promising next‐generation energy storage/conversion devices, considering their high energy density and low cost. However, the shuttle of polysulfides hinders the practical application of Li–S batteries, which leads to reduced cycling stability. Although chemical adsorption strategies have made significant progress in improving the cycling stability of Li–S batteries, the poor catalytic conversion ability of the polysulfide host results in an imbalance between chemical adsorption and catalytic conversion. Recent studies have revealed that metal oxides with adjustable electronic structures exhibit good catalytic ability as polysulfide hosts. However, there is currently no systematic review of the catalytic mechanism of metal oxides in Li–S batteries. Herein, the working principle and primary challenge of Li–S batteries are first introduced, followed by a categorization of catalyst design strategies. Furthermore, a comprehensive review of recent advancements in understanding the reaction mechanism of metal oxide catalysts in Li–S batteries is also provided. Finally, personal perspectives on the future development of Li–S batteries enhanced by metal oxide catalysis are offered. It is hoped that this review can provide valuable insights into the catalytic role of metal oxides in accelerating polysulfide conversion for Li–S batteries.
Catalytic Efficacy of a 2D Chemically Robust MOF for the Synthesis of Bioactive Diindolylmethane (DIM)‐Based Drug Molecules
Authors: Sahoo Rupam , Pramanik Bikram , Das Madhab C. 
Published: February 2025

AbstractSynthesis of biologically and pharmaceutically important drugs via organic transformations in one pot under mild conditions with efficient catalysts is of significant interest in terms of practical utility. Though metal‐organic frameworks (MOFs) prove their efficiencies in various catalytic reactions, synthesis of drug molecules employing MOF catalysts is still in its early stage, in fact, restricted to only 1,4‐Dihydropyridines (1,4‐DHP) based drug molecules synthesis. Although the Friedel–Crafts alkylation (FCA) reaction is one of the oldest reactions with a significant impact on drug molecules synthesis, surprisingly this reaction triggered by MOF catalyst is largely unexplored. Herein, we report a robust framework, MOF: IITKGP‐55, synthesized solely in aqueous medium, which exhibits its superior catalytic efficiencies for one‐pot FCA reaction with the well tolerance of various substrate scopes. Most importantly, based on this catalytic reaction, three drug molecules with bioactive diindolylmethane (DIM) core are synthesized for the first time, which was never realized by employing any sort of heterogeneous catalysts. Moreover, Arundine drug is crystallized and an in‐depth crystallographic analysis is performed. The superior catalytic efficiencies with excellent framework robustness highlight the potentiality of the developed framework and unwrap a new avenue for drug molecule synthesis via FCA reaction by employing heterogeneous catalysts.
Enhanced UV Stability of Perovskite Solar Modules via Downshifting Luminescent Organic–Inorganic Copper Halide Film with Near‐Unity Efficiency
Authors: Liu Zhe , Liu Zheng , Deng Liangliang , Guo Songhao , Wang Yanyan , Xie Lingling , Lv Ning , Yang Henan , Lü Xujie , Chen Bingkun , Zhan Yiqiang 
Published: February 2025

AbstractObtaining efficient perovskite solar modules (PSMs) with enhanced UV stability is essential for their practical applications, yet remains a significant challenge. In this work, a highly efficient organic–inorganic copper halide downshifting film that significantly enhances the UV stability of PSMs is demonstrated by converting high‐energy harmful UV photons into beneficial visible light photons that contribute to photovoltaic performance. The tetrapropylammonium (TPA) cation is selected as the main framework to synthesize a series of organic–inorganic copper halides, denoted as BrxIy. A near‐unity photoluminescence quantum yield (PLQY) of 99.51% can be achieved by precisely controlling the Br/I ratio to 2:4, denoted as Br2I4, which is one of the highest values reported to date. The dual self‐trapped excitons (STEs) luminescence mechanism is systematically investigated by both temperature‐dependent and pressure‐dependent photoluminescence experiments. This dual‐STEs mechanism enables the Br2I4 film to efficiently absorb UV photons and re‐emit visible photons, thereby mitigating the photodegradation of PSMs induced by high‐energy UV light. Finally, the Br2I4 film is demonstrated effective as a downshifting layer. The PSMs with Br2I4 film achieved an optimal efficiency of 22.24%, maintaining over 90% of their initial efficiency after exposure to a total UV dose of 66.07 kWh m−2.
Facile Growth of h‐BN Films by Using Surface‐Activated h‐BN Powders as Precursors
Authors: Chen Chen , Wang Qiang , Zhang Zongyuan , Liu Zhibo , Xu Chuan , Ren Wencai 
Published: February 2025

AbstractAtomically thick hexagonal boron nitride (h‐BN) films have gained increasing interest, such as nanoelectronics and protection coatings. Chemical vapor deposition (CVD) has been proven to be an efficient method for synthesizing h‐BN thin films, but its precursors are still limited. Here, it is reported that a novel and easily available precursor, surface‐activated h‐BN (As‐hBN), with NH3/N2 as an additional nitrogen source is used for CVD growth of monolayer h‐BN films on the Cu foils. The as‐grown h‐BN films can significantly enhance the anti‐oxidation ability of copper. Molecular dynamics simulations reveal that the reactivity of the As‐hBN precursors is attributed to the decomposition of unstable BO3 and O‐terminal edges on the surface under H2 atmosphere. This method provides a more reliable approach for fabricating h‐BN films.
On the Energy Consumption of ATL Transformations
Authors: Rubei Riccardo , Rocco Juri di , Ruscio Davide di 
Published: February 2025

AbstractBackgroundModel transformations play a crucial role in Model‐Driven Engineering (MDE), with the ATLAS Transformation Language (ATL) being a powerful technology for developing model‐to‐model transformations.MethodsThis paper presents a comprehensive investigation into the energy consumption of ATL transformations, aiming to identify possible correlations among transformation rules, model size, and metamodel structural characteristics. We conducted experiments on 52 ATL transformations, analyzing power usage and extending our inquiry to understand the impact of mutations on both models and transformations.ResultsThe experimental findings reveal relationships between the energy utilization of ATL transformations and the structural characteristics of metamodels. Furthermore, we establish a connection between energy consumption, model size, and the complexity of transformation processes.ConclusionThe insights gained from this research lay the groundwork for devising future energy‐efficient strategies while developing model transformations.
Archaeoseismology: Identifying Earthquake Effects in Ancient Sites
Authors: Sintubin Manuel 
Published: February 2025

Abstract
The burgeoning scientific discipline of archaeoseismology is the interdisciplinary study of—prehistoric to recent—earthquakes through a range of evidence in the archaeological record, from structural damage to manmade structures to changes in the cultural fabric of a society. The identification of potential earthquake archaeological effects in archaeological contexts is a first step in the archaeoseismological endavour. Relating these effects unambiguously to earthquakes remains challenging to archaeoseismologists, both in space and time. In common with paleoseismology, archaeoseismology aims at parameterizing ancient earthquakes and thus completing the earthquake catalogue of a region. New interdisciplinary developments in quantitative archaeoseismology turn archaeological sites into seismoscopes, becoming testing grounds to quantitatively assess site-specific ground motion effects. In common with archaeology, archaeoseismology aims at understanding the relationship between earthquakes and ancient societies. Learning about the earthquake culture of our ancestors may eventually serve as an impetus to establish and foster local earthquake cultures in earthquake-prone regions.