bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Gel’fand Problem on Expanding Tubular Domains in $$\mathbb {R}^2$$: Existence and the Morse Index of Solutions
Authors: Ghergu Marius , Miyamoto Yasuhito 
Published: November 2024

Abstract
We discuss the existence and the Morse index of solutions to the problem

$$\begin{aligned} {\left\{ \begin{array}{ll} \Delta U+\lambda e^U=0 & \text {in}\ \Omega _{R},\\ U=0 & \text {on}\ \partial \Omega _{R}, \end{array}\right. } \end{aligned}$$

Δ
U
+
λ

e
U

=
0

in

Ω
R

,

U
=
0

on

∂

Ω
R

,

where

$$\Omega _R$$

Ω
R

is a tubular domain in the plane with fixed width. We obtain the existence of an increasing sequence

$$R_k\rightarrow \infty $$

R
k

→
∞

as

$$k\rightarrow \infty $$

k
→
∞

and a corresponding sequence of solutions

$$\{U_{R_k}\}_{k\ge 1}$$

{

U

R
k

}

k
≥
1

to the above problem. We investigate the energy of such solutions and show that their Morse index

$${{\textsf{m}}}(U_{R_k})$$

m
(

U

R
k

)

satisfies

$${{\textsf{m}}}(U_{R_k})/R_k\rightarrow 2\sqrt{-\eta _1}$$

m

(

U

R
k

)

/

R
k

→
2

-

η
1

as

$$k\rightarrow \infty $$

k
→
∞

, where

$$\eta _1<0$$

η
1

<
0

is the first eigenvalue of the linearized 1D Gel’fand problem.
Recent developments in marginal zone lymphoma
Authors: Raderer Markus , Kiesewetter Barbara 
Published: November 2024

SummaryMarginal zone lymphoma is a relatively rare disease with a usually indolent clinical course. In clinical terms, three major subtypes—i.e., extranodal, nodal, and splenic marginal zone lymphomas—are encountered, which have specific clinical features. Clinical progress, however, has been limited in recent years as they are mostly lumped together with follicular lymphoma under the term “indolent lymphomas” in clinical trials. A talk given at the 2024 Annual Meeting of the Austrian Society of Hematology and Oncology aimed to summarize clinically relevant topics and progress in the field of marginal zone lymphoma, which is summarized in the current article.
Establishment and drug resistance characterization of paired organoids using human primary colorectal cancer and matched tumor deposit specimens
Authors: Deng Jiao , Qin Jerry H. , Li Xiaolan , Tao Deding , Feng Yongdong 
Published: November 2024

AbstractTumor deposits (TDs) represent a specific form tumor metastasis observed in colorectal cancer (CRC). The lack of successfully established cell lines for TDs, as well as the molecular mechanisms by which TDs occur remain largely unknown. Here, we established paired CRC organoids, including a human primary cancer organoid and its TD organoid, from a 46-year-old male patient with CRC. Further analysis revealed that, compared with primary tumor-derived cells, TD-derived cells exhibited enhanced proliferative, invasive and metastatic capabilities, and increased expression of stemness-related proteins. Furthermore, the present findings also demonstrated that TD-derived cells were more resistant to oxaliplatin or 5-FU. Transcriptomic profiling and qPCR revealed that TD-derived cells exhibited more alterations in fatty acid metabolism signaling and enhanced lipid synthesis ability compared to primary tumor-derived cells. Inhibition of lipid synthesis markedly decreased resistance to oxaliplatin in TD-derived cells. Taken together, the paired organoids established using CRC primary tumor and its TD specimens will provide valuable tools to study tumorigenicity, metastasis and chemoresistance in CRC. Notably, these models will provide novel insights to study tumor heterogeneity and lipid metabolism in CRC.
Dayuan Yin alleviates symptoms of HCoV-229E-induced pneumonia and modulates the Ras/Raf1/MEK/ERK pathway
Authors: Li Rui , Zhang Wen , Huang Bei , Sun Guotong , Xie Yifei , Song Junke , Wang Shumei , Du Guanhua 
Published: November 2024

AbstractViral pneumonia is characterized by inflammation in the lungs triggered by respiratory viruses. Dayuan Yin (DYY), a traditional Chinese medicine formula known for treating infectious diseases, is hypothesized to offer therapeutic benefits in treating viral pneumonia, although its specific molecular impacts remain understudied. This study aimed to evaluate the therapeutic effects of DYY in mitigating HCoV-229E virus-induced pneumonia in mice. This study employed an HCoV-229E virus-infected mouse model to investigate the therapeutic potential and underlying molecular mechanisms of DYY on virus-induced pneumonia. The respiratory function and organ indices post-treatment were assessed. Lung tissue and tracheal lesions were evaluated via immunohistochemistry. Spleen immune cell composition was analyzed using flow cytometry. Inflammatory cytokines and viral loads were quantified using hypersensitive multiplex electrochemiluminescence method and PCR analysis, respectively. The expression levels of MAS1, Ras, Raf1, MEK1/2, and ERK1/2 in lung tissues were determined through western blot analysis. DYY significantly improved respiratory function, and reduced organ pathology in infected mice. It effectively decreased viral loads and inflammatory cytokines such as IL-6, IL-1β, and TNF-α in lung tissues. Enhancements in immune response were evidenced by increased CD4/CD8 ratios in the spleen. DYY also notably upregulated MAS1 protein levels and suppressed the activation of the Ras/Raf1/MEK/ERK signaling pathway. DYY enhanced respiratory function and exerted significant antiviral and immunomodulatory effects in mice infected with the HCoV-229E virus, primarily by modulating MAS1 expression and inhibiting the Ras/Raf1/MEK/ERK pathway.
Graphical Abstract
In Vitro Assessment of Injectable Bone Marrow Aspirate Concentrates Compared to Injectable Platelet-Rich Fibrin
Authors: Fujioka-Kobayashi Masako , Koyanagi Masateru , Inada Ryo , Miyasaka Ayako , Satomi Takafumi 
Published: November 2024

Abstract
BACKGROUND:
Injectable platelet-rich fibrin (iPRF), a liquid form of PRF that is prepared from peripheral blood without anticoagulants, promotes tissue wound healing and regeneration. The present study focused on iPRF-like bone marrow aspirate concentrate (iBMAC) prepared without anticoagulant, and the regenerative potential of iPRF and iBMAC was compared in vitro.

METHODS:
iPRF and iBMAC were prepared from the same New Zealand white rabbits. The cytocompatibility and regenerative potential of each concentrate were evaluated using primary rabbit gingival fibroblasts and osteoblasts.

RESULTS:
Both gingival fibroblasts and osteoblasts treated with each concentrate exhibited excellent cell viability. Interestingly, compared to cells treated with iPRF, cells treated with iBMAC demonstrated significantly greater migration potential. Furthermore, higher mRNA levels of transforming growth factor-β (TGF-β), vascular endothelial growth factor (VEGF), and collagen I (COL1) were observed in gingival fibroblasts treated with iBMAC than in those treated with iPRF. Compared with osteoblasts treated with iPRF, osteoblasts treated with iBMAC exhibited greater differentiation potential, as indicated by increased osteocalcin (OCN) expression and mineralization capability.

CONCLUSION:
The results of the in vitro study suggest that, compared with iPRF, iBMAC may promote wound healing and bone regeneration more effectively. However, further preclinical and clinical studies are needed to confirm the regenerative potential of iBMAC in the body.
Transcultural adaptation and validation of the Eating Self-Efficacy Brief Scale (ESEBS): the Brazilian version
Authors: Feoli Ana Maria Pandolfo , da Silva Tainá Lopes , de Souza Urbanetto Janete , D’Amico Monica , Cerolini Silvia , Lombardo Caterina 
Published: November 2024

Abstract
Purpose
This study aimed to develop and validate the Brief Eating Self-Efficacy Scale (ESEBS-BR) in Brazilian Portuguese, addressing the lack of tools to assess eating self-efficacy beliefs in Portuguese-speaking populations.

Method
The study sought to cross-culturally adapt the scale, evaluate its internal structure, validate its reliability and validity, and explore potential associations between eating self-efficacy and eating behaviors. The cross-cultural adaptation process involved translation and back-translation, expert committees, and pre-testing. Scale validation was conducted with 228 participants, including reliability analyses, confirmatory factor analysis, and correlations with established measures of eating behavior.

Results
The ESEBS-BR, maintaining all 8 original items, demonstrated high reliability, with a two-factor structure model confirmed by confirmatory factor analysis. Significant correlations were found between ESEBS-BR scores and measures of binge eating and eating behaviors, validating its criterion validity.

Conclusions
The development and validation of the ESEBS-BR represent a significant contribution to the assessment of eating self-efficacy in the Brazilian population. The scale proved to be sensitive, reliable, and valid, offering an important tool for research and clinical interventions related to eating behavior.
Level of evidence: V, descriptive cross-sectional study.
Coevolution and dynamic processes: an introduction to this issue and avenues for future research
Authors: Almudi Isabel , Fatas-Villafranca Francisco , Foster John , Potts Jason 
Published: November 2024

AbstractIn this paper, drawing upon previous contributions to modern Schumpeterian economics, we argue that coevolution in economic systems operates when considering several evolving populations within a socio-economic system, these populations mutually shape their respective selection, learning, and/or novelty generation mechanisms. The properties that arise from coevolution should be analyzed as emerging from multiple populations in co-determination. The notion of coevolution appears not only in Schumpeterian economics but, in general, in many branches of heterodox thought. Likewise, it can also be found in Biology, Sociology, Political Science, History, Philosophy, Law, and Computational studies. In this introduction, after providing a neat definition of coevolution, we illustrate with formal examples how coevolution can be represented and, potentially, empirically tested. Finally, we present the contributions to the SI and suggest avenues for future research.
Investigating spatial and temporal characteristics and elements influencing running among residents of Nanchang city in green open areas
Authors: Fang Tianwei , Zhou Linguo , Cai Zhenrao , Tan Zhijin , Chen Chao , Zheng Jiali , Fang Chaoyang 
Published: November 2024

AbstractUnderstanding the exercise behaviors of urban residents in green open spaces is crucial for optimizing future urban spatial planning and improving residents’ quality of life. This study provides an in-depth analysis of residents’ running behaviors across 87 green open spaces in Nanchang City. Running data collected from the exercise platform Keep, coupled with urban geographic data and field research, reveals the spatial distribution and temporal variations of running activities, including running flow, distance, time, and pace. A set of indicator systems, such as total area, landscape form index, surrounding residential land area, and road conditions, is used to analyze its relationship with the environmental elements of green open spaces. The research results show that the running flow, running distance, and running time in green open spaces in Nanchang City are significantly influenced by different objective elements, while running pace shows little correlation with these environmental elements. Specifically, the study highlights the significant impact of elements such as total area and road length, as well as the number of nearby bus stops and population density, on residents’ running behaviors in Nanchang City’s green open spaces.
Enhancing Expert Decision-Making for Wastewater Treatment Plants with Seidel Laplacian Energy and Cosine Similarity Measure in Intuitionistic Fuzzy Graphs
Authors: Atheeque A. Mohamed , Basha S. Sharief 
Published: November 2024

AbstractWastewater treatment facilities’ main goal is to protect the public and environment from the hazardous and poisonous materials found in wastewater. Water treatment facilities were developed to speed up the natural process of cleansing water. A novel cosine similarity measure across intuitionistic fuzzy graphs has been proven to be more effective than certain present ones in group decision-making issues using example verification. This paper provides a unique approach for calculating expert-certified, well-known scores by finding the ambiguous information of intuitionistic fuzzy preference relations as well as the regular cosine similarity grades from one separable intuitionistic fuzzy preference relation to another. The new technique considers both "objective" and "subjective" information provided by experts. Using intuitionistic fuzzy preference relations, we provide workable techniques for judging experts’ eligible reputational ratings. This can be used to raise or decrease the relevance of the stated criteria in an evaluation that takes into account several competing elements. We give a solution to a decisional problem by using two effective methods: the newly constructed cosine similarity measure and the Seidel Laplacian energy (SLe+) of an intuitionistic fuzzy graph. Finally, two working procedures and circumstances are offered to show the effectiveness and superiority of the proposed techniques.
Random Oversampling-Based Diabetes Classification via Machine Learning Algorithms
Authors: Ashisha G. R. , Mary X. Anitha , Kanaga E. Grace Mary , Andrew J. , Eunice R. Jennifer 
Published: November 2024

AbstractDiabetes mellitus is considered one of the main causes of death worldwide. If diabetes fails to be treated and diagnosed earlier, it can cause several other health problems, such as kidney disease, nerve disease, vision problems, and brain issues. Early detection of diabetes reduces healthcare costs and minimizes the chance of serious complications. In this work, we propose an e-diagnostic model for diabetes classification via a machine learning algorithm that can be executed on the Internet of Medical Things (IoMT). The study uses and analyses two benchmarking datasets, the PIMA Indian Diabetes Dataset (PIDD) and the Behavioral Risk Factor Surveillance System (BRFSS) diabetes dataset, to classify diabetes. The proposed model consists of the random oversampling method to balance the range of classes, the interquartile range technique-based outlier detection to eliminate outlier data, and the Boruta algorithm for selecting the optimal features from the datasets. The proposed approach considers ML algorithms such as random forest, gradient boosting models, light gradient boosting classifiers, and decision trees, as they are widely used classification algorithms for diabetes prediction. We evaluated all four ML algorithms via performance indicators such as accuracy, F1 score, recall, precision, and AUC-ROC. Comparative analysis of this model suggests that the random forest algorithm outperforms all the remaining classifiers, with the greatest accuracy of 92% on the BRFSS diabetes dataset and 94% accuracy on the PIDD dataset, which is greater than the 3% accuracy reported in existing research. This research is helpful for assisting diabetologists in developing accurate treatment regimens for patients who are diabetic.
Insights into Algorithmic Decision-Making Systems via a Decolonial-Intersectional Lens: A Cross-Analysis Case Study
Authors: Alba Joel Tyler 
Published: November 2024

Abstract
As the line between governance, technology, and decision-making becomes ever-blurred, advocates and disparagers of Artificial Intelligence (AI) debate the impacts of its societal integration. Despite efforts to promote Fairness, Accountability, Transparency, and Ethics (FATE), vulnerable populations continue to be systematically marginalized and made “invisible” by the racialised, classed, and colonial institutions buttressing Algorithmic Decision-Making Systems (ADMS). To address these sociotechnical risks and acknowledge our privileged, Western “standpoint epistemology,” we employ a “metaparadigm perspective” to engage the literature and case studies through a critical theory lens. The cross-analysis of three case studies: Systems Theory, the United States’ “Blueprint for an AI Bill of Rights,” and predictive policing demonstrate that our current risk mitigation frameworks are incapable of addressing transhistorical injustices. Therefore, we call for operationalizing intersectional risk theory via the decolonial turn to address ADMS risks. On that note, we propose three algorithmic accountability recommendations. We theorize that through a reformulation of FATE principles to that of the algorithmic (decolonial) self, understanding power imbalances through marginalized risk discourses, and advancing intersectional ML decision-making practices, policymakers may be better positioned to enact equitable and just ADMS.
Insights for the immunotherapy in malignant melanoma: a new revolution
Authors: Xuan Jiangying , Gao Zixu , Wei Chuanyuan , Gu Jianying 
Published: November 2024

AbstractMelanoma is highly malignant and mainly occurs in the skin. Melanoma was first used in immunotherapy due to its high immunogenicity, which changed the therapeutic pattern of tumor patients. However, many patients do not benefit from immunotherapy because of its side effects, resistance and low sensitivity to anti-tumor responses. Recently, with a deeper understanding of tumor development, immunotherapy has emerged as a new era. In this review, we first elucidate the effects of immunotherapy on the tumor microenvironment (TME) and the mechanisms involved, as well as how immune cells exert immune promoting or immunosuppressive effects on the impact of immunotherapy. Then we mention the latest progress in immunotherapy of immune checkpoint blockades, such as CTLA-4, PD-1/PD-L1, TIM3 and LAG3. Biotherapies such as adoptive therapy, RNA vaccines and oncolytic virus are also included. Next, we elaborate on the problems and existing data of immunotherapy to explain the current clinical situation. To address these challenges, we introduce the combination strategies such as targeted therapy, radiotherapy, chemotherapy, and biotherapy. Numerous clinical data have showed that the combined treatment can significantly improve the therapeutic effect of melanoma patients. Overall, understanding the specific introduction of immunotherapy in melanoma may provide fresh perspectives for scientists to investigate therapeutic targets and identify new treatments.
Machine learning in neuroimaging of epilepsy: a narrative review
Authors: Perillo Teresa , Perillo Sandra , Pinto Antonio 
Published: November 2024

AbstractEpilepsy is defined as a disorder of the brain characterized by an enduring predisposition to epileptic seizures. It is a heterogeneous condition characterized by multiple seizure types and syndromes, numerous etiologies, and variable prognoses. Neuroimaging plays a crucial role in the evaluation of epilepsy, with magnetic resonance imaging being the best imaging modality to diagnose epileptogenic lesions. In recent years, Machine Learning has proved to be useful in epilepsy to enhance diagnosis and treatment. Aim of this review is to describe the most recent advanced of Machine Learning in Epilepsy.
Recommendations on Imaging in the Context of Alzheimer’s Disease-Modifying Therapies from the CCNA Imaging Workgroup
Authors: Duchesne Simon , Collins D. Louis , Barlow Laura , Bartha Robert , Black Sandra , Chertkow Howard , Dadar Mahsa , Joshi Manish , Rosa-Neto Pedro , Soucy Jean-Paul , Smith Eric E. 
Published: November 2024

ABSTRACT

Background:
Disease-modifying therapies (DMTs) for Alzheimer’s disease (AD) are emerging following successful clinical trials of therapies targeting amyloid beta (Aβ) protofibrils or plaques. Determining patient eligibility and monitoring treatment efficacy and adverse events, such as Aβ-related imaging abnormalities, necessitates imaging with MRI and PET. The Canadian Consortium on Neurodegeneration in Aging (CCNA) Imaging Workgroup aimed to synthesize evidence and provide recommendations on implementing imaging protocols for AD DMTs in Canada.

Methods:
The workgroup employed a Delphi process to develop these recommendations. Experts from radiology, neurology, biomedical engineering, nuclear medicine, MRI and medical physics were recruited. Surveys and meetings were conducted to achieve consensus on key issues, including protocol standardization, scanner strength, monitoring protocols based on risk profiles and optimal protocol lengths. Draft recommendations were refined through multiple iterations and expert discussions.

Results:
The recommendations emphasize standardized acquisition imaging protocols across manufacturers and scanner strengths to ensure consistency and reliability of clinical treatment decisions, tailored monitoring protocols based on DMTs’ safety and efficacy profiles, consistent monitoring regardless of perceived treatment efficacy and MRI screening on 1.5T or 3T scanners with adapted protocols. An optimal protocol length of 20–30 minutes was deemed feasible; specific sequences are suggested.

Conclusion:
The guidelines aim to enhance imaging data quality and consistency, facilitating better clinical decision-making and improving patient outcomes. Further research is needed to refine these protocols and address evolving challenges with new DMTs. It is recognized that administrative, financial and logistical capacity to deliver additional MRI and positron emission tomography scans require careful planning.
Monitoring-supported value generation for managing structures and infrastructure systems
Authors: Kamariotis Antonios , Chatzi Eleni , Straub Daniel , Dervilis Nikolaos , Goebel Kai , Hughes Aidan J. , Lombaert Geert , Papadimitriou Costas , Papakonstantinou Konstantinos G. , Pozzi Matteo , Todd Michael , Worden Keith 
Published: November 2024

Abstract
To maximize its value, the design, development and implementation of structural health monitoring (SHM) should focus on its role in facilitating decision support. In this position paper, we offer perspectives on the synergy between SHM and decision-making. We propose a classification of SHM use cases aligning with various dimensions that are closely linked to the respective decision contexts. The types of decisions that have to be supported by the SHM system within these settings are discussed along with the corresponding challenges. We provide an overview of different classes of models that are required for integrating SHM in the decision-making process to support the operation and maintenance of structures and infrastructure systems. Fundamental decision-theoretic principles and state-of-the-art methods for optimizing maintenance and operational decision-making under uncertainty are briefly discussed. Finally, we offer a viewpoint on the appropriate course of action for quantifying, validating, and maximizing the added value generated by SHM. This work aspires to synthesize the different perspectives of the SHM, Prognostic Health Management, and reliability communities, and provide directions to researchers and practitioners working towards more pervasive monitoring-based decision-support.