bluuebunny/crossref_metadata_2025_split huggingface dataset:
Therapeutic potential of Lawsonia inermis Linn: a comprehensive overview
Authors: Batiha Gaber El-Saber , Teibo John Oluwafemi , Shaheen Hazem M. , Babalola Benjamin Ayodipupo , Teibo Titilade Kehinde Ayandeyi , Al-kuraishy Hayder M. , Al-Garbeeb Ali I. , Alexiou Athanasios , Papadakis Marios 
Published: November 2023

AbstractLawsonia inermis Linn, commonly known as henna, is a member of the Lythraceae family and has been found to contain a variety of compounds with both industrial and medicinal applications in its stem, bark, roots, flowers, and seeds. This report provides a comprehensive review of the bioactive components, pharmacological activities, pharmacokinetics, and pharmacological side effects of Lawsonia inermis. Relevant materials were gathered from Google Scholar, PubMed, Scopus, and Web of Science and reviewed for important properties and updates about the plant. Lawsonia inermis contains a variety of bioactive compounds, including flavonoids, coumarins, triterpenoids, steroids, xanthones, polyphenols, fatty acids, alkaloids, quinones, tannins, leucocyandin, epicatechin, catechin, and quercetin. The plant is been traditionally used to treat numerous conditions, including ulcers, bronchitis, lumbago, hemicrania, leukoderma, scabies, boils, ophthalmic disorders, hair loss, and jaundice. It has also been found to possess a range of pharmacological activities, including antioxidant, anti-inflammatory, analgesic, antiparasitic, hepatoprotective, antifungal, antitumor, wound healing, and hypoglycemic effects. The potential of Lawsonia inermis for various biological applications is promising, and further studies are needed to fully explore its therapeutic benefits for various diseases of public health. Concern advances in drug development could enable the characterization of various bioactive constituents and facilitate their development and application for the benefit of humanity.
Open Innovation in Schools: A New Imperative for Organising Innovation in Education?
Authors: Pietsch Marcus , Cramer Colin , Brown Chris , Aydin Burak , Witthöft Jasmin 
Published: November 2023

AbstractSchools are considered knowledge-creating organisations that find it difficult to develop and implement innovations on their own. Knowledge mobilisation is seen as the key to overcoming this problem. In particular, the use of external sources of knowledge is regarded as an important lever for change. However, there is a lack of concepts and empirical studies in educational research on the extent to which external knowledge is used for innovation in schools. Based on a sample of N = 411 schools, this article explores whether the concept of open innovation can be used in the context of education. Originating from the field of innovation research, open innovation regimes are seen as imperative if organisations are to create and benefit from technology. Multinomial logistic regression analyses show that mobilising external knowledge significantly increases the likelihood of implementing innovations in schools. A machine-learning approach reveals that it is necessary to tailor open innovation regimes to the specific conditions of any given school. In particular, with regard to the use of new technologies and innovations in the field of digitalisation, open innovation can be a lever for change.
Low circulating levels of miR-17 and miR-126-3p are associated with increased mortality risk in geriatric hospitalized patients affected by cardiovascular multimorbidity
Authors: Marchegiani Francesca , Recchioni Rina , Di Rosa Mirko , Piacenza Francesco , Marcheselli Fiorella , Bonfigli Anna Rita , Galeazzi Roberta , Matacchione Giulia , Cardelli Maurizio , Procopio Antonio Domenico , Corsonello Andrea , Cherubini Antonio , Antonicelli Roberto , Lombardi Giovanni , Lattanzio Fabrizia , Olivieri Fabiola 
Published: November 2023

AbstractMultiMorbidity (MM), defined as the co-occurrence of two or more chronic conditions, is associated with poorer health outcomes, such as recurrent hospital readmission and mortality. As a group of conditions, cardiovascular disease (CVD) exemplifies several challenges of MM, and the identification of prognostic minimally invasive biomarkers to stratify mortality risk in patients affected by cardiovascular MM is a huge challenge. Circulating miRNAs associated to inflammaging and endothelial dysfunction, such as miR-17, miR-21-5p, and miR-126-3p, are expected to have prognostic relevance. We analyzed a composite profile of circulating biomarkers, including miR-17, miR-21-5p, and miR-126-3p, and routine laboratory biomarkers in a sample of 246 hospitalized geriatric patients selected for cardiovascular MM from the Report-AGE INRCA database and BioGER INRCA biobank, to evaluate the association with all-cause mortality during 31 days and 12 and 24 months follow-up. Circulating levels of miR-17, miR-126-3p, and some blood parameters, including neutrophil to lymphocyte ratio (NLR) and eGFR, were significantly associated with mortality in these patients. Overall, our results suggest that in a cohort of geriatric hospitalized patients affected by cardiovascular MM, lower circulating miR-17 and miR-126-3p levels could contribute to identify patients at higher risk of short- and medium-term mortality.
Some notes on the algebraic structure of linear recurrent sequences
Authors: Alecci Gessica , Barbero Stefano , Murru Nadir 
Published: November 2023

AbstractSeveral operations can be defined on the set of all linear recurrent sequences, such as the binomial convolution (Hurwitz product) or the multinomial convolution (Newton product). Using elementary techniques, we prove that this set equipped with the termwise sum and the aforementioned products is an R-algebra, given any commutative ring R with identity. Moreover, we provide explicitly a characteristic polynomial of the Hurwitz product and Newton product of any two linear recurrent sequences. Finally, we also investigate whether these R-algebras are isomorphic, considering also the R-algebras obtained using the Hadamard product and the convolution product.
Clinical, Epidemiological, and Laboratory Features of Mayaro Virus Infection: a Systematic Review
Authors: Silva-Ramos Carlos Ramiro , Mejorano-Fonseca Julián A. , Hidalgo Marylin , Rodríguez-Morales Alfonso J. , Faccini-Martínez Álvaro A. 
Published: November 2023

Abstract
Purpose of Review
Mayaro virus is an arbovirus that circulates in a wild cycle transmitted by Haemagogus janthinomys; however, case reports in urban areas suggest that other arthropods could be acting as vectors. It shares several similarities with Chikungunya virus, being highly probable that many cases are being misdiagnosed. The disease caused by this pathogen is known as Mayaro fever, which is restricted to regions of Central and South America, mainly the Amazon rainforest. Cases of Mayaro fever have continuously been increasing, suggesting that Mayaro virus would become another major epidemic arbovirus in America.

Recent Findings
Four electronic databases were searched for articles in English and Spanish using the keywords “Mayaro” and “case.” A total of 234 cases were retrieved in the 23 selected manuscripts. Of all cases, most of them were male young adults, reported as autochthonous cases occurring mainly in Peru and Brazil, who were performing activities in wild rural areas. Fever, arthralgia, headache, myalgia, and retro-orbital pain were the main clinical manifestations. Most of the patients had laboratory parameters within the normal range, joint complications were evidenced in few cases, and all of them recovered without specific treatment.

Summary
Mayaro fever appears to be a mild self-limited disease that affects mainly males at productive age that are in contact with wild environments. However, further studies are required to establish its true pathogenic potential of Mayaro virus.
Evaluation of Biodegradability and Biocompatibility of Pure Zinc Coated with Zinc Phosphate for Cardiovascular Stent Applications
Authors: Wu Guan-Lin , Yen Chin-En , Lin Yi-Syuan , Yeh Ming-Long 
Published: November 2023

Abstract
Purpose
Percutaneous coronary intervention is widely used as a primary treatment for cardiovascular diseases. In this regard, it has been revealed from studies that zinc is a potential material for use in stents due to its intrinsic physiological relevance, biocompatibility, biodegradability, and pro-regeneration properties. However, localized corrosion and burst release of zinc ions might cause an early implant failure and a risky environment for vascular remodeling. To resolve these drawbacks effectively, a coating of zinc phosphate on pure zinc was fabricated in this study using a microwave-assisted chemical conversion method.

Methods
In this study, a comprehensive analysis was conducted through materials characterization, electrochemical testing, immersion testing, in vitro testing, and hemocompatibility evaluation to quantify the effect of zinc phosphate coating on zinc cardiovascular stents.

Results
It is revealed that the microstructures of the coatings are mainly composed of zinc phosphate and sodium zinc phosphate. The presence of sodium zinc phosphate could improve corrosion behavior. The assessment of in vitro biocompatibility for the zinc phosphate coatings revealed satisfactory cell viability and a stable and smooth degradation surface for cell adhesion. Furthermore, the zinc phosphate coatings exhibited nonhemolytic properties and inhibitions to the adhesion of platelets.

Conclusion
The zinc phosphate coatings could exhibit a uniform degradation behavior and a positive biological effect on vascular remodeling, and therefore, these coatings could be a promising surface treatment used in stent optimization for zinc.
Analysis and Prediction of Dockless Shared Bike Demand Evolving Around Urban Rail Transit Stations: Case Study in Shenzhen, China
Authors: Zhao Yingping , Wu Yiling , Zhang Xinfeng , Wang Yaowei , Zhang Zhenduo , Lu Hongyu , Ma Dongfang 
Published: November 2023

AbstractThe emergence of dockless shared bikes (DSB) has led to their use as an important transfer mode to urban rail transit (URT) stations. However, in highly populated areas such as subway stations in peak hours, there is increasing concern about the imbalance between the demand and supply of shared bikes. To promote smoother subway transfer trips using shared bikes, it is very important to estimate the DSB demand, especially the disparity in the volume of bike pick-up and drop-off demand around subway stations. This research first utilizes the Shenzhen metro usage data and DSB usage data, analyzes data regarding subway and shared bike usage, discusses their potential transfer uses, and finds great disparity in DSB demand between different subway stations. The catchment area method is used to estimate bike usage as a potential transfer mode to the subway, where the catchment area is defined as a radius of 150 m from the subway station center. The DSB trip demand is categorized into two types: pick-up and drop-off. The most recent deep learning method, adaptive graph convolutional recurrent network (AGCRN), is used to predict the DSB demand more accurately because of its ability in enabling the modeling of relationships between entities in a self-adapted graph, and the prediction is compared with long short-term memory (LSTM), spatiotemporal neural network (STNN), diffusion convolutional recurrent neural network (DCRNN), and Graph WaveNet. Results show that methods with graphs (STNN, DCRNN, Graph WaveNet, and AGCRN) perform better than LSTM, and methods with adaptive graphs (Graph WaveNet and AGCRN) outperform methods with static graphs in terms of mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). DSB prediction results show that AGCRN performs the best in this study. More data, particularly land use data and URT station volume data, are expected to improve the predictive accuracy of the method due to potentially improved graph representation of station characteristics and subway station volume correlations. And with more accurate prediction results, it will be possible to achieve a better balancing strategy for bike operation optimization for better bike usage, and thus for a higher transfer rate of DSB to subway.
Time series quantum classifiers with amplitude embedding
Authors: Cuéllar M. P. , Cano C. , Ruiz L. G. B. , Servadei L. 
Published: November 2023

AbstractQuantum Machine Learning was born during the past decade as the intersection of Quantum Computing and Machine Learning. Today, advances in quantum computer hardware and the design of simulation frameworks able to run quantum algorithms in classic computers make it possible to extend classic artificial intelligence models to a quantum environment. Despite these achievements, several questions regarding the whole quantum machine learning pipeline remain unanswered, for instance the problem of classical data representation on quantum hardware, or the methodologies for designing and evaluating quantum models for common learning tasks such as classification, function approximation, clustering, etc. These problems become even more difficult to solve in the case of Time Series processing, where the context of past historical data may influence the behavior of the decision-making model. In this piece of research, we address the problem of Time Series classification using quantum models, and propose an efficient and compact representation of time series in quantum data using amplitude embedding. The proposal is capable of representing a time series of length n in $$log_2(n)$$

l
o

g
2

(
n
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computational units, and experiments conducted on benchmark time series classification problems show that quantum models designed for classification can also outperform the accuracy of classic methods.
A Comparison of the Most Intense Periods (MIPs) During Competitive Matches and Training Over an 8-Week Period in a Male Elite Field Hockey Team
Authors: Büchel Daniel , Döring Michael , Baumeister Jochen 
Published: November 2023

Abstract

Purpose
Wearables serve to quantify the on-court activity in intermittent sports such as field hockey (FH). Based on objective data, benchmarks can be determined to tailor training intensity and volume. Next to average and accumulated values, the most intense periods (MIPs) during competitive FH matches are of special interest, since these quantify the peak intensities players experience throughout the intermittent matches. The aim of this study was to retrospectively compare peak intensities between training and competition sessions in a male FH team competing in the first german division.

Methods
Throughout an 8-week in-season period, 372 individual activity datasets (144 datasets from competitive sessions) were recorded using the Polar Team Pro sensor (Kempele, Finland). MIPs were calculated applying a rolling window approach with predefined window length (1–5 min) and calculated for Total distance, High-Intensity-Running distance (> 16 km/h), Sprinting distance (> 20 km/h) and Acceleration load. Significant differences between training and competition MIPs were analysed through non-parametric statistical tests (P < 0.05).

Results
Analyses revealed higher MIPs during competition for all considered outcomes (P < 0.001). Effect size estimation revealed strongest effects for sprinting distance (d = 1.89 to d = 1.22) and lowest effect sizes for acceleration load (d = 0.92 to d = 0.49).

Conclusion
The present findings demonstrate that peak intensities during training do not reach those experienced during competitive sessions in a male FH team. Training routines such as manipulations of court-dimensions and team sizes might contribute to this discrepancy. Coaches should compare training and competition intensities to recalibrate training routines to optimize athletes’ preparation for competition.
The ECODENT Model for Enhancing Pro-environmental Behaviors in Dentists
Authors: Antoniadou Maria 
Published: November 2023

AbstractClimate change challenges people’s lives and sustainability. Environmental problems seem to derive from human behavior. Dentistry has a high ecological footprint that needs to be controlled worldwide. Thus, the behavior change of dentists is a necessary condition to improve the environmental situation. In this study, a system dynamics approach is used to analyze pro-environmental behavior in dentists. Systemic modelling and simulation allow researchers to predict how the population of non-eco-dentists will change if certain factors are affected and how much time will be needed by the system of a group of dental professionals to become eco-dentists. To this end, the ECODENT model is presented, which was developed using the Vensim software. It is based on the classic Word of Mouth (WOM) model, which simulates how the WOM influences the spread of information among colleagues and stakeholders (e.g., patients, auxiliary staff, and community). The model incorporates relevant factors such as dental income, state support, CaPex and OpEx, education hours, level of urbanization, and time to react. By changing each one of them, we can observe the changes in the groups of non-eco and eco dentists as well as the flow of the phenomenon/transition. The simulation of this model shows that by augmenting WOM parameters, state economic support, or education hours while diminishing CaPex and OpEx, changes in the pro-environmental behaviors of dentists will be reported in a shorter time. The ECODENT model can be used to further study pro-environmentalism in different dental groups and associations and design relevant eco-friendly educational programs.
Distilling base-and-meta network with contrastive learning for few-shot semantic segmentation
Authors: Chen Xinyue , Wang Yueyi , Xu Yingyue , Shi Miaojing 
Published: November 2023

AbstractCurrent studies in few-shot semantic segmentation mostly utilize meta-learning frameworks to obtain models that can be generalized to new categories. However, these models trained on base classes with sufficient annotated samples are biased towards these base classes, which results in semantic confusion and ambiguity between base classes and new classes. A strategy is to use an additional base learner to recognize the objects of base classes and then refine the prediction results output by the meta learner. In this way, the interaction between these two learners and the way of combining results from the two learners are important. This paper proposes a new model, namely Distilling Base and Meta (DBAM) network by using self-attention mechanism and contrastive learning to enhance the few-shot segmentation performance. First, the self-attention-based ensemble module (SEM) is proposed to produce a more accurate adjustment factor for improving the fusion of two predictions of the two learners. Second, the prototype feature optimization module (PFOM) is proposed to provide an interaction between the two learners, which enhances the ability to distinguish the base classes from the target class by introducing contrastive learning loss. Extensive experiments have demonstrated that our method improves on the PASCAL-5i under 1-shot and 5-shot settings, respectively.
Cross-species epigenetic regulation of nucleus accumbens KCNN3 transcripts by excessive ethanol drinking
Authors: Mulholland Patrick J. , Padula Audrey E. , Wilhelm Larry J. , Park Byung , Grant Kathleen A. , Ferguson Betsy M. , Cervera-Juanes Rita 
Published: November 2023

AbstractThe underlying genetic and epigenetic mechanisms driving functional adaptations in neuronal excitability and excessive alcohol intake are poorly understood. Small-conductance Ca2+-activated K+ (KCa2 or SK) channels encoded by the KCNN family of genes have emerged from preclinical studies as a key contributor to alcohol-induced functional neuroadaptations in alcohol-drinking monkeys and alcohol-dependent mice. Here, this cross-species analysis focused on KCNN3 DNA methylation, gene expression, and single nucleotide polymorphisms, including alternative promoters in KCNN3, that could influence surface trafficking and function of KCa2 channels. Bisulfite sequencing analysis of the nucleus accumbens tissue from alcohol-drinking monkeys and alcohol-dependent mice revealed a differentially methylated region in exon 1A of KCNN3 that overlaps with a predicted promoter sequence. The hypermethylation of KCNN3 in the accumbens paralleled an increase in the expression of alternative transcripts that encode apamin-insensitive and dominant-negative KCa2 channel isoforms. A polymorphic repeat in macaque KCNN3 encoded by exon 1 did not correlate with alcohol drinking. At the protein level, KCa2.3 channel expression in the accumbens was significantly reduced in very heavy-drinking monkeys. Together, our cross-species findings on epigenetic dysregulation of KCNN3 represent a complex mechanism that utilizes alternative promoters to potentially impact the firing of accumbens neurons. Thus, these results provide support for hypermethylation of KCNN3 as a possible key molecular mechanism underlying harmful alcohol intake and alcohol use disorder.
Inhibition of SF3B1 improves the immune microenvironment through pyroptosis and synergizes with αPDL1 in ovarian cancer
Authors: Wang Shourong , Liu Yao , Xiao Huimin , Chen Zhongshao , Yang Xiaohang , Yin Jingjing , Li Yingwei , Yuan Cunzhong , Yan Shi , Chen Gang , Gao Qinglei , Kong Beihua , Sun Chaoyang , Song Kun 
Published: November 2023

AbstractOvarian cancer is resistant to immune checkpoint blockade (ICB) treatment. Combination of targeted therapy and immunotherapy is a promising strategy for ovarian cancer treatment benefit from an improved immune microenvironment. In this study, Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) cohorts were used to screen prognosis and cytotoxic lymphocyte infiltration-associated genes in upregulated genes of ovarian cancer, tissue microarrays were built for further verification. In vitro experiments and mouse (C57/BL6) ovarian tumor (ID8) models were built to evaluate the synergistic effect of the combination of SF3B1 inhibitor and PD-L1 antibody in the treatment of ovarian cancer. The results show that SF3B1 is shown to be overexpressed and related to low cytotoxic immune cell infiltration in ovarian cancer. Inhibition of SF3B1 induces pyroptosis in ovarian cancer cells and releases mitochondrial DNA (mtDNA), which is englobed by macrophages and subsequently activates them (polarization to M1). Moreover, pladienolide B increases cytotoxic immune cell infiltration in the ID8 mouse model as a SF3B1 inhibitor and increases the expression of PD-L1 which can enhance the antitumor effect of αPDL1 in ovarian cancer. The data suggests that inhibition of SF3B1 improves the immune microenvironment of ovarian cancer and synergizes ICB immunotherapy, which provides preclinical evidence for the combination of SF3B1 inhibitor and ICB to ovarian cancer treatment.