bluuebunny/crossref_metadata_2025_split huggingface dataset:
Capsaicin-entangled Multi-walled Carbon Nanotubes against Breast cancer: A Theoretical and Experimental Approach
Authors: Radhakrishna Govardhan Katta , Ramesh Sameera Hammigi , Almeida Shannon D , Sireesha Golla , Ramesh Soundarya , Theivendren Panneerselvam , Chidamabaram Kumarappan , Ammunje Damodar Nayak , Kunjiappan Selvaraj , Pavadai Parasuraman 
Published: April 2024

Abstract

The incidence of breast cancer has gradually increased of late due to many etiological factors. Conventional treatment strategies suffer from a lack of solubility, low bioavailability at the target site, lack of target specificity, and indiscriminate drug distribution which leads to drug resistance. The present study aimed to deliver capsaicin into breast cancer cells through folic acid-conjugated capsaicin-loaded carboxylic acid-functionalised multi-walled carbon nanotubes (FA-CAP-COOHMWCNTs). FA-CAP-COOHMWCNTs was formulated and characterized by FTIR, SEM, TEM, XRD, zeta potential, and particle size analyser. In silico studies demonstrated that the active molecule, capsaicin can strongly bind onto C-SRC kinase receptor to suppress cancer progression. The in-vitro cellular viability of MCF-7 after 24 h treatment with 100 µg × mL-1 of FA-CAP-COOHMWCNTs was found to be 29.27±2.59%and IC50 value was observed to be 22.71 µg × mL-1. Subsequently, in vivo anticancer activity of FA-CAP-COOHMWCNTs was performed against 7,12-dimethylbenz(a) anthracene (DMBA)-induced breast cancer in female Wistar rats. After 21 days of treatment with FA-CAP-COOHMWCNTs, breast cancer-induced rats showed a significant reduction in mammary tumor size, along with elevated levels of antioxidant and detoxifying enzymes in serum/breast tissue. The medium dose (5 mg × kg-1 b.w) FA-CAP-COOHMWCNTs showed the maximum antioxidant properties with a significant reduction in tumor size, almost equivalent to the standard drug, Doxorubicin. Histopathological studies also showed near-normal architecture of breast tissue. Altogether, it can be interpreted that FA-CAP-COOHMWCNTs have antiproliferative efficacy of breast tumor progression in breast cancer-induced rats.
Interaction between the sphingolipid membrane rafts and the concentration of the amyloid complex and analyses
Authors: Nutini Alessandro , Sohail Ayesha 
Published: April 2024

Abstract

Amyloidosis diseases are a heterogeneous group of pathologies that have a strong impact on current society both in global health and in the economic system of each country. The analysis of how the entry of the toxic species identified into the amyloid oligomeric elements occurs is the main objective of much research in the sector. In this work, we analyze a hypothesis of the entry of oligomers into the membrane whereby, after a massive recruitment of the sphingolipid rafts thanks to their connection with the oligomers, a cleansing of the membrane itself occurs. This pathological event creates a sort of pore which, subsequently stabilized, allows the oligomers to enter the cell. In this work, we build an entry model based on dynamic networks that investigates the fundamental mechanisms that lead to the entry of amyloid oligomers into the cell after having formed a critical concentration in the oligomer-membrane complex which, thanks to the conformational changes affecting the oligomers themselves, carries out the cleansing of the membrane and the entry of these pathogenic elements. The global dynamic network is divided into subnetworks which are expressed by algorithms that identify both time and threshold concentration as the fundamental moment in triggering the entire pathological process.
A framework to predict zoonotic reservoirs under data uncertainty: a case study on betacoronaviruses
Authors: Tonelli Andrea , Blagrove Marcus , Wardeh Maya , Marco Moreno Di 
Published: April 2024

Abstract

1. Modelling approaches aimed at identifying currently unknown hosts of zoonotic diseases have the potential to make high-impact contributions to global strategies for zoonotic risk surveillance. However, geographical and taxonomic biases in host-pathogen associations might influence reliability of models and their predictions. 2. Here we propose a methodological framework to mitigate the effect of biases in host–pathogen data and account for uncertainty in models’ predictions. Our approach involves identifying “pseudo-negative” species and integrating sampling biases into the modelling pipeline. We present an application on the Betacoronavirus genus and provide estimates of mammal-borne betacoronavirus hazard at the global scale. 3. We show that the inclusion of pseudo-negatives in the analysis improves the overall performance of our model significantly (AUC = 0.82 and PR-AUC = 0.48, on average) compared to a model that does not use pseudo-negatives (AUC = 0.75 and PR-AUC = 0.39, on average), reducing the rate of false positives. Results of our application unveil currently unrecognised hotspots of betacoronavirus hazard in subequatorial Africa, and South America. 4. Our approach addresses crucial limitations in host–virus association modelling, with important downstream implications for zoonotic risk assessments. The proposed framework is adaptable to different multi-host disease systems and may be used to identify surveillance priorities as well as knowledge gaps in zoonotic pathogens’ host-range.
Elite Sport: Values and Goals for Athletes with Spinal Cord Injury
Authors: Fizzotti Gabriella , Tsampas Nikolas , Vigorelli Vanessa 
Published: April 2024

Abstract

Introduction: Sport’s ability to challenge social paradigm and boost self-esteem has made it an important tool to empower people in disability world. An elite athlete is defined who has reached the highest level of performance in a particular sport.
Material and Methods: 19 elite athletes, eleven males, eight females, were recruited. A semi structured interview guide was employed with the goal of gaining specific information and making better comparison.
Results: Through the evaluation of the questionnaires four themes emerged: challenge, rebirth, empowerment and icon.
Conclusion: According to literature this paper indicates that sports can have significant physical and psychosocial health benefits for people with disability. Sports provide a great social environment and can be a tool to overcome social barriers.
Protomix: A Python Package for 1H-NMR Data Preprocessing
Authors: Zniber Mohammed , Huynh Tan-Phat 
Published: April 2024

Abstract

Background
NMR-based metabolomics is a field driven by technological advancements, necessitating the use of advanced preprocessing tools. Despite this need, there is a remarkable scarcity of comprehensive and user-friendly preprocessing tools. To bridge this gap, we have developed Protomix – a Python package designed for metabolomics research. Protomix offers a set of automated, efficient, and user-friendly signal processing steps, tailored to streamline, and enhance the preprocessing phase in metabolomics studies.
Results
This package presents a comprehensive preprocessing pipeline compatible with various data analysis tools. It encompasses a suite of functionalities for data extraction, preprocessing, and interactive visualization. Additionally, it includes a tutorial in the form of a Python Jupyter notebook, specifically designed for the analysis of 1D 1H-NMR metabolomics data related to prostate cancer and benign prostatic hyperplasia.
Conclusions
Protomix simplifies the preprocessing phase and incorporates interactive data visualization, making it accessible to researchers of varying programming skills. Ongoing development and optimization will continue to solidify its contribution to the metabolomics field.
Impact of teacher training in computational thinking on educational innovation and use of Artificial Intelligence
Authors: Vidal Inés María González , García María del Carmen Romero , González Alain Antonio Rodríguez 
Published: April 2024

Abstract

The increase in virtual educational spaces has meant that teachers develop digital skills required in the 21st century. However, few studies explore teacher training in virtual environments linked to the concept of universalization of computational thinking to improve their performance in virtual teaching environments. The objective of this work is to analyze the impact of teacher training in universalization of computational thinking to enhance educational innovation in virtual teaching environments and the use of artificial intelligence (AI). Supported by the Google Scholar, Scopus and Springerlink databases, a systematic review of 75 scientific publications was carried out, using quantitative and qualitative scientific methods, as well as the Prism Model, which allowed organizing quality and selection criteria. The results show that the definition of computational thinking changes as its understanding materializes and develops in new generations of teachers based on a contemporary vision of the concept applied to all citizens. The curricular integration of programming and robotics in teacher training improves their digital literacy. At the same time, the educational practices resulting from the dual dialectic: experiment-conceptualize and act-reflect, supported by Kold's model, could favor educational innovation and the use of AI in educational environments within an ethical and social framework. The TRACK model is pointed out as a strategy to implement computational thinking in teacher training, considering three types of knowledge (namely: technological, pedagogical and the contents of the discipline) necessary for STEAM education, as well as improving teacher performance. in virtual teaching environments.
A novel murine model of infective endocarditis mimics human pathophysiology
Authors: Bartsch Benedikt , Ackerschott Ansgar , Zaidi Muntadher Al , Jamin Raul Nicolas , Nazir Mariam Louis Fathy , Altrogge Moritz , Fester Lars , Lambertz Jessica , Coburn Mark , Nickenig Georg , Zimmer Sebastian , Weisheit Christina Katharina 
Published: April 2024

Abstract

Incidence of Infective endocarditis (IE) and its mortality rate despite optimal medical therapy remain high. Early diagnosis and treatment initiation are challenging because the involved immunological processes are poorly understood due to a lack of suitable in vivo models and their difference to human pathophysiology.
Objectives:
To establish a novel reproducible murine IE model, based on wire injury (WI) induced endothelial damage.
Methods:
IE was established by inducing endothelial damage via wire injury followed by bacterial challenge with S. aureus using 104–6 colony-forming units (CFU). Cross-sections of valvular leaflets were prepared for scanning electron microscopy (SEM) and immunofluorescence microscopy to visualize valvular invasion of macrophages, neutrophils, and S. aureus. Bacterial cultivation was carried out from blood and valve samples.
Results:
Wire injury induced endothelial damage was observed in all mice after wire-injury in SEM imaging. We reliably induced IE using 105 (85%) and 106 (91%) CFU S. aureus after wire injury. We found significant neutrophilia in the blood and increased valvular immune cell and bacterial accumulations in IE mice.
Conclusion:
Our model allows for reliable IE induction and analysis of bacterial vegetation and immune cell infiltration in vivo and ex vivo. Valvular immune cell infiltration was similar to human pathophysiology.
Reconv: Repeated Convolutional Strategies for Advanced Image Inpainting
Authors: Jini.P  , K.K Rajkumar 
Published: April 2024

Abstract

Image inpainting is a promising but challenging approach that fills in huge free-form empty areas in images. Most of the recent papers concentrate on splitting masked image into two matrices of valid and invalid elements which makes the system more complex. This paper proposes a novel algorithm named ReConv which uses a repeated standard convolution operation which treats valid and invalid elements of an image in the same manner. The outcomes of our suggested method, ReConv, shows that, in comparison to earlier approaches, our system produces outputs that are more adaptable with good quality for real world applications. Our suggested technique enables users quickly modify faces, eliminate distracting items, change image layouts, and remove unwanted text. An extensive comparison study on two types of datasets validates our method. The effectiveness of the suggested strategy was evaluated using different measures such as PSNR, SSIM and FID. The results show that our recommended approach excels in performance compared to the existing modern methods.
Decoding Cyber Threats: Advanced Techniques In Malware Detection, Analysis, and URL Detection
Authors: Padole Geeta , Annareddy Ruthvik Reddy , Mothukuri Avinash , Aela Abhiram , Varma Koushik 
Published: April 2024

Abstract

The digital security landscape keeps on changing, with malware posing an extraordinary and growing threat across various cyber domains. Malware or malicious software is getting more complicated as it uses different techniques to enter systems, exploit their weaknesses and compromise the integrity of data in them. The increasing number of malware attacks can be attributed to the rapid developments in technology and the increased connectivity through internet that gives better avenues for attackers.
To mitigate these threats, this paper presents a project that combines advanced machine learning techniques aimed at generating powerful analysis tools for both URL and malware detection thereby distinguishing itself from traditional approaches depending on signature-based detection and heuristic methods only. XGBoost and LightGBM algorithms are used in URL analysis to classify URLs based on their structural properties or patterns which distinguish benign URLs from malicious ones. Therefore, this enables early warning systems before users click on any web link. On the other hand, malware analysis is focused on examining executable files to determine whether they are safe or not. It incorporates a wide range of machine learning algorithms such as Random Forest, KNN, Logistic Regression and Gradient Boosting beyond conventional signature-based antivirus methods. This approach allows for the dynamic learning from new malware behaviors, improving detection rates and adapting to evolving threats.
Antimycobacterial and anticancer properties of Myrtus communis leaf extract
Authors: Mir Mushtaq A. , Memish Lamis Ahmad , Elbehairi Serag Eldin , Bashir Nasreena , Saif Faris , Shati Ali A. , Alfaifi Mohammad Y. , Alamri Ahmad M. , Alkahtani Sultan Ahmad , Ahmad Irfan 
Published: April 2024

Abstract

Background: Plant-derived products or extracts are widely used in folk/traditional medicine to treat several infections, ailments, or disorders. A well-known medicinal herb, Myrtus communis is an evergreen fragrant plant native to the Mediterranean region that has been used for ages in traditional medicine around the world.
Materials and methods: The microplate alamarBlue assay and the well diffusion method were used to evaluate the zone of inhibition and MIC, respectively. Double disc diffusion method was used to investigate the synergy between antibiotics and the extract. Crystal violet method was used to investigate the biofilm development. The SulphoRhodamine-B assay and DNA flow cytometry were used to investigate the proliferation and subsequent distribution of cells among different phases of the cell cycle. The apoptotic and necrotic phases of the cancer cells were examined using flow cytometry in conjunction with Annexin V-FITC/PI labeling. Using the IBM SPSS statistical program, a one-way ANOVA with Tukey's post-hoc test was employed for statistical analysis.
Results: The ethanolic leaf extract of M. communis showed a strong growth inhibitory effect (zone of inhibition: 20.3±1.1 - 26.3±2.5 mm, MIC: 4.88 - 312.5 µg/mL, and MBC: 39.07 - 1250 μg/mL) against several rapid and slow-growing mycobacterial strains in a dose-dependent manner. Damage to the cell wall of bacterial cells was determined to be the cause of the antimycobacterial action. The extract inhibited the biofilm formation (MBIC of 9.7 µg/mL) and eradicated the already formed mature and ultra-mature biofilms of M. smegmatis with MBEC of 78 µg/mL and 156 µg/mL, respectively. Additionally, the extract exhibited potent anticancer effects against diverse cancer cell lines of the breast (MCF-7), liver (HepG2), cervix (HeLa), and colon (HCT116) (IC50 for HCT116: 83 ± 2.5, HepG2: 53.3 ± 0.6, MCF-7: 41.5 ± 0.6, and HeLa: 33.3 ± 3.6) by apoptosis after arresting the cells in G1 phase of the cell cycle.
Conclusion: These results suggest that M. communis leaf extract is a potential source of secondary metabolites that could be further developed as potential anti-cancer and anti-mycobacterial agents to treat diverse types of cancers and mycobacterial infections.
In-situ and ex-situ experimental investigation on the chalcopyrite replacement in saline solution at 310–365 °C and 15–25 MPa
Authors: Wang Luying , Li Heping , Liu Qingyou , Lin Sen , Li Shengbin 
Published: April 2024

Abstract
The replacement of chalcopyrite in NaCl solutions was investigated from 310 to 365 °C and 15 to 25 MPa with ex-situ surface characterization techniques and in-situ electrochemical methods. The replacement products and reactions were analyzed ex situ using Raman spectroscopy and a scanning electron microscope (SEM) with an energy-dispersive X-ray spectrometer (EDS). Chalcopyrite dissolution was monitored by open circuit potential (OCP), polarization curves, and electrochemical impedance spectroscopy (EIS). The ex-situ and in-situ measurements show that: (1) elevated temperature and pressure promote the oxidative dissolution of chalcopyrite by different pathways; (2) the altered chalcopyrite surface consists of an outer layer of hematite and an inner layer of covellite; (3) at elevated temperature, removal of more copper from chalcopyrite surface enhances chalcopyrite dissolution, and favors hematite formation over covellite in the alteration rind; and (4) at elevated pressure, more iron ions diffuse through the passive covellite layer by point defects, promoting chalcopyrite replacement by covellite over hematite. This study provides experimental evidence on the replacement of chalcopyrite by covellite, the precipitation of hematite, and the exchange of Fe/Cu ions between chalcopyrite and hydrothermal brine.
A Comprehensive Youth Diabetes Epidemiological Data Set and Web Portal: Resource Development and Case Studies
Authors: McDonough Catherine , Li Yan Chak , Vangeepuram Nita , Liu Bian , Pandey Gaurav 
Published: April 2024

Background
The prevalence of type 2 diabetes mellitus (DM) and pre–diabetes mellitus (pre-DM) has been increasing among youth in recent decades in the United States, prompting an urgent need for understanding and identifying their associated risk factors. Such efforts, however, have been hindered by the lack of easily accessible youth pre-DM/DM data.

Objective
We aimed to first build a high-quality, comprehensive epidemiological data set focused on youth pre-DM/DM. Subsequently, we aimed to make these data accessible by creating a user-friendly web portal to share them and the corresponding codes. Through this, we hope to address this significant gap and facilitate youth pre-DM/DM research.

Methods
Building on data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, we cleaned and harmonized hundreds of variables relevant to pre-DM/DM (fasting plasma glucose level ≥100 mg/dL or glycated hemoglobin  ≥5.7%) for youth aged 12-19 years (N=15,149). We identified individual factors associated with pre-DM/DM risk using bivariate statistical analyses and predicted pre-DM/DM status using our Ensemble Integration (EI) framework for multidomain machine learning. We then developed a user-friendly web portal named Prediabetes/diabetes in youth Online Dashboard (POND) to share the data and codes.

Results
We extracted 95 variables potentially relevant to pre-DM/DM risk organized into 4 domains (sociodemographic, health status, diet, and other lifestyle behaviors). The bivariate analyses identified 27 significant correlates of pre-DM/DM (P<.001, Bonferroni adjusted), including race or ethnicity, health insurance, BMI, added sugar intake, and screen time. Among these factors, 16 factors were also identified based on the EI methodology (Fisher P of overlap=7.06×10-6). In addition to those, the EI approach identified 11 additional predictive variables, including some known (eg, meat and fruit intake and family income) and less recognized factors (eg, number of rooms in homes). The factors identified in both analyses spanned across all 4 of the domains mentioned. These data and results, as well as other exploratory tools, can be accessed on POND.

Conclusions
Using NHANES data, we built one of the largest public epidemiological data sets for studying youth pre-DM/DM and identified potential risk factors using complementary analytical approaches. Our results align with the multifactorial nature of pre-DM/DM with correlates across several domains. Also, our data-sharing platform, POND, facilitates a wide range of applications to inform future youth pre-DM/DM studies.
Exploring Anesthesia Provider Preferences for Precision Feedback: Preference Elicitation Study
Authors: Landis-Lewis Zach , Andrews Chris A , Gross Colin A , Friedman Charles P , Shah Nirav J 
Published: April 2024

Abstract

Background
Health care professionals must learn continuously as a core part of their work. As the rate of knowledge production in biomedicine increases, better support for health care professionals’ continuous learning is needed. In health systems, feedback is pervasive and is widely considered to be essential for learning that drives improvement. Clinical quality dashboards are one widely deployed approach to delivering feedback, but engagement with these systems is commonly low, reflecting a limited understanding of how to improve the effectiveness of feedback about health care. When coaches and facilitators deliver feedback for improving performance, they aim to be responsive to the recipient’s motivations, information needs, and preferences. However, such functionality is largely missing from dashboards and feedback reports. Precision feedback is the delivery of high-value, motivating performance information that is prioritized based on its motivational potential for a specific recipient, including their needs and preferences. Anesthesia care offers a clinical domain with high-quality performance data and an abundance of evidence-based quality metrics.

Objective
The objective of this study is to explore anesthesia provider preferences for precision feedback.

Methods
We developed a test set of precision feedback messages with balanced characteristics across 4 performance scenarios. We created an experimental design to expose participants to contrasting message versions. We recruited anesthesia providers and elicited their preferences through analysis of the content of preferred messages. Participants additionally rated their perceived benefit of preferred messages to clinical practice on a 5-point Likert scale.

Results
We elicited preferences and feedback message benefit ratings from 35 participants. Preferences were diverse across participants but largely consistent within participants. Participants’ preferences were consistent for message temporality (α=.85) and display format (α=.80). Ratings of participants’ perceived benefit to clinical practice of preferred messages were high (mean rating 4.27, SD 0.77).

Conclusions
Health care professionals exhibited diverse yet internally consistent preferences for precision feedback across a set of performance scenarios, while also giving messages high ratings of perceived benefit. A “one-size-fits-most approach” to performance feedback delivery would not appear to satisfy these preferences. Precision feedback systems may hold potential to improve support for health care professionals’ continuous learning by accommodating feedback preferences.