Scientific Research Research Articles

"I Heard of PrEP-I Didn't Think I Needed it." Understanding the Formation of HIV Risk Perception Among People Who Inject Drugs.

Uptake of pre-exposure prophylaxis medication (PrEP) to prevent HIV among people who inject drugs (PWID) remains extremely low in the United States. West Virginia's rising HIV incidence and highest drug overdose rate in the nation makes it an important locus for opioid use and HIV risk interaction. In this pilot study we pioneered the use of Cultural Theory among PWID to understand HIV-related risk perception arising from four contrasting modes of social organization. Carried out during an HIV outbreak, we explored PrEP uptake qualitatively as a window onto risk perception. Of the 26 interviewees, 18 were HIV- and despite the medication's free availability from the health center where recruitment took place, none had taken PrEP, half considering they were not at risk. Intimate couples who showed characteristics of 'enclaves' considered the boundary around themselves protective against HIV, creating a safe space or 'invisible risk group'. Higher HIV risk was perceived among those who were housed compared to those living homeless. Beliefs about the causation of the local HIV outbreak and the validity of scientific research corresponded with characteristics of the contrasting modes of social organization and the approach is promising for informing public health interventions among PWID.

Solitude in old age: a scoping review of conceptualisations, associated factors and impacts.

This study aimed to map the existing scientific research about solitude in older adults, focusing on its conceptualizations, associated factors, and impacts. A scoping review was conducted, searching Scopus, Web of Science, PubMed, and Academic Search Complete databases for publications from 1985 until December 2023. Twenty-four studies were included. The main findings suggest a wide-ranging conceptualisation of solitude, with most studies (n = 16) adopting neutral definitions such as "being alone, without communicating with others"; and a subset of studies adopting definitions that alluded to "positive solitude" (as the choice of being with oneself, underscoring potential benefits). Individual characteristics were identified as key associated factors of solitude. Both positive impacts and negative impacts were reported. The variability in conceptualisation and impacts likely stems from methodological and theoretical differences in approaching solitude. This underscores the need for additional research to establish a widely accepted conceptual framework. Such consensus could enhance the understanding of the conditions contributing to positive and negative impacts associated with solitude and inform targeted interventions.

ConvTrans-CL: Ocean time series temperature data anomaly detection based context contrast learning

Ocean temperature data anomaly detection is instrumental in monitoring environmental changes and implementing measures to alleviate adverse consequences. This holds immense importance for marine environmental observation and scientific inquiry. However, existing anomaly detection methods encounter significant challenges in extracting features from data, which severely affects the performance of anomaly detection. Existing models have limitations in capturing the local contextual distribution features and high stochastic distribution trends of ocean temperature data. Therefore, this paper introduces the ConvTrans-CL model, which integrates the Transformer encoder with causal convolution and employs a contrastive learning approach. Causal convolution extracts the distributional features of short-time subsequences within a sliding window and integrates them into the self-attention mechanism, enabling the model to focus on the localized features of the data. Contrastive learning efficiently captures the long-range dependencies of time-series data by distinguishing between pairs of subsequences with adjacent time intervals and pairs of non-adjacent subsequences. This enables the model to capture the trend of high stochasticity in the distribution of the temperature data. Finally, we select temperature data from two sea areas that are susceptible to multiple environmental factors for experiments and compare the feature extraction and anomaly detection capabilities of ConvTrans-CL with other methods, confirming the superior performance of our method.

Quercetin as the primary xanthine oxidase inhibitor compound in Maclura tricuspidata leaf.

Maclura tricuspidata (MT) leaf demonstrated various health benefits, notably the inhibition of xanthine oxidase (XOD) activity, which is crucial in the management of hyperuricaemia and many diseases related to oxidative stress. This study aimed to identify the primary compound responsible for this inhibitory effect. Through a systematic investigation, MT leaf extracts were subjected to solvent-solvent partitioning using ethyl acetate, n-hexane, n-butanol, and dichloromethane. Further purification involved adsorption and desorption using Amberlite XAD-2 resin, followed by column chromatography on Silica Gel and Sephadex LH-20. The purified compounds were analysed using UPLC-QTOF-MS coupled with NMR spectroscopy. Our findings identified quercetin, a phenolic compound, as the most significant inhibitor of XOD activity in MT leaf, with an IC50 value of 212.92 μg/ml. This is the first report of purifying and identifying a single compound responsible for XOD inhibition in MT.

Polyploidy in maize: from evolution to breeding.

Polyploidy played an important role in the evolution of the three most important crops: wheat, maize and rice, each of them providing a unique model for studying allopolyploidy, segmental alloploidy or paleopolyploidy. However, its genetic and evolutionary role is still vague. The undelying mechanisms and consequences of polyploidy remain fundamental objectives in the study of eukaryotes. Maize is one of the underutilized crops at the polyploid level. This species has no stable natural polyploids, the existing ones being artificially obtained. From the experimental polyploid series of maize, only the tetraploid forms (4n = 40) are of interest. They are characterized by some valuable morphological, physiological and biochemical features, superior to the diploid forms from which they originated, but also by some drawbacks such as: reduced fertility, slower development, longer vegetation period, low productivity and adaptedness. Due to these barriers to using tetraploids in field production, maize tetraploids primarily found utility in scientific studies regarding genetic variability, inbreeding, heterosis and gene dosage effect. Since the first mention of a triploid maize plant to present, many scientists and schools, devoted their efforts to capitalize on the use of polyploidy in maize. Despite its common disadvantages as a crop, significant progress in developing tetraploid maize with good agronomic performance was achieved leading to registered tetraploid maize varieties. In this review we summarize and discuss the different aspects of polyploidy in maize, such as evolutionary context, methods of induction, morphology, fertility issue, inheritance patterns, gene expression and potential use.

Derivatization, solid-phase microextraction and gas chromatography for determination of dialdehydes—biomarkers of oxidative stress

Dialdehydes are often used as biomarkers, molecules of interest for diagnosis, medical studies, and scientific research. A well-known example is malondialdehyde, which is a biomarker of oxidative stress formed during lipid peroxidation. We used combination of derivatization with O-(1,2,3,4,5)-pentafluorobenzylhydroxylamine, headspace solid-phase microextraction and gas chromatography with flame ionization detector for the analysis of dialdehydes. This combination allows to ensure the stability of analytes by derivatization, to separate them from the matrix, to preconcentrate by solid-phase microextraction, and to perform determination of dialdehydes. All the parameters affecting sensitivity and selectivity (pH and time of derivatization, sorption time, temperature of sorption, salt addition) were studied specifically for malondialdehyde, glyoxal, and methylglyoxal. The addition of salting-out agent resulted in decrease in the signal; therefore, a possible matrix effect was tested using simulated body fluid as model solution with ion composition similar to blood plasma. Established conditions for this method for dialdehydes showed 84–102% recovery in the model solution and allowed to reach the limits of quantification 0.2–0.8 μmol/L, which is low enough for the analysis of real samples. This method has the potential to be automated and is environmentally friendly because it does not require organic solvents and single-used filters or cartridges.

Does Being In-Person Matter? Demonstrating the Feasibility and Reliability of Fully Remote Observational Data Collection.

Many conventional research methods employed in randomized controlled trials were not possible during the height of the COVID-19 pandemic. In particular, behavioral observations are nearly universally gathered in-person. Observational methods are valued for the rich, informative data they produce in comparison to non-observational methods and are a cornerstone of parenting and family research. COVID provided the opportunity to, and indeed necessitated, the transition to fully remote observation. However, little to no studies have investigated whether remotely collected observational data are methodologically sound. This paper assesses the feasibility of remote data collection by describing the transition between in-person and fully remote observational data collection during a Sequential, Multiple Assignment, Randomized Trial (SMART) of a parenting program that took place both before and during the pandemic. Using mixed-methods data from coders, the overall quality of video-recorded data collected both before and during COVID was examined. Coder reliability over time was assessed with intraclass correlation coefficients. Results suggest that the frequency of audio problems, the severity of visual problems, and the level of administration challenges decreased after transitioning to remote data collection. Additionally, coders showed good to excellent reliability coding remotely collected data, and reliability even improved on some measured tasks. Although challenges to remote data collection exist, this study demonstrated that observational data can be collected feasibly and reliably. As observational data collection is a key method to assess parenting practices, these findings should improve researcher confidence in utilizing remote observational methods in prevention science.

A reconstruction method of stress fields, bending moments and wave parameters for floating nuclear power plants through regular wave identification

Floating nuclear power plants (FNPPs) have the potential to supply abundant electricity, hot water, and freshwater resources for offshore islands and offshore oil and gas extraction platforms. However, the consequences of a nuclear reactor accident can be extremely serious. In addition, there may be many uncertain factors in the actual operation of FNPPs. These factors lead to higher structural safety requirements. So， developing more scientific structural monitoring methods is an effective way to enhance the structural safety of FNPP. Traditional hull monitoring schemes can only track bending and torsional forces on specific cross-sections, failing to identify the overall stress field of the entire vessel structure. The paper proposes a reconstruction method for the stress field, bending moment distribution, and wave parameters of FNPP using regular wave identification. Initially, irregular waves are decomposed into a linear combination of multiple regular waves with different amplitudes and frequencies. Subsequently, the strains induced by these regular waves on the FNPP are utilized as basic functions to establish a mathematical model for reconstructing the stress field. The stress field, bending moment distribution, and wave parameters of the FNPP can be reconstructed by identifying the combination coefficients of the regular wave loads. The ill-posedness of the mathematical model for stress field reconstruction has the potential to significantly impact the accuracy of the stress field reconstruction, which was addressed by optimizing the sensor installation position in this study. Meanwhile, the optimization of the sensor installation position greatly reduces the dimensionality of mathematical models. Finally, a numerical study, i.e. FNPP being designed by China, was used to validate the accuracy of the proposed stress field reconstruction algorithm. The reconstructed stress field of the FNPP closely matched the original stress distribution under various irregular wave conditions.

The Impact of Metaverse-Enabled Digital Transformation on Tourism Marketing

This exploratory study aims to provide a comprehensive overview of the published academic material on metaverse in the context of tourism marketing. This is done by examining leading journals with published articles related to the phenomenon, the authors shaping the discourse and conducting coauthorship analysis to reveal the collaborative networks. Additionally, the study identified common themes among the published documents to comprehensively understand the metaverse’s multifaceted impact. Drawing from peer‐reviewed research articles published until November 1, 2023 from prominent scientific databases, a bibliometric and content analysis of 26 articles was performed. Findings revealed that the use of the metaverse in the tourism industry is still in its infancy, with studies only gaining momentum in 2023. Despite the transformative potential of the metaverse, this study sheds light on the current state of academic research on the metaverse and tourism marketing, providing insights into its key contributors and thematic trends. The study bridges gaps in existing knowledge and contributes to explicating an avenue that guides future research and advances understanding in the field. The study argues that the metaverse is causing a paradigm shift in tourism marketing, making the research relevant to practitioners interested in digital transformations in tourism and thus have managerial and policy implications.

Thromboinflammation in COVID-19: Unraveling the interplay of coagulation and inflammation.

The coronavirus disease 2019 (COVID-19) pandemic has not only challenged global health systems but also spurred intense scientific inquiry into its pathophysiology. Among the multifaceted aspects of the disease, coagulation abnormalities have emerged as a significant contributor to morbidity and mortality. From endothelial dysfunction to dysregulated immune responses, various factors contribute to the hypercoagulable state seen in severe COVID-19 cases. The dysregulation of coagulation in COVID-19 extends beyond traditional thromboembolic events, encompassing a spectrum of abnormalities ranging from microvascular thrombosis to disseminated intravascular coagulation (DIC). Endothelial injury induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection triggers a cascade of events involving platelet activation, coagulation factor consumption, and fibrinolysis impairment. Moreover, the virus direct effects on immune cells and the cytokine storm further exacerbate the prothrombotic milieu. Unraveling this intricate web of interactions between viral pathogenesis and host responses is essential for elucidating novel therapeutic targets and refining existing management strategies for COVID-19-associated coagulopathy. In the quest to unravel the complex interplay between coagulation and COVID-19, numerous clinical and laboratory studies have yielded invaluable insights into potential biomarkers, prognostic indicators, and therapeutic avenues. Anticoagulation therapy has emerged as a cornerstone in the management of severe COVID-19, although optimal dosing regimens and patient selection criteria remain subjects of ongoing investigation. Additionally, innovative approaches such as targeting specific components of the coagulation cascade or modulating endothelial function hold promise for future therapeutic development.

Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare

AI-powered segmentation of hip and knee bony anatomy has revolutionized orthopedics, transforming pre-operative planning and post-operative assessment. Despite the remarkable advancements in AI algorithms for medical imaging, the potential for biases inherent within these models remains largely unexplored. This study tackles these concerns by thoroughly re-examining AI-driven segmentation for hip and knee bony anatomy. While advanced imaging modalities like CT and MRI offer comprehensive views, plain radiographs (X-rays) predominate the standard initial clinical assessment due to their widespread availability, low cost, and rapid acquisition. Hence, we focused on plain radiographs to ensure the utilization of our contribution in diverse healthcare settings, including those with limited access to advanced imaging technologies. This work provides insights into the underlying causes of biases in AI-based knee and hip image segmentation through an extensive evaluation, presenting targeted mitigation strategies to alleviate biases related to sex, race, and age, using an automatic segmentation that is fair, impartial, and safe in the context of AI. Our contribution can enhance inclusivity, ethical practices, equity, and an unbiased healthcare environment with advanced clinical outcomes, aiding decision-making and osteoarthritis research. Furthermore, we have made all the codes and datasets publicly and freely accessible to promote open scientific research.

Thermal Truncation of Heptamethine Cyanine Dyes.

Cyanine dyes are a class of organic, usually cationic molecules containing two nitrogen centers linked through conjugated polymethine chains. The synthesis and reactivity of cyanine derivatives have been extensively investigated for decades. Unlike the recently described phototruncation process, the thermal truncation (chain shortening) reaction is a phenomenon that has rarely been reported for these important fluorophores. Here, we present a systematic investigation of the truncation of heptamethine cyanines (Cy7) to pentamethine (Cy5) and trimethine (Cy3) cyanines via homogeneous, acid-base-catalyzed nucleophilic exchange reactions. We demonstrate how different substituents at the C3' and C4' positions of the chain and different heterocyclic end groups, the presence of bases, nucleophiles, and oxygen, solvent properties, and temperature affect the truncation process. The mechanism of chain shortening, studied by various analytical and spectroscopic techniques, was verified by extensive ab initio calculation, implying the necessity to model catalytic reactions by highly correlated wave function-based methods. In this study, we provide critical insight into the reactivity of cyanine polyene chains and elucidate the truncation mechanism and methods to mitigate side processes that can occur during the synthesis of cyanine derivatives. In addition, we offer alternative routes to the preparation of symmetrical and unsymmetrical meso-substituted Cy5 derivatives.

A cost modelling methodology based on machine learning for engineered-to-order products

Recent scientific studies are targeted at applying and assessing the effectiveness of Machine Learning (ML) approaches for cost estimation during the preliminary design phases. To train ML prediction models, comprehensive and structured datasets of historical data are required. This solution is inapplicable when such information is unavailable or sparse due to the lack of structured datasets. For engineered-to-order products, the number of historical records is often limited and strongly influenced by different purchasing or manufacturing strategies, thus requiring complex normalisation of such data.This method overcomes the above limitations by presenting an ML-based cost modelling methodology for the conceptual design that is applicable even when historical data are insufficient to train the prediction algorithms. The training dataset is generated through an analytical and automatic software tool for manufacturing cost estimation. Such a tool, starting from a 3D model of a product, can quickly and autonomously assess the related cost in different scenarios. An extensive and structured training dataset can be easily generated. The proposed methodology was based on CRISP-DM (Cross Industry Standard Process for Data Mining).Cost engineers of an Oil & Gas company used the method to develop parametric cost models for discs and spacers of an axial compressor. The solution guarantees lower error (7% vs 9%) and significant time-saving (minutes instead of hours) than estimations based on other approaches. Cost models are more comprehensive (capable of analysing different scenarios), explainable (not conceived as a black box), and self-learning (can be updated by extending the training dataset).

Can the sustainable development goal 9 support an untreated early childhood caries elimination agenda?

BackgroundEarly childhood caries (ECC) is a global public health challenge that requires innovation, infrastructure, and health system influences to bolster initiatives for its management and control. The aim of this scoping review was to investigate the published evidence on the association between ECC and the targets of the Sustainable Development Goal 9 (SDG9) concerned with industry, innovation, and infrastructure development.MethodsThe scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. A search was conducted in PubMed, Web of Science, and Scopus between July and August 2023 using a search strategy related to the promotion of resilient infrastructure, sustainable industries, scientific research and innovation, access to the internet and ECC. Only English language publications were included. Studies that solely examined ECC without reference to the SDG9 targets were excluded.ResultsThe search yielded 933 studies for review. After screening for the eligibility and removing duplicates, 916 unique articles remained for further screening. However, none of the identified studies provided data on the association between resilient infrastructure, sustainable industries, scientific research and innovation, access to the internet and ECC.ConclusionThere were no primary studies that assessed the association between ECC and SDG9, even though the plausibility of a potential relationship exists. Future studies are needed to generate evidence on the link between ECC and SDG9 as this link may contribute to the reduction in the proportion of children with untreated ECC.

Aortic Valve Calcium Score: Applications in Clinical Practice and Scientific Research—A Narrative Review

Aortic Valve Calcium Score: Applications in Clinical Practice and Scientific Research—A Narrative Review

Assessment of efficacy of chrysin in diabetes-associated cardiac complications in chick embryo and murine model.

Patients with type 2 diabetes or prolonged diabetic condition are webbed into cardiac complications. This study aimed to ascertain the utility of chick embryo as an alternative to the mammalian model for type 2 diabetes-induced cardiac complications and chrysin as a protective agent. Diabetes was activated in ovo model (chick embryo) using glucose along with β-hydroxybutyric acid. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, Alamar, and Kenacid blue assay were used to compare with chrysin-administered group. Blood glucose level, total cholesterol, triglyceride, and high-density lipoprotein were considered as endpoints. Diabetes was induced in Wistar albino rats by administering a high-fat diet and a subdued dose of streptozotocin (35mg/kg, b.w). Percentage of glycated hemoglobin, creatinine kinase-MB, tumor necrosis factor-α, and C-reactive protein were evaluated and compared with chrysin administered group. Chrysin treatment improved elevated blood glucose levels and dyslipidemia in a diabetic group of whole embryos. Condensed cellular growth and protein content as well as enhanced cytotoxicity in ovo were shielded by chrysin. Chrysin reduced cardiac and inflammatory markers in diabetic rats and provided cellular protection to damage the heart of diabetic rats. The protective action of chrysin in ovo model induced a secondary complication associated with diabetes, evidenced that the ovo model is an effective alternative in curtailing higher animal use in scientific research.

The Semiexperimental Approach at Work: Equilibrium Structure of Radical Species.

The so-called semiexperimental (SE) approach is a powerful technique for obtaining highly accurate equilibrium structures for isolated systems. This Featured Article describes its extension to open-shell species, thus providing the first systematic investigation on radical equilibrium geometries to be used for benchmarking purposes. The small yet significant database obtained demonstrates that there is no reduction in accuracy when moving from closed-shell species to radicals. We also provide an extension of the applicability of the SE approach to medium-/large-sized radicals by exploiting the so-called "Lego-brick" approach, which is based on the assumption that a molecular system can be seen as formed by smaller fragments for which the SE equilibrium structure is available. In this Featured Article we show that this model can be successfully applied also to open-shell species.

Breaking the financial bottleneck of local universities of China: an analysis of the current situation of university intellectual property

PurposeWith the global economic slowdown in 2023, most universities in the central and western regions of China may face the problem of shortage of government financial funding, which is directly related to the development of science and technology of the universities. With the support of the local government, most of the ordinary universities have shifted attention to the transfer and transformation of intellectual property based on school-enterprise cooperation, hoping to seek more commercialization income as an extra source of income. Therefore, this paper aimed to study the current situation of university intellectual property, i.e. the reserve and distribution of transferable patents, discipline distribution of patents, and regional cooperation of university patents.Design/methodology/approachA patent database of 10 key universities in Henan Province of China from 2012 to 2021 was established. The evaluation indicator model by the Entropy method was conducted to reveal the interaction mechanism between quantity indicators, quality indicators, and value indicators. Based on the data analysis, the development direction of the discipline construction and school-enterprise cooperation for local universities was clear.FindingsThe findings indicated that the number of patent applications from ten key universities in Henan Province has been increasing rapidly over the years. The trend for authorized invention patents has remained relatively stable. Compared with the patent applications and authorizations, the growth of transferable patents was notably slow. The significance of strategic layout of dominant disciplines and related high-value patents was not appreciated enough by Chinese government and universities. There was a large difference of dominant disciplines between 10 key universities, and the patent level of each university was directly proportional to the number of dominant disciplines. The university patent level heavily relied on the researchers' contributions and the distribution mechanism of scientific research resources within the university. Guangdong Province and the Yangtze River Delta of China, being economically developed regions, exhibited the most active engagement in patent technology cooperation. Conversely, the central and western regions displayed relatively lower participation in high-value patent research and development.Research limitations/implicationsThis study can provide a unique perspective to understand and solve the financial bottleneck problem of ordinary universities in the central and western regions of China. The discipline construction and school-enterprise cooperation are just one of the existing schemes, but it still cannot solve the financial problems faced by local universities in China towards the government.Practical implicationsThe findings of this study can serve as a foundation for further investigation into policies aimed at improving the financial problems of ordinary universities in the central and western regions of China.Originality/valueAs an important attempt, the study tried to focus the financial problem of universities in the central and western regions of China based on the evaluation of the current situation of university intellectual property. This research findings are of great value in finding optimal layout between university patents, disciplines and school-enterprise cooperation.

Treatment of endometriosis with mifepristone mediated by nanostructured lipid carriers.

Mifepristone, a progesterone receptor antagonist, was initially used to terminate early pregnancy. As scientific research advanced, it emerged to be effective in the treatment of various tumors and tumor-like conditions such as endometriosis. Despite the therapeutic potential of mifepristone, its therapeutic effect is still far from ideal because the drug is difficult to dissolve and to accumulate in the target tissue sites. To address this issue, mifepristone-loaded nanostructured lipid carriers (Mif-NLC) were prepared by a simple solvent diffusion method and their anti-endometriosis performance and mechanisms were initially investigated. By optimizing the preparation protocol, we obtained uniform and spheroidal Mif-NLC with an average particle size of 280nm. The encapsulation rate and drug loading capacity were 64.67% ± 0.15% and 2.7% ± 0.014%, respectively, as measured by UV spectrophotometry. The in vitro release kinetics indicated that mifepristone was released from NLC in a sustained-release manner. Compared with free mifepristone, Mif-NLC exhibited enhanced cellular uptake and inhibition of invasion activity in primary mesenchymal cells of endometriosis. A certain reduction in the size of endometriotic cysts was observed in animals compared to controls. The induction of autophagy via Mif-NLC may serve as the molecular mechanism underlying this effect. Furthermore, observation of uterine structures showed negligible toxic effects. This suggested that mifepristone encapsulated in NLC can improve its bioavailability and anti-endometriosis efficacy, which provided a new strategy for the treatment of endometriosis.

Meandering streamflows across landscapes and scales: a review and discussion

The study of meandering patterns created by geophysical flows is important for a number of fundamental and applied research topics, including stream and wetland restoration, land management, infrastructure design, oil exploration and production, carbon sequestration, flood-hazard mitigation and planetary palaeoenvironmental reconstructions. This volume, Meandering Streamflows: Patterns and Processes across Landscapes and Scales , contains 13 papers that present field, laboratory and numerical investigations of meandering channels found in distinct environmental and geological contexts and focus on how the interactions of different autogenic and allogenic processes, both in the horizontal and the vertical dimension, affect meander kinematics and the resulting morphology, sedimentology and stratigraphic architecture. In this introductory chapter, we offer an overview of the evolution of scientific research on meandering streams over time, aiming to review and discuss meandering patterns in both fluvial and non-fluvial settings. Additionally, we present a new compilation of data on meander morphological features, drawn from both existing literature and novel sources, encompassing over 8000 meander bends discovered across a diverse array of environments.