搜索结果：Journal of vascular nursing : official publication of the Society for Peripheral Vascular Nursing

We discuss the characteristics of the patterns of the vascular networks in a mathematical model for angiogenesis. Based on recent in vitro experiments, this mathematical model assumes that the elongation and bifurcation of blood vessels during angiogenesis are determined by the density of endothelial cells at the tip of the vascular network, and describes the dynamical changes in vascular network formation using a system of simultaneous ordinary differential equations. The pattern of formation strongly depends on the supply rate of endothelial cells by cell division, the branching angle, and also on the connectivity of vessels. By introducing reconnection of blood vessels, the statistical distribution of the size of islands in the network is discussed with respect to bifurcation angles and elongation factor distributions. The characteristics of the obtained patterns are analysed using multifractal dimension and other techniques.

Leaves of vascular plants are arranged regularly around stems, a phenomenon known as phyllotaxis. A constant angle between two successive leaves is called divergence angle. On the one side, the divergence angle $α_0$ of an initial pattern of leaf primordia at a shoot apex is most commonly an irrational number of about 137.5 degrees, called limit divergence. On the other side, the divergence $α$ of a final pattern of leaf traces in the vascular system of a mature stem is expressed in terms of a sequence of rational numbers, 1/2, 1/3, 2/5, 3/8, 5/13, 8/21, called phyllotactic fractions. The mathematical relationship between the initial divergence $α_0$, the final divergence $α$, and the number of internodes traversed by the leaf traces $n_c$ is investigated by means of a theoretical model of vascular phyllotaxis. It is shown that continuous changes of the trace length $n_c$ induce transitions between the fractional orders in the vascular structure. The vascular phyllotaxis transition suggests an evolutionary mechanism for the phenomenon of phyllotaxis. To provide supporting evidence for the model and mechanism, available experimental results for fossil remains of Lepidodendron and th

Data science has become increasingly essential for the production of official statistics, as it enables the automated collection, processing, and analysis of large amounts of data. With such data science practices in place, it enables more timely, more insightful and more flexible reporting. However, the quality and integrity of data-science-driven statistics rely on the accuracy and reliability of the data sources and the machine learning techniques that support them. In particular, changes in data sources are inevitable to occur and pose significant risks that are crucial to address in the context of machine learning for official statistics. This paper gives an overview of the main risks, liabilities, and uncertainties associated with changing data sources in the context of machine learning for official statistics. We provide a checklist of the most prevalent origins and causes of changing data sources; not only on a technical level but also regarding ownership, ethics, regulation, and public perception. Next, we highlight the repercussions of changing data sources on statistical reporting. These include technical effects such as concept drift, bias, availability, validity, accur

Publication patterns of 79 forest scientists awarded major international forestry prizes during 1990-2010 were compared with the journal classification and ranking promoted as part of the 'Excellence in Research for Australia' (ERA) by the Australian Research Council. The data revealed that these scientists exhibited an elite publication performance during the decade before and two decades following their first major award. An analysis of their 1703 articles in 431 journals revealed substantial differences between the journal choices of these elite scientists and the ERA classification and ranking of journals. Implications from these findings are that additional cross-classifications should be added for many journals, and there should be an adjustment to the ranking of several journals relevant to the ERA Field of Research classified as 0705 Forestry Sciences.

The aim of this report is to present a ranking of Nursing journals covered in Google Scholar Metrics (GSM), a Google product launched in 2012 to assess the impact of scientific journals from citation counts this receive on Google Scholar. Google has chosen to include only those journals that have published at least 100 papers and have at least one citation in a period of five years (2007-2011). Journal rankings are sorted by languages (showing the 100 papers with the greatest impact). This tool allows to sort by subject areas and disciplines, but only in the case of journals in English. In this case, it only shows the 20 journals with the highest h index. This option is not available for journals in the other nine languages present in Google (Chinese, Portuguese, German, Spanish, French, Korean, Japanese, Dutch and Italian). Google Scholar Metrics doesnt currently allow to group and sort all journals belonging to a scientific discipline. In the case of Nursing, in the ten listings displayed by GSM we can only locate 34 journals. Therefore, in an attempt to overcome this limitation, we have used the diversity of search procedures allowed by GSM to identify the greatest number of sci

As the aging population increases and the shortage of healthcare workers increases, the need to examine other means for caring for the aging population increases. One such means is the use of humanoid robots to care for social, emotional, and physical wellbeing of the people above 65. Understanding skilled and long term care nursing home administrators' perspectives on humanoid robots in caregiving is crucial as their insights shape the implementation of robots and their potential impact on resident well-being and quality of life. This authors surveyed two hundred and sixty nine nursing homes executives to understand their perspectives on the use of humanoid robots in their nursing home facilities. The data was coded and results revealed that the executives were keen on exploring other avenues for care such as robotics that would enhance their nursing homes abilities to care for their residents. Qualitative analysis reveals diverse perspectives on integrating humanoid robots in nursing homes. While acknowledging benefits like improved engagement and staff support, concerns persist about costs, impacts on human interaction, and doubts about robot effectiveness. This highlights compl

Objective. To perform a modern bibliometric analysis of the research based on the Roy Adaptation Model, a founding nursing model proposed by Sor Callista Roy in the1970s. Method. A descriptive and longitudinal study. We used information from the two dominant scientific databases, Web Of Science and SCOPUS. We obtained 137 publications from the Core Collection of WoS, and 338 publications from SCOPUS. We conducted our analysis using the software Bibliometrix, an R-package specialized in creating bibliometric analyses from a perspective of descriptive statistics and network analysis, including co-citation, co-keyword occurrence and collaboration networks. Results. Our quantitative results show the main actors around the research based on the model and the founding literature or references on which this research was based. We analyze the main keywords and how they are linked. Furthermore, we present the most prolific authors both in number of publications and in centrality in the network of coauthors. We present the most central institutions in the global network of collaboration. Conclusions. We highlight the relevance of this theoretical model in nursing and detail its evolution. Th

Various morphological and functional parameters of peripheral nerves and their vascular supply are indicative of pathological changes due to injury or disease. Based on recent improvements in optoacoustic image quality, we explore the ability of multispectral optoacoustic tomography, in tandem with ultrasound imaging (OPUS), to investigate the vascular environment and morphology of peripheral nerves in vivo in a pilot study on healthy volunteers. We showcase the unique ability of optoacoustic imaging to visualize the vasa nervorum by observing intraneurial vessels in healthy nerves in vivo for the first time. In addition, we demonstrate that the label-free spectral optoacoustic contrast of the perfused connective tissue of peripheral nerves can be linked to the endogenous contrast of haemoglobin and collagen. We introduce metrics to analyze the composition of tissue based on its optoacoustic contrast and show that the high-resolution spectral contrast reveals specific differences between nervous tissue and reference tissue in the nerve's surrounding. We discuss how this showcased extraction of peripheral nerve characteristics using multispectral optoacoustic and ultrasound imaging

The segmentation of 2D vascular structures via deep learning holds significant clinical value but is hindered by the scarcity of annotated data, severely limiting its widespread application. Developing a universal few-shot vascular segmentation model is highly desirable, yet remains challenging due to the need for extensive training and the inherent complexities of vascular imaging. In this work, we propose UniVG (Generative Data-engine Foundation Model for Universal Few-shot 2D Vascular Image Segmentation), a novel approach that learns the compositionality of vascular images and constructing a generative foundation model for robust vascular segmentation. UniVG enables the synthesis and learning of diverse and realistic vascular images through two key innovations: 1) Compositional learning for flexible and diverse vascular synthesis: It decomposes and recombines vascular structures with varying morphological features and diverse foreground-background configurations to generate richly diverse synthetic image-label pairs. 2) Few-shot generative adaptation for transferable segmentation: It fine-tunes pre-trained models with minimal annotated data to bridge the gap between synthetic an

Background: Telephone nursing is the first line of contact for many care-seekers and aims at optimizing the performance of the healthcare system by supporting and guiding patients to the correct level of care and reduce the amount of unscheduled visits. Good statistical models that describe the effects of telephone nursing are important in order to study its impact on healthcare resources and evaluate changes in telephone nursing procedures. Objective: To develop a valid model that captures the complex relationships between the nurse's recommendations, the patients' intended actions and the patients' health seeking behavior. Using the model to estimate the effects of telephone nursing on patient behavior, healthcare utilization, and infer potential cost savings. Methods: Bayesian ordinal regression modeling of data from randomly selected patients that received telephone nursing. Inference is based on Markov Chain Monte Carlo methods, model selection using the Watanabe-Akaike Information Criteria, and model validation using posterior predictive checks on standard discrepancy measures. Results and Conclusions: We present a robust Bayesian ordinal regression model that predicts 76% of

This study develops an economic model for a social planner who prioritizes health over short-term wealth accumulation during a pandemic. Agents are connected through a weighted undirected network of contacts, and the planner's objective is to determine the policy that contains the spread of infection below a tolerable incidence level, and that maximizes the present discounted value of real income, in that order of priority. The optimal unique policy depends both on the configuration of the contact network and the tolerable infection incidence. Comparative statics analyses are conducted: (i) they reveal the tradeoff between the economic cost of the pandemic and the infection incidence allowed; and (ii) they suggest a correlation between different measures of network centrality and individual lockdown probability with the correlation increasing with the tolerable infection incidence level. Using unique data on the networks of nursing and long-term homes in the U.S., we calibrate our model at the state level and estimate the tolerable COVID-19 infection incidence level. We find that laissez-faire (more tolerance to the virus spread) pandemic policy is associated with an increased numb

The City of Toronto Long Term Care Homes & Services (LTCH&S) division is one of the largest providers of long-term care in the Canadian province of Ontario, providing care to 2,640 residents at 10 homes across Toronto. Our collaboration with LTCH&S was initiated to facilitate the increasingly challenging task of scheduling nursing staff and reduce high absenteeism rate observed among the part-time nurses. We developed a spreadsheet-based scheduling tool to automate the generation of schedules and incorporate nurses' preferences for different shifts into the schedules. At the core of the scheduling tool is a hierarchical optimization model that generates a feasible schedule with the highest total preference score while satisfying the maximum possible demand. Feasible schedules had to abide by a set of complex seniority requirements which prioritized more senior nurses when allocating the available shifts. Our scheduling tool was implemented in a 391-bed home in Toronto. The tool allowed nursing managers to generate feasible schedules within a fraction of an hour, in contrast to the status-quo manual approach which could took up to tens of hours. In addition, the schedule

Nurses should rely on the best evidence, but tend to struggle with statistics, impeding research integration into clinical practice. Statistical significance, a key concept in classical statistics, and its primary metric, the p-value, are frequently misused. This topic has been debated in many disciplines but rarely in nursing. The aim is to present key arguments in the debate surrounding the misuse of p-values, discuss their relevance to nursing, and offer recommendations to address them. The literature indicates that the concept of probability in classical statistics is not easily understood, leading to misinterpretations of statistical significance. Much of the critique concerning p-values arises from such misunderstandings and imprecise terminology. Thus, some scholars have argued for the complete abandonment of p-values. Instead of discarding p-values, this article provides a comprehensive account of their historical context and the information they convey. This will clarify why they are widely used yet often misunderstood. The article also offers recommendations for accurate interpretation of statistical significance by incorporating other key metrics. To mitigate publication

The development of modern nursing and consequently nursing research in Ex- Yugoslavia is about a century old. To profile the development, volume, and content of nursing research we completed a performance and spatial bibliometric analysis combined with synthetic content analysis to identify the most productive countries and institutions, most prolific source titles, country cooperation, publication production trends, the content of research and hot topics. The corpus was harvested from the Web of Science All databases and contained 1380 papers. Slovenia was the most productive country, followed by Croatia and Serbia. The synthetic content analysis demonstrated that nursing research in ex-Yugoslavian countries is growing both in scope and number of publications, notwithstanding the fact that research content differs between countries and it seems that each country is focused on their local health problems. A substantial part of the research is published in national journals in national languages however, it is noteworthy to note that some ex-Yugoslavian authors have succeeded in publishing their research in top nursing journals. The study also revealed substantial international coop

Accurate identification of brain function is necessary to understand the neurobiology of cognitive ageing, and thereby promote well-being across the lifespan. A common tool used to investigate neurocognitive ageing is functional magnetic resonance imaging (fMRI). However, although fMRI data are often interpreted in terms of neuronal activity, the blood-oxygen-level-dependent (BOLD) signal measured by fMRI includes contributions of both vascular and neuronal factors, which change differentially with age. While some studies investigate vascular ageing factors, the results of these studies are not well known within the field of neurocognitive ageing and therefore vascular confounds in neurocognitive fMRI studies are common. In contrast to over 10,000 BOLD-fMRI papers on ageing, fewer than 20 have applied techniques to correct for vascular effects. However, neurovascular ageing is not only a confound in fMRI, but an important feature in its own right, to be assessed alongside measures of neuronal ageing. We review current approaches to dissociate neuronal and vascular components of BOLD-fMRI of regional activity and functional connectivity. We highlight emerging evidence that vascular

Recent advancements in large language models (LLMs) have significantly transformed medical systems. However, their potential within specialized domains such as nursing remains largely underexplored. In this work, we introduce NurseLLM, the first nursing-specialized LLM tailored for multiple choice question-answering (MCQ) tasks. We develop a multi-stage data generation pipeline to build the first large scale nursing MCQ dataset to train LLMs on a broad spectrum of nursing topics. We further introduce multiple nursing benchmarks to enable rigorous evaluation. Our extensive experiments demonstrate that NurseLLM outperforms SoTA general-purpose and medical-specialized LLMs of comparable size on different benchmarks, underscoring the importance of a specialized LLM for the nursing domain. Finally, we explore the role of reasoning and multi-agent collaboration systems in nursing, highlighting their promise for future research and applications.

The flexibility level allowed in nursing care delivery and uncertainty in infusion durations are very important factors to be considered during the chemotherapy schedule generation task. The nursing care delivery scheme employed in an outpatient chemotherapy clinic (OCC) determines the strictness of the patient-to-nurse assignment policies, while the estimation of infusion durations affects the trade-off between patient waiting time and nurse overtime. We study the problem of daily scheduling of patients, assignment of patients to nurses and chairs under uncertainty in infusion durations for an OCC that functions according to any of the three commonly used nursing care delivery models representing fully flexible, partially flexible, and inflexible care models, respectively. We develop a two-stage stochastic mixed-integer programming model that is valid for the three care delivery models to minimize expected weighted cost of patient waiting time and nurse overtime. We propose multiple variants of a scenario grouping-based decomposition algorithm to solve the model using data of a major university oncology hospital. The variants of the algorithm differ from each other according to th

Due to its high lethality amongst the elderly, the safety of nursing homes has been of central importance during the COVID-19 pandemic. With test procedures becoming available at scale, such as antigen or RT-LAMP tests, and increasing availability of vaccinations, nursing homes might be able to safely relax prohibitory measures while controlling the spread of infections (meaning an average of one or less secondary infections per index case). Here, we develop a detailed agent-based epidemiological model for the spread of SARS-CoV-2 in nursing homes to identify optimal prevention strategies. The model is microscopically calibrated to high-resolution data from nursing homes in Austria, including detailed social contact networks and information on past outbreaks. We find that the effectiveness of mitigation testing depends critically on the timespan between test and test result, the detection threshold of the viral load for the test to give a positive result, and the screening frequencies of residents and employees. Under realistic conditions and in absence of an effective vaccine, we find that preventive screening of employees only might be sufficient to control outbreaks in nursing h

Cerebral blood flow regulation is critical for brain function, and its disruption is implicated in various neurological disorders. Many existing models do not fully capture the complex, multiscale interactions among neuronal activity, astrocytic signaling, and vascular dynamics--especially in key brainstem regions. In this work, we present a 3D-1D-0D multiscale computational framework for modeling the neuro-glial-vascular unit (NGVU) in the dorsal vagal complex (DVC). Our approach integrates a quadripartite synapse model--which represents the interplay among excitatory and inhibitory neurons, astrocytes, and vascular smooth muscle cells--with a hierarchical description of vascular dynamics that couples a three-dimensional microcirculatory network with a one-dimensional macrocirculatory representation and a zero-dimensional synaptic component. By linking neuronal spiking, astrocytic calcium and gliotransmitter signaling, and vascular tone regulation, our model reproduces key features of functional hyperemia and elucidates the feedback loops that help maintain cerebral blood flow. Simulation results demonstrate that neurotransmitter release triggers astrocytic responses that modulate