Line Graphs Research Articles

Study of the Operating Cost of the Electric Pump for Mine Dewatering: Case of the SOMAÏR Uranium Mine

This study focuses on the operating cost of the electric pump used to extract water from the Artois mine owned by Société des Mines de l'AÏR. Exploitation of the SOMAÏR Artois mine uranium deposit is to be increased from the 20 m level to the 80, 100 or 120 m level; in the current phase of certain pits, access to the uranium deposit can only be achieved by a means that can draw off the water encountered. It is therefore essential to set up a drainage system to manage and evacuate the various water inflows. The main source of incoming water is the nearby water table, which is drained directly onto the mining unit. The current drainage system has two sumps capable of storing the various water inflows without incident. The energy balance for the three (3) years of pumping was drawn up using the electricity consumption data received. Peaks appear in August for the year 2021, followed by January 2020 and May 2022. The results show that specific energy is high, resulting in higher power consumption than water production. According to the graph of performance curves P = f(Q), the power ratings obtained for flows corresponding to the 24 m head are 25 kW at a flow rate Q = 49 l/s for pump 261; 35 kW, at a flow rate Q = 59 l/s for pump 259 and 35 kW at a flow rate Q = 105 l/s for pump 234.

Behavioral game of quarantine during the monkeypox epidemic: Analysis of deterministic and fractional order approach

This work concerns the epidemiology of infectious diseases like monkeypox (mpox) in humans and animals. Our models examine transmission scenarios, including transmission dynamics between humans, animals, and both. We approach this using evolutionary game theory, specifically the intervention game-theoretical (IGT) framework, to study how human behavior can mitigate disease transmission without perfect vaccines and treatments. To do this, we use non-pharmaceutical intervention, namely the quarantine policy, which demonstrates the delayed effect of the epidemic. Additionally, we contemplate quarantine-based behavioral intervention policies in deterministic and fractional-order models to show behavioral impact in the context of the memory effect. Firstly, we extensively analyzed the model's positivity and boundness of the solution, reproduction number, disease-free and endemic equilibrium, possible stability, existence, concavity, and Ulam-Hyers stability for the fractional order. Subsequently, we proceeded to present a numerical analysis that effectively illustrates the repercussions of varying quarantine-related factors, information probability, and protection probability. We aimed to comprehensively examine the effects of non-pharmaceutical interventions on disease control, which we conveyed through line graphs and 2D heat maps. Our findings underscored the significant influence of strict quarantine measures and the protection of both humans and animals in mitigating disease outbreaks. These measures not only significantly curtailed the spread of the disease but also delayed the occurrence of the epidemic's peak. Conversely, when quarantine maintenance policies were implemented at lower rates and protection levels diminished, we observed contrasting outcomes that exacerbated the situation. Eventually, our analysis revealed the emergence of animal reservoirs in cases involving disease transmission between humans and animals.

Analysis of Risk Factors and Construction of a Predictive Model for Readmission in Patients with Coronary Slow Flow Phenomenon.

Coronary slow flow phenomenon (CSFP) is a phenomenon in which distal vascular perfusion is delayed on angiography, but coronary arteries are not significantly narrowed and there is no other organic cardiac disease. Patients with CSFP may be repeatedly readmitted to the hospital because of chest pain or other symptoms of precordial discomfort, and there is a risk of adverse events. In order to investigate the risk factors affecting the readmission of CSFP patients, a prediction model was constructed with the aim of identifying patients at risk of readmission at an early stage and providing a reference for further clinical intervention. In this study, we collected clinical data from 397 CSFP patients between June 2021 and January 2023 in Xinjiang Medical University Hospital. Telephone follow-up clarified whether the patients were readmitted to the hospital. A predictive model for readmission of CSFP patients was constructed using multifactorial logistic regression. Nomogram was used to visualize the model and bootstrap was used to internally validate the model. ROC, DCA and Calibration curve were plotted to evaluate the calibration and discriminative ability of the column line graphs, respectively. Calibration and resolution of the column line graphs, respectively. A total of 34 of 397 CSFP patients experienced readmission. Smoking history, creatine kinase isoenzyme-MB, total cholesterol, and left ventricular ejection fraction were the predictors of readmission in patients with CSFP. The area under the curve of the Nomogram model was 0.87, which indicated that the model had good predictive ability and differentiation, and the DCA and Calibration curves also indicated that the model had good consistency and was clinically useful. A readmission prediction model for patients with CSFP may facilitate early identification of patients at potential risk for readmission and timely interventional therapy to improve patient prognosis.

Construction and validation of a perioperative concomitant lower extremity deep vein thrombosis line graph model in patients with aneurysmal subarachnoid hemorrhage

BackgroundTo develop and validate a nomogram for predicting the probability of deep venous thrombosis (DVT) in patients with aneurysmal subarachnoid hemorrhage (aSAH) during the perioperative period, using clinical features and readily available biochemical parameters. MethodsThe least absolute shrinkage and selection operator (LASSO) regression technique was employed for data dimensionality reduction and selection of predictive factors. A multivariable logistic regression analysis was conducted to establish a predictive model and nomogram for post-aSAH DVT. The discriminative ability of the model was determined by calculating the area under the curve (AUC). ResultsA total of 358 aSAH patients were included in the study, with an overall incidence of DVT of 20.9%. LASSO regression identified four variables, including age, modified Fisher grade, total length of hospital stay, and anticoagulation therapy, as highly predictive factors for post-aSAH DVT. The patients were randomly divided into a modeling group and a validation group in a 6:4 ratio to construct the nomogram. The AUCs of the modeling and validation groups were 0.8511 (95% CI, 0.7922–0.9099) and 0.8633 (95% CI, 0.7968–0.9298), respectively. ConclusionsThe developed nomogram exhibits good accuracy, discriminative ability, and clinical utility in predicting DVT, aiding clinicians in identifying high-risk individuals and implementing appropriate preventive and treatment measures.

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Impact of paving surface material on thermal conditions within a residential building

This paper presents the impact of paving surface material on thermal comfort in a residential building. The aim of the study was to demonstrate differences in temperature, measured near a building’s walls, depending on their location (relative to the cardinal directions) and the type of paving surface material outside the building (in its immediate vicinity, considering the cardinal directions). The study found differences in temperature values recorded near walls located on the south-west side, which faced a garden and a grassy surface, compared to the temperature of the walls that faced a street with asphalt and concrete paving blocks. It should be noted that the study was carried out in the summer, when the interior of the building was not heated. The facade of the building had not been additionally insulated and retained its original historical form (facade render). The method used in the study consisted of temperature measurements taken near the building’s walls using a Steinberg System weather station’s sensors. The measurement results supported the hypothesis that wall temperature varies depending on a space’s placement relative to the cardinal directions and the surface paving material in the space adjoining the building. The results of the study are presented using line graphs. The study is of scientific value and the results may also be useful in site development planning practice. The thermal conditions are a major factor that affecting the comfort of various types of buildings, including housing.

Development and validation of a cognitive dysfunction risk prediction model for the abdominal obesity population.

This study was aimed to develop a nomogram that can accurately predict the likelihood of cognitive dysfunction in individuals with abdominal obesity by utilizing various predictor factors. A total of 1490 cases of abdominal obesity were randomly selected from the National Health and Nutrition Examination Survey (NHANES) database for the years 2011-2014. The diagnostic criteria for abdominal obesity were as follows: waist size ≥ 102 cm for men and waist size ≥ 88 cm for women, and cognitive function was assessed by Consortium to Establish a Registry for Alzheimer's Disease (CERAD), Word Learning subtest, Delayed Word Recall Test, Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). The cases were divided into two sets: a training set consisting of 1043 cases (70%) and a validation set consisting of 447 cases (30%). To create the model nomogram, multifactor logistic regression models were constructed based on the selected predictors identified through LASSO regression analysis. The model's performance was assessed using several metrics, including the consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA) to assess the clinical benefit of the model. The multivariate logistic regression analysis revealed that age, sex, education level, 24-hour total fat intake, red blood cell folate concentration, depression, and moderate work activity were significant predictors of cognitive dysfunction in individuals with abdominal obesity (p < 0.05). These predictors were incorporated into the nomogram. The C-indices for the training and validation sets were 0.814 (95% CI: 0.875-0.842) and 0.805 (95% CI: 0.758-0.851), respectively. The corresponding AUC values were 0.814 (95% CI: 0.875-0.842) and 0.795 (95% CI: 0.753-0.847). The calibration curves demonstrated a satisfactory level of agreement between the nomogram model and the observed data. The DCA indicated that early intervention for at-risk populations would provide a net benefit, as indicated by the line graph. Age, sex, education level, 24-hour total fat intake, red blood cell folate concentration, depression, and moderate work activity were identified as predictive factors for cognitive dysfunction in individuals with abdominal obesity. In conclusion, the nomogram model developed in this study can effectively predict the clinical risk of cognitive dysfunction in individuals with abdominal obesity.

Finite element simulations of Herschel–Bulkley visco-plastic materials over a cylinder: Drag and lift correlation analysis

This study employs a two-dimensional and incompressible flow of Herschel–Bulkley visco-plastic materials in order to investigate the hydrodynamic forces that are acting on a barrier that is located close to the inlet of a channel. As the benchmark configuration, the flow domain that has been selected is a channel that still contains the impediment. The two important parameters of the Herschel–Bulkley Model (HBM) are the yield stress [Formula: see text] and power law index n. Obtaining special situations within the HBM, such as Newtonian, power-law, and Bingham fluids, can be accomplished by assigning certain values to these parameters at the appropriate times. Utilizing a numerical strategy grounded in the Finite Element Method (FEM), we tackle the nonlinearity of the governing equations as well as the viscosity models. As a result of this nonlinearity, FEM becomes an essential tool. The generation of a refined hybrid mesh is done in order to guarantee accuracy in the computations. The stable finite element pair ([Formula: see text]) has been selected for discretization purposes. The discretized nonlinear system is linearized with Newton’s method and subsequently, a direct linear solver PARDISO has been employed in the inner iterations. The pressure, velocity, and viscosity profiles are plotted for various values of n and Bingham number (Bn). In addition, the velocity behavior is observed along the y-direction in a channel through line graphs. Code validation is done as a special case [Formula: see text] and a good agreement is found with the results available in the literature. Finally, a correlation analysis has been performed for the drag coefficient [Formula: see text] and lift coefficient [Formula: see text].

Quality assessment and community detection methods for anonymized mobility data in the Italian Covid context

We discuss how to assess the reliability of partial, anonymized mobility data and compare two different methods to identify spatial communities based on movements: Greedy Modularity Clustering (GMC) and the novel Critical Variable Selection (CVS). These capture different aspects of mobility: direct population fluxes (GMC) and the probability for individuals to move between two nodes (CVS). As a test case, we consider movements of Italians before and during the SARS-Cov2 pandemic, using Facebook users’ data and publicly available information from the Italian National Institute of Statistics (Istat) to construct daily mobility networks at the interprovincial level. Using the Perron-Frobenius (PF) theorem, we show how the mean stochastic network has a stationary population density state comparable with data from Istat, and how this ceases to be the case if even a moderate amount of pruning is applied to the network. We then identify the first two national lockdowns through temporal clustering of the mobility networks, define two representative graphs for the lockdown and non-lockdown conditions and perform optimal spatial community identification on both graphs using the GMC and CVS approaches. Despite the fundamental differences in the methods, the variation of information (VI) between them assesses that they return similar partitions of the Italian provincial networks in both situations. The information provided can be used to inform policy, for example, to define an optimal scale for lockdown measures. Our approach is general and can be applied to other countries or geographical scales.

Identification and validation of NAD+ metabolism-related biomarkers in patients with diabetic peripheral neuropathy.

The mechanism of Nicotinamide Adenine Dinucleotide (NAD+) metabolism-related genes (NMRGs) in diabetic peripheral neuropathy (DPN) is unclear. This study aimed to find new NMRGs biomarkers in DPN. DPN related datasets GSE95849 and GSE185011 were acquired from the Gene Expression Omnibus (GEO) database. 51 NMRGs were collected from a previous article. To explore NMRGs expression in DPN and control samples, differential expression analysis was completed in GSE95849 to obtain differentially expressed genes (DEGs), and the intersection of DEGs and NMRGs was regarded as DE-NMRGs. Next, a protein-protein interaction (PPI) network based on DE-NMRGs was constructed and biomarkers were screened by eight algorithms. Additionally, Gene Set Enrichment Analysis (GSEA) enrichment analysis was completed, biomarker-based column line graphs were constructed, lncRNA-miRNA-mRNA and competing endogenouse (ce) RNA networks were constructed, and drug prediction was completed. Finally, biomarkers expression validation was completed in GSE95849 and GSE185011. 5217 DEGs were obtained from GSE95849 and 21 overlapping genes of DEGs and NMRGs were DE-NMRGs. Functional enrichment analysis revealed that DE-NMRGs were associated with glycosyl compound metabolic process. The PPI network contained 93 protein-interaction pairs and 21 nodes, with strong interactions between NMNAT1 and NAMPT, NADK and NMNAT3, ENPP3 and NUDT12 as biomarkers based on 8 algorithms. Expression validation suggested that ENPP3 and NUDT12 were upregulated in DPN samples (P < 0.05). Moreover, an alignment diagram with good diagnostic efficacy based on ENPP3 and NUDT12 were identified was constructed. GSEA suggested that ENPP3 was enriched in Toll like receptor (TLR) pathway, NUDT12 was enriched in maturity onset diabetes of the young and insulin pathway. Furthermore, 18 potential miRNAs and 36 Transcription factors (TFs) were predicted and the miRNA-mRNA-TF networks were constructed, suggesting that ENPP3 might regulate hsa-miR-34a-5p by affecting MYNN. The ceRNA network suggested that XLOC_013024 might regulate hsa-let-7b-5p by affecting NUDT12. 15 drugs were predicted, with 8 drugs affecting NUDT12 such as resveratrol, and 13 drugs affecting ENPP3 such as troglitazone. ENPP3 and NUDT12 might play key roles in DPN, which provides reference for further research on DPN.

The Realizability of Theta Graphs as Reconfiguration Graphs of Minimum Independent Dominating Sets

Abstract The independent domination number i(G) of a graph G is the minimum cardinality of a maximal independent set of G, also called an i(G)-set. The i-graph of G, denoted ℐ (G), is the graph whose vertices correspond to the i(G)-sets, and where two i(G)-sets are adjacent if and only if they differ by two adjacent vertices. Not all graphs are i-graph realizable, that is, given a target graph H, there does not necessarily exist a source graph G such that H = ℐ (G). We consider a class of graphs called “theta graphs”: a theta graph is the union of three internally disjoint nontrivial paths with the same two distinct end vertices. We characterize theta graphs that are i-graph realizable, showing that there are only finitely many that are not. We also characterize those line graphs and claw-free graphs that are i-graphs, and show that all 3-connected cubic bipartite planar graphs are i-graphs.

Design and pilot of an inclusive environmental education course for a community living under the unprecedented environmental emergencies in Pakistan- a case study

ABSTRACT This study presents the design and pilot of the environmental education course with one of the communities, living under environmental emergencies in Pakistan. The study employs the case study as a research approach. Community-based participatory research is used as a method. Forty participants are selected from one of the communities in Hunza, Gilgit-Baltistan, Pakistan. First, I gathered pre-intervention data through an online survey followed by focus group discussions. Next, together with participants, I designed and piloted a contextual environmental education course. Finally, I collected post-intervention data through an online survey and conducted focus group discussions. Data is analyzed using line graphs and narrative analysis. The course enhanced participants’ environmental literacy and equipped them with relevant strategies and contextual skills pertinent to supporting their communities during environmental catastrophes. Peace-building approaches, such as collaborative problem-solving case studies and development of conflict management skills, specifically enabled participants to understand the complexities of challenges and propose innovative solutions. Participants suggested the inclusion of models and standards of environmental education, and research components in the future course. They further recommended translating the course into a regional language, preparing local trainers, and introducing the same course via face-to-face mode to the illiterate population within their communities.

Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling

BackgroundThere is a need to develop and validate a widely applicable nomogram for predicting readmission of respiratory failure patients within 365 days.MethodsWe recruited patients with respiratory failure at the First People’s Hospital of Yancheng and the People’s Hospital of Jiangsu. We used the least absolute shrinkage and selection operator regression to select significant features for multivariate Cox proportional hazard analysis. The Random Survival Forest algorithm was employed to construct a model for the variables that obtained a coefficient of 0 following LASSO regression, and subsequently determine the prediction score. Independent risk factors and the score were used to develop a multivariate COX regression for creating the line graph. We used the Harrell concordance index to quantify the predictive accuracy and the receiver operating characteristic curve to evaluate model performance. Additionally, we used decision curve analysiso assess clinical usefulness.ResultsThe LASSO regression and multivariate Cox regression were used to screen hemoglobin, diabetes and pneumonia as risk variables combined with Score to develop a column chart model. The C index is 0.927 in the development queue, 0.924 in the internal validation queue, and 0.922 in the external validation queue. At the same time, the predictive model also showed excellent calibration and higher clinical value.ConclusionsA nomogram predicting readmission of patients with respiratory failure within 365 days based on three independent risk factors and a jointly developed random survival forest algorithm has been developed and validated. This improves the accuracy of predicting patient readmission and provides practical information for individualized treatment decisions.

Diverse Structure-Aware Relation Representation in Cross-Lingual Entity Alignment

Cross-lingual entity alignment (CLEA) aims to find equivalent entity pairs between knowledge graphs (KGs) in different languages. It is an important way to connect heterogeneous KGs and facilitate knowledge completion. Existing methods have found that incorporating relations into entities can effectively improve KG representation and benefit entity alignment, and these methods learn relation representation depending on entities, which cannot capture the diverse structures of relations. However, multiple relations in KG form diverse structures, such as adjacency structure and ring structure. This diversity of relation structures makes the relation representation challenging. Therefore, we propose to construct the weighted line graphs to model the diverse structures of relations and learn relation representation independently from entities. Especially, owing to the diversity of adjacency structures and ring structures, we propose to construct adjacency line graph and ring line graph, respectively, to model the structures of relations and to further improve entity representation. In addition, to alleviate the hubness problem in alignment, we introduce the optimal transport into alignment and compute the distance matrix in a different way. From a global perspective, we calculate the optimal 1-to-1 alignment bi-directionally to improve the alignment accuracy. Experimental results on two benchmark datasets show that our proposed method significantly outperforms state-of-the-art CLEA methods in both supervised and unsupervised manners.

Traditional Chinese Exercises for Cardiovascular Diseases: A Bibliometric Analysis.

Traditional Chinese exercises (TCEs) have great value in the prevention and effective treatment of cardiovascular diseases (CVD). Our purpose in this study was to summarize present research trends and future directions regarding the link between TCEs and CVD by bibliometrics analysis. We searched the Web of Science Core Collection (WoSCC) for all original articles and reviews on TCEs for CVD published in English before August 7, 2022 using CiteSpace 5.8.R3 and Microsoft Excel 2019 software, and we displayed the results in the form of network maps, line graphs, and tables. We initially obtained 725 articles. Our results showed that the United States was the most influential country in this line of research, with Harvard University the most prolific institution in the field, and, Evidence-Based Complementary and Alternative Medicine was the most productive journal for these articles. The highest-frequency keywords in this research area were Tai Chi, exercise, blood pressure, quality of life, and older adult. Additionally, important research topics included heart rate variability, quality of life, meta-analysis, Baduanjin exercise, and breathing exercise. In addition, our results revealed that among all the TCEs, Tai Chi, Baduanjin, and Qigong emerged as the most extensively studied. However, it's important to note our exclusive focus on literature published in English may have led to our missing important results. Future investigators should broaden their search to include other databases and languages to present a still more comprehensive overview of this field.

A retrospective analysis of malaria deaths in the pre- and intra- COVID 19 pandemic era, Ghana, 2016-2021.

Global efforts over the years have resulted in a 27% reduction in malaria incidence and an estimated 51% reduction in malaria mortality since 2000. Meanwhile, COVID-19 pandemic disrupted provision and utilization of malaria services, leading to a surge in malaria incidence and mortality. Globally, 627000 malaria deaths were recorded in 2020, representing about 69000 more deaths compared to 2019. Also, 14 million more cases of malaria were recorded in 2020 compared to 2019. This study sought to determine whether excess malaria deaths were recorded in Ghana during the COVID-19 pandemic era. This was a descriptive study on routine malaria mortality data in Ghana for the period 2016 to 2021. Data was retrieved from the District Health Information Management System using a data extraction guide. Excess mortality was defined as occurrence of malaria deaths more than expected value for the period 2020 and 2021. The expected number of mortalities for 2020 and 2021 were determined using 2016 to 2019 average. Excess mortality (P-score) was estimated using the formula: [(reported mortalities-expected mortalities)/expected mortalities X 100%]. Data were summarized and processed in Microsoft excel version 16.0. Malaria mortality in Ghana and its regions was described using tables and line graphs. An average of 535 malaria deaths per year were recorded nationwide from 2016 to 2020. About 50% (1603/3207) of deaths occurred in children aged less than five years. The p-scores for the country were -53% and -58% for 2020 and 2021 respectively. No region recorded excess all-age malaria mortality in 2020, rather significant reduction. Stratified by age, Greater Accra region reported 90% higher than expected deaths among persons aged five years and above in 2020 (p-score = 90%, 95% CI: 21-159). All regions reported reduction in under-five mortality in 2020. No significant excess malaria mortalities were reported among the regions in 2021. Although negative p-scores suggested a decline in malaria mortalities nationwide, some regions recorded excess deaths during the COVID-19 pandemic era. There is a need to integrate COVID-19 control activities with malaria control and prevention efforts to mitigate the impact of COVID-19 on malaria case management and mortality.

A novel self-adaptive SIS model based on the mutual interaction between a graph and its line graph.

We propose a new paradigm to design a network-based self-adaptive epidemic model that relies on the interplay between the network and its line graph. We implement this proposal on a susceptible-infected-susceptible model in which both nodes and edges are considered susceptible and their respective probabilities of being infected result in a real-time re-modulation of the weights of both the graph and its line graph. The new model can be considered as an appropriate perturbation of the standard susceptible-infected-susceptible model, and the coupling between the graph and its line graph is interpreted as a reinforcement factor that fosters diffusion through a continuous adjustment of the parameters involved. We study the existence and stability conditions of the endemic and disease-free states for general network topologies. Moreover, we introduce, through the asymptotic values in the endemic steady states, a new type of eigenvector centrality where the score of a node depends on both the neighboring nodes and the edges connected to it. We also investigate the properties of this new model on some specific synthetic graphs, such as cycle, regular, and star graphs. Finally, we perform a series of numerical simulations and prove their effectiveness in capturing some empirical evidence on behavioral adoption mechanisms.

Health Numeracy Is Associated With a Patient's Ability to Interpret Patient-Reported Outcome Measures Data.

Patient-reported outcome measures (PROMs) were originally developed as research tools; however, there is increasing interest in using PROMs to inform clinical care. Prior work has shown the benefits of implementing PROMs at the point of care, but a patient's health numeracy (their ability to understand and work with numbers) may affect their ability to interpret PROM results. We recruited patients presenting to an outpatient orthopedic clinic. Forty-nine patients completed a survey that included demographic information, the short-form General Health Numeracy Test, and accuracy questions about four PROM displays (bar graph, table, line graph, pictograph) that indicated the same PROM results. Patients with higher health numeracy answered all display accuracy questions correctly (P=.016). Patients who preferred using the table were more likely to answer display accuracy questions incorrectly (odds ratio, 0.013, P=.024). The two most frequently preferred PROM formats were bar graphs and tables, and most patients preferred to learn about their PROM function scores via a combination of displays and verbal discussions. Patient health numeracy is associated with the ability to correctly interpret visual displays of PROMs. Implementation of PROMs at point of care currently does not account for health numeracy. Efforts to account for health numeracy when using PROMs at point of care may improve the efficacy of using PROMs to improve outcomes in orthopedic surgery. [Orthopedics. 2024;47(5):e255-e260.].

A dynamical behavior of the coupled Broer-Kaup-Kupershmidt equation using two efficient analytical techniques.

The aim of the present study is to identify multiple soliton solutions to the nonlinear coupled Broer-Kaup-Kupershmidt (BKK) system, including beta, conformable, local-fractional, and M-truncated derivatives. The coupled Broer-Kaup-Kupershmidt system is employed for modelling nonlinear wave evolution in mathematical models of fluid dynamics, plasmic, optical, dispersive, and nonlinear long-gravity waves. The travelling wave solutions to the above model are found using the Unified and generalised Bernoulli sub-ODE techniques. By modifying certain parameter values, we may create bright soliton, squeezed bell-shaped wave, expanded v-shaped soliton, W-shaped wave, singular soliton, and periodic solutions. The four distinct kinds of derivatives are compared quite effectively using 2D line graphs. Also, contour plots and 3D graphics are given by using Mathematica 10. Lastly, any pair of propagating wave solutions has symmetrical geometrical forms.

Osteosarcoma neutrophil extracellular trap network-associated gene recurrence and metastasis model

Osteosarcoma (OS) is the most common malignancy in children and adolescents and has a high probability of recurrence and metastasis. A growing number of studies have shown that neutrophil extracellular traps (NETs) are strongly associated with cancer metastasis, but in osteosarcoma, genes associated with NETs that promote osteosarcoma recurrence and metastasis remain to be explored. We systematically investigated the gene expression patterns of NETs in OS samples from the GEO database. NETs molecular typing was evaluated based on NETs expression profiles, and the association between NETs molecular subtypes and immune microenvironment and metastatic features were explored. Ultimately, we constructed a signature model and column line graph associated with metastasis prediction and screened possible potential drugs for metastatic osteosarcoma. We established two different molecular subtypes of NETs, which showed significant differences in metastatic status, metastasis time, tumor immune microenvironment, and biological effects. We also constructed a NETs-related gene metastasis signature(NRGMS) to assess the expression pattern of NETs in patients to predict metastatic recurrence in osteosarcoma patients. We screened for TOMM40 and FH associated with metastatic recurrence in osteosarcoma patients. Overall, this study constructs a predictive model for osteosarcoma metastasis of NETs-related genes, which is expected to provide new insights into the metastasis of osteosarcoma.