Correlation Coefficient Research Articles

Comparative evaluation of drying methods for struvite produced from electrocoagulated source-separated urine: Implications for quality, energy and cost-effectiveness

Struvite precipitation from source-separated urine is crucial for waste utilization and sustainability. However, after precipitation, the high moisture content of struvite necessitates an additional drying process that can be costly and inefficient. In the present study, the performance of different drying methods—open sun drying, air drying, conventional drying (20–100 °C), and microwave drying (180–720 W) on the quality of struvite obtained from source-separated urine through electrocoagulation using Mg–Mg electrodes were evaluated. It was found that higher temperatures and power in the convective oven and microwave resulted in higher diffusivity (10−9-10−7 m2s−1), leading to reduced drying times. Different models were employed to comprehend the drying mechanism, and the one with the highest correlation coefficient (R2 = 0.99) and the lowest statistical values was selected. The key findings indicated that higher power and temperature levels were more cost-effective. However, characterization of the dried struvite using X-ray diffraction and Fourier-transformed infrared spectroscopy, disintegration of struvite crystals at temperatures above 60 °C in the conventional oven and 180 W in the microwave oven was observed. Based on the results, we conclude that sun drying is a cost-effective and environmentally friendly alternative for drying struvite without compromising its quality.

A New Strategy for Planning Urban Park Green Spaces by Considering Their Spatial Accessibility and Distributional Equity

Urban park green spaces (PGSs) are crucial social public resources that provide various ecological services and enhance human health and well-being. However, with the acceleration of urbanization, the accessibility and equity of urban PGS resources are increasingly threatened. This study aims to propose an integrated framework that considers accessibility and equity simultaneously to optimize the planning and layout of urban PGS within the high-speed ring road of Hefei City. This study first used an improved two-step floating catchment area (2SFCA) method to quantify the level of accessibility of urban PGS within the ring road. Then, with the use of Lorenz curves, Gini coefficients, and bivariate correlation coefficients, the equity characteristics of these PGSs were quantified and evaluated, followed by an analysis of their relationships to the accessibility levels. Based on this comprehensive evaluation, the particle swarm optimization (PSO) algorithm was employed to the areas with low accessibility and equity levels to propose targeted PGS optimization strategies. The results showed that the accessibility of PGS was unevenly distributed, exhibiting a clear spatial difference of “east–west clustering”. The number of subdistricts with good (52.24–94.78) and best (94.79–283.58) accessibility was four, which was less than one-tenth of the total number of subdistricts in the study area. At the subdistrict level, the Gini coefficients for the accessible area of all types of PGSs were substantially higher than the international warning line of 0.4, indicating a substantial inequity in the population’s access to PGS. The implemented PSO algorithm resulted in eight new parks being planned at the specific optimized locations. Based on the actual land use status of the selected sites, recommendations are provided for the planning and layout of PGS. This proposed framework offers valuable data and theoretical insights for urban public green space planning and design in similar regions.

Solubility evaluation of palm-based Mono-diacylglycerols (MDAGs) in food grade solvent (hexane, ethanol, acetone, water) using QSPR model approach

Mono-diacylglycerols (MDAGs) are emulsifiers widely used in the food industry. MDAGs based on refined bleached deodorized palm stearin (RBDPS) have not been widely developed. MDAGs have been synthesized using glicerolysis between RBDPS and glycerols. Due to their low solubility in food-grade solvents (hexane, ethanol, acetone, and water), it has been difficult to purify or fractionate MDAGs only by a simple method. This research attempted to develop a model of MDAGs solubility in food-grade solvents and understand the parameters that influence the solubility of MDAGs in food-grade solvents. The process used a combination of experimental data collection and quantitative structure–property relationship (QSPR) models. The QSPR model for MDAGs solubility in a single and binary solvent demonstrated that the descriptors of Δ dipole moment (Δµ), E HOMO, E LUMO, electronic energy, gap energy HOMO-LUMO (ΔE1, ΔE2) softness (S), electrophilicity index (ω), and electron transfer (ΔN) have a significant impact in determining the solubility of MDAGs. Both the single and the binary solvents solubility models can give satisfactory prediction results: the square of the correlation coefficient (R2pred) was 0.933 and 0.948, and the root-mean-square error (RMSE) was 0.075 and 0.031, respectively, for the whole data set. In addition, this paper provided a new and effective method for predicting the solubility of MDAGs in single and binary food-grade solvent systems.

Doubly stochastic subdomain mining with sample reweighting for unsupervised domain adaptive person re-identification

Clustering-based unsupervised domain adaptive person re-identification methods have achieved remarkable progress. However, existing works are easy to fall into local minimum traps due to the optimization of two variables, feature representation and pseudo labels. Besides, the model can also be hurt by the inevitable false assignment of pseudo labels. In order to solve these problems, we propose the Doubly Stochastic Subdomain Mining (DSSM) to prevent the nonconvex optimization from falling into local minima in this paper. And we also design a novel reweighting algorithm based on the similarity correlation coefficient between samples which is referred to as Maximal Heterogeneous Similarity (MHS), it can reduce the adverse effect caused by noisy labels. Extensive experiments on two popular person re-identification datasets demonstrate that our method outperforms other state-of-the-art works. The source code is available at https://github.com/Tchunansheng/DSSM.

Potential smoke-free dividend across local areas in England: a cross-sectional analysis

BackgroundThe value that might be added to local economies each year through the money that people who smoke tobacco would save if everyone quit smoking is called the ‘smoke-free dividend’....

Comparison of assessment of programmed death-ligand 1 (PD-L1) status in triple-negative breast cancer biopsies and surgical specimens

AimsProgrammed death-ligand 1 (PD-L1) status in triple-negative breast cancer (TNBC) is important for immune checkpoint inhibitor therapies but may vary between different immunohistochemical assays, scorings and the type of specimen...

Quadrat soil pollen signal reflects plant important values in forests and shrublands from subtropical China.

Pollen analysis, a crucial tool in botany and ecology for examining historical biotic dynamics, has elicited debate owing to its complex link with vegetation. The challenge lies in discerning the ecological significance of pollen data. In this study, we conducted detailed quadrat surveys on Jinhua Mountain, subtropical China, analyzing topsoil pollen to determine whether pollen signals accurately reflect key ecological components in the forests and shrublands. We performed direct comparisons between pollen and plant compositions and calculated pollen percentages and plant Important Values (IVs) for each quadrat. The results indicate greater homogeneity in pollen composition across the study area compared to plant composition, particularly in the high percentage of Pinus pollen. However, distinct plant communities exhibited significantly different pollen compositions, as evidenced by the multi-response permutation test. This divergence aligns with variations in the dominant plant species across different communities. There were significant correlations between pollen percentages and plant IVs, with correlation coefficients of 0.55 (p < 0.001) at the quadrat level and 0.78 (p < 0.001) at the taxon level. These results support the utility of pollen analysis for representing ecologically significant values in subtropical Chinese forests and shrublands. Such correlations might also be extrapolated to pollen-based paleoecological studies.

Phenotypic variability in the US upland cotton core set for root traits and water use efficiency at the late reproductive stage

AbstractTo incorporate root traits that improve water use efficiency (WUE) in cotton (Gossypium hirsutum L.) variety development, harnessing the genetic variability for root traits is essential. The objectives of this study were to characterize the US upland cotton core set for root traits and WUE and determine the traits associated with WUE. The core set includes 23 of the 381 accessions of the cotton diversity panel and represents 74% of the allelic diversity in US upland cotton. Plants were grown in polyvinyl chloride columns (125‐cm height, 15‐cm diameter) in a greenhouse in 2021–2022 and 2022–2023. Half of the columns contained a synthetic hardpan (1 cm thickness, 300 PSI penetration resistance) at a depth of 25 cm. Plants were harvested when &gt;50% of the population opened bolls. Based on 16 root‐ and shoot‐traits, water use, and WUE, Deltapine 14, Station Miller, and Southland M1 were the best performers, and Toole, Paymaster HS200, Western Stormproof, CD3HCABCUH‐1‐89, and PD 2164 were the poor performers irrespective of the presence or absence of hardpan stress to root growth. The WUE of the core set genotypes was positively correlated with very fine root (diameter &lt;0.25 mm) length, surface area, and volume and total root weight (correlation coefficient ≥0.45). These traits serve as beneficial root traits for developing new varieties with enhanced WUE. The identified genotypes and traits will be valuable for developing the next generation of water‐use‐efficient cotton varieties with a broad genetic base through advanced breeding techniques involving genomic tools and genetic diversity.

Junior university: fostering young minds’ interest in higher education

The Universidade Júnior (U.Jr.) program, initiated by the University of Porto (U.Porto), Portugal, is a comprehensive educational initiative conducted during the summer months, primarily targeting the 10–18 age group. The program aims to promote science, technology, arts, humanities, and sports knowledge among elementary and secondary-level students and to influence their vocational choices and higher education aspirations. The study analyses the relationship between participation in the U.Jr. program and subsequent enrollment in higher education at U.Porto. It utilises data collected from 2006 to 2022, comparing U.Jr. participants with students who enrolled as freshmen at U.Porto. A Pearson correlation coefficient was applied to establish the connection between these datasets. Data analysis reveals a significant positive relationship between participation in U.Jr. and the choice of U.Porto for higher education. The study shows that 22 out of 100 first-year students at U.Porto in 2021 had previously attended U.Jr. Moreover, the geographical provenance of participants and U.Porto first-year students showed a robust correlation. The findings suggest that U.Jr. has a substantial impact on attracting students to U.Porto and influencing their academic choices. The program’s diverse activities, coupled with its inclusive approach, have been instrumental in increasing the university’s attractiveness and helping mitigate the country’s low higher education rates. The study underscores the importance of such initiatives in shaping students’ educational trajectories and choices for higher education.

Testing the validity of three acute care assessment tools for assessing residents' performance during in situ simulation: the ACAT-SimSit study.

The assessment of technical and nontechnical skills in emergency medicine requires reliable and usable tools. Three Acute Care Assessment Tools (ACATs) have been developed to assess medical learners in their management of cardiac arrest (ACAT-CA), coma (ACAT-coma) and acute respiratory failure (ACAT-ARF). This study aims to analyze the reliability and usability of the three ACATs when used for in situ (bedside) simulation. This prospective multicenter validation study tested ACATs using interprofessional in situ simulations in seven emergency departments and invited training residents to participate in them. Each session was rated by two independent raters using ACAT. Intraclass correlation coefficients (ICC) were used to assess interrater reliability, and Cronbach's alpha coefficient was used to assess internal consistency for each ACAT. The correlation between ACATs' scores and the learners' level of performance was also assessed. Finally, a questionnaire and two focus groups were used to assess the usability of the ACATs. A total of 104 in situ simulation sessions, including 85 residents, were evaluated by 37 raters. The ICC for ACAT-CA, ACAT-coma and ACAT-ARF were 0.95 [95% confidence interval (CI), 0.93-0.98], 0.89 (95% CI, 0.77-0.95) and 0.92 (95%CI 0.83-0.96), respectively. The Cronbach's alphas were 0.79, 0.80 and 0.73, respectively. The ACAT-CA and ARF showed good construct validity, as third-year residents obtained significantly higher scores than first-year residents ( P < 0.001; P < 0.019). The raters supported the usability of the tools, even though they expressed concerns regarding the use of simulations in a summative way. This study reported that the three ACATs showed good external validity and usability.

A comparative study of three ultrasound techniques for liver fibrosis staging.

The aim of this study is to assess the agreement and performance of visual transient elastography (ViTE), sound touch elastography (STE), and sound touch quantification (STQ) for liver fibrosis staging using transient elastography (TE) as a standard. We finally enrolled 252 subjects with chronic liver disease (CLD) who underwent ViTE, STE, STQ, and TE examinations simultaneously in our hospital from October 2022 to July 2023. We evaluated the correlation and agreement between various technologies. And also evaluated the performances and optimal cutoffs of ViTE, STE, and STQ . The correlation coefficients of ViTE and TE, STE and TE, STQ and TE were 0.863, 0.709, and 0.727, respectively. The ICC among ViTE, STE, STQ, and TE was 0.911. The area under the receiver operating characteristics (AUROCs) of ViTE, STE, and STQ for detection of TE of ≥5 kPa, ≥10 kPa, ≥15 kPa, and ≥20 kPa were 0.867, 0.771, 0.804; 0.972, 0.935, 0.933; 0.998, 0.973, 0.968; and 1.000, 0.960, 0.954, respectively. The AUROCs of ViTE for detection of lower stages (TE≥5 kPa and ≥10 kPa) were significantly higher than STE and STQ in the overall cohort (ViTE vs. STE: Z = 2.766, for TE ≥5 kPa; ViTE vs. STE: Z = 2.145, ; ViTE vs. STQ: Z = 2.587, for TE ≥10 kPa) (all P < 0.05). These methods all have coincided with performance in more advanced stages (TE ≥15 kPa and ≥20 kPa) (all P > 0.05). These methods showed excellent correlation and agreement. ViTE performance in more advanced fibrosis differentiation is comparable to the STE and STQ while ViTE is more accurate than STE and STQ to identify patients with mild CLD stage, and can more effectively rule out compensated advanced CLD.

Quantitative Assessment of Breast Tumor: Comparison of Four Methods of Positioning Region of Interest for Synthetic Relaxometry and Diffusion Measurement

ObjectivesTo compare the differences in apparent diffusion coefficient (ADC) and synthetic magnetic resonance (MR) measurements of four region of interest (ROI) placement methods for breast tumor and to investigate their diagnostic performance. Methods110 (70 malignant, 40 benign) newly diagnosed breast tumors were evaluated. The patients underwent 3.0T MR examinations including diffusion-weighted imaging and synthetic MR. Two radiologists independently measured ADCs, T1 relaxation time (T1), T2 relaxation time (T2), and proton density (PD) using four ROI methods: round, square, freehand, and whole-tumor volume (WTV). The interclass correlation coefficient (ICC) was used to assess their measurement reliability. Diagnostic performance was evaluated using multivariate logistic regression analysis and the receiver operating characteristic (ROC) curves. ResultsThe mean values of all ROI methods showed good or excellent interobserver reproducibility (0.79~0.99) and showed the best diagnostic performance compared to the minimum and maximum values. The square ROI exhibited superior performance in differentiating between benign from malignant breast lesions, followed by the freehand ROI. T2, PD, and ADC values were significantly lower in malignant breast lesions compared to benign ones for all ROI methods (p < 0.05). Multiparameters of T2 + ADC demonstrated the highest AUC values (0.82~0.95), surpassing the diagnostic efficacy of ADC or T2 alone (p < 0.05). ConclusionsROI placement significantly influences ADC and synthetic MR values measured in breast tumors. Square ROI and mean values showed superior performance in differentiating benign and malignant breast lesions. The multiparameters of T2 + ADC surpassed the diagnostic efficacy of a single parameter.

Evaluation of next-generation sequencing versus next-generation flow cytometry for minimal-residual-disease detection in Chinese patients with multiple myeloma

PurposeTo evaluate the efficacy of next-generation sequencing (NGS) in minimal-residual-disease (MRD) monitoring in Chinese patients with multiple myeloma (MM).MethodsThis study analyzed 60 Chinese MM patients. During MRD monitoring in these patients’ post-therapy, clonal immunoglobulin heavy chain (IGH) rearrangements were detected via NGS using LymphoTrack assays. MRD monitoring was performed using NGS or next-generation flow cytometry (NGF), and the results were compared. Additionally, the sensitivity and reproducibility of the NGS method were assessed.ResultsThe MRD detection range of the NGS method was 10–6–10–1, which suggested good linearity, with a Pearson correlation coefficient of 0.985 and a limit of detection of 10–6. Intra- and inter-assay reproducibility analyses showed that NGS exhibited 100% reproducibility with low variability in clonal cells. At diagnosis, unique clones were found in 42 patients (70.0%) with clonal IGH rearrangements, which were used as clonality markers for MRD monitoring post-therapy. Comparison of NGS and NGF for MRD monitoring showed 79.1% concordance. No samples that tested MRD-positive via NGF were found negative via NGS, indicating the higher sensitivity of NGS. MRD could be detected using NGS in 6 of 7 samples before autologous hematopoietic stem-cell transplantation, and 5 of them tested negative post-transplantation. In contrast, the NGF method could detect MRD in only 1 sample pre-transplantation.ConclusionCompared with NGF, NGS exhibits higher sensitivity and reproducibility in MRD detection and can be an effective strategy for MRD monitoring in Chinese MM patients.

Comparison of ANN and SVR based models in sea level prediction for the Black Sea coast of Sinop

Seawater level oscillations are very critical to coastal construction, flood prevention and human living conditions. However, it is difficult to accurately project the daily future for seawater level due to the effects of wind, precipitation and other atmospheric conditions. For this reason, in this paper, artificial intelligence (AI) based Artificial Neural Networks (ANN) and Support Vector Regression (SVR) methods are applied for the estimation of seawater level in Sinop Coast. In addition, Multiple Linear Regression (MLR) is used as a benchmarking model. In this study, coefficient of determination (R2) and root mean square error (RMSE) were applied as model evaluation criteria. Besides, 15 minutes (approximately 22 months) sea water level data of Sinop Station were collected and used as is. The findings revealed that the ANN model can predict the water level for 1st, 2nd, 3rd, 4th days with correlation coefficients (R2) of 0.84, 0.67, 0.64, 0.63, respectively, and the SVR model can predict for 1st, 2nd days with correlation coefficients (R2) of 0.86, 0.66, respectively.

Exploring the depths of on‐water training in highly‐trained rowing athletes

AbstractThis investigation examined the association between on‐water rowing stroke‐rate (SR), velocity and heart‐rate (HR) in highly trained rowers (n = 8 male; n = 8 female and 19.3 ± 1.1 year) over a 4‐month real‐world (i.e., variable environment and boat class) training period. On‐water SR, prognostic velocity (percent of world's best velocity) and HR were captured for 1453 sessions via smart‐watch and chest‐strap HR monitor. Data was filtered and smoothed with individual HR training zones identified (T1–T5). Linear mixed modeling and an overlapping index () were used to assess differences in SR and prognostic velocity between HR zones. Correlation coefficient (r) was used to assess the SR and prognostic velocity relationship and progression of prognostic velocity at a SR of 20spm over time. There were significant differences in SR and prognostic velocity between HR zones (T1&gt;T2&gt;T3&gt;T4&gt;T5, p &lt; 0.001); however, data overlap between adjacent zones was substantial for both variables ( = 0.69–0.85; prognostic velocity: = 0.46–0.86). A significant, positive correlation (r = 0.50 and p &lt; 0.001) between SR and prognostic velocity was found. Progression of prognostic velocity at SR 20spm over the study duration was trivial (r = −0.01 and p = 0.71). Variables commonly used to prescribe and describe on‐water rowing training showed large variability in a real‐world training environment impacting the ability to accurately monitor training performance and progression.

Validity and test-retest reliability of the Swedish version of the Geriatric Depression Scale among very old adults.

The Geriatric Depression Scale (GDS) has shown good validity and reliability, but few studies have examined the GDS among very old adults or the Swedish translation. Evaluate the validity and reliability of the Swedish version of GDS-15 among very old adults. In the Umeå85 + /GErontological Regional DAtabase (GERDA) study, 387 participants were assessed with both the GDS-15 and the Montgomery-Åsberg Depression Rating Scale (MADRS). The mean age was 91years. Concurrent validity between the scales was calculated using Spearman's correlation. We used the Diagnostic and Statistical Manual of Mental Disorders (DSM) V symptom criteria for depression based on MADRS item scores to define depression. We calculated the Area Under the Curve (AUC) and found an optimal cut-off. A convenience sample with 60 individuals was used to calculate test-retest reliability with Cohen's kappa and Intraclass Correlation Coefficient (ICC). Spearman's correlation coefficients between total scores for GDS-15 and MADRS were 0.60. Cronbach's alpha for the whole scale was 0.73. The AUC was 0.90 for distinguishing major depression, and the recommended cut-off of ≥ 5 showed a sensitivity of 95.2% and specificity of 65.8%. The test-retest showed that Cohen's kappa was substantial (0.71) and the ICC was excellent (0.95). The Swedish version of the GDS-15 showed good validity and reliability among very old adults. The generally recommended cut-off of ≥ 5 seems reasonable to use with the Swedish version and among very old adults.

Reliability testing of the Health of the Nation Outcome Scales 2018.

The Health of the Nation Outcome Scales (HoNOS) is a widely used clinical measure designed to rate and monitor the outcomes of service users accessing specialist mental healthcare. Since its development (in 1996), numerous research studies have confirmed the HoNOS captures the aspects of care that it purports to (validity), and that clinicians' ratings are consistent both over time, and between different raters (reliability). In 2018, the HoNOS was reviewed with updates made to some terminology and other revisions intended to remove ambiguity in the guidance for raters. However, although the new version (HoNOS 2018) was accompanied by a recommendation that its validity and reliability be re-tested this was not undertaken. To our knowledge, this is the first study to re-assess the updated tool's reliability by measuring the level of agreement between different raters. Our findings confirm that there is an acceptable level of consistency between student mental health nurses that have been trained to use the (new) HoNOS 2018. The HoNOS is nationally mandated for use by all specialist mental healthcare providers in the UK. Our findings provide some assurance that, with appropriate update training and monitoring of organisational-level data sets, the original HoNOS glossary can safely be replaced with the HoNOS 2018 to ensure more contemporary routine outcome measurement can occur. INTRODUCTION: The Health of the Nation Outcome Scales (HoNOS) is a well-established clinician rated outcome measure for use in mental health services. Following an international review, an updated version (HoNOS 2018) was published with a recommendation that its psychometric properties be re-tested prior to widespread implementation. To date, only one such study has been published. To test the inter-rater agreement levels for HoNOS 2018. Third-year student mental health nurses received training to complete the HoNOS 2018. Following this timetabled session, they were each invited to independently rate two, randomly selected, videos of (simulated) patient interviews. The resulting data were then analysed to calculate the tool's internal consistency and inter-rater agreement levels. The 55 participants provided 106 ratings from four vignettes. Cronbach's alphas and McDonalds omegas confirmed the revised tool's internal consistency was acceptable. Average measure intraclass correlation coefficients for the four patient vignettes indicated excellent reliability. This study provides initial assurance that the HoNOS 2018 is a reliable clinician rated outcome measure suitable for use in routine clinical practice by relatively inexperienced mental health practitioners with limited training.

Correlation analysis and predicting modeling of pyrolysis gas based on landfill excavated waste pyrolysis characteristics

The contribution of excavated waste to waste management is multifaceted, including minimization, non-hazardous disposal, access to useable land resources, improved waste management techniques and public environmental awareness, consistent with recent circular economy initiatives. Pyrolysis can be converted into tar, pyrolysis gas and char with recyclable utilization, enriching the application of pyrolysis technology in the field of excavation waste. In this study, the pyrolysis system includes horizontal tube furnace, gas collection device and Micro GC. The excavated waste was pyrolyzed at a temperature of 500∼900 °C with a heating rate of 10 °C/min. Pyrolysis gases include H2, CO, CO2, CH4, C2H4, C2H6 and C3H8. Pyrolysis was divided into four stages, the main decomposition range is 230∼500 °C, with a weight loss rate of 68.49% and a co-pyrolysis behavior. As the temperature increases, the tar and char decreased and the gas production increased significantly, and the pyrolysis gas reached 47.02% at 900 °C. According to Pearson correlation coefficient analysis, the generation of H2 and CO is positively correlated with temperature. Therefore, the target products can be influenced by changing the parameters, when considering the practical utilization of the excavated waste pyrolysis products. On this basis, the prediction models were built by polynomial fitting method. This model can reduce the experimental exploration cycle, reduce the cost, and accurately predict the pyrolysis gas, which has practical guidance for the application of pyrolysis industry, and provides a theoretical basis for the resource recycling and energy recovery of landfill.

Type A standard uncertainty evaluation in one measurement through uncertainty propagation from voxel values’ distribution for computed tomography metrology

According to the guide to the expression of uncertainty in measurement, ‘type A evaluation’ generally requires repeated measurements, which are time-consuming for CT scans. To solve this problem, we developed a method for estimating the standard deviation of measurement results in one measurement through uncertainty propagation, which can be regarded as repeatability standard deviation to evaluate the type A standard uncertainty. The method first fits the CT voxel value distribution, uses the ISO50 method to determine the spatial distribution of surface points from the voxel value distribution and edge shape interpolation, and then derives the measurement results by fitting geometric parameters with the least square algorithm. Finally, the standard deviation of the measurement results is evaluated according to the distribution of the surface point position through uncertainty propagation. We performed simulations and experiments using the hole-plate with 28 holes to compare the uncertainty evaluated by our method and the type A standard uncertainty evaluated on the basis of a series of observations obtained under repeatability conditions. Both simulation and experimental results show that these two uncertainties follow the same statistical variation pattern. The Pearson correlation coefficients of the two uncertainties in simulation and experiment are 0.79 and 0.33, respectively, indicating that the uncertainty evaluated by the proposed method can directly replace the type A uncertainty or provide a reference similar to type A uncertainty for the evaluation of the combined uncertainty.

Global, regional, and national temporal trends of diet-related ischemic stroke mortality and disability from 1990 to 2019.

Stroke is the second leading cause of death and the third leading cause of disability in the general population worldwide. However, the changing trend of ischemic stroke burden attributable to various dietary risk factors has not been fully revealed and may contribute to a better understanding of stroke epidemiology. Our article aimed to evaluate the temporal trend of diet-related ischemic stroke burden to inform future research and policy-making. This analysis was based on the data from the Global Burden of Disease (GBD) Study 2019 (spanning years 1990 to 2019), and we used the joinpoint regression to model temporal trends in diet-related ischemic stroke burden across countries and regions of the world during the study period. Six specific dietary factors known to influence stroke risk, including sodium, red meat, fiber, vegetables, whole grains, and fruits, were evaluated in the GBD study to determine their individual and joint impact on ischemic stroke. The changing trend was primarily measured by the average annual percent change (AAPC). Age-standardized rates (ASRs) of mortality and years lived with disability (YLD) per 100,000 population were used to evaluate disease burden. Finally, the socioeconomic background, which was quantified as sociodemographic index (SDI), and its association with diet-related ischemic stroke burden were also explored with the Pearson correlation coefficient. During the study period, the ischemic stroke ASR of mortality attributable to overall dietary risk decreased by an average of 1.6% per year, while the ASR of YLD decreased by an average of 0.2% per year. High sodium diet was still a key driver of diet-related ischemic stroke, accounting for 8.4% and 11.0% of deaths and disabilities, respectively, in 2019. In addition, we found a negative association between temporal evolution of stroke burden and socioeconomic background (r = -0.6603 for mortality and r = -0.4224 for disability, P < 0.001), which suggested that the developing countries with weak social and economic foundation faced greater challenges from the ongoing burden of diet-related strokes compared with developed countries. Our study found declining trends and revealed the current status of diet-related ischemic stroke mortality and disability. Interdisciplinary countermeasures involving the development of effective food policies, evidence-based guidelines, and public education are needed in the future to combat this global epidemic. The data used for analysis were open-access and can be obtained from https://vizhub.healthdata.org/gbd-results/.