Consistency Index Research Articles

Regression generative adversarial network based on bounded losses for prediction of free calcium oxide in cement clinker

The data imbalance problem caused by multi-time scales phenomenon affects the prediction accuracy, validity and robustness of free calcium oxide (fCaO) content in cement clinker calcination process. Focusing on this problem, we propose an regression generative adversarial network model to predict fCaO content, which contains a generator, discriminator and predictor. Generator and discriminator are designed as bounded loss generative adversarial network to solve mode collapse problem and improve the stability. They learn actual data features in adversarial learning style and produce fake data to enlarge the scale and feature space of actual data to train predictor and finally achieve the prediction of fCaO content, which overcomes the problem of data imbalance. For performance assessment, we visually evaluate the validity of generated data from the perspective of univariate distribution and multivariate joint distribution and invent sequence change tendency consistency index (TCI) to evaluate the robustness of fCaO content prediction. Experiments implemented by cement production data demonstrate that the proposed model has advantages in accuracy, availability and robustness in fCaO content prediction, especially TCI is higher 22.69 percentage points than that of without data augmentation.

Construction of a prognostic model of lung adenocarcinoma based on machine learning

In order to more accurately predict the prognosis and survival of lung adenocarcinoma patients, this paper used the gene expression and clinical information data of lung adenocarcinoma patients in the open database of TCGA to jointly construct a prognosis model of lung adenocarcinoma. Three difference analysis methods and univariate cox regression analysis were used as the preliminary screening method. By comparing the variable selection ability of lasso regression and random survival forest, comparing the performance of cox proportional risk regression model and random survival forest model, and integrating clinical data, a model that can more accurately predict the prognosis of lung adenocarcinoma patients was constructed. After comparison and selection, lasso regression was used to select variables and cox proportional risk model was used as the prediction model. The consistency index of the model reached 0.712. The AUC for 1-year, 3-year and 5-year survival of lung adenocarcinoma patients in the validation set were 0.808, 0.816 and 0.754, respectively. After the fusion of clinical data, the 1-year, 3-year and 5-year survival prediction AUC in the validation set were 0.840, 0.836 and 0.865, respectively, indicating that the model had good predictive performance.

Construction and Validation of a Risk Prediction Model for Mild Cognitive Impairment in Non-Dialysis Chronic Kidney Disease Patient.

The aims of this study are to explore the factors affecting mild cognitive impairment in patients with chronic kidney disease (CKD) who are not undergoing dialysis and to construct and validate a nomogram risk prediction model. Using a convenience sampling method, 383 non-dialysis CKD patients from two tertiary hospitals in Chengdu were selected between February 2023 and August 2023 to form the modeling group. The patients were divided into a mild cognitive impairment group (n = 192) and a non-mild cognitive impairment group (n = 191), and factors such as demographics, disease data, and sleep disorders were compared between the two groups. Univariate and multivariate binary logistic regression analyses were used to identify independent influencing factors, followed by collinearity testing, and construction of the regression model. The final risk prediction model was presented through a nomogram and an online calculator, with internal validation using Bootstrap sampling. For external validation, 137 non-dialysis CKD patients from another tertiary hospital in Chengdu were selected between October 2023 and December 2023. In the modeling group, 192 (50.1%) of the non-dialysis CKD patients developed mild cognitive impairment, and in the validation group, 56 (40.9%) patients developed mild cognitive impairment, totaling 248 (47.7%) of all sampled non-dialysis CKD patients. Age, educational level, Occupation status, Use of smartphone, sleep disorders, hemoglobin, and platelet count were independent factors influencing the occurrence of mild cognitive impairment in non-dialysis CKD patients (all p < 0.05). The model evaluation showed an area under the ROC curve of 0.928, 95% CI (0.902, 0.953) in the modeling group, and 0.897, 95% CI (0.844, 0.950) in the validation group. The model's Youden index was 0.707, with an optimal cutoff value of 0.494, sensitivity of 0.853, and specificity of 0.854, indicating good predictive performance; calibration curves, Hosmer-Lemeshow test, and clinical decision curves indicated good calibration and clinical benefit. Internal validation results showed a consistency index (C-index) of 0.928, 95% CI (0.902, 0.953). The risk prediction model developed in this study shows excellent performance, demonstrating significant predictive potential for early screening of mild cognitive impairment in non-dialysis CKD patients. The application of this model will provide a reference for healthcare professionals, helping them formulate more targeted intervention strategies to optimize patient treatment and management outcomes.

Identification of prognostic RNA editing profiles for clear cell renal carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer and currently lacks effective biomarkers. This research aims to analyze and identify RNA editing profile associated with ccRCC prognosis through bioinformatics and in vitro experiments. Transcriptome data and clinical information for ccRCC were retrieved from the TCGA database, and RNA editing files were obtained from the Synapse database. Prognostic models were screened, developed, and assessed using consistency index analysis and independent prognostic analysis, etc. Internal validation models were also constructed for further evaluation. Differential genes were investigated using GO, KEGG, and GSEA enrichment analyses. Furthermore, qPCR was performed to determine gene expression in human renal tubular epithelial cells HK-2 and ccRCC cells A-498, 786-O, and Caki-2. An RNA editing-based risk score, that effectively distinguishes between high and low-risk populations, has been identified. It includes CHD3| chr17:7815229, MYO19| chr17:34853704, OIP5-AS1| chr15:41590962, MRI1| chr19:13883962, GBP4| chr1:89649327, APOL1| chr22:36662830, FCF1| chr14:75203040 edited sites or genes and could serve as an independent prognostic factor for ccRCC patients. qPCR results showed significant up-regulation of CHD3, MYO19, MRI1, APOL1, and FCF1 in A-498, 786-O, and Caki-2 cells, while the expression of OIP5-AS1 and GBP4 was significantly down-regulated. RNA editing site-based prognostic models are valuable in differentiating between high and low-risk populations. The seven identified RNA editing sites may be utilized as potential biomarkers for ccRCC.

Development and Validation of a Nomogram for Predicting the Severity of Coronary Artery Disease Based on Cardiopulmonary Exercise Testing.

As a major global health concern, coronary artery disease (CAD) demands precise, noninvasive diagnostic methods like cardiopulmonary exercise testing (CPET) for effective assessment and management, balancing the need for accurate disease severity evaluation with improved treatment decision-making. Our objective was to develop and validate a nomogram based on CPET parameters for noninvasively predicting the severity of CAD, thereby assisting clinicians in more effectively assessing patient conditions. This study analyzed 525 patients divided into training (367) and validation (183) cohorts, identifying key CAD severity indicators using least absolute shrinkage and selection operator (LASSO) regression. A predictive nomogram was developed, evaluated by average consistency index (C-index), the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA), confirming its reliability and clinical applicability. In our study, out of 25 variables, 6 were identified as significant predictors for CAD severity. These included age (OR = 1.053, P < .001), high-density lipoprotein (HDL, OR = 0.440, P = .002), hypertension (OR = 2.050, P = .007), diabetes mellitus (OR = 3.435, P < .001), anaerobic threshold (AT, OR = 0.837, P < .001), and peak kilogram body weight oxygen uptake (VO2/kg, OR = 0.872, P < .001). The nomogram, based on these predictors, demonstrated strong diagnostic accuracy for assessing CAD severity, with AUC values of 0.939 in the training cohort and 0.840 in the validation cohort, and also exhibited significant clinical utility. The nomogram, which is based on CPET parameters, was useful for predicting the severity of CAD and assisted in risk stratification by offering a personalized, noninvasive diagnostic approach for clinicians.

The Practice and Application of Enterprise Leadership Strategic Management Based on Fuzzy Decision Model

Abstract This paper gives a pseudo metric between any two hesitation fuzzy elements based on the closeness of the hesitation fuzzy elements; based on the hesitation fuzzy pseudo metric, a hesitation fuzzy multi-attribute decision-making method based on the pseudo metric closeness is proposed. Understanding the level of strategic management ability and enterprise leaders’ ability level can be achieved by measuring the size of fuzzy weighted closeness. From the perspective of group consistency analysis, a hesitation fuzzy attribute weight optimization model based on group consistency index maximization is established, and a hesitation fuzzy multi-attribute decision-making method based on group consistency and grey correlation analysis is proposed in combination with grey correlation analysis. Enterprise leaders’ strategic management abilities are assessed during the application stage using the hesitation fuzzy multi-attribute decision-making method. Analyzing the relationship between the strategic stage and the match between different types of strategic styles of business leaders and business performance is done using cross-linkage. The analysis results show that the distance value between the comprehensive fuzzy assessment value of each level of the leader’s strategic management capability and the semantic variable of each assessment value is at least 0.052, which is consistent with the overall assessment results of the five experts on the leader’s strategic management capability. The match between the strategic style of the business leader and the strategic stage in which the enterprise is located has a significant interaction effect on the performance indicators of the enterprise P=0.007&lt;0.01. The results verify the accuracy of the evaluation method of this paper and the existence of a correlation between the strategic leadership of the business leader and the performance of the enterprise.

Decomposition of air conditioning electricity consumption based on effective duration.

As the share of air conditioning electricity consumption within total grid electricity consumption grows, the decomposition of such consumption becomes increasingly crucial for assessing electricity usage patterns, devising consumption scheduling strategies, and maintaining the stability of the power grid. Although there is a strong correlation between apparent temperature and air conditioning electricity consumption, the literature currently available seldom explores the impact of apparent temperature on this consumption. Moreover, there is a scarcity of effective assessment indices to evaluate the efficacy of air conditioning electricity consumption breakdown. This study introduces a method for decomposing electricity consumption from air conditioning units, utilizing effective duration as a basis to tackle these issues. By employing an apparent temperature model as a constraint, this approach identifies the effective operating time of air conditioning and constructs a constrained convex optimization problem to estimate air conditioning power usage. Additionally, a novel evaluation index for the effectiveness of air conditioning electricity consumption decomposition is proposed, which includes penalties for negative decomposed consumption, alongside the traditional consistency index. Comparative experiments are conducted using real electricity consumption data from Fujian Province. Empirical results indicate that the methodology for air conditioning electricity consumption decomposition presented in this paper aligns more closely with actual conditions. Furthermore, the evaluation metrics introduced for the decomposition of air conditioning electricity consumption are adept at precisely gauging the quality of the air conditioning electricity consumption data.

Anoikis-related mRNA-lncRNA and DNA methylation profiles for overall survival prediction in breast cancer patients.

As a type of programmed cell death, anoikis resistance plays an essential role in tumor metastasis, allowing cancer cells to survive in the systemic circulation and as a key pathway for regulating critical biological processes. We conducted an exploratory analysis to improve risk stratification and optimize adjuvant treatment choices for patients with breast cancer, and identify multigene features in mRNA and lncRNA transcriptome profiles associated with anoikis. First, the variance selection method filters low information content genes in RNA sequence and then extracts the mRNA and lncRNA expression data base on annotation files. Then, the top ten key mRNAs are screened out through the PPI network. Pearson analysis has been employed to identify lncRNAs related to anoikis, and the prognosis-related lncRNAs are selected using Univariate Cox regression and machine learning. Finally, we identified a group of RNAs (including ten mRNAs and six lncRNAs) and integrated the expression data of 16 genes to construct a risk-scoring system for BRCA prognosis and drug sensitivity analysis. The risk score's validity has been evaluated with the ROC curve, Kaplan-Meier survival curve analysis and decision curve analysis (DCA). For the methylation data, we have obtained 169 anoikis-related prognostic methylation sites, integrated these sites with 16 RNA features and further used the deep learning model to evaluate and predict the survival risk of patients. The developed anoikis feature is demonstrated a consistency index (C-index) of 0.778, indicating its potential to predict the survival probability of breast cancer patients using deep learning methods.

Reliability, Validity, Modification and Expansion of the Chinese Version of the Disease-Specific Anxiety Questionnaire for Chronic Obstructive Pulmonary Disease.

To translate a disease-specific anxiety questionnaire on chronic obstructive pulmonary disease (COPD) and test its reliability and validity in China. The German version of the revised COPD Anxiety Questionnaire (CAF-R) was initially validated using step-by-step translation, back-translation, and cross-cultural adaptation. The reliability and validity of the Chinese version of the CAF-R (CAF-R-CN) were tested among 448 patients with COPD (mean age =71.42±9.33 years, 17.2% female) from four medical institutions in Suzhou, Jiangsu Province, using convenience sampling, from April 2022 to June 2023. The CAF-R-CN included six dimensions with a total of 25 items. The item-level content validity index was 0.860-1.000; the scale-level content validity index was 0.920. The structural validity χ2/df was 2.326, the root mean square error of approximation was 0.077, the comparative fit index was 0.924, and the Tucker-Lewis index was 0.912. The six-dimensional internal consistency index Cronbach's α coefficient was 0.696-0.910, and the test-retest reliability was 0.949. An optimal cut-off score of 50.5 was selected with a sensitivity of 0.786 and specificity of 0.870. The CAF-R-CN had satisfactory reliability and validity and can be used to identify and assess anxiety in COPD patients with a Chinese cultural background.

Analysis of the geotechnical and mineralogical characteristics of the Settat-Khouribga shale clay for potential civil engineering applications

This study provides a comprehensive analysis of clay shale from the Settat-Khouribga region, assessing its suitability for civil engineering applications through various tests. Grain size distribution, hardness (MDE, LA), and plasticity (ES 0/5, IP, VB) tests, along with consistency index evaluations following the "CSTCN" catalog, were conducted. X-ray diffraction (XRD) and mineralogical analyses identified key components such as quartz, calcite, feldspar, and iron oxide, with quartz being predominant. The results demonstrated that the shale clay is well-suited for construction purposes, exhibiting favorable grain size distribution and high resistance to wear and abrasion, which are crucial for material durability. Its low plasticity further indicates good mechanical stability in construction projects. SEM analysis revealed a microstructure dominated by quartz grains, montmorillonite, and illite lamellae, and the presence of micro fissures and pores suggests both strengths and potential durability concerns. Advanced statistical analyses, including Principal Component Analysis (PCA) and regression models, were employed to assess geotechnical properties. The study confirms the shale's compliance with construction standards, highlighting its potential for sustainable building practices. These findings contribute significantly to the field of geotechnical engineering, emphasizing the material's suitability for construction and its broader societal implications.

User Behavior Analysis and Optimization of Japanese Language Online Education Platforms

Abstract Japanese language instruction at universities has gained new life thanks to the quick growth of online learning via the Internet, microclasses, flipped classrooms, and other innovative teaching methods. This is the direction of future educational reform in colleges and universities. After gathering and pre-processing behavioral data of Japanese learners, this study builds a data analysis model of Japanese online learning user behavior based on the Japanese online education platform. In this model, user behavioral features are extracted and classified using the RFG-SVM model, which is based on SVM. Users with similar user behaviors are then clustered together using the FCM algorithm, and the association rule algorithm is utilized to explore the intricate relationship between user online learning behaviors and learning effects. Lastly, the FSQCA approach is used to investigate the optimization path of Japanese online education platforms after combining with example analysis. The most significant aspect of Japanese online learning is its online learning rate (0.7499). Users can be categorized into three groups: close cooperation (52.3%), active participation (6.1%), and weak participation (41.6%). Japanese online learners also exhibit better user behavior. The consistency indexes for Grouping H1: SI*LA*CC*HA, Grouping H2: ~SQ*CQ*SI*~LA*CC, and Grouping H3: SQ*CQ*SI*~LA*HA were 0.929, 0.959, and 0.965, respectively, with both social influence (SI) and habit (HA) serving as important requisites. This study helps to form a mature mechanism that prompts Japanese online education users to develop a continuous willingness to use the program, which contributes to the development of inclusive education to a certain extent.

DOES THE ACADEMIC ENVIRONMENT DEVELOP TEAMWORK EFFICIENCY AMONG STUDENTS?

Teamwork skills are considered essential for all areas of a person’s life, so universities take them into account when designing their curricula. These skills are especially important if we are talking about humanities fields. However, little is known about how students’ factors can affect the process of developing teamwork skills. Consequently, our study proposes to fill this gap and to analyze the mastery of teamwork skills by higher education students, measured by the efficiency of working in a team, in relation to teamwork styles, but also with the number of courses whose content allows the development of these skills. One Hundred Ninety-Five female students participated in this study, with an average age of M = 31.5, a standard deviation of SD = 9.2. The participants filled out a questionnaire that measured the efficiency of teamwork and the styles of teamwork based on specialized literature. Also, the questionnaires were first applied in a pilot study in which 50 female students participated and the Cronbach’s internal consistency index was 0.8 for the first questionnaire and 0.77 for the second. The results showed that there are statistically significant correlations between teamwork efficiency, teamwork styles, and the number of courses in which the development of these skills is encouraged, p &lt; 0.01. Moreover, we also noticed that the number of courses in which the development of these skills is encouraged has a mediating effect on the relationship between working styles in a team and the efficiency of working in a team, p &lt; 0.01. These results can support teaching staff in higher education to adopt the most suitable educational methods for the development of teamwork skills, essential skills, especially for students in the humanities domains.

Monte Carlo simulation with confusion matrix paradigm - A sample of internal consistency indices.

Monte Carlo simulation is a common method of providing empirical evidence to verify statistics used in psychological studies. A representative set of conditions should be included in simulation studies. However, several recently published Monte Carlo simulation studies have not included the conditions of the null distribution of the statistic in their evaluations or comparisons of statistics and, therefore, have drawn incorrect conclusions. This present study proposes a design based on a common statistic evaluation procedure in psychology and machine learning, using a confusion matrix with four cells: true positive, true negative, false negative modified, and false positive modified. To illustrate this design, we employ an influential Monte Carlo simulation study by Trizano-Hermosilla and Alvarado (2016), which concluded that the Omega-indexed internal consistency should be preferred over other alternatives. Our results show that Omega can report an acceptable level of internal consistency (i.e., > 0.7) in a population with no relationship between every two items in some conditions, providing novel empirical evidence for comparing internal consistency indices.

Structural, chemical and technofunctional properties pectin modification by green and novel intermediate frequency ultrasound procedure

The impact of intermediate frequency ultrasound (IFUS, 582, 864 and 1144 kHz), mode of operation (continue and pulsed) and ascorbic acid (Aa) addition on the structural, chemical and technofunctional properties of commercial citrus high methoxyl-grade pectin (HMP) was investigated. The chemical dosimetry of IFUS, monitored by the triiodide formation rate (I3−), demonstrated that the pulsed ratio (1900 ms on/100 ms off) at the three frequencies was similar to that of continue mode but IFUS1144 kHz produced more acoustic streaming demonstrated by the height liquid measured using image analysis. In presence of Aa, HMP presented higher fragmentation than in its absence. IFUS did not give rise any changes in the main functional groups of the HMP. In general, a reduction in molecular weight was observed, being the presence of Aa the most influencing factor. Regarding monosaccharides, IFUS modified the structure of homogalacturonan and rhamnogalacturonan-I and increased of GalA contents of the HMP in presence of Aa at the above three frequencies. A reducing of the consistency index (k) and increasing of the flow index (n) of HMP were showed by IFUS frequency and Aa addition. The emulsifying activity and stability index were increased for HMP treated by IFUS in continue mode at all frequencies and in presence of Aa. The results presented in this research shown the effectiveness of IFUS as tool to modify pectin into different structures with different functionalities.

An experimental investigation on the settling of disk particles in annular region: Wall effect and drag coefficient

Settling non-spherical and spherical particles in the annular region is common in many industrial applications. This work shows the intensification of the performance of settling particles in an annular channel by reducing the drag coefficient. The disk/cylinder particles settle in annular channels filled with Newtonian and non-Newtonian fluids. An aqueous solution of different concentrations of glycerin and Carboxymethylcellulose (CMC) was considered as the Newtonian and non-Newtonian fluids. The experimental results reported in the present work covered the 0.17 ≤ blockage ratio ≤0.55, 0.14 ≤ k (flow consistency index) ≤1.81 (Pa.sn), 0.64 ≤ n (flow behavior index) ≤1, and annular channel Reynolds number 0.40 ≤ Re ≤63.44 and 0.05 ≤ Re ≤43.02 in Newtonian and non-Newtonian fluids respectively. The wall factor, f, was increased with the increase in Re and the decrease in blockage ratio. At higher Reynolds numbers (Re), the particle’s wall factor was only influenced by the blockage ratio. The wall factor was reduced due to the particle’s shape change from cylinder to disk, i.e., decreasing the particle’s sphericity. The wall factor of particles is observed to be higher in non-Newtonian than in Newtonian fluids. The critical Reynolds number (Rec ) decreased with the H/d (Height/diameter of disk) ratio and sphericity of the studied particles. Both the drag coefficients, in the absence ( C D ∞ ) and presence ( C D ) of the wall effect, declined with the Reynolds number. Suitable correlations were developed for CD with low values of RMSLD (root mean square logarithmic deviation). The experimentally obtained drag coefficient data were also analyzed successfully using the laminar model available in Ansys (Fluent) −18.1. The computed velocity contours analyzed the wall effect and CD variations successfully. The use of the annular channel as a drag reduction device is encouraged due to the observed higher wall factor and decreased drag coefficient for particle settling in comparison to the non-annular channel.

Nomogram prediction model of cervical anastomotic leakage after esophageal cancer surgery

Objective: To retrospectively analyze the risk factors of anastomotic leakage in the neck after esophageal cancer and establish a nomogram prediction model that can accurately predict the occurrence of anastomotic leakage in the neck of the patient. Methods: The study retrospectively analyzed 702 patients who underwent radical esophageal cancer surgery between January 2010 and May 2015 at Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. A multivariate logistic regression model was used to determine the risk factors for neck anastomotic leak, and a nomogram model was constructed, internal validation methods were used to evaluate and verify the predictive effectiveness of the nomogram. Results: There were 702 patients in the whole group, 492 in the training group and 210 in the validation group. The incidence of postoperative cervical anastomotic leak was 16.1% (79/492) in 492 patients with esophageal cancer in the training group. Multifactorial analysis revealed calcification of the descending aorta (OR=2.12, 95% CI: 1.14, 3.94, P=0.018), calcification of the celiac artery (OR=2.29, 95% CI: 1.13, 4.64, P=0.022), peripheral vascular disease (OR=5.50, 95% CI: 1.64, 18.40, P=0.006), postoperative ventilator-assisted breathing (OR=5.33, 95% CI: 1.83, 15.56, P=0.002), pleural effusion or septic chest (OR=3.08, 95% CI: 1.11, 8.55, P=0.031), incisional fat liquefaction and infection (OR=3.49, 95% CI: 1.68, 7.27, P=0.001) were independent risk factors for the development of cervical anastomotic leak after esophageal cancer surgery. The results of the nomogram prediction model showed that the consistency indices of the training and external validation groups were 0.73 and 0.74, respectively (P<0.001), suggesting that the prediction model has good predictive efficacy. Conclusion: The nomogram prediction model can intuitively predict the incidence of postoperative cervical anastomotic leakage in patients with high prediction accuracy, which can help provide a clinical basis for preventing cervical anastomotic leak and individualized treatment of patients.

EFFECTS OF THE COVID-19 ON MEDICAL USE OF ELDERLY PATIENTS WITH HYPERTENSION: A NATIONWIDE COHORT STUDY IN KOREA.

Abstract COVID-19 pandemic disrupted healthcare services, including chronic disease management for vulnerable groups like older individuals with hypertension. This study aims to evaluate hypertension management in South Korea’s elderly population during the pandemic using treatment consistency indices like Continuity of Care (COC), Modified, Modified Continuity Index (MMCI), Most Frequent Provider Continuity (MFPC). This study used the the Korea Disease Control and Prevention Agency-COVID-19-National Health Insurance Service cohort (K-COV-N cohort) from the National Health Insurance Service (NHIS) between 2017 and 2021 The research included a total of 4,097,299 hypertensive patients aged 65 or older. We defined 2018 and 2019 as baseline period before COVID 19 pandemic, and 2020 and 2021 as COVID 19 period, and calculated the indices of medical continuity (Number of visits, COC, MMCI and MFPC) on a yearly basis were measured. The number of visits were decreased during COVID 19 period compared to baseline period ( 59.64 ±52.75 to 50.49 ± 50.33, p&amp;lt; 0.001) respectively. However COC, MMCI and MFPC were not decreased in baseline period compared to COVID-19 period ( 0.71 ± 0.21 vs. 0.71 ± 0.22, p&amp;lt; 0.001), (0.97 ± 0.05 vs. 0.96 ± 0.05, p&amp;lt; 0.001), and (0.8 ± 0.17 vs. 0.8 ± 0.17, p&amp;lt; 0.001) respectively. In conclusion, COVID-19 had no significant impact on continuity of care, but it did affect the frequency of outpatient visits of older hypertensive patients. However, this study highlights the importance of addressing healthcare inequalities, especially for older hypertensive patients, during pandemics and advocates for policy changes to ensure continued care for vulnerable populations.

Malnutrition and cerebral intraparenchymal damage in patients with thrombosis of dural sinuses and/or cerebral veins

BackgroundsThrombosis of dural sinuses and/or cerebral veins (CVT) is an uncommon form of cerebrovascular disease. Malnutrition is common in patients with cerebrovascular disease, and early assessment of malnutrition and individualized nutritional treatment have been reported to improve functional outcomes of these patients. As for CVT patients, little is known about whether these patients would suffer from malnutrition. Also, the correlation between malnutrition and cerebral intraparenchymal damage (CID) in CVT patients was rarely studied.MethodsPatients with CVT were retrospectively included in this observational study. Multivariate logistic regressions were used to investigate the effects of nutritional indexes on the risk of CID. Subsequently, we used the independent risk factors to construct the nomogram model, and the consistency index (C-index), calibration curve and decision curve analysis (DCA) to assess the reliability and applicability of the model.ResultsA total of 165 patients were included in the final analysis. Approximately 72.7% of CVT patients were regarded as malnourished by our malnutrition screening tools, and malnutrition is associated with an increased risk of CID. Prognostic Nutritional Index (PNI) (OR = 0.873; CI: 0.791, 0.963, p = 0.007) remained as an independent predictor for CID after adjustment for other risk factors. The nomogram model showed that PNI and gender have a great contribution to prediction. Besides, the nomogram model was consistent with the actual observations of CID risk (C-index = 0.65) and was of clinical significance.ConclusionsWe reported that malnutrition, as indicated by PNI, was associated with a higher incidence of CID in CVT patients. Also, we have constructed a nomogram for predicting the risk of CID in these patients.

Consistency Analysis of Multi-Source Remote Sensing Land Cover Products in Arid Regions—A Case Study of Xinjiang

Arid regions are considered to be among the most ecologically fragile and highly sensitive to environmental change globally, and land use and land cover conditions in the region directly influence large-scale ecosystem processes. Currently, thanks to diverse remote sensing platforms, geographers have developed an array of land cover products. However, there are differences between these products due to variations in spatio-temporal resolutions. In this context, assessing the accuracy and consistency of different land cover products is crucial for rationalizing the selection of land cover products to study global or regional environmental changes. In this study, Xinjiang Uygur Autonomous Region (XUAR) is taken as the study area, and the consistency and performance (type area deviation, spatial consistency, accuracy assessment, and other indexes) of the five land cover products (GlobeLand30, FROM_GLC30, CLCD, GLC_FCS30, and ESRI) were compared and analyzed. The results of the study show that (1) the GlobeLand30 product has the highest overall accuracy in the study area, with an overall accuracy of 84.06%, followed by ESA with 75.57%, while CLCD has the lowest overall accuracy of 70.05%. (2) The consistency between GlobeLand30 and CLCD (area correlation coefficient of 0.99) was higher than that among the other products. (3) Among the five products, the highest consistency was found for water bodies and permanent snow and ice, followed by bare land. In contrast, the consistency of these five products for grassland and forest was relatively low. (4) The full-consistency area accounts for 49.01% of the total study area. They were mainly distributed in areas with relatively homogeneous land cover types, such as the north and south of the Tianshan Mountains, which are dominated by bare land and cropland. In contrast, areas of inconsistency make up only 0.03% and are mostly found in heterogeneous areas, like the transitional zones with mixed land cover types in the Altai Mountains and Tianshan Mountains, or in areas with complex terrain. In terms of meeting practical user needs, GlobeLand30 offers the best comprehensive performance. GLC_FCS30 is more suitable for studies related to forests, while FROM_GLC30 and ESRI demonstrate greater advantages in identifying permanent ice and snow, whereas the performance of CLCD is generally average.

Development and validation of a nomogram for predicting overall survival in patients with early-onset endometrial cancer

BackgroundThis study aimed to investigate the differences in the clinicopathological characteristics of younger and older patients with endometrial cancer (EC) and develop a nomogram to assess the prognosis of early onset EC in terms of overall survival.MethodsPatients diagnosed with EC from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 were selected. Clinicopathological characteristics were compared between younger and older patients, and survival analysis was performed for both groups. Prognostic factors affecting overall survival in young patients with EC were identified using Cox regression. A nomogram was created and internal validation was performed using the consistency index, decision curve analysis, receiver operating characteristic curves, and calibration curves. External validation used data from 70 patients with early onset EC. Finally, Kaplan-Meier curves were plotted to compare survival outcomes across the risk subgroups.ResultsA total of 1042 young patients and 12,991 older patients were included in this study. Younger patients were divided into training (732) and validation (310) cohorts in a 7:3 ratio. Cox regression analysis identified age, tumorsize, grade, FIGO stage(International Federation of Gynecology and Obstetrics) and surgery as independent risk factors for overall survival, and a nomogram was constructed based on these factors. Internal and external validations demonstrated the good predictive power of the nomogram. In particular, the C-index for the overall survival nomogram was 0.832 [95% confidence interval (0.797–0.844)] in the training cohort and 0.839 (0.810–0.868) in the internal validation cohort. The differences in the Kaplan-Meier curves between the different risk subgroups were statistically significant.ConclusionsIn this study, a nomogram for predicting overall survival of patients with early onset endometrial cancer based on the SEER database was developed to help assess the prognosis of patients and guide clinical treatment.