Evaluation of the value of combined detection of tumor markers CA724,carcinoembryonic antigen,CA242,and CA19-9 in gastric cancer

BACKGROUND Gastric cancer is a global health concern that poses a significant threat to human well-being.AIM To detecting serum changes in carcinoembryonic antigen(CEA),carbohydrate antigens(CA)724,CA242,and CA19-9 expression among patients with gastric cancer.METHODS Eighty patients diagnosed with gastric cancer between January 2020 and January 2023 were included in the observation group,while 80 patients with benign gastric diseases were included in the control group.Both groups were tested for tumor markers(CA724,CEA,CA242,and CA19-9].Tumor marker indicators(CA724,CEA,CA242,and CA19-9)were compared between the two groups,assessing positive rates of tumor markers across various stages in the observation group.Additionally,single and combined detection of various tumor markers were examined.RESULTS The sensitivity,specificity,accuracy,positive predictive value,and negative predictive value observed for the combined detection of CA724,CEA,CA242,and CA19-9 were higher than those of CA724,CEA,CA242,and CA19-9 individually.Therefore,the combined detection of CA724,CEA,CA242,and CA19-9 has a high diagnostic accuracy and could reduce the occurrence of missed or misdiagnosed cases,facilitating the early diagnosis and treatment of patients.CONCLUSION CA724,CEA,CA242,and CA19-9 serum levels in gastric cancer patients significantly surpassed those in non-gastric cancer patients(P<0.05).Their combined detection can improve the diagnostic accuracy for gastric cancer,warranting clinical promotion.

Diagnostic value of serum vascular endothelial growth factor and interleukin-17 in primary hepatocellular carcinoma

BACKGROUND Despite significant advancements in the medical treatment of primary hepato-cellular carcinoma(PHC)in recent years,enhancing therapeutic effects and im-proving prognosis remain substantial challenges worldwide.AIM To investigate the expression levels of serum vascular endothelial growth factor(VEGF)and interleukin(IL)-17 in patients with PHC and evaluate their diagnostic value while exploring their relationship with patients’clinical characteristics.METHODS The study included 50 patients with confirmed PHC who visited Wuhan Han-yang Hospital from January 2021 to January 2022,and 50 healthy individuals from the same period served as the control group.Serum VEGF and IL-17 levels in both groups were measured by Enzyme-Linked Immunosorbent Assay,and their diagnostic value was assessed using receiver operating characteristic(ROC)curves.Pearson correlation analysis was performed to examine the relationship between serum VEGF and IL-17 levels.Pathological data of the PHC patients were analyzed to determine the relationship between serum VEGF and IL-17 levels and pathological characteristics.RESULTS Serum VEGF and IL-17 levels were significantly higher in the study group com-pared to the control group(P<0.05).No significant association was observed between serum VEGF and IL-17 levels and gender,age,combined cirrhosis,tumor diameter,or degree of differentiation(P>0.05).However,there was a significant relationship between clinical TNM stage,tumor metastasis,and serum VEGF and IL-17 levels(P<0.05).Correlation analysis revealed a positive correlation between serum VEGF and IL-17(P<0.05).ROC analysis demonstrated that both serum VEGF and IL-17 had good diagnostic efficacy for PHC.CONCLUSION Serum VEGF and IL-17 levels were significantly higher in PHC patients compared to healthy individuals.Their levels were closely related to pathological features such as tumor metastasis and clinical TNM stage,and there was a significant positive correlation between VEGF and IL-17.These biomarkers may serve as valuable reference in-dicators for the early diagnosis and treatment guidance of PHC.

Yield and Nutritive Values of Semi- and Non-Fall Dormant Alfalfa Cultivars under Late-Cutting Schedule in California’s Central Valley

California is one of the major alfalfa (Medicago sativa L) forage-producing states in the U.S, but its production area has decreased significantly in the last couple of decades. Selection of cultivars with high yield and nutritive value under late-cutting schedule strategy may help identify cultivars that growers can use to maximize yield while maintaining area for sustainable alfalfa production, but there is little information on this strategy. A field study was conducted to determine cumulative dry matter (DM) and nutritive values of 20 semi- and non-fall dormant (FD) ratings (FD 7 and FD 8 - 10, respectively) cultivars under 35-day cut in California’s Central Valley in 2020-2022. Seasonal cumulative DM yields ranged from 6.8 in 2020 to 37.0 Mg·ha−1 in 2021. Four FD 8 - 9 cultivars were the highest yielding with 3-yrs avg. DM greater than the lowest yielding lines by 46%. FD 7 cultivar “715RR” produced the highest crude protein (CP: 240 g·Kg−1) while FD 8 cultivar “HVX840RR” resulted in the highest neutral detergent fiber digestibility (NDFD: 484 g·Kg−1, 7% greater than the top yielding cultivars) but with DM yield intermediate. Yields and NDFD correlated positively but weakly indicating some semi- and non-FD cultivars performing similarly. These results suggest that selecting high yielding cultivars under 35-day cutting schedule strategy can be used as a tool to help growers to maximize yield while achieving good quality forages for sustainable alfalfa production in California’s Central Valley.

Dynamical distribution of continuous service time model involving non-Maxwellian collision kernel and value functions

The distribution of continuous service time in call centers is investigated.A non-Maxwellian collision kernel combining two different value functions in the interaction rule are used to describe the evolution of continuous service time,respectively.Using the statistical mechanical and asymptotic limit methods,Fokker–Planck equations are derived from the corresponding Boltzmann-type equations with non-Maxwellian collision kernels.The steady-state solutions of the Fokker–Planck equation are obtained in exact form.Numerical experiments are provided to support our results under different parameters.

Dirac method for nonlinear and non-homogenous boundary value problems of plates

The boundary value problem plays a crucial role in the analytical investigation of continuum dynamics. In this paper, an analytical method based on the Dirac operator to solve the nonlinear and non-homogeneous boundary value problem of rectangular plates is proposed. The key concept behind this method is to transform the nonlinear or non-homogeneous part on the boundary into a lateral force within the governing function by the Dirac operator, which linearizes and homogenizes the original boundary, allowing one to employ the modal superposition method for obtaining solutions to reconstructive governing equations. Once projected into the modal space, the harmonic balance method(HBM) is utilized to solve coupled ordinary differential equations(ODEs)of truncated systems with nonlinearity. To validate the convergence and accuracy of the proposed Dirac method, the results of typical examples, involving nonlinearly restricted boundaries, moment excitation, and displacement excitation, are compared with those of the differential quadrature element method(DQEM). The results demonstrate that when dealing with nonlinear boundaries, the Dirac method exhibits more excellent accuracy and convergence compared with the DQEM. However, when facing displacement excitation, there exist some discrepancies between the proposed approach and simulations;nevertheless, the proposed method still accurately predicts resonant frequencies while being uniquely capable of handling nonuniform displacement excitations. Overall, this methodology offers a convenient way for addressing nonlinear and non-homogenous plate boundaries.

Value Iteration-Based Cooperative Adaptive Optimal Control for Multi-Player Differential Games With Incomplete Information

This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the learning process and adapt their policies sequentially.Our method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based frameworks.Furthermore,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws.The efficacy of our method is illustrated by three examples.

Effect of FeSi additive in dual-chamber sample cup on thermal analysis characteristic values and vermiculating rate of compacted graphite iron

Thermal analysis plays a key role in the online inspection of molten iron quality.Different solidification process of molten iron can be reflected by thermal analysis curves,and silicon is one of important elements affecting the solidification of molten iron.In this study,FeSi75 was added in one chamber of the dual-chamber sample cup,and the influences of FeSi75 additive on the characteristic values of thermal analysis curves and vermiculating rate were investigated.The results show that with the increase of FeSi75,the start temperature of austenite formation TALfirstly decreases and then increases,but the start temperature of eutectic growth TSEF,the lowest eutectic temperature TEU,temperature at maximum eutectic reaction rate TEM,and highest eutectic temperature TERkeep always an increase.The temperature at final solidification point TEShas little change.The FeSi75 additive has different influences on the vermiculating rate of molten iron with different vermiculation,and the vermiculating rate increases for lower vermiculation molten iron while decreases for higher one.According to the thermal analysis curves obtained by a dual-chamber sample cup with 0.30wt.%FeSi75 additive in one chamber,the vermiculating rate of molten iron can be evaluated by comparing the characteristic values of these curves.The time differenceΔtERcorresponding to the highest eutectic temperature TERhas a closer relationship with the vermiculating rate,and a parabolic regression curve between the time differenceΔtERand vermiculating rateηhas been obtained within the range of 65%to 95%,which is suitable for the qualified melt.

Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.

Spatio-temporal evolution and topographic gradient effect of land use and ecosystem service value in the Lhasa River Basin

The Lhasa River Basin forms an essential human settlement area in the southern part of the Qinghai-Tibet Plateau.This study employed ecosystem service value(ESV)evaluation model,terrain gradient grading,and Geodetector to analyze land use and ESV in the Lhasa River Basin from 1985 to 2020.The findings reveal that:(1)From 1985 to 2020,grassland was the dominant land use.There was a trend of grassland reduction and the expansion of other land types.(2)ESV has increased over the research period(with a total increase of 0.84%),with higher values in the southeast and lower values in the northwest.Grassland contributed the most to ESV,and climate regulation and hydrological regulation were the ecosystem services that contribute the most to ESV.(3)Natural factors like NDVI and altitude,as well as economic factors like population density and distance from roads,influenced the spatial differentiation of ESV,the explanatory power of NDVI reached up to 0.47.The interaction between factors had a greater impact than individual factors.These research results can provide theoretical support for national spatial planning and ecological environment protection in the Lhasa River Basin and other similar areas.

How Sense of Ritual in Tourism Impacts Tourists’Co-Creation of Tourism Experience Values:A Study Based on the Theory of Value Co-Creation

Establishing a sense of ritual within tourism consumption scenarios offers tourists the opportunity for interactive engagement.Drawing upon the value co-creation theory,this study constructed an influence mechanism model to examine tourists'active engagement in the process of co-creating tourism experience values.It employed Partial Least Squares Structural Equation Modeling(PLS-SEM)to empirically test the proposed hypotheses.The findings demonstrate that the model constructed in the present study exhibits robust reliability,validity,and explanatory power.The perception of the sense of ritual in tourism exerts a significant positive influence on tourists’co-creation of tourism experience values,thereby significantly enhancing both the communitas and flow experienced by tourists during their travels.Moreover,such communitas and flow can mediate the influence of the sense of ritual in tourism on tourists’co-creation of tourism experience values.This study contributes to advancing the current research on tourists’co-creation of tourism experience values and the sense of ritual in tourism,thereby providing theoretical foundations for cultivating a sense of ritual within tourism consumption scenarios.

AHP-based Landscape Value Evaluation of Perennial Flower Border Application in Hefei City

An analytic hierarchy process(AHP)was employed to establish a landscape value evaluation model for 20 common species of perennial flowers in the flower border of Hefei City.With the overarching objective of determining the application value of the flower border,10 evaluation factors were identified and subsequently classified into 3 distinct grades.Grade I(L>2.5)was defined as perennial flower resources with the highest development and utilization value,including only one species of Salvia farinacea.Grade II(2.0≤L≤2.5)was defined as perennial flower varieties suitable for most areas,encompassing 9 species of plants,such asTaraxacummongolicum.Grade III(L<2.0)was defined as perennial flower varieties with low application value,encompassing 10 species of plants,such asRuellia brittoniana,but lacking the value of further popularization and application.Consequently,the proportion of their application in the flower border should be reduced.The evaluation results can serve as a theoretical foundation for the implementation of perennial flowers in urban flower borders.

Pan-immune-inflammation value as a prognostic biomarker for colon cancer and its variation by primary tumor location

BACKGROUND A growing body of research indicates significant differences between left-sided colon cancers(LCC)and right-sided colon cancers(RCC).Pan-immune-inflammation value(PIV)is a systemic immune response marker that can predict the prognosis of patients with colon cancer.However,the specific distinction between PIV of LCC and RCC remains unclear.AIM To investigate the prognostic and clinical significance of PIV in LCC and RCC patients.METHODS This multicenter retrospective cohort study included 1510 patients with colon cancer,comprising 801 with LCC and 709 with RCC.We used generalized lifting regression analysis to evaluate the relative impact of PIV on disease-free survival(DFS)in these patients.Kaplan-Meier analysis,as well as univariate and multivariate analyses,were used to examine the risk factors for DFS.The correlation between PIV and the clinical characteristics was statistically analyzed in these patients.RESULTS A total of 1510 patients{872 female patients(58%);median age 63 years[interquartile ranges(IQR):54-71];patients with LCC 801(53%);median follow-up 44.17 months(IQR 29.67-62.32)}were identified.PIV was significantly higher in patients with RCC[median(IQR):214.34(121.78-386.72)vs 175.87(111.92-286.84),P<0.001].After propensity score matching,no difference in PIV was observed between patients with LCC and RCC[median(IQR):182.42(111.88-297.65)vs 189.45(109.44-316.02);P=0.987].PIV thresholds for DFS were 227.84 in LCC and 145.99 in RCC.High PIV(>227.84)was associated with worse DFS in LCC[PIV-high:Adjusted hazard ratio(aHR)=2.39;95%confidence interval:1.70-3.38;P<0.001]but not in RCC(PIV-high:aHR=0.72;95%confidence interval:0.48-1.08;P=0.114).CONCLUSION These findings suggest that PIV may predict recurrence in patients with LCC but not RCC,underscoring the importance of tumor location when using PIV as a colon cancer biomarker.

Effects of Functional Organic Materials on Fresh Weight,Quality,Single-melon Weight and SPAD Value of Watermelon

[Objectives]This study was conducted to explore a functional organic material formula suitable for watermelon cultivation with high quality,high yield and high efficiency.[Methods]Four treatments were set in the experiment,namely four functional organic materials,garlic straw treatment(T_(1)),onion straw treatment(T_(2)),garlic straw+sheep manure treatment(T_(3))and onion straw+chicken manure treatment(T_(4)),to investigate the effects of different functional organic materials on fresh weight,quality,single-melon weight and SPAD value of watermelon.[Results]The effects of different functional organic materials on fresh weight,quality,single-melon weight and SPAD value of watermelon were quite different.The fresh weight,quality,single-melon weight and SPAD value of watermelon were higher in treatment T_(3)applying garlic straw and sheep manure and treatment T_(4)applying onion straw and chicken manure than in treatment T_(1)applying garlic straw and treatment T_(2)applying onion straw.Specifically,the fresh weight of whole plant was the highest in treatment T_(3),followed by treatment T_(4),and the values of the two treatments increased by 12.83%and 5.94%respectively compared with treatment T_(1);the weight of single melon was the highest in treatment T_(3),followed by treatment T_(4),and the values of the two treatments increased by 42.45%and 31.77%respectively compared with treatment T_(2);and the SPAD values of treatments T_(3)and T_(4)were significantly higher than those of treatments T_(1)and T_(2),and the value of treatment T_(3)was the largest.[Conclusions]This study provides theoretical support for the popularization and application of fertilization techniques combining organic fertilizers and reduced chemical fertilizers for watermelon.

Wavelet Multi-Resolution Interpolation Galerkin Method for Linear Singularly Perturbed Boundary Value Problems

In this study,a wavelet multi-resolution interpolation Galerkin method(WMIGM)is proposed to solve linear singularly perturbed boundary value problems.Unlike conventional wavelet schemes,the proposed algorithm can be readily extended to special node generation techniques,such as the Shishkin node.Such a wavelet method allows a high degree of local refinement of the nodal distribution to efficiently capture localized steep gradients.All the shape functions possess the Kronecker delta property,making the imposition of boundary conditions as easy as that in the finite element method.Four numerical examples are studied to demonstrate the validity and accuracy of the proposedwavelet method.The results showthat the use ofmodified Shishkin nodes can significantly reduce numerical oscillation near the boundary layer.Compared with many other methods,the proposed method possesses satisfactory accuracy and efficiency.The theoretical and numerical results demonstrate that the order of theε-uniform convergence of this wavelet method can reach 5.

Omni-Channel Retailing and Firm Performance from a Customer Lifetime Value Perspective

In the digital era,retailers are keen to find out whether omni-channel retailing helps improve long-term firm performance.In this paper,we employ machine learning techniques on a large consumption data set in order to measure customer lifetime value(CLV)as the basis for determining long-term firm performance,and we provide an empirical analysis of the relationship between omni-channel retailing and CLV.The results suggest that omni-channel retailing may effectively enhance CLV.Further analysis reveals that this process is influenced by heterogeneous consumer requirements and that significant differences exist in the extent to which the omni-channel transition may influence CLV depending on consumer preferences for diversity of commodities,sensitivity to the cost of contract performance,and sensitivity to warehousing costs.Hence,retailers should provide consumers with a complete portfolio of goods and services based on target consumers’heterogeneous requirements in order to increase omni-channel efficiency.

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

Clinical application value of long non-coding RNAs signatures of genomic instability in predicting prognosis of hepatocellular carcinoma

Hepatocellular carcinoma(HCC)presents challenges due to its high recurrence and metastasis rates and poor prognosis.While current clinical diagnostic and prognostic indicators exist,their accuracy remains imperfect due to their biol-ogical complexity.Therefore,there is a quest to identify improved biomarkers for HCC diagnosis and prognosis.By combining long non-coding RNA(lncRNA)expression and somatic mutations,Duan et al identified five representative lncRNAs from 88 lncRNAs related to genomic instability(GI),forming a GI-derived lncRNA signature(LncSig).This signature outperforms previously re-ported LncSig and TP53 mutations in predicting HCC prognosis.In this editorial,we comprehensively evaluate the clinical application value of such prognostic evaluation model based on sequencing technology in terms of cost,time,and practicability.Additionally,we provide an overview of various prognostic models for HCC,aiding in a comprehensive understanding of research progress in pro-gnostic evaluation methods.

Cut-off value of glycated hemoglobin A1c for detecting diabetic retinopathy in the Chinese population

BACKGROUND Glycated hemoglobin A1c(HbA1c)is considered the most suitable for diabetes mellitus diagnosis due to its accuracy and convenience.However,the effect of HbA1c on diabetic retinopathy(DR)in the Han and Korean populations in Jilin,China,remains inconclusive.AIM To determine the best cut-off of HbA1c for diagnosing DR among the Chinese.METHODS This cross-sectional study included 1933 participants from the Yanbian area of Jilin Province,China.Trained investigators employed a questionnaire-based survey,physical examination,laboratory tests,and fundus photography for the investigation.The best cut-off value for HbA1c was established via the receiver operating characteristic curve.The factors associated with HbA1c-associated risk factors were determined via linear regression.RESULTS The analysis included 887 eligible Chinese Han and Korean participants,591 of whom were assigned randomly to the training set and 296 to the validation set.The prevalence of DR was 3.27% in the total population.HbA1c of 6.2% was the best cut-off value in the training set,while it was 5.9% in the validation set.In both Chinese Han and Korean populations,an HbA1c level of 6.2% was the best cut-off value.The optimal cut-off values of fasting blood glucose(FBG)≥7 mmol/L and<7 mmol/L were 8.1% and 6.2% respectively in Han populations,while those in Korean populations were 6.9%and 5.3%,respectively.Age,body mass index,and FBG were determined as the risk factors impacting HbA1c levels.CONCLUSION HbA1c may serve as a useful diagnostic indicator for DR.An HbA1c level of 6.2% may be an appropriate cut-off value for DR detection in the Chinese population.

The Regularity of Solutions to Mixed Boundary Value Problems of Second-Order Elliptic Equations with Small Angles

This paper considers the regularity of solutions to mixed boundary value problems in small-angle regions for elliptic equations. By constructing a specific barrier function, we proved that under the assumption of sufficient regularity of boundary conditions and coefficients, as long as the angle is sufficiently small, the regularity of the solution to the mixed boundary value problem of the second-order elliptic equation can reach any order.

Approximation Maintenance When Adding a Conditional Value in Set-Valued Ordered Decision Systems

The integration of set-valued ordered rough set models and incremental learning signify a progressive advancement of conventional rough set theory, with the objective of tackling the heterogeneity and ongoing transformations in information systems. In set-valued ordered decision systems, when changes occur in the attribute value domain, such as adding conditional values, it may result in changes in the preference relation between objects, indirectly leading to changes in approximations. In this paper, we effectively addressed the issue of updating approximations that arose from adding conditional values in set-valued ordered decision systems. Firstly, we classified the research objects into two categories: objects with changes in conditional values and objects without changes, and then conducted theoretical studies on updating approximations for these two categories, presenting approximation update theories for adding conditional values. Subsequently, we presented incremental algorithms corresponding to approximation update theories. We demonstrated the feasibility of the proposed incremental update method with numerical examples and showed that our incremental algorithm outperformed the static algorithm. Ultimately, by comparing experimental results on different datasets, it is evident that the incremental algorithm efficiently reduced processing time. In conclusion, this study offered a promising strategy to address the challenges of set-valued ordered decision systems in dynamic environments.