Mercury（Hg） exists in different chemical forms presenting varied toxic potentials. It is necessary to explore an ecological risk assessment method for different mercury species in aquatic environment. The predicted ...Mercury（Hg） exists in different chemical forms presenting varied toxic potentials. It is necessary to explore an ecological risk assessment method for different mercury species in aquatic environment. The predicted no-effect concentrations（PNECs） for Hg（Ⅱ） and methyl mercury（Me Hg） in the aqueous phase, calculated using the species sensitivity distribution method and the assessment factor method, were 0.39 and 6.5 × 10-3μg/L, respectively. The partition theory of Hg between sediment and aqueous phases was considered, along with PNECs for the aqueous phase to conduct an ecological risk assessment for Hg in the sediment phase. Two case studies, one in China and one in the Western Black Sea, were conducted using these PNECs. The toxicity of mercury is heavily dependent on their forms,and their potential ecological risk should be respectively evaluated on the basis of mercury species.展开更多
The anode effect is a common failure in the aluminium electrolysis industry.If the anode effect cannot be accurately predicted,it will cause increased energy consumption,harmful gas generation and even equipment damag...The anode effect is a common failure in the aluminium electrolysis industry.If the anode effect cannot be accurately predicted,it will cause increased energy consumption,harmful gas generation and even equipment damage in the aluminium electrolysis.In this paper,an anode effect prediction framework using multi-model merging based on deep learning technology is proposed.Different models are used to process aluminium electrolysis cell condition parameters with high dimensions and different characteristics,and hidden key fault information is deeply mined.A stacked denoising autoencoder is utilized to denoise and extract features from a large number of longperiod parameter data.A long short-term memory network is implemented to identify the intrinsic links between the realtime voltage and current time series and the anode effect.By setting the model time step,the anode effect can be predicted precisely in advance,and the proposed method has good robustness and generalization.Moreover,the traditional Adam algorithm is improved,which enhances the performance and convergence speed of the model.The experimental results show that the classification accuracy and F1score of the model are 97.14% and 0.9579%,respectively.The prediction time can reach 15 min.展开更多
The prediction behaviors of some coherent plane wave equations for the effective velocities and attenuations of the coherent plane waves propagating through a composite material and for the effective elastic moduli of...The prediction behaviors of some coherent plane wave equations for the effective velocities and attenuations of the coherent plane waves propagating through a composite material and for the effective elastic moduli of the composites are studied. The numerical results obtained by Waterman & Truell's, Twersky's and Gubernatis's equations for Glass-Epoxy composites with various volume fractions are compared. It is found that the predictions by both Twersky's and Gubernatis's equations underestimate the effective velocities and the effective elastic moduli when compare with the predictions by Waterman & Truell's equation. Furthermore, the deviations are more evident for the shear wave than that for the longitudinal wave. But these deviations decrease gradually with the increase of the frequency and increase gradually with the increase of the volume fraction.展开更多
[Objective] The research aimed to analyze explanation effect of the European numerical prediction on temperature. [Method] Based on CMSVM regression method, by using 850 hPa grid point data of the European numerical p...[Objective] The research aimed to analyze explanation effect of the European numerical prediction on temperature. [Method] Based on CMSVM regression method, by using 850 hPa grid point data of the European numerical prediction from 2003 to 2009 and actual data of the maximum and minimum temperatures at 8 automatic stations in Qingyang City, prediction model of the temperature was established, and running effect of the business from 2008 to 2010 was tested and evaluated. [Result] The method had very good guidance role in real-time business running of the temperature prediction. Test and evaluation found that as forecast time prolonged, prediction accuracies of the maximum and minimum temperatures declined. When temperature anomaly was higher (actual temperature was higher than historical mean), prediction accuracy increased. Influence of the European numerical prediction was bigger. [Conclusion] Compared with other methods, operation of the prediction method was convenient, modeling was automatic, running time was short, system was stable, and prediction accuracy was high. It was suitable for implementing of the explanation work for numerical prediction product at meteorological station.展开更多
Heart disease is the leading cause of death worldwide.Predicting heart disease is challenging because it requires substantial experience and knowledge.Several research studies have found that the diagnostic accuracy o...Heart disease is the leading cause of death worldwide.Predicting heart disease is challenging because it requires substantial experience and knowledge.Several research studies have found that the diagnostic accuracy of heart disease is low.The coronary heart disorder determines the state that influences the heart valves,causing heart disease.Two indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep throat.This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness.At first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)algorithm.With this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was decreased.The CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods face.Our proposed method offers an illustrative framework that helps predict heart attacks with high accuracy.The proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.展开更多
Objective: To determine whether there was any association between resistance to antibiotics and decreased susceptibility to antiseptics and disinfectants and their importance in clinical practice. Methods: We studied ...Objective: To determine whether there was any association between resistance to antibiotics and decreased susceptibility to antiseptics and disinfectants and their importance in clinical practice. Methods: We studied a large number of microorganisms isolated from ICU patients (high percentage of cases of antibiotic resistance). The antibiogram (Kirby-Bauer) was determined and, in parallel, the bactericidal effect was assessed by two methods, according to the product used: 1) Effect on rough material (endodontic files) in 10 min, using five disinfectants;2) Effect on a skin equivalent (sterile cotton cloth) in 30 sec, for two alcohol solutions. A predictive equation of the bactericidal effects versus microorganisms’ antibiogram was obtained by multivariate methods. Results:?Bactericidal efficacy was very similar for all the products with the exception of 1% povidone-iodine. Within each product there were no significant differences between the three groups of microorganisms: “Enterobacteria”, “Non Fermentative Gram Negative Bacteria” and “cocci”. Multivariate study only obtained one significant equation: 1% chlorhexidine resistance was directly correlated with aztreonam resistance (OR = 2.16), while resistance to imipenem and to phosphomycin acted as protection factors (OR < 1). Conclusion: There is no necessary to change the indications for antiseptics or disinfectants in ICUs, except if aztreonam resistance is high. In which caseis better to use greater concentration than 1% of Chlorhexidine.展开更多
Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis...Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis is placed on comparing the properties of two LMM approaches: restricted maximum likelihood (REML) and minimum norm quadratic unbiased estimation (MINQUE) with and without resampling techniques being included. Bias, testing power, Type I error, and computing time were compared between REML and MINQUE approaches with and without Jackknife technique based on 500 simulated data sets. Results showed that MINQUE and REML methods performed equally regarding bias, Type I error, and power. Jackknife-based MINQUE and REML greatly improved power compared to non-Jackknife based linear mixed model approaches. Results also showed that MINQUE is more time-saving compared to REML, especially with the use of resampling techniques and large data set analysis. Results from the actual cotton data analysis were in agreement with our simulated results. Therefore, Jackknife-based MINQUE approaches could be recommended to achieve desirable power with reduced time for a large data analysis and model simulations.展开更多
The development of new drugs for therapeutic purposes has become very expensive and time-consuming in American and European countries.It is estimated that on the average 50 to 100 million dollars and 10 or more years ...The development of new drugs for therapeutic purposes has become very expensive and time-consuming in American and European countries.It is estimated that on the average 50 to 100 million dollars and 10 or more years from the time of patenting are required to make a new drug available for general prescription. Every new drug needs to be charac-展开更多
Background:Transrectal(TR)and transperineal(TP)biopsies are commonly used methods for diagnosing prostate cancer.However,their comparative effectiveness in conjunction with machine learning(ML)techniques remains under...Background:Transrectal(TR)and transperineal(TP)biopsies are commonly used methods for diagnosing prostate cancer.However,their comparative effectiveness in conjunction with machine learning(ML)techniques remains underexplored.This study aimed to evaluate the predictive accuracy of ML algorithms in detecting prostate cancer using data derived from TR and TP biopsies.Methods:The clinical records of patients who underwent prostate biopsy at King Saud University Medical City and King Faisal Specialist Hospital and Research Centerin Riyadh,Saudi Arabia,between 2018 and 2025 were analyzed.Data were used to train and testMLmodels,including eXtreme Gradient Boosting(XGBoost),Decision Tree,Random Forest,and Extra Trees.Results:The two datasets are comparable.The models demonstrated exceptional performance,achieving accuracies of up to 96.49%and 95.56%on TP and TR biopsy datasets,respectively.The area under the curve(AUC)values were also high,reaching 0.9988 for TP and 0.9903 for TR biopsy predictions.Conclusion:These findings highlight the potential of MLto enhance the diagnostic accuracy of prostate cancer detection irrespective of the biopsy method.However,TP biopsy data showed marginally higher accuracy,possibly because of the lower risk of contamination.While ML holds great promise for transforming prostate cancer care,further research is needed to address limitations.Collaboration between clinicians,data scientists,and researchers is crucial to ensure the clinical relevance and interpretability of ML models.展开更多
BACKGROUND Tumor deposits(TDs)are an independent predictor of poor prognosis in colorec-tal cancer(CRC)patients.Enhanced follow-up and treatment monitoring for TD+patients may improve survival rates and quality of lif...BACKGROUND Tumor deposits(TDs)are an independent predictor of poor prognosis in colorec-tal cancer(CRC)patients.Enhanced follow-up and treatment monitoring for TD+patients may improve survival rates and quality of life.However,the detection of TDs relies primarily on postoperative pathological examination,which may have a low detection rate due to sampling limitations.AIM To evaluate the spectral computed tomography(CT)parameters of primary tu-mors and the largest regional lymph nodes(LNs),to determine their value in predicting TDs in CRC.METHODS A retrospective analysis was conducted which included 121 patients with CRC whose complete spectral CT data were available.Patients were divided into the TDs+group and the TDs-group on the basis of their pathological results.Spectral CT parameters of the primary CRC lesion and the largest regional LNs were measured,including the normalized iodine concentration(NIC)in both the arte-rial and venous phases,and the LN-to-primary tumor ratio was calculated.Stati-stical methods were used to evaluate the diagnostic efficacy of each spectral para-meter.RESULTS Among the 121 CRC patients,33(27.2%)were confirmed to be TDs+.The risk of TDs positivity was greater in patients with positive LN metastasis,higher N stage and elevated carcinoembryonic antigen and cancer antigen 19-9 levels.The NIC(LNs in both the arterial and venous phases),NIC(primary tumors in the venous phase),and the LN-to-primary tumor ratio in both the arterial and venous phases were associated with TDs(P<0.05).In mul-tivariate logistic regression analysis,the arterial phase LN-to-primary tumor ratio was identified as an independent predictor of TDs,demonstrating the highest diagnostic performance(area under the curve:0.812,sensitivity:0.879,specificity:0.648,cutoff value:1.145).CONCLUSION The spectral CT parameters of the primary colorectal tumor and the largest regional LNs,especially the LN-to-primary tumor ratio,have significant clinical value in predicting TDs in CRC.展开更多
A calculation model based on effective medium theory has been developed for predicting elastic properties of dry carbonates with complex pore structures by integrating the Kuster-Toksǒz model with a differential meth...A calculation model based on effective medium theory has been developed for predicting elastic properties of dry carbonates with complex pore structures by integrating the Kuster-Toksǒz model with a differential method.All types of pores are simultaneously introduced to the composite during the differential iteration process according to the ratio of their volume fractions.Based on this model,the effects of pore structures on predicted pore-pressure in carbonates were analyzed.Calculation results indicate that cracks with low pore aspect ratios lead to pore-pressure overestimation which results in lost circulation and reservoir damage.However,moldic pores and vugs with high pore aspect ratios lead to pore-pressure underestimation which results in well kick and even blowout.The pore-pressure deviation due to cracks and moldic pores increases with an increase in porosity.For carbonates with complex pore structures,adopting conventional pore-pressure prediction methods and casing program designs will expose the well drilling engineering to high uncertainties.Velocity prediction models considering the influence of pore structure need to be built to improve the reliability and accuracy of pore-pressure prediction in carbonates.展开更多
BACKGROUND Early symptoms of hepatocellular carcinoma(HCC)are not obvious,and more than 70%of which does not receive radical hepatectomy,when first diagnosed.In recent years,molecular-targeted drugs combined with immu...BACKGROUND Early symptoms of hepatocellular carcinoma(HCC)are not obvious,and more than 70%of which does not receive radical hepatectomy,when first diagnosed.In recent years,molecular-targeted drugs combined with immunotherapy and other therapeutic methods have provided new treatment options for middle and advanced HCC(aHCC).Predicting the effect of targeted combined immunotherapy has become a hot topic in current research.AIM To explore the relationship between nodule enhancement in hepatobiliary phase and the efficacy of combined targeted immunotherapy for aHCC.METHODS Data from 56 patients with aHCC for magnetic resonance imaging with gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid were retrospectively collected.Signal intensity of intrahepatic nodules was measured,and the hepatobiliary relative enhancement ratio(RER)was calculated.Progression-free survival(PFS)of patients with high and low reinforcement of HCC nodules was compared.The model was validated using receiver operating characteristic curves.Univariate and multivariate logistic regression and Kaplan-Meier analysis were performed to explore factors influencing the efficacy of targeted immunization and PFS.RESULTS Univariate and multivariate analyses revealed that the RER,neutrophil-to-lymphocyte ratio,platelet-to-lymphocyte ratio,and prognostic nutritional index were significantly associated with the efficacy of tyrosine kinase inhibitors combined with immunotherapy(P<0.05).The area under the curve of the RER for predicting the efficacy of tyrosine kinase inhibitors combined with anti-programmed death 1 antibody in patients with aHCC was 0.876(95%confidence interval:0.781-0.971,P<0.05),the optimal cutoff value was 0.904,diagnostic sensitivity was 87.5%,and specificity was 79.2%.Kaplan-Meier analysis showed that neutrophil-to-lymphocyte ratio<5,plateletto-lymphocyte ratio<300,prognostic nutritional index<45,and RER<0.9 significantly improved PFS.CONCLUSION AHCC nodules enhancement in the hepatobiliary stage was significantly correlated with PFS.Imaging information and immunological indicators had high predictive efficacy for targeted combined immunotherapy and were associated with PFS.展开更多
基金financially supported in partial by National Key Technologies R&D Program of China (Research & Development on Suitable Key Technologies of the Village Environmental Monitoring, No. 2012BAJ24B01)
文摘Mercury（Hg） exists in different chemical forms presenting varied toxic potentials. It is necessary to explore an ecological risk assessment method for different mercury species in aquatic environment. The predicted no-effect concentrations（PNECs） for Hg（Ⅱ） and methyl mercury（Me Hg） in the aqueous phase, calculated using the species sensitivity distribution method and the assessment factor method, were 0.39 and 6.5 × 10-3μg/L, respectively. The partition theory of Hg between sediment and aqueous phases was considered, along with PNECs for the aqueous phase to conduct an ecological risk assessment for Hg in the sediment phase. Two case studies, one in China and one in the Western Black Sea, were conducted using these PNECs. The toxicity of mercury is heavily dependent on their forms,and their potential ecological risk should be respectively evaluated on the basis of mercury species.
基金financially supported by the General Program of National Natural Science Foundation of China(No.62373069)the Major Projects for Technological Transformation(No.H20201555)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project (No.CQYC202203091061)。
文摘The anode effect is a common failure in the aluminium electrolysis industry.If the anode effect cannot be accurately predicted,it will cause increased energy consumption,harmful gas generation and even equipment damage in the aluminium electrolysis.In this paper,an anode effect prediction framework using multi-model merging based on deep learning technology is proposed.Different models are used to process aluminium electrolysis cell condition parameters with high dimensions and different characteristics,and hidden key fault information is deeply mined.A stacked denoising autoencoder is utilized to denoise and extract features from a large number of longperiod parameter data.A long short-term memory network is implemented to identify the intrinsic links between the realtime voltage and current time series and the anode effect.By setting the model time step,the anode effect can be predicted precisely in advance,and the proposed method has good robustness and generalization.Moreover,the traditional Adam algorithm is improved,which enhances the performance and convergence speed of the model.The experimental results show that the classification accuracy and F1score of the model are 97.14% and 0.9579%,respectively.The prediction time can reach 15 min.
文摘The prediction behaviors of some coherent plane wave equations for the effective velocities and attenuations of the coherent plane waves propagating through a composite material and for the effective elastic moduli of the composites are studied. The numerical results obtained by Waterman & Truell's, Twersky's and Gubernatis's equations for Glass-Epoxy composites with various volume fractions are compared. It is found that the predictions by both Twersky's and Gubernatis's equations underestimate the effective velocities and the effective elastic moduli when compare with the predictions by Waterman & Truell's equation. Furthermore, the deviations are more evident for the shear wave than that for the longitudinal wave. But these deviations decrease gradually with the increase of the frequency and increase gradually with the increase of the volume fraction.
文摘[Objective] The research aimed to analyze explanation effect of the European numerical prediction on temperature. [Method] Based on CMSVM regression method, by using 850 hPa grid point data of the European numerical prediction from 2003 to 2009 and actual data of the maximum and minimum temperatures at 8 automatic stations in Qingyang City, prediction model of the temperature was established, and running effect of the business from 2008 to 2010 was tested and evaluated. [Result] The method had very good guidance role in real-time business running of the temperature prediction. Test and evaluation found that as forecast time prolonged, prediction accuracies of the maximum and minimum temperatures declined. When temperature anomaly was higher (actual temperature was higher than historical mean), prediction accuracy increased. Influence of the European numerical prediction was bigger. [Conclusion] Compared with other methods, operation of the prediction method was convenient, modeling was automatic, running time was short, system was stable, and prediction accuracy was high. It was suitable for implementing of the explanation work for numerical prediction product at meteorological station.
文摘Heart disease is the leading cause of death worldwide.Predicting heart disease is challenging because it requires substantial experience and knowledge.Several research studies have found that the diagnostic accuracy of heart disease is low.The coronary heart disorder determines the state that influences the heart valves,causing heart disease.Two indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep throat.This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness.At first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)algorithm.With this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was decreased.The CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods face.Our proposed method offers an illustrative framework that helps predict heart attacks with high accuracy.The proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.
文摘Objective: To determine whether there was any association between resistance to antibiotics and decreased susceptibility to antiseptics and disinfectants and their importance in clinical practice. Methods: We studied a large number of microorganisms isolated from ICU patients (high percentage of cases of antibiotic resistance). The antibiogram (Kirby-Bauer) was determined and, in parallel, the bactericidal effect was assessed by two methods, according to the product used: 1) Effect on rough material (endodontic files) in 10 min, using five disinfectants;2) Effect on a skin equivalent (sterile cotton cloth) in 30 sec, for two alcohol solutions. A predictive equation of the bactericidal effects versus microorganisms’ antibiogram was obtained by multivariate methods. Results:?Bactericidal efficacy was very similar for all the products with the exception of 1% povidone-iodine. Within each product there were no significant differences between the three groups of microorganisms: “Enterobacteria”, “Non Fermentative Gram Negative Bacteria” and “cocci”. Multivariate study only obtained one significant equation: 1% chlorhexidine resistance was directly correlated with aztreonam resistance (OR = 2.16), while resistance to imipenem and to phosphomycin acted as protection factors (OR < 1). Conclusion: There is no necessary to change the indications for antiseptics or disinfectants in ICUs, except if aztreonam resistance is high. In which caseis better to use greater concentration than 1% of Chlorhexidine.
文摘Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis is placed on comparing the properties of two LMM approaches: restricted maximum likelihood (REML) and minimum norm quadratic unbiased estimation (MINQUE) with and without resampling techniques being included. Bias, testing power, Type I error, and computing time were compared between REML and MINQUE approaches with and without Jackknife technique based on 500 simulated data sets. Results showed that MINQUE and REML methods performed equally regarding bias, Type I error, and power. Jackknife-based MINQUE and REML greatly improved power compared to non-Jackknife based linear mixed model approaches. Results also showed that MINQUE is more time-saving compared to REML, especially with the use of resampling techniques and large data set analysis. Results from the actual cotton data analysis were in agreement with our simulated results. Therefore, Jackknife-based MINQUE approaches could be recommended to achieve desirable power with reduced time for a large data analysis and model simulations.
文摘The development of new drugs for therapeutic purposes has become very expensive and time-consuming in American and European countries.It is estimated that on the average 50 to 100 million dollars and 10 or more years from the time of patenting are required to make a new drug available for general prescription. Every new drug needs to be charac-
文摘Background:Transrectal(TR)and transperineal(TP)biopsies are commonly used methods for diagnosing prostate cancer.However,their comparative effectiveness in conjunction with machine learning(ML)techniques remains underexplored.This study aimed to evaluate the predictive accuracy of ML algorithms in detecting prostate cancer using data derived from TR and TP biopsies.Methods:The clinical records of patients who underwent prostate biopsy at King Saud University Medical City and King Faisal Specialist Hospital and Research Centerin Riyadh,Saudi Arabia,between 2018 and 2025 were analyzed.Data were used to train and testMLmodels,including eXtreme Gradient Boosting(XGBoost),Decision Tree,Random Forest,and Extra Trees.Results:The two datasets are comparable.The models demonstrated exceptional performance,achieving accuracies of up to 96.49%and 95.56%on TP and TR biopsy datasets,respectively.The area under the curve(AUC)values were also high,reaching 0.9988 for TP and 0.9903 for TR biopsy predictions.Conclusion:These findings highlight the potential of MLto enhance the diagnostic accuracy of prostate cancer detection irrespective of the biopsy method.However,TP biopsy data showed marginally higher accuracy,possibly because of the lower risk of contamination.While ML holds great promise for transforming prostate cancer care,further research is needed to address limitations.Collaboration between clinicians,data scientists,and researchers is crucial to ensure the clinical relevance and interpretability of ML models.
文摘BACKGROUND Tumor deposits(TDs)are an independent predictor of poor prognosis in colorec-tal cancer(CRC)patients.Enhanced follow-up and treatment monitoring for TD+patients may improve survival rates and quality of life.However,the detection of TDs relies primarily on postoperative pathological examination,which may have a low detection rate due to sampling limitations.AIM To evaluate the spectral computed tomography(CT)parameters of primary tu-mors and the largest regional lymph nodes(LNs),to determine their value in predicting TDs in CRC.METHODS A retrospective analysis was conducted which included 121 patients with CRC whose complete spectral CT data were available.Patients were divided into the TDs+group and the TDs-group on the basis of their pathological results.Spectral CT parameters of the primary CRC lesion and the largest regional LNs were measured,including the normalized iodine concentration(NIC)in both the arte-rial and venous phases,and the LN-to-primary tumor ratio was calculated.Stati-stical methods were used to evaluate the diagnostic efficacy of each spectral para-meter.RESULTS Among the 121 CRC patients,33(27.2%)were confirmed to be TDs+.The risk of TDs positivity was greater in patients with positive LN metastasis,higher N stage and elevated carcinoembryonic antigen and cancer antigen 19-9 levels.The NIC(LNs in both the arterial and venous phases),NIC(primary tumors in the venous phase),and the LN-to-primary tumor ratio in both the arterial and venous phases were associated with TDs(P<0.05).In mul-tivariate logistic regression analysis,the arterial phase LN-to-primary tumor ratio was identified as an independent predictor of TDs,demonstrating the highest diagnostic performance(area under the curve:0.812,sensitivity:0.879,specificity:0.648,cutoff value:1.145).CONCLUSION The spectral CT parameters of the primary colorectal tumor and the largest regional LNs,especially the LN-to-primary tumor ratio,have significant clinical value in predicting TDs in CRC.
基金the financial support from the National Natural Science Foundation of China (No. 51274230)the Natural Science Foundation of Shandong Province (No. ZR2012EEL01)the Fundamental Research Funds for the Central Universities (No. 14CX02040A and No. 14CX06023A)
文摘A calculation model based on effective medium theory has been developed for predicting elastic properties of dry carbonates with complex pore structures by integrating the Kuster-Toksǒz model with a differential method.All types of pores are simultaneously introduced to the composite during the differential iteration process according to the ratio of their volume fractions.Based on this model,the effects of pore structures on predicted pore-pressure in carbonates were analyzed.Calculation results indicate that cracks with low pore aspect ratios lead to pore-pressure overestimation which results in lost circulation and reservoir damage.However,moldic pores and vugs with high pore aspect ratios lead to pore-pressure underestimation which results in well kick and even blowout.The pore-pressure deviation due to cracks and moldic pores increases with an increase in porosity.For carbonates with complex pore structures,adopting conventional pore-pressure prediction methods and casing program designs will expose the well drilling engineering to high uncertainties.Velocity prediction models considering the influence of pore structure need to be built to improve the reliability and accuracy of pore-pressure prediction in carbonates.
基金Supported by Natural Science Foundation of Henan Province,No.242300421286the research and practice project of higher education reform in Henan Province,No.2023SJGLX124Ythe research and practice project of higher education reform of Zhengzhou University,No.2023ZZUJGXM114.
文摘BACKGROUND Early symptoms of hepatocellular carcinoma(HCC)are not obvious,and more than 70%of which does not receive radical hepatectomy,when first diagnosed.In recent years,molecular-targeted drugs combined with immunotherapy and other therapeutic methods have provided new treatment options for middle and advanced HCC(aHCC).Predicting the effect of targeted combined immunotherapy has become a hot topic in current research.AIM To explore the relationship between nodule enhancement in hepatobiliary phase and the efficacy of combined targeted immunotherapy for aHCC.METHODS Data from 56 patients with aHCC for magnetic resonance imaging with gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid were retrospectively collected.Signal intensity of intrahepatic nodules was measured,and the hepatobiliary relative enhancement ratio(RER)was calculated.Progression-free survival(PFS)of patients with high and low reinforcement of HCC nodules was compared.The model was validated using receiver operating characteristic curves.Univariate and multivariate logistic regression and Kaplan-Meier analysis were performed to explore factors influencing the efficacy of targeted immunization and PFS.RESULTS Univariate and multivariate analyses revealed that the RER,neutrophil-to-lymphocyte ratio,platelet-to-lymphocyte ratio,and prognostic nutritional index were significantly associated with the efficacy of tyrosine kinase inhibitors combined with immunotherapy(P<0.05).The area under the curve of the RER for predicting the efficacy of tyrosine kinase inhibitors combined with anti-programmed death 1 antibody in patients with aHCC was 0.876(95%confidence interval:0.781-0.971,P<0.05),the optimal cutoff value was 0.904,diagnostic sensitivity was 87.5%,and specificity was 79.2%.Kaplan-Meier analysis showed that neutrophil-to-lymphocyte ratio<5,plateletto-lymphocyte ratio<300,prognostic nutritional index<45,and RER<0.9 significantly improved PFS.CONCLUSION AHCC nodules enhancement in the hepatobiliary stage was significantly correlated with PFS.Imaging information and immunological indicators had high predictive efficacy for targeted combined immunotherapy and were associated with PFS.
