BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To...BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To provide fair organ distribution,predictive mortality scores have been developed.AIM To compare the Acute Physiology and Chronic Health Evaluation IV(APACHE IV),balance of risk(BAR),and model for end-stage liver disease(MELD)scores as predictors of mortality.METHODS Retrospective cohort study,which included 283 adult patients in the postoperative period of deceased donor liver transplantation from 2014 to 2018.RESULTS The transplant recipients were mainly male,with a mean age of 58.1 years.Donors were mostly male,with a mean age of 41.6 years.The median cold ischemia time was 3.1 hours,and the median intensive care unit stay was 5 days.For APACHE IV,a mean of 59.6 was found,BAR 10.7,and MELD 24.2.The 28-day mortality rate was 9.5%,and at 90 days,it was 3.5%.The 28-day mortality prediction for APACHE IV was very good[area under the curve(AUC):0.85,P<0.001,95%CI:0.76-0.94],P<0.001,BAR(AUC:0.70,P<0.001,95%CI:0.58–0.81),and MELD(AUC:0.66,P<0.006,95%CI:0.55-0.78),P<0.008.At 90 days,the data for APACHE IV were very good(AUC:0.80,P<0.001,95%CI:0.71–0.90)and moderate for BAR and MELD,respectively,(AUC:0.66,P<0.004,95%CI:0.55–0.77),(AUC:0.62,P<0.026,95%CI:0.51–0.72).All showed good discrimination between deaths and survivors.As for the best value for liver transplantation,it was significant only for APACHE IV(P<0.001).CONCLUSION The APACHE IV assessment score was more accurate than BAR and MELD in predicting mortality in deceased donor liver transplant recipients.展开更多
BACKGROUND Split liver transplantation(SLT)effectively expands the donor pool but carries a higher risk of early postoperative complications(EPC)due to the extensive transection surface and altered hemodynamics of par...BACKGROUND Split liver transplantation(SLT)effectively expands the donor pool but carries a higher risk of early postoperative complications(EPC)due to the extensive transection surface and altered hemodynamics of partial grafts.AIM To establish an interpretable machine learning framework to identify risk factors for EPC in adult recipients undergoing right tri-segment SLT.METHODS We retrospectively analyzed 109 adult SLT recipients,including 37 who developed EPC.A comprehensive set of perioperative donor and recipient variables was evaluated using four machine learning algorithms(random forest,support vector machine,extreme gradient boosting,and logistic regression).SHapley Additive exPlanations were employed to rank variable importance.Independent predictors were further validated through multivariate logistic regression,and a diagnostic nomogram was constructed.Restricted cubic spline,receiver operating characteristic,and survival analyses were conducted to evaluate model performance and clinical outcomes.RESULTS EPC occurred in 33.9%of recipients.Among the machine learning models,random forest demonstrated the best predictive performance.SHapley Additive exPlanations analysis identified the log-transformed systemic immune-inflammation index(LnSII),albumin-to-fibrinogen ratio,model for end-stage liver disease(MELD)score,partial lobectomy of segment IV(IV PL),intraoperative blood loss,and operation time as major contributors to the model.Multivariate logistic regression confirmed LnSII,MELD scores,IV PL,and blood loss as independent predictors of EPC.The nomogram constructed from these factors showed good discrimination and calibration(area under the curve=0.788,95%confidence interval:0.734-0.906).Kaplan-Meier analysis revealed that both LnSII and MELD scores were associated with five-year overall survival(P<0.05),while MELD score and IV PL were significantly correlated with early postoperative liver function recovery.CONCLUSION IV PL during right tri-segment SLT appears to reduce the risk of EPC and enhance postoperative liver function recovery.Together with LnSII,blood loss,and MELD score,these factors offer a reliable foundation for individualized perioperative risk stratification and management.展开更多
The efficient recovery of silver(Ag)from retired photovoltaic(PV)panels is crucial for resource sustainability and envi-ronmental protection.This study developed an environmentally friendly leaching method using ammon...The efficient recovery of silver(Ag)from retired photovoltaic(PV)panels is crucial for resource sustainability and envi-ronmental protection.This study developed an environmentally friendly leaching method using ammonia(NH_(3)·H_(2)O)and hydrogen peroxide(H_(2)O_(2)),achieving the selective dissolution of Ag from retired crystalline silicon solar panels.Meanwhile,nonprecious metals such as aluminum(Al)and lead(Pb),which are commonly found in PV cells,were barely dissolved,dem-onstrating the excellent selectivity of this method for Ag.Light irradiation significantly improved the dissolution efficiency of Ag and reduced the amount of the reagent used.Ag dissolution occurred owing to a dual-pathway synergistic effect,which stemmed from the direct oxidation of Ag by H_(2)O_(2).The strongly oxidizing hydroxyl radicals generated by photocatalysis accelerated the oxidation and dissolution of Ag.In addition,NH 3·H_(2)O effectively promoted the dissolution and stabilization of oxidation products by forming soluble Ag–NH3·H2O complexes([Ag(NH3)2]+).This article reports an efficient,selective,and environmentally friendly strategy of Ag recovery and elucidates the radical-mediated dissolution mechanism under light-driven conditions,offering a feasible way for sustainably recovering valuable metals from retired PV panels.展开更多
A reliable and accurate HPLC/UV method was developed for the quantitative determination of astragaloside IV in ＇Huang-Qi-Si-Wu＇ Capsules, a widely used prescription of traditional Chinese medicines （TCM）. The chro...A reliable and accurate HPLC/UV method was developed for the quantitative determination of astragaloside IV in ＇Huang-Qi-Si-Wu＇ Capsules, a widely used prescription of traditional Chinese medicines （TCM）. The chromatographic separation conditions employed for HPLC/UV were optimized using a Hypersil-ODS column （250 mm^4.6 mm, 5.0 pm） with isocratic elution. Acetonitrile-water （32：68, v/v） were used as the mobile phase pumped at a flow rate of 1.0 mL/min and a detection wavelength at 203 nm was used. The method was fully validated with respect to linearity, precision, accuracy, specificity and robustness. The validated method was applied successfully to the quantification of astragaloside IV in the extract of ＇Huang-Qi- Si-Wu＇ Capsules from different production batches. The results indicate that the established HPLC/UV method is suitable for the quantitative analysis and quality control of ＇Huang-Qi-Si-Wu＇ Capsules and other related botanical drugs.展开更多
In the current era marked by energy shortages,the advancement of nuclear energy stands as an inevitable progression.The reprocessing of spent nuclear fuel plays a crucial role in determining the sustainability of nucl...In the current era marked by energy shortages,the advancement of nuclear energy stands as an inevitable progression.The reprocessing of spent nuclear fuel plays a crucial role in determining the sustainability of nuclear energy as a viable energy source.Among these processes,the separation and recovery of Pu(Ⅳ)from high-level liquid waste(HLLW)hold paramount significance in terms of safety and strategic implications.Herein,this work focused on the synthesis of two acid-and radiation-resistant pyridine-based sp^(2)c-COFs(COF-IHEP3 and COF-IHEP4),followed by the creation of two pyridine-based ionized sp^(2)c-COFs named COF-IHEP3-CH_(3)NO_(3)and COF-IHEP4-CH3NO3through post-modification.These materials have potential anion exchange capacity for the selective separation of Pu(Ⅳ)in highly acidic conditions.Notably,in 8 mol/L nitric acid solution,COF-IHEP3-CH3NO3demonstrated the capability to eliminate plutonium within 20 min in 98%removal efficiency with a Kdvalue of 2450 m L/g.Experimental and theoretical analysis suggest that the ionized sp^(2)c-COFs exhibit exceptional stability,selectivity,and prevention of secondary contamination towards Pu(Ⅳ)in the presence of multiple ions environments.In short,this work provides an appropriate anion exchange strategy to design ionic sp^(2)c-COFs as a promising platform for Pu(Ⅳ)recovery from HLLW.展开更多
BACKGROUND Stage IV pancreatic cancer(PC)has a poor prognosis and lacks individualized prognostic tools.Current survival prediction models are limited,and there is a need for more accurate,personalized methods.The Sur...BACKGROUND Stage IV pancreatic cancer(PC)has a poor prognosis and lacks individualized prognostic tools.Current survival prediction models are limited,and there is a need for more accurate,personalized methods.The Surveillance,Epidemiology,and End Results(SEER)database offers a valuable resource for studying large patient cohorts,yet machine learning-based nomograms for stage IV PC prognosis remain underexplored.This study hypothesizes that a machine learning-based nomogram can predict cancer-specific survival(CSS)and overall survival(OS)with high accuracy in stage IV PC patients.AIM To construct and validate a machine learning-based nomogram for predicting survival in stage IV PC patients using real-world data.METHODS Clinical data from stage IV PC patients diagnosed via pathology from 2000 to 2019 INTRODUCTION Pancreatic cancer(PC)is a significant human health issue and,by 2025,is projected to surpass breast cancer as the third leading cause of cancer-related deaths[1].In the United States,an estimated 66440 new cases and 51750 deaths due to PC were reported in 2024.PC is often asymptomatic in its early stages,with more than half of patients presenting with distant organ metastasis at the time of initial diagnosis[2].Consequently,the prognosis is very poor,with a 5-year relative survival rate of only 12.8%[2]In clinical practice,considerable heterogeneity in survival outcomes has been observed among patients with stage IV PC,highlighting the need for an individualized survival prediction tool for this population.Nomograms,which are visual tools incorporating multiple prognostic factors to predict patient survival,aid in person-alized treatment planning and clinical decision-making and are widely used in cancer prognosis evaluation[3-6].Machine learning,a core technique within artificial intelligence,employs algorithms to analyze data,learn from patterns,and predict real-world events with high accuracy,and is increasingly applied in health assessment,medical decision-making,prognosis,and personalized treatment[7-9].This study leverages the large sample size and comprehensive clinical data from the United State Surveillance,Epidemiology,and End Results(SEER)database to develop a prognostic nomogram for stage IV PC patients using machine learning,with the aim of providing individualized prognostic assessments to improve clinical decision-making.展开更多
AIM: Nonalcoholic steatohepatitis (NASH) is a severe form of nonalcoholic fatty liver disease (NAFLD), and progresses to the end stage of liver disease. Biochemical markers of liver fibrosis are strongly associated wi...AIM: Nonalcoholic steatohepatitis (NASH) is a severe form of nonalcoholic fatty liver disease (NAFLD), and progresses to the end stage of liver disease. Biochemical markers of liver fibrosis are strongly associated with the degree of histological liver fibrosis in patients with chronic liver disease. However, data are few on the usefulness of markers in NAFLD patients. The aim of this study was to identify better noninvasive predictors of hepatic fibrosis, with special focus on markers of liver fibrosis, type VI collagen 7S domain and hyaluronic acid. METHODS: One hundred and twelve patients with histologically proven NAFLD were studied. RESULTS: The histological stage of NAFLD correlated with several clinical and biochemical variables, the extent of hepatic fibrosis and the markers of liver fibrosis were relatively strong associated. The best cutoff values to detect NASH were assessed by using receiver operating characteristic analysis: type VI collagen 75 domain ≥5.0 ng/mL, hyaluronic acid ≥43 ng/mL. Both markers had a high positive predictive value: type VI collagen 7S domain, 86% and hyaluronic acid, 92%. Diagnostic accuracies of these markers were evaluated to detect severe fibrosis. Both markers showed high negative predictive values: type VI collagen 7S domain (≥5.0 ng/mL), 84% and hyaluronic acid (≥50 ng/mL), 78%, and were significantly and independently associated with the presence of NASH or severe fibrosis by logistic regression analysis. CONCLUSION: Both markers of liver fibrosis are useful in discriminating NASH from fatty liver alone or patients with severe fibrosis from patients with non-severe fibrosis.展开更多
Platinum-based anticancer agents such as cisplatin and its analogues are widely used for treating multiple cancers. However, due to the inferior water-solubility, chemoresistance and consequent adverse side effects, t...Platinum-based anticancer agents such as cisplatin and its analogues are widely used for treating multiple cancers. However, due to the inferior water-solubility, chemoresistance and consequent adverse side effects, their clinical applications are limited. Herein, choles Pt(IV), a lipophilic platinum(IV) prodrug was synthesized for manufacture of Choles Pt(IV)-Liposomes aiming to resolve the predefined obstacles encountered by platinum drugs. Following systematic screening, Choles Pt(IV)-Liposomes showed a small particle size(105.6 nm), the rapid release of platinum(Pt) ions, and notable apoptosis of cancer cells.In addition, according to the fluidity and safety results of animal experiments in mice, Choles Pt(IV)-Liposomes also showed better therapeutic effect, which significantly inhibited the growth of patientderived xenograft tumors of hepatocellular carcinoma with an inhibition ratio of 80.7%, and effectively alleviated the drug toxicity brought by traditional platinum drugs. Overall, this study provides a promising route to enhance the therapeutic efficiency of platinum drugs in cancer treatment.展开更多
文摘BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To provide fair organ distribution,predictive mortality scores have been developed.AIM To compare the Acute Physiology and Chronic Health Evaluation IV(APACHE IV),balance of risk(BAR),and model for end-stage liver disease(MELD)scores as predictors of mortality.METHODS Retrospective cohort study,which included 283 adult patients in the postoperative period of deceased donor liver transplantation from 2014 to 2018.RESULTS The transplant recipients were mainly male,with a mean age of 58.1 years.Donors were mostly male,with a mean age of 41.6 years.The median cold ischemia time was 3.1 hours,and the median intensive care unit stay was 5 days.For APACHE IV,a mean of 59.6 was found,BAR 10.7,and MELD 24.2.The 28-day mortality rate was 9.5%,and at 90 days,it was 3.5%.The 28-day mortality prediction for APACHE IV was very good[area under the curve(AUC):0.85,P<0.001,95%CI:0.76-0.94],P<0.001,BAR(AUC:0.70,P<0.001,95%CI:0.58–0.81),and MELD(AUC:0.66,P<0.006,95%CI:0.55-0.78),P<0.008.At 90 days,the data for APACHE IV were very good(AUC:0.80,P<0.001,95%CI:0.71–0.90)and moderate for BAR and MELD,respectively,(AUC:0.66,P<0.004,95%CI:0.55–0.77),(AUC:0.62,P<0.026,95%CI:0.51–0.72).All showed good discrimination between deaths and survivors.As for the best value for liver transplantation,it was significant only for APACHE IV(P<0.001).CONCLUSION The APACHE IV assessment score was more accurate than BAR and MELD in predicting mortality in deceased donor liver transplant recipients.
基金Supported by Tianjin Key Medical Discipline Construction Project,No.TJYXZDXK-3-006ATianjin Municipal Health Commission General Fund Project,No.TJWJ2024MS017+3 种基金Key Project of Tianjin Science and Technology Bureau Applied Basic Research,No.23JCZDJC01200The Independent Research Fund of the Institute of Transplant Medicine at Nankai University,No.NKTM2023004The General Project of the China Medicine Education Association,No.ZJWYH-2023-YIZHI-028General Project of Scientific Research Plan of Tianjin Municipal Education Commission,No.2024ZX013。
文摘BACKGROUND Split liver transplantation(SLT)effectively expands the donor pool but carries a higher risk of early postoperative complications(EPC)due to the extensive transection surface and altered hemodynamics of partial grafts.AIM To establish an interpretable machine learning framework to identify risk factors for EPC in adult recipients undergoing right tri-segment SLT.METHODS We retrospectively analyzed 109 adult SLT recipients,including 37 who developed EPC.A comprehensive set of perioperative donor and recipient variables was evaluated using four machine learning algorithms(random forest,support vector machine,extreme gradient boosting,and logistic regression).SHapley Additive exPlanations were employed to rank variable importance.Independent predictors were further validated through multivariate logistic regression,and a diagnostic nomogram was constructed.Restricted cubic spline,receiver operating characteristic,and survival analyses were conducted to evaluate model performance and clinical outcomes.RESULTS EPC occurred in 33.9%of recipients.Among the machine learning models,random forest demonstrated the best predictive performance.SHapley Additive exPlanations analysis identified the log-transformed systemic immune-inflammation index(LnSII),albumin-to-fibrinogen ratio,model for end-stage liver disease(MELD)score,partial lobectomy of segment IV(IV PL),intraoperative blood loss,and operation time as major contributors to the model.Multivariate logistic regression confirmed LnSII,MELD scores,IV PL,and blood loss as independent predictors of EPC.The nomogram constructed from these factors showed good discrimination and calibration(area under the curve=0.788,95%confidence interval:0.734-0.906).Kaplan-Meier analysis revealed that both LnSII and MELD scores were associated with five-year overall survival(P<0.05),while MELD score and IV PL were significantly correlated with early postoperative liver function recovery.CONCLUSION IV PL during right tri-segment SLT appears to reduce the risk of EPC and enhance postoperative liver function recovery.Together with LnSII,blood loss,and MELD score,these factors offer a reliable foundation for individualized perioperative risk stratification and management.
基金supported by the National Science Foundation of China(Nos.22525606,22176128,22236005,22406131,22506126)the Innovation Program of Shanghai Municipal Education Commission(No.2023ZKZD50)+13 种基金Shanghai Leading Talent Program of Eastern Talent Plan(No.LJ2023002)Shanghai Government(Nos.22dz1205400,23520711100)Chinese Education Ministry Key Laboratory and International Joint Laboratory on Resource ChemistryShanghai Eastern Scholar ProgramThe authors also thank Fellowship of China National Postdoctoral Program for Innovative Talents(No.BX20240229)the China Postdoctoral Science(No.2024M762100)the Foundation the Shanghai Science and Technology Commission Project(No.24ZR1455700)Shanghai Post-doctoral Excellence Pro-gram(No.2024787)the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Com-mission(No.24CGA49)the“111 Innovation and Talent Recruitment Base on Photochemical and Energy Materials”(No.D18020)Yunnan University Collaborative Innovation Center(Qujing Green Photovoltaic Industry Collaborative Innovation Center)Technology Talent and Platform Plan Project of Yunnan Provincial Department of Science and Technology(No.202305AF150088)Shanghai Engineering Research Center of Green Energy Chemical Engineering(No.18DZ2254200)Shanghai Frontiers Science Center of Biomimetic Catalysis.
文摘The efficient recovery of silver(Ag)from retired photovoltaic(PV)panels is crucial for resource sustainability and envi-ronmental protection.This study developed an environmentally friendly leaching method using ammonia(NH_(3)·H_(2)O)and hydrogen peroxide(H_(2)O_(2)),achieving the selective dissolution of Ag from retired crystalline silicon solar panels.Meanwhile,nonprecious metals such as aluminum(Al)and lead(Pb),which are commonly found in PV cells,were barely dissolved,dem-onstrating the excellent selectivity of this method for Ag.Light irradiation significantly improved the dissolution efficiency of Ag and reduced the amount of the reagent used.Ag dissolution occurred owing to a dual-pathway synergistic effect,which stemmed from the direct oxidation of Ag by H_(2)O_(2).The strongly oxidizing hydroxyl radicals generated by photocatalysis accelerated the oxidation and dissolution of Ag.In addition,NH 3·H_(2)O effectively promoted the dissolution and stabilization of oxidation products by forming soluble Ag–NH3·H2O complexes([Ag(NH3)2]+).This article reports an efficient,selective,and environmentally friendly strategy of Ag recovery and elucidates the radical-mediated dissolution mechanism under light-driven conditions,offering a feasible way for sustainably recovering valuable metals from retired PV panels.
基金Scientific and Technological Innovation Project Foundation of Shanxi,China(Grant No.20090321099)
文摘A reliable and accurate HPLC/UV method was developed for the quantitative determination of astragaloside IV in ＇Huang-Qi-Si-Wu＇ Capsules, a widely used prescription of traditional Chinese medicines （TCM）. The chromatographic separation conditions employed for HPLC/UV were optimized using a Hypersil-ODS column （250 mm^4.6 mm, 5.0 pm） with isocratic elution. Acetonitrile-water （32：68, v/v） were used as the mobile phase pumped at a flow rate of 1.0 mL/min and a detection wavelength at 203 nm was used. The method was fully validated with respect to linearity, precision, accuracy, specificity and robustness. The validated method was applied successfully to the quantification of astragaloside IV in the extract of ＇Huang-Qi- Si-Wu＇ Capsules from different production batches. The results indicate that the established HPLC/UV method is suitable for the quantitative analysis and quality control of ＇Huang-Qi-Si-Wu＇ Capsules and other related botanical drugs.
基金supported by the National Natural Science Foundation of China(Nos.U2067212,22176191)the National Science Fund for Distinguished Young Scholars(No.21925603)。
文摘In the current era marked by energy shortages,the advancement of nuclear energy stands as an inevitable progression.The reprocessing of spent nuclear fuel plays a crucial role in determining the sustainability of nuclear energy as a viable energy source.Among these processes,the separation and recovery of Pu(Ⅳ)from high-level liquid waste(HLLW)hold paramount significance in terms of safety and strategic implications.Herein,this work focused on the synthesis of two acid-and radiation-resistant pyridine-based sp^(2)c-COFs(COF-IHEP3 and COF-IHEP4),followed by the creation of two pyridine-based ionized sp^(2)c-COFs named COF-IHEP3-CH_(3)NO_(3)and COF-IHEP4-CH3NO3through post-modification.These materials have potential anion exchange capacity for the selective separation of Pu(Ⅳ)in highly acidic conditions.Notably,in 8 mol/L nitric acid solution,COF-IHEP3-CH3NO3demonstrated the capability to eliminate plutonium within 20 min in 98%removal efficiency with a Kdvalue of 2450 m L/g.Experimental and theoretical analysis suggest that the ionized sp^(2)c-COFs exhibit exceptional stability,selectivity,and prevention of secondary contamination towards Pu(Ⅳ)in the presence of multiple ions environments.In short,this work provides an appropriate anion exchange strategy to design ionic sp^(2)c-COFs as a promising platform for Pu(Ⅳ)recovery from HLLW.
基金Supported by Mianyang Health and Health Committee 2023 Scientific Research Project,No.202309Chengdu University of Traditional Chinese Medicine University-Hospital Joint Innovation Fund,No.LH202402010Mianyang Chinese Medicine Association 2024 Traditional Chinese Medicine Inheritance and Innovation Science and Technology Project,No.MYSZYYXH-202426.
文摘BACKGROUND Stage IV pancreatic cancer(PC)has a poor prognosis and lacks individualized prognostic tools.Current survival prediction models are limited,and there is a need for more accurate,personalized methods.The Surveillance,Epidemiology,and End Results(SEER)database offers a valuable resource for studying large patient cohorts,yet machine learning-based nomograms for stage IV PC prognosis remain underexplored.This study hypothesizes that a machine learning-based nomogram can predict cancer-specific survival(CSS)and overall survival(OS)with high accuracy in stage IV PC patients.AIM To construct and validate a machine learning-based nomogram for predicting survival in stage IV PC patients using real-world data.METHODS Clinical data from stage IV PC patients diagnosed via pathology from 2000 to 2019 INTRODUCTION Pancreatic cancer(PC)is a significant human health issue and,by 2025,is projected to surpass breast cancer as the third leading cause of cancer-related deaths[1].In the United States,an estimated 66440 new cases and 51750 deaths due to PC were reported in 2024.PC is often asymptomatic in its early stages,with more than half of patients presenting with distant organ metastasis at the time of initial diagnosis[2].Consequently,the prognosis is very poor,with a 5-year relative survival rate of only 12.8%[2]In clinical practice,considerable heterogeneity in survival outcomes has been observed among patients with stage IV PC,highlighting the need for an individualized survival prediction tool for this population.Nomograms,which are visual tools incorporating multiple prognostic factors to predict patient survival,aid in person-alized treatment planning and clinical decision-making and are widely used in cancer prognosis evaluation[3-6].Machine learning,a core technique within artificial intelligence,employs algorithms to analyze data,learn from patterns,and predict real-world events with high accuracy,and is increasingly applied in health assessment,medical decision-making,prognosis,and personalized treatment[7-9].This study leverages the large sample size and comprehensive clinical data from the United State Surveillance,Epidemiology,and End Results(SEER)database to develop a prognostic nomogram for stage IV PC patients using machine learning,with the aim of providing individualized prognostic assessments to improve clinical decision-making.
文摘AIM: Nonalcoholic steatohepatitis (NASH) is a severe form of nonalcoholic fatty liver disease (NAFLD), and progresses to the end stage of liver disease. Biochemical markers of liver fibrosis are strongly associated with the degree of histological liver fibrosis in patients with chronic liver disease. However, data are few on the usefulness of markers in NAFLD patients. The aim of this study was to identify better noninvasive predictors of hepatic fibrosis, with special focus on markers of liver fibrosis, type VI collagen 7S domain and hyaluronic acid. METHODS: One hundred and twelve patients with histologically proven NAFLD were studied. RESULTS: The histological stage of NAFLD correlated with several clinical and biochemical variables, the extent of hepatic fibrosis and the markers of liver fibrosis were relatively strong associated. The best cutoff values to detect NASH were assessed by using receiver operating characteristic analysis: type VI collagen 75 domain ≥5.0 ng/mL, hyaluronic acid ≥43 ng/mL. Both markers had a high positive predictive value: type VI collagen 7S domain, 86% and hyaluronic acid, 92%. Diagnostic accuracies of these markers were evaluated to detect severe fibrosis. Both markers showed high negative predictive values: type VI collagen 7S domain (≥5.0 ng/mL), 84% and hyaluronic acid (≥50 ng/mL), 78%, and were significantly and independently associated with the presence of NASH or severe fibrosis by logistic regression analysis. CONCLUSION: Both markers of liver fibrosis are useful in discriminating NASH from fatty liver alone or patients with severe fibrosis from patients with non-severe fibrosis.
基金financially supported by the GDNRC [Guangdong Nature Resource Center](2020)(037)National Natural Science Foundation of China (Nos. 81773642, 52073139)+3 种基金the Natural Science Foundation of Guangdong Province (No. 2019A1515011619)Guangdong Provincial Science and Technology Department (No.2016A030311015)the Key R&D Plan of Chenzhou (No.ZDYF202008)the Discipline Leader Startup Fund of Huazhong University of Science and Technoloy Union Shenzhen Hospital (No.YN2021002)。
文摘Platinum-based anticancer agents such as cisplatin and its analogues are widely used for treating multiple cancers. However, due to the inferior water-solubility, chemoresistance and consequent adverse side effects, their clinical applications are limited. Herein, choles Pt(IV), a lipophilic platinum(IV) prodrug was synthesized for manufacture of Choles Pt(IV)-Liposomes aiming to resolve the predefined obstacles encountered by platinum drugs. Following systematic screening, Choles Pt(IV)-Liposomes showed a small particle size(105.6 nm), the rapid release of platinum(Pt) ions, and notable apoptosis of cancer cells.In addition, according to the fluidity and safety results of animal experiments in mice, Choles Pt(IV)-Liposomes also showed better therapeutic effect, which significantly inhibited the growth of patientderived xenograft tumors of hepatocellular carcinoma with an inhibition ratio of 80.7%, and effectively alleviated the drug toxicity brought by traditional platinum drugs. Overall, this study provides a promising route to enhance the therapeutic efficiency of platinum drugs in cancer treatment.
