The constrained weighted-non-negative matrix factorization(CW-NMF)hybrid receptor model was applied to study the influence of steelmaking activities on PM_(2.5)(particulate matter with equivalent aerodynamic diameter ...The constrained weighted-non-negative matrix factorization(CW-NMF)hybrid receptor model was applied to study the influence of steelmaking activities on PM_(2.5)(particulate matter with equivalent aerodynamic diameter less than 2.5μm)composition in Dunkerque,Northern France.Semi-diurnal PM_(2.5)samples were collected using a high volume sampler in winter 2010 and spring 2011 and were analyzed for trace metals,water-soluble ions,and total carbon using inductively coupled plasma–atomic emission spectrometry(ICP-AES),ICP-mass spectrometry(ICP-MS),ionic chromatography and micro elemental carbon analyzer.The elemental composition shows that NO_(3)^(-),SO_(4)^(2-),NH_4~+and total carbon are the main PM_(2.5)constituents.Trace metals data were interpreted using concentration roses and both influences of integrated steelworks and electric steel plant were evidenced.The distinction between the two sources is made possible by the use Zn/Fe and Zn/Mn diagnostic ratios.Moreover Rb/Cr,Pb/Cr and Cu/Cd combination ratio are proposed to distinguish the ISW-sintering stack from the ISW-fugitive emissions.The a priori knowledge on the influencing source was introduced in the CW-NMF to guide the calculation.Eleven source profiles with various contributions were identified:8 are characteristics of coastal urban background site profiles and 3 are related to the steelmaking activities.Between them,secondary nitrates,secondary sulfates and combustion profiles give the highest contributions and account for 93%of the PM_(2.5)concentration.The steelwork facilities contribute in about 2%of the total PM_(2.5)concentration and appear to be the main source of Cr,Cu,Fe,Mn,Zn.展开更多
为了寻求合理简化的流域地形指数水文模型TOPMODEL(Topographic Index model)用于大尺度的陆面模式,推导了土壤表层饱和导水率k0、衰减因子f和地下水补给速率R空间都可变的扩展的TOPMODEL,并将f空间非均匀分布的TOPMODEL与陆面模式SSiB...为了寻求合理简化的流域地形指数水文模型TOPMODEL(Topographic Index model)用于大尺度的陆面模式,推导了土壤表层饱和导水率k0、衰减因子f和地下水补给速率R空间都可变的扩展的TOPMODEL,并将f空间非均匀分布的TOPMODEL与陆面模式SSiB4耦合(SSiB4/GTOP)。通过耦合模型在f空间非均匀条件下进行实际流域的水文模拟,分析f空间非均匀对流域土壤湿度、蒸散发、地表径流、基流和总径流的影响。主要结论有:(1)k0和R的空间变化并不改变经典TOPMODEL原有关系式,只要定义新的地形指数,k0和R空间非均匀TOPMODEL与空间均匀的TOPMODEL并无区别;(2) f空间变化条件下由于局地的地下水埋深还与局地的f值有关,地形指数相同的区域具有水文相似性这一结论不再成立;(3)与f空间均匀的模拟结果相比较,f随海拔高度h i增加而线性减小使模拟的流域土壤湿度、地表径流和流域蒸散减小但使基流和总径流增加;(4) f空间非均匀对流域水文模拟结果有影响,但其影响明显小于流域地形因子的影响。展开更多
Spinal and bulbar muscular atrophy is a neurodegenerative disease caused by extended CAG trinucleotide repeats in the androgen receptor gene,which encodes a ligand-dependent transcription facto r.The mutant androgen r...Spinal and bulbar muscular atrophy is a neurodegenerative disease caused by extended CAG trinucleotide repeats in the androgen receptor gene,which encodes a ligand-dependent transcription facto r.The mutant androgen receptor protein,characterized by polyglutamine expansion,is prone to misfolding and forms aggregates in both the nucleus and cytoplasm in the brain in spinal and bulbar muscular atrophy patients.These aggregates alter protein-protein interactions and compromise transcriptional activity.In this study,we reported that in both cultured N2a cells and mouse brain,mutant androgen receptor with polyglutamine expansion causes reduced expression of mesencephalic astrocyte-de rived neurotrophic factor.Overexpressio n of mesencephalic astrocyte-derived neurotrophic factor amelio rated the neurotoxicity of mutant androgen receptor through the inhibition of mutant androgen receptor aggregation.Conversely.knocking down endogenous mesencephalic astrocyte-derived neurotrophic factor in the mouse brain exacerbated neuronal damage and mutant androgen receptor aggregation.Our findings suggest that inhibition of mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor is a potential mechanism underlying neurodegeneration in spinal and bulbar muscular atrophy.展开更多
Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glyc...Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential.Methods:First,gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO),and glycolysis-related genes were obtained from the Molecular Signatures Database(MSigDB).Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained,which were used in nonnegative matrix factorization(NMF)to identify clusters.The correlation between clusters and clinical features was discussed,and the differentially expressed genes(DEGs)between the two clusters were investigated.Based on the DEGs,we investigated the biological differences between clusters,including immune cell infiltration,mutation,tumor immune dysfunction and exclusion,immune function,and checkpoint genes.To establish the prognostic model,the genes were filtered based on univariable Cox regression,LASSO,and multivariable Cox regression.Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model.A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting.Result:The genes obtained from MSigDB were enriched in glycolysis functions.Two clusters were identified by NMF analysis based on 272 glycolysis-related genes,and a prognostic model based on DEGs between the two clusters was finally established.The prognostic model consisted of LAMPS,SPRN,ATOH1,TANC1,ETV1,TDRD1,KLK14,MESP2,POSTN,CRIP2,NAT1,AKR7A3,PODXL,CARTPT,and PCDHGB2.All sample,training,and test cohorts from The Cancer Genome Atlas(TCGA)and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups.The area under the ROC curve showed great performance of this prognostic model.Conclusion:A prognostic model based on glycolysis-related genes was established,with great performance and potential significance to the clinical application.展开更多
Rank determination issue is one of the most significant issues in non-negative matrix factorization (NMF) research. However, rank determination problem has not received so much emphasis as sparseness regularization pr...Rank determination issue is one of the most significant issues in non-negative matrix factorization (NMF) research. However, rank determination problem has not received so much emphasis as sparseness regularization problem. Usually, the rank of base matrix needs to be assumed. In this paper, we propose an unsupervised multi-level non-negative matrix factorization model to extract the hidden data structure and seek the rank of base matrix. From machine learning point of view, the learning result depends on its prior knowledge. In our unsupervised multi-level model, we construct a three-level data structure for non-negative matrix factorization algorithm. Such a construction could apply more prior knowledge to the algorithm and obtain a better approximation of real data structure. The final bases selection is achieved through L2-norm optimization. We implement our experiment via binary datasets. The results demonstrate that our approach is able to retrieve the hidden structure of data, thus determine the correct rank of base matrix.展开更多
BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR...BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR)is a common polypectomy proce-dure in clinical practice,but it has a high postoperative recurrence rate.Currently,there is no predictive model for the recurrence of colorectal polyps after EMR.AIM To construct and validate a machine learning(ML)model for predicting the risk of colorectal polyp recurrence one year after EMR.METHODS This study retrospectively collected data from 1694 patients at three medical centers in Xuzhou.Additionally,a total of 166 patients were collected to form a prospective validation set.Feature variable screening was conducted using uni-variate and multivariate logistic regression analyses,and five ML algorithms were used to construct the predictive models.The optimal models were evaluated based on different performance metrics.Decision curve analysis(DCA)and SHapley Additive exPlanation(SHAP)analysis were performed to assess clinical applicability and predictor importance.RESULTS Multivariate logistic regression analysis identified 8 independent risk factors for colorectal polyp recurrence one year after EMR(P<0.05).Among the models,eXtreme Gradient Boosting(XGBoost)demonstrated the highest area under the curve(AUC)in the training set,internal validation set,and prospective validation set,with AUCs of 0.909(95%CI:0.89-0.92),0.921(95%CI:0.90-0.94),and 0.963(95%CI:0.94-0.99),respectively.DCA indicated favorable clinical utility for the XGBoost model.SHAP analysis identified smoking history,family history,and age as the top three most important predictors in the model.CONCLUSION The XGBoost model has the best predictive performance and can assist clinicians in providing individualized colonoscopy follow-up recommendations.展开更多
BACKGROUND Ischemic heart disease(IHD)impacts the quality of life and has the highest mortality rate of cardiovascular diseases globally.AIM To compare variations in the parameters of the single-lead electrocardiogram...BACKGROUND Ischemic heart disease(IHD)impacts the quality of life and has the highest mortality rate of cardiovascular diseases globally.AIM To compare variations in the parameters of the single-lead electrocardiogram(ECG)during resting conditions and physical exertion in individuals diagnosed with IHD and those without the condition using vasodilator-induced stress computed tomography(CT)myocardial perfusion imaging as the diagnostic reference standard.METHODS This single center observational study included 80 participants.The participants were aged≥40 years and given an informed written consent to participate in the study.Both groups,G1(n=31)with and G2(n=49)without post stress induced myocardial perfusion defect,passed cardiologist consultation,anthropometric measurements,blood pressure and pulse rate measurement,echocardiography,cardio-ankle vascular index,bicycle ergometry,recording 3-min single-lead ECG(Cardio-Qvark)before and just after bicycle ergometry followed by performing CT myocardial perfusion.The LASSO regression with nested cross-validation was used to find the association between Cardio-Qvark parameters and the existence of the perfusion defect.Statistical processing was performed with the R programming language v4.2,Python v.3.10[^R],and Statistica 12 program.RESULTS Bicycle ergometry yielded an area under the receiver operating characteristic curve of 50.7%[95%confidence interval(CI):0.388-0.625],specificity of 53.1%(95%CI:0.392-0.673),and sensitivity of 48.4%(95%CI:0.306-0.657).In contrast,the Cardio-Qvark test performed notably better with an area under the receiver operating characteristic curve of 67%(95%CI:0.530-0.801),specificity of 75.5%(95%CI:0.628-0.88),and sensitivity of 51.6%(95%CI:0.333-0.695).CONCLUSION The single-lead ECG has a relatively higher diagnostic accuracy compared with bicycle ergometry by using machine learning models,but the difference was not statistically significant.However,further investigations are required to uncover the hidden capabilities of single-lead ECG in IHD diagnosis.展开更多
BACKGROUND Stomal complications though small in early postoperative period,but poses significant morbidity,therapeutic challenge,delay in adjuvant treatment and sometimes even leads to mortality.Predictive model for e...BACKGROUND Stomal complications though small in early postoperative period,but poses significant morbidity,therapeutic challenge,delay in adjuvant treatment and sometimes even leads to mortality.Predictive model for early detection of stomal complications is important to improve the outcome.A model including patients and disease related factors,intraoperative surgical techniques and biochemical markers would be a better determinant to anticipate early stomal complications.Incorporation of emerging tools and technology such as artificial intelligence(AI),will further improve the prediction.AIM To identify various risk factors and models for prediction of early post operative stomal complications in colorectal cancer(CRC)surgery.METHODS Published literatures on early postoperative stomal complications in CRC surgery were systematically reviewed between 1995 and 2024 from online search engines PubMed and MEDLINE.RESULTS Twenty-four observational studies focused on identifying various risk factors for early post operative stomal complications in CRC surgery were analyzed.Stomal complications in CRC are influenced by several factors such as disease factors,patient-specific characteristics,and surgical techniques.There are some biomarkers and tools loke AI which may play significant roles in early detection.CONCLUSION Careful analysis of these factors,changes in biochemical parameters,and application of AI,a predictive model for stomal complications can be generated,to help in early detection,prompt action to achieve better outcomes.展开更多
Angiosperms experienced one of the most remarkable radiations of land plants and are now the dominant autotrophs in terrestrial ecosystems.Recent phylogenomic studies based on large-scale data from plastid,mitochondri...Angiosperms experienced one of the most remarkable radiations of land plants and are now the dominant autotrophs in terrestrial ecosystems.Recent phylogenomic studies based on large-scale data from plastid,mitochondrial,or nuclear transcriptomes/genomes and increased taxon sampling have provided unprecedent resolution into the phylogeny of flowering plants.However,owing to ancient rapid radiations,the interrelationships among the five lineages of Mesangiospermae,the vast majority of angiosperms,remain contentious.Here we show that,although plastid and mitochondrial genomes lack sufficient phylogenetic signal for resolving deeper phylogeny,the relationships among five mesangiosperm lineages can be confidently resolved under better-fitting models using genome-scale data.According to our Bayesian cross-validation and model test in a maximum likelihood framework,siteheterogeneous models(e.g.,CAT-GTR+G4,LG+C20+F+G)outperform site-homogeneous or partition models often used in previous studies.Under site-heterogeneous models,the approximately unbiased test favored our preferred tree recovered from various datasets:Ceratophyllales(coontails)are robustly recovered as sister to monocots,and they together are sister to the clade comprising magnoliids,Chloranthales,and eudicots.Our phylogenomic analyses resolve the last enigma of the deeper phylogeny of angiosperms and emphasize the efficacy of modeling compositional heterogeneity in resolving rapid radiations of plants.展开更多
Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-...Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework.展开更多
BACKGROUND Anxiety and depression are highly prevalent among patients with cervical cancer(CC).However,few studies have systematically analyzed the psychological effects of tumor stage,treatment methods,and related fa...BACKGROUND Anxiety and depression are highly prevalent among patients with cervical cancer(CC).However,few studies have systematically analyzed the psychological effects of tumor stage,treatment methods,and related factors on these patients,or developed predictive models for these outcomes.AIM To identify factors influencing anxiety and depression in patients with CC and construct predictive models.METHODS We retrospectively analyzed data from 119 patients with CC treated at the Gynecology Department of Suzhou Ninth People’s Hospital between January 2017 and May 2025.Clinical data,psychological hope levels at diagnosis,and Self-Rating Anxiety Scale and Self-Rating Depression Scale scores during treatment were collected.Influencing factors were identified,and predictive models were developed.The model performance was evaluated using receiver operating characteristic(ROC)curves and the Hosmer-Lemeshow goodness-of-fit test.RESULTS During treatment,64.71%of the patients experienced anxiety and 52.10%experienced depression.Significant differences in family income,tumor stage,treatment modality,and hope level were observed between patients with and without anxiety/depression(P<0.05).Multivariate analysis showed that a family monthly income<5000 yuan,stage III-IV tumor,comprehensive treatment,and low hope level were independent risk factors(P<0.05).The predictive formula for anxiety was as follows:Logit(P)=0.795×monthly income+0.594×tumor stage+1.095×treatment method+1.184×hope level−9.176;for depression:Logit(P)=0.432×monthly income+0.518×tumor stage+0.727×treatment method+1.095×hope level−8.541.The area under the ROC curves were 0.865 for anxiety and 0.837 for depression.Goodness-of-fit test confirmed no overfitting(P>0.05).CONCLUSION Family income,tumor stage,treatment method,and hope level are key determinants of anxiety and depression in patients with CC.Predictive models incorporating these factors can effectively assess risk of anxiety and depression during treatment.展开更多
基金financially supported by the Nord-Pas-de-Calais Region Councilthe Ministry of Higher Education and Research+1 种基金the European Regional Development FundsAdib Kfoury acknowledges the“Pole Metropolitain Cote d'Opale”(PMCO)for its PhD financial support
文摘The constrained weighted-non-negative matrix factorization(CW-NMF)hybrid receptor model was applied to study the influence of steelmaking activities on PM_(2.5)(particulate matter with equivalent aerodynamic diameter less than 2.5μm)composition in Dunkerque,Northern France.Semi-diurnal PM_(2.5)samples were collected using a high volume sampler in winter 2010 and spring 2011 and were analyzed for trace metals,water-soluble ions,and total carbon using inductively coupled plasma–atomic emission spectrometry(ICP-AES),ICP-mass spectrometry(ICP-MS),ionic chromatography and micro elemental carbon analyzer.The elemental composition shows that NO_(3)^(-),SO_(4)^(2-),NH_4~+and total carbon are the main PM_(2.5)constituents.Trace metals data were interpreted using concentration roses and both influences of integrated steelworks and electric steel plant were evidenced.The distinction between the two sources is made possible by the use Zn/Fe and Zn/Mn diagnostic ratios.Moreover Rb/Cr,Pb/Cr and Cu/Cd combination ratio are proposed to distinguish the ISW-sintering stack from the ISW-fugitive emissions.The a priori knowledge on the influencing source was introduced in the CW-NMF to guide the calculation.Eleven source profiles with various contributions were identified:8 are characteristics of coastal urban background site profiles and 3 are related to the steelmaking activities.Between them,secondary nitrates,secondary sulfates and combustion profiles give the highest contributions and account for 93%of the PM_(2.5)concentration.The steelwork facilities contribute in about 2%of the total PM_(2.5)concentration and appear to be the main source of Cr,Cu,Fe,Mn,Zn.
基金supported by the National Key R&D Program of China,No.2021YFA0805200(to SY)the National Natural Science Foundation of China,No.31970954(to SY)two grants from the Department of Science and Technology of Guangdong Province,Nos.2021ZT09Y007,2020B121201006(both to XJL)。
文摘Spinal and bulbar muscular atrophy is a neurodegenerative disease caused by extended CAG trinucleotide repeats in the androgen receptor gene,which encodes a ligand-dependent transcription facto r.The mutant androgen receptor protein,characterized by polyglutamine expansion,is prone to misfolding and forms aggregates in both the nucleus and cytoplasm in the brain in spinal and bulbar muscular atrophy patients.These aggregates alter protein-protein interactions and compromise transcriptional activity.In this study,we reported that in both cultured N2a cells and mouse brain,mutant androgen receptor with polyglutamine expansion causes reduced expression of mesencephalic astrocyte-de rived neurotrophic factor.Overexpressio n of mesencephalic astrocyte-derived neurotrophic factor amelio rated the neurotoxicity of mutant androgen receptor through the inhibition of mutant androgen receptor aggregation.Conversely.knocking down endogenous mesencephalic astrocyte-derived neurotrophic factor in the mouse brain exacerbated neuronal damage and mutant androgen receptor aggregation.Our findings suggest that inhibition of mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor is a potential mechanism underlying neurodegeneration in spinal and bulbar muscular atrophy.
基金supported by the Public Health Research Project in Futian District,Shenzhen(Grant Nos.FTWS2020026,FTWS2021073).
文摘Background:Establishing an appropriate prognostic model for PCa is essential for its effective treatment.Glycolysis is a vital energy-harvesting mechanism for tumors.Developing a prognostic model for PCa based on glycolysis-related genes is novel and has great potential.Methods:First,gene expression and clinical data of PCa patients were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO),and glycolysis-related genes were obtained from the Molecular Signatures Database(MSigDB).Gene enrichment analysis was performed to verify that glycolysis functions were enriched in the genes we obtained,which were used in nonnegative matrix factorization(NMF)to identify clusters.The correlation between clusters and clinical features was discussed,and the differentially expressed genes(DEGs)between the two clusters were investigated.Based on the DEGs,we investigated the biological differences between clusters,including immune cell infiltration,mutation,tumor immune dysfunction and exclusion,immune function,and checkpoint genes.To establish the prognostic model,the genes were filtered based on univariable Cox regression,LASSO,and multivariable Cox regression.Kaplan–Meier analysis and receiver operating characteristic analysis validated the prognostic value of the model.A nomogram of the risk score calculated by the prognostic model and clinical characteristics was constructed to quantitatively estimate the survival probability for PCa patients in the clinical setting.Result:The genes obtained from MSigDB were enriched in glycolysis functions.Two clusters were identified by NMF analysis based on 272 glycolysis-related genes,and a prognostic model based on DEGs between the two clusters was finally established.The prognostic model consisted of LAMPS,SPRN,ATOH1,TANC1,ETV1,TDRD1,KLK14,MESP2,POSTN,CRIP2,NAT1,AKR7A3,PODXL,CARTPT,and PCDHGB2.All sample,training,and test cohorts from The Cancer Genome Atlas(TCGA)and the external validation cohort from GEO showed significant differences between the high-risk and low-risk groups.The area under the ROC curve showed great performance of this prognostic model.Conclusion:A prognostic model based on glycolysis-related genes was established,with great performance and potential significance to the clinical application.
文摘Rank determination issue is one of the most significant issues in non-negative matrix factorization (NMF) research. However, rank determination problem has not received so much emphasis as sparseness regularization problem. Usually, the rank of base matrix needs to be assumed. In this paper, we propose an unsupervised multi-level non-negative matrix factorization model to extract the hidden data structure and seek the rank of base matrix. From machine learning point of view, the learning result depends on its prior knowledge. In our unsupervised multi-level model, we construct a three-level data structure for non-negative matrix factorization algorithm. Such a construction could apply more prior knowledge to the algorithm and obtain a better approximation of real data structure. The final bases selection is achieved through L2-norm optimization. We implement our experiment via binary datasets. The results demonstrate that our approach is able to retrieve the hidden structure of data, thus determine the correct rank of base matrix.
文摘BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR)is a common polypectomy proce-dure in clinical practice,but it has a high postoperative recurrence rate.Currently,there is no predictive model for the recurrence of colorectal polyps after EMR.AIM To construct and validate a machine learning(ML)model for predicting the risk of colorectal polyp recurrence one year after EMR.METHODS This study retrospectively collected data from 1694 patients at three medical centers in Xuzhou.Additionally,a total of 166 patients were collected to form a prospective validation set.Feature variable screening was conducted using uni-variate and multivariate logistic regression analyses,and five ML algorithms were used to construct the predictive models.The optimal models were evaluated based on different performance metrics.Decision curve analysis(DCA)and SHapley Additive exPlanation(SHAP)analysis were performed to assess clinical applicability and predictor importance.RESULTS Multivariate logistic regression analysis identified 8 independent risk factors for colorectal polyp recurrence one year after EMR(P<0.05).Among the models,eXtreme Gradient Boosting(XGBoost)demonstrated the highest area under the curve(AUC)in the training set,internal validation set,and prospective validation set,with AUCs of 0.909(95%CI:0.89-0.92),0.921(95%CI:0.90-0.94),and 0.963(95%CI:0.94-0.99),respectively.DCA indicated favorable clinical utility for the XGBoost model.SHAP analysis identified smoking history,family history,and age as the top three most important predictors in the model.CONCLUSION The XGBoost model has the best predictive performance and can assist clinicians in providing individualized colonoscopy follow-up recommendations.
基金Supported by Government Assignment,No.1023022600020-6RSF Grant,No.24-15-00549Ministry of Science and Higher Education of the Russian Federation within the Framework of State Support for the Creation and Development of World-Class Research Center,No.075-15-2022-304.
文摘BACKGROUND Ischemic heart disease(IHD)impacts the quality of life and has the highest mortality rate of cardiovascular diseases globally.AIM To compare variations in the parameters of the single-lead electrocardiogram(ECG)during resting conditions and physical exertion in individuals diagnosed with IHD and those without the condition using vasodilator-induced stress computed tomography(CT)myocardial perfusion imaging as the diagnostic reference standard.METHODS This single center observational study included 80 participants.The participants were aged≥40 years and given an informed written consent to participate in the study.Both groups,G1(n=31)with and G2(n=49)without post stress induced myocardial perfusion defect,passed cardiologist consultation,anthropometric measurements,blood pressure and pulse rate measurement,echocardiography,cardio-ankle vascular index,bicycle ergometry,recording 3-min single-lead ECG(Cardio-Qvark)before and just after bicycle ergometry followed by performing CT myocardial perfusion.The LASSO regression with nested cross-validation was used to find the association between Cardio-Qvark parameters and the existence of the perfusion defect.Statistical processing was performed with the R programming language v4.2,Python v.3.10[^R],and Statistica 12 program.RESULTS Bicycle ergometry yielded an area under the receiver operating characteristic curve of 50.7%[95%confidence interval(CI):0.388-0.625],specificity of 53.1%(95%CI:0.392-0.673),and sensitivity of 48.4%(95%CI:0.306-0.657).In contrast,the Cardio-Qvark test performed notably better with an area under the receiver operating characteristic curve of 67%(95%CI:0.530-0.801),specificity of 75.5%(95%CI:0.628-0.88),and sensitivity of 51.6%(95%CI:0.333-0.695).CONCLUSION The single-lead ECG has a relatively higher diagnostic accuracy compared with bicycle ergometry by using machine learning models,but the difference was not statistically significant.However,further investigations are required to uncover the hidden capabilities of single-lead ECG in IHD diagnosis.
文摘BACKGROUND Stomal complications though small in early postoperative period,but poses significant morbidity,therapeutic challenge,delay in adjuvant treatment and sometimes even leads to mortality.Predictive model for early detection of stomal complications is important to improve the outcome.A model including patients and disease related factors,intraoperative surgical techniques and biochemical markers would be a better determinant to anticipate early stomal complications.Incorporation of emerging tools and technology such as artificial intelligence(AI),will further improve the prediction.AIM To identify various risk factors and models for prediction of early post operative stomal complications in colorectal cancer(CRC)surgery.METHODS Published literatures on early postoperative stomal complications in CRC surgery were systematically reviewed between 1995 and 2024 from online search engines PubMed and MEDLINE.RESULTS Twenty-four observational studies focused on identifying various risk factors for early post operative stomal complications in CRC surgery were analyzed.Stomal complications in CRC are influenced by several factors such as disease factors,patient-specific characteristics,and surgical techniques.There are some biomarkers and tools loke AI which may play significant roles in early detection.CONCLUSION Careful analysis of these factors,changes in biochemical parameters,and application of AI,a predictive model for stomal complications can be generated,to help in early detection,prompt action to achieve better outcomes.
基金supported by the National Natural Science Foundation of China(42222201,42288201)。
文摘Angiosperms experienced one of the most remarkable radiations of land plants and are now the dominant autotrophs in terrestrial ecosystems.Recent phylogenomic studies based on large-scale data from plastid,mitochondrial,or nuclear transcriptomes/genomes and increased taxon sampling have provided unprecedent resolution into the phylogeny of flowering plants.However,owing to ancient rapid radiations,the interrelationships among the five lineages of Mesangiospermae,the vast majority of angiosperms,remain contentious.Here we show that,although plastid and mitochondrial genomes lack sufficient phylogenetic signal for resolving deeper phylogeny,the relationships among five mesangiosperm lineages can be confidently resolved under better-fitting models using genome-scale data.According to our Bayesian cross-validation and model test in a maximum likelihood framework,siteheterogeneous models(e.g.,CAT-GTR+G4,LG+C20+F+G)outperform site-homogeneous or partition models often used in previous studies.Under site-heterogeneous models,the approximately unbiased test favored our preferred tree recovered from various datasets:Ceratophyllales(coontails)are robustly recovered as sister to monocots,and they together are sister to the clade comprising magnoliids,Chloranthales,and eudicots.Our phylogenomic analyses resolve the last enigma of the deeper phylogeny of angiosperms and emphasize the efficacy of modeling compositional heterogeneity in resolving rapid radiations of plants.
基金supported by the National Natural Science Foundation of China(62272078)Chongqing Natural Science Foundation(CSTB2023NSCQ-LZX0069)the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202300210)
文摘Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework.
基金Supported by 2024 Hospital-Level Research Start-up Fund,No.YK202426Suzhou Wujiang District"Science and Education for Health"Project,No.WWK202201Development Fund Project of the Affiliated Hospital of Xuzhou Medical University,No.XYFY202423.
文摘BACKGROUND Anxiety and depression are highly prevalent among patients with cervical cancer(CC).However,few studies have systematically analyzed the psychological effects of tumor stage,treatment methods,and related factors on these patients,or developed predictive models for these outcomes.AIM To identify factors influencing anxiety and depression in patients with CC and construct predictive models.METHODS We retrospectively analyzed data from 119 patients with CC treated at the Gynecology Department of Suzhou Ninth People’s Hospital between January 2017 and May 2025.Clinical data,psychological hope levels at diagnosis,and Self-Rating Anxiety Scale and Self-Rating Depression Scale scores during treatment were collected.Influencing factors were identified,and predictive models were developed.The model performance was evaluated using receiver operating characteristic(ROC)curves and the Hosmer-Lemeshow goodness-of-fit test.RESULTS During treatment,64.71%of the patients experienced anxiety and 52.10%experienced depression.Significant differences in family income,tumor stage,treatment modality,and hope level were observed between patients with and without anxiety/depression(P<0.05).Multivariate analysis showed that a family monthly income<5000 yuan,stage III-IV tumor,comprehensive treatment,and low hope level were independent risk factors(P<0.05).The predictive formula for anxiety was as follows:Logit(P)=0.795×monthly income+0.594×tumor stage+1.095×treatment method+1.184×hope level−9.176;for depression:Logit(P)=0.432×monthly income+0.518×tumor stage+0.727×treatment method+1.095×hope level−8.541.The area under the ROC curves were 0.865 for anxiety and 0.837 for depression.Goodness-of-fit test confirmed no overfitting(P>0.05).CONCLUSION Family income,tumor stage,treatment method,and hope level are key determinants of anxiety and depression in patients with CC.Predictive models incorporating these factors can effectively assess risk of anxiety and depression during treatment.
