Objective:Little progress has been made in recent years using first-line chemotherapy,including gemcitabine combined with nab-paclitaxel,FOLFIRINOX,and NALIRIFOX,for advanced pancreatic adenocarcinoma(APC).In addition...Objective:Little progress has been made in recent years using first-line chemotherapy,including gemcitabine combined with nab-paclitaxel,FOLFIRINOX,and NALIRIFOX,for advanced pancreatic adenocarcinoma(APC).In addition,the optimal second-line chemotherapy regimen has not been determined.This study aimed to compare the effectiveness of different types of second-line chemotherapy for APC.Methods:Patients with APC who received first-line treatment from January 2008 to January 2021 were considered eligible for this retrospective analysis.The primary and secondary endpoints were overall survival(OS)and progression-free survival(PFS),respectively.Results:Four hundred and thirty-seven and 617 patients were treated with 5-fluorouracil-and gemcitabine-based chemotherapy as first-line treatment,respectively.Demographic and clinical features,except age and liver metastasis,were comparable between the two groups(P<0.05).The median OS was 8.8 and 7.8 months in patients who received a 5-fluorouracil-and gemcitabine-based combined regimen for first-line therapy,respectively(HR=1.244,95%CI=1.090–1.419;P<0.001).The median OS was 5.6 and 1.9 months in patients who received second-line chemotherapy and supportive care,respectively(HR=0.766,95%CI=0.677–0.867;P<0.001).The median PFS was not significantly differently between gemcitabine or 5-fluorouracil monotherapy and combination therapy.Conclusions:A 5-fluorouracil-or gemcitabine-based combined regimen was shown to be as effective as a single 5-fluorouracil or gemcitabine regimen as second-line therapy for patients with APC.展开更多
The coarse grained(CG)model implements the molecular dynamics simulation by simplifying atom properties and interaction between them.Despite losing certain detailed information,the CG model is still the first-thought ...The coarse grained(CG)model implements the molecular dynamics simulation by simplifying atom properties and interaction between them.Despite losing certain detailed information,the CG model is still the first-thought option to study the large molecule in long time scale with less computing resource.The deep learning model mainly mimics the human studying process to handle the network input as the image to achieve a good classification and regression result.In this work,the TorchMD,a MD framework combining the CG model and deep learning model,is applied to study the protein folding process.In 3D collective variable(CV)space,the modified find density peaks algorithm is applied to cluster the conformations from the TorchMD CG simulation.The center conformation in different states is searched.And the boundary conformations between clusters are assigned.The string algorithm is applied to study the path between two states,which are compared with the end conformations from all atoms simulations.The result shows that the main phenomenon of protein folding with TorchMD CG model is the same as the all-atom simulations,but with a less simulating time scale.The workflow in this work provides another option to study the protein folding and other relative processes with the deep learning CG model.展开更多
Performing cluster analysis on molecular conformation is an important way to find the representative conformation in the molecular dynamics trajectories.Usually,it is a critical step for interpreting complex conformat...Performing cluster analysis on molecular conformation is an important way to find the representative conformation in the molecular dynamics trajectories.Usually,it is a critical step for interpreting complex conformational changes or interaction mechanisms.As one of the density-based clustering algorithms,find density peaks(FDP)is an accurate and reasonable candidate for the molecular conformation clustering.However,facing the rapidly increasing simulation length due to the increase in computing power,the low computing efficiency of FDP limits its application potential.Here we propose a marginal extension to FDP named K-means find density peaks(KFDP)to solve the mass source consuming problem.In KFDP,the points are initially clustered by a high efficiency clustering algorithm,such as K-means.Cluster centers are defined as typical points with a weight which represents the cluster size.Then,the weighted typical points are clustered again by FDP,and then are refined as core,boundary,and redefined halo points.In this way,KFDP has comparable accuracy as FDP but its computational complexity is reduced from O(n^(2))to O(n).We apply and test our KFDP method to the trajectory data of multiple small proteins in terms of torsion angle,secondary structure or contact map.The comparing results with K-means and density-based spatial clustering of applications with noise show the validation of the proposed KFDP.展开更多
基金This work was supported by the National Key Research and Development Program of China(Grant No.2021YFA1201100)the National Natural Science Foundation of China(Grant No.82072657).
文摘Objective:Little progress has been made in recent years using first-line chemotherapy,including gemcitabine combined with nab-paclitaxel,FOLFIRINOX,and NALIRIFOX,for advanced pancreatic adenocarcinoma(APC).In addition,the optimal second-line chemotherapy regimen has not been determined.This study aimed to compare the effectiveness of different types of second-line chemotherapy for APC.Methods:Patients with APC who received first-line treatment from January 2008 to January 2021 were considered eligible for this retrospective analysis.The primary and secondary endpoints were overall survival(OS)and progression-free survival(PFS),respectively.Results:Four hundred and thirty-seven and 617 patients were treated with 5-fluorouracil-and gemcitabine-based chemotherapy as first-line treatment,respectively.Demographic and clinical features,except age and liver metastasis,were comparable between the two groups(P<0.05).The median OS was 8.8 and 7.8 months in patients who received a 5-fluorouracil-and gemcitabine-based combined regimen for first-line therapy,respectively(HR=1.244,95%CI=1.090–1.419;P<0.001).The median OS was 5.6 and 1.9 months in patients who received second-line chemotherapy and supportive care,respectively(HR=0.766,95%CI=0.677–0.867;P<0.001).The median PFS was not significantly differently between gemcitabine or 5-fluorouracil monotherapy and combination therapy.Conclusions:A 5-fluorouracil-or gemcitabine-based combined regimen was shown to be as effective as a single 5-fluorouracil or gemcitabine regimen as second-line therapy for patients with APC.
基金supported by the National Natural Science Foundation of China(No.31800615 and No.21933010)。
文摘The coarse grained(CG)model implements the molecular dynamics simulation by simplifying atom properties and interaction between them.Despite losing certain detailed information,the CG model is still the first-thought option to study the large molecule in long time scale with less computing resource.The deep learning model mainly mimics the human studying process to handle the network input as the image to achieve a good classification and regression result.In this work,the TorchMD,a MD framework combining the CG model and deep learning model,is applied to study the protein folding process.In 3D collective variable(CV)space,the modified find density peaks algorithm is applied to cluster the conformations from the TorchMD CG simulation.The center conformation in different states is searched.And the boundary conformations between clusters are assigned.The string algorithm is applied to study the path between two states,which are compared with the end conformations from all atoms simulations.The result shows that the main phenomenon of protein folding with TorchMD CG model is the same as the all-atom simulations,but with a less simulating time scale.The workflow in this work provides another option to study the protein folding and other relative processes with the deep learning CG model.
基金Professor Hong Yu at Intelligent Fishery Innovative Team(No.C202109)in School of Information Engineering of Dalian Ocean University for her support of this workfunded by the National Natural Science Foundation of China(No.31800615 and No.21933010)。
文摘Performing cluster analysis on molecular conformation is an important way to find the representative conformation in the molecular dynamics trajectories.Usually,it is a critical step for interpreting complex conformational changes or interaction mechanisms.As one of the density-based clustering algorithms,find density peaks(FDP)is an accurate and reasonable candidate for the molecular conformation clustering.However,facing the rapidly increasing simulation length due to the increase in computing power,the low computing efficiency of FDP limits its application potential.Here we propose a marginal extension to FDP named K-means find density peaks(KFDP)to solve the mass source consuming problem.In KFDP,the points are initially clustered by a high efficiency clustering algorithm,such as K-means.Cluster centers are defined as typical points with a weight which represents the cluster size.Then,the weighted typical points are clustered again by FDP,and then are refined as core,boundary,and redefined halo points.In this way,KFDP has comparable accuracy as FDP but its computational complexity is reduced from O(n^(2))to O(n).We apply and test our KFDP method to the trajectory data of multiple small proteins in terms of torsion angle,secondary structure or contact map.The comparing results with K-means and density-based spatial clustering of applications with noise show the validation of the proposed KFDP.
