Yaxiao Zhang
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View article: A novel minimally invasive fixation method for flail chest management in a Canine model: an animal research
A novel minimally invasive fixation method for flail chest management in a Canine model: an animal research Open
View article: Retraction Note: MiR-519d-3p enhances the sensitivity of non-small-cell lung cancer to tyrosine kinase inhibitors
Retraction Note: MiR-519d-3p enhances the sensitivity of non-small-cell lung cancer to tyrosine kinase inhibitors Open
View article: Supplementary file from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary file from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
Supplementary file
View article: Supplementary Figure S5 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary Figure S5 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
The generalizability of PKU-M model in prediction of SPNs
View article: Supplementary Tables from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary Tables from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
Supplementary Tables
View article: Supplementary Figure S1 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary Figure S1 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
ROC curves of each center in the independent validation cohort
View article: Supplementary Figure S6 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary Figure S6 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
Illustration of the CADx product (RX)
View article: Supplementary file from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary file from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
Supplementary file
View article: Supplementary Figure S3 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary Figure S3 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
ROC curves of each center in the prospective comparison cohort
View article: Data from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Data from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
Purpose:Nodule evaluation is challenging and critical to diagnose multiple pulmonary nodules (MPNs). We aimed to develop and validate a machine learning–based model to estimate the malignant probability of MPNs to guide decision-making.Exp…
View article: Supplementary Figure S4 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary Figure S4 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
Risk category judged by clinicians
View article: Supplementary Figure S1 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary Figure S1 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
ROC curves of each center in the independent validation cohort
View article: Supplementary Figure S5 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary Figure S5 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
The generalizability of PKU-M model in prediction of SPNs
View article: Supplementary Figure S4 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary Figure S4 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
Risk category judged by clinicians
View article: Supplementary Tables from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary Tables from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
Supplementary Tables
View article: Data from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Data from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
Purpose:Nodule evaluation is challenging and critical to diagnose multiple pulmonary nodules (MPNs). We aimed to develop and validate a machine learning–based model to estimate the malignant probability of MPNs to guide decision-making.Exp…
View article: Supplementary Figure S2 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary Figure S2 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
Comparison of AUCs between PKU-M model, Brock model, PKU model, Mayo model, and VA model in subgroups
View article: Supplementary Figure S3 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary Figure S3 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
ROC curves of each center in the prospective comparison cohort
View article: Supplementary Figure S6 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary Figure S6 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
Illustration of the CADx product (RX)
View article: Supplementary Figure S2 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Supplementary Figure S2 from Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
Comparison of AUCs between PKU-M model, Brock model, PKU model, Mayo model, and VA model in subgroups
View article: A Novel Minimally Invasive Fixation Method for Flail Chest Management in a Canine Model: An Animal Research
A Novel Minimally Invasive Fixation Method for Flail Chest Management in a Canine Model: An Animal Research Open
Background: Multiple rib fractures can lead to flail chest with up to 35% mortality rate due to severe pulmonary complications. Current treatments of flail chest remain controversial. Studies have shown that surgical treatments can improve…
View article: Additional file 1 of A novel minimally invasive fixation method for flail chest management in a Canine model: an animal research
Additional file 1 of A novel minimally invasive fixation method for flail chest management in a Canine model: an animal research Open
Supplementary Material 1
View article: Circ-ATIC Serves as a Sponge of miR-326 to Accelerate Esophageal Squamous Cell Carcinoma Progression by Targeting ID1
Circ-ATIC Serves as a Sponge of miR-326 to Accelerate Esophageal Squamous Cell Carcinoma Progression by Targeting ID1 Open
In the previous studies, circular RNA (circRNA) has been shown to be closely related to the occurrence and development of various cancers. However, the role and mechanism of circ-ATIC in the progression of esophageal squamous cell carcinom…
View article: Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts Open
Purpose: Nodule evaluation is challenging and critical to diagnose multiple pulmonary nodules (MPNs). We aimed to develop and validate a machine learning–based model to estimate the malignant probability of MPNs to guide decision-making. E…
View article: LncRNA DANCR Promotes Lung Cancer by Sequestering miR-216a
LncRNA DANCR Promotes Lung Cancer by Sequestering miR-216a Open
Background: Long noncoding RNAs (lncRNAs) are a new class of cancer regulators. Here, we aimed to investigate the diagnostic and therapeutic values of an lncRNA, differentiation antagonizing noncoding RNA (DANCR), in lung cancer. Methods: …
View article: MicroRNA-124 suppresses proliferation and glycolysis in non–small cell lung cancer cells by targeting AKT–GLUT1/HKII
MicroRNA-124 suppresses proliferation and glycolysis in non–small cell lung cancer cells by targeting AKT–GLUT1/HKII Open
Non–small cell lung cancer accounts for 85% of all types of lung cancer and is the leading cause of worldwide cancer-associated mortalities. MiR-124 is epigenetically silenced in various types of cancer and plays important roles in tumor d…
View article: Consensus of High-Order Linear Multiagent Systems with Multitype Switching Topologies Based on the Dynamic Dwell Time Approach
Consensus of High-Order Linear Multiagent Systems with Multitype Switching Topologies Based on the Dynamic Dwell Time Approach Open
This paper investigates the consensus problem of high-order continuous-time linear multiagent systems (LMASs) with multitype switching topologies which include both consensusable and unconsensusable communication topologies. A linear trans…
View article: Endothelial PAS domain-containing protein 1 confers TKI-resistance by mediating EGFR and MET pathways in non-small cell lung cancer cells
Endothelial PAS domain-containing protein 1 confers TKI-resistance by mediating EGFR and MET pathways in non-small cell lung cancer cells Open
Mutations in epidermal growth factor receptor (EGFR) rendering it constitutively active is one of the major causes for metastatic non-small-cell lung cancer (NSCLC), and EGFR-targeted therapies utilizing tyrosine kinase inhibitors (TKIs) a…