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View article: Constructing epoxy polymer with significantly increased dielectric strength through molecular design by introducing deep trap
Constructing epoxy polymer with significantly increased dielectric strength through molecular design by introducing deep trap Open
The demand for epoxy resin (EP) with superior dielectric strength is critical in advanced power equipment. Here, we aimed to construct EP with enhanced dielectric strength via molecular design. Simulations indicated that substituting the C…
View article: The RC/HDL-c Ratio: A Superior Predictor of Coronary Artery Calcification Severity Compared to Remnant Cholesterol Alone
The RC/HDL-c Ratio: A Superior Predictor of Coronary Artery Calcification Severity Compared to Remnant Cholesterol Alone Open
Background We investigated the associations among remnant cholesterol (RC) and high-density lipoprotein cholesterol (HDL-c) with coronary artery calcification (CACS), quantified by noncontrast computed tomography, aiming to determine wheth…
View article: Achieving ultrahigh surface flashover voltage of epoxy resin in vacuum by ultraviolet irradiation
Achieving ultrahigh surface flashover voltage of epoxy resin in vacuum by ultraviolet irradiation Open
Surface flashover that occurs on the surface of epoxy resin (EP) is one of the main causes of insulation failure in the power system. The newly emerging polar groups on the surface are highly desirable for enhancing the surface flashover p…
View article: Development of epoxy resin with superior breakdown strength: A Review
Development of epoxy resin with superior breakdown strength: A Review Open
Epoxy resin (EP) has been widely utilized in electrical equipment and electronic devices due to its fascinating electric, thermal, and mechanical properties. However, the complex insulation structures of modern power devices in high-voltag…
View article: High-temperature dielectric with excellent capacitive performance enabled by rationally designed traps in blends
High-temperature dielectric with excellent capacitive performance enabled by rationally designed traps in blends Open
Polymer dielectrics with excellent capacitive performance are urgently needed in advanced electrical and electronic systems. However, due to the dramatic increase in the conduction loss, the energy density and efficiency of polymers degrad…
View article: Effect of electron beam irradiation on surface molecule and flashover voltage of epoxy composites
Effect of electron beam irradiation on surface molecule and flashover voltage of epoxy composites Open
The unsatisfactory insulating properties of solid–gas interfaces seriously restrict the development of high‐voltage electrical equipment and threaten their power supply stability. Electron‐beam irradiation (EBI) can effectively improve the…
View article: Tuning aggregation state in PTMA/PVP blends for high energy storage
Tuning aggregation state in PTMA/PVP blends for high energy storage Open
Dielectric capacitors supported by all-organic materials show great potentials in advanced electronic and electric devices. However, the contradiction between increases in dielectric constant and breakdown strength severely prevents the de…
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: Human Papillomavirus 16 E6 Promotes Angiogenesis of Lung Cancer by SNHG1
Human Papillomavirus 16 E6 Promotes Angiogenesis of Lung Cancer by SNHG1 Open
Human papillomavirus (HPV) is a risk factor for lung cancer. However, the mechanisms underlying is not known. Long noncoding RNAs (lncRNAs) have been found to play an important part in the occurrence and development of lung cancer due to t…
View article: Integrative Analyses of Circulating mRNA and lncRNA Expression Profile in Plasma of Lung Cancer Patients
Integrative Analyses of Circulating mRNA and lncRNA Expression Profile in Plasma of Lung Cancer Patients Open
Circulating-free RNAs (cfRNAs) have been regarded as potential biomarkers for “liquid biopsy” in cancers. However, the circulating messenger RNA (mRNA) and long noncoding RNA (lncRNA) profiles of lung cancer have not been fully characteriz…
View article: Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis
Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis Open
Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-c…
View article: Recent advances on polaprezinc for medical use (Review)
Recent advances on polaprezinc for medical use (Review) Open
The present study described the chemical and biological properties of zinc complex of L-carnosine (L-CAZ; generic name, polaprezinc; chemical name, catena-(S)-[µ-[N(α)-(3-aminopropionyl) histidinato (2-) N1, N2, O: N(τ)]-zinc], molecular f…
View article: Assessment of an Exhaled Breath Test Using High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry to Detect Lung Cancer
Assessment of an Exhaled Breath Test Using High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry to Detect Lung Cancer Open
This diagnostic study's results suggest that a breath test with HPPI-TOFMS is feasible and accurate for lung cancer detection, which may be useful for future lung cancer screenings.