Search

ABSTRACT: Duck hepatitis A virus type 1 (DHAV-1) can cause severe liver damage in infected ducklings and is a fatal and contagious pathogen that endangers the Chinese duck industry. The objective of this study was to explore the correlation mechanism of liver metabolism-gut microbiota in DHAV-1 infection. Briefly, liquid chromatography-mass spectrometry and 16S rDNA sequencing combined with multivariate statistical analysis were used to evaluate the effects of DHAV-1 infection on liver metabolism, gut microbiota regulation, and other potential mechanisms in ducklings. In DHAV-1-infected ducklings at 72 h postinfection, changes were found in metabolites associated with key metabolic pathways such as lipid metabolism, sugar metabolism, and nucleotide metabolism, which participated in signaling networks and ultimately affecting the function of the liver. The abundance and composition of gut microbiota were also changed, and gut microbiota is significantly involved in lipid metabolism in the liver. The evident correlation between gut microbiota and liver metabolites indicates that DHAV-host gut microbiome interactions play important roles in the development of duck viral hepatitis (DVH).

Background: Accurate, noninvasive, and reliable assessment of epidermal growth factor receptor (EGFR) mutation status and EGFR molecular subtypes is essential for treatment plan selection and individualized therapy in lung adenocarcinoma (LUAD). Radiomics models based on 18F‐FDG PET/CT have great potential in identifying EGFR mutation status and EGFR subtypes in patients with LUAD. The validation of multi‐center data, model visualization, and interpretation are significantly important for the management, application and trust of machine learning predictive models. However, few EGFR‐related research involved model visualization and interpretation, and multi‐center trial. Purpose: To develop explainable optimal predictive models based on handcrafted radiomics features (HRFs) extracted from multi‐center 18F‐FDG PET/CT to predict EGFR mutation status and molecular subtypes in LUAD. Methods: Baseline 18F‐FDG PET/CT images of 383 LUAD patients from three hospitals and one public data set were collected. Further, 1808 HRFs were extracted from the primary tumor regions using Pyradiomics. Predictive models were built based on cross‐combination of seven feature selection methods and seven machine learning algorithms. Yellowbrick and explainable artificial intelligence technology were used for model visualization and interpretation. Receiver operating characteristic curve, classification report and confusion matrix were used for model performance evaluation. Clinical applicability of the optimal models was assessed by decision curve analysis. Results: STACK feature selection method combined with light gradient boosting machine (LGBM) reached optimal performance in identifying EGFR mutation status ([area under the curve] AUC = 0.81 in the internal test cohort; AUC = 0.62 in the external test cohort). Random forest feature selection method combined with LGBM reached optimal performance in predicting EGFR mutation molecular subtypes (AUC = 0.89 in the internal test cohort; AUC = 0.61 in the external test cohort). Conclusions: Explainable machine learning models combined with radiomics features extracted from multi‐center/scanner 18F‐FDG PET/CT have certain potential to identify EGFR mutation status and subtypes in LUAD, which might be helpful to the treatment of LUAD. [ABSTRACT FROM AUTHOR]

ABSTRACT This study investigated the fatigue fracture mechanisms and life prediction of clinched joints made from titanium alloy TA1. The fatigue tests revealed that TA1 titanium alloy clinched joints exhibited failure characterized by fracture of the lower plate at three distinct fatigue load levels. Additionally, finite element analysis indicated that cold work hardening enhanced the fatigue performance of these joints. Observations of fracture surfaces using scanning electron microscopy identified the crack source and its propagation path, which correlated with the location of maximum principal stress from the finite element simulations. Fretting wear was also observed in this critical region. Furthermore, fatigue life predictions for TA1 titanium alloy clinched joints were made using Paris' law and the local strain approach. Both methods closely matched experimental results across different fatigue life intervals. Overall, the local strain approach exhibited superior predictive capability compared to Paris' law, taking into account various influencing factors. [ABSTRACT FROM AUTHOR]

Background: Accurate, noninvasive, and reliable assessment of epidermal growth factor receptor (EGFR) mutation status and EGFR molecular subtypes is essential for treatment plan selection and individualized therapy in lung adenocarcinoma (LUAD). Radiomics models based on 18 F-FDG PET/CT have great potential in identifying EGFR mutation status and EGFR subtypes in patients with LUAD. The validation of multi-center data, model visualization, and interpretation are significantly important for the management, application and trust of machine learning predictive models. However, few EGFR-related research involved model visualization and interpretation, and multi-center trial., Purpose: To develop explainable optimal predictive models based on handcrafted radiomics features (HRFs) extracted from multi-center 18 F-FDG PET/CT to predict EGFR mutation status and molecular subtypes in LUAD., Methods: Baseline 18 F-FDG PET/CT images of 383 LUAD patients from three hospitals and one public data set were collected. Further, 1808 HRFs were extracted from the primary tumor regions using Pyradiomics. Predictive models were built based on cross-combination of seven feature selection methods and seven machine learning algorithms. Yellowbrick and explainable artificial intelligence technology were used for model visualization and interpretation. Receiver operating characteristic curve, classification report and confusion matrix were used for model performance evaluation. Clinical applicability of the optimal models was assessed by decision curve analysis., Results: STACK feature selection method combined with light gradient boosting machine (LGBM) reached optimal performance in identifying EGFR mutation status ([area under the curve] AUC = 0.81 in the internal test cohort; AUC = 0.62 in the external test cohort). Random forest feature selection method combined with LGBM reached optimal performance in predicting EGFR mutation molecular subtypes (AUC = 0.89 in the internal test cohort; AUC = 0.61 in the external test cohort)., Conclusions: Explainable machine learning models combined with radiomics features extracted from multi-center/scanner 18 F-FDG PET/CT have certain potential to identify EGFR mutation status and subtypes in LUAD, which might be helpful to the treatment of LUAD., (© 2024 American Association of Physicists in Medicine.)