No abstract

The objectives of this study were to investigate the transfer of aflatoxin from feed to milk and to evaluate the effects of Solis Mos (SM; Novus International Inc., St. Charles, MO) on milk aflatoxin M1, plasma biochemical parameters, and ruminal fermentation of dairy cows fed varying doses of aflatoxin B1 (AFB1). Three groups of 8 multiparous Holstein cows in late lactation (days in milk = 271 ± 29; milk yield = 21.6 ± 3.1 kg/d) were assigned to 1 of 3 experiments in a crossover design. Cows in experiment 1 received no aflatoxin, cows in experiment 2 received 20 µg of AFB1/kg of dry matter, and cows in experiment 3 received 40 µg of AFB1/kg of dry matter. Cows in each experiment were assigned to 1 of 2 treatments: control or 0.25% SM. Each experiment consisted of 2 consecutive periods with the first 4 d (d 1 to 4) as adaptation, followed by AFB1 challenge for 7 d (d 5 to 11), and finally clearance for 5 d (d 12 to 16) in each period. Samples of total mixed ration and milk were collected on d 1, 2, and 10 to 14 of each period. Blood samples were collected from the coccygeal vein on d 1, 11, and 14 of each period. Rumen fluid was collected by oral stomach tube 2 h after the morning feeding on d 1 and 11 of each period. Adding SM to basal or AFB1-contaminated diets at 0.25% had no effect on lactation performance, liver function, or immune response. However, addition of SM improved antioxidative status, as indicated by increased plasma concentrations of superoxide dismutase and reduced malondialdehyde regardless of dietary AFB1 level. Addition of SM to the AFB1-free diet eliminated the background AFM1 in milk and increased total ruminal volatile fatty acid (99.6 vs. 94.2 mM) concentrations. Adding SM to the AFB1-contaminated diet in experiment 2 decreased the AFM1 concentration (88.4 vs. 105.3 ng/L) and the transfer of aflatoxin to milk (0.46 vs. 0.56%), and increased total volatile fatty acid concentration (99.8 vs. 93.4 mM). Adding SM to diets with 40 µg/kg of AFB1 did not elicit changes in rumen parameters or AFM1 output. These results indicated that adding SM to diets containing 0 or 20 µg of AFB1/kg decreased milk AFM1 concentration, improved antioxidative status, and altered rumen fermentation, whereas adding SM to a diet containing 40 µg of AFB1/kg did not reduce AFB1 transfer but did increase the antioxidant status of the liver.

Background: We aim to analyze the ability to detect epithelial growth factor receptor (EGFR) mutations on chest CT images of patients with lung adenocarcinoma using radiomics and/or multi-level residual convolutionary neural networks (MCNNs). Methods: We retrospectively collected 1,010 consecutive patients in Shanghai Chest Hospital from 2013 to 2017, among which 510 patients were EGFR-mutated and 500 patients were wild-type. The patients were randomly divided into a training set (810 patients) and a validation set (200 patients) according to a balanced distribution of clinical features. The CT images and the corresponding EGFR status measured by Amplification Refractory Mutation System (ARMS) method of the patients in the training set were utilized to construct both a radiomics-based model (M Radiomics) and MCNNs-based model (M MCNNs). The M Radiomics and M MCNNs were combined to build the Model Radiomics+MCNNs (M Radiomics+MCNNs). Clinical data of gender and smoking history constructed the clinical features-based model (M Clinical). M Clinical was then added into M Radiomics , M MCNNs , and M Radiomics+MCNNs to establish the Model Radiomics+Clinical (M Radiomics+Clinical), the Model MCNNs+Clinical (M MCNNs+Clinical) and the Model Radiomics+MCNNs+Clinical (M Radiomics+MCNNs+Clinical). All the seven models were tested in the validation set to ascertain whether they were competent to detect EGFR mutations. The detection efficiency of each model was also compared in terms of area under the curve (AUC), sensitivity and specificity. Results: The AUC of the M Radiomics , M MCNNs and M Radiomics+MCNNs to predict EGFR mutations was 0.740, 0.810 and 0.811 respectively. The performance of M MCNNs was better than that of M Radiomics (P=0.0225). The addition of clinical features did not improve the AUC of the M Radiomics (P=0.623), the M MCNNs (P=0.114) and the M Radiomics+MCNNs (P=0.058). The M Radiomics+MCNNs+Clinical demonstrated the highest AUC value of 0.834. The M MCNNs did not demonstrate any inferiority when compared with the M Radiomics+MCNNs (P=0.742) and the M Radiomics+MCNNs+Clinical (P=0.056). Conclusions: Both of the M Radiomics and the M CNNs could predict EGFR mutations on CT images of patients with lung adenocarcinoma. The M MCNNs outperformed the M Radiomics in the detection of EGFR mutations. The combination of these two models, even added with clinical features, is not significantly more efficient than M MCNNs alone.