The Influence of Dietary Gallic Acid on Growth Performance and Plasma Antioxidant Status of High and Low Weaning Weight Piglets.

peer reviewedThis study evaluated the effects of dietary gallic acid (GA) on growth performance, diarrhea incidence and plasma antioxidant status of weaned piglets regardless of whether weaning weight was high or low. A total of 120 weaned piglets were randomly allocated to four treatments in a 42-day experiment with a 2 × 2 factorial treatment arrangement comparing different weaning weights (high weight (HW) or low weight (LW), 8.49 ± 0.18 kg vs. 5.45 ± 0.13 kg) and dietary treatment (without supplementation (CT) or with supplementation of 400 mg/kg of GA). The results showed that HW piglets exhibited better growth performance and plasma antioxidant capacity. Piglets supplemented with GA had higher body weight (BW) on day 42 and average daily gain (ADG) from day 0 to 42 compared to the control piglets, which is mainly attributed to the specific improvement on BW and ADG of LW piglets by the supplementation of GA. The decreased values of diarrhea incidence were seen in piglets fed GA, more particularly in LW piglets. In addition, dietary GA numerically reduced malondialdehyde (MDA) content in plasma of LW piglets. In conclusion, our study suggests that dietary GA may especially improve the growth and health in LW weaned piglets

T-SPOT with CT image analysis based on deep learning for early differential diagnosis of nontuberculous mycobacteria pulmonary disease and pulmonary tuberculosis

Objectives: This study aimed to establish a diagnostic algorithm combining T-SPOT with computed tomography image analysis based on deep learning (DL) for early differential diagnosis of nontuberculous mycobacteria pulmonary disease (NTM-PD) and pulmonary tuberculosis (PTB). Methods: A total of 1049 cases were enrolled, including 467 NTM-PD and 582 PTB cases. A total of 320 cases (160 NTM-PD and 160 PTB) were randomized as the testing set and were analyzed using T-SPOT combined with the DL model. The testing cases were first divided into T-SPOT-positive and -negative groups, and the DL model was then used to separate the cases into four subgroups further. Results: The precision was found to be 91.7% for the subgroup of T-SPOT-negative and DL classified as NTM-PD, and 89.8% for T-SPOT-positive and DL classified as PTB, which covered 66.9% of the total cases, compared with the accuracy rate of 80.3% of T-SPOT alone. In the other two remaining groups, where the T-SPOT prediction was inconsistent with the DL model, the accuracy was 73.0% and 52.2%, separately. Conclusion: Our study shows that the new diagnostic system combining T-SPOT with DL based computed tomography image analysis can greatly improve the classification precision of NTM-PD and PTB when the two methods of prediction are consistent