Weaning is considered to be one of the most critical periods in pig production, which is related to the economic benefits of pig farms. However, in actual production, many piglets are often subjected to weaning stress due to the sudden separation from the sow, the changes in diet and living environment, and other social challenges. Weaning stress often causes changes in the morphology and function of the small intestine of piglets, disrupts digestion and absorption capacity, destroys intestinal barrier function, and ultimately leads to reduced feed intake, increased diarrhea rate, and growth retardation. Therefore, correctly understanding the effects of weaning stress on intestinal health have important guiding significance for nutritional regulation of intestinal injury caused by weaning stress. In this review, we mainly reviewed the effects of weaning stress on the intestinal health of piglets, from the aspects of intestinal development, and intestinal barrier function, thereby providing a theoretical basis for nutritional strategies to alleviate weaning stress in mammals in future studies.

The aim of the present study was to investigate the effects of intrauterine growth retardation (IUGR) on the intestinal morphology, intestinal epithelial cell apoptosis, intestinal antioxidant capacity, intestinal glucose absorption capacity, and intestinal barrier function of piglets during the suckling period. A total of eight normal-birth-weight (NBW) piglets and eight IUGR newborn piglets (Duroc × Landrace × Yorkshire) were selected from eight litters, one NBW and one IUGR newborn piglet per litter. In each litter, piglets with birth weight of 1.54 ± 0.04   kg (within one SD of the mean birth weight) were selected as NBW piglets and piglets with birth weight of 0.82 ± 0.03   kg (two SD below the mean birth weight) were selected as IUGR piglets. At 21 days of age, all piglets were killed by exsanguinations for sampling. The results showed the body weight (BW) of IUGR piglets on day 0, day 7, day 14, and day 21, and the body weight gain (BWG) of IUGR piglets was significantly lower than that of NBW piglets. IUGR piglets exhibited impaired intestinal morphology, raised enterocyte apoptosis, and increased oxidative damage. It showed that IUGR leads to a lower antioxidant capacity and glucose absorption in the jejunum. In accordance, IUGR caused the intestinal barrier dysfunction by impairing tight junctions and increasing intestinal inflammatory injury. Collectively, these results add to our understanding that IUGR affects intestinal health of suckling piglets via altering intestinal antioxidant capacity, glucose uptake, tight junction, and immune responses, and the slow growth of piglets with IUGR may be associated with intestinal injury.

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) magnetic nanocomposites were prepared and then applied in the Cu(II) removal from aqueous solutions. Scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy and superconduction quantum interference device magnetometer were performed to characterize the nZVI/rGO nanocomposites. In order to reduce the number of experiments and the economic cost, response surface methodology (RSM) combined with artificial intelligence (AI) techniques, such as artificial neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), has been utilized as a major tool that can model and optimize the removal processes, because a tremendous advance has recently been made on AI that may result in extensive applications. Based on RSM, ANN-GA and ANN-PSO were employed to model the Cu(II) removal process and optimize the operating parameters, e.g., operating temperature, initial pH, initial concentration and contact time. The ANN-PSO model was proven to be an effective tool for modeling and optimizing the Cu(II) removal with a low absolute error and a high removal efficiency. Furthermore, the isotherm, kinetic, thermodynamic studies and the XPS analysis were performed to explore the mechanisms of Cu(II) removal process.

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Reduced graphene oxide-supported Fe3O4 (Fe3O4/rGO) composites were applied in this study to remove low-concentration mercury from aqueous solutions with the aid of an artificial neural network (ANN) modeling and genetic algorithm (GA) optimization. The Fe3O4/rGO composites were prepared by the solvothermal method and characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), atomic force microscopy (AFM), N2-sorption, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR) and superconduction quantum interference device (SQUID). Response surface methodology (RSM) and ANN were employed to model the effects of different operating conditions (temperature, initial pH, initial Hg ion concentration and contact time) on the removal of the low-concentration mercury from aqueous solutions by the Fe3O4/rGO composites. The ANN-GA model results (with a prediction error below 5%) show better agreement with the experimental data than the RSM model results (with a prediction error below 10%). The removal process of the low-concentration mercury obeyed the Freudlich isotherm and the pseudo-second-order kinetic model. In addition, a regeneration experiment of the Fe3O4/rGO composites demonstrated that these composites can be reused for the removal of low-concentration mercury from aqueous solutions.