Impaired epithelial barrier function disrupts immune homeostasis and increases inflammation in intestines, leading to many intestinal diseases. Cathelicidin peptides suppress intestinal inflammation and improve intestinal epithelial barrier function independently of their antimicrobial activity. In this study, we investigated the effects of Cathelicidin-WA (CWA) on intestinal epithelial barrier function, as well as the underlying mechanism, by using enterohemorrhagic Escherichia coli (EHEC)-infected mice and intestinal epithelial cells. The results showed that CWA attenuated EHEC-induced clinical symptoms and intestinal colitis, as did enrofloxacin (Enro). CWA decreased IL-6 production in the serum, jejunum, and colon of EHEC-infected mice. Additionally, CWA alleviated the EHEC-induced disruption of mucin-2 and goblet cells in the intestine. Interestingly, CWA increased the mucus layer thickness, which was associated with increasing expression of trefoil factor 3, in the jejunum of EHEC-infected mice. CWA increased the expression of tight junction proteins in the jejunum of EHEC-infected mice. Using intestinal epithelial cells and a Rac1 inhibitor in vitro, we demonstrated that the CWA-mediated increases in the tight junction proteins might depend on the Rac1 pathway. Furthermore, CWA improved the microbiota and short-chain fatty acid concentrations in the cecum of EHEC-infected mice. Although Enro and CWA had similar effects on intestinal inflammation, CWA was superior to Enro with regard to improving intestinal epithelial barrier and microbiota in the intestine. In conclusion, CWA attenuated EHEC-induced inflammation, intestinal epithelial barrier damage, and microbiota disruption in the intestine of mice, suggesting that CWA may be an effective therapy for many intestinal diseases.

No abstract

No abstract

It is imperative to accurately predict the remaining useful life (RUL) of lithium-ion batteries to ensure the reliability and safety of related industries and facilities. In view of the noise sequence embedded in the measured aging data of lithium-ion batteries and the strong nonlinear characteristics of the aging process, this study proposes a method for predicting lithium-ion batteries’ RUL based on the wavelet threshold denoising and transformer model. To specify, firstly, the wavelet threshold denoising method is adopted to preprocess the measured discharging capacity data of lithium-ion batteries to eliminate some noise signals. Second, based on the denoised data, the transformer model output’s full connection layer is applied to replace the decoder layer for establishing the RUL prediction model of lithium-ion batteries. Finally, the discharging capacity of each charging–discharging cycle is predicted iteratively, and then the RUL of lithium-ion batteries can be calculated eventually. Two groups of lithium-ion batteries’ aging data from the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland and the laboratory at Anqing Normal University (AQNU) are employed to verify the proposed method, individually. The experimental results demonstrate that this method can overcome the impacts of data measurement noise, effectively predict the RUL of lithium-ion batteries, and present a sound generalization ability and high accuracy.

No abstract