Lactobacillus plantarum 24-7 improves postoperative bloating and hard stools by modulating intestinal microbiota in patients with congenital heart disease: a randomized controlled trial.

Gastrointestinal symptoms are a common postoperative complication in patients with congenital heart disease (CHD), affecting their postoperative recovery. Probiotic intervention may be a promising therapeutic approach to alleviate postoperative gastrointestinal symptoms. This study aimed to evaluate the potential of Lactobacillus plantarum 24-7 (L. plantarum 24-7) in mitigating postoperative gastrointestinal symptoms and promoting patient recovery. Adult CHD patients scheduled for surgical intervention were recruited. One hundred and twenty patients were randomized and received L. plantarum or placebo intervention twice daily for ten days. Gastrointestinal symptoms were assessed utilizing the Gastrointestinal Symptom Rating Scale (GSRS). Various postoperative variables were analyzed across both groups. Alterations in gut microbiota were evaluated through 16S rRNA sequencing. 112 patients completed the study, with 55 in the probiotic group and 57 in the placebo group. While the disparity in overall postoperative GSRS scores between the two groups did not reach statistical significance (P = 0.067), marked differences were observed in bloating (P = 0.004) and hard stool (P = 0.030) scores. Furthermore, individuals within the probiotic group exhibited lower postoperative neutrophil counts (P = 0.007) and concurrently higher lymphocyte counts (P = 0.001). Variations in the diversity and composition of postoperative gut microbiota were discerned between the probiotic and placebo groups. Remarkably, no probiotic-related adverse events were documented. Supplementation with L. plantarum was well-tolerated and demonstrated partial efficacy in ameliorating gastrointestinal symptoms in postoperative CHD patients. Modulating the gut microbiota may be a potential mechanism by which L. plantarum exerts clinical benefits.

Gap Measurement of Point Machine Using Adaptive Wavelet Threshold and Mathematical Morphology.

A point machine's gap is an important indication of its healthy status. An edge detection algorithm is proposed to measure and calculate a point machine's gap from the gap image captured by CCD plane arrays. This algorithm integrates adaptive wavelet-based image denoising, locally adaptive image binarization, and mathematical morphology technologies. The adaptive wavelet-based image denoising obtains not only an optimal denoising threshold, but also unblurred edges. Locally adaptive image binarization has the advantage of overcoming the local intensity variation in gap images. Mathematical morphology may suppress speckle spots caused by reflective metal surfaces in point machines. The subjective and objective evaluations of the proposed method are presented by using point machine gap images from a railway corporation in China. The performance between the proposed method and conventional edge detection methods has also been compared, and the result shows that the former outperforms the latter.