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Abstract Background Coccidiosis, a prominent intestinal protozoan disease, carries significant economic implications for the poultry industry. The aim of this study was to evaluate the effects of Fengqiang Shengtai (BLES), a probiotics product, and coccidiosis vaccine in modulating the intestinal microbiome and providing insight into mitigating the occurrence and management of avian coccidiosis. Methods Broilers included in the study were divided into four pre-treatment groups: the Pre-Con group (commercial diet), Pre-BLES group (BLES supplement), Pre-Vac group (coccidiosis vaccination) and Pre-Vac-BLES group (combined vaccination and BLES). Body weight gain, feed consumption and feed conversion ratio were monitored from age 25 to 55 days. Cecum contents were collected at 8 and 15 days of age for comparative analysis of intestinal microbiomes. In the Pre-BLES and Pre-Vac-BLES groups, probiotics were administered at a dose of 0.01 g per chicken between ages 3 to 6 days and 10–13 days. At 3 days of age, chickens in the Pre-Vac and Pre-Vac-BLES groups were vaccinated with 1700 sporulated oocysts of the live coccidiosis vaccine per chicken. At the age of 25 days, Eimeria spp. challenge experiments were performed based on the aforementioned immunization strategy, and the oocysts per gram (OPG) in the feces, intestinal lesion score and intestinal pathological characteristics were evaluated. Specifically, 30 chickens were randomly selected from each group and orally administered 34,000 sporulated oocysts of Eimeria spp. per chicken, re-defined as Eimeria group, BLES-Eimeria group, Vac-Eimeria group and Vac-BLES-Eimeria group, respectively. Additionally, 30 chickens were randomly selected from the Pre-Con group and included as negative control without Eimeria spp. challenge. Intestinal microbiota was sequenced and analyzed when the broilers were 32 days old. Results A significant improvement was observed in body weight gain of the broilers in the Pre-BLES and Pre-Vac-BLES group at 45 days of age. Analysis of the intestinal microbiota revealed a positive correlation between the experimental groups receiving BLES and coccidiosis vaccines at 8 and 15 days of age with the Enterococcus genus and Lachnospiraceae NK4A136 group, respectively. In addition to the reduced lesion score and OPG values, the combination of coccidiosis vaccine and BLES also reduced the intestinal epithelial abscission induced by coccidiosis vaccines. The results of intestinal microbial function prediction demonstrated that N-glycan biosynthesis and ferroptosis were the prominent signal pathways in the Vac-BLES-Eimeria group. Conclusions Taken together, the results of the present study suggest that supplementation of BLES with coccidiosis vaccine represents a promising strategy for improving growth performance, alleviating clinical manifestations and inducing favorable alterations to the intestinal microbiota in broiler chickens affected by coccidiosis.

Abstract In this study, the self-extracted constipation treatment of traditional Chinese medicine extracts was applied to constipated rats. To explore the mechanism and role of the Chinese medicine for the treatment of constipation, the 16S rRNA sequencing and qRT-PCR technology were used to analyze the intestinal flora. We found that the relative abundance of Firmicutes with constipation was significantly higher accounted for 86.7%, while the gut microbiota was significantly changed after taking a certain dose of Chinese medicine, greatly increased the relative abundance of Lactobacillus accounted for 23.1%, enhanced the symbiotic relationships of Lactobacillus with other intestinal flora. The total copies of intestinal bacteria in the constipated rats decreased after taking the traditional Chinese medicine. Finally, this study results provides a theoretical basis for the treatment and understand the mechanism and effect of traditional Chinese medicine on rate constipation.

Abstract Background Recently, deep learning technologies have rapidly expanded into medical image analysis, including both disease detection and classification. As far as we know, migraine is a disabling and common neurological disorder, typically characterized by unilateral, throbbing and pulsating headaches. Unfortunately, a large number of migraineurs do not receive the accurate diagnosis when using traditional diagnostic criteria based on the guidelines of the International Headache Society. As such, there is substantial interest in developing automated methods to assist in the diagnosis of migraine. Methods To the best of our knowledge, no studies have evaluated the potential of deep learning technologies in assisting with the classification of migraine patients. Here, we used deep learning methods in combination with three functional measures (the amplitude of low-frequency fluctuations, regional homogeneity and regional functional correlation strength) based on rs-fMRI data to distinguish not only between migraineurs and healthy controls, but also between the two subtypes of migraine. We employed 21 migraine patients without aura, 15 migraineurs with aura, and 28 healthy controls. Results Compared with the traditional support vector machine classifier, which has an accuracy of 83.67%, our Inception module-based convolutional neural network approach showed a significant improvement in classification output (over 86.18%). Our data also indicate that the Inception module-based CNN performs better than the AlexNet-based CNN (Inception module-based CNN reached an accuracy of 99.25%). Finally, we also found that regional functional correlation strength (RFCS) could be regarded as the optimum input out of the three indices (ALFF, ReHo, RFCS). Conclusions Overall, our study shows that combining the three functional measures of rs-fMRI with deep learning classification is a powerful method to distinguish between migraineurs and healthy individuals. Our data also highlight that deep learning-based frameworks could be used to develop more complicated models or systems to aid in clinical decision making in the future.