Antibiotics affect gut microbial composition, leading to Gut–Brain-Axis imbalance and neurobehavioral changes. However, the intestinal dysbacteriosis associated behavior changes are not consistently reported. It is not clear whether these changes are transient or permanent. The neuroprotective effect of probiotics against intestinal dysbacteriosis induced alternations needs to be determined either. In the present study, oral antibiotic mixture including Ampicillin, Streptomycin, and Clindamycin was utilized to induce intestinal dysbacteriosis in mice. Antibiotics application triggered mechanical allodynia in von frey test and spontaneous pain in open field test. It also resulted in increased anxiety and depressive-like behaviors and damaged spatial memory performance. After application of probiotics, the mechanical allodynia and spontaneous pain were alleviated significantly. The anxiety behaviors, depressive-like behaviors and recognitive performance were ameliorative as well. By using Fos protein as a marker, it is found that the sensory, emotion and memory related brain regions were activated in mice with intestinal dysbacteriosis. Our study is not only helpful for enriching our basic knowledge for understanding the changed pain responses and related brain disorders in antibiotics-induced dysbacteriosis mice, but also beneficial for providing a more comprehensive mechanistic explanation for the regulation of antibiotics and probiotics on gut microbiota and relevant alternations in animal neurological behaviors.
Concerns about the environmental impacts of used and discarded products have recently led to enactment of laws that regulate the amounts of hazardous substances and recyclable content in products. The laws also make the Original Equipment Manufacturers (OEMs) responsible for recovery and proper treatment of these end-of-life products. In this two part paper, we present methodologies for OEMs to use the Product Lifecycle Management (PLM) framework to effectively meet the challenges posed by these regulations. In this second part, we outline a methodology that can enable case-by-case selection of the treatment strategy for incoming end-of-life products. To extract product information required for this selection, we develop rule-based heuristics for identification of joints directly from CAD assembly models, along with their type, size, location and orientation.
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