Objective:To investigate effect of Yiqi Huoxue Huazhuo Jiedu Prescription(YHHJP)on inflammatory factors of brain tissues,tight junction between brain and colon tissues,intestinal flora and bacterial metabolites in cerebral ischemia reperfusion injury(CIRI)rats based on brain-gut axis.Methods:Fifty male SD rats of SPF grade were randomly divided into sham-operation group(Sham),model group(MCAO),low,medium,high doses YHHJP groups(TCM-L/TCM-M/TCM-H).Middle cerebral artery occlusion model was established according to Zea Longa methods.Neurological function defects were detected 3 days after administra-tion.TTC staining was used to calculate infarct size of brain tissue.Pathological changes of brain tissue were observed by Nissl staining,and pathological changes of brain and colon tissues were observed by HE staining.IL-1β,IL-6,TNF-α in brain tissue and LPS con-tent in serum were detected by ELISA,and D-LA content in serum was detected by biochemical method.Gene expressions of ZO-1 and Claudin-5 in brain tissue and gene expressions of ZO-1,Claudin-1 in colon tissue were studied by Real-time fluorescent quantita-tive PCR.Intestinal flora were detected by 16S rDNA high-throughput sequencing.Results:Compared with Sham group,pathological damage of brain and colon tissue were serious in MCAO group,intestinal flora structure was significantly different,neural function im-pairment was aggravated,infarct size was increased,IL-1β,IL-6,TNF-α contents in brain tissue,and LPS,D-LA contents in serum were increased,gene expressions of ZO-1 and Claudin-5 in brain tissue and gene expressions of ZO-1 and Claudin-1 in colon tissue were decreased significantly(P<0.05).Compared with MCAO group,pathological damage of brain and colon tissue of rats were relieved in TCM-L,TCM-M,TCM-H groups,disturbance of intestinal microflora microecology was improved,neurological function impairment and infarct size were markedly decreased,IL-1β,IL-6,TNF-α contents in brain tissue were decreased,gene expressions of ZO-1 and Claudin-1 in colon tissue were increased significantly(P<0.05);LPS and D-LA contents in serum were decreased in YH-HJP medium and high doses groups,while gene expressions of ZO-1 and Claudin-5 in brain tissue were increased significantly(P<0.05).Conclusion:YHHJP has a good effect on improving CIRI,whose mechanism may be related to regulating diversity of intestinal flora,reducing release of intestinal bacterial metabolites LPS and D-LA,increasing gene expressions of tight junction proteins ZO-1,Claudin-5 and Claudin-1,and down-regulating secretion of proinflammatory cytokine.

This paper aims to detect the antigens in porcine circovirus type 2 (PCV2) vaccines by high-performance size-exclusion chromatography (HPSEC) coupled with multi-angle laser light scattering (MALLS). With purified inactivated PCV2 and PCV2 virus-like particles (VLP) as references, two inactivated vaccines (a and b) and two VLP vaccines (c and d) for PCV2 from four manufacturers were analyzed by HPSEC-MALLS after demulsification. The antigen peaks in HPSEC-MALLS were identified by PCV2 antigen test strips, Western blotting and transmission electron microscope (TEM). The repeatability and linearity of the method were investigated. The results showed the virus antigens in the two inactivated vaccines were eluted at about 13.3 min in HPSEC. The molecular weight of these antigens was 2.61×10⁶ (±4.34%) Da and 2.40×10⁶ (±2.51%) Da, respectively, as calculated by MALLS. The antigen peaks of the two VLP vaccines also appeared at 13.3 min and the molecular weight was 2.09×10⁶ (±2.94%) Da and 2.88×10⁶ (±11.85%) Da, respectively, which was close to the theoretical molecular weight of PCV2. Moreover, an antigen peak of VLP vaccine c was observed at 11.4 min and the molecular weight was 4.37×10⁶ (±0.42%) Da. The antigen was verified to be the dimer of VLP by TEM. Vaccine d and purified Cap VLP antigens were tested repeatedly, and the RSD of the peak area (n=3) was all < 1.5%, indicating that the method was repeatable. The purified VLP were diluted in serial and tested for linearity. The result suggested good linear relationship between the peak area of VLP or VLP aggregates and the protein concentration of the sample with R² of 0.999 and 0.997, respectively. Thus, the method met the requirement for quantification and aggregate analysis. This method is accurate and efficient in in vitro quality evaluation and improvement of PCV2 vaccine.
Animals ; Antibodies, Viral ; Capsid Proteins ; Chromatography, Gel ; Circoviridae Infections/prevention & control* ; Circovirus ; Lasers ; Swine ; Vaccines, Inactivated ; Vaccines, Virus-Like Particle ; Viral Vaccines

BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts. METHODS: We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models. RESULTS: The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749). CONCLUSIONS: The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population. TRIAL REGISTRATION Chictr.org, ChiCTR 1800018895.
Humans ; Aged ; Sarcopenia/diagnosis* ; Deep Learning ; Aging ; Algorithms ; Biomarkers