Human umbilical cord mesenchymal stem cells (hUCMSCs) promotes the recovery of ovarian function in a rat model of premature ovarian failure (POF), Gynecological Endocrinology,

Oxylipids are potent lipid mediators associated with inflammation-induced colon carcinomas and colon tumor survival. Therefore, oxylipid profiles may be useful as novel biomarkers of colon polyp presence. The aim of this study was to investigate the relationship between plasma non-esterified oxylipids and the presence of colon polyps. A total of 123 Caucasian men, ages 48 to 65, were categorized into three groups: those with no polyps, those with one or more hyperplastic polyps, and those with one or more adenomas. Plasma non-esterified oxylipids were analyzed using solid phase extraction and quantified using a targeted HPLC tandem mass spectrometric analysis. Statistical analyses included Kruskal-Wallis one-way ANOVA with Dunn’s test for multiple comparison and generalized linear models to adjust for confounding factors such as age, anthropometrics, and smoking status. In general, monohydroxy omega-6-derived oxylipids were significantly increased in those with polyps. Concentrations of 5-hydroxyeicosatetraenoic acid (HETE) and 11-HETE were significantly higher in those with hyperplastic polyps and adenomas compared to those with no polyps. Arachidonic acid-derived HETEs were significantly associated with colon polyp types, even after adjusting for age, smoking, and body mass index or waist circumference in regression models. Since many of these oxylipids are formed through oxygenation by lipoxygenases (i.e., 5-, 12-, and 15-HETE, and 15- hydroxyeicosatrienoic acid [HETrE]) or auto-oxidative reactions (i.e., 11-HETE), this may indicate that lipoxygenase activity and lipid peroxidation are increased in those with colon polyps. In addition, since oxylipids such as 5-, 12-, and 15-HETE are signaling molecules involved in inflammation regulation, these oxylipids may have important functions in inflammation-associated polyp presence. Future studies should be performed in a larger cohorts to investigate if these oxylipids are useful as potential biomarkers of colon polyps.

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A bioanalytical method using mixed-mode solid phase extraction and UltraPerformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was developed for the analysis of morphine, morphine-3beta-glucuronide, morphine-6beta-glucuronide, 6-acetylmorphine, morphine N-oxide, and 10-hydroxymorphine in porcine plasma. All six compounds, along with four deuterated internal standards, were simultaneously extracted using mixed-mode strong cation exchange SPE in a 96-well microElution plate format. Due to analyte instability, a neutralizing solvent was used during the elution step to minimize degradation of 6-acetylmorphine. Separation was subsequently performed in 8 minutes on a 2.1 x 100 mm, 1.8 microm C(18 )column designed for retention of extremely polar compounds using a formic acid and methanol gradient. Analytes were detected by positive electrospray ionization in multiple reaction monitoring mode using a fast-scanning triple quadrupole mass spectrometer. Recovery was 73-123% depending on the analyte, and inter-day variability was less than 6%. Linearity was determined in porcine plasma by spiking the analytes prior to SPE. Correlation coefficients were >or= 0.998, and% deviation from the actual concentrations was less than 15%. The lower limit of quantitation (LLOQ) for all compounds was between 0.1 and 0.25 ng/mL.

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Purpose The objective of this study was to investigate the key glycolysis-related genes linked to immune cell infiltration in endometriosis and to develop a new endometriosis (EMS) predictive model. Methods A training set and a test set were created from the Gene Expression Omnibus (GEO) public database. We identified five glycolysis-related genes using least absolute shrinkage and selection operator (LASSO) regression and the random forest method. Then, we developed and tested a prediction model for EMS diagnosis. The CIBERSORT method was used to compare the infiltration of 22 different immune cells. We examined the relationship between key glycolysis-related genes and immune factors in the eutopic endometrium of women with endometriosis. In addition, Gene Ontology (GO)-based semantic similarity and logistic regression model analyses were used to investigate core genes. Reverse real-time quantitative PCR (RT-qPCR) of 5 target genes was analysed. Results The five glycolysis-related hub genes (CHPF, CITED2, GPC3, PDK3, ADH6) were used to establish a predictive model for EMS. In the training and test sets, the area under the curve (AUC) of the receiver operating characteristic curve (ROC) prediction model was 0.777, 0.824, and 0.774. Additionally, there was a remarkable difference in the immune environment between the EMS and control groups. Eventually, the five target genes were verified by RT-qPCR. Conclusion The glycolysis-immune-based predictive model was established to forecast EMS patients’ diagnosis, and a detailed comprehension of the interactions between endometriosis, glycolysis, and the immune system may be vital for the recognition of potential novel therapeutic approaches and targets for EMS patients.

Purpose: The objective of this study was to investigate the key glycolysis-related genes linked to immune cell infiltration in endometriosis and to develop a new endometriosis(EMS) predictive model.Methods: A training set and a test set were created from the NCBI GEO public database. We identified five glycolysis-related genes using LASSO and the Random Forest method. Then we developed and tested a prediction model for EMS diagnosis. The method CIBERSORT was used to compare the infiltration of 22 different immune cells. We looked into the relationship between key glycolysis-related genes and immune factors in eutopic endometrial of women with endometriosis. Besides, GO-based semantic similarity and logistic regression model analyses were used to investigate core genes.Results: The five glycolysis-related hub genes (CHPF, CITED2, GPC3, PDK3, ADH6) were used to establish a predictive model for EMS. In the training and test set, the AUC of the ROC prediction model was 0.777, 0.824, and 0.774, respectively. Additionally, there was a remarkable difference in the immune environment between EMS and control. Conclusion: The glycolysis-immune-based predictive model was established to forecast EMS patients’ diagnosis, and a detailed comprehension of the interactions between endometriosis, glycolysis, and the immune system, may be vital for the recognition of potential novel therapeutic approaches and targets for EMS patients.