PATIENTS AND METHODS: Materials and methods: The study involved 134 ST-segment elevation myocardial infarction patients. Occurrence of post-percutaneous coronary (PCI) intervention epicardial blood flow of TIMI <3 or myocardial blush grade 0-1 along with ST resolution <70% within 2 hours after PCI was qualified as the no-reflow condition. Left ventricle remodeling was defined after 6-months as an increase in left ventricle end-diastolic volume and/or end-systolic volume by more than 10%.
RESULTS: Results: A logistic regression formula was evaluated. Included biomarkers were macrophage migration inhibitory factor and sST2, left ventricle ejection fraction: Y=exp(-39.06+0.82EF+0.096ST2+0.0028MIF) / (1+exp(-39.06+0.82EF+0.096ST2+0.0028MIF)). The estimated range is from 0 to 1 point. Less than 0.5 determines an adverse outcome, and more than 0.5 is a good prognosis. This equation, with sensitivity of 77 % and specificity of 85%, could predict the development of adverse left ventricle remodeling six months after a coronary event (AUC=0.864, CI 0.673 to 0.966, p<0.05).
CONCLUSION: Conclusions: A combination of biomarkers gives a significant predicting result in the formation of adverse left ventricular remodeling after ST-segment elevation myocardial infarction.
OBJECTIVE: This systematic review aimed to investigate the available literature on the shared molecular mechanisms of neuroinflammation in AD and epilepsy.
METHODS: The search included in this systematic review was obtained from 5 established databases. A total of 2,760 articles were screened according to inclusion criteria. Articles related to the modulation of the inflammatory biomarkers commonly associated with the progression of AD and epilepsy in all populations were included in this review.
RESULTS: Only 7 articles met these criteria and were chosen for further analysis. Selected studies include both in vitro and in vivo research conducted on rodents. Several neuroinflammatory biomarkers were reported to be involved in the cross-talk between AD and epilepsy.
CONCLUSION: Neuroinflammation was directly associated with the advancement of AD and epilepsy in populations compared to those with either AD or epilepsy. However, more studies focusing on common inflammatory biomarkers are required to develop standardized monitoring guidelines to prevent the manifestation of epilepsy and delay the progression of AD in patients.
METHODS: Participants were recruited in Intensive Care Units (ICUs) from multiple UK hospitals, including fifty-nine patients with abdominal sepsis, eighty-four patients with pulmonary sepsis, forty-two SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), sampled at four time points, in addition to thirty healthy control donors. Multiple clinical parameters were measured, including SOFA score, with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools.
RESULTS: Nineteen high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed 'indicators of inflammation' (I°I), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty entities, termed 'SIRS or Sepsis' (S°S) biomarkers, were differentially expressed between sepsis and SIRS (FC>2·0, p-value<0.05).
DISCUSSION: The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758). The I°I and S°S signatures performed variably in other independent gene expression datasets, this may be due to technical variation in the study/assay platform.
AREAS COVERED: The differential bacterial composition identified from BEVs isolated from biofluids between patients and healthy controls may be valuable for detecting diseases. Therefore, BEVs may serve as novel diagnostic markers. Literature search on PubMed and Google Scholar databases was conducted. In this special report, we outline the commonly used approach for investigating BEVs in biofluids, the 16S ribosomal RNA gene sequencing of V3-V4 hypervariable regions, and the recent studies exploring the potential of BEVs as biomarkers for various diseases.
EXPERT OPINION: The emerging field of BEVs offers new possibilities for the diagnosis of various types of diseases, although there remain issues that need to be resolved in this research area to implement BEVs in clinical applications. Hence, it is important for future studies to take these challenges into consideration when investigating the diagnostic value of BEVs.
METHODS: A total of 30 women aged 20-24 years old were randomly divided into three groups. Measurement of betatrophin levels using Enzyme-Linked Immunosorbent Assay (ELISA). Data analysis techniques used were one-way ANOVA and parametric linear correlation.
RESULTS: The results showed that the average levels of betatrophin pre-exercise were 200.40 ± 11.03 pg/mL at CON, 203.07 ± 42.48 pg/mL at MIE, 196.62 ± 21.29 pg/mL at MCE, and p=0.978. Average levels of betatrophin post-exercise were 226.65 ± 18.96 pg/mL at CON, 109.31 ± 11.23 pg/mL at MIE, 52.38 ± 8.18 pg/mL at MCE, and p=0.000. Pre-exercise betatrophin levels were positively correlated with age, BMI, FM, WHR, FBG, and PBF (p≤0.001).
CONCLUSIONS: Our study showed that betatrophin levels are decreased by 10 min post-MIE and post-MCE. However, moderate-intensity continuous exercise is more effective in lowering betatrophin levels than moderate-intensity interval exercise. In addition, pre-exercise betatrophin levels also have a positive correlation with obesity markers.