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.
METHODS: A total of 32 obese women were selected as subjects and administered the interventions of low-intensity combination exercise (Q2), moderate-intensity combination exercise (Q3), and high-intensity combination exercise (Q4). ELISA was used to measure irisin and IL-6 levels in all samples. Statistical analysis used one-way ANOVA and Turkey's-Honest Significant Difference (HSD) post hoc test.
RESULTS: The mean Δ IL-6 levels in the control groups (Q1), Q2, Q3, and Q4 were 0.27 ± 2.54, 2.07 ± 2.55, 5.99 ± 6.25, and 7.98 ± 2.82 pg/mL with (p=0.015). The mean Δ irisin levels were 0.06 ± 0.81 ng/mL in Q1, 0.59 ± 0.67 ng/mL in Q2, 1.99 ± 1.65 ng/mL in Q3, 4.63 ± 3.57 ng/mL in Q4 with (p=0.001).
CONCLUSIONS: This study proved that all three types of combined exercise intensity increased myokine levels, such as irisin and IL-6. However, high-intensity combination exercise provided the most optimal improvement in myokine levels in obese women. Future studies are needed to design long-term exercise programs specifically for obese adolescent women using the findings from this study.
METHODS: Bacterial DNA was extracted from biopsy samples of patients presenting dyspepsia symptoms with H. pylori positive from cultures and histology. DNA was amplified from the V3-V4 regions of the 16S rRNA gene. In-vitro E-test was used to detect antibiotic resistance. Microbiome community analysis was conducted through α-diversity, β-diversity, and relative abundance.
RESULTS: Sixty-nine H. pylori positive samples were eligible after quality filtering. Following resistance status to five antibiotics, samples were classified into 24 sensitive, 24 single resistance, 16 double resistance, 5 triple resistance. Samples were mostly resistant to metronidazole (73.33%; 33/45). Comparation of four groups displayed significantly elevated α-diversity parameters under the multidrug resistance condition (all P <0.05). A notable change was observed in triple-resistant compared to sensitive (P <0.05) and double-resistant (P <0.05) groups. Differences in β-diversity by UniFrac and Jaccard were not significant in terms of the resistance (P = 0.113 and P = 0.275, respectively). In the triple-resistant group, the relative abundance of Helicobacter genera was lower, whereas that of Streptococcus increased. Moreover, the linear discriminant analysis effect size (LEfSe) was associated with the presence of Corynebacterium and Saccharimonadales in the single-resistant group and Pseudomonas and Cloacibacterium in the triple-resistant group.
CONCLUSION: Our results suggest that the resistant samples showed a higher trend of diversity and evenness than the sensitive samples. The abundance of H. pylori in the triple-resistant samples decreased with increasing cohabitation of pathogenic bacteria, which may support antimicrobial resistance. However, antibiotic susceptibility determined by the E-test may not completely represent the resistance status.