OBJECTIVE: To investigate the relationship between a +ve postoperative Upper Instrumented Vertebra (UIV) (≥0°) tilt angle and the risk of medial shoulder/neck and lateral shoulder imbalance among Lenke 1 and 2 Adolescent Idiopathic Scoliosis (AIS) patients following Posterior Spinal Fusion.
SUMMARY OF BACKGROUND DATA: Current UIV selection strategy has poor correlation with postoperative shoulder balance. The relationship between a +ve postoperative UIV tilt angle and the risk of postoperative shoulder and neck imbalance was unknown.
METHODS: One hundred thirty-six Lenke 1 and 2 AIS patients with minimum 2 years follow-up were recruited. For medial shoulder and neck balance, patients were categorized into positive (+ve) imbalance (≥+4°), balanced, or negative (-ve) imbalance (≤-4°) groups based on T1 tilt angle/Cervical Axis measurement. For lateral shoulder balance, patients were classified into +ve imbalance (≥+3°) balanced, and -ve imbalance (≤-3°) groups based on Clavicle Angle (Cla-A) measurement. Linear regression analysis identified the predictive factors for shoulder/neck imbalance. Logistic regression analysis calculated the odds ratio of shoulder/neck imbalance for patients with +ve postoperative UIV tilt angle.
RESULTS: Postoperative UIV tilt angle and preoperative T1 tilt angle were predictive of +ve medial shoulder imbalance. Postoperative UIV tilt angle and postoperative PT correction were predictive of +ve neck imbalance. Approximately 51.6% of patients with +ve medial shoulder imbalance had +ve postoperative UIV tilt angle. Patients with +ve postoperative UIV tilt angle had 14.9 times increased odds of developing +ve medial shoulder imbalance and 3.3 times increased odds of developing +ve neck imbalance. Postoperative UIV tilt angle did not predict lateral shoulder imbalance.
CONCLUSION: Patients with +ve postoperative UIV tilt angle had 14.9 times increased odds of developing +ve medial shoulder imbalance (T1 tilt angle ≥+4°) and 3.3 times increased odds of developing +ve neck imbalance (cervical axis ≥+4°).
LEVEL OF EVIDENCE: 4.
METHODS: The N. oleracea fractions were obtained using solid phase extraction (SPE). A metabolomics approach that coupled the use of proton nuclear magnetic resonance (1H NMR) with multivariate data analysis (MVDA) was applied to distinguish the metabolite variations among the N. oleracea fractions, as well as to assess the correlation between metabolite variation and the studied bioactivities (DPPH free radical scavenging and α-glucosidase inhibitory activities). The bioactive fractions were then subjected to ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) analysis to profile and identify the potential bioactive constituents.
RESULTS: The principal component analysis (PCA) discriminated EF and MF from the other fractions with the higher distributions of phenolics. Partial least squares (PLS) analysis revealed a strong correlation between the phenolics and the studied bioactivities in the EF and the MF. The UHPLC-MS/MS profiling of EF and MF had tentatively identified the phenolics present. Together with some non-phenolic metabolites, a total of 37 metabolites were tentatively assigned.
CONCLUSIONS: The findings of this work supported that N. oleracea is a rich source of phenolics that can be potential antioxidants and α-glucosidase inhibitors for the management of diabetes. To our knowledge, this study is the first report on the metabolite-bioactivity correlation and UHPLC-MS/MS analysis of N. oleracea fractions.
OBJECTIVE: This study investigated the metabolite changes along the developmental stages of a local stevia cultivar.
METHODOLOGY: Stevia leaves were harvested at 4 different developmental stages (early vegetative, late vegetative, budding, and flowering). Samples were then subjected to LC-MS metabolomics analysis to determine the metabolite variations.
RESULTS: A total of 55 metabolites, comprising phenolic acids, flavonoids, and terpenoids were identified by MS/MS analysis of the stevia leaf extracts, revealing a metabolite profile which was comparatively similar with those of cultivars grown in other countries. PLS-DA differentiated the early vegetative stage stevia leaf samples from those of the later stages by higher content of phenolic acids. The leaf metabolomes of the later 3 stages (late vegetative, budding, and flowering) were collectively richer in flavonoids. Meanwhile, the content of steviol glycosides is highest during the late vegetative and budding stages.
CONCLUSION: The present study provided, for the first time, a general overview of the metabolite variations with regard to the different developmental stages of stevia. The information may facilitate decision making of suitable harvesting times for higher yields of steviol glycosides or a more balanced metabolite profile in terms of pharmacologically useful metabolites.
RESULTS: Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities.
CONCLUSION: Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry.