NMR methods to monitor the enzymatic depolymerization of heparin

Heparin and the related glycosaminoglycan, heparan sulfate, are polydisperse linear polysaccharides that mediate numerous biological processes due to their interaction with proteins. Because of the structural complexity and heterogeneity of heparin and heparan sulfate, digestion to produce smaller oligosaccharides is commonly performed prior to separation and analysis. Current techniques used to monitor the extent of heparin depolymerization include UV absorption to follow product formation and size exclusion or strong anion exchange chromatography to monitor the size distribution of the components in the digest solution. In this study, we used 1H nuclear magnetic resonance (NMR) survey spectra and NMR diffusion experiments in conjunction with UV absorption measurements to monitor heparin depolymerization using the enzyme heparinase I. Diffusion NMR does not require the physical separation of the components in the reaction mixture and instead can be used to monitor the reaction solution directly in the NMR tube. Using diffusion NMR, the enzymatic reaction can be stopped at the desired time point, maximizing the abundance of larger oligosaccharides for protein-binding studies or completion of the reaction if the goal of the study is exhaustive digestion for characterization of the disaccharide composition. In this study, porcine intestinal mucosa heparin was depolymerized using the enzyme heparinase I. The unsaturated bond formed by enzymatic cleavage serves as a UV chromophore that can be used to monitor the progress of the depolymerization and for the detection and quantification of oligosaccharides in subsequent separations. The double bond also introduces a unique multiplet with peaks at 5.973, 5.981, 5.990, and 5.998 ppm in the 1H-NMR spectrum downfield of the anomeric region. This multiplet is produced by the proton of the C-4 double bond of the non-reducing end uronic acid at the cleavage site. Changes in this resonance were used to monitor the progression of the enzymatic digestion and compared to the profile obtained from UV absorbance measurements. In addition, in situ NMR diffusion measurements were explored for their ability to profile the different-sized components generated over the course of the digestion

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified