A combination of fecal calprotectin and human beta-defensin 2 facilitates diagnosis and monitoring of inflammatory bowel disease

Inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) show a large overlap in clinical presentation, which presents diagnostic challenges. As a consequence, invasive and burdensome endoscopies are often used to distinguish between IBD and IBS. Here, we aimed to develop a noninvasive fecal test that can distinguish between IBD and IBS and reduce the number of endoscopies. We used shotgun metagenomic sequencing to analyze the composition and function of gut microbiota of 169 IBS patients, 447 IBD patients and 1044 population controls and measured fecal Calprotectin (FCal), human beta defensin 2 (HBD2), and chromogranin A (CgA) in these samples. These measurements were used to construct training sets (75% of data) for logistic regression and machine learning models to differentiate IBS from IBD and inactive from active IBD. The results were replicated on test sets (remaining 25% of the data) and microbiome data obtained using 16S sequencing. Fecal HBD2 showed high sensitivity and specificity for differentiating between IBD and IBS (sensitivity = 0.89, specificity = 0.76), while the inclusion of microbiome data with biomarkers (HBD2 and FCal) showed a potential for improvement in predictive power (optimal sensitivity = 0.87, specificity = 0.93). Shotgun sequencing-based models produced comparable results using 16S-sequencing data. HBD2 and FCal were found to have predictive power for IBD disease activity (AUC approximate to 0.7). HBD2 is a novel biomarker for IBD in patients with gastro-intestinal complaints, especially when used in combination with FCal and potentially in combination with gut microbiome data

L(+) and D(−) Lactate Are Increased in Plasma and Urine Samples of Type 2 Diabetes as Measured by a Simultaneous Quantification of L(+) and D(−) Lactate by Reversed-Phase Liquid Chromatography Tandem Mass Spectrometry

Background. Plasma and urinary levels of D-lactate have been linked to the presence of diabetes. Previously developed techniques have shown several limitations to further evaluate D-lactate as a biomarker for this condition. Methods. D- and L-lactate were quantified using ultraperformance liquid chromatography tandem mass spectrometry with labelled internal standard. Samples were derivatized with diacetyl-L-tartaric anhydride and separated on a C18-reversed phase column. D- and L-lactate were analysed in plasma and urine of controls, patients with inflammatory bowel disease (IBD), and patients with type 2 diabetes (T2DM). Results. Quantitative analysis of D- and L-lactate was achieved successfully. Calibration curves were linear (r2 > 0.99) over the physiological and pathophysiological ranges. Recoveries for urine and plasma were between 96% and 113%. Inter- and intra-assay variations were between 2% and 9%. The limits of detection of D-lactate and L-lactate in plasma were 0.7 μmol/L and 0.2 μmol/L, respectively. The limits of detection of D-lactate and L-lactate in urine were 8.1 nmol/mmol creatinine and 4.4 nmol/mmol creatinine, respectively. Plasma and urinary levels of D- and L-lactate were increased in patients with IBD and T2DM as compared with controls. Conclusion. The presented method proved to be suitable for the quantification of D- and L-lactate and opens the possibility to explore the use of D-lactate as a biomarker

Two randomized crossover multicenter studies investigating gastrointestinal symptoms after bread consumption in individuals with noncoeliac wheat sensitivity: do wheat species and fermentation type matter?

Background: Many individuals reduce their bread intake due to the belief that wheat is the cause of their gastrointestinal (GI) symptoms. Different grains and processing methods may impact tolerability. Objective: We investigated the effects of six different types of bread on GI symptoms in individuals with self-reported non-coeliac wheat sensitivity (NCWS). Methods: Two parallel randomised double-blind crossover multicentre studies were conducted. NCWS individuals, in whom coeliac disease and wheat allergy were ruled out, received five slices of (study A, n=20) yeast fermented (YF) or (study B, n=20) sourdough fermented (SF) bread made of bread wheat, spelt or emmer on three separate intervention days. Each test day was preceded by a run-in period of 3 days and separated by a wash-out period of at least 7 days. GI symptoms were evaluated by change in symptom score (test day minus average of the 3-day run-in period) on a 0-100mm visual analogue scale (delta VAS). Responders were defined as an increase in delta VAS of at least 15mm for overall GI symptoms, abdominal discomfort, abdominal pain, bloating and/or flatulence. Results: The overall change in GI symptoms did not differ between breads of different grains (YF p=0.267; SF p=0.144). The number of responders was also comparable for both YF (6 to wheat, 5 to spelt, and 7 to emmer, p=0.761) and SF breads (9 to wheat, 7 to spelt, and 8 to emmer, p=0.761). Conclusion: The majority of NCWS individuals experienced GI symptoms for at least one of the breads, but on a group level, no differences were found between different grain types for either YF or SF breads. Clinical Trial Registry: ClinicalTrials.gov, NCT0408447

Electrons in a ferromagnetic metal with a domain wall

We present theoretical description of conduction electrons interacting with a domain wall in ferromagnetic metals. The description takes into account interaction between electrons. Within the semiclassical approximation we calculate the spin and charge distributions, particularly their modification by the domain wall. In the same approximation we calculate local transport characteristics, including relaxation times and charge and spin conductivities. It is shown that these parameters are significantly modified near the wall and this modification depends on electron-electron interaction.Comment: 10 pages with 4 figure

Trade-Offs Between Carbon Stocks and Timber Recovery in Tropical Forests are Mediated by Logging Intensity

Forest degradation accounts for ~70% of total carbon losses from tropical forests. Substantial emissions are from selective logging, a land-use activity that decreases forest carbon density. To maintain carbon values in selectively logged forests, climate change mitigation policies and government agencies promote the adoption of reduced-impact logging (RIL) practices. However, whether RIL will maintain both carbon and timber values in managed tropical forests over time remains uncertain. In this study, we quantify the recovery of timber stocks and aboveground carbon at an experimental site where forests were subjected to different intensities of RIL (4, 8, and 16 trees/ha). Our census data span 20 years postlogging and 17 years after the liberation of future crop trees from competition in a tropical forest on the Guiana Shield, a globally important forest carbon reservoir. We model recovery of timber and carbon with a breakpoint regression that allowed us to capture elevated tree mortality immediately after logging. Recovery rates of timber and carbon were governed by the presence of residual trees (i.e., trees that persisted through the first harvest). The liberation treatment stimulated faster recovery of timber albeit at a carbon cost. Model results suggest a threshold logging intensity beyond which forests managed for timber and carbon derive few benefits from RIL, with recruitment and residual growth not sufficient to offset losses. Inclusion of the breakpoint at which carbon and timber gains outpaced postlogging mortality led to high predictive accuracy, including out-of-sample R2 values \u3e90%, and enabled inference on demographic changes postlogging. Our modeling framework is broadly applicable to studies that aim to quantify impacts of logging on forest recovery. Overall, we demonstrate that initial mortality drives variation in recovery rates, that the second harvest depends on old growth wood, and that timber intensification lowers carbon stocks

Integrated fecal microbiome–metabolome signatures reflect stress and serotonin metabolism in irritable bowel syndrome

To gain insight into the complex microbiome-gut-brain axis in irritable bowel syndrome (IBS) several modalities of biological and clinical data must be combined. We aimed to identify profiles of faecal microbiota and metabolites associated with IBS and to delineate specific phenotypes of IBS that represent potential pathophysiological mechanisms. Faecal metabolites were measured using proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy and gut microbiome using Shotgun Metagenomic Sequencing (MGS) in a combined dataset of 142 IBS patients and 120 healthy controls (HC) with extensive clinical, biological and phenotype information. Data were analysed using support vector classification and regression and kernel t-SNE. Microbiome and metabolome profiles could distinguish IBS and HC with an area-under-the-receiver-operator-curve (AUC) of 77.3% and 79.5%, respectively, but this could be improved by combining microbiota and metabolites to 83.6%. No significant differences in predictive ability of the microbiome-metabolome data were observed between the three classical, stool pattern-based, IBS subtypes. However, unsupervised clustering showed distinct subsets of IBS patients based on faecal microbiome-metabolome data. These clusters could be related plasma levels of serotonin and its metabolite 5-hydroxyindoleacetate, effects of psychological stress on gastrointestinal symptoms, onset of IBS after stressful events, medical history of previous abdominal surgery, dietary caloric intake and IBS symptom duration. Furthermore, pathways in metabolic reaction networks were integrated with microbiota data, that reflect the host-microbiome interactions in IBS. The identified microbiome-metabolome signatures for IBS, associated with altered serotonin metabolism and unfavourable stress-response related to gastrointestinal symptoms, support the microbiota-gut-brain link in the pathogenesis of IBS

Grammar disruption in a patient with Neuro-Sweet syndrome

This paper for the first time reports detailed neurolinguistic findings in a patient with Neuro-Sweet syndrome. In this patient the presenting symptoms of central nervous system (CNS) involvement primarily consisted of a selective grammar deficit restricted to spontaneous speech. On MRI a left prefrontal ischemic stroke (superior part BA 6) and two small subcortical left parietal infarctions were found. Neurolinguistic analyses, however, did not reveal a profile consistent with any observations of agrammatism caused by structural damage to the language areas critically involved in grammatical processing. It is hypothesized that selectively distorted grammar might reflect disruption of the frontosubcortical network involved in language processing. Prefrontal neurobehavioral abnormalities associated with functional disruption of the inferior medial frontal regions as demonstrated by SPECT, additionally suggest that agrammatic symptoms may be linked to a higher-level cognitive disorder following encephalopathic CNS involvement