Human intestinal microbiota plays an important role in the maintenance of host health by providing energy, nutrients, and immunological protection. Intestinal dysfunction is a frequent complaint in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) patients, and previous reports suggest that dysbiosis, i.e. the overgrowth of abnormal populations of bacteria in the gut, is linked to the pathogenesis of the disease. We used high-throughput 16S rRNA gene sequencing to investigate the presence of specific alterations in the gut microbiota of ME/CFS patients from Belgium and Norway. 43 ME/CFS patients and 36 healthy controls were included in the study. Bacterial DNA was extracted from stool samples, PCR amplification was performed on 16S rRNA gene regions, and PCR amplicons were sequenced using Roche FLX 454 sequencer. The composition of the gut microbiota was found to differ between Belgian controls and Norwegian controls: Norwegians showed higher percentages of specific Firmicutes populations (Roseburia, Holdemania) and lower proportions of most Bacteroidetes genera. A highly significant separation could be achieved between Norwegian controls and Norwegian patients: patients presented increased proportions of Lactonifactor and Alistipes, as well as a decrease in several Firmicutes populations. In Belgian subjects the patient/control separation was less pronounced, however some abnormalities observed in Norwegian patients were also found in Belgian patients. These results show that intestinal microbiota is altered in ME/CFS. High-throughput sequencing is a useful tool to diagnose dysbiosis in patients and could help designing treatments based on gut microbiota modulation (antibiotics, pre and probiotics supplementation).

The bioadhesive characteristics of tablets for oral use made from modified starch, polyacrylic acid (PAA), polyethylene glycol (PEG) and sodium carboxymethylcellulose (CMC) were investigated. Adhesion force and energy were determined in-vitro and maximal adhesion time was evaluated in-vivo in human subjects. In-vitro, PAA showed the best bioadhesive properties, followed by modified maize starch and PEG with a mol. wt of 300,000-400,000 daltons. The presence of 0.1 mg of fluoride as NaF did not lead to significant differences in adhesion force and energy for the same formulation. The in-vivo bioadhesion was not strongly correlated to the in-vitro data. PAA, despite its excellent adhesion, proved to be irritating to the mucosa. PEG with a mol, wt of 200,000 daltons was subject to erosion. CMC showed good bioadhesive properties but the mechanical strength of the tablets was low. Modified maize starch tablets containing 5% (w/w) PAA and PEG with a mol. wt of 300,000 daltons proved to be the most suitable formulations for a fluoride-slow-release tablet with bioadhesive properties. In-vitro, the tablets released all of the fluoride within the 8 h period, with a high initial release. The release rate was related to the water absorption rate of the tablets. The PAA-containing formulations and the CMC formulations had the fastest release. In-vivo, fluoride levels with a minimum of 150 and a maximum of 1000 micrograms mL-1 were maintained for 8 h in the oral cavity. These fluoride levels were sustained significantly longer than those obtained with the administration of fourfold the amount of fluoride in the form of a fluoride-containing toothpaste. The release characteristics in-vivo exhibited a high variation. The use of bioadhesive polymers in oral pharmacotherapy seems promising.

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Using the largest available database of 328 blood-brain distribution (logBB) values, a quantitative benchmark was proposed to allow for a consistent comparison of the predictive accuracy of current and future logBB/quantitative structure-activity relationship (-QSAR) models. The usefulness of the benchmark was illustrated by comparing the global and k-nearest neighbors (kNN) multiple-linear regression (MLR) models based on the linear free-energy relationship (LFER) descriptors, and one non-LFER-based MLR model. The leave-one-out (LOO) and leave-group-out Monte Carlo (MC) cross-validation results (q(2) = 0.766, qms = 0.290, and qms(mc) = 0.311) indicated that the LFER-based kNN-MLR model was currently one of the most accurate predictive logBB-QSAR models. The LOO, MC, and kNN-MLR methods have been implemented in the QSAR-BENCH program, which is freely available from www.dmitrykonovalov.org for academic use.