Oxygenated Hydrogel Promotes Re-Epithelialization and Reduces Inflammation in a Perturbed Wound Model in Rat.

Chronic wounds can take months or even years to heal and require proper medical intervention. Normal wound healing processes require adequate oxygen supply. Accordingly, destroyed or inefficient vasculature leads to insufficient delivery to peripheral tissues and impair healing. Oxygen is critical for vital processes such as proliferation, collagen synthesis and antibacterial defense. Hyperbaric oxygen therapy (HBOT) is commonly used to accelerate healing however, this can be costly and requires specialized training and equipment. Efforts have turned to the development of topical oxygen delivery systems. Oxysolutions has developed oxygenated gels (P407, P407/P188, nanocellulose based gel (NCG)) with high levels of dissolved oxygen. This study aims to evaluate the efficacy of these newly developed oxygenated products by assessing their impact on healing rates in a rat perturbed wound model. Here, P407/P188 oxygenated gels demonstrated greater re-epithelialization distances compared to its controls at Day 3. In addition, all oxygenated gels had a higher proportion of wounds with complete wound closure. All three oxygenated gels also minimized further escalation in inflammation from Day 3 to Day 10. This highlights the potential of this newly-developed oxygenated gels as an alternative to existing oxygen therapies.

Bead-beating artefacts in the Bacteroidetes to Firmicutes ratio of the human stool metagenome.

We evaluated bead-beating cell-lysis in analysing the human stool metagenome, since this is a key step. We observed that two different bead-beating instruments from the same producer gave a three-fold difference in the Bacteroidetes to Firmicutes ratio. This illustrates that bead-beating can have a major impact on downstream metagenome analyses.

Found in transcription: accurate Parkinson's disease classification in peripheral blood.

BACKGROUND: A blood-based test for the early detection of Parkinson's disease (PD) would be an important diagnostic tool and useful for patient selection when developing novel drugs or treatments for the disease. OBJECTIVE: Here, we aimed to identify potential biomarkers associated with PD. METHODS: We applied gene expression profiling to the study of peripheral blood from 75 healthy control subjects and 79 PD patients at different stages of the disease. Healthy control subjects were matched for age and gender with PD subjects, and the diagnosis of patients was based on clinical evaluation by specialists in movement disorders. RNA was extracted from the blood samples and the gene expressions were measured using the Illumina HumanHT-12 v4.0 Expression BeadChip. RESULTS: Our results support previous studies that gene expression in blood may be instrumental in the search for molecular biomarkers for PD. Single cross-validation results show that PD can be correctly classified from healthy controls with an agreement of 88% to clinical diagnosis. De novo PD patients are classified with a sensitivity of 87%, which is close to what was achieved for the patients having a confirmed PD diagnosis with disease duration <5 and >5 years (93% and 88%). A double cross-validation procedure showed that using a selected set of around 650 informative genes, similar results are achieved. Functional analysis of the selected genes showed genes significantly associated to mitochondrial dysfunction, protein ubiquitination, gene expression and cell death. CONCLUSIONS: PD affects gene expression in blood, suggesting the potential for the development of a blood-based gene expression test.

Microarray-based prediction of Parkinson's disease using clinical data as additional response variables.

Recent discoveries and developments in the field of genomics have led to the commercialization of novel diagnostic devices for studying disease or estimating therapeutic outcomes in individual patients. With this emerging field, the emphasis is shifting to integration of clinical research into product development. Data acquisition is primary in the initial exploratory phase of product development, and during the process of sample collection and data generation in clinical microarray studies, great amounts of additional information, such as demographic, clinical, and study design variables associated with the data, are often accumulated and made available. Including additional information in classification has been addressed in many different ways. However, in previous studies, the additional information have consistently been treated as extra predictors, which can be a problem for future prediction if such information are not available or collectable for the new samples. We instead propose to adopt a method called canonical partial least squares, which for our purpose, only uses the additional information at the model building stage to stabilize the construction of a classifier for disease status from microarray data. The canonical partial least squares method is compared with regular partial least squares for the classification of Parkinson's disease from gene expression in peripheral blood samples and also through computer simulations. The present study showed that including clinical data in the model building produces simpler and more stable models for prediction of Parkinson's disease from gene expression data.