A bioengineering investigation of cervical collar design and fit: Implications on skin health.

BACKGROUND: Cervical collars restrict cervical spine movement to minimise the risk of spinal cord injury. Collars apply mechanical loading to the skin putting it at risk of skin damage. Indeed, cervical collar-related pressure ulcers are unacceptably prevalent, especially at the occiput, mandibles, and chin. Collar design and fit are often key considerations for prevention. METHODS: This comprehensive study evaluated four commercial prehospital and acute care cervical collars. Pressure, microclimate, transepidermal water loss and skin hydration were measured at the interface between the device and the skin. Range of motion restriction was measured to evaluate effective immobilisation. Head, neck, and shoulder morphology was evaluated using three-dimensional scans. FINDINGS: The occiput experienced significantly higher interface pressures than the chin and mandibles for most collar designs. Interface pressure at the occiput was significantly higher for the Stiffneck extrication collar compared to the other collar designs. The Stiffneck collar also provided the most movement restriction, though not significantly more than other designs. Relative humidity at the device skin interface was significantly higher for the Stiffneck and Philadelphia collars corresponding to closed cell foam padding, in contrast to the open cell foams lined with permeable fabric used in the other collars. Collar discomfort correlated with both occipital pressure and skin humidity. INTERPRETATION: The occiput is at increased risk of cervical collar-related pressure ulcers during supine immobilisation, especially for Stiffneck extrication collars. Lined open-cell foams could be used to minimise skin humidity and increase comfort.

Differential metabolism between biofilm and suspended Pseudomonas aeruginosa cultures in bovine synovial fluid by 2D NMR-based metabolomics.

Total joint arthroplasty is a common surgical procedure resulting in improved quality of life; however, a leading cause of surgery failure is infection. Periprosthetic joint infections often involve biofilms, making treatment challenging. The metabolic state of pathogens in the joint space and mechanism of their tolerance to antibiotics and host defenses are not well understood. Thus, there is a critical need for increased understanding of the physiological state of pathogens in the joint space for development of improved treatment strategies toward better patient outcomes. Here, we present a quantitative, untargeted NMR-based metabolomics strategy for Pseudomonas aeruginosa suspended culture and biofilm phenotypes grown in bovine synovial fluid as a model system. Significant differences in metabolic pathways were found between the suspended culture and biofilm phenotypes including creatine, glutathione, alanine, and choline metabolism and the tricarboxylic acid cycle. We also identified 21 unique metabolites with the presence of P. aeruginosa in synovial fluid and one uniquely present with the biofilm phenotype in synovial fluid. If translatable in vivo, these unique metabolite and pathway differences have the potential for further development to serve as targets for P. aeruginosa and biofilm control in synovial fluid.

COLMARq: A Web Server for 2D NMR Peak Picking and Quantitative Comparative Analysis of Cohorts of Metabolomics Samples.

Highly quantitative metabolomics studies of complex biological mixtures are facilitated by the resolution enhancement afforded by 2D NMR spectra such as 2D 13C-1H HSQC spectra. Here, we describe a new public web server, COLMARq, for the semi-automated analysis of sets of 2D HSQC spectra of cohorts of samples. The workflow of COLMARq includes automated peak picking using the deep neural network DEEP Picker, quantitative cross-peak volume extraction by numerical fitting using Voigt Fitter, the matching of corresponding cross-peaks across cohorts of spectra, peak volume normalization between different spectra, database query for metabolite identification, and basic univariate and multivariate statistical analyses of the results. COLMARq allows the analysis of cross-peaks that belong to both known and unknown metabolites. After a user has uploaded cohorts of 2D 13C-1H HSQC and optionally 2D 1H-1H TOCSY spectra in their preferred format, all subsequent steps on the web server can be performed fully automatically, allowing manual editing if needed and the sessions can be saved for later use. The accuracy, versatility, and interactive nature of COLMARq enables quantitative metabolomics analysis, including biomarker identification, of a broad range of complex biological mixtures as is illustrated for cohorts of samples from bacterial cultures of Pseudomonas aeruginosa in both its biofilm and planktonic states.

Cadaverine Is a Switch in the Lysine Degradation Pathway in Pseudomonas aeruginosa Biofilm Identified by Untargeted Metabolomics.

There is a critical need to accurately diagnose, prevent, and treat biofilms in humans. The biofilm forming P. aeruginosa bacteria can cause acute and chronic infections, which are difficult to treat due to their ability to evade host defenses along with an inherent antibiotic-tolerance. Using an untargeted NMR-based metabolomics approach, we identified statistically significant differences in 52 metabolites between P. aeruginosa grown in the planktonic and lawn biofilm states. Among them, the metabolites of the cadaverine branch of the lysine degradation pathway were systematically decreased in biofilm. Exogenous supplementation of cadaverine caused significantly increased planktonic growth, decreased biofilm accumulation by 49% and led to altered biofilm morphology, converting to a pellicle biofilm at the air-liquid interface. Our findings show how metabolic pathway differences directly affect the growth mode in P. aeruginosa and could support interventional strategies to control biofilm formation.

COLMAR Lipids Web Server and Ultrahigh-Resolution Methods for Two-Dimensional Nuclear Magnetic Resonance- and Mass Spectrometry-Based Lipidomics.

Accurate identification of lipids in biological samples is a key step in lipidomics studies. Multidimensional nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical tool for this purpose as it provides comprehensive structural information on lipid composition at atomic resolution. However, the interpretation of NMR spectra of complex lipid mixtures is currently hampered by limited spectral resolution and the absence of a customized lipid NMR database along with user-friendly spectral analysis tools. We introduce a new two-dimensional (2D) NMR metabolite database "COLMAR Lipids" that was specifically curated for hydrophobic metabolites presently containing 501 compounds with accurate experimental 2D 13C-1H heteronuclear single quantum coherence (HSQC) chemical shift data measured in CDCl3. A new module in the public COLMAR suite of NMR web servers was developed for the (semi)automated analysis of complex lipidomics mixtures (http://spin.ccic.osu.edu/index.php/colmarm/index2). To obtain 2D HSQC spectra with the necessary high spectral resolution along both 13C and 1H dimensions, nonuniform sampling in combination with pure shift spectroscopy was applied allowing the extraction of an abundance of unique cross-peaks belonging to hydrophobic compounds in complex lipidomics mixtures. As shown here, this information is critical for the unambiguous identification of underlying lipid molecules by means of the new COLMAR Lipids web server, also in combination with mass spectrometry, as is demonstrated for Caco-2 cell and lung tissue cell extracts.

Identification of Unknown Metabolomics Mixture Compounds by Combining NMR, MS, and Cheminformatics.

Metabolomics aims at the comprehensive identification of metabolites in complex mixtures to characterize the state of a biological system and elucidate their roles in biochemical pathways. For many biological samples, a large number of spectral features observed by NMR spectroscopy and mass spectrometry (MS) belong to unknowns, i.e., these features do not belong to metabolites that have been previously identified, and their spectral information is not available in databases. By combining NMR, MS, and combinatorial cheminformatics, the analysis of unknowns can be pursued in complex mixtures requiring minimal purification. This chapter describes the SUMMIT MS/NMR approach covering sample preparation, NMR and MS data collection and processing, and the identification of likely unknowns with the use of cheminformatics tools and the prediction of NMR spectral properties.