Associations between ward climate and patient characteristics in a secure forensic mental health service

Ward climate is associated with patient satisfaction and, potentially, with improved outcomes but increased understanding of its relationship with individual patient characteristics is required. We investigated relationships between patient (N = 63) gender, perceived risk, risk behaviour, therapeutic engagement (session attendance), psychopathology and ward climate in a forensic psychiatric hospital. Lower security levels were significantly associated with better patient cohesion (PC), experienced safety (ES) and therapeutic hold (TH). Female gender predicted PC and ES. Higher perceived risk was associated with lower PC after controlling for security level and gender. Diagnosis of personality disorder or psychosis was associated with higher ES. Lower levels of engagement predicted greater TH. The relationship between patient characteristics and ward climate in forensic settings is complex. Prospective studies are needed to further establish determinants of ward climate, particularly those aspects of patient risk that are associated with poorer PC

A Novel Computerized Cell Count Algorithm for Biofilm Analysis

<div><p>Biofilms are the preferred sessile and matrix-embedded life form of most microorganisms on surfaces. In the medical field, biofilms are a frequent cause of treatment failure because they protect the bacteria from antibiotics and immune cells. Antibiotics are selected according to the minimal inhibitory concentration (MIC) based on the planktonic form of bacteria. Determination of the minimal biofilm eradicating concentration (MBEC), which can be up to 1,000-fold greater than the MIC, is not currently conducted as routine diagnostic testing, primarily because of the methodical hurdles of available biofilm assessing protocols that are time- and cost-consuming. Comparative analysis of biofilms is also limited as most quantitative methods such as crystal violet staining are indirect and highly imprecise. In this paper, we present a novel algorithm for assessing biofilm resistance to antibiotics that overcomes several of the limitations of alternative methods. This algorithm aims for a computer-based analysis of confocal microscope 3D images of biofilms after live/dead stains providing various biofilm parameters such as numbers of viable and dead cells and their vertical distributions within the biofilm, or biofilm thickness. The performance of this algorithm was evaluated using computer-simulated 2D and 3D images of coccal and rodent cells varying different parameters such as cell density, shading or cell size. Finally, genuine biofilms that were untreated or treated with nitroxoline or colistin were analyzed and the results were compared with quantitative microbiological standard methods. This novel algorithm allows a direct, fast and reproducible analysis of biofilms after live/dead staining. It performed well in biofilms of moderate cell densities in a 2D set-up however the 3D analysis remains still imperfect and difficult to evaluate. Nevertheless, this is a first try to develop an easy but conclusive tool that eventually might be implemented into routine diagnostics to determine the MBEC and to improve outcomes of patients with biofilm-associated infections.</p></div

Concentration-response activities of nitroxoline and colistin against <i>P</i>. <i>aeruginosa</i> biofilms measured by different methods.

<p>(A-B) Direct count (<i>N</i> /cm²) of viable and dead bacteria by qBA; (C-D) Determination of viable cells on agar (CFU/mL); (E-F) Direct determination of the area (<i>A</i>) covered by green- and red-stained bacteria by qBA; (G-H) Crystal violet absorption.</p

Accuracy of cell counting per Z-layer and of the total cell number of simulated 3D biofilms depending on <i>I</i> and <i>P</i> filters.

<p>Accuracy of cell counting per Z-layer and of the total cell number of simulated 3D biofilms depending on <i>I</i> and <i>P</i> filters.</p

Comparison of a 2D and 3D analysis by qBA of an <i>E</i>. <i>coli</i> biofilm.

<p>(A) Analyzed biofilm layers scanned by CLSM (green and red channels overlapping). (B) Histogram of the viable (green) and dead (red) cells estimated in a 2D (dotted lines) and 3D (solid lines) setting. (C) Allocated (red crosses) local grayscale maxima in three neighboring layers (as indicated by the red dotted square in A). Biofilm images in A and B were processed by increasing the intensity and contrast of the signals for illustrative purpose.</p

Histograms of viable and dead cells of <i>P</i>. <i>aeruginosa</i> biofilms treated by PBS or various concentrations of antibiotics.

<p>(A) PBS treatment; (B, D, F, H) Nitroxoline treatment and (C, E, G, I) colistin treatment (corresponding to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154937#pone.0154937.g006" target="_blank">Fig 6</a>). Viable cells are represented by green lines and dead cells in red lines. Error bars indicate the standard error of the mean (SEM) for three independent experiments. Concentrations of antibiotics are indicated above the corresponding histograms.</p

Principals of image processing and adaptive segmentation and binarization.

<p>(A) 2D CLSM image of one Z-layer; (B) Schematic example of a 3D grayscale histogram section (<i>g</i> = f[x, y]); (C) Schematic illustration of the Z-allocation of the cells; (D) 2D projection image of all Z-layers and the local grayscale maxima (indicated as red crosses); (E) Schematic illustration of the window adjustment (<i>w</i>, <i>w)</i> by prolongation and local intensity; (F) Segmented image by seeded region growing algorithm.</p

Accuracy of the cell counting (<i>N</i>) and the calculation of the biomass (<i>A</i>) depending on coloration of the cells.

<p>Accuracy of the cell counting (<i>N</i>) and the calculation of the biomass (<i>A</i>) depending on coloration of the cells.</p

Accuracy of cell counts of a simulated 3D biofilm containing 995 dead cells.

<p>Accuracy of cell counts of a simulated 3D biofilm containing 995 dead cells.</p

CLSM images of <i>P</i>. <i>aeruginosa</i> PA01 biofilms after 24 hours growth treated with PBS or nitroxoline or colistin for 3.5 hours.

<p>(A) PBS treatment; (B) 160 μg/mL nitroxoline; (C) 160 μg/mL colistin; (D) 320 μg/mL nitroxoline; (E) 320 μg/mL colistin (F) 640 μg/mL nitroxoline; (G) 640 μg/mL colistin. Viable cells are visible in green (SYTO 9) and dead cells in red (propidium iodide). All images present only sections of approximately 50 x 50 μm (X x Y) and variable Z-sizes (depending on biofilm thickness).</p