Contamination of surfaces in hospitals and food industry by bacterial biofilms is a serious health risk. Of concern is their resistance to routine antibacterials and disinfectants. This requires the development of novel approaches to biofilm detachment. The study evaluates the effectiveness of cationic polymer micelles (CPMs) against pre‐formed biofilms. CPMs based on different polycations were used. The hydrodynamic radius of the particles ranged from 16 to 360 nm. Biofilms of Escherichia coli 420, Pseudomonas aeruginosa PAO1, Staphylococcus aureus 29213 and Bacillus subtilis 168 were cultivated for 24 h then the pre‐formed biofilms were treated with the CPMs for 2, 4 or 6 h. Biofilm biomass was evaluated by the crystal violet assay, and live/dead fluorescence test was applied for bacterial viability. The ability of CPMs to interact with pre‐formed biofilms of the model strains was evaluated. We observed that the most effective CPMs were those based on poly(2‐(dimethylamino)ethyl methacrylate) copolymers which reduced the biofilm biomass three‐ to four‐fold compared with the treatment of the biofilm with water. Significantly reduced vitality of the bacteria in the biofilms was registered by the live/dead stain. The results indicate the applicability of the CPMs for disinfection of biofilm‐contaminated surfaces and the treatment of wounds.
It is generally accepted that bacteria in biofilm are more resistant to antibacterials than their planktonic counterparts. For numerous antibiotics, it has been shown that minimal inhibitory concentrations (MICs) for bacteria grown in broth are much lower than the minimal biofilm inhibition concentrations. While sub-inhibitory concentrations, that is, amounts of antibacterials below the MIC, do not either influence or suppress to some extent or other the bacterial growth in liquid media, these same amounts of drugs, natural substances, etc., may have diverse effects on bacterial biofilms, ranging from suppression to stimulation of the sessile growth and varying with regard to the bacterial species and strains. This is a source of additional risks for both biofilm infection of host tissues and contamination indwelling devices. When considering the data for biofilm modulation, differences in experimental protocols should be taken into account, as well as the strain-specific mechanisms of biofilm formation.
In the present work we applied a recently developed procedure for multidimensional data clustering to processing of spectral satellite images. The core of our approach lays in projection of multidimensional image to a two dimensional one. The main aim is to discover points with similar characteristics. This was done by clustering of the resulting image. The processing technique exploits equilibrium states of a kind of recurrent neural network -Echo state network (ESN) -that are obtained after intrinsic plasticity (IP) tuning of the ESN using multidimensional data as inputs. The proposed in our previous work automated procedure for multidimensional data clustering is further refined and tested on the satellite image data. The obtained number and position of clusters of a multispectral image of a mountain region in Bulgaria is compared with the classification of the region landscape given by the Ministry of Regional Development and Public Works.
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