Breaking barriers in Candida spp. detection with Electronic Noses and artificial intelligence.

The timely and accurate diagnosis of candidemia, a severe bloodstream infection caused by Candida spp., remains challenging in clinical practice. Blood culture, the current gold standard technique, suffers from lengthy turnaround times and limited sensitivity. To address these limitations, we propose a novel approach utilizing an Electronic Nose (E-nose) combined with Time Series-based classification techniques to analyze and identify Candida spp. rapidly, using culture species of C. albicans, C.kodamaea ohmeri, C. glabrara, C. haemulonii, C. parapsilosis and C. krusei as control samples. This innovative method not only enhances diagnostic accuracy and reduces decision time for healthcare professionals in selecting appropriate treatments but also offers the potential for expanded usage and cost reduction due to the E-nose's low production costs. Our proof-of-concept experimental results, carried out with culture samples, demonstrate promising outcomes, with the Inception Time classifier achieving an impressive average accuracy of 97.46% during the test phase. This paper presents a groundbreaking advancement in the field, empowering medical practitioners with an efficient and reliable tool for early and precise identification of candidemia, ultimately leading to improved patient outcomes.

Diffusion maps of Bacillus subtilis biofilms via magnetic resonance imaging highlight a complex network of channels.

Bacillus subtilis microorganism when cultivated under chemically-defined conditions developed a biofilm with an unusual pattern of wrinkles on the surface. Some questions were raised about whether there was a special function of these wrinkles for the biofilm itself, since they resembled microchannels that could be involved in the transport of nutrients within the biofilm. Since the diffusion is the main mechanism for nutrient transport to biofilm from the medium, the role of these wrinkled structures in the whole diffusion within the biofilm was investigated using diffusion-weighted magnetic resonance imaging (DW-MRI). Data from these diffusion images was used to generate 2D diffusion maps which highlighted the striking channel features of the biofilm surface. The diffusion maps revealed a network of interconnected channels, with self-diffusion coefficients higher in the microchannels than in other regions of the biofilms. Polar plots made from 2D diffusion maps obtained from the plane of the biofilm show an anisotropy of the diffusion inside the microchannels, with the diffusion higher when along the principal direction of the microchannels. These results agree with the model, that the buckling of the biofilm surface from the B. subtilis creates microchannels that can enhance diffusion throughout the biofilm.