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Antibiotic resistance challenges the treatment of bacterial biofilm-related infections, but the use of nanoparticles as a treatment is a promising strategy to overcome bacterial infections. This study applied nitrogen-doped titanium dioxide (N-TiO2) conjugated with folic acid (FA) on biofilm-forming resistant bacteria. The photocatalytic effect of TiO2 nanoparticles (NPs) was studied under ultraviolet (UV), visible light, and dark conditions at 60, 120, and 180 min against planktonic cells and biofilms of Staphylococcus aureus, methicillin-resistant Staphylococcus aureus (MRSA), and Pseudomonas aeruginosa. TiO2 NPs were in the anatase phase, spherical shaped with sizes of 10-13 nm, and effectively doped and conjugated with N and FA. The FA-conjugated nanoparticles (N-TiO2-FA and FA-TiO2) were shown to have a bactericidal effect on all bacteria between 60 and 180 min under UV and visible light conditions. Concerning biofilms, N-TiO2-FA was shown to have a highly disruptive effect on all bacterial biofilms under UV irradiation at 180 min. Meanwhile, the nanoparticles did not show DNA damaging potential and they had no cytostatic effect, indicating that these NPs are biocompatible. In sum, nanoparticle conjugation with FA promoted photocatalytic effectiveness, revealing the promise this nanomaterial holds as a biocompatible antimicrobial agent.
RESUMO
BACKGROUND: Breakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial-temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA). RESULTS: Roots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001. CONCLUSIONS: The experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.
