Inhibitory effect of 405 nm laser light on bacterial biofilm in urethral stent.

The clinical use of urethral stents is usually complicated by various adverse effects, including dysuria, fever, and urinary tract infection (UTI). Biofilms (formed by bacteria, such as Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus) adhering to the stent cause UTIs in stented patients (approximately 11%). The undesirable consequences of antibiotics use include bacterial resistance, weight gain, and type 1 diabetes, which occur when antibiotics are used for a long time. We aimed to assess the efficacy of a new optical treatment with a 405 nm laser to inhibit bacterial growth in a urethral stent in vitro. The urethral stent was grown in S. aureus broth media for three days to induce biofilm formation under dynamic conditions. Various irradiation times with the 405 nm laser light were tested (5, 10, and 15 min). The efficacy of the optical treatment on biofilms was evaluated quantitatively and qualitatively. The production of reactive oxygen species helped eliminate the biofilm over the urethral stent after 405 nm irradiation. The inhibition rate corresponded to a 2.2 log reduction of colony-forming units/mL of bacteria after 0.3 W/cm2 of irradiation for 10 min. The treated stent showed a significant reduction in biofilm formation compared with the untreated stent, as demonstrated by SYTO 9 and propidium iodide staining. MTT assays using the CCD-986sk cell line revealed no toxicity after 10 min of irradiation. We conclude that optical treatment with 405 nm laser light inhibits bacterial growth in urethral stents with no or minimal toxicity.

Automated Structural Analysis and Quantitative Characterization of Scar Tissue Using Machine Learning.

An analysis of scar tissue is necessary to understand the pathological tissue conditions during or after the wound healing process. Hematoxylin and eosin (HE) staining has conventionally been applied to understand the morphology of scar tissue. However, the scar lesions cannot be analyzed from a whole slide image. The current study aimed to develop a method for the rapid and automatic characterization of scar lesions in HE-stained scar tissues using a supervised and unsupervised learning algorithm. The supervised learning used a Mask region-based convolutional neural network (RCNN) to train a pattern from a data representation using MMDetection tools. The K-means algorithm characterized the HE-stained tissue and extracted the main features, such as the collagen density and directional variance of the collagen. The Mask RCNN model effectively predicted scar images using various backbone networks (e.g., ResNet50, ResNet101, ResNeSt50, and ResNeSt101) with high accuracy. The K-means clustering method successfully characterized the HE-stained tissue by separating the main features in terms of the collagen fiber and dermal mature components, namely, the glands, hair follicles, and nuclei. A quantitative analysis of the scar tissue in terms of the collagen density and directional variance of the collagen confirmed 50% differences between the normal and scar tissues. The proposed methods were utilized to characterize the pathological features of scar tissue for an objective histological analysis. The trained model is time-efficient when used for detection in place of a manual analysis. Machine learning-assisted analysis is expected to aid in understanding scar conditions, and to help establish an optimal treatment plan.