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Abstract Background 2'-Fucosyllactose (2-FL) is a functional oligosaccharide present in human milk which protects against the infection of enteric pathogens. Because 2-FL can be synthesized through the enzymatic fucosylation of lactose with guanosine 5′-diphosphate (GDP)-l-fucose by α-1,2-fucosyltransferase (FucT2), an 2-FL producing Escherichia coli can be constructed through overexpressing genes coding for endogenous GDP- l-fucose biosynthetic enzymes and heterologous fucosyltransferase. Results The gene for FucT2 from Helicobacter pylori was introduced to the GDP- l-fucose producing recombinant E. coli BL21 star(DE3) strain. However, only small amount of 2-FL was produced in a batch fermentation because the E. coli BL21star(DE3) strain assimilated lactose instead of converting to 2-FL. As an alternative host, the E. coli JM109(DE3) strain which is incapable of assimilating lactose was chosen as a 2-FL producer. Whole cell biosynthesis of 2-FL from lactose was investigated in a series of batch fermentations using various concentrations of lactose. The results of batch fermentations showed that lactose was slowly assimilated by the engineered E. coli JM109(DE3) strain and 2-FL was synthesized without supplementation of another auxiliary sugar for cell growth. A maximum 2-FL concentration of 1.23 g/l was obtained from a batch fermentation with 14.5 g/l lactose. The experimentally obtained yield (g 2-FL/g lactose) corresponded to 20% of the theoretical maximum yield estimated by the elementary flux mode (EFM) analysis. Conclusions The experimental 2-FL yield in this study corresponded to about 20% of the theoretical maximum yield, which suggests further modifications via metabolic engineering of a host strain or optimization of fermentation processes might be carried out for improving 2-FL yield. Improvement of microbial production of 2-FL from lactose by engineered E. coli would increase the feasibility of utilizing 2-FL as a prebiotic in various foods.

Key information for the maintenance and diagnosis of structures including bridges can be obtained from the processing of digital images acquired by unmanned aerial vehicle (UAV). However, low-quality images caused by various problems such as UAV movement, inspection environment, and camera parameters can lead to inappropriate structural evaluation due to the difficulty of digital image processing. Therefore, an appropriate assessment method for image quality considering the deterioration of the inspection image in the structural inspection procedure is required. In this study, a new image quality assessment (IQA) using a convolutional neural network (CNN) is proposed in consideration of various degradation factors that may occur in the structure inspection image. The first stage presents a method to obtain consistent quality against various interference factors of deterioration that may occur in inspection images. Adjusting the camera parameters minimizes the degradation of the inspection image. Subsequently, low- and high-quality images are distinguished according to the proposed image acquisition method. The second stage is the classification of the inspection dataset using the CNN-based image quality classifier model through training of data classified according to their quality. Experimental validation of the proposed method shows that the results are similar to the Human Visual System (HVS), which means subjective quality classification, and that the inspection image can be classified with more accurate and shorter processing time.

Background: Approximately half of the patients with advanced pancreatic ductal adenocarcinoma (PDAC) receive subsequent lines of chemotherapy. Recently, the liposomal irinotecan (nal-IRI) plus 5-fluorouracil/leucovorin (5-FU/LV) regimen is recommended as subsequent lines of chemotherapy. However, little is known about the predictive factors for the nal-IRI + 5-FU/LV regimen, especially in patients with previous irinotecan (IRI) exposure. Objectives: We investigated the predictive factors associated with nal-IRI + 5-FU/LV treatment in patients with PDAC. Design: Multicenter, retrospective cohort study. Methods: This study included patients with advanced PDAC who received the nal-IRI + 5-FU/LV regimen for palliative purposes. Results: Overall, 268 patients were treated with nal-IRI + 5-FU/LV. The median overall survival (OS) was 7.9 months (95% confidence interval (CI): 7.0–8.8), while the median progression-free survival (PFS) was 2.6 months (95% CI: 1.9–3.2). An albumin level of<4.0 g/dL, neutrophil-to-lymphocyte ratio (NLR) of ⩾3.5, liver or peritoneal metastasis, and a history of >3 lines of palliative chemotherapy were associated with worse OS. An NLR of ⩾3.5 and liver metastasis were significant predictive factors for worse PFS. Previous exposure to IRI was not a significant predictor. Patients without prior IRI (no-IRI) treatment showed relatively longer OS and PFS compared to IRI responders and nonresponders, but these differences were not significant when compared specifically to the responders (OS: 8.8 vs 8.1 months, p = 0.388; PFS: 3.6 vs 2.6 months, p = 0.126). Conclusion: An NLR of ⩾3.5 and liver metastasis were associated with worse PFS. Prior IRI exposure was not a significant predictive factor for OS and PFS, especially in IRI responders. [ABSTRACT FROM AUTHOR]

Urinary and vascular catheters are among the most commonly used medical devices. However, infections caused by biofilm formation on the surface of catheters are a major cause of healthcare-associated infections. Traditional methods, such as using antimicrobials to prevent such infections, generally have short-term effects, and treatment is challenging owing to the emergence of antimicrobial-resistant bacteria. This review aims to evaluate the limitations of conventional catheter-related infection prevention efficacy, such as currently used antimicrobials, and analyze the efficacy and limitations of potential alternatives to prevent catheter-related infections that have not yet been commercialized, classified by the transition stages of biofilm formation. We intend to provide profound insights into the ideal technologies for preventing catheter-associated tract infections and present perspectives on future directions in this field. [ABSTRACT FROM AUTHOR]

Duodenal mucosal resurfacing (DMR) by thermal ablation of the duodenal mucosa is a minimally invasive endoscopic procedure for controlling metabolic syndrome (MS). However, thermal energy can cause adverse effects due to deep mucosal injury, necessitating an additional mucosal lifting process, which complicate the procedures. Therefore, we aimed to develop a similar procedure using non-thermal photodynamic therapy (PDT) for DMR using a highly functional metal stent covered with photosensitizers (PSs) to minimize the potential risks of thermal ablation injury. We developed a novel PS stent enabling the controlled release of radical oxygen species with specific structures to prevent stent migration and duodenal stricture after ablation and performed an animal study (n = 8) to demonstrate the feasibility and safety of PDT for DMR. The stents were placed for 7 days to prevent duodenal strictures after PDT. To confirm PDT efficacy, we stained for gastric inhibitory polypeptide (GIP) and glucose transporter isoform 1. The PS stents were deployed, and PDT was applied without evidence of duodenal stricture, pancreatitis, or hemorrhage in any of the pigs. Microscopic evaluation indicated apoptosis of the mucosal cells in the irradiated duodenum on days 7 and 14, which recovered after day 28. Immunohistochemistry revealed suppressed GIP expression in the mucosal wall of the irradiated duodenum. Endoscopic PDT for DMR using PS stents could be applied safely in a porcine model and may result in decreased GIP secretion, which is a crucial mechanism in MS treatment. Further clinical studies are required to explore its safety and efficacy in patients with MS. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have been increasingly utilized for facility safety inspections due to their superior safety, cost effectiveness, and inspection accuracy compared to traditional manpower-based methods. High-resolution images captured by UAVs directly contribute to identifying and quantifying structural defects on facility exteriors, making image quality a critical factor in achieving accurate results. However, motion blur induced by external factors such as vibration, low light conditions, and wind during UAV operation significantly degrades image quality, leading to inaccurate defect detection and quantification. To address this issue, this research proposes a deblurring network using a Generative Adversarial Network (GAN) to eliminate the motion blur effect in UAV images. The GAN-based motion deblur network represents an image inpainting method that leverages generative models to correct blurry artifacts, thereby generating clear images. Unlike previous studies, this proposed approach incorporates deblur and blur learning modules to realistically generate blur images required for training the generative models. The UAV images processed using the motion deblur network are evaluated using a quality assessment method based on local blur map and other well-known image quality assessment (IQA) metrics. Moreover, in the experiment of crack detection utilizing the object detection system, improved detection results are observed when using enhanced images. Overall, this research contributes to improving the quality and accuracy of facility safety inspections conducted with UAV-based inspections by effectively addressing the challenges associated with motion blur effects in UAV-captured images.

As technology advances, the use of unmanned aerial vehicles (UAVs) and image sensors for structural monitoring and diagnostics is becoming increasingly critical. This approach enables the efficient inspection and assessment of structural conditions. Furthermore, the integration of deep learning techniques has been proven to be highly effective in detecting damage from structural images, as demonstrated in our study. To enable effective learning by deep learning models, a substantial volume of data is crucial, but collecting appropriate instances of structural damage from real-world scenarios poses challenges and demands specialized knowledge, as well as significant time and resources for labeling. In this study, we propose a methodology that utilizes a generative adversarial network (GAN) for image-to-image translation, with the objective of generating synthetic structural damage data to augment the dataset. Initially, a GAN-based image generation model was trained using paired datasets. When provided with a mask image, this model generated an RGB image based on the annotations. The subsequent step generated domain-specific mask images, a critical task that improved the data augmentation process. These mask images were designed based on prior knowledge to suit the specific characteristics and requirements of the structural damage dataset. These generated masks were then used by the GAN model to produce new RGB image data incorporating various types of damage. In the experimental validation conducted across the three datasets to assess the image generation for data augmentation, our results demonstrated that the generated images closely resembled actual images while effectively conveying information about the newly introduced damage. Furthermore, the experimental validation of damage detection with augmented data entailed a comparative analysis between the performance achieved solely with the original dataset and that attained with the incorporation of additional augmented data. The results for damage detection consistently demonstrated that the utilization of augmented data enhanced performance when compared to relying solely on the original images.

According to data from the Ministry of Employment and Labor in Korea, a significant portion of fatal accidents on construction sites occur due to collisions between construction workers and equipment, with many of these collisions being attributed to worker negligence. This study introduces a method for accurately localizing construction equipment and workers on-site, delineating areas prone to collisions as ‘a danger area of a collision’, and defining collision risk states. Utilizing advanced deep learning models which specialize in object detection, video footage obtained from strategically placed closed-circuit television (CCTV) cameras across the construction site is analyzed. The positions of each detected object are determined using transformation or homography matrices representing the conversion relationship between a sufficiently flat reference plane and image coordinates. Additionally, ‘a danger area of a collision’ is proposed for evaluating equipment collision risk based on the moving equipment’s speed, and the validity of this area is verified. Through this, the paper presents a system designed to preemptively identify potential collision risks, particularly when workers are located within the ‘danger area of a collision’, thereby mitigating accident risks on construction sites.