Analysis of ion transport properties of Glycine max HKT transporters and identifying a regulation of GmHKT1;1 by the non-functional GmHKT1;4.

The HKT transporter plays an important role for plants in response to salt stress, but the transport property of the soybean HKT transporters at the molecular level is still unclear. Here, using Xenopus oocyte as a heterologous expression system and two-electrode voltage-clamp technique, we identified four HKT transporters, GmHKT1;1, GmHKT1;2, GmHKT1;3, and GmHKT1;4, which all belong to type I subfamily, but having distinct ion transport properties. While GmHKT1;1, GmHKT1;2 and GmHKT1;3 function as Na+ transporters, GmHKT1;1 is less selective against K+ than the two other transporters. Astonishingly, GmHKT1;4, which lacks transmembrane segments and has no ion permeability, is significantly expressed, and its gene expression pattern is different from the other three GmHKTs under salt stress. Interestingly, GmHKT1;4 reduced the Na+/K+ currents mediated by GmHKT1;1. Further study showed that the transport ability of GmHKT1;1 regulated by GmHKT1;4 was related to the structural differences in the first intracellular domain and the fourth repeat domain. Overall, we have identified one unique GmHKT member, GmHKT1;4, which modulates the Na+ and K+ transport ability of GmHKT1;1 via direct interaction. Thus, we have revealed a new type of HKTs interaction model for altering their ion transport properties.

Dietary rutin improves the antidiarrheal capacity of weaned piglets by improving intestinal barrier function, antioxidant capacity and cecal microbiota composition.

BACKGROUND: Early weaning is prone to damage intestinal barrier function, resulting in diarrhea, whereas rutin, as a natural flavonoid with multiple biological functions, shows potential in piglets. Therefore, the effects of dietary rutin on growth, antidiarrheal, barrier function, antioxidant status and cecal microbiota of weaned piglets were investigated with the control group (CON) (basal diet) and Rutin (basal diet+500 mg kg-1 rutin) groups fed for 14 days. RESULTS: The results showed that dietary 500 mg kg-1 rutin significantly decreased diarrhea index, serum diamine oxidase activity and total aerobic bacterial population in mesenteric lymph nodes, whereas it significantly increased the gain-to-feed ratio (G:F) and serum growth hormone content, jejunal villus height and villus height to crypt depth ratio, and also enhanced jejunal claudin-1 and zonula occludens-1 mRNA and protein expression. Meanwhile, dietary rutin significantly decreased inflammation-associated mRNA expression, malondialdehyde (MDA) content, swollen mitochondrial number and mitochondrial area in the jejunum, whereas it increased the total superoxide dismutase (T-SOD) and glutathione peroxidase activities and activated the Nrf2 signaling pathway. Moreover, dietary rutin significantly increased Firmicutes abundance and decreased Campylobacterota abundance, which were closely associated with the decreased diarrhea index and MDA content or increased Claudin-1 expression and T-SOD activity. CONCLUSION: Dietary 500 mg kg-1 rutin increased G:F by improving intestinal morphology, and alleviated diarrhea by enhancing intestinal barrier, which might be associated with the enhanced antioxidant capacity via activating the Nrf2/Keap1 signaling pathway and the improved cecal microbial composition in weaned piglets. © 2024 Society of Chemical Industry.

Comprehensive advancement in endoscopy: optical design, algorithm enhancement, and clinical validation for merged WLI and CBI imaging.

As endoscopic imaging technology advances, there is a growing clinical demand for enhanced imaging capabilities. Although conventional white light imaging (WLI) endoscopy offers realistic images, it often cannot reveal detailed characteristics of the mucosa. On the other hand, optical staining endoscopy, such as Compound Band Imaging (CBI), can discern subtle structures, serving to some extent as an optical biopsy. However, its image brightness is low, and the colors can be abrupt. These two techniques, commonly used in clinical settings, have complementary advantages. Nonetheless, they require different lighting conditions, which makes it challenging to combine their imaging strengths on living tissues. In this study, we introduce a novel endoscopic imaging technique that effectively combines the advantages of both WLI and CBI. Doctors don't need to manually switch between these two observation modes, as they can obtain the image information of both modes in one image. We calibrated an appropriate proportion for simultaneous illumination with the light required for WLI and CBI. We designed a new illumination spectrum tailored for gastrointestinal examination, achieving their fusion at the optical level. Using a new algorithm that focuses on enhancing specific hemoglobin tissue features, we restored narrow-band image characteristics lost due to the introduction of white light. Our hardware and software innovations not only boost the illumination brightness of the endoscope but also ensure the narrow-band feature details of the image. To evaluate the reliability and safety of the new endoscopic system, we conducted a series of tests in line with relevant international standards and validated the design parameters. For clinical trials, we collected a total of 256 sets of images, each set comprising images of the same lesion location captured using WLI, CBI, and our proposed method. We recruited four experienced clinicians to conduct subjective evaluations of the collected images. The results affirmed the significant advantages of our method. We believe that the novel endoscopic system we introduced has vast potential for clinical application in the future.

A Monocular Variable Magnifications 3D Laparoscope System Using Double Liquid Lenses.

During minimal invasive surgery (MIS), the laparoscope only provides a single viewpoint to the surgeon, leaving a lack of 3D perception. Many works have been proposed to obtain depth and 3D reconstruction by designing a new optical structure or by depending on the camera pose and image sequences. Most of these works modify the structure of the conventional laparoscopes and cannot provide 3D reconstruction of different magnification views. In this study, we propose a laparoscopic system based on double liquid lenses, which provide doctors with variable magnification rates, near observation, and real-time monocular 3D reconstruction. Our system composes of an optical structure that can obtain auto magnification change and autofocus without any physically moving element, and a deep learning network based on the Depth from Defocus (DFD) method, trained to suit inconsistent camera intrinsic situations and estimate depth from images of different focal lengths. The optical structure is portable and can be mounted on conventional laparoscopes. The depth estimation network estimates depth in real-time from monocular images of different focal lengths and magnification rates. Experiments show that our system provides a 0.68-1.44x zoom rate and can estimate depth from different magnification rates at 6fps. Monocular 3D reconstruction reaches at least 6mm accuracy. The system also provides a clear view even under 1mm close working distance. Ex-vivo experiments and implementation on clinical images prove that our system provides doctors with a magnified clear view of the lesion, as well as quick monocular depth perception during laparoscopy, which help surgeons get better detection and size diagnosis of the abdomen during laparoscope surgeries.

A Fast-scanning Protocol of Optical Coherence Tomography Angiography Based on Structure-Aware Attention.

Oral diseases have imposed a heavy social and financial burden on many countries and regions. If left untreated, severe cases can lead to malignant tumours. Common devices can no longer meet the high-resolution and non-invasive requirement, while Optical Coherence Tomography Angiography (OCTA) provides an ideal perspective for detecting vascular microcirculation. However, acquiring high-quality OCTA images takes time and can result in unpredictable motion artefacts. Therefore, we propose a systematic workflow for rapid OCTA data acquisition. Initially, we implement a fourfold reduction in sampling points to enhance the scanning speed. Then, we apply a deep neural network for rapid image reconstruction, elevating the resolution to the level achieved through full scanning. Specifically, it is a hybrid attention model with a structure-aware loss to extract local and global information on angiography, which improves the visualisation performance and quantitative metrics of numerous classical and recent-presented models by 3.536%-9.943% in SSIM and 0.930%-2.946% in MS-SSIM. Through this approach, the time of constructing one OCTA volume can be reduced from nearly 30 s to about 3 s. The rapid-scanning protocol of high-quality imaging also presents feasibility for future real-time detection applications.

A robot-assisted tracheal intubation system based on a soft actuator?

PURPOSE: Tracheal intubation is the gold standard of airway protection and constitutes a pivotal life-saving technique frequently employed in emergency medical interventions. Hence, in this paper, a system is designed to execute tracheal intubation tasks automatically, offering a safer and more efficient solution, thereby alleviating the burden on physicians. METHODS: The system comprises a tracheal tube with a bendable front end, a drive system, and a tip endoscope. The soft actuator provides two degrees of freedom for precise orientation. It is fabricated with varying-hardness silicone and reinforced with fibers and spiral steel wire for flexibility and safety. The hydraulic actuation system and tube feeding mechanism enable controlled bending and delivery. Object detection of key anatomical features guides the robotic arm and soft actuator. The control strategy involves visual servo control for coordinated robotic arm and soft actuator movements, ensuring accurate and safe tracheal intubation. RESULTS: The kinematics of the soft actuator were established using a constant curvature model, allowing simulation of its workspace. Through experiments, the actuator is capable of 90° bending as well as 20° deflection on the left and right sides. The maximum insertion force of the tube is 2 N. Autonomous tracheal intubation experiments on a training manikin were successful in all 10 trials, with an average insertion time of 45.6 s. CONCLUSION: Experimental validation on the manikin demonstrated that the robot tracheal intubation system based on a soft actuator was able to perform safe, stable, and automated tracheal intubation. In summary, this paper proposed a safe and automated robot-assisted tracheal intubation system based on a soft actuator, showing considerable potential for clinical applications.

Better Rough than Scarce: Proximal Femur Fracture Segmentation with Rough Annotations.

Proximal femoral fracture segmentation in computed tomography (CT) is essential in the preoperative planning of orthopedic surgeons. Recently, numerous deep learning-based approaches have been proposed for segmenting various structures within CT scans. Nevertheless, distinguishing various attributes between fracture fragments and soft tissue regions in CT scans frequently poses challenges, which have received comparatively limited research attention. Besides, the cornerstone of contemporary deep learning methodologies is the availability of annotated data, while detailed CT annotations remain scarce. To address the challenge, we propose a novel weakly-supervised framework, namely Rough Turbo Net (RT-Net), for the segmentation of proximal femoral fractures. We emphasize the utilization of human resources to produce rough annotations on a substantial scale, as opposed to relying on limited fine-grained annotations that demand a substantial time to create. In RT-Net, rough annotations pose fractured-region constraints, which have demonstrated significant efficacy in enhancing the accuracy of the network. Conversely, the fine annotations can provide more details for recognizing edges and soft tissues. Besides, we design a spatial adaptive attention module (SAAM) that adapts to the spatial distribution of the fracture regions and align feature in each decoder. Moreover, we propose a fine-edge loss which is applied through an edge discrimination network to penalize the absence or imprecision edge features. Extensive quantitative and qualitative experiments demonstrate the superiority of RT-Net to state-of-the-art approaches. Furthermore, additional experiments show that RT-Net has the capability to produce pseudo labels for raw CT images that can further improve fracture segmentation performance and has the potential to improve segmentation performance on public datasets. The code is available at: https://github.com/zyairelu/RT-Net.