Neoagarooligosaccharide Protects against Hepatic Fibrosis via Inhibition of TGF-β/Smad Signaling Pathway

Hepatic fibrosis occurs when liver tissue becomes scarred from repetitive liver injury and inflammatory responses; it can progress to cirrhosis and eventually to hepatocellular carcinoma. Previously, we reported that neoagarooligosaccharides (NAOs), produced by the hydrolysis of agar by β-agarases, have hepatoprotective effects against acetaminophen overdose-induced acute liver injury. However, the effect of NAOs on chronic liver injury, including hepatic fibrosis, has not yet been elucidated. Therefore, we examined whether NAOs protect against fibrogenesis in vitro and in vivo. NAOs ameliorated PAI-1, α-SMA, CTGF and fibronectin protein expression and decreased mRNA levels of fibrogenic genes in TGF-β-treated LX-2 cells. Furthermore, downstream of TGF-β, the Smad signaling pathway was inhibited by NAOs in LX-2 cells. Treatment with NAOs diminished the severity of hepatic injury, as evidenced by reduction in serum alanine aminotransferase and aspartate aminotransferase levels, in carbon tetrachloride (CCl₄)-induced liver fibrosis mouse models. Moreover, NAOs markedly blocked histopathological changes and collagen accumulation, as shown by H&E and Sirius red staining, respectively. Finally, NAOs antagonized the CCl₄-induced upregulation of the protein and mRNA levels of fibrogenic genes in the liver. In conclusion, our findings suggest that NAOs may be a promising candidate for the prevention and treatment of chronic liver injury via inhibition of the TGF-β/Smad signaling pathway.     

Comparison of yield and metabolites according to the types of hilling materials utilized during Glehnia littoralis sprout vegetable cultivation

Food Science and Biotechnology - Various hilling materials (rice hulls, pine sawdust, and perlite) were compared to produce sprout vegetables using beach silvertop (Glehnia littoralis Fr. Schm. ex...     

Facile preparation and strong adhesive strength of honeycomb polyurethane films with small pore diameter

With the continuous development of bionics, such as, geckos and virginia creeper with both superhydrophobic and super-adhesive, the surface wetting and super-adhesive properties of various porous materials have attracted extensive attention of the scientific and medical communities. Here, the honeycomb polyurethane (PU) porous films with strong adhesion were successfully prepared by microphase separation method and the effects of growth parameters on their microstructure and adhesive strength to ice were investigated. It was found that a high relative humidity (e.g., 100%) and a low solution concentration (e.g., 2%) facilitated the formation of ordered honeycomb PU porous films, and as-prepared PU pores with average pore diameter as small as 5 μm are better ordered and more uniform than these in related documents. Although the contact angle of water droplets on the surface of PU porous films increased from the premodification value of 85–130° to more than 160° after surface modification with polydopamine (PDA), the corresponding rolling angle remained approximately constant (180°), indicating that the surface of PU porous films has strong adhesion similar to geckos and virginia creeper. Furthermore, at lower temperature, the PU porous films exhibited the high adhesive strength of 142.13 kPa on ice, which was strongly dependent on the porous microstructures and surface compositions. The improved adhesive behavior to ice of honeycomb PU porous films modified with PDA provides new strategies for surface modification of materials and potential applications in medical domain.     

Progressive NO2 Sensors with Rapid Alarm and Persistent Memory-Type Responses for Wide-Range Sensing Using Antimony Triselenide Nanoflakes

Antimony triselenide (Sb₂Se₃) nanoflake-based nitrogen dioxide (NO₂) sensors exhibit a progressive bifunctional gas-sensing performance, with a rapid alarm for hazardous highly concentrated gases, and an advanced memory-type function for low-concentration (<1 ppm) monitoring repeated under potentially fatal exposure. Rectangular and cuboid shaped Sb₂Se₃ nanoflakes, comprising van der Waals planes with large surface areas and covalent bond planes with small areas, can rapidly detect a wide range of NO₂ gas concentrations from 0.1 to 100 ppm. These Sb₂Se₃ nanoflakes are found to be suitable for physisorption-based gas sensing owing to their anisotropic quasi-2D crystal structure with extremely enlarged van der Waals planes, where they are humidity-insensitive and consequently exhibit an extremely stable baseline current. The Sb₂Se₃ nanoflake sensor exhibits a room-temperature/low-voltage operation, which is noticeable owing to its low energy consumption and rapid response even under a NO₂ gas flow of only 1 ppm. As a result, the Sb₂Se₃ nanoflake sensor is suitable for the development of a rapid alarm system. Furthermore, the persistent gas-sensing conductivity of the sensor with a slow decaying current can enable the development of a progressive memory-type sensor that retains the previous signal under irregular gas injection at low concentrations.     

A Case Report of Germline Compound Heterozygous Mutations in the BRCA1 Gene of an Ovarian and Breast Cancer Patient

The germline carrier of the BRCA1 pathogenic mutation has been well proven to confer an increased risk of breast and ovarian cancer. Despite BRCA1 biallelic pathogenic mutations being extremely rare, they have been reported to be embryonically lethal or to cause Fanconi anemia (FA). Here we describe a patient who was a 48-year-old female identified with biallelic pathogenic mutations of the BRCA1 gene, with no or very subtle FA-features. She was diagnosed with ovarian cancer and breast cancer at the ages of 43 and 44 and had a strong family history of breast and gynecological cancers.     

Multimodal sensing and active continuous closed‐loop feedback for achieving reliable manipulation in the outdoor physical world

The use of field robots can greatly decrease the amount of time, effort, and associated risk compared to if human workers were to carryout certain tasks such as disaster response. However, transportability and reliability remain two main issues for most current robot systems. To address the issue of transportability, we have developed a lightweight modularizable platform named AeroArm. To address the issue of reliability, we utilize a multimodal sensing approach, combining the use of multiple sensors and sensor types, and the use of different detection algorithms, as well as active continuous closed‐loop feedback to accurately estimate the state of the robot with respect to the environment. We used Challenge 2 of the 2017 Mohammed Bin Zayed International Robotics Competition as an example outdoor manipulation task, demonstrating the capabilities of our robot system and approach in achieving reliable performance in the fields, and ranked fifth place internationally in the competition.     

Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations

Sustainable and efficient food supply chain has become an essential component of one’s life. The model proposed in this paper is deeply linked to people's quality of life as a result of which there is a large incentive to fulfil customer demands through it. This proposed model can enhance food quality by making the best possible food quality accessible to customers, construct a sustainable logistics system considering its environmental impact and ensure the customer demand to be fulfilled as fast as possible. In this paper, an extended model is examined that builds a unified planning problem for efficient food logistics operations where four important objectives are viewed: minimising the total expense of the system, maximising the average food quality along with the minimisation of the amount of CO₂ emissions in transportation along with production and total weighted delivery lead time minimisation. A four objective mixed integer linear programming model for intelligent food logistics system is developed in the paper. The optimisation of the formulated mathematical model is proposed using a modified multi-objective particle swarm optimisation algorithm with multiple social structures: MO-GLNPSO (Multi-Objective Global Local Near-Neighbour Particle Swarm Optimisation). Computational results of a case study on a given dataset as well as on multiple small, medium and large-scale datasets followed by sensitivity analysis show the potency and effectiveness of the introduced method. Lastly, there has been a scope for future study displayed which would lead to the further progress of these types of models.