HDAC6 Inhibition Prevents TNF-α-Induced Caspase 3 Activation in Lung Endothelial Cell and Maintains Cell-Cell Junctions

Pro-inflammatory mediators such as TNF-α induce caspase activation in endothelial cells, which leads to degradation of cellular proteins, induction of apoptotic signaling, and endothelial cell dysfunction. New therapeutic agents that can inhibit caspase activation may provide protection against inflammatory injury to endothelial cells. In the present study, we examined the effects of selective histone deacetylase 6 (HDAC6) inhibition on TNF-α induced caspase 3 activation and cell-cell junction dysfunction in lung endothelial cells. We also assessed the protective effects of HDAC6 inhibition against lung inflammatory injury in a mouse model of endotoxemia. We demonstrated that selective HDAC6 inhibition or knockdown of HDAC6 expression was able to prevent caspase 3 activation in lung endothelial cells and maintain lung endothelial cell-cell junctions. Mice pre-treated with HDAC6 inhibitors exhibited decreased endotoxin-induced caspase 3 activation and reduced lung vascular injury as indicated by the retention of cell-cell junction protein VE-Cadherin level and alleviated lung edema. Collectively, our data suggest that HDAC6 inhibition is a potent therapeutic strategy against inflammatory injury to endothelial cells

Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation

Social networks are considered to be heterogeneous graph neural networks (HGNNs) with deep learning technological advances. HGNNs, compared to homogeneous data, absorb various aspects of information about individuals in the training stage. That means more information has been covered in the learning result, especially sensitive information. However, the privacy-preserving methods on homogeneous graphs only preserve the same type of node attributes or relationships, which cannot effectively work on heterogeneous graphs due to the complexity. To address this issue, we propose a novel heterogeneous graph neural network privacy-preserving method based on a differential privacy mechanism named HeteDP, which provides a double guarantee on graph features and topology. In particular, we first define a new attack scheme to reveal privacy leakage in the heterogeneous graphs. Specifically, we design a two-stage pipeline framework, which includes the privacy-preserving feature encoder and the heterogeneous link reconstructor with gradients perturbation based on differential privacy to tolerate data diversity and against the attack. To better control the noise and promote model performance, we utilize a bi-level optimization pattern to allocate a suitable privacy budget for the above two modules. Our experiments on four public benchmarks show that the HeteDP method is equipped to resist heterogeneous graph privacy leakage with admirable model generalization

Editorial: Exploring sustainable strategies for active compounds from low-quality crops: Extraction, package, and development

With the explosive growth of the global population and the rapid development of agriculture and food technology, people have shown a huge demand for high-quality and diverse food (Tripathi et al., 2019), followed by the rapid development and expansion of the food processing industry. However, this also raises new problems. To pursue products with higher nutritional value on the basis of ensuring cost stability and work efficiency, food enterprises will produce a series of low-quality by-products in each link of the food industry. They directly waste numerous low-quality raw materials, and of course, numerous nutrients contained in them can also be wasted (Spiker et al., 2017). Considering the large global population base, how to realize the value-added of low-quality crops has become a hot topic in recent years (AliAkbari et al., 2021). In view of this, many researchers have put forward some ideas, such as using low-value crops as biomass energy (Jin et al., 2018; Ganesh et al., 2022), developing New foods (Ganesh et al., 2022), using advanced processing technology to produce high value-added products (Kewuyemi et al., 2022) and improving the extraction process of active substances (Putnik et al., 2018) etc. One strategy we have noticed is to extract and utilize the nutrient-active substances contained in these low-quality crops (Kita et al., 2023), to realize the reuse of extracted materials. In the future, we can not only realize the value-added of low-quality crops, but also use these extracted active substances to develop more functional foods to meet the needs of consumers, and even try to solve some technical problems faced by the current food industry to obtain products with higher market value. This paper analyzes the content of the four latest related studies to gain insight into the significance of these research results for the food processing industry and functional food development, and to provide theoretical guidance for future research directions

Response surface methodology used for statistical optimization of jiean-peptide production by Bacillus subtilise

Response surface methodology (RSM) was used for statistical optimization of jiean-peptide (JAA) production by Bacillus subtilise ZK8 cells adsorbed on wood chips to form a novel fermentation system. The Plackett-Burman design was used in the first step to evaluate the effects of eight factors, including six fermentation medium components and two cell adsorption conditions. Among the variables screened, soybean meal hydrolysate (SMH) and MgSO4\ub77H2O in the fermentation medium had significant effects on JAA production. In the second step, the concentrations of SMH and MgSO4\ub77H2O were further optimized using central composite designs and response surface analysis. The optimized concentration of SMH and MgSO4\ub77H2O was 24% (v/v) and 0.38% (w/v), respectively, which increased the production of JAA in a shake flask system by 41% relative to optimization of a single variable component of the culture medium

Hollow mesoporous silica nanoparticles for intracellular delivery of fluorescent dye

In this study, hollow mesoporous silica nanoparticles (HMSNs) were synthesized using the sol-gel/emulsion approach and its potential application in drug delivery was assessed. The HMSNs were characterized, by transmission electron microscopy (TEM), Scanning Electron Microscopy (SEM), nitrogen adsorption/desorption and Brunauer-Emmett-Teller (BET), to have a mesoporous layer on its surface, with an average pore diameter of about 2 nm and a surface area of 880 m2/g. Fluorescein isothiocyanate (FITC) loaded into these HMSNs was used as a model platform to assess its efficacy as a drug delivery tool. Its release kinetic study revealed a sequential release of FITC from the HMSNs for over a period of one week when soaked in inorganic solution, while a burst release kinetic of the dye was observed just within a few hours of soaking in organic solution. These FITC-loaded HMSNs was also found capable to be internalized by live human cervical cancer cells (HeLa), wherein it was quickly released into the cytoplasm within a short period of time after intracellular uptake. We envision that these HMSNs, with large pores and high efficacy to adsorb chemicals such as the fluorescent dye FITC, could serve as a delivery vehicle for controlled release of chemicals administered into live cells, opening potential to a diverse range of applications including drug storage and release as well as metabolic manipulation of cells

Simulation of Land Use Change and Habitat Quality in the Yellow River Basin under Multiple Scenarios

Habitat quality is the key to regional ecological restoration and green development, and land use change is an essential factor affecting habitat quality. Studying the spatial and temporal evolution characteristics of land use change and habitat quality under multiple scenarios is significant for regional ecological restoration and management, and for preventing future ecological and environmental risks. We used the improved Logistic-CA-Markov (Logistic-Cellular Automata-Markov) and InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) models to establish the spatial patterns of habitat quality in the Yellow River Basin from 2000 to 2040 and analyzed the characteristics of land use and habitat quality changes under scenarios of natural development (S1), ecological protection (S2), and urban expansion (S3). The results showed that in 2000, 2005, 2010, 2015, and 2020, the main land use types in the watershed were dryland and grassland, accounting for more than 72%. Paddy land, dryland, woodland, middle-coverage grassland, and unused land all showed decreasing trends, whereas all other land types showed increasing trends. Influenced by human activities and the environment, the watershed habitat quality was low, with 80% of the areas with middle to low grades, but the overall trend was rising. The spatial variability in habitat quality of the watershed was significant, with habitat quality improvements in the central and northern regions and continued deterioration around the cities in the southern and western parts. The spatial autocorrelation and aggregation of habitat quality in the watershed were strong, and future land use patterns in the study area had a significant relationship with human activities. Simulation of future scenarios revealed ecological conservation catalytic effects on habitat quality in the study area, whereas urban expansion deteriorated watershed habitat quality. This study could provide support for future ecological conservation decisions

Simulation of Land Use Change and Habitat Quality in the Yellow River Basin under Multiple Scenarios

Habitat quality is the key to regional ecological restoration and green development, and land use change is an essential factor affecting habitat quality. Studying the spatial and temporal evolution characteristics of land use change and habitat quality under multiple scenarios is significant for regional ecological restoration and management, and for preventing future ecological and environmental risks. We used the improved Logistic-CA-Markov (Logistic-Cellular Automata-Markov) and InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) models to establish the spatial patterns of habitat quality in the Yellow River Basin from 2000 to 2040 and analyzed the characteristics of land use and habitat quality changes under scenarios of natural development (S1), ecological protection (S2), and urban expansion (S3). The results showed that in 2000, 2005, 2010, 2015, and 2020, the main land use types in the watershed were dryland and grassland, accounting for more than 72%. Paddy land, dryland, woodland, middle-coverage grassland, and unused land all showed decreasing trends, whereas all other land types showed increasing trends. Influenced by human activities and the environment, the watershed habitat quality was low, with 80% of the areas with middle to low grades, but the overall trend was rising. The spatial variability in habitat quality of the watershed was significant, with habitat quality improvements in the central and northern regions and continued deterioration around the cities in the southern and western parts. The spatial autocorrelation and aggregation of habitat quality in the watershed were strong, and future land use patterns in the study area had a significant relationship with human activities. Simulation of future scenarios revealed ecological conservation catalytic effects on habitat quality in the study area, whereas urban expansion deteriorated watershed habitat quality. This study could provide support for future ecological conservation decisions