DEGAP: Dynamic elongation of a genome assembly path.

Genome assembly remains to be a major task in genomic research. Despite the development over the past decades of different assembly software programs and algorithms, it is still a great challenge to assemble a complete genome without any gaps. With the latest DNA circular consensus sequencing (CCS) technology, several assembly programs can now build a genome from raw sequencing data to contigs; however, some complex sequence regions remain as unresolved gaps. Here, we present a novel gap-filling software, DEGAP (Dynamic Elongation of a Genome Assembly Path), that resolves gap regions by utilizing the dual advantages of accuracy and length of high-fidelity (HiFi) reads. DEGAP identifies differences between reads and provides 'GapFiller' or 'CtgLinker' modes to eliminate or shorten gaps in genomes. DEGAP adopts an iterative elongation strategy that automatically and dynamically adjusts parameters according to three complexity factors affecting the genome to determine the optimal extension path. DEGAP has already been successfully applied to decipher complex genomic regions in several projects and may be widely employed to generate more gap-free genomes.

Whole-genome sequencing of allotetraploid bermudagrass reveals the origin of Cynodon and candidate genes for salt tolerance.

Bermudagrass (Cynodon dactylon) is a globally distributed, extensively used warm-season turf and forage grass with high tolerance to salinity and drought stress in alkaline environments. However, the origin of the species and genetic mechanisms for salinity tolerance in the species are basically unknown. Accordingly, we set out to study evolution divergence events in the Cynodon genome and to identify genes for salinity tolerance. We developed a 604.0 Mb chromosome-level polyploid genome sequence for bermudagrass 'A12359' (n = 18). The C. dactylon genome comprises 2 complete sets of homoeologous chromosomes, each with approximately 30 000 genes, and most genes are conserved as syntenic pairs. Phylogenetic study showed that the initial Cynodon species diverged from Oropetium thomaeum approximately 19.7-25.4 million years ago (Mya), the A and B subgenomes of C. dactylon diverged approximately 6.3-9.1 Mya, and the bermudagrass polyploidization event occurred 1.5 Mya on the African continent. Moreover, we identified 82 candidate genes associated with seven agronomic traits using a genome-wide association study, and three single-nucleotide polymorphisms were strongly associated with three salt resistance genes: RAP2-2, CNG channels, and F14D7.1. These genes may be associated with enhanced bermudagrass salt tolerance. These bermudagrass genomic resources, when integrated, may provide fundamental insights into evolution of diploid and tetraploid genomes and enhance the efficacy of comparative genomics in studying salt tolerance in Cynodon.

π-π Interaction-Induced Organic Long-wavelength Room-Temperature Phosphorescence for In Vivo Atherosclerotic Plaque Imaging.

Room-temperature phosphorescent (RTP) materials have great potential for in vivo imaging because they can circumvent the autofluorescence of biological tissues. In this study, a class of organic-doped long-wavelength (≈600 nm) RTP materials with benzo[c][1,2,5] thiadiazole as a guest was constructed. Both host and guest molecules have simple structures and can be directly purchased commercially at a low cost. Owing to the long phosphorescence wavelength of the doping system, it exhibited good tissue penetration (10 mm). Notably, these RTP nanoparticles were successfully used to image atherosclerotic plaques, with a signal-to-background ratio (SBR) of 44.52. This study provides a new approach for constructing inexpensive red organic phosphorescent materials and a new method for imaging cardiovascular diseases using these materials.

Identification of tumor immunophenotypes associated with immunotherapy response in bladder cancer.

OBJECTIVES: This study aims to reveal immunophenotypes associated with immunotherapy response in bladder cancer, identify the signature genes of immune subtypes, and provide new molecular targets for improving immunotherapy response. METHODS: Bladder cancer immunophenotypes were characterized in the bulk RNA sequencing dataset GSE32894 and Imvigor210, and gene expression signatures were established to identify the immunophenotypes. Expression of gene signatures were validated in single-cell RNA sequencing dataset GSE145140 and human proteins expression data source. Investigation of Immunotherapy Response was performed in IMvigor210 dataset. Prognosis of tumor immunophenotypes was further analyzed. RESULTS: Inflamed and immune-excluded immunophenotypes were characterized based on the tumor immune cell scores. Risk score models that were established rely on RNA sequencing profiles and overall survival of bladder cancer cohorts. The inflamed tumors had lower risk scores, and the low-risk tumors were more likely to respond to atezolizumab, receiving complete response/partial response (CR/PR). Patients who responded to atezolizumab had higher SRRM4 and lower NPHS1 and TMEM72 expression than the non-responders. SRRM4 expression was a protective factor for bladder cancer prognosis, while the NPHS1 and TMEM72 showed the opposite pattern. CONCLUSION: This study provided a novel classification method for tumor immunophenotypes. Bladder cancer immunophenotypes can predict the response to immune checkpoint blockade. The immunophenotypes can be identified by the expression of signature genes.

Using Feature Engineering and Principal Component Analysis for Monitoring Spindle Speed Change Based on Kullback-Leibler Divergence with a Gaussian Mixture Model.

Machining is a crucial constituent of the manufacturing industry, which has begun to transition from precision machinery to smart machinery. Particularly, the introduction of artificial intelligence into computer numerically controlled (CNC) machine tools will enable machine tools to self-diagnose during operation, improving the quality of finished products. In this study, feature engineering and principal component analysis were combined with the online and real-time Gaussian mixture model (GMM) based on the Kullback-Leibler divergence's measure to achieve the real-time monitoring of changes in manufacturing parameters. Based on the attached accelerometer device's vibration signals and current sensing of the spindle, the developed GMM unsupervised learning was successfully used to diagnose the spindle speed changes of a CNC machine tool during milling. The F1-scores with improved experimental results for X, Y, and Z axes were 0.95, 0.88, and 0.93, respectively. The established FE-PCA-GMM/KLD method can be applied to issue warnings when it predicts a change in the manufacturing process parameter. A smart sensing device for diagnosing the machining status can be fabricated for implementation. The effectiveness of the developed method for determining the manufacturing parameter changes was successfully verified by experiments.

Manipulating XXY Planar Platform Positioning Accuracy by Computer Vision Based on Reinforcement Learning.

With the rise of Industry 4.0 and artificial intelligence, the demand for industrial automation and precise control has increased. Machine learning can reduce the cost of machine parameter tuning and improve high-precision positioning motion. In this study, a visual image recognition system was used to observe the displacement of an XXY planar platform. Ball-screw clearance, backlash, nonlinear frictional force, and other factors affect the accuracy and reproducibility of positioning. Therefore, the actual positioning error was determined by inputting images captured by a charge-coupled device camera into a reinforcement Q-learning algorithm. Time-differential learning and accumulated rewards were used to perform Q-value iteration to enable optimal platform positioning. A deep Q-network model was constructed and trained through reinforcement learning for effectively estimating the XXY platform's positioning error and predicting the command compensation according to the error history. The constructed model was validated through simulations. The adopted methodology can be extended to other control applications based on the interaction between feedback measurement and artificial intelligence.

A Morphing Point-to-Point Displacement Control Based on Long Short-Term Memory for a Coplanar XXY Stage.

In this study, visual recognition with a charge-coupled device (CCD) image feedback control system was used to record the movement of a coplanar XXY stage. The position of the stage is fedback through the image positioning method, and the positioning compensation of the stage is performed by the image compensation control parameter. The image resolution was constrained and resulted in an average positioning error of the optimized control parameter of 6.712 µm, with the root mean square error being 2.802 µm, and the settling time being approximately 7 s. The merit of a long short-term memory (LSTM) deep learning model is that it can identify long-term dependencies and sequential state data to determine the next control signal. As for improving the positioning performance, LSTM was used to develop a training model for stage motion with an additional dial indicator with an accuracy of 1 µm being used to record the XXY position information. After removing the assisting dial indicator, a new LSTM-based XXY feedback control system was subsequently constructed to reduce the positioning error. In other words, the morphing control signals are dependent not only on time, but also on the iterations of the LSTM learning process. Point-to-point commanded forward, backward and repeated back-and-forth repetitive motions were conducted. Experimental results revealed that the average positioning error achieved after using the LSTM model was 2.085 µm, with the root mean square error being 2.681 µm, and a settling time of 2.02 s. With the assistance of LSTM, the stage exhibited a higher control accuracy and less settling time than did the CCD imaging system according to three positioning indices.

Enhancing UAV Visual Landing Recognition with YOLO's Object Detection by Onboard Edge Computing.

A visual camera system combined with the unmanned aerial vehicle (UAV) onboard edge computer should deploy an efficient object detection ability, increase the frame per second rate of the object of interest, and the wide searching ability of the gimbal camera for finding the emergent landing platform and for future reconnaissance area missions. This paper proposes an approach to enhance the visual capabilities of this system by using the You Only Look Once (YOLO)-based object detection (OD) with Tensor RTTM acceleration technique, an automated visual tracking gimbal camera control system, and multithread programing for image transmission to the ground station. With lightweight edge computing (EC), the mean average precision (mAP) was satisfied and we achieved a higher frame per second (FPS) rate via YOLO accelerated with TensorRT for an onboard UAV. The OD compares four YOLO models to recognize objects of interest for landing spots at the home university first. Then, the trained dataset with YOLOv4-tiny was successfully applied to another field with a distance of more than 100 km. The system's capability to accurately recognize a different landing point in new and unknown environments is demonstrated successfully. The proposed approach substantially reduces the data transmission and processing time to ground stations with automated visual tracking gimbal control, and results in rapid OD and the feasibility of using NVIDIA JetsonTM Xavier NX by deploying YOLOs with more than 35 FPS for the UAV. The enhanced visual landing and future reconnaissance mission capabilities of real-time UAVs were demonstrated.

A compensation mechanism for air pollutants generated by tourism-related land-based transportation: An exergy-based case study from Macao.

This paper discusses the compensation standard for exhaust pollution and devises a compensation mechanism for Macao's tourism-related transport sector based on an integration of chemical exergy and universal exergy, using data on gasoline consumption by automobile sector retrieved from the transportation industry. The results reveal that: (1) the exergy values of air pollutant emissions increased from 1.53 × 1012 kJ in 2010 to 2.03 × 1012 kJ in 2019 (an increase of 1.33 times), and the exergy of CO, NOx, and SO2 emissions accounted for 77.5%, 20.4% and 2.1% of total exhaust emissions in Macao respectively. (2) In 2019, the monetary value of emission exergy, and the environmental costs of air pollution, were 1.7 times greater than in 2010. (3) If Light Rail Transit is compensated for, then the mean interval's values of the upper and lower limits of the compensation standard are 0.55 USD and 0.05 USD, respectively. When gasoline tax is used as a means of compensation it is necessary to raise its rate by about 8% based on the tax rate. A three-stage bargaining game model is used to provide evidence that this compensation standard is practical and acceptable.

Diagnostic accuracy of circulating microRNAs for hepatitis C virus-associated hepatocellular carcinoma: a systematic review and meta-analysis.

AIMS: The purpose of this study was to perform an assessment of circulating microRNAs (miRNAs) as promising biomarker for hepatitis C virus (HCV)-associated hepatocellular carcinoma (HCV-HCC) through a meta-analysis. METHODS: A comprehensive literatures search extended up to March 1, 2020 in PubMed, Cochrane library, Embase, Web of Science, Scopus and Ovid databases. The collected data were analyzed by random-effects model, the pooled sensitivity (SEN), specificity (SPE), positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were used to explore the diagnostic performance of circulating miRNAs. Meta-regression and subgroup analysis were further carried out to explore the heterogeneity. RESULTS: A total of 16 articles including 3606 HCV-HCC patients and 3387 HCV patients without HCC were collected. The pooled estimates indicated miRNAs could distinguish HCC patients from chronic hepatitis C (CHC) and HCV-associated liver cirrhosis (HCV-LC), with a SEN of 0.83 (95% CI, 0.79-0.87), a SPE of 0.77 (95% CI, 0.71-0.82), a DOR of 17 (95% CI, 12-28), and an AUC of 0.87 (95% CI, 0.84-0.90). The combination of miRNAs and AFP showed a better diagnostic accuracy than each alone. Subgroup analysis demonstrated that diagnostic accuracy of miRNAs was better for plasma types, up-regulated miRNAs, and miRNA clusters. There was no evidence of publication bias in Deeks' funnel plot. CONCLUSIONS: Circulating miRNAs, especially for miRNA clusters, have a relatively high diagnostic value for HCV-HCC from CHC and HCV-LC.

Automated Machine Learning System for Defect Detection on Cylindrical Metal Surfaces.

Metal workpieces are indispensable in the manufacturing industry. Surface defects affect the appearance and efficiency of a workpiece and reduce the safety of manufactured products. Therefore, products must be inspected for surface defects, such as scratches, dirt, and chips. The traditional manual inspection method is time-consuming and labor-intensive, and human error is unavoidable when thousands of products require inspection. Therefore, an automated optical inspection method is often adopted. Traditional automated optical inspection algorithms are insufficient in the detection of defects on metal surfaces, but a convolutional neural network (CNN) may aid in the inspection. However, considerable time is required to select the optimal hyperparameters for a CNN through training and testing. First, we compared the ability of three CNNs, namely VGG-16, ResNet-50, and MobileNet v1, to detect defects on metal surfaces. These models were hypothetically implemented for transfer learning (TL). However, in deploying TL, the phenomenon of apparent convergence in prediction accuracy, followed by divergence in validation accuracy, may create a problem when the image pattern is not known in advance. Second, our developed automated machine-learning (AutoML) model was trained through a random search with the core layers of the network architecture of the three TL models. We developed a retraining criterion for scenarios in which the model exhibited poor training results such that a new neural network architecture and new hyperparameters could be selected for retraining when the defect accuracy criterion in the first TL was not met. Third, we used AutoKeras to execute AutoML and identify a model suitable for a metal-surface-defect dataset. The performance of TL, AutoKeras, and our designed AutoML model was compared. The results of this study were obtained using a small number of metal defect samples. Based on TL, the detection accuracy of VGG-16, ResNet-50, and MobileNet v1 was 91%, 59.00%, and 50%, respectively. Moreover, the AutoKeras model exhibited the highest accuracy of 99.83%. The accuracy of the self-designed AutoML model reached 95.50% when using a core layer module, obtained by combining the modules of VGG-16, ResNet-50, and MobileNet v1. The designed AutoML model effectively and accurately recognized defective and low-quality samples despite low training costs. The defect accuracy of the developed model was close to that of the existing AutoKeras model and thus can contribute to the development of new diagnostic technologies for smart manufacturing.

Dissociation of water on atomic oxygen-covered Rh nanoclusters supported on graphene/Ru(0001).

We studied the dissociation of water (H2O*, with * denoting adspecies) on atomic oxygen (O*)-covered Rh nanoclusters (RhO* ) supported on a graphene film grown on a Ru(0001) surface [G/Ru(0001)] under ultrahigh-vacuum conditions and with varied surface-probe techniques and calculations based on density-functional theory. The graphene had a single rotational domain; its lattice expanded by about 5.7% to match the Ru substrate structurally better. The Rh clusters were grown by depositing Rh vapors onto G/Ru(0001); they had an fcc phase and grew in (111) orientation. Water adsorbed on the Rh clusters was dissociated exclusively in the presence of O*, like that on a Rh(111) single-crystal surface. Contrary to the case on Rh(111)O* , excess O* (even at a saturation level) on small RhO* clusters (diameter of 30-34 Å) continued to promote, instead of inhibiting, the dissociation of water; the produced hydroxyl (OH*) increased generally with the concentration of O* on the clusters. The difference results from more reactive O* on the RhO* clusters. O* on RhO* clusters activated the dissociation via both the formation of hydrogen bonds with H2O* and abstraction of H directly from H2O*, whereas O* on Rh(111)O* assisted the dissociation largely via the formation of hydrogen bonds, which was readily obstructed with an increased O* coverage. As the disproportionation (2 OH* → H2O* + O*) is endothermic on the RhO* clusters but exothermic on Rh(111)O* , OH* produced on RhO* clusters showed a thermal stability superior to that on the Rh(111)O* surface-thermally stable up to 400 K.

Impact of Muscle Mass on Survival in Patients with Sepsis: A Systematic Review and Meta-Analysis.

INTRODUCTION: The aim of this study is to investigate the association between loss of muscle mass and prognosis of sepsis. METHODS: Six databases, including PubMed, Embase, Cochrane Library, Web of Science, Scopus, and Ovid, were searched by the deadline of August 18, 2020. A meta-analysis was conducted on the collected data by means of a random-effects model. The quality of each included article was assessed according to the Newcastle-Ottawa Scale. RESULTS: Out of 1,819 references, 6 articles and 1 conference abstract were included. Sepsis patients with a loss of muscle mass or sarcopenia had higher mortality (risk ratio [RR]: 1.94, 95% confidence intervals [CI]: 1.59-2.37; I-squared = 18.7%, p < 0.001). The RR of mortality within 30 days (RR: 2.31, 95% CI: 1.78-2.99, p < 0.001) was higher than that of mortality over 30 days. Loss of psoas muscle mass, as evaluated by CT, showed the highest RR of sepsis mortality. In addition, based on data on overall survival retrieved from 4 trials, the pooled hazard ratio (HR) for patients with a loss of muscle mass or sarcopenia was 3.04. Subgroup analysis showed that survival time was the main source of heterogeneity for the overall HR. Furthermore, the scanning areas of muscle mass in survival patients were 0.33 cm2/m2 higher than those measured in deceased patients. CONCLUSION: A loss of muscle mass, as evaluated by CT scan, was associated with a poor outcome in sepsis.

Use of Long Short-Term Memory for Remaining Useful Life and Degradation Assessment Prediction of Dental Air Turbine Handpiece in Milling Process.

The complexity of the internal components of dental air turbine handpieces has been increasing over time. To make operations reliable and ensure patients' safety, this study established long short-term memory (LSTM) prediction models with the functions of learning, storing, and transmitting memory for monitoring the health and degradation of dental air turbine handpieces. A handpiece was used to cut a glass porcelain block back and forth. An accelerometer was used to obtain vibration signals during the free running of the handpiece to identify the characteristic frequency of these vibrations in the frequency domain. This information was used to establish a health index (HI) for developing prediction models. The many-to-one and many-to-many LSTM frameworks were used for machine learning to establish prediction models for the HI and degradation trajectory. The results indicate that, in terms of HI predicted for the testing dataset, the mean square error of the many-to-one LSTM framework was lower than that that of a logistic regression model, which did not have a memory framework. Nevertheless, high accuracies were achieved with both of the two aforementioned approaches. In general, the degradation trajectory prediction model could accurately predict the degradation trend of the dental handpiece; thus, this model can be a useful tool for predicting the degradation trajectory of real dental handpieces in the future.

Structural and Binding Effects of Chemical Modifications on Thrombin Binding Aptamer (TBA).

The thrombin binding aptamer (TBA) is a promising nucleic acid-based anticoagulant. We studied the effects of chemical modifications, such as dendrimer Trebler and NHS carboxy group, on TBA with respect to its structures and thrombin binding affinity. The two dendrimer modifications were incorporated into the TBA at the 5' end and the NHS carboxy group was added into the thymine residues in the thrombin binding site of the TBA G-quadruplex (at T4, T13 and both T4/T13) using solid phase oligonucleotide synthesis. Circular dichroism (CD) spectroscopy confirmed that all of these modified TBA variants fold into a stable G-quadruplex. The binding affinity of TBA variants with thrombin was measured by surface plasmon resonance (SPR). The binding patterns and equilibrium dissociation constants (KD) of the modified TBAs are very similar to that of the native TBA. Molecular dynamics simulations studies indicate that the additional interactions or stability enhancement introduced by the modifications are minimized either by the disruption of TBA-thrombin interactions or destabilization elsewhere in the aptamer, providing a rational explanation for our experimental data. Overall, this study identifies potential positions on the TBA that can be modified without adversely affecting its structure and thrombin binding preference, which could be useful in the design and development of more functional TBA analogues.

Synthesis and Characterization of Melatonin-Loaded Chitosan Microparticles Promote Differentiation and Mineralization in Preosteoblastic Cells.

In terms of a novel scaffold with well good osteoinductive and osteoconductive capacity, melatonin (Mel) possesses positive effects on chemical linkage in scaffold structures, which may allow osteogenic differentiation. The aim of this study is to fabricate Mel-loaded chitosan (CS) microparticles (MPs) as a novel bone substitute through generating a Mel sustained release system from Mel-loaded CS MPs and evaluating its effect on the osteogenic capacity of MC3T3-E1 in vitro. The physical-chemical characteristics of the prepared CS MPs were examined by both Fourier transform infrared spectroscopy and scanning electron microscopy. The released profile and kinetics of Mel from MPs were quantified, and the bioactivity of the released Mel on preosteoblastic MC3T3-E1 cells was characterized in vitro. An in vitro drug release assay has shown high encapsulation efficiency and sustained release of Mel over the investigation period. In an osteogenesis assay, Mel-loaded CS MPs have significantly enhanced alkaline phosphatase (ALP) mRNA expression and ALP activity compared with the control group. Meanwhile, the osteoblast-specific differentiation genes, including runt related transcription factor 2 (Runx2), bone morphogentic protein-2 (Bmp2), collagen I (Col I), and osteocalcin (Ocn), were also significantly upregulated. Furthermore, quantificational alizarin red-based assay demonstrated that Mel-loaded CS MPs notably enhanced the calcium deposit of MC3T3-E1 compared with controls. In essence, Mel-loaded CS MPs can control the release of Mel for a period of time to accelerate osteogenic differentiation of preosteoblast cells in vitro.

Clinical characteristics and prognostic factors of liver cirrhosis patients with systemic inflammatory response syndrome.

AIM: Our objective is to study the clinical characteristics of cirrhosis patients with SIRS and investigate its prognostic factors. METHODS: We analyzed 285 consecutive patients and their data were evaluated retrospectively. Data were compared in patients with/without SIRS during hospitalization. Univariate and multivariate Cox regression analyses were undertaken separately for cirrhotic patients with SIRS to assess predictive factors for 90-day mortality. RESULTS: The mortality was 38.24% (52/136) in patients with SIRS and 6.04% (9/149) in patients without SIRS for 90-day follow-up (P < 0.001). The univariate analysis showed gastrointestinal hemorrhage (P < 0.001), hepatic encephalopathy (P < 0.001), albumin <30 g/L (P < 0.037), creatinine (Cr) >175 µmol/L (P < 0.001), cholinesterase(ChE) activity <3000 U/L (P = 0.019), white blood cell count ≥10 000 (109/L) (P = 0.018), neutrophils ≥80% (P = 0.018), C-reactive protein (CRP) ≥25 mg/L (P < 0.001), procalcitonin ≥1.0 ng/mL (P = 0.007), Child-Pugh class C (P < 0.001), septicemia (P < 0.001), pulmonary infection (P < 0.001),multi-site infection (P = 0.001), acute-on-chronic liver failure (ACLF) (P < 0.001), and advanced hepatocellular carcinoma (HCC) (P < 0.001). In multivariate analysis, only Cr ≥175 µmol/L (hazard ratio [HR] = 2.768; confidence interval [CI], 1.53-5.04; P = 0.001), C-reactive protein ≥25 mg/L (HR = 3.179; CI, 1.772-7.03; P = 0.004), multi-site infection (HR = 19.427; CI, 7.484-50.431; P < 0.001), ACLF (HR = 7.308; CI, 3.048-17.521; P < 0.001), advanced HCC (HR = 2.523; CI, 1.019-6.248; P = 0.045) were independent predictors of 90-day mortality in cirrhotic patients with SIRS. CONCLUSION: Cr ≥ 175 µmol/L, CRP ≥ 25 mg/L, multi-site infection, ACLF, and advanced HCC independently predicted a higher rate of 90-day mortality in liver cirrhosis with SIRS.

Tumor Necrosis Factor α-Dependent Neutrophil Priming Prevents Intestinal Ischemia/Reperfusion-Induced Bacterial Translocation.

BACKGROUND: Intestinal ischemia/reperfusion (I/R) causes barrier impairment and bacterial influx. Protection against I/R injury in sterile organs by hypoxic preconditioning (HPC) had been attributed to erythropoietic and angiogenic responses. Our previous study showed attenuation of intestinal I/R injury by HPC for 21 days in a neutrophil-dependent manner. AIM: To investigate the underlying mechanisms of neutrophil priming by HPC, and explore whether adoptive transfer of primed neutrophils is sufficient to ameliorate intestinal I/R injury. METHODS: Rats raised in normoxia (NM) and HPC for 3 or 7 days were subjected to sham operation or superior mesenteric artery occlusion for I/R challenge. Neutrophils isolated from rats raised in NM or HPC for 21 days were intravenously injected into naïve controls prior to I/R. RESULTS: Similar to the protective effect of HPC-21d, I/R-induced mucosal damage was attenuated by HPC-7d but not by HPC-3d. Naïve rats reconstituted with neutrophils of HPC-21d rats showed increase in intestinal phagocytic infiltration and myeloperoxidase activity, and barrier protection against I/R insult. Elevated free radical production, and higher bactericidal and phagocytic activity were observed in HPC neutrophils compared to NM controls. Moreover, increased serum levels of tumor necrosis factor α (TNFα) and cytokine-induced neutrophil chemoattractant-1 (CINC-1) were seen in HPC rats. Naïve neutrophils incubated with HPC serum or recombinant TNFα, but not CINC-1, exhibited heightened respiratory burst and bactericidal activity. Lastly, neutrophil priming effect was abolished by neutralization of TNFα in HPC serum. CONCLUSIONS: TNFα-primed neutrophils by HPC act as effectors cells for enhancing barrier integrity under gut ischemia.

Preparation and characterization of antioxidant nanoparticles composed of chitosan and fucoidan for antibiotics delivery.

In this study, we developed novel chitosan/fucoidan nanoparticles (CS/F NPs) using a simple polyelectrolyte self-assembly method and evaluated their potential to be antioxidant carriers. As the CS/F weight ratio was 5/1, the CS/F NPs were spherical and exhibited diameters of approximately 230-250 nm, as demonstrated by TEM. These CS/F NPs maintained compactness and stability for 25 day in phosphate-buffered saline (pH 6.0-7.4). The CS/F NPs exhibited highly potent antioxidant effects by scavenging 1,1-diphenyl-2-picrylhydrazyl (DPPH), reducing the concentration of intracellular reactive oxygen species (ROS) and superoxide anion (O2-) in stimulated macrophages. The DPPH scavenging effect of CS/F NPs primarily derives from fucoidan. Furthermore, these CS/F NPs activated no host immune cells into inflammation-mediated cytotoxic conditions induced by IL-6 production and NO generation. The MTT cell viability assay revealed an absence of toxicity in A549 cells after exposure to the formulations containing 0.375 mg NPs/mL to 3 mg NPs/mL. Gentamicin (GM), an antibiotic, was used as a model drug for an in vitro releasing test. The CS/F NPs controlled the release of GM for up to 72 h, with 99% of release. The antioxidant CS/F NPs prepared in this study could thus be effective in delivering antibiotics to the lungs, particularly for airway inflammatory diseases.