Carbon Monoxide Inhibits the Expression of Proteins Associated with Intestinal Mucosal Pyroptosis in a Rat Model of Sepsis Induced by Cecal Ligation and Puncture.

BACKGROUND Carbon monoxide (CO) has anti-inflammatory effects and protects the intestinal mucosal barrier in sepsis. Pyroptosis, or cell death associated with sepsis, is mediated by caspase-1 activation. This study aimed to investigate the role of CO on the expression of proteins associated with intestinal mucosal pyroptosis in a rat model of sepsis induced by cecal ligation and puncture (CLP). MATERIAL AND METHODS The rat model of sepsis was developed using CLP. Male Sprague-Dawley rats (n=120) were divided into six study groups: the sham group (n=20); the CLP group (n=20); the hemin group (treated with ferric chloride and heme) (n=20); the zinc protoporphyrin IX (ZnPPIX) group (n=20); the CO-releasing molecule 2 (CORM-2) group (n=20); and the inactive CORM-2 (iCORM-2) group (n=20). Hemin and CORM-2 were CO donors, and ZnPPIX was a CO inhibitor. In the six groups, the seven-day survival curves, the fluorescein isothiocyanate (FITC)-labeled dextran 4000 Da (FD-4) permeability assay, levels of intestinal pyroptosis proteins caspase-1, caspase-11, and gasdermin D (GSDMD) were measured by confocal fluorescence microscopy. Proinflammatory cytokines interleukin (IL)-18, IL-1ß, and high mobility group box protein 1 (HMGB1) were measured by Western blot and enzyme-linked immunosorbent assay (ELISA). RESULTS CO reduced the mortality rate in rats with sepsis and reduced intestinal mucosal permeability and mucosal damage. CO also reduced the expression levels of IL-18, IL-1ß, and HMGB1, and reduced pyroptosis by preventing the cleavage of caspase-1 and caspase-11. CONCLUSIONS In a rat model of sepsis induced by CLP, CO had a protective role by inhibiting intestinal mucosal pyroptosis.

High-Throughput Sequencing Analysis of the Endophytic Bacterial Diversity and Dynamics in Roots of the Halophyte Salicornia europaea.

Endophytic bacterial communities of halophyte Salicornia europaea roots were analyzed by 16S rRNA gene pyrosequencing. A total of 20,151 partial 16S rRNA gene sequences were obtained. These sequences revealed huge amounts of operational taxonomic units (OTUs), that is, 747-1405 OTUs in a root sample, at 3 % cut-off level. Root endophytes mainly comprised four phyla, among which Proteobacteria was the most represented, followed by Bacteroidetes, Actinobacteria, and Firmicutes. Gammaproteobacteria was the most abundant class of Proteobacteria, followed by Betaproteobacteria and Alphaproteobacteria. Genera Pantoea, Halomonas, Azomonas, Serpens, and Pseudomonas were shared by all growth periods. A marked difference in endophytic bacterial communities was evident in roots from different host life-history stages. Gammaproteobacteria increased during the five periods, while Betaproteobacteria decreased. The richest endophytic bacteria diversity was detected in the seedling stage. Endophytic bacteria diversity was reduced during the flowering stage and fruiting stage. The five libraries contained 2321 different OTUs with 41 OTUs in common. As a whole, this study first surveys communities of endophytic bacteria by tracing crucial stages in the process of halophyte growth using high-throughput sequencing methods.

Isolation of Endophytic Plant Growth-Promoting Bacteria Associated with the Halophyte Salicornia europaea and Evaluation of their Promoting Activity Under Salt Stress.

Several reports have highlighted that many plant growth-promoting endophytic bacteria (PGPE) can assist their host plants in coping with various biotic and abiotic stresses. However, information about the PGPE colonizing in the halophytes is still scarce. This study was designed to isolate and characterize PGPE from salt-accumulating halophyte Salicornia europaea grown under extreme salinity and to evaluate in vitro the bacterial mechanisms related to plant growth promotion. A total of 105 isolates were obtained from the surface-sterilized roots, stems, and assimilation twigs of S. europaea. Thirty-two isolates were initially selected for their ability to produce 1-aminocyclopropane-1-carboxylate deaminase as well as other properties such as production of indole-3-acetic acid and phosphate-solubilizing activities. The 16S rRNA gene-sequencing analysis revealed that these isolates belong to 13 different genera and 19 bacterial species. For these 32 strains, seed germination and seedling growth in axenically grown S. europaea seedlings at different NaCl concentrations (50-500 mM) were quantified. Five isolates possessing significant stimulation of the host plant growth were obtained. The five isolates were identified as Bacillus endophyticus, Bacillus tequilensis, Planococcus rifietoensis, Variovorax paradoxus, and Arthrobacter agilis. All the five strains could colonize and can be reisolated from the host plant interior tissues. These results demonstrate that habitat-adapted PGPE isolated from halophyte could enhance plant growth under saline stress conditions.

Differentiating Benign From Malignant Ovarian Masses With Solid Components: Diagnostic Performance of CEUS Combined With IOTA Simple Rules and O-RADS.

OBJECTIVE: This study aimed to apply the International Ovarian Tumor Analysis (IOTA) Simple Rules (SR), the Ovarian-Adnexal Reporting and Data System (O-RADS) and contrast-enhanced ultrasound (CEUS) in an identical cohort of Chinese patients and to analyze their performance in discrimination of ovarian masses with solid components. METHODS: This was a two-center retrospective study that included a total of 94 ovarian lesions in 86 women enrolled from January 2018 to February 2023. The lesions were classified by using the IOTA terminology and CEUS was performed for the lesions exhibiting solid components on ultrasonography, IOTA SR and O-RADS were applied, and CEUS images were analyzed retrospectively. We assessed the time to wash-in, time to peak intensity (PI), PI compared to myometrium, and time to wash-out, and observed statistically significant differences between benign and malignant lesions in the first three parameters. CEUS characteristics were employed to determine CEUS scores for benign (score 0) and malignant (score 3) lesions. Subsequently, the lesions were reassessed based on the IOTA SR and O-RADS classifications and CEUS scores. The sensitivity, specificity, and area under the receiver-operating-characteristics curve (AUC) of the different models were also determined. RESULTS: Among the 94 ovarian lesions, 46 (48.9%) were benign and 48 (51.1%) were malignant. It was found that in the 60 lesions to which the SR could be applied, the sensitivity, specificity, and AUC was 0.900, 0.667, and 0.783, respectively. The sensitivity, specificity, and AUC of O-RADS was observed to be 1.000, 0.283 and 0.641, respectively. When SR and O-RADS were combined with CEUS, their sensitivity, specificity, and AUC values were increased to 0.917, 0.891, 0.904, and 0.958, 0.783, 0.871, respectively. CONCLUSION: IOTA SR and O-RADS exhibited relatively low specificity in differentiating malignant from benign ovarian lesions with the solid components, and their diagnostic performance can be significantly improved when combined with CEUS.

Automatic detection of thyroid nodules with a real-time artificial intelligence system in a real clinical scenario and the associated influencing factors.

BACKGROUND: At present, most articles mainly focused on the diagnosis of thyroid nodules by using artificial intelligence (AI), and there was little research on the detection performance of AI in thyroid nodules. OBJECTIVE: To explore the value of a real-time AI based on computer-aided diagnosis system in the detection of thyroid nodules and to analyze the factors influencing the detection accuracy. METHODS: From June 1, 2022 to December 31, 2023, 224 consecutive patients with 587 thyroid nodules were prospective collected. Based on the detection results determined by two experienced radiologists (both with more than 15 years experience in thyroid diagnosis), the detection ability of thyroid nodules of radiologists with different experience levels (junior radiologist with 1 year experience and senior radiologist with 5 years experience in thyroid diagnosis) and real-time AI were compared. According to the logistic regression analysis, the factors influencing the real-time AI detection of thyroid nodules were analyzed. RESULTS: The detection rate of thyroid nodules by real-time AI was significantly higher than that of junior radiologist (P = 0.013), but lower than that of senior radiologist (P = 0.001). Multivariate logistic regression analysis showed that nodules size, superior pole, outside (near carotid artery), close to vessel, echogenicity (isoechoic, hyperechoic, mixed-echoic), morphology (not very regular, irregular), margin (unclear), ACR TI-RADS category 4 and 5 were significant independent influencing factors (all P < 0.05). With the combination of real-time AI and radiologists, junior and senior radiologist increased the detection rate to 97.4% (P < 0.001) and 99.1% (P = 0.015) respectively. CONCLUSONS: The real-time AI has good performance in thyroid nodule detection and can be a good auxiliary tool in the clinical work of radiologists.

B-lymphocyte-induced maturation protein-1 inhibits inflammation and pyroptosis to alleviate sepsis injury.

Liver and lung tissue damage caused by sepsis is still one of the causes of death. B-lymphocyte-induced maturation protein-1 (Blimp-1) has a protective role in inflammation-related disease. However, whether Blimp-1 can regulate cell pyroptosis and affect disease progression in sepsis is still unclear. Animal and cell models were established by the cecal ligation and puncture method and lipopolysaccharides (LPS)-induced RAW 264.7 cells, respectively, and the role of Blimp-1 in regulation inflammatory response and pyroptosis was verified. The changes of inflammation and pyroptosis in liver and lung tissues of septic mice were determined by the addition of TAK-242 (TLR4 inhibitor). Cell pyroptosis and the level of inflammation was detected after Blimp-1 knockdown and TAK-242 treatment in the cell model. The expression of Blimp-1 was continuously increased in a septic mice model. After treatment with TAK-242, the expression of Blimp-1, pyroptosis and inflammatory levels were reduced in mice. In the LPS-induced cell model, cell injury by knockout Blimp-1 was increased, and cell activity was restored after TAK-242 intervention. Overexpression of Blimp-1 relieved LPS-induced cellular inflammatory damage and pyroptosis. Our study had shown that Blimp-1 could improve septic damage by regulating the level of cellular inflammation and pyroptosis in sepsis.

A watershed-scale assessment of cost-effectiveness of sediment abatement with flow diversion terraces.

Soil conservation beneficial management practices (BMPs) are effective at controlling soil loss from farmlands and minimizing water pollution in agricultural watersheds. However, costs associated with implementing and maintaining these practices are high and often deter farmers from using them. Consequently, it is necessary to conduct cost-benefit analysis of BMP implementation to assist decision-makers with planning to provide the greatest level of environmental protection with limited resources and funding. The Soil and Water Assessment Tool (SWAT) was used to evaluate the efficacy of flow diversion terraces (FDT) in abating sediment yield at the outlet of Black Brook Watershed (BBW), northwestern New Brunswick. Different FDT-implementation scenarios were expressed as the ratio of land area protected by FDT to the total cultivated area. From this analysis, we found that average annual sediment yield decreased exponentially with increased FDT protection. When the proportion of FDT-protected areas was low, sediment reductions caused by FDT increased sharply with increasing use of FDT. Similarly, marginal sediment yield abatement costs (dollar per tonne of sediment reduction) increased exponentially with increasing proportion of FDT-protected area. The results indicated that increasing land protection with FDT from 6 to 50% would result in a reduction of about 2.1 tonne ha(-1) yr(-1) and costs of sediment reduction increased from $7 to $12 per tonne. Increasing FDT-protected cropland from 50 to 100%, a reduction of about 0.9 tonne of sediment ha(-1) yr(-1) would occur and the costs would increase from $12 to $53 per tonne of sediment yield reduction.

Identification of key genes and pathways using bioinformatics analysis in septic shock children.

BACKGROUND AND HYPOTHESIS: Sepsis is still one of the reasons for serious infectious diseases in pediatric intensive care unit patients despite the use of anti-infective therapy and organ support therapy. As it is well-known, the effect of single gene or pathway does not play a role in sepsis. We want to explore the interaction of two more genes or pathways in sepsis patients for future works. We hypothesize that the discovery from the available gene expression data of pediatric sepsis patients could know the process or improve the situation. METHODS AND RESULTS: The gene expression profile dataset GSE26440 of 98 septic shock samples and 32 normal samples using whole blood-derived RNA samples were generated. A total of 1,108 upregulated and 142 downregulated differentially expressed genes (DEGs) were identified in septic shock children using R software packages. The Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were analyzed using DAVID software; Gene Set Enrichment Analysis method was also used for enrichment analysis of the DEGs. The protein-protein interaction (PPI) network and the top 10 hub genes construction of the DEGs were constructed via plug-in Molecular Complex Detection and cytoHubba of Cytoscape software. From the PPI network, the top 10 hub genes, which are all upregulated DEGs in the septic shock children, were identified as GAPDH, TNF, EGF, MAPK3, IL-10, TLR4, MAPK14, IL-1ß, PIK3CB, and TLR2. Some of them were involved in one or more significant inflammatory pathways, such as the enrichment of tumor necrosis factor (TNF) pathway in the activation of mitogen-activated protein kinase activity, toll-like receptor signaling pathway, nuclear factor-κB signaling pathway, PI3K-Akt signaling pathway, and TNF signaling pathway. These findings support future studies on pediatric septic shock.

Production of high-resolution forest-ecosite maps based on model predictions of soil moisture and nutrient regimes over a large forested area.

Forest ecosite reflects the local site conditions that are meaningful to forest productivity as well as basic ecological functions. Field assessments of vegetation and soil types are often used to identify forest ecosites. However, the production of high-resolution ecosite maps for large areas from interpolating field data is difficult because of high spatial variation and associated costs and time requirements. Indices of soil moisture and nutrient regimes (i.e., SMR and SNR) introduced in this study reflect the combined effects of biogeochemical and topographic factors on forest growth. The objective of this research is to present a method for creating high-resolution forest ecosite maps based on computer-generated predictions of SMR and SNR for an area in Atlantic Canada covering about 4.3 × 106 hectares (ha) of forestland. Field data from 1,507 forest ecosystem classification plots were used to assess the accuracy of the ecosite maps produced. Using model predictions of SMR and SNR alone, ecosite maps were 61 and 59% correct in identifying 10 Acadian- and Maritime-Boreal-region ecosite types, respectively. This method provides an operational framework for the production of high-resolution maps of forest ecosites over large areas without the need for data from expensive, supplementary field surveys.

[Classification method of deciduous-conifer mixed forest in jilin province based on GIS-TM remote sensing image].

To improve the accuracy of automatic classification and identification of TM remote sensing images in forest area, an expert system for automatically classifying and identifying deciduous-conifer mixed forest was built up, based on the GIS technique, quantitative analysis on the internal relations between geographic factors such as DEM and slope aspect and environment factors like soil type, and qualitative analysis on the spectrum information and preclassification information of sensing images, aimed to build a classification knowledge system. Taking the TM remote sensing image of Wangqing Forest Bureau in Jilin Province as an example, the study showed that this expert system could obviously reduce the influence of mixed pixel and terrain shadow. The classification precision of this system was increased by 14.22%, compared with that of Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) unsupervised classification, and the Kappa index was 0.7556, which could help to classify needle, deciduous and mixed forests. Introducing GIS data into the expert system could also solve the problem that TM remote sensing image could not do, due to the loss of correct spectrum value in cloudy and shady area.