A pH-responsive, injectable and self-healing chitosan-coumarin hydrogel based on Schiff base and hydrogen bonds.

Smart hydrogels have shown great potential applications in disease treatment due to their controlled and local drug-release ability. Herein, a smart hydrogel with pH-responsive, injectable, and self-healing properties for controlled release of taxifolin (TFL) was prepared by freezing-thawing and photo-crosslinking methods. The crosslinking network of hydrogels (CS-CA hydrogels) was constructed by the hydrogen bonds, Schiff base bonds, and cyclobutane rings using chitosan (CS) and coumarin (CA) as raw materials. The CS-CA hydrogel demonstrated a compressive strength of 1.04 MPa, a self-healing efficiency of 99.9 %, and could maintain structural and functional integrity after injection. In addition, the drug release rate and shape of the CS-CA hydrogels were tunable due to its pH sensitivity. The TFL cumulative release reached 60 % within 12 h at pH = 4, and after equilibration, the cumulative release of TFL at pH = 4 (80 %) was significantly higher than at pH = 9.2 (50 %). The CCK8 experiment showed that the resulting hydrogel had no cytotoxicity. Meanwhile, subcutaneous implantation experiments in mice showed that the CS-CA hydrogels had favorable biodegradability and compatibility.

Distribution and abundance of oil-degrading bacteria in seawater of the Yellow Sea and Bohai Sea, China.

Petroleum hydrocarbons are widespread in seawater. As an important sea area in northern China, the content and distribution of petroleum hydrocarbons in seawater need our attention because of the high toxicity and lasting polluting effects on the ecological environment of the Yellow Sea and Bohai Sea. In addition, there are few reports comparing the diversity of oil-degrading bacteria before and after enrichment. Therefore, we collected surface seawater from 10 sites in the Yellow Sea and Bohai Sea in the autumn of 2020 to study the distribution characteristics of total petroleum hydrocarbons (TPH) and the diversity of oil-degrading bacteria. The concentration of TPH was 81.65 µg/L-139.55 µg/L at ten sites in the Bohai Sea and the Yellow Sea, which conformed to the China Grade II water quality standard (GB3097-1997). Moreover, the pristine/phytane (PR/PH) value of most sites was close to 1, indicating that the area was obviously polluted by exogenous petroleum hydrocarbons. We found that oil-degrading bacteria in the seawater of the Yellow Sea and the Bohai Sea had a good degradation effect on C11-C14 short chain alkanes (degradation rate of 59.19-73.22 %) and C1-C4 phenanthrene (degradation rate of 48.19-60.74 %). In terms of the diversity of oil-degrading bacteria, Gammaproteobacteria and Alphaproteobacteria dominated the enriched bacterial communities. Notably, the relative abundance of Alcanivorax changed significantly before and after enrichment. We proposed that surface seawater in the Bohai Sea and Yellow Sea could form oil-degrading bacteria mainly composed of Alcanivorax, which had great potential for oil pollution remediation.

Gramella oceanisediminis sp. nov., isolated from deep-sea sediment of the Indian Ocean.

A taxonomic study was carried out on strain GC03-9T, which was isolated from deep-sea sediment of the Indian Ocean. The bacterium was Gram-stain-negative, catalase-positive, oxidase-negative, rod-shaped and gliding motile. Growth was observed at salinities of 0-9 % and at temperatures of 10-42 °C. The isolate could degrade gelatin and aesculin. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain GC03-9T belonged to the genus Gramella, with the highest sequence similarity to Gramella bathymodioli JCM 33424T (97.9 %), followed by Gramella jeungdoensis KCTC 23123T (97.2 %) and other species of the genus Gramella (93.4-96.3 %). The average nucleotide identity and the digital DNA-DNA hybridization estimate values between strain GC03-9T and G. bathymodioli JCM 33424T and G. jeungdoensis KCTC 23123T were 25.1 and 18.7 % and 82.47 and 75.69 %, respectively. The principal fatty acids were iso-C15 : 0 (28.0 %), iso-C17 : 0 3OH (13.4 %), summed feature 9 (iso-C17 : 1 ω9c and/or 10-methyl C16 : 0; 13.3 %) and summed feature 3 (C16 : 1 ω7c and/or C16 : 1 ω6c; 11.0 %). The G+C content of the chromosomal DNA was 41.17 mol%. The respiratory quinone was determined to be menaquinone-6 (100 %). Phosphatidylethanolamine, one unknown phospholipid, three unknown aminolipids and two unknown polar lipids were present. The combined genotypic and phenotypic data showed that strain GC03-9T represents a novel species within the genus Gramella, for which the name Gramella oceanisediminis sp. nov. is proposed, with the type strain GC03-9T (=MCCC M25440T=KCTC 92235T).

Biodiversity and degradation potential of oil-degrading bacteria isolated from sediments of hydrothermal and non-hydrothermal areas of the Southwest Mid-Indian Ocean Ridge.

In this study, sediments from eight sites were collected from hydrothermal areas (e.g., the Tiancheng, Tianzuo, and Longqi hydrothermal areas) and non-hydrothermal area on the Southwest Mid-Indian Ocean Ridge. Using crude oil as the only carbon and energy source, 162 strains of culturable oil-degrading bacteria were isolated and obtained. The rate of oil degradation of the consortia was 39.48-46.00% in hydrothermal and non-hydrothermal areas. High-throughput sequencing found that the alpha diversity indices (e.g., Shannon and Simpson) of the communities in hydrothermal areas were higher than those in non-hydrothermal area. The species diversities of the oil-degrading bacteria were different among different hydrothermal areas. The composition of the oil-degrading bacterial species in the Tianzuo hydrothermal area tended to be more similar to that in the non-hydrothermal area. This similarity is attributed to the changes in the bacterial community that followed the cessation of hydrothermal vent eruptions at this site. The Alphaproteobacteria abundance of the oil-degrading bacteria was significantly different in oil-degrading bacteria between the hydrothermal and non-hydrothermal areas.

A Novel Pathological Images and Genomic Data Fusion Framework for Breast Cancer Survival Prediction.

Survival analysis is a valid solution for cancer treatments and outcome evaluations. Due to the wide application of medical imaging and genome technology, computer-aided survival analysis has become a popular and promising area, from which we can get relatively satisfactory results. Although there are already some impressive technologies in this field, most of them make some recommendations using single-source medical data and have not combined multi-level and multi-source data efficiently. In this paper, we propose a novel pathological images and gene expression data fusion framework to perform the survival prediction. Different from previous methods, our framework can extract correlated multi-scale deep features from whole slide images (WSIs) and dimensionality reduced gene expression data respectively for jointly survival analysis. The experiment results demonstrate that the integrated multi-level image and genome features can achieve higher prediction accuracy compared with single-source features.