Prediction of lymph node metastasis in patients with papillary thyroid cancer based on radiomics analysis and intraoperative frozen section analysis: A retrospective study.

INTRODUCTION: To evaluate the diagnostic efficiency among the clinical model, the radiomics model and the nomogram that combined radiomics features, frozen section (FS) analysis and clinical characteristics for the prediction of lymph node (LN) metastasis in patients with papillary thyroid cancer (PTC). METHODS: A total of 208 patients were randomly divided into two groups randomly with a proportion of 7:3 for the training groups (n = 146) and the validation groups (n = 62). The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for the selection of radiomics features extracted from ultrasound (US) images. Univariate and multivariate logistic analyses were used to select predictors associated with the status of LN. The clinical model, radiomics model and nomogram were subsequently established by logistic regression machine learning. The area under the curve (AUC), sensitivity and specificity were used to evaluate the diagnostic performance of the different models. The Delong test was used to compare the AUC of the three models. RESULTS: Multivariate analysis indicated that age, size group, Adler grade, ACR score and the psammoma body group were independent predictors of lymph node metastasis (LNM). The results showed that in both the training and validation groups, the nomogram showed better performance than the clinical model, albeit not statistically significant (p > .05), and significantly outperformed the radiomics model (p < .05). However, the nomogram exhibits a slight improvement in sensitivity that could reduce the incidence of false negatives. CONCLUSION: We propose that the nomogram holds substantial promise as an effective tool for predicting LNM in patients with PTC.

Functional and Transcriptomic Characterization of a Dye-decolorizing Fungus from Taxus Rhizosphere.

We isolated three laccase-producing fungus strains from Taxus rhizosphere. Myrotheium verrucaria strain DJTU-sh7 had the highest laccase activity of 216.2 U/ml, which was increased to above 300 U/ml after optimization. DJTU-sh7 had the best decolorizing effect for three classes of reactive dyes. The DJTU-sh7-containing fungal consortium displayed the robust decolorizing ability. Both color removal efficiency and chemical oxygen demand were increased in the consortium mediated biotransformation. Transcriptome changes of M. verrucaria elicited by azo dye and phenolic were quantified by the high throughput transcriptome sequencing, and the activities of the selected oxidases and reductases were determined. The possible involvement of oxidases and reductases, especially laccase, aryl alcohol oxidase, and ferric reductase in the biotransformation of dye and phenolic compounds was revealed at both transcriptomic and phenotypic levels. Revealing the transcriptomic mechanisms of fungi in dealing with organic pollutants facilitates the fine-tuned manipulation of strains in developing novel bioremediation and biodegradation strategies.We isolated three laccase-producing fungus strains from Taxus rhizosphere. Myrotheium verrucaria strain DJTU-sh7 had the highest laccase activity of 216.2 U/ml, which was increased to above 300 U/ml after optimization. DJTU-sh7 had the best decolorizing effect for three classes of reactive dyes. The DJTU-sh7-containing fungal consortium displayed the robust decolorizing ability. Both color removal efficiency and chemical oxygen demand were increased in the consortium mediated biotransformation. Transcriptome changes of M. verrucaria elicited by azo dye and phenolic were quantified by the high throughput transcriptome sequencing, and the activities of the selected oxidases and reductases were determined. The possible involvement of oxidases and reductases, especially laccase, aryl alcohol oxidase, and ferric reductase in the biotransformation of dye and phenolic compounds was revealed at both transcriptomic and phenotypic levels. Revealing the transcriptomic mechanisms of fungi in dealing with organic pollutants facilitates the fine-tuned manipulation of strains in developing novel bioremediation and biodegradation strategies.

Unearthing microbial diversity of Taxus rhizosphere via MiSeq high-throughput amplicon sequencing and isolate characterization.

The species variability and potential environmental functions of Taxus rhizosphere microbial community were studied by comparative analyses of 15 16S rRNA and 15 ITS MiSeq sequencing libraries from Taxus rhizospheres in subtropical and temperate regions of China, as well as by isolating laccase-producing strains and polycyclic aromatic hydrocarbon (PAH)-degrading strains. Total reads could be assigned to 2,141 Operational Taxonomic Units (OTUs) belonging to 31 bacteria phyla and 2,904 OTUs of at least seven fungi phyla. The abundance of Planctomycetes, Actinobacteria, and Chloroflexi was higher in T. cuspidata var. nana and T. × media rhizospheres than in T. mairei rhizosphere (NF), while Acidobacteria, Proteobacteria, Nitrospirae, and unclassified bacteria were more abundant in the latter. Ascomycota and Zygomycota were predominant in NF, while two temperate Taxus rhizospheres had more unclassified fungi, Basidiomycota, and Chytridiomycota. The bacterial/fungal community richness and diversity were lower in NF than in other two. Three dye decolorizing fungal isolates were shown to be highly efficient in removing three classes of reactive dye, while two PAH-degrading fungi were able to degrade recalcitrant benzo[a]pyrene. The present studies extend the knowledge pedigree of the microbial diversity populating rhizospheres, and exemplify the method shift in research and development of resource plant rhizosphere.