Recently, many small non-coding RNAs (sRNAs) with important regulatory roles have been identified in bacteria. As their eukaryotic counterparts, a major class of bacterial trans-encoded sRNAs acts by basepairing with target mRNAs, resulting in changes in translation and stability of the mRNA. RNA interference (RNAi) has become a powerful gene silencing tool in eukaryotes. However, such an effective RNA silencing tool remains to be developed for prokaryotes. In this study, we described first the use of artificial trans-encoded sRNAs (atsRNAs) for specific gene silencing in bacteria. Based on the common structural characteristics of natural sRNAs in Gram-negative bacteria, we developed the designing principle of atsRNA. Most of the atsRNAs effectively suppressed the expression of exogenous EGFP gene and endogenous uidA gene in Escherichia coli. Further studies demonstrated that the mRNA base pairing region and AU rich Hfq binding site were crucial for the activity of atsRNA. The atsRNA-mediated gene silencing was Hfq dependent. The atsRNAs led to gene silencing and RNase E dependent degradation of target mRNA. We also designed a series of atsRNAs which targeted the toxic genes in Staphyloccocus aureus, but found no significant interfering effect. We established an effective method for specific gene silencing in Gram-negative bacteria.

Background: The use of magnetic resonance imaging (MRI) in diagnosis of neonatal acute bilirubin encephalopathy (ABE) in newborns has been limited by its difficulty in differentiating confounding image contrast changes associated with normal myelination. This study aims to demonstrate the feasibility of building a machine learning prediction model based on radiomics features derived from MRI to better characterize and distinguish ABE from normal myelination.Methods: In this retrospective study, we included 32 neonates with clinically confirmed ABE and 29 age-matched controls with normal myelination. Radiomics features were extracted from the manually segmented region of interest (ROI) on T1-weighted spin echo images, followed by the feature selection using two-sample independent t-test, least absolute shrinkage and selection operator (Lasso) regression, and Pearson's correlation matrix. Additional feature quantifying the relative mean intensity of ROI was defined and calculated. A prediction model based on the selected features was built to classify ABE and normal myelination using multiple machine learning classifiers and a leave-one-out cross-validation scheme. Receiver operating characteristics (ROC) analysis was used to evaluate the prediction performance with the area under the curve (AUC) and feature importance ranked based on the Fisher score.Results: Among 1319 radiomics features, one radiologist-defined intensity-based feature and 12 texture features were selected as the most discriminative features. Based on these features, decision trees had the best classification performance with the largest AUC of 0.946, followed by support vector machine (SVM), tree-bagger, logistic regression, Naïve Bayes, discriminant analysis, and k-nearest neighborhood (KNN), which have an AUC of 0.931, 0.925, 0.905, 0.891, 0.883, and 0.817, respectively. The relative mean intensity outperformed other 12 texture features in differentiating ABE from controls.Conclusions: The results from this study demonstrated a new strategy of characterizing ABE-induced intensity and morphological changes in MRI, which are difficult to be recognized, interpreted, or quantified by the routine experience and visual-based reading strategy. With more quantitative and objective measurements, the reported machine learning assisted radiomics features-based approach can improve the diagnosis and support clinical decision-making.

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A series of 1-aryl-5-(4-arylpiperazine-1-carbonyl)-1H-tetrazols as microtubule destabilizers were designed, synthesised and evaluated for anticancer activity. Based on bioisosterism, we introduced the tetrazole moiety containing the hydrogen-bond acceptors as B-ring of XRP44X analogues. The key intermediates ethyl 1-aryl-1H-tetrazole-5-carboxylates 10 can be simply and efficiently prepared via a microwave-assisted continuous operation process. Among the compounds synthesised, compound 6-31 showed noteworthy potency against SGC-7901, A549 and HeLa cell lines. In mechanism studies, compound 6-31 inhibited tubulin polymerisation and disorganised microtubule in SGC-7901 cells by binding to tubulin. Moreover, compound 6-31 arrested SGC-7901cells in G2/M phase. This study provided a new perspective for development of antitumor agents that target tubulin.