Manganese(I)-Catalyzed Enantioselective C(sp2)-C(sp3) Bond-Forming for the Synthesis of Skipped Dienes with Synergistic Aminocatalysis.

Mn(I)-catalyzed enantioselective C-C bond-forming reactions represent a great challenge in homogeneous catalysis primarily due to a limited understanding of its mechanistic principles. Herein, we have developed an interesting catalytic strategy that leverages a synergistic combination of a dimeric manganese(I) catalyst and a chiral aminocatalyst to address this issue. A range of conjugated dienals and trienals can exclusively proceed 1,4-hydroalkenylation by using readily available aromatic and aliphatic alkenyl boronic acids as coupling partners, producing a rich library of skipped diene aldehydes in synthetically useful yields and high levels of enantioselectivities. Notably, downstream transformations of these products can not only afford a concise approach to construct enantioenriched skipped trienes but also realize enantioselective total synthesis of analogues to (-)-Blepharocalyxin D in four steps. DFT calculations suggest the 1,4-hydroalkenylation is kinetically more favorable than 1,6-hydroalkenylation.

GM-lncLoc: LncRNAs subcellular localization prediction based on graph neural network with meta-learning.

In recent years, a large number of studies have shown that the subcellular localization of long non-coding RNAs (lncRNAs) can bring crucial information to the recognition of lncRNAs function. Therefore, it is of great significance to establish a computational method to accurately predict the subcellular localization of lncRNA. Previous prediction models are based on low-level sequences information and are troubled by the few samples problem. In this study, we propose a new prediction model, GM-lncLoc, which is based on the initial information extracted from the lncRNA sequence, and also combines the graph structure information to extract high level features of lncRNA. In addition, the training mode of meta-learning is introduced to obtain meta-parameters by training a series of tasks. With the meta-parameters, the final parameters of other similar tasks can be learned quickly, so as to solve the problem of few samples in lncRNA subcellular localization. Compared with the previous methods, GM-lncLoc achieved the best results with an accuracy of 93.4 and 94.2% in the benchmark datasets of 5 and 4 subcellular compartments, respectively. Furthermore, the prediction performance of GM-lncLoc was also better on the independent dataset. It shows the effectiveness and great potential of our proposed method for lncRNA subcellular localization prediction. The datasets and source code are freely available at https://github.com/JunzheCai/GM-lncLoc .

Identification and validation of a potential key gene SGOL1 for poor prognosis in hepatocellular carcinoma based on a bioinformatics approach.

Objective: Hepatocellular carcinoma (HCC) is one of the most prevalent types of cancer worldwide. Shugoshin 1 (SGOL1) plays a crucial role in cell mitosis and its aberrant expression level in human tumors has shown to promote chromosomal instability (CIN) and accelerate tumor growth. SGOL1 expression level in HCC cells and tissues, whether it has an influence on HCC patients' prognosis, and its mechanism of action have not yet been studied. Methods: We carried out the bioinformatics analysis of SGOL1 expression level and survival analysis in 8 different malignancies, including HCC. In addition, we analyzed SGOL1 expression level in HCC tissues, as well as HCC patients' clinical features, enrichment analysis of SGOL1 function and mechanism of action in HCC and tumor immune cells. The effects of SGOL1 expression level and cell viability on HCC were confirmed by in vitro cytological assays. Results: It was found that SGOL1 mRNA expression level was significantly higher in several tumor tissues, including HCC, than in corresponding normal tissues, and the elevated SGOL1 expression level was strongly associated with HCC patients' poor prognosis. It was also revealed that SGOL1 expression level in HCC tissue was positively correlated with disease stage, tumor grade, and tumor size, and the results of multivariate logistic regression analysis showed that SGOL1 was one of the independent influential factors of the prognosis of HCC. Enrichment analysis revealed that SGOL1 expression level in HCC tissue was mainly associated with tumor proliferation, cell cycle, and other factors. The results of the immune infiltration analysis indicated that SGOL1 expression level was associated with immune cell infiltration and immune checkpoints in HCC. In vitro experiments demonstrated the high SGOL1 expression level in HCC tissues and cells, and silencing of SGOL1 resulted in altered cell cycle markers and decreased proliferation, invasion, and migration of HCC cells. Conclusion: The findings revealed that SGOL1 is highly expressed in HCC tissues, it is a biomarker of a poor prognosis, which may be related to immune cell infiltration in HCC, and may enhance the proliferation, invasion, and migration of HCC cells. The results may provide new insights into targeted treatment of HCC and improve HCC patients' prognosis.

A Novel Coumarin-based Fluorescent Probe with Aggregation Induced Emission for Detecting CN- and its Applications in Bioimaging.

Although cyanogen ion (CN-) plays important role in industry which also bring acute environmental pollution. More serious, trace CN- enters the human body can cause serious consequences and even death. Therefore, it is of great significance to detect trace CN- with high sensitivity. Herein, a novel aggregation-induced emission (AIE) probe C-BH was synthesized based on coumarin matrix. Probe C-BH showed high selectivity and sensitivity toward CN- by dual channel response due to the excited state intramolecular proton transfer (ESIPT). The low detection limit was calculated to be 0.05 µM. Moreover, probe C-BH was successfully used for imaging CN- in living cells and zebrafish due to its low toxicity and excellent optical properties.