An efficient computer-aided structural elucidation strategy for mixtures using an iterative dynamic programming algorithm.

The identification of chemical structures in natural product mixtures is an important task in drug discovery but is still a challenging problem, as structural elucidation is a time-consuming process and is limited by the available mass spectra of known natural products. Computer-aided structure elucidation (CASE) strategies seek to automatically propose a list of possible chemical structures in mixtures by utilizing chromatographic and spectroscopic methods. However, current CASE tools still cannot automatically solve structures for experienced natural product chemists.

NP-StructurePredictor: Prediction of Unknown Natural Products in Plant Mixtures.

Identification of the individual chemical constituents of a mixture, especially solutions extracted from medicinal plants, is a time-consuming task. The identification results are often limited by challenges such as the development of separation methods and the availability of known reference standards. A novel structure elucidation system, NP-StructurePredictor, is presented and used to accelerate the process of identifying chemical structures in a mixture based on a branch and bound algorithm combined with a large collection of natural product databases.

Rule-based classification models of molecular autofluorescence.

Fluorescence-based detection has been commonly used in high-throughput screening (HTS) assays. Autofluorescent compounds, which can emit light in the absence of artificial fluorescent markers, often interfere with the detection of fluorophores and result in false positive signals in these assays. This interference presents a major issue in fluorescence-based screening techniques.

Metabolomic Dynamic Analysis of Hypoxia in MDA-MB-231 and the Comparison with Inferred Metabolites from Transcriptomics Data.

Hypoxia affects the tumor microenvironment and is considered important to metastasis progression and therapy resistance. Thus far, the majority of global analyses of tumor hypoxia responses have been limited to just a single omics level. Combining multiple omics data can broaden our understanding of tumor hypoxia.

Butylidenephthalide suppresses human telomerase reverse transcriptase (TERT) in human glioblastomas.

Background: Telomerase is widely expressed in most human cancers, but is almost undetectable in normal somatic cells and is therefore a potential drug target. Using the human telomerase promoter platform, the naturally occurring compound butylidenephthalide (BP) was selected for subsequent investigation of antitumor activity in vitro and in vivo.

Methods: We treated human glioblastoma cells with BP and found a dose-dependent decrease in human telomerase reverse transcriptase (hTERT) mRNA expression and a concomitant increase in p16 and p21 expression.

Overexpression of the orphan receptor Nur77 and its translocation induced by PCH4 may inhibit malignant glioma cell growth and induce cell apoptosis.

Background: In previous study, n-butylidenephthalide (BP), a natural compound from Angelica sinensis, has anti-glioblastoma multiform (GBM) cell effects. In this study, we modified BP structure to increase anti-GBM cell effects. The anti-GBM cell effects of one derivative of BP, (Z)-N-(2-(dimethylamino)ethyl)-2-(3-((3-oxoisobenzofuran-1(3H)-ylidene)methyl)phenoxy)acetamide (PCH4) were tested in vitro and in vivo.

Expression of Nur77 induced by an n-butylidenephthalide derivative promotes apoptosis and inhibits cell growth in oral squamous cell carcinoma.

In spite of numerous advances, the 5-year survival rate for head and neck squamous cell cancer has remained largely stagnant and few new anti-tumor drugs have been developed. PCH4, a derivative of n-butylidenephthalide, has been investigated for its anti-tumor effects on oral squamous cell carcinoma (OSCC). The aim of this study was to investigate the anti-tumor mechanism of a potential target gene, Nur77, in OSCC cells, which can be induced by PCH4 treatment.