A living cell-based fluorescent reporter for high-throughput screening of anti-tumor drugs / 药物分析学报

Suppression of cellular O-linked β-N-acetylglucosaminylation (O-GlcNAcylation) can repress prolifera-tion and migration of various cancer cells,which opens a new avenue for cancer therapy.Based on the regulation of insulin gene transcription,we designed a cell-based fluorescent reporter capable of sensing cellular O-GlcNAcylation in HEK293T cells.The fluorescent reporter mainly consists of a reporter (green fluorescent protein (GFP)),an internal reference (red fluorescent protein),and an operator (neuronal differentiation 1),which serves as a "sweet switch" to control GFP expression in response to cellular O-GlcNAcylation changes.The fluorescent reporter can efficiently sense reduced levels of cellular O-GlcNAcylation in several cell lines.Using the fluorescent reporter,we screened 120 natural products and obtained one compound,sesamin,which could markedly inhibit protein O-GlcNAcylation in HeLa and human colorectal carcinoma-116 cells and repress their migration in vitro.Altogether,the present study demonstrated the development of a novel strategy for anti-tumor drug screening,as well as for con-ducting gene transcription studies.

A data-driven method for syndrome type identification and classification in traditional Chinese medicine / 中西医结合学报

The efficacy of traditional Chinese medicine (TCM) treatments for Western medicine (WM) diseases relies heavily on the proper classification of patients into TCM syndrome types. The authors developed a data-driven method for solving the classification problem, where syndrome types were identified and quantified based on statistical patterns detected in unlabeled symptom survey data. The new method is a generalization of latent class analysis (LCA), which has been widely applied in WM research to solve a similar problem, i.e., to identify subtypes of a patient population in the absence of a gold standard. A well-known weakness of LCA is that it makes an unrealistically strong independence assumption. The authors relaxed the assumption by first detecting symptom co-occurrence patterns from survey data and used those statistical patterns instead of the symptoms as features for LCA. This new method consists of six steps: data collection, symptom co-occurrence pattern discovery, statistical pattern interpretation, syndrome identification, syndrome type identification and syndrome type classification. A software package called Lantern has been developed to support the application of the method. The method was illustrated using a data set on vascular mild cognitive impairment.

Identification and classification of traditional Chinese medicine syndrome types among senior patients with vascular mild cognitive impairment using latent tree analysis / 中西医结合学报

<p><b>OBJECTIVE</b>To treat patients with vascular mild cognitive impairment (VMCI) using traditional Chinese medicine (TCM), it is necessary to classify the patients into TCM syndrome types and to apply different treatments to different types. In this paper, we investigate how to properly carry out the classification for patients with VMCI aged 50 or above using a novel data-driven method known as latent tree analysis (LTA).</p><p><b>METHOD</b>A cross-sectional survey on VMCI was carried out in several regions in Northern China between February 2008 and February 2012 which resulted in a data set that involves 803 patients and 93 symptoms. LTA was performed on the data to reveal symptom co-occurrence patterns, and the patients were partitioned into clusters in multiple ways based on the patterns. The patient clusters were matched up with syndrome types, and population statistics of the clusters are used to quantify the syndrome types and to establish classification rules.</p><p><b>RESULTS</b>Eight syndrome types are identified: Qi deficiency, Qi stagnation, Blood deficiency, Blood stasis, Phlegm-dampness, Fire-heat, Yang deficiency, and Yin deficiency. The prevalence and symptom occurrence characteristics of each syndrome type are determined. Quantitative classification rules are established for determining whether a patient belongs to each of the syndrome types.</p><p><b>CONCLUSION</b>A solution for the TCM syndrome classification problem for patients with VMCI and aged 50 or above is established based on the LTA of unlabeled symptom survey data. The results can be used as a reference in clinic practice to improve the quality of syndrome differentiation and to reduce diagnosis variances across physicians. They can also be used for patient selection in research projects aimed at finding biomarkers for the syndrome types and in randomized control trials aimed at determining the efficacy of TCM treatments of VMCI.</p>

Latent Structure Analysis and Syndrome Differentiation for Integration of Traditional Chinese Medicine and Western Medicine (III)：Establishment of Classification Rules / 世界科学技术-中医药现代化

Objective: In China, doctors at TCM hospitals and clinics often divide patients with a Western medicine (WM) disease into several syndrome classes from the TCM perspective and treat patients in different classes using different principles. A key problem is how to carry out the classification properly. We propose an evidence-based ap-proach for solving the problem where evidence is obtained by analyzing unlabeled symptom data using latent tree models.Method: In previous work, we have shown how latent tree analysis of symptom data can be used to identify TCM syndrome classes among patients with a WM disease. In the paper, we investigate how to establish classification rules for distinguishing between the classes.Results: We have applied the method to a data set about Vascular Mild Cognitive Impairment that involves 93 symptoms and 803 patients. Nine syndrome types are identified, along with the corresponding classification rules. Conclusions: An evidence-based approach to the TCM patient classification prob-lem has been developed. The approach can be used to answer the following questions about a WM disease: What TCM syndrome classes are there? What are the sizes of the classes? What are the statistical characteristics of each class? How can one differentiate between the different classes?