Background and purpose: Traditional Chinese Medicine (TCM) is widely practised and is viewed as an attractive alternative to conventional medicine. Quantitative information about TCM prescriptions, constituent herbs and herbal ingredients is necessary for studying and exploring TCM.Experimental approach: We manually collected information on TCM in books and other printed sources in Medline. The Traditional Chinese Medicine Information Database TCM‐ID, at http://bidd.group/TCMID/, was introduced for providing comprehensive information about all aspects of TCM including prescriptions, constituent herbs, herbal ingredients, molecular structure and functional properties of active ingredients, therapeutic and side effects, clinical indication and application and related matters.Results: TCM‐ID currently contains information for 1,588 prescriptions, 1,313 herbs, 5,669 herbal ingredients, and the 3D structure of 3,725 herbal ingredients. The value of the data in TCM‐ID was illustrated by using some of the data for an in‐silico study of molecular mechanism of the therapeutic effects of herbal ingredients and for developing a computer program to validate TCM multi‐herb preparations.Conclusions and Implications: The development of systems biology has led to a new design principle for therapeutic intervention strategy, the concept of ‘magic shrapnel’ (rather than the ‘magic bullet’), involving many drugs against multiple targets, administered in a single treatment. TCM offers an extensive source of examples of this concept in which several active ingredients in one prescription are aimed at numerous targets and work together to provide therapeutic benefit. The database and its mining applications described here represent early efforts toward exploring TCM for new theories in drug discovery.British Journal of Pharmacology (2006) 149, 1092–1103. doi:10.1038/sj.bjp.0706945

Early determination of the prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is important to guide clinical management and decrease mortality. The aim of this study was to develop a new simplified prognostic score to accurately predict outcomes in patients with HBV-ACLF. Prospective clinical data from 2,409 hospitalized patients with acute deterioration of HBV-related chronic liver disease were used to develop a new prognostic score that was validated in an external group. A total of 954 enrolled patients with HBV-ACLF were diagnosed based on the Chinese Group on the Study of Severe Hepatitis B-ACLF (COSSH-ACLF) criteria. Six predictive factors were significantly related to 28-day mortality and constituted a new prognostic score (=1.649×ln(international normalized ratio)+0.457×hepatic encephalopathy score+0.425×ln(neutrophil)+0.396×ln(total bilirubin)+0.576×ln(serum urea)+0.033×age). The C-indices of the new score for 28-/90-day mortality (0.826/0.809) were significantly higher than those of 4 other scores (COSSH-ACLF, 0.793/0.784; CLIF-C ACLF, 0.792/0.770; MELD, 0.731/0.727; MELD-Na, 0.730/0.726; all p <0.05). The prediction error rates of the new score for 28-day mortality were significantly lower than those of the 4 other scores: COSSH-ACLF (15.9%), CLIF-C ACLF (16.3%), MELD (35.3%) and MELD-Na (35.6%). The probability density function evaluation and risk stratification of the new score also showed the highest predictive values for mortality. These results were then validated in an external cohort. A new prognostic score based on 6 predictors, without an assessment of organ failure, can accurately predict short-term mortality in patients with HBV-ACLF and might be used to guide clinical management. Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is a complex syndrome that is associated with a high short-term mortality rate. We developed a simplified prognostic score for patients suffering from this condition based on a prospective multicentre cohort. This new score had better predictive ability than 4 other commonly used scores.

Background: Sepsis is defined as a systemic inflammatory response to microbial infections with multiple organ dysfunction. This study analysed untargeted metabolomics combined with proteomics of serum from patients with sepsis to reveal the underlying pathological mechanisms involved in sepsis. Methods: A total of 63 patients with sepsis and 43 normal controls were enrolled from a prospective multicentre cohort. The biological functions of the metabolome were assessed by coexpression network analysis. A molecular network based on metabolomics and proteomics data was constructed to investigate the key molecules. Results: Untargeted metabolomics analysis revealed widespread dysregulation of amino acid metabolism, which regulates inflammation and immunity, in patients with sepsis. Seventy-three differentially expressed metabolites (|log₂ fold change| > 1.5, adjusted P value < 0.05 and variable importance in the projection (VIP) > 1.5) that could predict sepsis were identified. External validation of the hub metabolites was consistent with the derivation results (area under the receiver operating characteristic curve (AUROC): 0.81–0.96/0.62–1.00). The pentose phosphate pathway was found to be related to sepsis-associated encephalopathy. Phenylalanine metabolism was associated with sepsis-associated acute kidney injury. The key molecular alterations of the multiomics network in sepsis compared to normal controls implicate acute inflammatory response, platelet degranulation, myeloid cell activation involved in immune response and phenylalanine, tyrosine and tryptophan biosynthesis, and arginine biosynthesis. Conclusions: Integrated analysis of untargeted metabolomics and proteomics revealed characteristic metabolite and protein alterations in sepsis, which were mainly involved in inflammation-related pathways and amino acid metabolism. This study depicted the pathological characteristics and pathways involved in sepsis and potential therapeutic targets.

Objective Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) pathophysiology remains unclear. This study aims to characterise the molecular basis of HBV-ACLF using transcriptomics. Methods Four hundred subjects with HBV-ACLF, acute-on-chronic hepatic dysfunction (ACHD), liver cirrhosis (LC) or chronic hepatitis B (CHB) and normal controls (NC) from a prospective multicentre cohort were studied, and 65 subjects (ACLF, 20; ACHD, 10; LC, 10; CHB, 10; NC, 15) among them underwent mRNA sequencing using peripheral blood mononuclear cells (PBMCs). Results The functional synergy analysis focusing on seven bioprocesses related to the PBMC response and the top 500 differentially expressed genes (DEGs) showed that viral processes were associated with all disease stages. Immune dysregulation, as the most prominent change and disorder triggered by HBV exacerbation, drove CHB or LC to ACHD and ACLF. Metabolic disruption was significant in ACHD and severe in ACLF. The analysis of 62 overlapping DEGs further linked the HBV-based immune-metabolism disorder to ACLF progression. The signatures of interferon-related, neutrophil-related and monocyte-related pathways related to the innate immune response were significantly upregulated. Signatures linked to the adaptive immune response were downregulated. Disruptions of lipid and fatty acid metabolism were observed during ACLF development. External validation of four DEGs underlying the aforementioned molecular mechanism in patients and experimental rats confirmed their specificity and potential as biomarkers for HBV-ACLF pathogenesis. Conclusions This study highlights immune-metabolism disorder triggered by HBV exacerbation as a potential mechanism of HBV-ACLF and may indicate a novel diagnostic and treatment target to reduce HBV-ACLF-related mortality.

Experience in clinical practice and research in systems pharmacology suggested the limitations of the current one-drug-one-target paradigm in new drug discovery. Single-target drugs may not always produce desired physiological effects on the entire biological system, even if they have successfully regulated the activities of their designated targets. On the other hand, multicomponent therapy, in which two or more agents simultaneously interact with multiple targets, has attracted growing attention. Many drug combinations consisting of multiple agents have already entered clinical practice, especially in treating complex and refractory diseases. Drug combination database (DCDB), launched in 2010, is the first available database that collects and organizes information on drug combinations, with an aim to facilitate systems-oriented new drug discovery. Here, we report the second major release of DCDB (Version 2.0), which includes 866 new drug combinations (1363 in total), consisting of 904 distinctive components. These drug combinations are curated from ∼140 000 clinical studies and the food and drug administration (FDA) electronic orange book. In this update, DCDB collects 237 unsuccessful drug combinations, which may provide a contrast for systematic discovery of the patterns in successful drug combinations. Database URL:http://www.cls.zju.edu.cn/dcdb/

The involvement of cytokinins (CTKs) in the repression of phosphate (Pi)‐starvation signalling has been widely documented. However, the full physiological and molecular relevance of this role remains unclear. To gain further insights into the regulation system of CTK repression of Pi‐starvation signalling, a global analysis of gene expression events in rice seedlings under Pi starvation, and the exogenous CTK treatment under Pi‐sufficient (+P) and Pi‐deficient (−P) conditions, was conducted using oligonucleotide array analysis. Physiological and biochemical adaptation was observed after 10 d Pi starvation in rice seedlings. A global reduction of the Pi‐starvation signalling was detected after 3 d treatment of exogenous CTK. Expression profiling data indicate that, together with a significant increase of intracellular Pi content, many expression changes responsive to Pi starvation were reversed by exogenous CTK treatment while CTK‐responsive genes behaved normally under −P condition. These results suggest that the interplay of CTK signal and Pi‐starvation response can be partially explained by the rise of Pi concentration after exogenous CTK treatment. Microarray data also revealed that a small number of genes have different CTK response patterns under different Pi levels, suggesting a subtle interaction between CTK and Pi‐starvation signalling pathway.