Purpose Immunological functions are altered following physical injury. The magnitude of the immunological response is dependent on the initial injury. However, variability in the immune response exists within and between patients where only some patients are at risk of developing complications such as systemic inflammatory response syndrome after injury. This systematic review and meta-analysis assessed whether lipopolysaccharide (LPS) induced cytokine production capacity of leucocytes can be used as a functional test to predict the risk of developing complications after injury. Methods Medline, Embase and Web of Science were systematically searched to identify articles that investigated the association between LPS induced cytokine production capacity in leucocytes and any clinical outcome after surgery or trauma. Where sufficient information was supplied, a meta-analysis was performed to determine the overall clinical outcomes. Results A total of 25 articles out of 6765 abstracts identified through the literature search were included in this review. Most articles described a positive association between cytokine production capacity and the development of inflammatory complications (n = 15/25). Coincidingly, the meta-analysis demonstrated that TNFα (Hedges g: 0.63, 95% CI 0.23, 1.03), IL-6 (Hedges g: 0.76, 95% CI 0.41, 1.11) and IL-8 (Hedges g: 0.93, 95% CI 0.46, 1.39) production capacity was significantly higher, one day after injury, in patients who developed inflammatory complications compared to patients who did not following trauma or surgical intervention. No significant difference was observed for IL-1β. Conclusion The associations of elevated LPS-induced cytokine production capacity with the risk of developing inflammatory complications are consistent with previous theories that proposed excessive inflammation is accompanied by antiinflammatory mechanisms that results in a period of immunosuppression and increased risk of secondary complications. However, immunological biomarkers for risk stratification is still a developing field of research where further investigations and validations are required.

Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.

Background: The oxidative stress hypothesis is challenging the dominant position of amyloid-β (Aβ) in the field of understanding the mechanisms of Alzheimer’s disease (AD), a complicated and untreatable neurodegenerative disease. Objective: The goal of the present study was to uncover the oxidative stress mechanisms causing AD, as well as the potential therapeutic targets and neuroprotective drugs against oxidative stress mechanisms. Methods: In this study, a systematic workflow combining pharmacological experiments and computational prediction were proposed. 222 drugs and natural products were collected first and then tested on SH-SY5Y cells to obtain phenotypic screening data on neuroprotection. The preliminary screening data were integrated with drug-target interactions (DTIs) and multi-scale biomedical data, which were analyzed with statistical tests and gene set enrichment analysis. A polypharmacology network was further constructed for investigation. Results: 340 DTIs were matched in multiple databases, and 222 cell viability ratios were calculated for experimental compounds. We identified significant potential therapeutic targets based on oxidative stress mechanisms for AD, including NR3C1, SHBG, ESR1, PGR, and AVPR1A, which might be closely related to neuroprotective effects and pathogenesis. 50% of the top 14 enriched pathways were found to correlate with AD, such as arachidonic acid metabolism and neuroactive ligand-receptor interaction. Several approved drugs in this research were also found to exert neuroprotective effects against oxidative stress mechanisms, including beclometasone, methylprednisolone, and conivaptan. Conclusion: Our results indicated that NR3C1, SHBG, ESR1, PGR, and AVPR1A were promising therapeutic targets and several drugs may be repurposed from the perspective of oxidative stress and AD.

Alzheimer's disease (AD), a neurodegenerative disease with no cure, affects millions of people worldwide and has become one of the biggest healthcare challenges. Some investigated compounds play anti-AD roles at the cellular or the animal level, but their molecular mechanisms remain unclear. In this study, we designed a strategy combining network-based and structure-based methods together to identify targets for anti-AD sarsasapogenin derivatives (AAs). First, we collected drug-target interactions (DTIs) data from public databases, constructed a global DTI network, and generated drug-substructure associations. After network construction, network-based models were built for DTI prediction. The best bSDTNBI-FCFP_4 model was further used to predict DTIs for AAs. Second, a structure-based molecular docking method was employed for rescreening the prediction results to obtain more credible target proteins. Finally, in vitro experiments were conducted for validation of the predicted targets, and Nrf2 showed significant evidence as the target of anti-AD compound AA13. Moreover, we analyzed the potential mechanisms of AA13 for the treatment of AD. Generally, our combined strategy could be applied to other novel drugs or compounds and become a useful tool in identification of new targets and elucidation of disease mechanisms. Our model was deployed on our NetInfer web server (http:// lmmd.ecust.edu.cn/netinfer/).