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Nine undescribed steroidal alkaloid glycosides, unibrasolanosides A-F, unibraverazosides A-B, and unibratomatoside A, were isolated from the bulbs of Fritillaria unibracteata P. K. HsiaoK. C. Hsia (Liliaceae). Their structures were elucidated by HRESIMS and 1D and 2D NMR data analyses as well as chemical methods and single-crystal X-ray diffraction analyses. Further investigation revealed that eight steroidal alkaloid glycosides displayed moderate anti-inflammatory activity in vivo in a CuSO

In this paper, the solubility and diffusivity of CO2/C2H4 in ionic liquids ([Emmim][Tf2N], [Pmmim][Tf2N], [Bmmim][Tf2N], [Hmmim][Tf2N]) are measured at the temperature range of 303-333 K with a tube-in-tube reactor. The new device can greatly decrease the measurement time to several minutes in the tube-in-tube reactor. The solubility data is determined by the steady-state method with less than 10% deviation compared with previous reports. The diffusivity data is measured by an unsteady-state method within 3 min. The diffusion coefficient of CO2 in ionic liquids can be predicted with Morgan’s diffusivity correlation equation. For the diffusivity of C2H4 in ionic liquids, an empirical formula is developed and the predicted values using this correlation agree well with measured values with less than 17% deviation. The measured solubility and diffusivity data of CO2/C2H4 in ionic liquids and corresponding correlations can help to develop a more efficient process involving CO2/C2H4/ionic liquids system.

Introduction: Highly aggressive thoracic neoplasms characterized by SMARCA4 (BRG1) deficiency and undifferentiated round cell or rhabdoid morphology have been recently described and proposed to represent thoracic sarcomas. However, it remains unclear whether such tumors may instead represent sarcomatoid carcinomas, and how their clinicopathologic characteristics compare with those of nonsarcomatoid SMARCA4-deficient non–small cell lung carcinomas (SD-NSCC). Methods: We identified 22 SMARCA4-deficient thoracic sarcomatoid tumors (SD-TSTs) with round cell and/or rhabdoid morphology and 45 SD-NSCCs, and comprehensively analyzed their clinicopathologic, immunohistochemical, and genomic characteristics using 341–468 gene next-generation sequencing and other molecular platforms. Results: The relationship of SD-TSTs with NSCC was supported by (1) the presence of NSCC components juxtaposed with sarcomatoid areas in five cases, (2) focal expression of NSCC lineage markers TTF1 or p40 in four additional cases, (3) smoking history in all except one patient (mean = 51 pack-years), accompanied by genomic smoking signature, and (4) high tumor mutation burden (mean = 14.2 mutations per megabase) and mutations characteristic of NSCC in a subset. Compared with SD-NSCCs, SD-TSTs exhibited considerably larger primary tumor size (p < 0.0001), worse survival (p = 0.004), and more frequent presentation at younger age (30–50 years) despite heavier smoking history. Distinctive pathologic features of SD-TSTs included consistent lack of adhesion molecule claudin-4, SMARCA2 (BRM) codeficiency, and frequent expression of stem cell markers. Conclusions: SD-TSTs represent primarily smoking-associated undifferentiated/de-differentiated carcinomas rather than primary thoracic sarcomas. Despite their histogenetic relationship with NSCC, these tumors have unique clinicopathologic characteristics, supporting their recognition as a distinct entity. Further studies are warranted to determine therapeutic approaches to this novel class of exceptionally aggressive thoracic tumors.

Additional file 1.

As one of the primary contributors to high clinical attrition rates of drugs, toxicity evaluation is of critical significance to new drug discovery. Unsurprisingly, a vast number of computational methods have been developed at various stages of development pipeline to evaluate potential adverse drug reactions (ADRs). Despite previous success of these methods on individual ADR or certain drug family, there are great challenges to toxicity evaluation. In this study, a novel strategy was developed to predict the drug-ADR associations by combining deep learning and the biomedical tripartite network. This heterogeneous network contains biomedical linked data of three entities, for example, drugs, targets, and ADRs. For the first time, GraRep, a deep learning method for distributed representations, is introduced to learn graph representations and identify hidden features from the tripartite network which are further used for ADR prediction. Through this approach, drug-ADR associations could possibly be discovered from a systemic perspective. The accuracy of our method is 0.95 based on internal resource validation and 0.88 based on external resource validation. Moreover, our results show the prediction accuracy using the tripartite network is better than the one with bipartite network, suggesting the model performance can be improved with further enrichment on information. According to the result of 10-fold cross validation, the deep learning model outperforms two traditional methods (topology-based measures and chemical structure-based measures). Additionally, predictive models are also constructed using other deep learning methods, and comparable results are achieved. In summary, the biomedical tripartite network-based deep learning model proposed here proves to offer a promising solution for prediction of ADRs.

Cardiovascular diseases seriously endanger human health and life. The accompanying myocardial injury has been a focus of attention in society. Chinese medicine,serving as a natural and precious reservoir for the research and development of new drugs,is advantageous in resisting myocardial injury due to its multi-component,multi-pathway,and multi-target characteristics. In recent years,with the extensive application of culture method for isolated cardiomyocytes,a cost-effective,controllable in vitro model of cardiomyocyte injury with uniform samples is becoming a key tool for mechanism research on cardiomyocyte injury and drug development.A good in vitro model can reduce experimental and manpower cost,and also accurately stimulate clinical changes to reveal the mechanism. Therefore,the selection and establishment of in vitro model are crucial for the in-depth research. This study summarized the modeling principles,evaluation indicators,and application of more than ten models reflecting different clinical conditions,such as injuries induced by hypoxia-reoxygenation,hypertrophy,oxidative stress,inflammation,internal environmental disturbance,and toxicity. Furthermore,we analyzed advantages and technical difficulties,aiming to provide a reference for in-depth research on myocardial injury mechanism and drug development.

We previously identified a chemotherapy-induced paracrine inflammatory loop that paradoxically mitigates the anti-tumor effect of chemotherapy and triggers metastatic propagation in breast and lung cancer models. Therefore, we sought to further validate and translate these findings into patient care by coupling the anti-TNF-α drug certolizumab pegol with standard cisplatin doublet chemotherapy. Here we first validate the anti-metastatic effect of certolizumab in a liver-metastatic Lewis Lung Carcinoma model. We then evaluate the safety, efficacy, and pharmacodynamic effects of certolizumab with cisplatin and pemetrexed in an open label Phase 1 clinical trial (NCT02120807) of eighteen adult patients with stage IV lung adenocarcinomas. The primary outcome is maximum tolerated dose. Secondary outcomes are response rate and progression-free survival (PFS); pharmacodynamic changes in blood and tumor are evaluated as a correlative outcome. There were nine partial responses among 16 patients evaluable (56%, 95% CI 30 to 80%). The median duration of response was 9.0 months (range 5.9 to 42.6 months) and median PFS was 7.1 months (95% CI 6.3 to NR). The standard 400 mg dose of certolizumab, added to cisplatin and pemetrexed, is well-tolerated and, as a correlative endpoint, demonstrates potent pharmacodynamic inhibition of peripheral cytokines associated with the paracrine inflammatory loop.

Ethnopharmacological relevance Shenqi Fuzheng (SQ) is a renowned traditional Chinese medicine extracted from Radix Codonopsis and Radix Astragali. Although SQ is widely used to treat myocardial ischemia-reperfusion (I/R) injury, the molecular mechanisms supporting its clinical application remain elusive. Aim of the study The purpose of current study was to understand its cardioprotective effects at the molecular level using network pharmacology approach. Materials and method In an I/R injury animal model, the beneficial pharmacological activities of SQ were confirmed by decreased infarct range observed on drug treated rats versus control group. Additionally, several serum biochemical indicators were in concord with this observation. Subsequently, a microarray experiment was performed to reveal the influence on injured heart at the gene expression level by this TCM injection. We then proposed a network analysis algorithm NTRA to discover the key nodes based on both disease network structure and transcriptomics. Using NRIODN, a method developed by our group previously, the holistic changes on the gene network induced by for I/R injury and SQ treatment were evaluated. Results Pathway enrichment analysis of highly ranked genes by NTRA showed that PPAR and apoptosis pathways were highly related to I/R injury. Finally, western blot results showed increased level of the PPARα and BAX protein in the heart after injection treatment which confirmed the hypothesis. Conclusion In conclusion, our results suggest that SQ injection exerts protective effect against myocardial ischemia-reperfusion injury through multiple pathways, including myocardial energy metabolism improvement, cell adhesion inhibition, inflammatory reaction perturbation, myocardial apoptosis reduction and ventricular remodeling avoidance.

Additional file 2: Supplementary Table ST5. Summary statistics table.

Summary Selecting appropriate cell lines to represent a disease is crucial for the success of biomedical research, because the usage of less relevant cell lines could deliver misleading results. However, systematic guidance on cell line selection is unavailable. Here we developed a clinical Genomics-guided Prioritizing Strategy for Cancer Cell Lines (CCL-cGPS) and help to guide this process. Statistical analyses revealed CCL-cGPS selected cell lines were among the most appropriate models. Moreover, we observed a linear correlation between the drug response and CCL-cGPS score of cell lines for breast and thyroid cancers. Using RT4 cells selected by CCL-GPS, we identified mebendazole and digitoxin as candidate drugs against bladder cancer and validate their promising anticancer effect through in vitro and in vivo experiments. Additionally, a web tool was developed. In conclusion, CCL-cGPS bridges the gap between tumors and cell lines, presenting a helpful guide to select the most suitable cell line models., Graphical Abstract, Highlights • Cell lines were ranked by the resemblance of transcriptional signatures to tumors • Among 44 tumor subtypes, CCL-cGPS provides proper cell lines for each subtype • CCL-cGPS was verified by the computational analysis, in vitro and in vivo assays • A web tool was developed to guide the selection of the most suitable cell lines, Cancer; Cancer Systems Biology; Genomics

Background Previous studies reported that early progression of disease (POD) after initial therapy predicted poor overall survival (OS) in patients with follicular lymphoma (FL). Here, we investigated whether pre-treatment imaging modality had an impact on prognostic significance of POD. Methods In this retrospective study, we identified 1088 patients with grade I–IIIA FL; of whom, 238 patients with stage II–IV disease were initially treated with rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP), and 346 patients were treated with rituximab-based chemotherapy. Patients (N = 484) from the FOLL05 study served as an independent validation cohort. We risk-stratified patients based on pre-treatment radiographic imaging (positron-emission tomography [PET] versus computed tomography [CT]) and early POD status using event-defining and landmark analyses. A competing risk analysis evaluated the association between early POD and histologic transformation. Results In the discovery cohort, patients with POD within 24 months (PFS24) of initiating R-CHOP therapy had a 5-year OS of 57.6% for CT-staged patients compared with 70.6% for PET-staged patients. In the validation cohort, the 5-year OS for patients with early POD was 53.9% and 100% in CT- and PET-staged patients, respectively. The risk of histologic transformation in patients whose disease progressed within one year of initiating therapy was higher in CT-staged patients than in PET-staged patients (16.7% versus 6.3%, respectively), which was associated with a 9.7-fold higher risk of death. Conclusion In FL, pre-treatment PET staging reduced the prognostic impact of early POD compared with CT staging. Patients with early POD and no histologic transformation have an extended OS with standard therapy.