Prophylactic and therapeutic treatment with a synthetic analogue of a parasitic worm product prevents experimental arthritis and inhibits IL-1β production via NRF2-mediated counter-regulation of the inflammasome

Rheumatoid arthritis (RA) remains a debilitating autoimmune condition as many patients are refractory to existing conventional and biologic therapies, and hence successful development of novel treatments remains a critical requirement. Towards this, we now describe a synthetic drug-like small molecule analogue, SMA-12b, of an immunomodulatory parasitic worm product, ES-62, which acts both prophylactically and therapeutically against collagen-induced arthritis (CIA) in mice. Mechanistic analysis revealed that SMA-12b modifies the expression of a number of inflammatory response genes, particularly those associated with the inflammasome in mouse bone marrow-derived macrophages and indeed IL-1β was the most down-regulated gene. Consistent with this, IL-1β was significantly reduced in the joints of mice with CIA treated with SMA-12b. SMA-12b also increased the expression of a number of genes associated with anti-oxidant responses that are controlled by the transcription factor NRF2 and critically, was unable to inhibit expression of IL-1β by macrophages derived from the bone marrow of NRF2−/− mice. Collectively, these data suggest that SMA-12b could provide the basis of an entirely novel approach to fulfilling the urgent need for new treatments for RA

Oral medications including clomiphene citrate or aromatase inhibitors with gonadotropins for controlled ovarian stimulation in women undergoing in vitro fertilisation

We thank:• Richard Kirubakaran, Cochrane South Asia, Prof. BV Moses Centre for Evidence-Informed Health Care and Health Policy, Christian Medical College, Vellore, India;• Marian Showell, Information Specialist for the Cochrane Gynaecology and Fertility Group;• Editorial team of the Cochrane Gynaecology and Fertility Group for their support and assistancePeer reviewedPublisher PD

MicroRNA profiling of cisplatinresistant oral squamous cell carcinoma cell lines enriched withcancer-stem-cell-like and epithelial-mesenchymal transition-type features

Oral cancer is of major public health problem in India. Current investigation was aimed to identify the specific deregulated miRNAs which are responsible for development of resistance phenotype through regulating their resistance related target gene expression in oral squamous cell carcinoma (OSCC). Cisplatin-resistant OSCC cell lines were developed from their parental human OSCC cell lines and subsequently characterised. The resistant cells exhibited enhanced proliferative, clonogenic capacity with significant up-regulation of P-glycoprotein (ABCB1), c-Myc, survivin, β-catenin and a putative cancer-stem-like signature with increased expression of CD44, whereas the loss of E-cadherin signifies induced EMT phenotype. A comparative analysis of miRNA expression profiling in parental and cisplatin-resistant OSCC cell lines for a selected sets (deregulated miRNAs in head and neck cancer) revealed resistance specific signature. Moreover, we observed similar expression pattern for these resistance specific signature miRNAs in neoadjuvant chemotherapy treated and recurrent tumours compared to those with newly diagnosed primary tumours in patients with OSCC. All these results revealed that these miRNAs play an important role in the development of cisplatin-resistance mainly through modulating cancer stem-cell-like and EMT-type properties in OSCC

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

<p>Abstract</p> <p>Background</p> <p>The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.</p> <p>Methods</p> <p>We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.</p> <p>Results</p> <p>Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).</p> <p>Conclusions</p> <p>We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.</p