IGF-1R inhibition sensitizes breast cancer cells to ATM-Related Kinase (ATR) inhibitor and cisplatin

The complexity of the IGF-1 signalling axis is clearly a roadblock in targeting this receptor in cancer therapy. Here, we sought to identify mediators of resistance, and potential co-targets for IGF-1R inhibition. By using an siRNA functional screen with the IGF-1R tyrosine kinase inhibitor (TKI) BMS-754807 in MCF-7 cells we identified several genes encoding components of the DNA damage response (DDR) pathways as mediators of resistance to IGF-1R kinase inhibition. These included ATM and Ataxia Telangiectasia and RAD3-related kinase (ATR). We also observed a clear induction of DDR in cells that were exposed to IGF-1R TKIs (BMS-754807 and OSI-906) as indicated by accumulation of γ-H2AX, and phosphorylated Chk1. Combination of the IGF-1R/IR TKIs with an ATR kinase inhibitor VE-821 resulted in additive to synergistic cytotoxicity compared to either drug alone. In MCF-7 cells with stably acquired resistance to the IGF-1R TKI (MCF-7-R), DNA damage was also observed, and again, dual inhibition of the ATR kinase and IGF-1R/IR kinase resulted in synergistic cytotoxicity. Interestingly, dual inhibition of ATR and IGF-1R was more effective in MCF-7-R cells than parental cells. IGF-1R TKIs also potentiated the effects of cisplatin in a panel of breast cancer cell lines. Overall, our findings identify induction of DDR by IGF-1R kinase inhibition as a rationale for co-targeting the IGF-1R with ATR kinase inhibitors or cisplatin, particularly in cells with acquired resistance to TKIs

Obesity in adults: a 2022 adapted clinical practice guideline for Ireland

This Clinical Practice Guideline (CPG) for the management of obesity in adults in Ireland, adapted from the Canadian CPG, defines obesity as a complex chronic disease characterised by excess or dysfunctional adiposity that impairs health. The guideline reflects substantial advances in the understanding of the determinants, pathophysiology, assessment, and treatment of obesity. It shifts the focus of obesity management toward improving patient-centred health outcomes, functional outcomes, and social and economic participation, rather than weight loss alone. It gives recommendations for care that are underpinned by evidence-based principles of chronic disease management; validate patients' lived experiences; move beyond simplistic approaches of "eat less, move more" and address the root drivers of obesity. People living with obesity face substantial bias and stigma, which contribute to increased morbidity and mortality independent of body weight. Education is needed for all healthcare professionals in Ireland to address the gap in skills, increase knowledge of evidence-based practice, and eliminate bias and stigma in healthcare settings. We call for people living with obesity in Ireland to have access to evidence-informed care, including medical, medical nutrition therapy, physical activity and physical rehabilitation interventions, psychological interventions, pharmacotherapy, and bariatric surgery. This can be best achieved by resourcing and fully implementing the Model of Care for the Management of Adult Overweight and Obesity. To address health inequalities, we also call for the inclusion of obesity in the Structured Chronic Disease Management Programme and for pharmacotherapy reimbursement, to ensure equal access to treatment based on health-need rather than ability to pay

Issues in the employment of early school leavers.

A good deal of previous ESRI research was directed at the situation of school leavers and the factors easing the transition from education to working life. This study looks at this topic from the employer's point of view. A detailed survey of employers shows some 70 per cent have never employed an early school leaver. Those who have, recruited them mainly for lower skilled positions. They felt the main problems encountered by these young people in employment relate to the poor level of education and skills attained. Employers felt that in general young people have good personal skills but need much better pre-employment training

Towards automatic data cleansing and classification of valid historical data an incremental approach based on MDD

The project Death and Burial Data: Ireland 1864-1922 (DBDIrl) examines the relationship between historical death registration data and burial data to explore the history of power in Ireland from 1864 to 1922. Its core Big Data arises from historical records from a variety of heterogeneous sources, some aspects are pre-digitized and machine readable. A huge data set (over 4 million records in each source) and its slow manual enrichment (ca 7,000 records processed so far) pose issues of quality, scalability, and creates the need for a quality assurance technology that is accessible to non-programmers. An important goal for the researcher community is to produce a reusable, high-level quality assurance tool for the ingested data that is domain specific (historic data), highly portable across data sources, thus independent of storage technology. This paper outlines the step-wise design of the finer granular digital format, aimed for storage and digital archiving, and the design and test of two generations of the techniques, used in the first two data ingestion and cleaning phases. The first small scale phase was exploratory, based on metadata enrichment transcription to Excel, and conducted in parallel with the design of the final digital format and the discovery of all the domain-specific rules and constraints for the syntax and semantic validity of individual entries. Excel embedded quality checks or database-specific techniques are not adequate due to the technology independence requirement. This first phase produced a Java parser with an embedded data cleaning and evaluation classifier, continuously improved and refined as insights grew. The next, larger scale phase uses a bespoke Historian Web Application that embeds the Java validator from the parser, as well as a new Boolean classifier for valid and complete data assurance built using a Model-Driven Development technique that we also describe. This solution enforces property constraints directly at data capture time, removing the need for additional parsing and cleaning stages. The new classifier is built in an easy to use graphical technology, and the ADD-Lib tool it uses is a modern low-code development environment that auto-generates code in a large number of programming languages. It thus meets the technology independence requirement and historians are now able to produce new classifiers themselves without being able to program. We aim to infuse the project with computational and archival thinking in order to produce a robust data set that is FAIR compliant (Free Accessible Inter-operable and Re-useable) </p