Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

An integrative approach for building personalized gene regulatory networks for precision medicine

Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare

Mobiware: QOS-aware middleware for mobile multimedia communications

Next generation wireless communications system will be required to support the seamless delivery of voice, video and data with high quality. Delivering hard Quality of Service (QOS) guarantees in the wireless domain is complex due to large-scale mobility requirements, limited radio resources and fluctuating network conditions. To address this challenge we are developing a QOS-aware middleware platform called mobiware which contains the complexity of supporting multimedia applications operating over wireline and wireless ATM networks. Mobiware is a software middleware platform based on xbind and CORBA technology that is designed to operate between the application and radio-link layers of future wireless media systems. Mobiware provides value-added QOS support by allowing mobile multimedia applications to operate transparently during handoff and periods of persistent QOS fluctuation. Keywords Middleware, mobile communications, adaptive algorithms, active transport, QOS 1 INTRODUCTION..

Processionary Moths and Associated Urtication Risk: Global Change–Driven Effects

Processionary moths carry urticating setae, which cause health problems in humans and other warm-blooded animals. The pine processionary moth Thaumetopoea pityocampa has responded to global change (climate warming and increased global trade) by extending its distribution range. The subfamily Thaumetopoeinae consists of approximately 100 species. An important question is whether other processionary moth species will similarly respond to these specific dimensions of global change and thus introduce health hazards into new areas. We describe, for the first time, how setae are distributed on different life stages (adult, larva) of major groups within the subfamily. Using the available data, we conclude that there is little evidence that processionary moths as a group will behave like T. pityocampa and expand their distributional range. The health problems caused by setae strongly relate to population density, which may, or may not, be connected to global change