Medical Developments (United Kingdom)

Efficient regression testing is essential for maintaining code quality in large-scale distributed systems where resource management challenges can lead to storage and memory overuse. This paper introduces an automated cleanup module integrated into the HPCC Systems Regression Test Engine (RTE), designed to streamline the deletion of workunits generated during a single regression test run. By dynamically parsing log files and programmatically interacting with the ECLWatch interface, the module automates the cleanup process, significantly reducing the manual effort. The module achieves this with minimal overhead, introducing a latency of only 15 minutes for processing 3,000 workunits. This overhead accounts for only about 5 % of the total execution time of the Regression Test Engine (RTE), highlighting the optimized design of the system. A custom logging system facilitates troubleshooting by logging key events. Extensive testing across Ubuntu-based local setups and Kubernetes-powered Cloud environments validated the module's scalability, reliability, and substantial improvements in storage utilization. Unlike existing tools focused on test execution, this work uniquely automates post-test cleanup, offering a novel enhancement to regression testing workflows in both local and Cloud-based environments. The module has been integrated into the HPCC Systems opensource GitHub repository, making the source code publicly available for future enhancements.

Abstract Objectives To assess the effectiveness, onset of action and safety of MED3000 gel over a 24-week treatment period in male patients clinically diagnosed with ED. Methods 100 male patients clinically diagnosed with ED were randomised into two treatment groups, 50 to MED3000 and 50 to Tadalafil (5mg) in 11 Investigational sites in USA, Poland Georgia and Bulgaria. Results Both Co-Primary end points were achieved (P<0.001) (1) Mean IIEF-EF change from baseline at 24 weeks (2) Minimal clinically important difference (MCID) exceeded at 24 weeks). 61% of MED3000 users exceeded the MCID at 24 weeks The mean IIEF score at weeks 4, 8, 12, 16, 20, 24 was 4.59, 5.20, 5.12, 5.83, 5.57 5.73 Secondary Endpoint 10 minute onset of action FDA agreed standard for achieving a 10 minute onset of action was achieved. A key differentiator from PDEi's Effective in Mild, Moderate and Severe ED MED3000 also delivered a clinically meaningful outcome over 24 weeks in accordance with the Rosen 2, 5 and 7 criteria (Rosen et al 2011)1 in mild, moderate and severe ED subjects Adverse Events (<2 incidences) are detailed in figure 1. Reference 1 – Rosen RC et al “Minimal clinically important differences in the erectile function domain in the International Index of Erectile Function Scale Eur Urol 2011;60(5):1010-6 Conclusions MED3000 has been shown to deliver a clinically meaningful response and is fast-acting. It was also shown to be clinically effective in mild, moderate and severe ED. Results were generally consistent with a previously conducted clinical study used for European approval MED3000 was shown to have minimal adverse events. Its “drug-free” formula provides a further margin of safety with no propensity for adverse drug interactions. This profile makes it suitable for OTC use. FDA filing for marketing authorisation as a De Novo medical device occured in October 2022. Conflicts of Interest Professor David Ralph is a consultant to FMD Tim Holland and Ken James are employees of FMD The study was funded by FMD (Not for promotional purposes in the US).

Background:Natalizumab is a highly effective treatment for patients with relapsing–remitting multiple sclerosis (RRMS). Treatment with natalizumab has been associated with progressive multifocal leukoencephalopathy (PML), a rare yet serious disease of the brain. Published studies have quantified the PML risk by the presence of anti-JC virus antibodies, previous immunosuppressant use, and duration of natalizumab treatment.Objectives:The aim of this analysis was to evaluate the net benefits and risks for patients with RRMS receiving natalizumab treatment compared with fingolimod, interferon-β, and no treatment across PML risk sub-groups.Research design and methods:Based on previously validated MS model structures, a Markov cohort model was developed to assess the impact of treatment on quality-adjusted life years (QALYs). Natalizumab-treated patients were classified by PML risk sub-groups and analysed separately for short-term (2 years) and long-term (20 years) time horizons.Main outcome measures:Main outcome measures included total QALYs by PML risk sub-group and the increase in PML risk associated with natalizumab treatment which offsets the quality of life benefit of comparator treatments.Results:Results showed higher QALYs with natalizumab versus all other comparators across PML risk sub-groups over both time horizons. For the QALYs of natalizumab to equal the QALYs of fingolimod, interferon-β, and no treatment, the risk of PML would have to increase 4.6–84.2 times, 24.0–444.3 times, and 5.7–106.1 times, respectively (short term), and 1.4–123.4 times, 1.5–138.3 times, and 2.2–193.7 times, respectively (long term).Conclusion:This study shows that natalizumab generates the most net health benefits in terms of quality-adjusted life years compared with fingolimod, interferon-β, or no treatment, even when the risk of natalizumab-associated PML is taken into consideration. This study is limited by the availability of published data around natalizumab-associated PML, as well as the constraints of the model used to conduct the analysis.

The Globally Harmonised System of Classification (GHS) is a framework within which the intrinsic hazards of substances may be determined and communicated. It is not a legislative instrument per se, but is enacted into national legislation with the appropriate legislative instruments. GHS covers many aspects of effects upon health and the environment, including adverse effects upon sexual function and fertility or on development. Classification for these effects is based upon observations in humans or from properly designed experiments in animals, although only the latter is covered herein. The decision to classify a substance based upon experimental data, and the category of classification ascribed, is determined by the level of evidence that is available for an adverse effect on sexual function and fertility or on development that does not arise as a secondary non-specific consequence of other toxic effect. This document offers guidance on the determination of level of concern as a measure of adversity, and the level of evidence to ascribe classification based on data from tests in laboratory animals.