How to design and set up a clinical trial part 2 : protocols and approvals

Data from clinical trials involving human participants are essential in establishing an evidence base about the safety and effectiveness of our treatments. This second article describes the steps involved in designing and setting up a clinical trial, from writing a protocol to gaining the necessary approvals. Acquiring some knowledge about how to set up a clinical trial will allow the conscientious clinician to use the most relevant information to provide the highest possible standards of clinical care for his/her patients

Vibrational interference of Raman and high-harmonic generation pathways

Experiments have shown that the internal vibrational state of a molecule can affect the intensity of high harmonic light generated from that molecule. This paper presents a model which explains this modulation in terms of interference between different vibrational states occurring during the high harmonic process. In addition, a semiclassical model of the continuum electron propagation is developed which connects with rigorous treatments of the electron-ion scattering

Preplanned Studies: Orofacial Clefts in High Prevalence Area of Birth Defects — Five Counties, Shanxi Province, China, 2000–2020

What is already known on this topic?: The prevalence of structural birth defects, especially neural tube defects, decreased after national folic acid (FA) supplementation initiation. / What is added by this report?: The prevalence of orofacial clefts (OFCs) in five counties of Shanxi Province in northern China, including most subtypes except cleft palate, showed a downward trend in the past two decades. In this study, pre-perinatal prevalence increased due to earlier detection. / What are the implications for public health practice?: Periconceptional supplementation with FA may contribute to the decline in OFCs prevalence, while the effect on the OFCs subtype needs further investigation. Continuing to advocate for earlier supplementation (3 months before conception) and increased supplementation frequency (daily consumption) could promote further reduction in the prevalence of OFCs. Specific surveillance of this effect in the era of universal three-child policy is warranted

Doping dependent charge order correlations in electron-doped cuprates

Understanding the interplay between charge order (CO) and other phenomena (e.g. pseudogap, antiferromagnetism, and superconductivity) is one of the central questions in the cuprate high-temperature superconductors. The discovery that similar forms of CO exist in both hole- and electron-doped cuprates opened a path to determine what subset of the CO phenomenology is universal to all the cuprates. Here, we use resonant x-ray scattering to measure the charge order correlations in electron-doped cuprates (La2-xCexCuO4 and Nd2-xCexCuO4) and their relationship to antiferromagnetism, pseudogap, and superconductivity. Detailed measurements of Nd2-xCexCuO4 show that CO is present in the x = 0.059 to 0.166 range, and that its doping dependent wavevector is consistent with the separation between straight segments of the Fermi surface. The CO onset temperature is highest between x = 0.106 and 0.166, but decreases at lower doping levels, indicating that it is not tied to the appearance of antiferromagnetic correlations or the pseudogap. Near optimal doping, where the CO wavevector is also consistent with a previously observed phonon anomaly, measurements of the CO below and above the superconducting transition temperature, or in a magnetic field, show that the CO is insensitive to superconductivity. Overall these findings indicate that, while verified in the electron-doped cuprates, material-dependent details determine whether the CO correlations acquire sufficient strength to compete for the ground state of the cuprates.Comment: Supplementary information available upon reques

Industry-scale application and evaluation of deep learning for drug target prediction

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.Web of Science121art. no. 2

Simulation‐Based Medical Emergencies Education for Dental Students: A Three‐Year Evaluation

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153561/1/jddjde019084.pd

Literature review on enhancing integration of disaster risk and climate change adaptation in Irish emergency planning

The scope of the present literature review is under the remit of a wider project entitled Enhancing Integration of Disaster Risk and Climate Change Adaptation into Irish Emergency Planning which is funded under the Environmental Protection Agency's Climate Topic 3 funding call. The project, which began in March 2020, is due to run until March 2021. The objective of the project is to help institutions responsible to further 'climate-proof' emergency planning and risk management systems in Ireland to the increasing risk of extreme hydrometeorological events, by addressing national policy and decision-making processes, as well as local and regional planning and response mechanisms