Whey-derived peptides interactions with ACE by molecular docking as a potential predictive tool of natural ACE inhibitors

Several milk/whey derived peptides possess high in vitro angiotensin I-converting enzyme (ACE) inhibitory activity. However, in some cases, poor correlation between the in vitro ACE inhibitory activity and the in vivo antihypertensive activity has been observed. The aim of this study is to gain insight into the structure-activity relationship of peptide sequences present in whey/milk protein hydrolysates with high ACE inhibitory activity, which could lead to a better understanding and prediction of their in vivo antihypertensive activity. The potential interactions between peptides produced from whey proteins, previously reported as high ACE inhibitors such as IPP, LIVTQ, IIAE, LVYPFP, and human ACE were assessed using a molecular docking approach. The results show that peptides IIAE, LIVTQ, and LVYPFP formed strong H bonds with the amino acids Gln 259, His 331, and Thr 358 in the active site of the human ACE. Interestingly, the same residues were found to form strong hydrogen bonds with the ACE inhibitory drug Sampatrilat. Furthermore, peptides IIAE and LVYPFP interacted with the amino acid residues Gln 259 and His 331, respectively, also in common with other ACE-inhibitory drugs such as Captopril, Lisinopril and Elanapril. Additionally, IIAE interacted with the amino acid residue Asp 140 in common with Lisinopril, and LIVTQ interacted with Ala 332 in common with both Lisinopril and Elanapril. The peptides produced naturally from whey by enzymatic hydrolysis interacted with residues of the human ACE in common with potent ACE-inhibitory drugs which suggests that these natural peptides may be potent ACE inhibitors

Measuring sleep in critically ill patients: beware the pitfalls

Survivors of critical illness frequently report poor sleep while in the intensive care unit (ICU), and sleep deprivation has been hypothesized to lead to emotional distress, ICU delirium and neurocognitive dysfunction, prolongation of mechanical ventilation, and decreased immune function. Thus, the careful study of sleep in the ICU is essential to understanding possible relationships with adverse clinical outcomes. Such research, however, must be conducted using sleep measurement techniques that have important limitations in this unique setting. Polysomnography (PSG) is considered the gold standard but is cumbersome, time consuming, and expensive. As such, alternative methods of sleep measurement such as actigraphy, processed electroencephalography monitors, and subjective observation are often used. Though helpful in some instances, data obtained using these methods can often be inaccurate and misleading. Even PSG itself must be interpreted with caution in this population due to effects of critical illness and associated treatments

Patients' experiences and preferences for primary care delivery : a focus group analysis

Peer reviewedPublisher PD

A systematic review and narrative synthesis of pharmacist-led education-based antimicrobial stewardship interventions and their effect on antimicrobial use in hospital inpatients

Acknowledgements The authors would like to thank Dr Peerawat Jinathongthai and Dr Sisira Donsamak (Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Thailand) who advised and contributed in the literature search. Funding TM has received the Royal Thai Government Scholarship for his doctoral study (scholarship number ST G5397) at The University of Bath, Bath, UK. None of the other authors were funded by a specific grant for this research from any funding agency in the public, commercial, or non-for-profit sectors.Peer reviewedPostprin

Weaving Together Indigenous and Western Knowledge in Science Education: Reflections and Recommendations

The Culturally Responsive Indigenous Science (CRIS) project was a collaborative effort between three Tribal communities in the Pacific Northwest and faculty and students from Washington State University, many of who are Tribal citizens. The project was designed to integrate Indigenous Traditional Ecological Knowledge (ITEK) and Western Knowledge into science curricula and professional learning opportunities. At the end of the 5-year grant project, members of the CRIS team (including Tribal and university partners) gathered to reflect on the work accomplished and the lessons learned about the process of integrating ITEK within science education. In this conceptual paper, the authors discuss four key takeaways from their reflections: 1) Creating relational space for cultural values and practices, 2) Indigenous science education requires many educators with diverse expertise, 3) Respecting Tribal and individual autonomy and timelines, and 4) Remembering who the work is meant to serve. In summary, the authors highlighted important recommendations to be considered when weaving together ITEK and Western science to better serve and engage Native American youth

Subgrouping siblings of people with autism: Identifying the broader autism phenotype.

We investigate the broader autism phenotype (BAP) in siblings of individuals with autism spectrum conditions (ASC). Autistic traits were measured in typical controls (n = 2,000), siblings (n = 496), and volunteers with ASC (n = 2,322) using the Autism-Spectrum Quotient (AQ), both self-report and parent-report versions. Using cluster analysis of AQ subscale scores, two sibling subgroups were identified for both males and females: a cluster of low-scorers and a cluster of high-scorers. Results show that while siblings as a group have intermediate levels of autistic traits compared to control individuals and participants with ASC, when examined on a cluster level, the low-scoring sibling group is more similar to typical controls while the high-scoring group is more similar to the ASC clinical group. Further investigation into the underlying genetic and epigenetic characteristics of these two subgroups will be informative in understanding autistic traits, both within the general population and in relation to those with a clinical diagnosis. Autism Res 2016, 9: 658-665. © 2015 The Authors Autism Research published by Wiley Periodicals, Inc. on behalf of International Society for Autism Research.This work was supported by grants from the Autism Research Trust, the MRC, and the Wellcome Trust to SBC. CA was supported by NIHR CLAHRC EoE during the period of this work.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/aur.154

Measuring autistic traits in the general population: a systematic review of the Autism-Spectrum Quotient (AQ) in a nonclinical population sample of 6,900 typical adult males and females.

The Autism-Spectrum Quotient (AQ) is a self-report measure of autistic traits. It is frequently cited in diverse fields and has been administered to adults of at least average intelligence with autism and to nonclinical controls, as well as to clinical control groups such as those with schizophrenia, prosopagnosia, anorexia, and depression. However, there has been no empirical systematic review of the AQ since its inception in 2001. The present study reports a comprehensive systematic review of the literature to estimate a reliable mean AQ score in individuals without a diagnosis of an autism spectrum condition (ASC), in order to establish a reference norm for future studies. A systematic search of computerized databases was performed to identify studies that administered the AQ to nonclinical participant samples representing the adult male and female general population. Inclusion was based on a set of formalized criteria that evaluated the quality of the study, the usage of the AQ, and the population being assessed. After selection, 73 articles, detailing 6,934 nonclinical participants, as well as 1,963 matched clinical cases of ASC (from available cohorts within each individual study), were analyzed. Mean AQ score for the nonclinical population was 16.94 (95% CI 11.6, 20.0), while mean AQ score for the clinical population with ASC was found to be 35.19 (95% CI 27.6, 41.1). In addition, in the nonclinical population, a sex difference in autistic traits was found, although no sex difference in AQ score was seen in the clinical ASC population. These findings have implications for the study of autistic traits in the general population. Here, we confirm previous norms with more rigorous data and for the first time establish average AQ scores based on a systematic review, for populations of adult males and females with and without ASC. Finally, we advise future researchers to avoid risk of bias by carefully considering the recruitment strategy for both clinical and nonclinical groups and to demonstrate transparency by reporting recruitment methods for all participants.This research was supported by the Medical Research Council UK, the Wellcome Trust, and the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care East of England at Cambridgeshire and Peterborough NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health

Physics-Constrained Deep Learning for Climate Downscaling

The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and, therefore, often generate coarse-resolution predictions. Statistical downscaling, including super-resolution methods from deep learning, can provide an efficient method of upsampling low-resolution data. However, despite achieving visually compelling results in some cases, such models frequently violate conservation laws when predicting physical variables. In order to conserve physical quantities, we develop methods that guarantee physical constraints are satisfied by a deep learning downscaling model while also improving their performance according to traditional metrics. We compare different constraining approaches and demonstrate their applicability across different neural architectures as well as a variety of climate and weather datasets. Besides enabling faster and more accurate climate predictions, we also show that our novel methodologies can improve super-resolution for satellite data and standard datasets

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling

Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computationally expensive to resolve complex climate processes at high spatial resolution. As one way to speed up climate simulations, neural networks have been used to downscale climate variables from fast-running low-resolution simulations, but high-resolution training data are often unobtainable or scarce, greatly limiting accuracy. In this work, we propose a downscaling method based on the Fourier neural operator. It trains with data of a small upsampling factor and then can zero-shot downscale its input to arbitrary unseen high resolution. Evaluated both on ERA5 climate model data and on the Navier-Stokes equation solution data, our downscaling model significantly outperforms state-of-the-art convolutional and generative adversarial downscaling models, both in standard single-resolution downscaling and in zero-shot generalization to higher upsampling factors. Furthermore, we show that our method also outperforms state-of-the-art data-driven partial differential equation solvers on Navier-Stokes equations. Overall, our work bridges the gap between simulation of a physical process and interpolation of low-resolution output, showing that it is possible to combine both approaches and significantly improve upon each other.Comment: Presented at the ICLR 2023 workshop on "Tackling Climate Change with Machine Learning