Relative antagonism of mutants of the CGRP receptor extracellular loop 2 domain (ECL2) using a truncated competitive antagonist (CGRP8-37):evidence for the dual involvement of ECL2 in the two-domain binding model

The second extracellular loop (ECL2) of the G protein-coupled receptor (GPCR) family is important for ligand interaction and drug discovery. ECL2 of the family B cardioprotective calcitonin gene-related peptide (CGRP) receptor is required for cell signaling. Family B GPCR ligands have two regions; the N-terminus mediates receptor activation, and the remainder confers high-affinity binding. Comparing antagonism of CGRP8-37 at a number of point mutations of ECL2 of the CGRP receptor, we show that the ECL2 potentially facilitates interaction with up to the 18 N-terminal residues of CGRP. This has implications for understanding family B GPCR activation and for drug design at the CGRP receptor

Understanding the molecular functions of the second extracellular loop (ECL2) of the calcitonin gene-related peptide (CGRP) receptor using a comprehensive mutagenesis approach

The extracellular loop 2 (ECL2) region is the most conserved of the three ECL domains in family B G protein-coupled receptors (GPCRs) and has a fundamental role in ligand binding and activation across the receptor super-family. ECL2 is fundamental for ligand-induced activation of the calcitonin gene related peptide (CGRP) receptor, a family B GPCR implicated in migraine and heart disease. In this study we apply a comprehensive targeted non-alanine substitution analysis method and molecular modelling to the functionally important residues of ECL2 to reveal key molecular interactions. We identified an interaction network between R274/Y278/D280/W283. These amino acids had the biggest reduction in signalling following alanine substitution analysis and comprise a group of basic, acidic and aromatic residues conserved in the wider calcitonin family of class B GPCRs. This study identifies key and varied constraints at each locus, including diverse biochemical requirements for neighbouring tyrosine residues and a W283H substitution that recovered wild-type (WT) signalling, despite the strictly conserved nature of the central ECL2 tryptophan and the catastrophic effects on signalling of W283A substitution. In contrast, while the distal end of ECL2 requires strict conservation of hydrophobicity or polarity in each position, mutation of these residues never has a large effect. This approach has revealed linked networks of amino acids, consistent with structural models of ECL2 and likely to represent a shared structural framework at an important ligand-receptor interface that is present across the family B GPCRs

Opportunities and challenges for identifying undiagnosed Rare Disease patients through analysis of primary care records: long QT syndrome as a test case

BackgroundPatients with rare genetic diseases frequently experience significant diagnostic delays. Routinely collected data in the electronic health record (EHR) may be used to help identify patients at risk of undiagnosed conditions. Long QT syndrome (LQTS) is a rare inherited cardiac condition associated with significant morbidity and premature mortality. In this study, we examine LQTS as an exemplar disease to assess if clinical features recorded in the primary care EHR can be used to develop and validate a predictive model to aid earlier detection.Methods1495 patients with an LQTS diagnostic code and 7475 propensity-score matched controls were identified from 10.5 million patients’ electronic primary care records in the UK’s Clinical Practice Research Datalink (CPRD). Associated clinical features recorded before diagnosis (with p < 0.05) were incorporated into a multivariable logistic regression model, the final model was determined by backwards regression and validated by bootstrapping to determine model optimism.ResultsThe mean age at LQTS diagnosis was 58.4 (SD 19.41). 18 features were included in the final model. Discriminative accuracy, assessed by area under the curve (AUC), was 0.74, (95% CI 0.73, 0.75) (optimism 6%). Features occurring at significantly greater frequency before diagnosis included: epilepsy, palpitations, syncope, collapse, mitral valve disease and irritable bowel syndrome.ConclusionThis study demonstrates the potential to develop primary care prediction models for rare conditions, like LQTS, in routine primary care records and highlights key considerations including disease suitability, finding an appropriate linked dataset, the need for accurate case ascertainment and utilising an approach to modelling suitable for rare events

Mapping agricultural land in Afghanistan’s opium provinces using a generalised deep learning model and medium resolution satellite imagery

Understanding the relationship between land use and opium production is critical for monitoring the dynamics of poppy cultivation and developing an effective counter narcotics policy in Afghanistan. However, mapping agricultural land accurately and rapidly is challenging, as current methods require resource-intensive and time consuming manual image-interpretation. Deep convolutional neural nets have been shown to greatly reduce the manual effort in mapping agriculture from satellite imagery but require large amounts of densely labelled training data for model training. Here we develop a generalised model using past images and labels from different medium resolution satellite sensors for fully automatic agricultural land classification using the latest medium resolution satellite imagery. The model (FCN-8) is first trained on Disaster Monitoring Constellation (DMC) satellite images from 2007 to 2009. The effect of shape, texture and spectral features on model performance are investigated along with normalisation in order to standardise input medium resolution imagery from DMC, Landsat-5, Landsat-8, and Sentinel-2 for transfer learning between sensors and across years. Textural features make the highest contribution to overall accuracy (∼73%) while the effect of shape is minimal. The model accuracy on new images, with no additional training, is comparable to visual image interpretation (overall > 95%, user accuracy > 91%, producer accuracy > 85%, and frequency weighted intersection over union > 67%). The model is robust and was used to map agriculture from archive images (1990) and can be used in other areas with similar landscapes. The model can be updated by fine tuning using smaller, sparsely labelled datasets in the future. The generalised model was used to map the change in agricultural area in Helmand Province, showing the expansion of agricultural land into former desert areas. Training generalised deep learning models using data from both new and long-term EO programmes, with little or no requirement for fine tuning, is an exciting opportunity for automating image classification across datasets and through time that can improve our understanding of the environment.Natural Environment Research Council (NERC): NE/M009009/

A mixed-method pilot study to improve patient satisfaction in rural Uganda

This article reports on a mixed-method longitudinal field study that was conducted using a tablet-based app capturing data on patients’ satisfaction with an outpatient clinic in Kalungu District, Uganda. The app was developed, piloted, and refined using clinician and patient feedback. Findings were reported and discussed in staff meetings, with change in reported levels of satisfaction assessed using descriptive statistical analysis and Chi2 tests. Qualitative data were collected. Satisfaction was relatively high at baseline and increased by 4.4%, and staff found the feedback actionable. Patients reported fewer delays and better treatment after introducing the app, with the proportion of “very dissatisfied” patients decreasing from 2.3% to zero after six weeks

Deep Purple Payload Qualifies for NASA Launch, Could Provide New Method for Real-Time Space-Domain Awareness

In January 2024, the Lawrence Livermore National Laboratory (LLNL) Space Program fully assembled and qualified its Deep Purple payload. It will be on board NASA\u27s Pathfinder Technology Demonstrator-R (PTD-R), scheduled to launch on the SpaceX Transporter 11 in July 2024. The Livermore team designed, developed, qualified, and delivered the Deep Purple payload in approximately one year and has now been integrated into the PTD-R satellite, a 6U (36 cm x 23 cm x 10 cm) bus constructed by a Laboratory Collaborative Research and Development Agreement (CRADA) partner Terran Orbital. NASA\u27s Small Spacecraft Technology Programs\u27 PTD-R space vehicle containing the Deep Purple payload was developed to replace an earlier NASA technological demonstration (PTD-2). LLNL utilized this opportunity to rapidly prototype a payload utilizing a new design for LLNL-developed ultra-violet (UV, 230nm – 310nm) and short-wave infrared (SWIR, 1000nm – 1700nm) monolithic Cassegrain telescopes. Deep Purple will demonstrate LLNL\u27s monolithic UV and SWIR optical sensing platforms from space for the first time via two co-boresighted, 85mm aperture telescopes. A new compact electronics module and a novel, lightweight, carbon-composite optical housing and radiator save considerable weight, cost, and lead time while boosting optical performance. The dual optical module and the electronics are contained in a 23 cm x 15 cm x 10 cm package (about the size of a loaf of bread). The Deep Purple payload showcases the rapid development, test, and build cycles needed for responsive space missions. The modular optical housing developed for PTD-R allows future missions to rapidly integrate and gang telescopes for an even quicker time-to-flight. What traditionally takes years can now be accomplished in just a few months. Once operational, Deep Purple will perform simultaneous UV and SWIR observations from high-UV stars and the Milky Way\u27s galactic bulge. Furthermore, it will attempt to capture time-domain galactic and extra-galactic events as well as demonstrate real-time space domain awareness using these unique sensing bands. The Space Program at LLNL continues to demonstrate its leadership in developing and delivering small satellite tools and capabilities

The paradox of tolerance: parasite extinction due to the evolution of host defence.

Host defence against parasite infection can rely on two broad strategies: resistance and tolerance. The spread of resistance traits usually lowers parasite prevalence and decreases selection for higher defence. Conversely, tolerance mechanisms increase parasite prevalence and foster selection for more tolerance. Here we examine the potential for the host to drive parasites to extinction through the evolution of one or other defence mechanism. We analysed theoretical models of resistance and tolerance evolution in both the absence and the presence of a trade-off between defence and reproduction. In the absence of costs, resistance evolves towards maximisation and, consequently, parasite extinction. Tolerance also evolves towards maximisation but the positive feedback between tolerance and disease prevents the disappearance of the parasite. On the contrary, when defence comes with costs it is impossible for the host to eliminate the infection through resistance, because costly resistance is selected against when parasites are at low prevalence. We uncover that the only path to disease clearance in the presence of costs is through tolerance. Paradoxically, however, it is by lowering tolerance -and hence increasing disease-induced mortality- that extinction can occur. We also show that such extinction can occur even in the case of parasite counter-adaptation. Our results emphasise the importance of tolerance as a defence strategy, and identify key questions for future research