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Lived experience of fully closed-loop insulin delivery in adolescents with type 1 diabetes and HbA1c above target.
Full text linkAIMS: The aim of this qualitative study was to explore the impact of using the CamAPS HX fully closed-loop system, which does not require carbohydrate counting, meal announcements or pre-meal bolusing, on the daily lives of adolescents with type 1 diabetes and HbA1c above the recommended target (≥7.5 % [58 mmol/mol]). METHODS: Twelve adolescents took part in virtual semi-structured interviews. Data was analyzed thematically using an inductive-deductive approach. Study participants also completed quality of life questionnaires. RESULTS: All interviewees reported reduced effort in managing diabetes, as they no longer needed to count carbohydrates or bolus, and worried less about their glucose levels. This led to improved quality of life, with a greater sense of freedom and normalcy, particularly around meals. A few also noted benefits in physical activity, sleep, work and social life. Interviewees expressed dissatisfaction with the algorithm's slow response to postprandial glucose spikes, and the need for a tethered pump. Questionnaires showed no significant differences in hypoglycaemia fear or diabetes distress between study periods but reflected a positive experience with the closed-loop system. CONCLUSIONS: In adolescents with type 1 diabetes, fully closed-loop insulin delivery reduced the daily burden of self-management, leading to improved quality of life. CLINICAL TRIAL REGISTRATION: NCT05653050
Large-scale meta-analysis and precision functional assays identify FANCM regions in which PTVs confer different risks for ER-negative and triple-negative breast cancer.
Full text linkThe breast cancer risk conferred by germline protein truncating variants (PTVs) in known and putative breast cancer genes has been extensively investigated. However, the effect of FANCM PTVs on breast cancer risk remains unclear. Our previous clinical, genetic and functional results on the N-terminal p.Arg658∗ and the two C-terminal p.Gln1701∗ and p.Gly1906Alafs∗12 variants suggested that FANCM PTVs may confer different risks for ER-negative (ER-neg) and triple-negative (TN) breast cancer subtypes. Here, we performed meta-analyses of seven studies totaling 144 681 breast cancer cases and 123 632 controls. FANCM PTVs were tested for association with breast cancer risk overall and the disease clinical subtypes by single variant and burden analyses. Two CRISPR-Cas9-based functional assays were also conducted to test the fitness of cells after knock-in of the p.Arg658∗, p.Gln1701∗ and p.Gly1906Alafs∗12 PTVs and the sensitivity of different FANCM regions to genome editing. Our results suggest that the N-terminal FANCM region upstream of p.Tyr725 harbors essential functions, whereas downstream regions appear dispensable. This is supported by our genetic data which indicate that all FANCM PTVs, excluding the two C-terminal p.Gln1701∗ and p.Gly1906Alafs∗12, are associated with an increased risk of ER-neg (OR = 1.41, P = 0.023) and TN (OR = 1.64, P = 0.0023). Notably, PTVs upstream of AA position 670 are associated with a moderate risk of developing TN breast cancer, and that even when the p.Arg658∗ carriers were excluded from the analysis. Importantly, our results confirm previous data indicating that p.Arg658∗ carriers are at moderate risk of developing ER-neg (OR = 2.08, P = 0.030) and TN (OR = 3.26; P = 0.0034), whereas carriers of p.Gln1701∗ and p.Gly1906Alafs∗12 should not be considered at increased risk. Our data are useful for counseling carriers of FANCM PTVs, but further analyses are warranted to obtain more precise risk estimates
Copper-rich fluids arising from sulfide resorption by hydrous arc melts.
Full text linkIncreasing global demand for copper (Cu) related to the energy transition requires that we understand the mechanisms by which Cu is enriched in the upper crust via magmatism. Porphyry Cu deposits (PCDs) are associated with arc volcanic systems and form under rare circumstances by precipitation from Cu-rich magmatic fluids. Here we develop models to delineate the magmatic conditions under which the Cu concentration and flux may be maximised in exsolved hydrous magmatic fluids. We show that ubiquitous sulfide saturation is a critical limitation on the Cu and sulfur load of exsolved magmatic fluids, owing to the strong partitioning of Cu into sulfide. Sulfide saturation in arc magmas may usually only be avoided under the most hydrous or oxidised conditions, which the global volcanic rock record suggests is not commonplace. However, thermally mature arc crust is likely to develop deep crustal cumulate zones in which sulfides may accumulate over time. When sulfide-undersaturated water-rich mafic melts percolate through these zones they may resorb sulfides during reactive flow. On volatile saturation, Cu-rich fluids will be generated that are viable precursors to PCDs
Structural Phase Transitions and Magnetic Characterization of Ba 2 GdNbO 6 for Low-Temperature Magnetocaloric Refrigeration
Full text linkBa2GdNbO6 has previously been reported to adopt either monoclinic, tetragonal, or cubic symmetry at room temperature. Using high-resolution synchrotron X-ray diffraction, neutron diffraction and neutron pair distribution function analysis we find that the compound adopts a tetragonal I4/m double-perovskite structure at room temperature (with a weak, temperature-independent second-order Jahn–Teller distortion in the NbO6 octahedra) and undergoes a phase transition to a monoclinic P21/n symmetry upon cooling to 2.4 K. Only upon heating above room temperature to T ≈ 450 K does Ba2GdNbO6 reversibly transition to a cubic Fm 3̅ m symmetry. Magnetic susceptibility measurements indicate predominant paramagnetic behavior down to 1.8 K, with minimal ferromagnetic short-range correlations (θ = 0.20(5) K) and a small exchange interaction (J 1 = −0.0032(8) K). At 2 K and 9 T, the compound exhibits a maximum magnetic entropy change of −ΔS m = 15.75 J K–1 mol–1 and an adiabatic temperature change of ΔT ad = 21 K, making it a promising candidate for low-temperature magnetocaloric applications. Heat capacity measurements confirm a rigid crystal lattice (T D = 267(3) K) and a corresponding small lattice entropy contribution in the low-temperature regime, highlighting the potential of Ba2GdNbO6 for effective cooling capability in magnetocaloric devices at cryogenic temperatures. This study elucidates the structural and magnetic characteristics of Ba2GdNbO6 and attests to its promise for low-temperature magnetocaloric refrigeration
Palliative management of malignant gastric outlet obstruction: A practice review.
Full text linkBACKGROUND: Malignant gastric outlet obstruction is a frequent complication in advanced gastric, pancreatic and duodenal cancers, and can cause nausea, vomiting, pain, and malnutrition. No individual guideline comprehensively addresses the palliative management of malignant gastric-outlet-obstruction symptoms. AIM: To develop evidence-based recommendations for non-surgical palliative management of malignant gastric-outlet-obstruction, incorporating pharmacological, decompressive, endoscopic and nutritional strategies. DESIGN: Practice review using scoping methodology to evaluate the strength of evidence supporting guideline-recommended interventions. DATA SOURCES: We identified relevant national and international guidelines via web searches and the TRIP database; guideline reference lists were hand-searched for relevant primary studies; a supplementary structured MEDLINE search (2024) captured emerging research. RESULTS: A multidisciplinary team approach, with early evaluation of prognosis and patient preference, should guide intervention choice. Pharmacological therapies (opioids, antiemetics, antisecretory agents) are frequently used for symptom control, but evidence of their efficacy in malignant gastric-outlet-obstruction is limited. Duodenal stenting remains first-line for endoscopic palliation, while endoscopic gastroenterostomy has emerging evidence supporting its effectiveness and should be considered. Nasogastric or venting gastrostomy decompression is advised for acute obstruction when endoscopy is not feasible, but prolonged NG tube use should be avoided. Early nutritional assessment is recommended, although the optimal modality and duration of nutritional supplementation have yet to be determined. CONCLUSIONS: Patient-centred, multidisciplinary care is essential for malignant gastric-outlet-obstruction palliation. Further research is needed to establish optimal drug combinations. Duodenal stenting remains first-line, but guidelines should incorporate newer minimally-invasive interventions such as endoscopic gastroenterostomy. Standardised quality-of-life measures are required for developing integrated care pathways
Robust and interpretable high-dimensional machine learning for predictive cancer medicine
Full text linkGaining actionable insights from complex, high-dimensional biological data is challenging and often relies upon dimensionality reduction techniques. These methods reveal structure within data, potentially exposing meaningful biological patterns.
In predictive cancer medicine, it is common to employ linear dimensionality reduction methods due to their inherent interpretability. However, recent advances in machine learning have sparked considerable interest in more flexible, non-linear dimensionality reduction techniques.
In this thesis, I propose and build upon extensions to the variational autoencoder (VAE), a probabilistic latent variable model that leverages neural networks to learn latent representations. Specifically, I propose a VAE variant that generates latent representations which explicitly capture genetic dependencies in cancers. I incorporate this into a framework for prediction using these learned interpretable representations as inputs. I demonstrate that this process allows interpretable and biologically meaningful prediction on a variety of tasks in cancer medicine.
Extending this two-stage learning framework, I propose a series of models which instead jointly learn interpretable representations and downstream prediction models while effectively leveraging informative auxiliary data to encourage the formation of useful representations.
Complementing these latent variable modelling approaches, I also explore interpretability directly within the original high-dimensional feature space using interpretable graph neural networks (GNNs). In particular, I focus on developing interpretable GNNs that are suited to problems involving large biomedical graphs. I apply these methods to identify genetic dependencies and biological interactions that drive drug response in cancer cell lines. These GNN-based methods provide transparent explanations by explicitly selecting and highlighting relevant features from high-dimensional data projected onto large biomedical graphs.
I demonstrate the effectiveness and interpretability of the proposed methodologies through extensive evaluation on diverse datasets, including high-dimensional transcriptomics data from cancer cell lines, high-throughput in vitro screening datasets, and clinical cancer cohorts, as well as simulated datasets. These approaches not only improve predictive performance and generalisation but also provide transparent, biologically interpretable insights, demonstrating their potential utility in personalised cancer medicine
Community-led landscape regeneration: A review of and framework for engagement in restoration initiatives.
Full text linkWith growing calls for people-centred and equitable approaches to regeneration and restoration, this review paper contributes to enhancing understanding of the role of communities in restoring landscapes across the world. Addressing the lack of clarity around tangible pathways for equitable and inclusive forms of landscape regeneration, we focus on exploring the practices and forms through which communities engage with landscape regeneration and restoration. We undertake a systematic review of an international selection of community-based landscape regeneration initiatives worldwide to better understand how communities engage with, manage and lead regeneration practices. We map landscape regeneration and restoration initiatives across international contexts based on four themes around community organisation, land ownership, engagement and land values. Borne out of this review, we propose an analytical framework for community-based landscape regeneration in order to support and mobilise more democratic and socially just approaches to ecological regeneration initiatives
Early Diagnosis, Early Stratification, and Early Intervention to Deliver Precision Medicine in IBD.
Full text linkDespite huge advances in understanding the molecular basis of IBD, clinical management has continued to rely on a "trial and error" approach. In addition, a therapeutic ceiling has emerged whereby even the most effective interventions are only beneficial for approximately 30% of patients. Consequently, several tools have been developed to aid stratification and guide treatment-decisions. We review the potential application for many of these precision medicine approaches, which are now almost within reach. We highlight the importance of early action (and avoiding inaction) to ensure the best outcomes for patients and how combining early action with precision tools will likely ensure the right treatment is delivered at the right time and place for each individual person living with IBD. The lack of clinical impact to date from precision medicine, despite much hype and investment, should be tempered with the knowledge that clinical translation can take a long time, and many promising breakthroughs might be ready for clinical implementation in the near future. We discuss some of the remaining challenges and barriers to overcome for clinical adoption. We also highlight that early recognition, early diagnosis, early stratification, and early intervention go hand in hand with precision medicine tools. It is the combination of these approaches that offer the greatest opportunity to finally deliver on the promise of precision medicine in IBD
Informing conservation problems and actions using an indicator of extinction risk: A detailed assessment of applying the LIFE metric
Full text linkExtinction is a critical issue, with land-use change the main threat to many terrestrial species. Understanding and tackling this requires global, comparable, and scalable metrics that link land-cover change to extinction risk and are useable across diverse conservation contexts. Here, we demonstrate the flexibility of the new Land-cover change Impacts on Future Extinctions (LIFE) metric through five distinct case studies. First, we explore the near real-time quantification of biodiversity harms in tropical hotspots by integrating LIFE with forest loss data. Second, we couple LIFE with crop distribution and trade data to assess variation in extinction impacts mediated by food consumption – specifically of apples in the UK. Third, we test LIFE's suitability for use in biodiversity compensation through a hypothetical scenario in Sumatra. Fourth, we use LIFE to prioritize competing conservation investments by comparing benefits of area-based projects in Honduras. Finally, we combine LIFE with counterfactual methods to evaluate the effectiveness of a long-term conservation project in Sierra Leone. Together, these examples show that LIFE offers actionable insights into a geographically and thematically wide range of conservation challenges, from land-use planning to sustainable consumption. Like all global metrics, LIFE's broad applicability relies on assumptions and simplifications. It should be used cautiously, and alongside local knowledge and ground-truthing, especially for restoration, offsetting, or fine-scale analysis, and in poorly studied areas. By providing an accompanying “How-to” guide, we aim to ensure LIFE can be used widely to inform understanding of the extinction crisis and support tangible actions to halt it
AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network
No full textAdvances in machine learning have transformed structural biology, enabling swift and accurate
prediction of protein structure from sequence. However, challenges persist in capturing sidechain
packing, condition-dependent conformational dynamics, and biomolecular interactions, primar-
ily due to scarcity of high-quality training data. Emerging techniques, including cryo-electron
tomography (cryo-ET) and high-throughput crystallography, promise vast new sources of struc-
tural data, but translating experimental observations into mechanistically interpretable atomic
models remains a key bottleneck. Here, we address these challenges by improving the efficiency of
structural analysis through combining experimental measurements with a landmark protein struc-
ture prediction method – AlphaFold2. We present an augmentation of AlphaFold2, ROCKET,
that refines its predictions using cryo-EM, cryo-ET, and X-ray crystallography data, and demon-
strate that this approach captures biologically important structural variation that AlphaFold2
does not. By performing structure optimization in the space of coevolutionary embeddings,
rather than Cartesian coordinates, ROCKET automates difficult modeling tasks, such as flips
of functional loops, domain rearrangements, and building in low signal-to-noise regimes that
are beyond the scope of current state-of-the-art methods and, in some instances, even manual
human modeling. These abilities unlock a new horizon for scalable and automated model build-
ing. Crucially, ROCKET does not require retraining of AlphaFold2 and is readily adaptable to
multimers, ligand-cofolding, and other data modalities. Conversely, our differentiable crystal-
lographic and cryo-EM target functions are capable of augmenting other structure prediction
methods. ROCKET thus provides an extensible framework for the integration of experimental
observables with biomolecular machine learning