Accurate staging of chick embryonic tissues via deep learning of salient features.

Recent work shows that the developmental potential of progenitor cells in the HH10 chick brain changes rapidly, accompanied by subtle changes in morphology. This demands increased temporal resolution for studies of the brain at this stage, necessitating precise and unbiased staging. Here, we investigated whether we could train a deep convolutional neural network to sub-stage HH10 chick brains using a small dataset of 151 expertly labelled images. By augmenting our images with biologically informed transformations and data-driven preprocessing steps, we successfully trained a classifier to sub-stage HH10 brains to 87.1% test accuracy. To determine whether our classifier could be generally applied, we re-trained it using images (269) of randomised control and experimental chick wings, and obtained similarly high test accuracy (86.1%). Saliency analyses revealed that biologically relevant features are used for classification. Our strategy enables training of image classifiers for various applications in developmental biology with limited microscopy data.

Comparison of genotypic features between two groups of antibiotic resistant Klebsiella pneumoniae clinical isolates obtained before and after the COVID-19 pandemic from Egypt.

Klebsiella pneumoniae is a common pathogen capable of causing a wide range of infections. Antibiotic resistance complicates treatment of these infections significantly. We are comparing resistance levels and genotypes among two collections of K. pneumoniae clinical isolates from Alexandria Main University Hospital (AMUH). We used disc diffusion and Minimum Inhibitory Concentration (MIC) by microbroth dilution to assess resistance levels and performed whole genome sequencing (WGS) to describe multilocus sequence types (MLST) and resistance gene presence. Among a collection of 56 K. pneumoniae clinical isolates (19 from 2019 to 37 from 2021), multidrug resistance (MDR) was 33% and 10%, extended drug resistance (XDR) was 24% and 46% and pan-drug resistance (PDR) was 43% and 43%, respectively. We identified 15 MLST STs including two novel types (ST-6118 and ST-6119 ). ST-101 and ST-383 were common between the two collections; ST-101 was the most common genotype in 2019 (28.6%) and ST-147 was most common in 2021 (25%). Ampicillin/sulbactam, amikacin, cefepime, ceftriaxone and ertapenem MICs were significantly higher in 2021. Prevalence of aph(3') - Ia, aph(3')-VI, mphA was significantly higher in 2021. The increasing resistance levels and the persistence of some MDR/XDR genotypes is concerning. Understanding mechanisms of resistance will inform infection control and antimicrobial stewardship plans to prevent evolution and spread of XDR and PDR strains.

Improved reference genome of Aedes aegypti informs arbovirus vector control.

Female Aedes aegypti mosquitoes infect more than 400 million people each year with dangerous viral pathogens including dengue, yellow fever, Zika and chikungunya. Progress in understanding the biology of mosquitoes and developing the tools to fight them has been slowed by the lack of a high-quality genome assembly. Here we combine diverse technologies to produce the markedly improved, fully re-annotated AaegL5 genome assembly, and demonstrate how it accelerates mosquito science. We anchored physical and cytogenetic maps, doubled the number of known chemosensory ionotropic receptors that guide mosquitoes to human hosts and egg-laying sites, provided further insight into the size and composition of the sex-determining M locus, and revealed copy-number variation among glutathione S-transferase genes that are important for insecticide resistance. Using high-resolution quantitative trait locus and population genomic analyses, we mapped new candidates for dengue vector competence and insecticide resistance. AaegL5 will catalyse new biological insights and intervention strategies to fight this deadly disease vector.

Clarifying status of DNNs as models of human vision.

On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN-human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in the literature. In our view, these latter issues are contributing to many unjustified claims regarding DNN-human correspondences in vision and other domains of cognition. We explore all these issues in this response.

Rooting the Animal Tree of Life.

Identifying our most distant animal relatives has emerged as one of the most challenging problems in phylogenetics. This debate has major implications for our understanding of the origin of multicellular animals and of the earliest events in animal evolution, including the origin of the nervous system. Some analyses identify sponges as our most distant animal relatives (Porifera-sister hypothesis), and others identify comb jellies (Ctenophora-sister hypothesis). These analyses vary in many respects, making it difficult to interpret previous tests of these hypotheses. To gain insight into why different studies yield different results, an important next step in the ongoing debate, we systematically test these hypotheses by synthesizing 15 previous phylogenomic studies and performing new standardized analyses under consistent conditions with additional models. We find that Ctenophora-sister is recovered across the full range of examined conditions, and Porifera-sister is recovered in some analyses under narrow conditions when most outgroups are excluded and site-heterogeneous CAT models are used. We additionally find that the number of categories in site-heterogeneous models is sufficient to explain the Porifera-sister results. Furthermore, our cross-validation analyses show CAT models that recover Porifera-sister have hundreds of additional categories and fail to fit significantly better than site-heterogenuous models with far fewer categories. Systematic and standardized testing of diverse phylogenetic models suggests that we should be skeptical of Porifera-sister results both because they are recovered under such narrow conditions and because the models in these conditions fit the data no better than other models that recover Ctenophora-sister.

Arms race and fluctuating selection dynamics in Pseudomonas aeruginosa bacteria coevolving with phage OMKO1.

Experimental evolution studies have examined coevolutionary dynamics between bacteria and lytic phages, where two models for antagonistic coevolution dominate: arms-race dynamics (ARD) and fluctuating-selection dynamics (FSD). Here, we tested the ability for Pseudomonas aeruginosa to coevolve with phage OMKO1 during 10 passages in the laboratory, whether ARD versus FSD coevolution occurred, and how coevolution affected a predicted phenotypic trade-off between phage resistance and antibiotic sensitivity. We used a unique "deep" sampling design, where 96 bacterial clones per passage were obtained from the three replicate coevolving communities. Next, we examined phenotypic changes in growth ability, susceptibility to phage infection and resistance to antibiotics. Results confirmed that the bacteria and phages coexisted throughout the study with one community undergoing ARD, whereas the other two showed evidence for FSD. Surprisingly, only the ARD bacteria demonstrated the anticipated trade-off. Whole genome sequencing revealed that treatment populations of bacteria accrued more de novo mutations, relative to a control bacterial population. Additionally, coevolved bacteria presented mutations in genes for biosynthesis of flagella, type-IV pilus and lipopolysaccharide, with three mutations fixing contemporaneously with the occurrence of the phenotypic trade-off in the ARD-coevolved bacteria. Our study demonstrates that both ARD and FSD coevolution outcomes are possible in a single interacting bacteria-phage system and that occurrence of predicted phage-driven evolutionary trade-offs may depend on the genetics underlying evolution of phage resistance in bacteria. These results are relevant for the ongoing development of lytic phages, such as OMKO1, in personalized treatment of human patients, as an alternative to antibiotics.

Deep problems with neural network models of human vision.

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data.

Variability in carbapenemase activity of intrinsic OxaAb (OXA-51-like) ß-lactamase enzymes in Acinetobacter baumannii.

OBJECTIVES: To measure the variability in carbapenem susceptibility conferred by different OxaAb variants, characterize the molecular evolution of oxaAb and elucidate the contribution of OxaAb and other possible carbapenem resistance factors in the clinical isolates using WGS and LC-MS/MS. METHODS: Antimicrobial susceptibility tests were performed on 10 clinical Acinetobacter baumannii isolates. Carbapenem MICs were evaluated for all oxaAb variants cloned into A. baumannii CIP70.10 and BM4547, with and without their natural promoters. Molecular evolution analysis of the oxaAb variants was performed using FastTree and SplitsTree4. Resistance determinants were studied in the clinical isolates using WGS and LC-MS/MS. RESULTS: Only the OxaAb variants with I129L and L167V substitutions, OxaAb(82), OxaAb(83), OxaAb(107) and OxaAb(110) increased carbapenem MICs when expressed in susceptible A. baumannii backgrounds without an upstream IS element. Carbapenem resistance was conferred with the addition of their natural upstream ISAba1 promoter. LC-MS/MS analysis on the original clinical isolates confirmed overexpression of the four I129L and L167V variants. No other differences in expression levels of proteins commonly associated with carbapenem resistance were detected. CONCLUSIONS: Elevated carbapenem MICs were observed by expression of OxaAb variants carrying clinically prevalent substitutions I129L and L167V. To drive carbapenem resistance, these variants required overexpression by their upstream ISAba1 promoter. This study clearly demonstrates that a combination of IS-driven overexpression of oxaAb and the presence of particular amino acid substitutions in the active site to improve carbapenem capture is key in conferring carbapenem resistance in A. baumannii and other mechanisms are not required.

Ten simple rules for writing Dockerfiles for reproducible data science.

Computational science has been greatly improved by the use of containers for packaging software and data dependencies. In a scholarly context, the main drivers for using these containers are transparency and support of reproducibility; in turn, a workflow's reproducibility can be greatly affected by the choices that are made with respect to building containers. In many cases, the build process for the container's image is created from instructions provided in a Dockerfile format. In support of this approach, we present a set of rules to help researchers write understandable Dockerfiles for typical data science workflows. By following the rules in this article, researchers can create containers suitable for sharing with fellow scientists, for including in scholarly communication such as education or scientific papers, and for effective and sustainable personal workflows.

A Maximum Entropy Model of Bounded Rational Decision-Making with Prior Beliefs and Market Feedback.

Bounded rationality is an important consideration stemming from the fact that agents often have limits on their processing abilities, making the assumption of perfect rationality inapplicable to many real tasks. We propose an information-theoretic approach to the inference of agent decisions under Smithian competition. The model explicitly captures the boundedness of agents (limited in their information-processing capacity) as the cost of information acquisition for expanding their prior beliefs. The expansion is measured as the Kullblack-Leibler divergence between posterior decisions and prior beliefs. When information acquisition is free, the homo economicus agent is recovered, while in cases when information acquisition becomes costly, agents instead revert to their prior beliefs. The maximum entropy principle is used to infer least biased decisions based upon the notion of Smithian competition formalised within the Quantal Response Statistical Equilibrium framework. The incorporation of prior beliefs into such a framework allowed us to systematically explore the effects of prior beliefs on decision-making in the presence of market feedback, as well as importantly adding a temporal interpretation to the framework. We verified the proposed model using Australian housing market data, showing how the incorporation of prior knowledge alters the resulting agent decisions. Specifically, it allowed for the separation of past beliefs and utility maximisation behaviour of the agent as well as the analysis into the evolution of agent beliefs.

Vectors as Epidemiological Sentinels: Patterns of Within-Tick Borrelia burgdorferi Diversity.

Hosts including humans, other vertebrates, and arthropods, are frequently infected with heterogeneous populations of pathogens. Within-host pathogen diversity has major implications for human health, epidemiology, and pathogen evolution. However, pathogen diversity within-hosts is difficult to characterize and little is known about the levels and sources of within-host diversity maintained in natural populations of disease vectors. Here, we examine genomic variation of the Lyme disease bacteria, Borrelia burgdorferi (Bb), in 98 individual field-collected tick vectors as a model for study of within-host processes. Deep population sequencing reveals extensive and previously undocumented levels of Bb variation: the majority (~70%) of ticks harbor mixed strain infections, which we define as levels Bb diversity pre-existing in a diverse inoculum. Within-tick diversity is thus a sample of the variation present within vertebrate hosts. Within individual ticks, we detect signatures of positive selection. Genes most commonly under positive selection across ticks include those involved in dissemination in vertebrate hosts and evasion of the vertebrate immune complement. By focusing on tick-borne Bb, we show that vectors can serve as epidemiological and evolutionary sentinels: within-vector pathogen diversity can be a useful and unbiased way to survey circulating pathogen diversity and identify evolutionary processes occurring in natural transmission cycles.

Fostering Transformation by Hearing Voices: Evaluating a 6-Second, Low-Fidelity Simulation.

BACKGROUND: The stigma of psychosis, with the accompanying symptoms of auditory and visual hallucinations, can affect a nurse's ability to provide safe, effective care. Increasing knowledge of the patient's perspective during auditory hallucinations can increase the nurse's ability to be empathetic and engage in a therapeutic alliance. OBJECTIVE: To evaluate the efficacy of a six-second auditory hallucination simulation to increase empathy in preclinical undergraduate nursing students. DESIGN: This descriptive, content analysis, qualitative study evaluated narratives written by students in a pre-licensure baccalaureate nursing student population, assessing empathy, insight, knowledge, and therapeutic communication. Students experienced the 6-second auditory hallucination simulation as part of preclinical instruction, and then they wrote a self-reflection. RESULTS: More than 200 self-reflections were collected, with a randomized final sample of 82 narratives evaluated. CONCLUSION: Self-reflections indicated that the experience of the 6-second hearing voices simulation increased efficacy, insight, knowledge, and intention to use therapeutic communication.

Supervised learning for infection risk inference using pathology data.

BACKGROUND: Antimicrobial Resistance is threatening our ability to treat common infectious diseases and overuse of antimicrobials to treat human infections in hospitals is accelerating this process. Clinical Decision Support Systems (CDSSs) have been proven to enhance quality of care by promoting change in prescription practices through antimicrobial selection advice. However, bypassing an initial assessment to determine the existence of an underlying disease that justifies the need of antimicrobial therapy might lead to indiscriminate and often unnecessary prescriptions. METHODS: From pathology laboratory tests, six biochemical markers were selected and combined with microbiology outcomes from susceptibility tests to create a unique dataset with over one and a half million daily profiles to perform infection risk inference. Outliers were discarded using the inter-quartile range rule and several sampling techniques were studied to tackle the class imbalance problem. The first phase selects the most effective and robust model during training using ten-fold stratified cross-validation. The second phase evaluates the final model after isotonic calibration in scenarios with missing inputs and imbalanced class distributions. RESULTS: More than 50% of infected profiles have daily requested laboratory tests for the six biochemical markers with very promising infection inference results: area under the receiver operating characteristic curve (0.80-0.83), sensitivity (0.64-0.75) and specificity (0.92-0.97). Standardization consistently outperforms normalization and sensitivity is enhanced by using the SMOTE sampling technique. Furthermore, models operated without noticeable loss in performance if at least four biomarkers were available. CONCLUSION: The selected biomarkers comprise enough information to perform infection risk inference with a high degree of confidence even in the presence of incomplete and imbalanced data. Since they are commonly available in hospitals, Clinical Decision Support Systems could benefit from these findings to assist clinicians in deciding whether or not to initiate antimicrobial therapy to improve prescription practices.

Divergent, coexisting Pseudomonas aeruginosa lineages in chronic cystic fibrosis lung infections.

RATIONALE: Pseudomonas aeruginosa, the predominant cause of chronic airway infections of patients with cystic fibrosis, exhibits extensive phenotypic diversity among isolates within and between sputum samples, but little is known about the underlying genetic diversity. OBJECTIVES: To characterize the population genetic structure of transmissible P. aeruginosa Liverpool Epidemic Strain in chronic infections of nine patients with cystic fibrosis, and infer evolutionary processes associated with adaptation to the cystic fibrosis lung. METHODS: We performed whole-genome sequencing of P. aeruginosa isolates and pooled populations and used comparative analyses of genome sequences including phylogenetic reconstructions and resolution of population structure from genome-wide allele frequencies. MEASUREMENTS AND MAIN RESULTS: Genome sequences were obtained for 360 isolates from nine patients. Phylogenetic reconstruction of the ancestry of 40 individually sequenced isolates from one patient sputum sample revealed the coexistence of two genetically diverged, recombining lineages exchanging potentially adaptive mutations. Analysis of population samples for eight additional patients indicated coexisting lineages in six cases. Reconstruction of the ancestry of individually sequenced isolates from all patients indicated smaller genetic distances between than within patients in most cases. CONCLUSIONS: Our population-level analysis demonstrates that coexistence of distinct lineages of P. aeruginosa Liverpool Epidemic Strain within individuals is common. In several cases, coexisting lineages may have been present in the infecting inoculum or assembled through multiple transmissions. Divergent lineages can share mutations via homologous recombination, potentially aiding adaptation to the airway during chronic infection. The genetic diversity of this transmissible strain within infections, revealed by high-resolution genomics, has implications for patient segregation and therapeutic strategies.

OXA ß-lactamases.

The OXA ß-lactamases were among the earliest ß-lactamases detected; however, these molecular class D ß-lactamases were originally relatively rare and always plasmid mediated. They had a substrate profile limited to the penicillins, but some became able to confer resistance to cephalosporins. From the 1980s onwards, isolates of Acinetobacter baumannii that were resistant to the carbapenems emerged, manifested by plasmid-encoded ß-lactamases (OXA-23, OXA-40, and OXA-58) categorized as OXA enzymes because of their sequence similarity to earlier OXA ß-lactamases. It was soon found that every A. baumannii strain possessed a chromosomally encoded OXA ß-lactamase (OXA-51-like), some of which could confer resistance to carbapenems when the genetic environment around the gene promoted its expression. Similarly, Acinetobacter species closely related to A. baumannii also possessed their own chromosomally encoded OXA ß-lactamases; some could be transferred to A. baumannii, and they formed the basis of transferable carbapenem resistance in this species. In some cases, the carbapenem-resistant OXA ß-lactamases (OXA-48) have migrated into the Enterobacteriaceae and are becoming a significant cause of carbapenem resistance. The emergence of OXA enzymes that can confer resistance to carbapenems, particularly in A. baumannii, has transformed these ß-lactamases from a minor hindrance into a major problem set to demote the clinical efficacy of the carbapenems.

Are 100 enough? Inferring acanthomorph teleost phylogeny using Anchored Hybrid Enrichment.

BACKGROUND: The past decade has witnessed remarkable progress towards resolution of the Tree of Life. However, despite the increased use of genomic scale datasets, some phylogenetic relationships remain difficult to resolve. Here we employ anchored phylogenomics to capture 107 nuclear loci in 29 species of acanthomorph teleost fishes, with 25 of these species sampled from the recently delimited clade Ovalentaria. Previous studies employing multilocus nuclear exon datasets have not been able to resolve the nodes at the base of the Ovalentaria tree with confidence. Here we test whether a phylogenomic approach will provide better support for these nodes, and if not, why this may be. RESULTS: After using a novel method to account for paralogous loci, we estimated phylogenies with maximum likelihood and species tree methods using DNA sequence alignments of over 80,000 base pairs. Several key relationships within Ovalentaria are well resolved, including 1) the sister taxon relationship between Cichlidae and Pholidichthys, 2) a clade containing blennies, grammas, clingfishes, and jawfishes, and 3) monophyly of Atherinomorpha (topminnows, flyingfishes, and silversides). However, many nodes in the phylogeny associated with the early diversification of Ovalentaria are poorly resolved in several analyses. Through the use of rarefaction curves we show that limited phylogenetic resolution among the earliest nodes in the Ovalentaria phylogeny does not appear to be due to a deficiency of data, as average global node support ceases to increase when only 1/3rd of the sampled loci are used in analyses. Instead this lack of resolution may be driven by model misspecification as a Bayesian mixed model analysis of the amino acid dataset provided good support for parts of the base of the Ovalentaria tree. CONCLUSIONS: Although it does not appear that the limited phylogenetic resolution among the earliest nodes in the Ovalentaria phylogeny is due to a deficiency of data, it may be that both stochastic and systematic error resulting from model misspecification play a role in the poor resolution at the base of the Ovalentaria tree as a Bayesian approach was able to resolve some of the deeper nodes, where the other methods failed.

STDP in lateral connections creates category-based perceptual cycles for invariance learning with multiple stimuli.

Learning to recognise objects and faces is an important and challenging problem tackled by the primate ventral visual system. One major difficulty lies in recognising an object despite profound differences in the retinal images it projects, due to changes in view, scale, position and other identity-preserving transformations. Several models of the ventral visual system have been successful in coping with these issues, but have typically been privileged by exposure to only one object at a time. In natural scenes, however, the challenges of object recognition are typically further compounded by the presence of several objects which should be perceived as distinct entities. In the present work, we explore one possible mechanism by which the visual system may overcome these two difficulties simultaneously, through segmenting unseen (artificial) stimuli using information about their category encoded in plastic lateral connections. We demonstrate that these experience-guided lateral interactions robustly organise input representations into perceptual cycles, allowing feed-forward connections trained with spike-timing-dependent plasticity to form independent, translation-invariant output representations. We present these simulations as a functional explanation for the role of plasticity in the lateral connectivity of visual cortex.

Clonal hematopoiesis of indeterminate potential in patients with immunoglobulin light-chain AL amyloidosis.

ABSTRACT: Immunoglobulin light-chain (AL) amyloidosis is characterized by the deposition of misfolded monoclonal free light chains, with cardiac complications accounting for patient mortality. Clonal hematopoiesis of indeterminate potential (CHIP) has been associated with worse cardiovascular outcomes in the general population. Its significance in AL amyloidosis remains unclear. We collected clinical information and outcome data on 76 patients with a diagnosis of AL amyloidosis who underwent deep targeted sequencing for myeloid neoplasia-associated mutations between April 2018 and August 2023. Variant allele frequency was set at 2% to call CHIP-associated mutations. CHIP mutations were present in patients with AL amyloidosis at a higher frequency compared with age-matched control individuals. Sixteen patients (21%) had at least 1 CHIP mutation. DNMT3A was the most frequent mutation (7/16; 44%). Compared with patients without CHIP, patients with CHIP had a higher prevalence of t(11;14) translocation (69% vs 25%, respectively; P = .004). Furthermore, among patients with renal involvement, those with CHIP had a lower Palladini renal stage (P = .001). At a median follow-up of 32.5 months, the presence of CHIP was not associated with worse overall survival or major organ dysfunction progression-free survival. Larger studies and longer follow-up are needed to better define the impact of CHIP in patients with AL amyloidosis.

Manchester Intermittent Diet in Gestational Diabetes Acceptability Study (MIDDAS-GDM): a two-arm randomised feasibility protocol trial of an intermittent low-energy diet (ILED) in women with gestational diabetes and obesity in Greater Manchester.

INTRODUCTION: The prevalence of gestational diabetes mellitus (GDM) is rising in the UK and is associated with maternal and neonatal complications. National Institute for Health and Care Excellence guidance advises first-line management with healthy eating and physical activity which is only moderately effective for achieving glycaemic targets. Approximately 30% of women require medication with metformin and/or insulin. There is currently no strong evidence base for any particular dietary regimen to improve outcomes in GDM. Intermittent low-energy diets (ILEDs) are associated with improved glycaemic control and reduced insulin resistance in type 2 diabetes and could be a viable option in the management of GDM. This study aims to test the safety, feasibility and acceptability of an ILED intervention among women with GDM compared with best National Health Service (NHS) care. METHOD AND ANALYSIS: We aim to recruit 48 women with GDM diagnosed between 24 and 30 weeks gestation from antenatal clinics at Wythenshawe and St Mary's hospitals, Manchester Foundation Trust, over 13 months starting in November 2022. Participants will be randomised (1:1) to ILED (2 low-energy diet days/week of 1000 kcal and 5 days/week of the best NHS care healthy diet and physical activity advice) or best NHS care 7 days/week until delivery of their baby. Primary outcomes include uptake and retention of participants to the trial and adherence to both dietary interventions. Safety outcomes will include birth weight, gestational age at delivery, neonatal hypoglycaemic episodes requiring intervention, neonatal hyperbilirubinaemia, admission to special care baby unit or neonatal intensive care unit, stillbirths, the percentage of women with hypoglycaemic episodes requiring third-party assistance, and significant maternal ketonaemia (defined as ≥1.0 mmol/L). Secondary outcomes will assess the fidelity of delivery of the interventions, and qualitative analysis of participant and healthcare professionals' experiences of the diet. Exploratory outcomes include the number of women requiring metformin and/or insulin. ETHICS AND DISSEMINATION: Ethical approval has been granted by the Cambridge East Research Ethics Committee (22/EE/0119). Findings will be disseminated via publication in peer-reviewed journals, conference presentations and shared with diabetes charitable bodies and organisations in the UK, such as Diabetes UK and the Association of British Clinical Diabetologists. TRIAL REGISTRATION NUMBER: NCT05344066.