Towards the development of next-generation MergoTM — a nucleic acid-based delivery platform

Nucleic acids offer great potential for therapeutic and drug delivery applications, but their clinical translation is hindered by several biological barriers. This thesis addresses some of these challenges by optimising Sixfold's Mergo system, a self-assembled RNA construct, for the in vivo delivery of siRNAs. Focusing on stability, cellular uptake, endosomal escape, and off-target effects, we performed a comprehensive investigation of Mergo's performance in different biological matrices.To ensure efficient delivery of intact therapeutics, stabilising Mergo systems against nucleolytic degradation is essential. Through detailed analysis of Mergo and other RNA constructs in biological matrices, we identified vulnerable regions and implemented strategies to enhance nuclease resistance. These strategies included specific chemical modifications and the design of novel linkers engineered for controlled siRNA release at the target site.Further investigation focused on the critical steps of cellular uptake and endosomal escape, which are known bottlenecks for nucleic acid-based therapies. While cellular uptake of Mergo was readily achieved, endosomal escape was identified as the rate-limiting steps for Mergo in vitro.Finally, to assess the safety and efficacy of Mergo, we employed a combination of in silico, in vitro, and in vivo studies to evaluate both on- and off-target effects. This work not only provided preliminary insights into Mergo's potential for therapeutic delivery but also established a valuable methodology for evaluating future RNA-based delivery systems and therapeutics.In summary, this thesis investigates the challenges and opportunities of the Mergo delivery system. By addressing key barriers, this work aims to accelerate the development of Mergo as a promising delivery platform for clinical applications. Furthermore, as Mergo is RNA-based, this research contributes to a broader understanding of the challenges and opportunities within the RNA delivery and therapeutics field

Discovery of novel gut-derived <i>Klebsiella pneumoniae</i> phages and mucus-interacting gut phage proteins

The rise in antimicrobial resistance and the stagnation in antibiotic development have intensified interest in alternative therapies. Bacteriophages, viruses of bacteria, have significant potential, for both classical phage therapy and for microbiome modulation. Klebsiella pneumoniae is a high-risk pathogen due to its multidrug resistance and virulence, causing acute tissue infections as well as chronic gut colonisations that may drive other diseases, like inflammatory bowel disease (IBD). Consequently, Klebsiella phages are highly sought after, yet their therapeutic application is hindered by key gaps, including the lack of standardised accessible phage and strain collections, limited understanding of the roles of Klebsiella in IBD, and poor knowledge of phage ecology in the gut. To address these, this thesis showcases the establishment of KlebPhaCol, an open-source collection of 52 newly isolated Klebsiella phages and 74 Klebsiella strains, including clinical and reference strains. This resource includes gut-relevant phages infecting the IBD-associated K. pneumoniae ST323 strain and enables the centralised study of Klebsiella-phage interactions. It further explores the nematode Caenorhabditis elegans as a simple in vivo model for Klebsiella-driven gut inflammation, and investigates phage ecology within the intestinal mucosa, commonly disrupted in IBD. Our findings reveal genomic and functional features of KlebPhaCol, including the discovery of a novel phage family linked to the gut environment, Felixviridae. We also show Klebsiella induces intestinal distension in C. elegans, suggesting a tractable model for studying Klebsiella-driven inflammation. Finally, we uncover a repertoire of 6,302 putative phage-encoded mucus-interacting proteins, many with evidence of bacterial origin and plausible retained functionality as well as 390 hits derived from Klebsiella phages, offering insights on potential mechanisms by which Klebsiella spp. may achieve gut colonisation and contribute to disease. In conclusion, this thesis lays foundational resources and insights into Klebsiella-phage interactions, advancing phage therapy developments and deepening the understanding of phage ecology in the gut

The association between maternal adiposity and breastfeeding initiation and duration: evidence from the Southampton Women's Survey

Background: most mothers in the UK are not meeting the breastfeeding recommendations set by the World Health Organisation (WHO, Breastfeeding - Recommendations. https://www.who.int/health-topics/breastfeeding#tab=tab_2, 2023). Maternal obesity has variably been associated with lower initiation and shorter duration of breastfeeding, but few studies have examined the impact of maternal adiposity estimated from skinfold thicknesses.Aim: to investigate the relationship between maternal adiposity and breastfeeding initiation and duration.Methods: data from 2,873 mother-offspring pairs in the Southampton Women’s Survey (SWS) mother-offspring cohort study were used to assess the relationship between a mother’s adiposity and breastfeeding initiation and duration. The exposure variables were body mass index (BMI) and body fat percentage, calculated from 4-site skinfold thickness measurements measured prior to conception. The outcome variables were breastfeeding initiation and duration. All analyses were adjusted for confounders identified using a Directed Acyclic Graph.Results: after adjustment for confounders, no associations were found between maternal BMI (RR 0.99 per 5 kg/m2, 95% CI 0.97, 1.01) or body fat percentage (RR 0.99 per 5%, 95% CI 0.97, 1.00) and initiation of breastfeeding. After adjustment for confounders, amongst women who initiated breastfeeding, higher maternal BMI (β −0.09 SDs per 5 kg/m2, 95% CI −0.13, −0.04) and body fat percentage (β −0.10 SDs per 5%, 95% CI −0.16, −0.04) were associated with shorter breastfeeding duration.Conclusions: in this study maternal adiposity had little impact on breastfeeding initiation but higher maternal adiposity was associated with shorter breastfeeding duration. This study suggests that more support should be given to mothers with overweight and obesity to promote longer duration of breastfeeding

Anatomical, physiological and inflammatory characterization of non-culprit vessels in patients undergoing primary PCI for ST-elevation myocardial infarction in the presence of multivessel disease: rationale and design of the PICNIC study

Background: up to 50% of patients presenting with ST-elevation myocardial infarction (STEMI) have multivessel coronary artery disease (CAD). Randomized trials suggest that complete revascularization improves outcomes, but the mechanism and identification of patients who benefit remain unclear. This study aims to assess the association between blood and coronary imaging biomarkers and clinical events, to identify patient-, vessel-, and lesion-specific risk in STEMI patients with bystander disease.Method: PICNIC is a multicenter, international, prospective, observational study enrolling 320 patients with STEMI and multivessel CAD undergoing primary PCI of the culprit vessel without complete revascularization. Participants will undergo blood sampling for inflammatory markers and coronary CT angiography (CTCA) to assess: (1) plaque burden and morphology, (2) artificial intelligence-enabled fractional flow reserve derived from CTCA (FFRCT) analysis of plaque and hemodynamic features, and (3) fat attenuation index (FAI) to evaluate perivascular inflammation.The primary analysis will evaluate the association between a composite 24-month clinical endpoint (including all-cause mortality, myocardial infarction, ischemia-driven revascularization as first layer and cardiac arrest, heart failure, stroke, and ventricular tachyarrhythmia (second layer)) and: (1) serum inflammatory markers, and (2) anatomical and physiological characteristics of non–infarct-related arteries (NIRA) assessed by CTCA, FFRCT, and FAI. Statistical and machine learning methods will be applied to determine which combinations of clinical, imaging, and biomarker data best predict patient-, vessel-, and lesion-specific risk.Conclusion: PICNIC will characterize the anatomical, physiological, and inflammatory features of NIRA lesions in STEMI patients treated with culprit-only PCI in order to develop an AI-based risk prediction model. If such a model is successful it could be used to inform personalized revascularization strategies.<br/

Towards fibre-like loss for photonic integration from violet to near-infrared

Over the past decades, remarkable progress has been made in reducing the loss of photonic integrated circuits (PICs) within the telecom band, facilitating on-chip applications spanning low-noise optical and microwave synthesis, to lidar and photonic artificial intelligence engines. However, several obstacles arise from the marked increase in material absorption and scattering losses at shorter wavelengths, which prominently elevate power requirements and limit performance in the visible and near-visible spectrum. Here we present an ultralow-loss PIC platform based on germano-silicate—the material underlying the extraordinary performance of optical fibre—but realized by a fully CMOS-foundry-compatible process. These PICs achieve resonator Q factors surpassing 180 million from violet to telecom wavelengths. They also attain a 10-dB higher quality factor without thermal treatment in the telecom band, expanding opportunities for heterogeneous integration with active components. Other features of this platform include readily engineered waveguide dispersion, acoustic mode confinement and large-mode-area-induced thermal stability—each demonstrated by soliton microcomb generation, stimulated Brillouin lasing and low-frequency-noise self-injection locking, respectively. The success of these germano-silicate PICs can ultimately enable fibre-like loss onto a chip, leading to an additional 20-dB improvement in waveguide loss over the current highest performance photonic platforms. Moreover, the performance abilities demonstrated here bridge ultralow-loss PIC technology to optical clocks, precision navigation systems and quantum sensors

Improving robustness to domain shift in machine learning

Machine learning models often underperform when the test data characteristics differ from the training data, a phenomenon known as domain shift. Improving robustness to domain shift has been a longstanding goal in machine learning, and is crucial to the widespread deployment of AI. This thesis addresses four underexplored but important aspects of this field: imbalanced domain adaptation, dataset filtering, model selection, and variance reduction of domain alignment losses. To this end, novel algorithms, perspectives, methodologies, and theoretical results are introduced, resulting in improved out-of-domain performance on these tasks. Particular emphasis is placed on developing methods that are both theoretically grounded and practically useful, and understanding their assumptions and limitations.A central motivating application for this work is the automated detection and classification of marine mammal vocalisations, where domain shift is especially prevalent. This thesis serves to underscore the importance of adopting robust training and evaluation practices in this context. To support progress in this area, a novel domain shift benchmark based on humpback whale detection is also introduced.Overall, this thesis contributes to advancing the reliability and trustworthiness of machine learning models, at a time when AI systems are increasingly being deployed to dynamic, uncertain, and open-ended settings