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Elevated body mass index is associated with delayed protective airway mucosal immune responses in mild SARS-CoV-2 infection
No full textBackground
SARS-CoV-2 viral load in the upper respiratory tract (URT) typically peaks and declines within days of infection, even in individuals without prior infection or vaccination. Although this implicates the URT innate immune response in effectively restricting viral replication, the nature of the protective responses and how they are affected by demographic factors is poorly defined.
Methods
We recruited 54 seronegative household contacts of recently diagnosed COVID-19 cases and prospectively collected URT samples during and after exposure. Among the 39 individuals who became infected, we quantified airway mucosal cytokine and chemokine responses and virus-specific nasal IgA using Meso Scale Discovery assays, and assessed associations with demographic factors, viral load, and symptoms.
Findings
Participants with higher BMI had higher URT viral loads and more marked symptoms. This was significantly associated with delayed induction of protective inflammatory mediators in the airway mucosa but not in blood. Induction of virus-specific nasal IgA at 1-week post-infection also correlated
with lower viral load.
Interpretation
Elevated BMI retards initial airway mucosal innate immune responses to infection, which may partially explain the pronounced adverse impact of higher BMI on clinical and virological outcomes in COVID-19.
Funding
This work is supported by the NIHR Health Protection Research Unit in Respiratory Infections, Imperial College London in partnership with the UK Health Security Agency (Grant number: NIHR200927; AL) and the Medical Research Council (Grant number: MR/X004058/1). Infrastructure support for this research was provided by the NIHR Imperial Biomedical Research Centre (BRC)
OCT embedding (for spatial transcriptomics) and flash freezing (for single nucleus RNA-seq) of human myometrial and decidual tissues
No full textPreprint versio
Outcomes in children with enterovirus meningitis in London, England: a retrospective multicentre cohort study, 2013 - 2023
No full textNon-polio enteroviruses (EV) are the most common cause of meningitis in children. We conducted a retrospective case series of long-term outcomes in 243 children between 1/1/2013 and 31/12/2023 across four tertiary centres in London. Adverse outcomes were associated with absence of fever at presentation (odds ratio, OR 4.65, 95% CI 1.03, 20.83), presence of seizures (OR 7.40, 95% CI 1.05, 51.96) and presence of comorbidities at baseline (OR 5.27, 95% CI 1.18, 23.47). Full recovery was seen in 153/160 (95.6%) of children who were under 3 months of age. These data may help clinicians to counsel parents and policymakers on streamlining care pathways following hospital discharge
The characteristics of people with COPD who enrol in home-based pulmonary rehabilitation versus centre-based pulmonary rehabilitation: a nationwide cross-sectional study
No full textObjectives
Home-based pulmonary rehabilitation (PR) is increasingly offered as an alternative to centre-based PR. This study explores differences in the characteristics of people with COPD enrolling in home-based versus centre-based PR in England and Wales and assesses whether availability of home-based PR is associated with increased enrolment.
Methods
This study used data from the UK 2023-24 National Respiratory Audit Programme PR audit. Eligible people had a primary condition of COPD, complete mental health and geographic data, and attended an initial assessment at a centre that completed the clinical and organisational audit. For the primary analysis only, people were further restricted to those enrolled on a purely home-based or centre-based programme at a centre that offered both options. Enrolment was defined as having attended
an initial assessment and having at least one scheduled PR session with a defined start date.
93 Differences in characteristics were assessed using Chi-square and Kruskal-Wallis tests. The association between home-based PR and enrolment was assessed using a mixed-effects logistic
regression model.
Results
13719/29981 (45.8%) people were eligible for inclusion in the primary analysis and 25039/29981 (83.5%) were eligible for the secondary analysis. Those who enrolled in a home-based programme were more likely to: be female (58.6% vs 48.2%; p<0.001); be more deprived (55.7% versus 46.6% in IMD quintiles 1 or 2, p<0.001); have a greater mental health burden (28.2% versus 22.2% with at
least 1 cognitive impairment recorded, p<0.001); and classified their symptom burden as more severe at assessment (CAT score 23 versus 22, p <0.001). Home-based PR was unavailable for 9099/25039 (36.3%) people. Availability of home-based PR was not associated with reduced non-enrolment in PR when compared with centres that did not offer home-based PR (adj-OR for non-enrolment: 0.79; 95%CI:0.51-1.23)).
Conclusion
Healthcare providers and those developing home-based PR digital applications should consider tailoring their approach to those who are most likely to opt in, who tend to be younger, female, and have a higher burden of respiratory symptoms and mental health comorbidities
Decoding heart signals through the ear: machine learning methods for arrhythmia detection and monitoring
No full textTimely detection and continuous monitoring of heart conditions, such as arrhythmias, is essential for minimizing the risk of severe complications like stroke and heart failure. 12-Lead electrocardiogram (ECG) is the gold standard technique to assess the electrical activity of the heart. Despite significant advancements in wearable health technologies, current alternative solutions such as wrist-worn, chest-straps or hand-held devices show limitations in diagnostic accuracy, comfort and long-term monitoring ability.
Recent studies reveal that the cardiac dipole propagates from the chest to the head, such that an ECG signal can be recorded by measuring the potential difference between the two ear canals. In-ear ECG offers an alternative continuous cardiovascular monitoring solution merging user-convenience and functionality.
This thesis addresses the challenges of in-ear ECG associated with low signal-to-noise ratio and interference from noncardiac physiological signals, mainly EEG, which have limited its clinical applications so far.
The PhD work reported in this thesis can be summarised in three main contributions. First, a signal denoising model is introduced, leveraging a convolutional autoencoder to significantly improve the SNR of in-ear ECG signals, while preserving subject-specific morphological waveforms and enabling downstream tasks such as R-peak detection.
Second, heart rate variability (HRV) features extracted from denoised in-ear ECG signals are used to explore the relationship between autonomic nervous system and physiological states, showcasing its application in the classification of breathing rates.
Third, the thesis develops a robust framework for atrial fibrillation (AFIB) and atrial flutter (AFL) detection through a clinical study. The proposed machine learning framework achieves performance comparable to gold-standard ECG systems. The feasibility of transfer learning, along with AI model interpretability, highlight the effectiveness of in-ear ECG in a clinical setting for diagnosis of AFIB/AFL patients.
Potential future research avenues are explored to offer insights into the future applications of in-ear ECG.Open Acces
"Now that the baby is out, I can be vaccinated": a qualitative study on COVID-19 vaccine hesitancy in pregnant women in Kilifi, Kenya
No full textCOVID-19 vaccines are safe and effective in pregnancy, but vaccine hesitancy limits uptake and effectiveness. This study explored COVID-19 vaccine hesitancy in pregnancy in Kilifi, coastal Kenya, to elicit reasons for vaccine hesitancy and acceptance, and to compile misconceptions around vaccination in pregnancy. Twenty-three in-depth interviews were conducted with pregnant women, mothers who had given birth in the previous 2 years and health workers (community health promoters, nurses, and supervisors). Data were analyzed using thematic template analysis based on the Vaccine Hesitancy Determinants Matrix. Concern about vaccine safety for the unborn baby was a major driver of hesitancy. Many pregnant women had limited knowledge of the potential benefits to the unborn baby, leading to postponing vaccination until after pregnancy. The initial government exclusion of pregnant women from vaccination led many to believe that vaccines were unsafe in pregnancy, long after the eligibility was revised. Aggressive promotion of the vaccine by the government was a source of mistrust and misconceptions. Integrating COVID-19 vaccination into routine antenatal care improved acceptance and development and dissemination of local guidelines boosted healthcare workers' confidence in offering vaccines to pregnant women. Future rollouts of vaccines for pregnant women should consider vaccination within antenatal care clinics alongside other routine pregnancy vaccines to enhance vaccine acceptance
Directional ice templating for cathode and anode of lithium ion batteries
No full textThis thesis investigates the development of sustainable and scalable fabrication strategies for lithium-ion battery electrodes using Directional Ice Templating (DIT). The aim was to overcome the limitations of conventional slurry coating, particularly the use of toxic NMP solvents and the lack of microstructural control. Chapter 1 outlined the background and motivation, emphasising the importance of thick, low-tortuosity electrodes for next-generation energy storage. Chapter 2 described the materials and experimental methods, including aqueous slurry design, fabrication procedures, and characterisation techniques.Chapter 3 demonstrated the feasibility of an aqueous DIT process to fabricate NMC811 cathodes with vertically aligned structures. This architecture enhanced electronic and ionic transport kinetics while eliminating the need for NMP solvent. Surface-sensitive characterisation confirmed the chemical stability of NMC811 during aqueous processing, and electrochemical testing showed that DIT cathodes delivered higher capacities and energy densities than slurry-coated electrodes. Chapter 4 addressed scalability and developed a directional extreme supercooling process to fabricate ultra-thick electrodes with mass loadings up to 70 mg cm⁻² at large scale. A systematic calendaring study identified 30% reduction as the critical threshold for balancing vertical alignment and packing density. At this point, DIT cathodes achieved high volumetric capacity, low impedance, and superior pouch-cell energy density compared to slurry-coating controls, demonstrating industrial relevance.
Chapter 5 extended DIT to graphite anodes. Thick, free-standing DIT anodes exhibited high mass loading, excellent reversibility, and lithium-ion diffusion coefficients several orders of magnitude higher than slurry-coated electrodes, demonstrating that DIT can overcome thickness limitations on both cathode and anode sides. Together, these results establish DIT as a sustainable, scalable, and versatile electrode fabrication strategy with significant implications for high-energy-density lithium-ion batteries in electric vehicles and grid-scale storage.Open Acces
Self-determination theory in HCI: advancing the field
No full textSelf-determination theory (SDT) has been widely successful in HCI. It offers ready concepts, measures, and theoretical propositions for third wave HCI topics like user experience, fun, wellbeing, motivation, or user autonomy. Still, HCI applications of SDT have been partial, at times superficial, and disconnecting– leaving great unfulfilled potential which motivated the present special issue. In this introduction, we present SDT to interested scholars; chart its use across HCI to date; and outline six advances to move HCI toward more intentional applications of SDT. As the articles from this issue illustrate, future growth areas of SDT in HCI are in extending domain-specific models and applications; harnessing underused parts of theory; computational formalisation; extending levels of analysis; facilitating design translation; and engaging in a cross-disciplinary dialogue on autonomy
Developing a foundation model in in-home monitoring data for healthcare applications
No full textWe propose a foundation model for in-home monitoring data to identify behavioral patterns and verify their clinical relevance in people living with dementia (PLWD). This method combines self-supervised representation learning with clinical outcome-oriented validation, focusing on agitation, urinary tract infections (UTIs), and cognitive decline. We encode multimodal patient data (sensors, activity patterns, EHRs) into structured latent representations using a pre-trained language model and a PageRank-based state transition model. Using Retrieval Augmented Generation (RAG), we synthesize privacy-preserving virtual cohorts statistically consistent with real data.
Validation indicates that this model outperforms traditional methods in predicting agitation, UTIs, and cognitive scores. Specifically, the ADAS-Cog prediction MAE was reduced to 10.46 (95% CI: 8.31-12.63), compared to 11.41 for the baseline. For UTI prediction, the model achieved 0.795 accuracy on real data and 0.906 on synthetic data. Sensitivity and PPV on synthetic data reached 0.909 and 0.957, respectively, surpassing real data metrics. The generated virtual cohort demonstrated high fidelity, achieving a Jensen-Shannon Distance of 0.2623 and Frechet Inception Distance (FID) of 0.0212. While agitation classification remained challenging (accuracy 52.3%), highlighting the need for prior knowledge, the overall two-stage coding architecture successfully extracted deep behavioral patterns. This work advances personalized dementia care by providing clinically instructive insights and secure, large-scale health monitoring through high-fidelity virtual cohort generation.Open Acces
Grain size as a record of tectono-climatic forcing: examples from fluvial and deltaic systems
No full textThis thesis explores how grain size can be used as a physical signal to decode tectonic and climatic forcing in sedimentary systems. Two natural field-based settings are presented: Pleistocene uplifted and modern Gilbert-type deltas in the Gulf of Corinth, Greece, and a near-natural gravel-bed river in western Switzerland (Sense River).
Methodologically, the thesis integrates classical field sampling of sediment calibre (Wolman counts and photo-granulometry) with UAV structure-from-motion and machine-learning segmentation to produce spatially continuous, grain-resolved datasets. A self-similarity framework places modern and stratigraphic grain-size distributions within common, dimensionless axes, allowing grain-size distributions to be interpreted without constraining detailed hydraulics.
Applied to rift-margin deltas, I show that topset grain fining and stratal architecture jointly quantify fault growth, interaction, and linkage. Steep-to-gentle shifts in fining correspond to changes in accommodation, sediment supply, and sediment routing, illustrating how fault configuration governs accommodation and sedimentation preservation. In the modern river case study, I use high-resolution mapping (ca. 1.86 million grains) to show spatially organised grain mobility in response to a moderately large flood event. Despite substantial patch-scale reworking, reach-scale distributions retain a stable self-similar form, validating dynamic equilibrium maintained by particle exchange.
I use these data to establish a grain-size framework enabling comparison between modern fluvial dynamics and long-term stratigraphic records. The thesis resolves distinct controls across scales; climate acts at seasonal scales by reorganising patches and bars in ways predictable from local hydraulics, while tectonics modulates accommodation and sediment routing over 10⁴-10⁵ years, shaping grain-size distributions preserved in basin fills.
My approach is transferable across fluvial, deltaic, and coastal systems. It enhances the interpretability of grain-size records, enables reconstruction of tectonic forcing, and clarifies which components of hydrologic variability are likely to be archived in modern and stratigraphic systems, offering a practical tool for decoding past landscape dynamics.Open Acces