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Acceptability of supporting lay-carer administration of anticipatory subcutaneous medications at home: a qualitative study using the theoretical framework of acceptability
No full textBackground
Globally, evidence indicates that most people prefer to receive care and die at home, provided high-quality care is available. However, systemic and logistical challenges often prevent this outcome. Palliate is a nurse-led intervention aiming to address these barriers, supporting lay-carers in administering end-of-life subcutaneous medications to their loved ones, through training, written guidance, and documentation.
Aim
To explore the perceptions and experiences of patients, carers, and healthcare professionals regarding the acceptability of the Palliate intervention, using the Theoretical Framework of Acceptability (TFA), including perceived barriers and opportunities to its implementation.
Methods
A qualitative study was conducted using semi-structured interviews with healthcare professionals, patients, carers, and policy-makers, informed by the TFA. Data were analysed thematically using deductive analysis.
Results
Thirty participants, including people with a diagnosis of advanced illness, carers, and a range of healthcare professionals involved in end-of-life care, provided perspectives on the acceptability of the intervention in end-of-life care. Participants described potential benefits, including improved symptom management, reduced waiting times for medication, and increased empowerment for families to support care at home. Concerns were raised about carer burden, emotional responsibility, and the need for professional oversight. Some participants spoke from direct experience of administering or supporting the intervention in practice, providing insights into both its practical value and the challenges of implementation.
Conclusion
While the Palliate intervention was generally viewed as acceptable and potentially beneficial, its broader implementation requires careful consideration. Its acceptability was conditional on carers receiving clear training, ongoing professional support, and being able to participate voluntarily. These findings offer new insights into the boundaries of lay caregiving and have implications for the implementation of family-administered end-of-life care within health systems. Further research is needed to evaluate its safety, impact, and feasibility in diverse contexts before wider adoption can be recommended
Audio-visual speech generation and representation learning
No full textThe progress of Deep Learning has led to the widespread adoption of interfaces and intelligent devices for human-computer interaction. These often rely on audio-visual communication and must interact with a variety of end users across diverse and potentially challenging real-world settings. This requires learning good representations of audio-visual speech, which we explore in this thesis. Progress in many audio-visual speech tasks is constrained by the scarcity of labelled, “in-the- wild” data. To this end, we collect the KAN-AV dataset, which is a large-scale audio and video dataset of speech captured “in-the-wild”. It contains age, kinship and identity annotations, allowing us to investigate challenging problems and particularly the problem of learning cross-modal representations, i.e., comparing and matching samples from different modalities. Crucially, the dataset contains samples across ages for each subject, enabling us to investigate the learning of age-invariant representations. We then turn to the task of speech-driven facial animation and explore a principled approach to combining audio-visual latent representations. We introduce the polynomial fusion layer, formulating a joint representation that includes higher-order interactions. We demonstrate the effectiveness of this approach in experiments with audio-visual speech datasets. The remainder of the thesis investigates the generation of speech from silent videos. We explore pre-training the decoder of a video-to-speech model on large volumes of audio-only data. Concretely, we pre-train audio encoder-decoder models and then fine-tune their decoders for video-to-speech synthesis. We demonstrate that this approach improves the reconstructed speech in both raw waveform and mel spectrogram generation. Finally, we note that previous works either employ silent video inputs only, or video and audio inputs and discard the audio input pathway during inference. We propose a method to include audio and video inputs during both training and inference, by synthesizing the input audio first. Our experiments show that this method outperforms previous approaches.Open Acces
Oral nalbuphine in idiopathic pulmonary fibrosis–associated cough
No full textImportance For patients with idiopathic pulmonary fibrosis (IPF), cough impairs quality of life; effective treatments for IPF-associated cough are needed.
Objective To determine if nalbuphine extended release (ER), a κ opioid receptor agonist and μ-opioid receptor antagonist, decreases cough compared with placebo in patients with IPF-associated cough.
Design, Setting, and Participants In this randomized, double-blind, placebo-controlled phase 2b trial conducted at 52 sites in 10 countries, patients with IPF, chronic cough for at least 8 weeks, and a Cough Severity Numerical Rating Scale (0, no cough; 10, worst possible cough) score of 4 or higher were enrolled from February 2024 to February 2025, with last follow-up in April 2025. Statistical analyses were conducted from May to August 2025.
Intervention Patients were randomized 1:1:1:1 to receive nalbuphine ER at doses of 27 mg, 54 mg, or 108 mg or placebo twice daily for 6 weeks.
Main Outcomes and Measures The primary outcome was the relative change from baseline in 24-hour cough frequency (coughs/h), measured with a digital cough monitor, for nalbuphine ER compared with placebo at week 6. The key secondary outcome was the relative change from baseline in the patient-reported cough frequency (Evaluating Respiratory Symptoms in IPF cough subscale; scores range from 0-4, lower scores indicate lesser cough frequency) at week 6.
Results Of the 223 patients screened, 165 were randomized (42, 43, 40, and 40 to receive nalbuphine ER 27 mg, 54 mg, and 108 mg, and placebo, respectively) and 160 were included in the primary analysis (median age, 71 [range, 51-85] years; 28.5% female). The baseline mean (SD) cough count was 28.3 (27.4) coughs/h. In the nalbuphine ER 27 mg, 54 mg, and 108 mg twice-daily groups, the mean relative decrease in the cough count and the absolute decrease in coughs/h were 47.9% (from 24.6 to 11.9; P = .008), 53.4% (from 28.0 to 14.9; P < .001), and 60.2% (from 31.5 to 11.9; P < .001), respectively, compared with placebo (16.9%; from 29.4 to 28.1 coughs/h). For the key secondary outcome of patient-reported cough frequency at week 6, the relative and absolute changes were −31.4% (from 2.3 to 1.5; P = .14), −40.6% (from 2.6 to 1.4; P = .004), and −40.2% (from 2.4 to 1.4; P < .005) in the 27-mg, 54-mg, and 108-mg groups, respectively, compared with –21.9% (from 2.6 to 1.9) with placebo.
Conclusions and Relevance For patients with IPF-associated chronic cough, all 3 doses of nalbuphine ER reduced objective cough frequency and the 2 higher doses improved patient-reported cough frequency at 6 weeks
An automated microfluidics platform for accelerating kinetics studies of alcohol electrooxidations
No full textThis study presents an automated flow platform to accelerate the kinetics study of small-molecule alcohol electrooxidations. Its microfluidics design and programmable workflow enable efficient, reliable, and reproducible characterizations, achieved by precise control of temperature, electrolyte composition, electrode potential, and in-process electrode cleaning. The validated platform determines apparent reaction orders and activation energies as functions of electrode potential across six alcohol systems, covering 114 conditions in a single run and reducing manual effort by over 50% compared to conventional one-variable-at-a-time methods. The kinetic data provide valuable mechanistic insights, showing that ethanol, 1-propanol, cyclohexanol, and 1,3-propanediol favor a potential-dependent oxidation pathway, whereas ethylene glycol and glycerol with higher hydroxyl-to-carbon ratios prefer an indirect oxidation pathway. This automation-assisted approach is broadly applicable to a range of electrochemical reactions, paving the way for self-driving experimentation for data-driven mechanistic understanding
Polymyxin B lethality requires energy-dependent outer membrane disruption
No full textPreprint versionPolymyxin antibiotics target lipopolysaccharide (LPS) in both membranes of the bacterial cell envelope, leading to bacterial killing through a mechanism that remains poorly understood. Here, we demonstrate that metabolic activity is essential for polymyxin lethality and leverage this insight to determine its mode of action. Polymyxin B (PmB) efficiently killed exponential phase E. coli but was unable to eliminate stationary phase cells unless a carbon source was available. Antibiotic lethality correlated with surface protrusions, LPS loss, and a significant reduction in outer membrane (OM) barrier function, processes that required LPS synthesis and transport. While the energy-dependent OM disruption was not directly lethal, it facilitated PmB access to the inner membrane (IM), which the antibiotic permeabilised in an energy-independent manner, leading to cell death. Finally, we show that the polymyxin resistance determinant MCR-1 prevents PmB-mediated LPS loss and OM protrusions and thereby renders the antibiotic ineffective.
Interpolation techniques for ultrasonic data
No full textMany applications where ultrasound is used for diagnostics exist where limited data is preventing a particular approach from being fully exploited; for example, sufficient data availability would allow the qualification of non-destructive evaluation (NDE) methods in-silico, and would potentially also enable the training of machine learning algorithms related to ultrasound and its applications. Real, experimental ultrasonic data is often scarce, and while it is already known that finite element (FE) modelling produces data which is sufficiently realistic to augment real data, the computational cost associated with its generation at the scales required for the aforementioned purposes is often prohibitive. In this work, we propose the use of interpolation techniques in combination with results from FE modelling to rapidly generate more data without the need to solve additional FE models. We present the relevant methods to achieve this, and validate them through four exemplary cases of increasing complexity. Validation is achieved through the comparison of interpolation-generated results to those generated by full FE modelling, demonstrating that our method is capable of producing results for different physical setups and signals of various degrees of complexity. The results were typically within less than 1% away from the expected, but generated at a fraction of the typical computational cost, and, while the validation cases examined are of interest to the NDE community, the method extends to other fields where ultrasonic data is of interest
Cancer-selective targeting of the NF-κB pathway via GADD45β/MKK7 inhibition: Results from the phase I trial of the first-in-class inhibitor, DTP3, in patients with relapsed or refractory multiple myeloma
No full textI-Card: a generative AI-supported intelligent design method card deck
No full textA design method card deck helps designers understand and provoke thinking by presenting each method in a simple format and allow designers to switch between methods seamlessly by maintaining the same simple format across the deck. However, recent observations have shown designers hesitate to use a card deck due to the lack of support, while other tools have provided identified support with generative AI. Through a formative study, we identified the specific support designers need when applying the design method cards and intentions in integrating generative AI. Accordingly, we developed the intelligent design method card deck, I-Card, which integrates generative AI to provide applicable design methods, design knowledge and data support, and interactive and dynamic support. A user study demonstrates that I-Card improved the design efficiency and applicability by offering personalized guidance, enhanced decision-making with comprehensive data generation and provided more design inspiration via interactive support
Primal S-matrix bootstrap with dispersion relations
No full textWe propose a new method for constructing the consistent space of scattering amplitudes by parameterizing the imaginary parts of partial waves and utilizing dispersion relations, crossing symmetry, and full unitarity. Using this framework, we explicitly compute bounds on the leading couplings and examine the Regge behaviors of the constructed amplitudes. The method also readily accommodates spinning bound states, which we use to constrain glueball couplings. By incorporating dispersion relations, our approach inherently satisfies the Froissart-Martin/Jin-Martin bounds or softer high-energy behaviors by construction. This, in turn, allows us to formulate a new class of fractionally subtracted dispersion relations, through which we investigate the sensitivity of coupling bounds to the asymptotic growth rate