Performance Testing of a Homemade Aerosol Generator for Pulmonary Administration of Dry Powder Formulations to Mice

A challenge in the development of dry powder formulations for inhalation is the poor reproducibility of their administration to small laboratory animals. The currently used devices for the pulmonary administration of dry powder formulations to small rodents often function sub-optimally as they use the same puff of air for both powder dispersion and aerosol delivery. As a result, either the air volume and flow rate are too low for complete powder deagglomeration or they are too high for effective aerosol delivery to the lungs of the animal. Therefore, novel and better devices are desired. We here present an aerosol generator designed to administer a pre-generated aerosol to the lungs of mice. By mapping the complex relationship between the airflow rate, delivery time and emitted dose, we were able to control the amount of powder being delivered from the aerosol generator. The emitted aerosol had a size range favorable for lung deposition and could be measured reproducibly. Nevertheless, in vivo fluorescent imaging still revealed considerable differences between the mice in terms of the dose deposited and the distribution of powder over the lungs, suggesting that a certain biological variation in lung deposition is inevitable.</p

Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image Segmentation

This work presents a novel framework CISFA (Contrastive Image synthesis and Self-supervised Feature Adaptation)that builds on image domain translation and unsupervised feature adaptation for cross-modality biomedical image segmentation. Different from existing works, we use a one-sided generative model and add a weighted patch-wise contrastive loss between sampled patches of the input image and the corresponding synthetic image, which serves as shape constraints. Moreover, we notice that the generated images and input images share similar structural information but are in different modalities. As such, we enforce contrastive losses on the generated images and the input images to train the encoder of a segmentation model to minimize the discrepancy between paired images in the learned embedding space. Compared with existing works that rely on adversarial learning for feature adaptation, such a method enables the encoder to learn domain-independent features in a more explicit way. We extensively evaluate our methods on segmentation tasks containing CT and MRI images for abdominal cavities and whole hearts. Experimental results show that the proposed framework not only outputs synthetic images with less distortion of organ shapes, but also outperforms state-of-the-art domain adaptation methods by a large margin

Dynamic Network Slicing Using Deep Reinforcement Learning

Nowadays network slicing is one of the biggest drivers of new elements in the 5G network business. This is because this paradigm allows the creation of independent slices, with their virtually and logically separated radio, network and computational resources. Using network slicing, operators sell infrastructure resources of any kind to tenants, while tenants use these resources to sell services to their customers, the end users. In this context, a problem that is essential to solve is how to improve the operator’s profit, ensuring compliance with the requests’ SLAs and distributing network resources in order to increase its usage rate. This dissertation proposes to design two algorithms based on DRL for slice admission in the transport network, learning which request to accept and reject while guaranteeing the requirements of the tenants requests. The contributions of this study start with the formalization of the problem of slice admission, followed by its simulation and implementation of DRL agents using Containernet, the Ryu controller, OpenAI Gym and the PyTorch framework. The result is two DRL-based algorithms capable of achieving good performances in this simulated scenario.Atualmente o network slicing é um dos maiores potenciadores de novos elementos no negócio das redes 5G. Isto deve-se ao facto de este paradigma permitir a criação de slices independentes, com os seus recursos rádio, de rede e computacionais virtual e logicamente separados. Utilizando network slicing, as operadoras poderão vender recursos de infraestrutura de qualquer tipo a tenants. Os tenants utilizam estes recursos para vender serviços aos seus clientes, os utilizadores finais. Neste contexto, um problema que é fundamental resolver é o de como melhorar o lucro da operadora, garantindo o cumprimento dos SLAs dos pedidos e distribuindo os recursos da rede de forma a aumentar a sua utilização. Nesta dissertação propõe-se desenhar dois algoritmos baseados em DRL para a admissão de slices na rede de transporte, aprendendo que pedidos aceitar e rejeitar, procurando satisfazer sempre os requisitos dos pedidos dos tenants. Os contributos deste estudo passam pela formalização do problema da admissão de slices na rede, seguindo-se a sua simulação e implementação dos agentes utilizando conjuntamente o Containernet, o controlador Ryu, o OpenAI Gym e o framework PyTorch. O resultado são dois algoritmos baseados em DRL capazes de atingir boas performances neste cenário simulado

Glycemic, Gastrointestinal, Hormonal and Appetitive Responses to Pearl Millet or Oats Porridge Breakfasts: a Randomized, Crossover Trial in Healthy Humans

Whole grain cereal breakfast consumption has been associated with beneficial effects on glucose and insulin metabolism as well as satiety. Pearl millet is a popular ancient grain variety that can be grown in hot, dry regions. However, little is known about its health effects. This study investigated the effect of a pearl millet porridge (PMP) compared with a well-known Scottish oats porridge (SOP) on glycaemic, gastrointestinal, hormonal and appetitive responses. In a randomized, two way crossover trial, 26 healthy participants consumed two iso-energetic/volumetric PMP or SOP breakfast meals, served with a drink of water. Blood samples for glucose, insulin, GLP-1, GIP and PYY, gastric volumes and appetite ratings were collected for two hours postprandially, followed by an ad libitum meal and food intake records for the remainder of the day. The incremental area under the curve (iAUC2h) for blood glucose was not significantly different between the porridges (p ˃ 0.05). The iAUC2h gastric volume was larger for PMP compared with SOP (p = 0.045). The iAUC2h GIP concentration was significantly lower for PMP compared with SOP (p = 0.001). Other hormones and appetite responses were similar between meals. In conclusion, this study reports, for the first time, data on glycaemic and physiological responses to a pearl millet breakfast, showing that this ancient grain could represent a sustainable, alternative, with health-promoting characteristics comparable to oats. GIP is an incretin hormone linked to triacylglycerol absorption in adipose tissue, therefore the lower GIP response for PMP may be an added health benefit

Efficacy of a gamified digital therapy for speech production in people with chronic aphasia (iTalkBetter): behavioural and imaging outcomes of a phase II item-randomised clinical trial

Background Aphasia is among the most debilitating of symptoms affecting stroke survivors. Speech and language therapy (SLT) is effective, but many hours of practice are required to make clinically meaningful gains. One solution to this ‘dosage’ problem is to automate therapeutic approaches via self-supporting apps so people with aphasia (PWA) can amass practice as it suits them. However, response to therapy is variable and no clinical trial has yet identified the key brain regions required to engage with word-retrieval therapy. Methods Between Sep 7, 2020 and Mar 1, 2022 at University College London in the UK, we carried out a phase II, item-randomised clinical trial in 27 PWA using a novel, self-led app, ‘iTalkBetter’, which utilises confrontation naming therapy. Unlike previously reported apps, it has a real-time utterance verification system that drives its adaptive therapy algorithm. Therapy items were individually randomised to provide balanced lists of ‘trained’ and ‘untrained’ items matched on key psycholinguistic variables and baseline performance. PWA practised with iTalkBetter over a 6-week therapy block. Structural and functional MRI data were collected to identify therapy-related changes in brain states. A repeated-measures design was employed. The trial was registered at ClinicalTrials.gov (NCT04566081). Findings iTalkBetter significantly improved naming ability by 13% for trained items compared with no change for untrained items, an average increase of 29 words (SD = 26) per person; beneficial effects persisted at three months. PWA’s propositional speech also significantly improved. iTalkBetter use was associated with brain volume increases in right auditory and left anterior prefrontal cortices. Task-based fMRI identified dose-related activity in the right temporoparietal junction. Interpretation Our findings suggested that iTalkBetter significantly improves PWAs’ naming ability on trained items. The effect size is similar to a previous RCT of computerised therapy, but this is the first study to show transfer to a naturalistic speaking task. iTalkBetter usage and dose caused observable changes in brain structure and function to key parts of the surviving language perception, production and control networks. iTalkBetter is being rolled-out as an app for all PWA and anomia: https://www.ucl.ac.uk/icn/research/research-groups/neurotherapeutics/projects/digital-interventions-neuro-rehabilitation-0 so that they can increase their dosage of practice-based SLT