Why were COVID-19 infections lower than expected amongst people who are homeless in London, UK in 2020? Exploring community perspectives and the multiple pathways of health inequalities in pandemics.

High rates of COVID-19 infections and deaths amongst people who are homeless in London, UK were feared. Rates however stayed much lower than expected throughout 2020; an experience that compares to other settings globally. This study sought a community level perspective to explore this rate of infections, and through this explore relationships between COVID-19 and existing health inequalities. Analyses are reported from ongoing qualitative studies on COVID-19 and homeless health service evaluation in London, UK. Repeated in-depth telephone interviews were implemented with people experiencing homelessness in London (n=17; 32 interviews in total) as well as street outreach workers, nurses and hostel staff (n=10) from September 2020 to early 2021. Thematic analysis generated three themes to explore peoples' experiences of, and perspectives on, low infections: people experiencing homelessness following, creating and breaking social distancing and hygiene measures; social distancing in the form of social exclusion as a long-running feature of life; and a narrative of 'street immunity' resulting from harsh living conditions. Further study is needed to understand how these factors combine to prevent COVID-19 and how they relate to different experiences of homelessness. This community perspective can ensure that emerging narratives of COVID-19 prevention success don't ignore longer running causes of homelessness and reinforce stigmatising notions of people who are homeless as lacking agency. Our findings aid theorisation of how health inequalities shape pandemic progression: severe exclusion may substantially delay epidemics in some communities, although with considerable other non-COVID-19 impacts

Prevention of COVID-19 among populations experiencing multiple social exclusions.

Despite the development of effective vaccines against SARS-CoV-2 and an encouraging start to its roll out in many countries, in the coming months and years targeted prevention strategies will still be vital for socially marginalised groups. People experiencing multiple levels of exclusion related to homelessness, drug use, sex work, migration and their intersection can be particularly vulnerable to infection and morbidity with SARS-CoV-2 and will be less likely to benefit from population-wide prevention approaches such as contact tracing and mass vaccination. The recommendation by the Joint Committee on Vaccine and Immunisation in the UK to prioritise vaccination of people experiencing homelessness and rough sleepers is welcome, but will require ongoing vaccination programmes to ensure optimal coverage as well as targeted testing in coming years. There is a high risk that individuals who are homeless or otherwise socially excluded will be unable to be vaccinated and remain vulnerable to COVID-19 infection, limiting the potential for overall UK population coverage of COVID-19 vaccination to remain below the herd immunity threshold. In this editorial, we consider existing evidence on ‘what works’ in vaccine provision and contact tracing among socially excluded populations, as well as learning from the response so far including the provision of emergency accommodation and vaccine delivery. We set out strategies for interventions and priority research questions, emphasising the importance of co-production in research and service delivery, to prevent ongoing transmission of SARS-CoV-2 and future infectious disease outbreaks.</p

Preclinical evaluation of FLT190, a liver-directed AAV gene therapy for Fabry disease

Fabry disease is an X-linked lysosomal storage disorder caused by loss of alpha-galactosidase A (α-Gal A) activity and is characterized by progressive accumulation of glycosphingolipids in multiple cells and tissues. FLT190, an investigational gene therapy, is currently being evaluated in a Phase 1/2 clinical trial in patients with Fabry disease (NCT04040049). FLT190 consists of a potent, synthetic capsid (AAVS3) containing an expression cassette with a codon-optimized human GLA cDNA under the control of a liver-specific promoter FRE1 (AAV2/S3-FRE1-GLAco). For mouse studies FLT190 genome was pseudotyped with AAV8 for efficient transduction. Preclinical studies in a murine model of Fabry disease (Gla-deficient mice), and non-human primates (NHPs) showed dose-dependent increases in plasma α-Gal A with steady-state observed 2 weeks following a single intravenous dose. In Fabry mice, AAV8-FLT190 treatment resulted in clearance of globotriaosylceramide (Gb3) and globotriaosylsphingosine (lyso-Gb3) in plasma, urine, kidney, and heart; electron microscopy analyses confirmed reductions in storage inclusion bodies in kidney and heart. In NHPs, α-Gal A expression was consistent with the levels of hGLA mRNA in liver, and no FLT190-related toxicities or adverse events were observed. Taken together, these studies demonstrate preclinical proof-of-concept of liver-directed gene therapy with FLT190 for the treatment of Fabry disease

Peer advocacy and access to healthcare for people who are homeless in London, UK: a mixed method impact, economic and process evaluation protocol.

INTRODUCTION: People who are homeless experience higher morbidity and mortality than the general population. These outcomes are exacerbated by inequitable access to healthcare. Emerging evidence suggests a role for peer advocates-that is, trained volunteers with lived experience-to support people who are homeless to access healthcare. METHODS AND ANALYSIS: We plan to conduct a mixed methods evaluation to assess the effects (qualitative, cohort and economic studies); processes and contexts (qualitative study); fidelity; and acceptability and reach (process study) of Peer Advocacy on people who are homeless and on peers themselves in London, UK. People with lived experience of homelessness are partners in the design, execution, analysis and dissemination of the evaluation. ETHICS AND DISSEMINATION: Ethics approval for all study designs has been granted by the National Health Service London-Dulwich Research Ethics Committee (UK) and the London School of Hygiene and Tropical Medicine's Ethics Committee (UK). We plan to disseminate study progress and outputs via a website, conference presentations, community meetings and peer-reviewed journal articles

Benchmarking Tool Development for Commercial Buildings' Energy Consumption Using Machine Learning

IUPUIThis thesis investigates approaches to classify and anticipate the energy consumption of commercial office buildings using external and performance benchmarking to reduce the energy consumption. External benchmarking in the context of building energy consumption considers the influence of climate zones that significantly impact a building's energy needs. Performance benchmarking recognizes that different types of commercial buildings have distinct energy consumption patterns. Benchmarks are established separately for each building type to provide relevant comparisons. The first part of this thesis is about providing a benchmarking baseline for buildings to show their consumption levels. This involves simulating the buildings based on standards and developing a model based on real-time results. Software tools like Open Studio and Energy Plus were utilized to simulate buildings representative of different-sized structures to organize the benchmark energy consumption baseline. These simulations accounted for two opposing climate zones—one cool and humid and one hot and dry. To ensure the authenticity of the simulation, details, which are the building envelope, operational hours, and HVAC systems, were matched with ASHRAE standards. Secondly, the neural network machine learning model is needed to predict the consumption of the buildings based on the trend data came out of simulation part, by training a comprehensive set of environmental characteristics, including ambient temperature, relative humidity, solar radiation, wind speed, and the specific HVAC (Heating, Ventilation, and Air Conditioning) load data for both heating and cooling of the building. The model's exceptional accuracy rating of 99.54% attained across all, which comes from the accuracy of training, validation, and test about 99.6%, 99.12%, and 99.42%, respectively, and shows the accuracy of the predicted energy consumption of the building. The validation check test confirms that the achieved accuracy represents the optimal performance of the model. A parametric study is done to show the dependency of energy consumption on the input, including the weather data and size of the building, which comes from the output data of machine learning, revealing the reliability of the trained model. Establishing a Graphic User Interface (GUI) enhances accessibility and interaction for users. In this thesis, we have successfully developed a tool that predicts the energy consumption of office buildings with an impressive accuracy of 99.54%. Our investigation shows that temperature, humidity, solar radiation, wind speed, and the building's size have varying impacts on energy use. Wind speed is the least influential component for low-rise buildings but can have a more substantial effect on high-rise structures

An Intelligent Hybrid Segmentation Model Based on Fuzzy Logic, Discrete Wavelet Transform In Digital Imaging for Detection of Gastric Cancer

Gastric cancer is the first rank of cancer in Iran. If the disease is detected in early stages, treatment is probability to be furthered and treatment costs will be reduced. Due to the complexity of pathologic images and the fundamental challenges in these images, such as the poor contrast between the cells, cell overlapping and the contradiction in tissue coloring, the process of diagnosing this type of disease is difficult and therefore needs a proper method to eliminate these Problems. In this research, a smart model is proposed to solve these problems. Then a fuzzy-based system, a discrete wavelet transform, The region's growth and the voting mechanism are used to identify the cells. Then, an advanced morphological method was presented for separating overlapping cells. Then the cell's feature were extracted and based on it, the cells are classified using the support vector machine algorithm (SVM) with the RBF kernel. The proposed algorithm was applied to a dataset of patients including 96 Microscopy Images from Baghiyatallah Hospital in Tehran. The proposed model was evaluated using the ROC curve analysis. The results were approved by expert pathologists and reveal accuracy of 92.12% in detection of normal and cancerous cells and 94.14% in detection of benign and malignant cells which are promising for early diagnosis of this type of cancer

Recent Advances on Capacitive Proximity Sensors: From Design and Materials to Creative Applications

Capacitive proximity sensors (CPSs) have recently been a focus of increased attention because of their widespread applications, simplicity of design, low cost, and low power consumption. This mini review article provides a comprehensive overview of various applications of CPSs, as well as current advancements in CPS construction approaches. We begin by outlining the major technologies utilized in proximity sensing, highlighting their characteristics and applications, and discussing their advantages and disadvantages, with a heavy emphasis on capacitive sensors. Evaluating various nanocomposites for proximity sensing and corresponding detecting approaches ranging from physical to chemical detection are emphasized. The matrix and active ingredients used in such sensors, as well as the measured ranges, will also be discussed. A good understanding of CPSs is not only essential for resolving issues, but is also one of the primary forces propelling CPS technology ahead. We aim to examine the impediments and possible solutions to the development of CPSs. Furthermore, we illustrate how nanocomposite fusion may be used to improve the detection range and accuracy of a CPS while also broadening the application scenarios. Finally, the impact of conductance on sensor performance and other variables that impact the sensitivity distribution of CPSs are presented

Recent Advances on Capacitive Proximity Sensors: From Design and Materials to Creative Applications

Capacitive proximity sensors (CPSs) have recently been a focus of increased attention because of their widespread applications, simplicity of design, low cost, and low power consumption. This mini review article provides a comprehensive overview of various applications of CPSs, as well as current advancements in CPS construction approaches. We begin by outlining the major technologies utilized in proximity sensing, highlighting their characteristics and applications, and discussing their advantages and disadvantages, with a heavy emphasis on capacitive sensors. Evaluating various nanocomposites for proximity sensing and corresponding detecting approaches ranging from physical to chemical detection are emphasized. The matrix and active ingredients used in such sensors, as well as the measured ranges, will also be discussed. A good understanding of CPSs is not only essential for resolving issues, but is also one of the primary forces propelling CPS technology ahead. We aim to examine the impediments and possible solutions to the development of CPSs. Furthermore, we illustrate how nanocomposite fusion may be used to improve the detection range and accuracy of a CPS while also broadening the application scenarios. Finally, the impact of conductance on sensor performance and other variables that impact the sensitivity distribution of CPSs are presented