Some recent developments and testing strategies relating to the passive fire protection of concrete using intumescent coatings: a review

PurposeIn the present article, the authors have conducted a review on some of the recent developments given in the literature pertaining to the passive protection of concrete structures using intumescent coatings. Here, the main thrust is placed on the spalling phenomenon of concrete elements when exposed to elevated temperatures and fires.Design/methodology/approachIn this context, it has been long established that prolonged thermal insult on concrete members will lead to egress of water, both physically bound as well as those present as water of hydration within the concrete matrix, in the form of steam through microchannels and associated pathways of least resistance, often resulting in the flaking of the surface of the structure. The latter process can ultimately lead to the exposure of the ferrous-based reenforcement elements, for instance, to higher temperatures, thus inducing melting. This, in turn, can result in substantial loss of strength and load-bearing capacity of the structural element that is already undergoing disintegration of its base matrix owing to heat/fire. Even though spalling of concrete structures has long been recognized as a serious problem that can often lead to catastrophic failure of infrastructures, such as buildings, bridges and tunnels, the utility of intumescent coating as a mitigation strategy is relatively new and has not been explored to its fullest possible extent. Therefore, in the latter parts of the review, the authors have endeavored to discuss the different types of intumescent coatings, their modes of actions and, in particular, their wider applicability in terms of protecting concrete elements from detrimental effects of severe or explosive spalling.FindingsGiven that spalling of concrete components is still a very serious issue that can result in loss of lives and destruction of critical infrastructures, there is an urgent need to formulate better mitigating strategies, through novel means and methods. The use of the intumescent coating in this context appears to be a promising way forward but is one that seems to be little explored so far. Therefore, a more systematic investigation is highly warranted in this area, especially, as the authors envisage a greater activity in the building and commissioning of more infrastructures worldwide incommensurate with augmented economic activities during the post-COVID recovery period.Originality/valueThe authors have conducted a review on some of the recent developments given in the literature pertaining to the passive protection of concrete structures using intumescent coatings. The authors have also included the results from some recent tests carried out at the facilities using a newly commissioned state-of-the-art furnace.

How Face Masks Affect the Use of Echolocation by Individuals With Visual Impairments During COVID-19: International Cross-sectional Online Survey.

BACKGROUND: Although a critical safety measure, preliminary studies have suggested that the use of a face mask may pose a problem for some users with disabilities. To date, little is known about how the wearing of a traditional face mask may pose a barrier to individuals with visual impairments who draw on auditory cues and echolocation techniques during independent travel. OBJECTIVE: The goal of this study was to document the difficulties, if any, encountered during orientation and mobility due to the use of a face mask during the COVID-19 pandemic and the strategies used to address these barriers. METHODS: In total, 135 individuals aged 18 years and older who self-identified as being blind, being deafblind, or having low vision and who could communicate in either English or French completed an anonymous cross-sectional online survey between March 29 and August 23, 2021. RESULTS: In total, 135 respondents (n=52, 38.5%, men; n=83, 61.5%, women) between the ages of 18 and 79 (mean 48.22, SD 14.48) years participated. Overall, 78 (57.7%) self-identified as blind and 57 (42.3%) as having low vision. In addition, 13 (9.6%) identified as having a combined vision and hearing loss and 3 (2.2%) as deafblind. The most common face coverings used were cloth (n=119, 88.1%) and surgical masks (n=74, 54.8%). Among the barriers raised, participants highlighted that face masks made it more difficult to locate people (n=86, 63.7%), communicate with others (n=101, 74.8%), and locate landmarks (n=82, 60.7%). Although the percentage of those who used a white cane before the pandemic did not substantially change, 6 (14.6%) of the 41 participants who were guide dog users prior to the pandemic reported no longer working with a guide dog at the time of the survey. Moreover, although guide dog users reported the highest level of confidence with independent travel before the pandemic, they indicated the lowest level of confidence a year after the pandemic began. CONCLUSIONS: These results suggest that participants were less able to draw on nonvisual cues during independent travel and social interactions due to the use of a facemask, contributing to a reduction in perceived self-confidence and independence. Findings inform the development of evidence-based recommendations to address identified barriers.

ACE2 chromogenic immunostaining protocol optimized for formalin-fixed paraffin-embedded human tissue sections.

Angiotensin-converting enzyme 2 (ACE2) is a key cellular entry factor for severe acute respiratory syndrome coronavirus 2. Hence, identifying cell types that express ACE2 is important for understanding the pathophysiology of coronavirus disease 2019. We performed extensive testing of multiple primary antibodies across various human tissue types. Here, we describe an optimized protocol for immunostaining of ACE2 in formalin-fixed paraffin-embedded human pancreas, small intestine, and kidney tissue sections obtained from organ donors and autopsies. For complete details on the use and execution of this protocol, please refer to Kusmartseva et al. (2020).

Expression of SARS-CoV-2 Entry Factors in the Pancreas of Normal Organ Donors and Individuals with COVID-19.

Diabetes is associated with increased mortality from severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Given literature suggesting a potential association between SARS-CoV-2 infection and diabetes induction, we examined pancreatic expression of angiotensin-converting enzyme 2 (ACE2), the key entry factor for SARS-CoV-2 infection. Specifically, we analyzed five public scRNA-seq pancreas datasets and performed fluorescence in situ hybridization, western blotting, and immunolocalization for ACE2 with extensive reagent validation on normal human pancreatic tissues across the lifespan, as well as those from coronavirus disease 2019 (COVID-19) cases. These in silico and ex vivo analyses demonstrated prominent expression of ACE2 in pancreatic ductal epithelium and microvasculature, but we found rare endocrine cell expression at the mRNA level. Pancreata from individuals with COVID-19 demonstrated multiple thrombotic lesions with SARS-CoV-2 nucleocapsid protein expression that was primarily limited to ducts. These results suggest SARS-CoV-2 infection of pancreatic endocrine cells, via ACE2, is an unlikely central pathogenic feature of COVID-19-related diabetes.

Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning.

Introduction The need to streamline patient management for coronavirus disease-19 (COVID-19) has become more pressing than ever. Chest X-rays (CXRs) provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge the severity of COVID-19 lung infections (and pneumonia in general) that can be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. Methods Images from a public COVID-19 database were scored retrospectively by three blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A neural network model that was pre-trained on large (non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for our task. Results This study finds that training a regression model on a subset of the outputs from this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE. Conclusions These results indicate that our model's ability to gauge the severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. To enable follow up work, we make our code, labels, and data available online.

Virtual visits in palliative care: about time or against the grain?

While the additional value from adding the option of virtual visits is not in question, numerous issues are raised around how to decide between face-to-face and virtual visits in individual cases and how best to set up such provision within an organisation. With only limited palliative care-specific literature and no time to set up and evaluate pilots, we had to get on and set up a prototype 'virtual visits' model, retro-fitting guidance and a supporting ethical framework. We looked at the issues spanning clinical, ethical and logistics domains; identifying areas of benefit as well as drawbacks, some specific to the rushed implementation because of COVID-19's infective risks and the 'rules' of lockdown, but many are generic areas to help guide longer term service design. Unsurprisingly, it appears clear that a 'one-size-fits-all' mentality is a poor fit for the individualised needs of the heterogeneous palliative care population. Virtual visits have great potential even if they are not a panacea.

COVID-19 Image Data Collection: Prospective Predictions Are the Future (preprint)

Across the world's coronavirus disease 2019 (COVID-19) hot spots, the need to streamline patient diagnosis and management has become more pressing than ever. As one of the main imaging tools, chest X-rays (CXRs) are common, fast, non-invasive, relatively cheap, and potentially bedside to monitor the progression of the disease. This paper describes the first public COVID-19 image data collection as well as a preliminary exploration of possible use cases for the data. This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19. It was manually aggregated from publication figures as well as various web based repositories into a machine learning (ML) friendly format with accompanying dataloader code. We collected frontal and lateral view imagery and metadata such as the time since first symptoms, intensive care unit (ICU) status, survival status, intubation status, or hospital location. We present multiple possible use cases for the data such as predicting the need for the ICU, predicting patient survival, and understanding a patient's trajectory during treatment. Data can be accessed here: https://github.com/ieee8023/covid-chestxray-dataset

Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning (preprint)

Purpose: The need to streamline patient management for COVID-19 has become more pressing than ever. Chest X-rays provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections (and pneumonia in general) that can be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. Methods: Images from a public COVID-19 database were scored retrospectively by three blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A neural network model that was pre-trained on large (non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for our task. Results: This study finds that training a regression model on a subset of the outputs from an this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE. Conclusions: These results indicate that our model's ability to gauge severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the intensive care unit (ICU). A proper clinical trial is needed to evaluate efficacy. To enable this we make our code, labels, and data available online at https://github.com/mlmed/torchxrayvision/tree/master/scripts/covid-severity and https://github.com/ieee8023/covid-chestxray-dataset

An antibiotic stewardship programme to reduce inappropriate antibiotic prescribing for acute respiratory infections in rural Chinese primary care facilities: study protocol for a clustered randomised controlled trial.

BACKGROUND: Inappropriate prescribing of antibiotics for acute respiratory infections at the primary care level represents the major source of antibiotic misuse in healthcare, and is a major driver for antimicrobial resistance worldwide. In this study we will develop, pilot and evaluate the effectiveness of a comprehensive antibiotic stewardship programme in China's primary care hospitals to reduce inappropriate prescribing of antibiotics for acute respiratory infections among all ages. METHODS: We will use a parallel-group, cluster-randomised, controlled, superiority trial with blinded outcome evaluation but unblinded treatment (providers and patients). We will randomise 34 primary care hospitals from two counties within Guangdong province into the intervention and control arm (1:1 overall ratio) stratified by county (8:9 within-county ratio). In the control arm, antibiotic prescribing and management will continue through usual care. In the intervention arm, we will implement an antibiotic stewardship programme targeting family physicians and patients/caregivers. The family physician components include: (1) training using new operational guidelines, (2) improved management and peer-review of antibiotic prescribing, (3) improved electronic medical records and smart phone app facilitation. The patient/caregiver component involves patient education via family physicians, leaflets and videos. The primary outcome is the proportion of prescriptions for acute respiratory infections (excluding pneumonia) that contain any antibiotic(s). Secondary outcomes will address how frequently specific classes of antibiotics are prescribed, how frequently key non-antibiotic alternatives are prescribed and the costs of consultations. We will conduct a qualitative process evaluation to explore operational questions regarding acceptability, cultural appropriateness and burden of technology use, as well as a cost-effectiveness analysis and a long-term benefit evaluation. The duration of the intervention will be 12 months, with another 24 months' post-trial long-term follow-up. DISCUSSION: Our study is one of the first trials to evaluate the effect of an antibiotic stewardship programme in primary care settings in a low- or middle-income country (LMIC). All interventional activities will be designed to be embedded into routine primary care with strong local ownership. Through the trial we intend to impact on clinical practice and national policy in antibiotic prescription for primary care facilities in rural China and other LMICs. TRIAL REGISTRATION: ISRCTN, ID: ISRCTN96892547. Registered on 18 August 2019.

COVID-19 Image Data Collection (preprint)

This paper describes the initial COVID-19 open image data collection. It was created by assembling medical images from websites and publications and currently contains 123 frontal view X-rays.