Judicious use of the Critical Care Outreach Team: A QIP to reduce the inappropriate use of Early Warning Scores through targeted communication

Introduction: Early Warning Scores (EWS) use physiological parameters to create an aggregate score alerting medical teams to patient deterioration. Although vital tools for triggering referrals to critical care services in appropriate patients, the score does not take account of patients with persistently altered physiology or patients who are not deemed suitable for escalation to critical care. In these instances, EWS can result in the over-monitoring of patients and inappropriate contact of already strained critical care outreach services.1-2 Guidelines state that in such circumstances routine recording of EWS may be stopped.1 The COVID-19 pandemic has placed unprecedented demands on already overstretched resources in the critical care services,3 in particular on the Critical Care Outreach Team (CCOT). This makes their judicious use, and this QIP, ever more pertinent. Objective(s): In our trust, it was found that despite documented decisions not to escalate patients to critical care, these patients were still being monitored according to EWS, resulting in the inappropriate call out of the CCOT. We introduced measures to improve the proportion of inpatients with treatment limitations in place that had these limitations documented on their EWS charts, with the overall aim of reducing the number of inappropriate call-out of the CCOT. Method(s): We performed two snapshot audits on acute medical (control) and general medical wards (intervention) in a large district general hospital between the years 2018-2019. We obtained the percentage of patients with treatment limitations in place who had this documented on their EWS charts before and after improvement measures. Firstly, a paper prompt on the EWS chart was used in both control and intervention wards. Secondly, targeted communication interventions to general medical wards only. Targeted communication was not repeated after the second audit. A third snapshot audit was completed a year after improvement measures (2020) to identify whether improvements were sustained. Result(s): There was no significant difference in EWS amendment in the acute medical ward, where only a paper prompt was used. However, where targeted communication was used (general medical ward), there was a statistically significant improvement in review and amendment of EWS scores between the first and second audit in the intervention ward (37.2% vs 59.1%, p=0.017). However, this improvement was not sustained when the audit was repeated a year later. Conclusion(s): The proportion of inpatients with treatment escalation limitation decisions in place that have EWS amended can be improved by targeted communication, but paper prompts alone are not sufficient. However, these improvements are not sustained without repeated communication. The importance of appropriate amendment of EWS has two key benefits. Firstly, it reduces inappropriate and futile monitoring of end-of-life patients, allowing them to have a more dignified death. Secondly, instead of performing repeated observations (nursing staff) or patient reviews (CCOT) that will not alter management, nursing staff can better utilise their time in providing palliative support where appropriate (particularly considering current visiting restrictions), and the ever- stretched CCOT can be used more judiciously.

Proteases of SARS Coronaviruses

Coronaviruses such as SARS and SARS-CoV-2 have established themselves as a global health concern after causing an epidemic and a pandemic in the last twenty years. Understanding the life cycle of such viruses is critical to reveal their pathogenic potential. As one of the essential viral enzymes, SARS proteases are indispensable for the processing of viral polypeptides and for the replication of the virus. SARS-CoV and SARS-CoV-2 encode for 2 viral proteases: the main protease (3CLpro) and the papain-like protease (PLPro), which are conserved among different coronaviruses and are absent in humans. This review summarizes the existing literature on the structure and function of these proteases;highlighting the similarity and differences between the enzymes of SARS and SARS-CoV-2. It also discusses the development of inhibitors to target viral proteases. © 2023 Elsevier Inc. All rights reserved.

COVID-19: Recent Advances in Lung Ultrasound

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV 2) has become a global threat that has led to tremendous societal instability. The SARS-CoV-2 can exhibit a drastic variation in terms of the signs and symptoms in the patient's body. This virus manifests its existence through cough, fever, sore throat, body aches, chest pain, headaches, and dyspnoea. These can lead to life-threatening respiratory insufficiency, thereby affecting several other organs such as the kid-ney, heart, lungs, liver, and nervous system. The lungs are the primary target site for SARS-CoV-2 and several diagnoses are being deployed in real time for treatment purposes. Although chest CT is the standard method for early diagnosis and management of Coronavirus Disease (COVID-19), lung ultrasound (US) has some merits over chest CT and may be used in addition to it in the workup of COVID-19. The goal of our review is to look at the observations of the reports on lung ultrasound in COVID-19 patients and the current advances.Copyright © 2023 Bentham Science Publishers.

Deep learning: An approach to encounter pandemic effect of novel corona virus (COVID-19)

Researchers around the world are struggling to discover ground-breaking equip-ment aimed at building a great healthcare structure to fight the novel corona virus for the duration of this global epidemic. How deep learning (DL) encountered the COVID-19 epidemic and what are the current guidelines for exploring the potential in COVID-19 are the subject to walk around. Over time, genetic material of novel corona viruses mutates itself and changed its characteristics to create different vari¬ants of viruses. These distinctive variants can trigger different waves of destructive infection in different parts of world. The substantiation of DL pertinences on the precedent pandemic motivates the professionals by giving an innovative trend to organize this outburst to make it least effective. The main target of this article is to study the utility of deep learning-based approaches on COVID-19 and also their credibility in terms of containment of the pandemic based on recent works around the globe. The study has listed down recent works within DL approaches regarding marking out of virus-affected people, investigation of its protein formation, vaccine & medicine finding, virus relentlessness, and contamination to direct the enduring eruption. DL is endowed with a suitable contrivance intended for rapid selection COVID-19 along with pronouncement possible high-risk patients, which possibly will be cooperative for medical resource optimization and early prevention prior to patients suffering rigorous indication. In this study, the wide-ranging consequence of DL on several magnitudes to be in command of novel coronavirus (COVID-19) is discussed, and attempts are made to investigate it. Despite rich studies being con¬ducted through DL algorithms, there are still many limitations and contradictions in the area of COVID research. The continuous evolution of DL on coronavirus handles contamination and is costly to create the right resolution task. Apart from this, in this work, a DL-based pandemic analysis has been done using the received dataset from about 55 hospitals in West Bengal, India. According to some research scientists, we may enter the third and fourth waves too, thus this work will be help¬ful for further research activity in the years to come. Finally, it is expected this work will help many researchers throughout the world get some opportunity to find out the final remedy to get rid of this deadly virus. © 2023 Scrivener Publishing LLC. All rights reserved.

Face Mask Detector Using Convolutional Neural Networks

A convolutional neural network (CNN) has one or more layers and is mainly used for image processing, classification, segmentation. CNN is commonly used for satellite image capturing or classifying hand written letters and digits. In this particular project, a convolutional neural network is trained to predict whether a person is wearing a mask or not. The training is done by using a set of masked and unmasked images which constitutes the training data. The performance of the trained model is evaluated on the test dataset, and the accuracy of the prediction is observed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Prenatal alcohol prevention in the UK: mapping the landscape through systematic collaborative review

BACKGROUND: UK policy makers have called for urgent action to reduce prenatal alcohol exposure (PAE), but evidence on what is effective is scarce. We aimed to identify, evaluate, and synthesise evidence on content, process aspects, and effectiveness of UK PAE prevention initiatives. METHODS: We conducted a systematic search of published and grey literature on UK PAE prevention (PROSPERO: CRD42020209460);consultations with 61 academic, practice, policy, third sector, and public stakeholders;and semi-structured 12 interviews with pregnant people (who were aged ≥18 years and ≥12 weeks' gestation) and service providers to discuss experiences of PAE prevention. Participants were purposively sampled to cover each UK region and identified through maternity sites, social media and, for stakeholder consultees, researcher networks. Information from relevant PAE prevention initiatives from the literature was independently extracted by two reviewers. Ethical approval and informed consent were obtained for interviews, which were recorded and transcribed. Qualitative evidence was synthesised using thematic analysis. Quantitative data will be summarised using descriptive statistics and meta-analysis. FINDINGS: We identified 14 PAE prevention initiatives through literature searches (22 of 4064 results were eligible), stakeholder consultation, and interviews. Initiatives included screening and intervention, campaigns, and education or training. Seven initiatives were identified in the north of England. Two initiatives were identified in Scotland and two in Wales. The East of England, West Midlands, and South East of England had one each. None were identified in Southwest of England or Northern Ireland. Barriers to prevention included absence of resources, excessive workload, concerns around blame, and COVID-19. Enablers included workforce training and trust between pregnant people and service providers. Effectiveness of evidence was scarce. INTERPRETATION: Key strengths include extensive searches and multidisciplinary consultation. Data collection and analyses are ongoing and will be finalised before November, 2022. This research will provide a comprehensive analysis of current provision, providing crucial evidence to inform research and practice. FUNDING: The National Institute for Health and Care Research.

Face Mask Detector Using Convolutional Neural Networks

A convolutional neural network (CNN) has one or more layers and is mainly used for image processing, classification, segmentation. CNN is commonly used for satellite image capturing or classifying hand written letters and digits. In this particular project, a convolutional neural network is trained to predict whether a person is wearing a mask or not. The training is done by using a set of masked and unmasked images which constitutes the training data. The performance of the trained model is evaluated on the test dataset, and the accuracy of the prediction is observed.

Assessing the impact of COVID-19 on visual acuity for diabetic macular edema patients treated with aflibercept in the UK

Purpose : Assessing the impact of COVID-19 on visual acuity (VA) in eyes treated for Diabetic Macular Edema. Methods : Anonymized data from 21 UK centers were extracted from Medisoft for eyes receiving treatment with aflibercept and with VA data in the pre-COVID baseline period (01/10/19 to 30/03/20, N=3,248). Comparisons for period 1 (01/04/20 to 30/09/20, N=2,077)-lockdown following RCOphth Medical Retinal Management Plan, period 2 (01/10/20 to 30/03/21, N=1,850)-intermittent lockdown and period 3 (01/04/21 to 30/09/21, N=1,111;20 centers)-easing of COVID-19 restrictions. VA change was compared for baseline VA, <7 vs. ≥7 injections before period 1 and for eyes losing ≥5 ETDRS letters in period 1. Results : The mean change in VA for eyes with a baseline VA of ≤35 letters, was +4.9, +2.5 and +1.7 letters from baseline to period 1, period 1 to 2 and period 2 to 3, respectively. For baseline VA of 36-55 letters, +0.6, +1.7 and -0.2 letters, from baseline to period 1, period 1 to 2 and period 2 to 3, respectively. For baseline VA of 56-75 letters, +1.9, zero and -0.5 letters, from baseline to period 1, period 1 to 2 and period 2 to 3, respectively. For baseline VA of >75 letters, -4.3, -0.5 and zero letters, from baseline to period 1, period 1 to 2 and period 2 to 3, respectively. For eyes receiving <7 injections before period 1, the mean change in VA was -1.9 letters (N=1,335) from baseline to period 1, +0.5 letters (N=992) from period 1 to 2 and +0.1 letters (N=592) from period 2 to 3. For ≥7 injections before period 1, the mean change in VA was -3.4 letters (N=742) from baseline to period 1, -0.4 letters (N=515) from period 1 to 2 and -1.1 letters (N=303) from period 2 to 3. For eyes losing ≥5 letters before period 1, the mean change in VA when receiving ≥1 injection in period 2 was +3.9 letters (N=283) from period 1 to 2 and -0.1 letters (N=140) from period 2 to 3. For eyes not retreated in period 2, the mean change in VA was -2.9 letters (N=162) from period 1 to 2 and zero letters (N=73) from period 2 to 3. Conclusions : Visual gain between time periods was more likely for lower baseline vision. For eyes with <7 or ≥7 injections before period 1, the mean VA change was a loss in vision in the first period with little change in later periods. For eyes with ≥5 letter loss in period 1, subsequent visual gain was more likely if treatment continued.

CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines

Convincing people to get vaccinated against COVID-19 is a key societal challenge in the present times. As a first step towards this goal, many prior works have relied on social media analysis to understand the specific concerns that people have towards these vaccines, such as potential side-effects, ineffectiveness, political factors, and so on. Though there are datasets that broadly classify social media posts into Anti-vax and Pro-Vax labels, there is no dataset (to our knowledge) that labels social media posts according to the specific anti-vaccine concerns mentioned in the posts. In this paper, we have curated CAVES, the first large-scale dataset containing about 10k COVID-19 anti-vaccine tweets labelled into various specific anti-vaccine concerns in a multi-label setting. This is also the first multi-label classification dataset that provides explanations for each of the labels. Additionally, the dataset also provides class-wise summaries of all the tweets. We also perform preliminary experiments on the dataset and show that this is a very challenging dataset for multi-label explainable classification and tweet summarization, as is evident by the moderate scores achieved by some state-of-the-art models. © 2022 ACM.

What's up with WhatsApp in supplementing surgical education: an objective assessment.

INTRODUCTION: This is the first study aimed at objectively quantifying the benefit of virtual education using WhatsApp-based discussion groups. METHODS: A prospective, non-randomised interventional study was undertaken in the Department of General Surgery, at a tertiary care centre in Kolkata, India, with 200 undergraduate students over a period of 5 days each for 2 weeks, with the first week acting as a control arm. A WhatsApp group was created consisting of 197 eligible undergraduates, faculty members and the authors of this study. Each day, three questions were posted on this group. The second week involved an hour-long WhatsApp-based discussion between the participants and the faculty. Responses were recorded and compared for improvements between the two weeks. Participant feedback was collected and analysed. RESULTS: Statistically significant improvements were observed in the study group compared with the control group in rates of one in three, two in three and three in three correct responses (p=0.01649, 0.01146 and 0.00946, respectively). A total of 68 (51.92%) feedback respondents were satisfied with the programme. Convenience of use was the principal reason behind satisfaction in 79 respondents (60.31%), whereas 62 participants (47.33%) reported lack of hands-on training as a major drawback. CONCLUSIONS: WhatsApp was found to be a satisfactory supplement to traditional medical teaching. It can be implemented to fill lapses in medical education, especially in light of the COVID-19 pandemic, which has caused great disruption to traditional teaching methods. Research is needed to assess the feasibility of incorporating it into the curriculum.

The experiences of autistic adults during the COVID-19 pandemic in the UK and implications for autism services development

Purpose COVID-19 has been challenging for many in the UK. This is no different to many with autism spectrum disorder. Based on the experiences and issues raised by a small group of autistic women in an ongoing support group, consideration if this holds true for the wider adult autistic community across further lockdowns and restrictions to public life was explored. Design/methodology/approach An online questionnaire was created based on the issues raised. Participants indicated the degree to which they agreed or disagreed with each statement. Findings Autistic adults experienced an increase in anxiety and poor mental health, which in turn has exacerbated autistic features, such as rigidity. The data indicates that autistic adults can adapt to change provided there is support in maintaining routines. Research limitations/implications The research is limited due to the small number of participants (N = 120), as well as national variations in service provision. Practical implications Our data raises wider questions about the nature of support for autistic adults without cognitive impairments during times of crises and how services can respond and may even be shaped in the future to provide support that is cost-effective and relevant to autistic adults. Social implications To ensure that services have an awareness of how crises impact on autistic adults and how relatively simple changes may avert poor mental health. Originality/value That the creation of local support networks, and the ability to access these, is a key feature of autism-specific support.

Why are some coronavirus variants more infectious?

Since the start of the pandemic, SARS-CoV-2 has infected almost 200 million human hosts and is set to encounter and gain entry in many more in the coming months. As the coronavirus flourish, the evolutionary pressure selects those variants that can complete the infection cycle faster and reproduce in large numbers compared to others. This increase in infectivity and transmissibility coupled with the immune response from high viral load may cause moderate to severe disease. Whether this leads to enhanced virulence in the prevalent Alpha and Delta variants is still not clear. This review describes the different types of SARS-CoV-2 variants that are now prevalent, their emergence, the mutations responsible for their growth advantages, and how they affect vaccine efficacy and increase chances of reinfection. Finally, we have also summarized the efforts made to recognize and predict the mutations, which can cause immune escape and track their emergence through impactful genomic surveillance.

Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach

We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimensional emotion regression, thereby utilizing the inter-relatedness between the tasks. Co-training especially helps in improving the performance of the classification task as we outperform the strongest baselines with 3.4%, 11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores respectively on the AIT dataset [17]. We also achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset [27]. For the regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset [5] for the Valence (V) and Dominance (D) affect dimensions respectively. We conclude our work with a case study on COVID-19 tweets posted by Indians that further helps in establishing the efficacy of our proposed solution. © 2021 ACM.

IoT-cloud based healthcare model for COVID-19 detection: an enhanced k-Nearest Neighbour classifier based approach

COVID - 19 affected severely worldwide. The pandemic has caused many causalities in a very short span. The IoT-cloud-based healthcare model requirement is utmost in this situation to provide a better decision in the covid-19 pandemic. In this paper, an attempt has been made to perform predictive analytics regarding the disease using a machine learning classifier. This research proposed an enhanced KNN (k NearestNeighbor) algorithm eKNN, which did not randomly choose the value of k. However, it used a mathematical function of the dataset's sample size while determining the k value. The enhanced KNN algorithm eKNN has experimented on 7 benchmark COVID-19 datasets of different size, which has been gathered from standard data cloud of different countries (Brazil, Mexico, etc.). It appeared that the enhanced KNN classifier performs significantly better than ordinary KNN. The second research question augmented the enhanced KNN algorithm with feature selection using ACO (Ant Colony Optimization). Results indicated that the enhanced KNN classifier along with the feature selection mechanism performed way better than enhanced KNN without feature selection. This paper involves proposing an improved KNN attempting to find an optimal value of k and studying IoT-cloud-based COVID - 19 detection.

Effect of COVID-19 lockdown on sleep behavior and screen exposure time: an observational study among Indian school children

Lockdown is an important measure that has been globally adopted to reduce the spread of the contagious disease caused by SARS CoV-2. The imposed schedule and confinement led to extensive use of digital media and rise in sedentary activity drastically. The escalated duration of screen exposure causes disruption in sleep behavior. An online survey was conducted to comprehend the effect of lockdown on sleep behavior and screen exposure time on school children. Screen exposure time involved with various electronic gadgets was also analyzed. It was observed that the social jet lag and sleep debt were significantly less during lockdown than before it. Inertia during lockdown significantly increased. The difference between screen exposure time on weekdays before lockdown and weekends during lockdown was identified to be the highest. Three clusters based on sleep behavior and duration of screen time were identified of which Cluster 2 revealed simultaneous existence of high sleep duration and screen time. These baseline data on sleep parameters and duration of exposure to the screen will help us in devising approaches to mitigate the evident disruption this unprecedented phase has brought about.