Cross-neutralizing antibody against emerging Omicron subvariants of SARS-CoV-2 in infection-naïve individuals with homologous BNT162b2 or BNT162b2(WT+ BA.4/5) bivalent booster vaccination (preprint)

Since the emergence of SARS-CoV-2, different variants and subvariants successively emerged to dominate global virus circulation as a result of immune evasion, replication fitness or both. COVID-19 vaccines continue to be updated in response to the emergence of antigenically divergent viruses, the first being the bivalent RNA vaccines that encodes for both the Wuhan-like and Omicron BA.5 subvariant spike proteins. Repeated infections and vaccine breakthrough infections have led to complex immune landscapes in populations making it increasingly difficult to assess the intrinsic neutralizing antibody responses elicited by the vaccines. Hong Kong’s intensive COVID-19 containment policy through 2020–2021 permitted us to identify sera from a small number of infection naïve individuals who received 3 doses RNA vaccine BNT162b2 of vaccines encoding the Wuhan-like spike who were boosted with a fourth dose monovalent Wuhan-like (WT) vaccine or the bivalent Wuhan-like and BA.4/5 spike (WT + BA.4/5) expressing vaccine. While neutralizing antibody to wild-type virus was comparable in both vaccine groups, BNT162b2 bivalent vaccine elicited significantly higher plaque neutralizing antibodies to Omicron subvariants BA.5, XBB.1.5, XBB.1.16, XBB.1.9.1, XBB.2.3.2, EG.5.1, HK.3, BA.2.86 and JN.1, compared to BNT162b2 monovalent vaccine. The single amino acid substitution that differentiates the spike of JN.1 from BA.2.86 resulted in a profound antigenic change.

Unfolded Von Willebrand Factor Binds Protein S and Reduces Anticoagulant Activity (preprint)

Protein S (PS), the critical plasma cofactor for the anticoagulants tissue factor (TF) pathway inhibitor (TFPI) and activated protein C (APC), circulates in two functionally distinct pools: free (anticoagulant) or bound to complement component 4b-binding protein (C4BP) (anti-inflammatory). Acquired free PS deficiency is detected in several viral infections, but its cause is unclear. Here, we identified a shear-dependent interaction between PS and von Willebrand Factor (VWF) by mass spectrometry. Consistently, plasma PS and VWF comigrated in both native and agarose gel electrophoresis. The PS/VWF interaction was blocked by TFPI but not APC, suggesting an interaction with the C-terminal sex hormone binding globulin (SHBG) region of PS. Microfluidic systems, mimicking arterial laminar flow or disrupted turbulent flow, demonstrated that PS stably binds VWF as VWF unfolds under turbulent flow. PS/VWF complexes also localized to platelet thrombi under laminar arterial flow. In thrombin generation-based assays, shearing plasma decreased PS activity, an effect not seen in the absence of VWF. Finally, free PS deficiency in COVID-19 patients, measured using an antibody that binds near the C4BP binding site in SHBG, correlated with changes in VWF, but not C4BP, and with thrombin generation. Our data suggest that PS binds to a shear-exposed site on VWF, thus sequestering free PS and decreasing its anticoagulant activity, which would account for the increased thrombin generation potential. As many viral infections present with free PS deficiency, elevated circulating VWF, and increased vascular shear, we propose that the PS/VWF interaction reported here is a likely contributor to virus-associated thrombotic risk.

Comprehensive contact tracing during an outbreak of alpha-variant SARS-CoV-2 in a rural community reveals less viral genomic diversity and higher household secondary attack rates than expected (preprint)

Sequencing of SARS-CoV-2 genomes throughout the COVID-19 pandemic has generated a wealth of data on viral evolution across populations, but only a few studies have so far explored SARS-CoV-2 evolution across transmission networks of tens to hundreds of persons. Here, we couple data from SARS-CoV-2 sequencing with contact tracing data from an outbreak with a single origin in a rural Norwegian community where samples from all exposed persons were collected prospectively. A total of 134 nasopharyngeal samples were positive by PCR. Among the 121 retrievable genomes, 81 were identical to the genome of the introductor, thus demonstrating that genomics offers limited additional value to manual contact-tracing. In the cases where mutations were discovered, five small genetic clusters were identified. We observed a household secondary attack rate of 67%, with 92% of household members infected among households with secondary transmission, suggesting that SARS-CoV-2 introduction into large families are likely to affect all household members. ImportanceIn outbreak investigations, obtaining a full overview of infected individuals within a population is seldom acheived. We here present an example of just that, where a single introduction of B1.1.7 SARS-CoV-2 within a rural community allowed for tracing of the virus, from an introductor via dissemination through larger gatherings, into households. The outbreak occurred before widespread vaccination, allowing for a "natural" outbreak development with community lock-down. We show through sequencing that the virus can infect up to five consecutive persons without gaining mutations, thereby showing that contact tracing seems more important than sequencing for local outbreak investigations. We also show how families with small children are less likely to contain spread to all family members if SARS-CoV-2 enters the household either by a child or a caregiver, as isolation of the primary infected is difficult in such scenarios.

Prediction of Pneumonia and COVID-19 Using Deep Neural Networks (preprint)

Pneumonia, caused by bacteria and viruses, is a rapidly spreading viral infection with global implications. Prompt identification of infected individuals is crucial for containing its transmission. This study explores the potential of medical image analysis to address this challenge. We propose machine-learning techniques for predicting Pneumonia from chest X-ray images. Chest X-ray imaging is vital for Pneumonia diagnosis due to its accessibility and cost-effectiveness. However, interpreting X-rays for Pneumonia detection can be complex, as radiographic features can overlap with other respiratory conditions. We evaluate the performance of different machine learning models, including DenseNet121, Inception Resnet-v2, Inception Resnet-v3, Resnet50, and Xception, using chest X-ray images of pneumonia patients. Performance measures and confusion matrices are employed to assess and compare the models. The findings reveal that DenseNet121 outperforms other models, achieving an accuracy rate of 99.58%. This study underscores the significance of machine learning in the accurate detection of Pneumonia, leveraging chest X-ray images. Our study offers insights into the potential of technology to mitigate the spread of pneumonia through precise diagnostics.

Internet of Things (IoT) based Health Monitor Device

Health is the centre of human enlightenment. Due to the recent Covid outbreak and several environmental pollutions, checking one's vitals regularly has become a necessity. Ours is an IoT-based device that measures a user's heart rate, blood oxygen level and body temperature. The device is compact and portable, making it easy for users to wear. The readings are measured and shown on an OLED display with the help of sensors. The data is also available on the cloud. A webpage and a mobile application were developed to view the data from the cloud. Individual graphs of the vitals with time are available on the mobile application. This can be used for progress measurement and statistical analyses. Authorized personnel can access the patient's vitals. This creates a scope for Tele-medication in rural and underdeveloped regions. Besides, one can also view his/her vitals for personal health routine. © 2022 IEEE.

Differences in Muslim philanthropic behavior during the COVID-19 pandemic based on demographic factors

The COVID-19 pandemic that hit the country has caused a huge impact, especially on the economic sector. The approach taken by the government in dealing with the pandemic crisis by implementing a movement control order (MCO) have, to some extent, affected the source of income of the community. In order to survive, these underprivileged people are in need of financial assistance from the government, agencies and the kind-hearted people. Islam places great emphasis on welfare, especially in helping the needy with their daily needs. Therefore, this study aims to examine the level of philanthropic behavior of Muslims during the COVID-19 pandemic as well as to analyze the differences in Muslim philanthropies' behavior during the occurrence of this pandemic based on demographic factors, namely gender, age, education level, marital status, area of residence, household income and employment sector. To achieve this goal, a total of 424 respondents had answered a set of questionnaires distributed during the movement control order (MCO) period as a time limit via a simple sampling method. The results of the study found that five out of the seven demographic factors tested, namely age, level of education, area of residence, household income and employment sector showed significant differences in philanthropic behavior among the respondents. These findings also prove that demographic factors do also influence the decision-making process to donate among Muslims in Melaka. Therefore, all parties need to take a role in explaining and providing awareness on the importance of this philanthropic activity so that the burden borne by the community affected by the COVID-19 pandemic can be eased. © 2023 Author(s).

Effect of Distant Education on Children's Academic Achievement in Primary Schools During Corona Pandemic in Dubai Emirate- Field Study

This study aimed to examine the effect of distance education on the level of educational achievement of children during the Corona period in ten primary schools in the Emirate of Dubai. To achieve the objectives of the study the researchers adopted the descriptive analytical approach. The quantitative method of data collection had been applied using the electronic questionnaire tool consisted of four main axes for data collection and had been distributed to the study sample consisted of 190 students' parents and administrators selected by using simple random techniques. The results of the study indicated that the participation of students in the educational process, and in the establishment of appropriate educational programs and applications for the transmission to distance learning have contributed to reducing the negative effects of the process of shifting from traditional education / face-to-face education classroom teaching to virtual classroom (ZOOM).The study recommended the need for strengthening distance education mechanisms, which contribute in developing the student's interests, tendencies, attitudes, concentrating on the study material, and using of safe and secured electronic devices to increase the search for additional information to reach the correct knowledge. Also, the school administration should have good e-learning plan ahead with required financial credits that will help in overcoming the crisis and mange distance learning processes to reach future objectives successfully. © 2022 Owner/Author.

What are People in Bali Doing in Pandemic: Understanding the Shape of Health Behavior and the Pitutur Tua

In the early occurrence of the Covid-19 pandemic, Indonesian and the world regarded Bali having a unique mechanism. Balinese or Bali residents are assumed excellently immune or difficulty infected from Covid-19. Interestingly, Balinese do not show overwhelmed panic as occurs in other areas although the Covid-19 cases in Bali are not low. There are various reasons why Balinese's actions to protect their health consider beliefs, religion, and culture. This paper is a survey of the Balinese's distinctive perception and behavior to face Covid-19. The information was gained from comments, opinions, and information about typical behavior posted on mass media and social media. The main source of the information was the WhatsApp groups of Banjar community (60 people), Bali academicians (76 people), Hindu organization alumni (113 people), and Bali medical staff (30 people). This study collected the information and conversations during early months of the pandemic in 2020 and 2021 when the Covid-19 cases skyrocketed and the government implemented the Community Activities Restrictions Enforcement (PPKM). This study concludes that the belief in the power of nature makes the sincerity and acceptance more easily appear. Moreover, tradition strengthens the trust. Support in social life also increases cooperative actions to the rules or advices of the Bali government and the custom village assembly to protect society from the pandemic.Copyright © 2021 Muslim OT et al.

Pathophysiology of thromboembolism in patients with COVID-19

Introduction and aim. A small number of critically ill patients with coronavirus disease (COVID-19) develop thromboembolism (arterial or venous), both micro- and macrovascular complications such as deep vein thrombosis, pulmonary embolism, and pulmonary arterial thrombosis. The objective of the study is to describe the pathophysiology of venous thromboembolism in patients with COVID-19. Material and methods. In this article a narrative review regarding pathophysiology of thromboembolism in patients with COVID-19. Analysis of the literature. The development of coagulopathy is a consequence of the intense inflammatory response associated with hypercoagulability, platelet activation, and endothelial dysfunction. The pathophysiology that relates pulmonary thromboembolism (PTE) with COVID-19 is associated with a hypercoagulable state. PTE is suspected in hospitalized patients presenting dyspnea, decreased oxygen requirement, hemodynamic instability, and dissociation between hemodynamic and respiratory changes. In COVID-19-associated coagulopathy, initially, patients present with elevated levels of fibrinogen and D-dimer, with minimal changes in prothrombin time and platelet count. The main risk factor for the development of pulmonary embolism is the increase in D-dimer that is associated with the development of PTE. The administration of iodine-based contrast agent to patients with COVID-19 would affect P-creatinine and renal function, where Ultrasound is viewed as cost-effective and highly portable, can be performed at the bedside. Conclusion. Acute respiratory distress syndrome severity in patients with COVID-19 can explain PTE as a consequence of an exaggerated immune response. © 2022 Publishing Office of the University of Rzeszow. All Rights Reserved.

Role of technology in motivating the graduate students in conducting research amidst the Covid-19 pandemic

Technology is already widely used in graduate school, especially in this new normal. Textbooks and references are reliable sources of reference materials while doing research;however, due to the pandemic, technology should be used to deliver the content of graduate school courses. However, not all graduate students have the motivation to conduct research. Thus, this study looked into the role of technology in motivating the students in the graduate studies in conducting research. This study employed the qualitative research approach and the convenience sampling was utilized to elicit the participants' perspectives on the role of technology in motivating graduate students in conducting research amidst the Covid-19 pandemic. An unstructured survey questionnaire was distributed to ten participants via google link. The findings were evaluated and interpreted in view of the documentary analysis. It has been discovered that there was a great impact of the role of technology in the graduate students to be motivated in conducting a research. Majority of the respondents believed that the use of technology paly a vital role in the conduct of research. This makes their life simpler and easier when supported with various technological tools, support from the adviser and the institution. The drawbacks, on the other side, can be utilized to strengthen the technological techniques of both professors and students in order to increase the motivation of the graduate students in their research writing. It was also found out that the graduate students are more motivated to conduct a study when they are provided with the library resources, support from the adviser, and support from the management or the institution. The research further suggests the potential solutions to the graduate students' problems through the use of themes that will form part of the role of technology in motivating the graduate students to do their thesis writing. This implies that technology has a greater impact in motivating the graduate students to write their thesis writing based on the result of the study. © 2023 Author(s).

Explainable Mortality Prediction Model for Congestive Heart Failure with Nature-Based Feature Selection Method

A mortality prediction model can be a great tool to assist physicians in decision making in the intensive care unit (ICU) in order to ensure optimal allocation of ICU resources according to the patient's health conditions. The entire world witnessed a severe ICU patient capacity crisis a few years ago during the COVID-19 pandemic. Various widely utilized machine learning (ML) models in this research field can provide poor performance due to a lack of proper feature selection. Despite the fact that nature-based algorithms in other sectors perform well for feature selection, no comparative study on the performance of nature-based algorithms in feature selection has been conducted in the ICU mortality prediction field. Therefore, in this research, a comparison of the performance of ML models with and without feature selection was performed. In addition, explainable artificial intelligence (AI) was used to examine the contribution of features to the decision-making process. Explainable AI focuses on establishing transparency and traceability for statistical black-box machine learning techniques. Explainable AI is essential in the medical industry to foster public confidence and trust in machine learning model predictions. Three nature-based algorithms, namely the flower pollination algorithm (FPA), particle swarm algorithm (PSO), and genetic algorithm (GA), were used in this study. For the classification job, the most widely used and diversified classifiers from the literature were used, including logistic regression (LR), decision tree (DT) classifier, the gradient boosting (GB) algorithm, and the random forest (RF) algorithm. The Medical Information Mart for Intensive Care III (MIMIC-III) dataset was used to collect data on heart failure patients. On the MIMIC-III dataset, it was discovered that feature selection significantly improved the performance of the described ML models. Without applying any feature selection process on the MIMIC-III heart failure patient dataset, the accuracy of the four mentioned ML models, namely LR, DT, RF, and GB was 69.9%, 82.5%, 90.6%, and 91.0%, respectively, whereas with feature selection in combination with the FPA, the accuracy increased to 71.6%, 84.8%, 92.8%, and 91.1%, respectively, for the same dataset. Again, the FPA showed the highest area under the receiver operating characteristic (AUROC) value of 83.0% with the RF algorithm among all other algorithms utilized in this study. Thus, it can be concluded that the use of feature selection with FPA has a profound impact on the outcome of ML models. Shapley additive explanation (SHAP) was used in this study to interpret the ML models. SHAP was used in this study because it offers mathematical assurances for the precision and consistency of explanations. It is trustworthy and suitable for both local and global explanations. It was found that the features that were selected by SHAP as most important were also most common with the features selected by the FPA. Therefore, we hope that this study will help physicians to predict ICU mortality for heart failure patients with a limited number of features and with high accuracy.

Social Cognition Approaches to Understanding and Changing COVID-19 Preventive Behaviors

Until pharmacological measures are rolled out on a global scale, reducing the transmission of COVID-19 and preventing future outbreaks require the continued promotion of behaviors known to effectively limit the spread of infections (e.g., physical distancing, hand hygiene practices). The success of such behavioral measures, however, relies on widespread compliance, highlighting the importance of theory-and evidence-based behavior change interventions targeting COVID-19 preventive behaviors. Theories of social cognition have recently been applied to COVID-19 preventive behaviors to identify the key modifiable determinants of behavior that can be targeted by strategies or techniques in interventions. The identification of key modifiable determinants of COVID-19 preventive behaviors permits the matching of evidence-based behavior change strategies or techniques proposed to affect change in the targeted determinants. The goal of this chapter is to provide an overview of the social cognition literature and interventions targeting key psychological constructs as means to adopt and maintain COVID-19 preventive behaviors. The chapter also provides example materials used in behavior change interventions based on social cognition theory, which may have application across a broad range of COVID-19 preventive behaviors. First, the chapter provides an overview of social cognition theories that have been used to explain and predict health and social behavior and how behavior change occurs. Next, the emerging research evidence of potentially modifiable social cognition determinants of COVID-19 preventive behaviors are reviewed. Finally, a step-by-step example of how interventionists might develop a theory-based intervention targeting change in an important COVID-19 preventive behavior is provided. The chapter concludes with a summary of some key challenges and future directions in moving forward behavior change research in this area. Overall, this chapter provides information useful to the design and development of effective behavior change interventions targeting the adoption and maintenance of COVID-19 preventive behaviors which are relevant to current and future pandemic contexts. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Ozone use in surface disinfection: an integrative review

Objective: To analyze the scientific evidence regarding the effectiveness of using ozone to disinfect surfaces based on an integrative literature review.Methods: A search was carried out in the SciELO, MEDLINE, LILACS, PubMed, Science Direct databases. Eleven articles published January 2010 to August 2021 were analyzed. All employed the experimental laboratory research model and achieved different levels of disinfection by O3, however, with varied surfaces and products tested, in addition to different methodological procedures.Results: The majority had an inhibition rate by O3 equal to or greater than 90%, thus proving the effectiveness of this agent as a surface disinfectant, even with variations in parameter values such as concentration and exposure time, in all selected articles, even those that did not prove the effectiveness of O3.Conclusion: This review shows the inhibitory power that O3 has on different pathogens, even if there are variables in the factors used for this purpose, highlighting it in front of other disinfectants. Thus, it corroborates the composition of surface disinfection protocols and decision-making among managers and committees about sanitizing technologies.

Use of metolazone as an adjunct therapy to loop diuretics in diuretic resistant acute decompensation of heart failure: A systematic review and meta-analysis: Metolazone in diuretic resistant AHF: A Meta-Analysis

Introduction: Loop diuretics are the first-line treatment for volume overload in acute decompensation of congestive heart failure (AHF). Loop diuretic resistance is common due to pharmacologic tachyphylaxis. Therefore, thiazide and thiazide-like diuretics are often used as add-on therapy to combine two different pharmacologic mechanisms. This systemic review and meta-analysis aimed to synthesize the current evidence on the efficacy and safety of metolazone and other thiazide-like diuretics in AHF. Method(s): PRISMA guidelines were followed in conducting this systematic review. PubMed, Scopus, PubMed Central, and Embase databases were searched using relevant keywords for studies published before 5 Jan 2022. and title screening was performed, followed by full-text screening using the Covidence software. Data were extracted, and analysis was done using Cochrane Review Manager (RevMan v5.1). The results were reported in odds ratio and mean difference with 95% confidence intervals. Result(s): Out of 2999 studies identified by database search, eight studies met the inclusion criteria (2 RCTs and 6 cohort studies). Pooled analysis using a random-effects model showed no difference in mean difference among the metolazone group and control group for 24 hours total urine output (MD 69.32, 95% CI -638.29 to 776.94;n = 551;I2 = 84%), change in urine output in 24 hours (MD -284.09, 95% CI -583.99 to 15.81;n = 345;I2 = 0%), 48 hours total urine output (MD -465.62, 95% CI -1302.22 to 370.99;n = 242;I2 = 0%) and urine output at 72 hours (MD -13.24, 95% CI -90.88 to 64.40;n = 205;I2 = 0%). However, studies with furosemide only in the comparator arm, 24 hours of total urine outcome favored metolazone (MD 692.70, 95% CI 386.59 to 998.82;n = 334;I2 = 0%). There was no difference between the two groups in the rate of adverse events, loss of weight, mortality, or readmission rates. Conclusion(s): Metolazone therapy in diuretic resistant AHF may improves urine output and facilitates achieving a net negative balance. Thus, metolazone and thiazide-like diuretics can be used as add-on therapy in acute decompensation of heart failure, especially in diuretic resistance.Copyright © 2023 The Author(s)

Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?

Memes can sway people's opinions over social media as they combine visual and textual information in an easy-to-consume manner. Since memes instantly turn viral, it becomes crucial to infer their intent and potentially associated harmfulness to take timely measures as needed. A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities. Here, we aim to understand whether the meme glorifies, vilifies, or victimizes each entity it refers to. To this end, we address the task of role identification of entities in harmful memes, i.e., detecting who is the 'hero', the 'villain', and the 'victim' in the meme, if any. We utilize HVVMemes - a memes dataset on US Politics and Covid-19 memes, released recently as part of the CONSTRAINT@ACL-2022 shared-task. It contains memes, entities referenced, and their associated roles: hero, villain, victim, and other. We further design VECTOR (Visual-semantic role dEteCToR), a robust multi-modal framework for the task, which integrates entity-based contextual information in the multi-modal representation and compare it to several standard unimodal (text-only or image-only) or multi-modal (image+text) models. Our experimental results show that our proposed model achieves an improvement of 4% over the best baseline and 1% over the best competing stand-alone submission from the shared-task. Besides divulging an extensive experimental setup with comparative analyses, we finally highlight the challenges encountered in addressing the complex task of semantic role labeling within memes. © 2023 Association for Computational Linguistics.

Covid-19 X-Ray Image Detection using ResNet50 and VGG16 in Convolution Neural Network

COVID-19 is an outbreak of disease which is created by China. COVID-19 is originated by coronavirus (CoV), generally created mutation pattern with 'SARS-CoV2' or '2019 novel coronavirus'. It is declared by the World Health Organization of 2019 in December. COVID-19 is a contagious virus and contiguous disease that will create the morality of life. Even though it is detected in an early stage it can be incurable if the severity is more. The throat and nose samples are collected to identify COVID-19 disease. We collected the X-Ray images to identify the virus. We propose a system to diagnose the images using Convolutional Neural Network (CNN) models. Dataset used consists of both Covid and Normal X-ray images. Among Convolutional Neural Network (CNN) models, the proposed models are ResNet50 and VGG16. RESNET50 consists of 48 convolutional, 1 MaxPool, and Average Pool layers, and VGG16 is another convolutional neural network that consists of 16 deep layers. By using these two models, the detection of COVID-19 is done. This research is designed to help physicians for successful detection of COVID-19 disease at an early stage in the medical field. © 2022 IEEE.

COVID-19 VACCINE PERCEPTION AND HESITANCY AMONG PATIENTS WITH AUTOIMMUNE RHEUMATIC DISEASE (AIRD) IN MALAYSIA

BackgroundPatients with autoimmune rheumatic disease (AIRD) are at risk of severe COVID-19 infection and vaccine has been demonstrated to be able to reduce the severity of infection. Malaysia has a low flu vaccination coverage rate (approximately 3%) and hence it is important to assess the perception and hesitancy of COVID-19 vaccine especially among the vulnerable group.ObjectivesTo study the perception of COVID-19 vaccine and to determine the prevalence of vaccine hesitancy among AIRD patients in Malaysia.MethodsThis was a cross-sectional survey using online Google Forms® that was conducted among adult AIRD patients (18 years and older) from August 2021 until February 2022. Patients were recruited from the outpatient clinics as well as distribution of the survey through social medias. The survey was in English and Malay language. The survey collected data on the socio-demographic background, prior history of other vaccination after the age of 18 and COVID-19 vaccination with reasons of hesitancy, defined as being unsure or unwilling to be vaccinated. The survey also assessed the patients' perception by specifying the level of agreement to COVID-19 vaccine statements using the Likert response scale: 1-Strongly disagree;2- Disagree;3-Neither agree nor disagree;4-Agree;5-Strongly agree.ResultsA total of 162 patients participated in the survey and majority of them were females (87.7%). Our multi-racial cohort consisted of Malay (n=103, 63.5%), followed by Chinese (n=38, 23.5%), Sabahan Bumiputra (n=12, 7.4%) and Indian (n=7, 4.3%). More than half (n=107,66.6%) have not had any history of other vaccination after the age of 18. Only 16.7% (n=27) agreed/strongly agreed that COVID-19 vaccine can be given to patients with co-morbidities and 24.1 (n=39) agreed/strongly agreed that COVID-19 vaccine can be given to patients who have history of allergy to other drugs or food. At the time of the survey, vast majority of the respondents have received at least the 1st dose of Covid-19 vaccine (n=148, 91.4%). A total of 9 (5.6%) patients were hesitant to be vaccinated (6 were unsure and 3 patients were not willing to be vaccinated). The commonest reasons of being unsure or not willing to be vaccinated was worried of the vaccine's adverse effects (66.7%), worried of the blood clot complication (33,3%), worried of disease flare post-vaccine (33,3%), worried of allergic reaction (22.2%), lack of information on the safety of the vaccine in patients with AIRD from government and media (22.2%), face mask and social distancing measures were adequate (22.2%). Statistical analysis revealed that more patients who had vaccine hesitancy were from the lower socioeconomic status (income <1066 Euro/month), 88.9% vs 11.1%, p=0.03 but no association with ethnicity, education status, marital status or place of residence (urban vs rural).ConclusionCOVID-19 vaccine hesitancy is low in Malaysian patients with AIRD but patients with a low socioeconomic status are prone to have vaccine hesitancy. More education on the vaccine's efficacy and safety especially among patients with co-morbidities are warranted.Reference[1]Knowledge, acceptance and perception on COVID-19 vaccine among Malaysians: A web-based survey. Mohamed NA, Solehan HM, Mohd Rani MD, Ithnin M, Che Isahak CI (2021) Knowledge, acceptance and perception on COVID-19 vaccine among Malaysians: A web-based survey. PLOS ONE 16(8): e0256110.Acknowledgements:NIL.Disclosure of InterestsSyahrul Sazliyana Shaharir Speakers bureau: Pfizer,Novartis, Lydia Kamaruzaman: None declared, Theepa Nesam Mariamutu: None declared, Mohd Shahrir Mohamed Said: None declared, Azmawati Mohammed Nawi: None declared, Wan Syamimee Wan Ghazali: None declared, Malehah Mohd Noh: None declared.

Invasive aspergillosis in patients with COVID-19 in intensive care units: results of a multicenter study.

Objective. To study risk factors, clinical and radiological features and effectiveness of the treatment of invasive aspergillosis (IA) in adult patients with COVID-19 (COVID-IA) in intensive care units (ICU). Materials and methods. A total of 60 patients with COVID-IA treated in ICU (median age 62 years, male - 58%) were included in this multicenter prospective study. The comparison group included 34 patients with COVID-IA outside the ICU (median age 62 years, male - 68%). ECMM/ISHAM 2020 criteria were used for diagnosis of CAPA, and EORTC/MSGERC 2020 criteria were used for evaluation of the treatment efficacy. A case-control study (one patient of the main group per two patients of the control group) was conducted to study risk factors for the development and features of CAPA. The control group included 120 adult COVID-19 patients without IA in the ICU, similar in demographic characteristics and background conditions. The median age of patients in the control group was 63 years, male - 67%. Results. 64% of patients with COVID-IA stayed in the ICU. Risk factors for the COVID-IA development in the ICU: chronic obstructive pulmonary disease (OR = 3.538 [1.104-11.337], p = 0.02), and prolonged (> 10 days) lymphopenia (OR = 8.770 [4.177-18.415], p = 0.00001). The main location of COVID-IA in the ICU was lungs (98%). Typical clinical signs were fever (97%), cough (92%), severe respiratory failure (72%), ARDS (64%) and haemoptysis (23%). Typical CT features were areas of consolidation (97%), hydrothorax (63%), and foci of destruction (53%). The effective methods of laboratory diagnosis of COVID-IA were test for galactomannan in BAL (62%), culture (33%) and microscopy (22%) of BAL. The main causative agents of COVID-IA are A. fumigatus (61%), A. niger (26%) and A. flavus (4%). The overall 12-week survival rate of patients with COVID-IA in the ICU was 42%, negative predictive factors were severe respiratory failure (27.5% vs 81%, p = 0.003), ARDS (14% vs 69%, p = 0.001), mechanical ventilation (25% vs 60%, p = 0.01), and foci of destruction in the lung tissue on CT scan (23% vs 59%, p = 0.01). Conclusions. IA affects predominantly ICU patients with COVID-19 who have concomitant medical conditions, such as diabetes mellitus, hematological malignancies, cancer, and COPD. Risk factors for COVID-IA in ICU patients are prolonged lymphopenia and COPD. The majority of patients with COVID-IA have their lungs affected, but clinical signs of IA are non-specific (fever, cough, progressive respiratory failure). The overall 12-week survival in ICU patients with COVID-IA is low. Prognostic factors of poor outcome in adult ICU patients are severe respiratory failure, ARDS, mechanical ventilation as well as CT signs of lung tissue destruction.Copyright © 2022, Interregional Association for Clinical Microbiology and Antimicrobial Chemotherapy. All rights reserved.

The impact of the Covid-19 pandemic on the diabetes antenatal clinic

Aims: The Covid-19 pandemic and subsequent restrictions impacted both health outcomes and clinical practice. We explored the impact on the diabetes antenatal clinic (DANC) attendance and outcomes. Method(s): Pre and during pandemic periods were defined as January 2019 to February 2020 and March 2020 to March 2022, respectively. DANC attendance, maternal and perinatal data were analysed. Adverse neonatal outcomes included stillbirth, neonatal hypoglycaemia, jaundice, shoulder dystocia and respiratory distress. Result(s): DANC attendance increased in the pandemic compared to the pre-pandemic period (297 (Interquartile range (IQR) 269-358) vs 196 (IQR 176-211) monthly, p < 0.001) with 36.7% (IQR 33-49) virtual appointments, representing a 34% overall increase. Body mass index (BMI) increased (29.7 kg/m2 (IQR 26.4-32.2) vs 31.4 kg/ m2 (IQR 26.5-34.2)) during the pandemic (p = 0.007), but maternal age and parity remained unchanged. There was no difference in gestational age at delivery;however, induction rates reduced from 58.5% to 37.5% (p = 0.0009) and spontaneous vaginal deliveries increased from 13.7% to 34.5% during the pandemic (p = 0.0004). Instrumental deliveries reduced from 21.5% to 11.3% (p = 0.03) but there was no change in number of caesarean sections including emergency ones. There was no difference in the rates of macrosomia or neonatal admissions. There was an overall reduction in adverse neonatal outcomes (37/102 (36.2%) vs 33/142 (23.2%) p = 0.03). Conclusion(s): Clinic numbers and maternal BMI increased during the pandemic. However, delivery and perinatal outcomes improved. Out data are reassuring and align with other studies indicating maternity outcomes did not deteriorate during the pandemic, possibly explained by improved care provision and organisation culture under crisis.