Emotional risk work during the pandemic: Healthcare professionals' perceptions from a COVID-19 ward

In March 2020, COVID-19 wards were established in hospitals in Denmark. Healthcare professionals from a variety of specialities and wards were transferred to these new wards to care for patients admitted with severe COVID-19 infections. Based on ethnographic fieldwork in a COVID-19 ward at a hospital in Copenhagen, Denmark, including focus group interviews with nursing staff, we intended to explore practices in a COVID-19 ward by seeking insight into the relation between the work carried out and the professionals' ways of talking about it. We used a performative approach of studying how the institutional ways of handling pandemic risk work comes into being and relates to the health professionals' emerging responses. The empirical analysis pointed at emotional responses by the nursing staff providing COVID-19 care as central. To explore these emotional responses we draw on the work of Mary Douglas and Deborah Lupton's concept of the ‘emotion-risk-assemblage'. Our analysis provides insight into how emotions are contextually produced and linked to institutional risk understandings. We show that work in the COVID-19 ward was based on an institutional order that was disrupted during the pandemic, producing significant emotions of insecurity. Although these emotions are structurally produced, they are simultaneously internalised as feelings of incompetence and shame.

ANTIBIOTIC POINT PREVALENCE SURVEY DURING THE PANDEMIC IN A COVID-19 DESIGNATED HOSPITAL IN MALAYSIA

Recent reports have shown that antibiotics were commonly prescribed in COVID-19 designated hospitals throughout the pandemic in spite of it being ineffective in treating viruses such as SARS-COV 2 which is the pathogen responsible for causing COVID-19. We conducted a cross-sectional Point Prevalence Survey (PPS) involving all wards in Hospital Sungai Buloh. Each ward was audited within one day within the period of two weeks (1st December 2021 till 14th December 2021). All in-patients receiving IV or oral antibiotics at 8am on the day of survey were included in the study. A total of 200 out of 664 in-patients (30%) were treated with antibiotics during the study period. COVID ICU recorded the highest prevalence of patient on antibiotics (83%) followed by General Medical (43%). Majority of patients received antibiotics for empirical therapy (80%, 160/200) and community-acquired pneumonia was the most common indication documented (36.5%, 75/205), followed by hospital-acquired pneumonia, with 23.4% of total documented indication (48/205). We found that in half of the patients (104/200), clinicians did not document the indication of antibiotic. Rate of prescription that was compliant to guideline was higher than that of non-compliant to guideline from total of 139 cases recorded (68% vs 32%). We found that there was a significant association between rate of compliant to guideline with respiratory diseases (χ² = 5.37, p<0.05). Twenty-seven percent of patients received antibiotics for respiratory diseases not according to guideline recommendation. Majority of cases of non-compliance to guideline, were patient with respiratory diseases (58.7%, 27 out of 46 cases). This pandemic has had an impact on the use of antibiotics, where its use has been found to increase drastically, especially in critical and severe patients. This high use makes adherence to the guidelines become important and should be an ongoing indicator, also it can be used as a guide for antimicrobial stewardship intervention. [ FROM AUTHOR] Copyright of International Journal of Infectious Diseases is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Application Of Neural Network For The Detection Of Covid-19 Or Viral Pneumonia

A bacterial infection in the lungs can cause viral pneumonia, a disease. Later the middle of December 2019, there have been multiple episodes of pneumonia in Wuhan City, China, with no known cause;it has since been discovered that this pneumonia is actually a new respiratory condition brought on by coronavirus infection. Humans who have lung abnormalities are more likely to develop high-risk conditions;this risk can be decreased with much quicker and more effective therapy. The symptoms of Covid-19 pneumonia are similar to those of viral pneumonia;they are not distinctive. X-ray or Computed Tomography (CT) scan images are used to identify lung abnormalities. Even for a skilled radiologist, it might be challenging to identify Covid-19/Viral pneumonia by looking at the X-ray images. For prompt and effective treatment, accurate diagnosis is essential. In this epidemic condition, delayed diagnosis can cause the number of cases to double, hence a suitable tool is required is necessary for the early identification of Covid-19. This paper highlights various AI techniques as a part of our contribution to swift identification and curie Covid-19 to front-line corona. The safety of Covid-19 people who have viral pneumonia is a concern. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), two AI technologies from Deep Learning (DL), were utilized to identify Covid-19/Viral pneumonia. The Algorithm is taught utilizing non-public local hospitals or Covid-19 wards, as well as X-ray images of healthy lungs, fake lungs from viral pneumonia, and ostentatious lungs from Covid-19 that are all publicly available. The model is also validated over a lengthy period of time using the transfer learning technique. The results correspond with clinically tested positive Covid-19 patients who underwent Swap testing conducted by medical professionals, giving us an accuracy of 78 to 82 percent. We discovered that each DL model has a unique expertise after testing the various models. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

An Efficient Medicine Identification and Delivery System Based on Mobile Manipulation Robot

In recent years, with the emergence of COVID-19, the shortage of medical resources has become increasingly obvious. However, current environments such as hospital wards still require a large number of medical staff to deliver medicines. In this paper, we propose a mobile robot that can complete medicine grabbing and delivery in a hospital ward scenario. First, a lightweight neural network is built to improve the detection efficiency of Faster R-CNN algorithm for boxed medicine. Then, the pose of the robotic arm grasping the pill box is determined by point cloud matching to control the mechanical grasping of the pill box. Finally, a discomfort function representing the collision risk between the robot and the pedestrian is incorporated into the Risk-RRT algorithm to improve the navigation performance of the algorithm. By building a real experimental platform, the experiments verify the performance of our proposed medicine delivery robot system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Seasonal and spatial variations of bioaerosols and antibiotic resistance bacteria in different wards of the hospital

Introduction: Transmission of bioaerosols through the air is known as an important route for a wide range of nosocomial infections. Therefore, in the present study, we aimed to evaluate the type and diversity of bioaerosols and antibiotic resistance of bacterial bioaerosols in the indoor environments of Sina educational and treatment hospital, Tabriz, Iran. Methods and materials: 150 samples of bacteria and fungi (75 fungi and 75 bacteria) bioaerosol samples were collected on petri dish containing Sabouraud dextrose agar from February to March and June to July 2020 in three periods of daytime (morning, noon and evening) according to National Institute for Occupational Safety and Health (NIOSH 0-800) standard. After sampling, fungal and bacterial samples were incubated and the disk diffusion agar method (Kirby-Bauer) was used for assessing the antibiotic resistance. Results: The concentration of bioaerosols varied significantly in different wards. In addition, the concentration of bioaerosols in winter was observed to be higher than in summer. The highest and lowest airborne fungal concentrations were found in burns operating room and men's infectious ward (49 CFU/m3) and children's burns ward (28 CFU/m3), respectively. The predominantly isolated bacteria were Streptococcus spp. (38%) and Staphylococcus spp. (37%). Also, the main isolated fungi belonged to the genera Aspergillus (75.9%) and Penicillium (22.5%). The highest rates of antibiotic resistance were observed for colistin (100%) in Gram-negative and penicillin (84.2%) in Gram-positive. Conclusion: Timely and regular disinfection of hospital wards can affect the density of bioaerosols. Owing to the prevalence of COVID-19 epidemic in the world, the staff and patients often were wearing masks, gloves and special clothing as well as using disinfectants to prevent coronavirus infection in wards during the summer sampling. © 2022 Tehran University of Medical Sciences. Published by Tehran University of Medical Sciences.

Length of Hospital Stay and Survival of Hospitalized COVID-19 Patients During the Second Wave of the Pandemic: A Single Centre Retrospective Study from Slovenia.

Background: As of writing, there are no publications pertaining to the prediction of COVID-19-related outcomes and length of stay in patients from Slovene hospitals. Objectives: To evaluate the length of regular ward and ICU stays and assess the survival of COVID-19 patients to develop better prediction models to forecast hospital capacity and staffing demands in possible further pandemic peaks. Methods: In this retrospective, single-site study we analysed the length of stay and survival of all patients, hospitalized due to the novel coronavirus (COVID-19) at the peak of the second wave, between November 18th 2020 and January 27th 2021 at the University Clinic Golnik, Slovenia. Results: Out of 407 included patients, 59% were male. The median length of stay on regular wards was 7.5 (IQR 5-13) days, and the median ICU length of stay was 6 (IQR 4-11) days. Age, male sex, and ICU stay were significantly associated with a higher risk of death. The probability of dying in 21 days at the regular ward was 14.4% (95% CI [10.9-18%]) and at the ICU it was 43.6% (95% CI [19.3-51.8%]). Conclusion: The survival of COVID-19 is strongly affected by age, sex, and the fact that a patient had to be admitted to ICU, while the length of hospital bed occupancy is very similar across different demographic groups. Knowing the length of stay and admission rate to ICU is important for proper planning of resources during an epidemic.

Simultaneous monitoring of SARS-CoV-2 and bacterial profiles from the air of hospital environments with COVID-19-affected patients.

The SARS-CoV-2 presence and the bacterial community profile in air samples collected at the Intensive Care Unit (ICU) of the Operational Unit of Infectious Diseases of Santa Caterina Novella Hospital in Galatina (Lecce, Italy) have been evaluated in this study. Air samplings were performed in different rooms of the ICU ward with and without COVID-19 patients. No sample was found positive to SARS-CoV-2, according to Allplex 2019-nCoV Assay. The airborne bacterial community profiles determined by the 16S rRNA gene metabarcoding approach up to the species level were characterized by richness and biodiversity indices, Spearman correlation coefficients, and Principal Coordinate Analysis. Pathogenic and non-pathogenic bacterial species, also detected in outdoor air samples, were found in all collected indoor samples. Staphylococcus pettenkoferi, Corynebacterium tuberculostearicum, and others coagulase-negative staphylococci, detected at high relative abundances in all the patients' rooms, were the most abundant pathogenic species. The highest mean relative abundance of S. pettenkoferi and C. tuberculostearicum suggested that they were likely the main pathogens of COVID-19 patients at the ICU ward of this study. The identification of nosocomial pathogens representing potential patients' risks in ICU COVID-19 rooms and the still controversial airborne transmission of the SARS-CoV-2 are the main contributions of this study. Supplementary Information: The online version contains supplementary material available at 10.1007/s10453-022-09754-7.

Delivering an Online Course on ‘COVID-19 Vaccination Campaign: Administration in Safety of the Anti SARS-COV 2/COVID-19 VACCINE’ for Nursing Students at Italian Universities

Many Italian universities had numerous nursing students attending hospital wards for administrating anti SARS-COV 2/COVID-19 vaccines. The training of nursing students was necessary to facilitate good practices, disseminate knowledge about anti SARS-COV 2/COVID-19 vaccines. On 22 December 2021, the Italian National Institute of Health (NIH) created a course that aimed to promote the anti-SARS-CoV-2/COVID-19 vaccination strategy in the country, providing the basic skills, tools and technical-scientific contents necessary to guarantee all phases of the vaccination campaign, including the safe administration of vaccines and counteract vaccination hesitation through the involvement and informed participation of health and social health personnel towards the population. The purpose of this paper was to describe the method used by the Sapienza University of Rome in delivering the Italian NIH course nursing students at Italian universities. The research group in charge of delivering the course decided to use the Google Classroom platform. From the 03/02/2022 to 25/03/2022, 3154 students from 46 Italian universities attended the course. This paper represents a clear advantage in the field of e-learning, not only because it describes an effective method for delivering a course to many students but also because it demonstrates how health professions students can be protected while allowing them to continue or restart internships in health facilities more safely and with more awareness. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Social Interaction & Service Robot for Hospital Wards

As the era of industrial revolution 5.0 has begun, most of the robots are developed to have cyber inter-physical functionalities which are deemed to replace human activities. However, robots are rarely being utilized in the health care sector. In a medical institution, countless activities and events are happening simultaneously. Most of these are very precise, lifesaving and are on a time constraint. Heavy machinery and equipment are required to execute such events which is time-consuming and inconvenient. The robot specified helps with regular processes occurring on a day-to-day basis in the institution such as taking vitals and sanitization as well as transporting products on the go intelligently and safely. This robot is good at mapping rooms using the internal GPS, the robot can effectively communicate and output simple messages with the patients via, a display screen. Human intervention plays a vital role in preventing the health care workers from coming in contact with the covid-19. © 2022 IEEE.

ANALYSIS OF PATH PLANNING ALGORITHMS FOR SERVICE ROBOTS IN HOSPITAL ENVIRONMENT

Medical robotics is an interdisciplinary domain devoted to the development of electromechanical machines for therapeutic purposes. It has the history of 34 years in enabling the new medical treatments by giving physicians additional powers or by assisting them. The pandemic COVID-19 has changed the world wherein humans are hiding in the masks and doctors are highly pressurized to save the lives of humans. On this note, this paper is concerned with the service robots in a hospital ward to offer medication and monitor patients to help the doctors. The purpose of this paper is to compare two well-known grid-based algorithms BFS & DFS and conclude with the optimal path finding algorithm for the medical robots in a virtualized ward of 20x20 grid plan. The results are simulated in MATLAB and it is found that BFS is more worthy in terms of finding an optimal path for service robot in a hospital ward. © 2021 IEEE.

Investigation of SARS CoV-2 virus in environmental surface.

The prevalent respiratory viruses such as SARS-CoV-2 probably persist for a long time on fomites and environmental surfaces. Some recent studies have detected SARS-CoV-2 RNA on the surface of cell phones, door handles and other items in the inhabited sites of confirmed cases. For the aim of this study, a total of 50 environmental surface samples of SARS-CoV-2 was collected from Imam Khomeini Hospital in Ardabil. Forty-one environmental surface samples were proved negative for SARS-CoV-2 RNA while nine surface samples were positive. Our findings regarding surfaces contaminated with the virus are consistent with the results of recent similar researches as it was revealed that a number of different samples taken from hospital surfaces such as handles, cupboards, light switches, and door handles were positive for the presence of SARS-Cov-2.