ABSTRACT
The psychological impact of COVID-19 on Health Care Workers (HCWs) has been widely reported. Few studies have sought to examine HCWs personal models of COVID-19 utilising an established theoretical framework. We undertook a mixed methods study of beliefs about COVID-19 held by HCWs in the Mid-West and South of Ireland during the first and third waves of COVID-19. Template analysis was undertaken on the free text responses of 408 HCWs about their perceptions of the Cause of COVID-19 as assessed by the Brief Illness Perception Questionnaire (B-IPQ). Responses were re-examined in the same cohort for stability at 3 months follow-up (n = 100). This analytic template was subsequently examined in a new cohort (n = 253) of HCWs in the third wave. Female HCWs perceived greater emotional impact of COVID-19 than men (t = -4.31, df405, p < 0.01). Differences between occupational groups were evident in relation to Timeline (F4,401 = 3.47, p < 0.01), Treatment Control (F4,401 = 5.64, p < 0.001) and Concerns about COVID-19 (F4,401 = 3.68, p < 0.01). Administration staff believed that treatment would be significantly more helpful and that COVID-19 would last a shorter amount of time than medical/nursing staff and HSCP. However, administration staff were significantly more concerned than HSCP about COVID-19. Template analysis on 1059 responses to the Cause items of the B-IPQ identified ten higher order categories of perceived Cause of COVID-19. The top two Causes identified at both Waves were 'individual behavioural factors' and 'overseas travel'. This study has progressed our understanding of the models HCWs hold about COVID-19 over time, and has highlighted the utility of the template analysis approach in analysing free-text questionnaire data. We suggest that group and individual occupational identities of HCWs may be of importance in shaping HCWs responses to working through COVID-19.
ABSTRACT
Background Modern cytometry can simultaneously measure dozens of markers, empowering investigation of complex phenotypes. However, manual gating relies on previous biological knowledge, and clustering/dimension-reduction tools fail to capture discrete phenotypes. Consequently, complex phenotypes with potential biological importance are often overlooked. To address this, we developed PhenoComb, an R package that allows agnostic exploration of complex phenotypes by assessing the frequencies of all marker combinations in cytometry datasets. Methods PhenoComb uses signal intensity thresholds to assign markers to discrete states (e.g. negative, low, high). As Pheno- Comb works in a memory-safe manner, time and disk space are the only constraints to the number of markers and discrete states that can be evaluated. Next, the number of cells per sample from all possible marker combinations are counted and frequencies assessed. PhenoComb provides several approaches to perform statistical comparisons, evaluate the relevance of phenotypes, and assess the independence of identified phenotypes. PhenoComb also allows users to guide analysis by adjusting several function arguments such as identifying parent populations of interest, filtering low-frequency populations, and defining a maximum marker complexity. PhenoComb is compatible with local computer or server-based use. Results In testing of PhenoComb's performance on synthetic datasets, computation on 16 markers was completed in the scale of minutes and up to 26 markers in hours. We applied PhenoComb to two publicly available datasets: an HIV flow cytometry dataset (12 markers and 421 samples) and the COVIDome CyTOF dataset (40 markers and 99 samples). In the HIV dataset, PhenoComb identified immune phenotypes associated with HIV seroconversion, including those highlighted in the original publication. In the COVID dataset, we identified several immune phenotypes with altered frequencies in infected individuals relative to healthy individuals. Conclusions PhenoComb is a unique and powerful tool for agnostically assessing phenotypes. By more fully utilizing the high-dimension data in single cell datasets, PhenoComb empowering exploratory data analysis and discovery of phenotypes for further characterization.
ABSTRACT
Purpose: The Coronavirus Disease 2019 (COVID-19) pandemic prompted widespread vaccination for the immunosuppressed population starting in January 2021 with minimal information on safety outcomes. The purpose of this study is to evaluate the relationship between kidney pathological changes and mRNA-based COVID-19 vaccines in three kidney transplant recipients. Method(s): We conducted a single-center retrospective case review of three kidney transplant recipients with biopsy-proven acute rejection or pathological changes after 2-dose COVID-19 mRNA vaccination. Renal function, maintenance immunosuppressant regimens, and pathology slides at baseline and post-rejection are recorded. Possible factors associated with the development of rejection were analyzed. Result(s): All participants were male, two received related-living donor transplants and one received a deceased donor transplant. The mean age was 44.3 years. Average time from 2nd COVID-19 vaccine to confirmed rejection or pathological changes was 33.7 days. Two patients received mRNA-1273 COVID-19 mRNA vaccine and one received the BNT162b2 COVID-19 mRNA vaccine. All three allograft biopsies demonstrated findings consistent with acute active antibody mediated rejection and thrombotic microangiopathy. One allograft biopsy also demonstrated findings consistent with collapsing focal segmental glomerular sclerosis. As of November 26, 2021, there have been over 26 reports of solid organ rejection or failure to the Vaccine Adverse Event Reporting System (VAERS) for the COVID-19 mRNA vaccines highlighting the need for further investigation. Conclusion(s): Immunization with COVID-19 mRNA vaccine has potential to precipitate clinically significant immune response to renal allografts leading to acute allograft rejection, thrombotic microangiopathy, and collapsing focal segmental glomerular sclerosis.
ABSTRACT
We present an open source, fully wireless potentiostat (the “NanoStat”) for applications in electrochemistry, sensing, biomedical diagnostics, and nanotechnology, based on only 2 integrated circuit chips: A digital microcontroller with integrated on board WiFi and file/web server hardware/software, and an analog front end. This versatile platform is fully capable of all modern electrochemisty assays, including cyclic voltammetry, square wave voltammetry, chronoamperometry, and normal pulse voltammetry. The user interface is a web browser connected over http. All the code (firmware, HTML5, JavaScript) is hosted by the NanoStat itself without the need for any additional software. The total size is 4×40×20 mm and battery operation for 6 h is demonstrated, possible to extend to weeks or months in sleep mode. We anticipate that the applications of this could be very broad, from biomedical sensing in the clinic, to remote monitoring of unattended “motes”, to even possibly sensing aerial pathogens such as COVID in large public spaces without the need for anything other than a web browser for remote monitoring from anywhere in the world. Finally, we propose to use this software suite as a basis (kernel) of a fully open source, general purpose, web based electrochemistry software suite, ed from the hardware, which we call “OpenEChem”. © 2022
ABSTRACT
Background The clinical frailty score (CFS) is a 9 point validated outcome measure used to measure function, mobility, cognition and co-morbidities in patients aged 65 or older. The physiotherapy department was restructured due to COVID-19 pandemic. This resulted in the formation of a mixed specialty team which consisted of Frailty Intervention Team (FIT), Medical Respiratory, Acute Medicine Service, Orthopaedics, General Rehabilitation and Care of the Older Person (COTOP). This service review aimed to identify frailty using the CFS across services and to compare CFS versus age, length of stay, falls history and discharge outcomes. Methods The CFS data was collected over two weeks. Inclusion criteria included patients who scored ≥4 on the CFS. Exclusion criteria included patients aged under 65. Variables such as age, history of falls, LOS and discharge destinations were compared across all services using Microsoft Excel. Results 166 patients were included, the average CFS was 5.24 and the average age was 77.2 years. COTOP had the oldest (Av. age 85.4), frailest (Av. CFS 6.1) and longest avLOS (25.3 days) across all services. Frailty was prevalent across all services, with 81% of patients on the medical respiratory service classed as frail. Patients who scored a CFS of ≥4 had higher falls risk and greater LOS. Of the medical respiratory cohort only 12% were discharged to rehabilitation with 77% discharged home. Orthopaedics had the highest percentage of patients discharged to rehabilitation (44%), followed by the general rehabilitation (34%). Only 14% of the COTOP patients were transferred to rehabilitation. Conclusion A high incidence of frailty and falls history was identified across all services. Patients who scored lower on the CFS resulted in reduced LOS and were more likely to be discharged directly home. Proactive screening and detection of frailty allows for targeted interventions that may improve outcomes and inform early discharge planning.